diff --git a/docs/.nojekyll b/docs/.nojekyll deleted file mode 100644 index e69de29..0000000 diff --git a/docs/Example analysis.html b/docs/Example analysis.html deleted file mode 100644 index aa0f0c3..0000000 --- a/docs/Example analysis.html +++ /dev/null @@ -1,1727 +0,0 @@ - - - - - - - - - - - -Yingying Yu - Example analysis - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Example analysis

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Author
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Yingying Yu

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November 1, 2023

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Introduction

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Menopause is a natural biological event triggered by the decline in ovarian follicular function and a reduction in circulating blood estrogen levels (Greendale, Lee, and Arriola 1999). According to the World Health Organization (WHO 2021), the majority of women experience menopause in their forties and fifties, typically defined as twelve consecutive months without menstruation, with no other underlying cause. Notably, during pregnancy, women do not have their regular menstrual period for approximately nine months due to the profound impact on ovarian activity.

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Research Objective

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The objective of this study is to investigate whether the age at which natural menopause occurs is influenced by the number of childbirths among American women. This research utilizes data from the 2017-18 National Health and Nutrition Examination Survey (NHANES). Our hypothesis posits that an increased age at menopause is associated with a higher number of childbirths.

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Note:

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This analysis is conducted for researchers, doctors, or anyone who is interested in the risk factors of menopause.

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Methodology

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Study population

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Performed by the National Center for Health Statistics (NCHS) within the Centers for Disease Control and Prevention (CDC), the NHANES is a program of cross-sectional studies collecting data regarding the health and nutritional status of Americans. This study is an analysis of the 2017-2018 data for female participants located in different counties across the nation, including demographics, socioeconomic, body measurements, reproductive health-related information gathered by interviews and physical examinations. The dataset can be download on the (NHANES CDC 2019) website.

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Measures

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The outcome of interest in this study is the age at natural menopause of a participant. Age at menopause was obtained from the self-reported question: ‘About how old were you when you had your last menstrual period?’ In this study, we defined age at menopause as the age in years when the woman had her last menstrual period.

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The primary covariate in this analysis is the number of childbirths. The number of childbirths was obtained from the self-reported questions: ‘How many of your deliveries resulted in a live birth?’ In this study, we used number of childbirths as a continuous variable (0–10 childbirths).

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After reviewing previous literatures, the following factors are included as additional covariates to address for potential confounding. Age in years was analyzed as continuous variable. Smoking status was dichotomized as “never” and “ever”. Alcohol use was dichotomized as “never” and “ever”. Educational level was categorized as “Less than high school diploma”, “High school diploma”, “College” and “College graduate or above”. Body mass index was analyzed as continues variable measured in kg/m2.

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Statistical Analysis

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Data of the outcome variable and covariates will be extracted from five sub-data files and combined into a single analytic dataset prior to analysis.

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# Load data
-library("foreign")
-BodyMeasure <- read.xport("data/BMX_J.XPT")
-Demographic <- read.xport("data/DEMO_J.XPT")
-Alcohol <- read.xport("data/ALQ_J.XPT")
-Smoking <- read.xport("data/SMQ_J.XPT")
-Reproductive <- read.xport("data/RHQ_J.XPT")
-BMI <- BodyMeasure[,c('SEQN','BMXBMI')] 
-Demo <- Demographic[, c('SEQN','RIDAGEYR','RIDRETH3','DMDEDUC2')]
-AlcUse <- Alcohol[,c('SEQN','ALQ111')]
-SmoUse <- Smoking[,c('SEQN','SMQ020')]
-FeHealth <- Reproductive[,c('SEQN','RHQ060','RHQ171','RHQ420')]
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-# Merge data
-data <- merge(BMI, Demo, by = "SEQN", all.x = T, all.y = F)
-data <- merge(AlcUse, data, by = "SEQN", all.x = T, all.y = F)
-data <- merge(SmoUse, data, by = "SEQN", all.x = F, all.y = T)
-data <- merge(FeHealth, data, by = "SEQN", all.x = T, all.y = F)
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-# Change column names
-colnames(data) <- c('ID','age_meno','livebirths','pills','smoke','drink','bmi',
-                    'age_sur','race','education')
-head(data,5)
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     ID age_meno livebirths pills smoke drink  bmi age_sur race education
-1 93705       50          2     1     1     1 31.7      66    4         2
-2 93708       48          3     1     2     2 23.7      66    6         1
-3 93709       NA         NA    NA     1    NA 38.9      75    4         4
-4 93714       52          1     1     1     1 39.9      54    4         4
-5 93719       NA         NA     2    NA    NA   NA      NA   NA        NA
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-Data Dictionary -
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The variables on the above data set are:

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NameMeaning
IDParticipant ID
age_menoMenopausal age
livebirthsNumber of deliveries
pillsOral contraceptive use
smokeSmoking status
drinkDrinking status
bmiBMI index
age_surAge at survey time
raceRace/ethnicity
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# drop NA and weird values
-library("tidyr")
-library("dplyr")
-data <- drop_na(data) 
-data <- data[data$age_meno != 999, ] # exclude those dont know when is their last period
-data <- data[data$pills != 9, ] # exclude those dont know whether they ever taken birth control pills
-data <- data[data$education != 9, ] # exclude those dont know their education level
-data <- arrange(data)
-head(data,5)
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     ID age_meno livebirths pills smoke drink  bmi age_sur race education
-1 93705       50          2     1     1     1 31.7      66    4         2
-2 93708       48          3     1     2     2 23.7      66    6         1
-3 93714       52          1     1     1     1 39.9      54    4         4
-4 93721       50          4     1     2     1 35.9      60    1         1
-5 93722       43          2     1     2     1 23.8      60    3         4
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# Quick look on menopausal age and number of delivery
-data %>% 
-  group_by(livebirths) %>% 
-  summarise(mean = mean(age_meno))
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# A tibble: 11 × 2
-   livebirths  mean
-        <dbl> <dbl>
- 1          0  48.5
- 2          1  43.9
- 3          2  44.4
- 4          3  45.2
- 5          4  44.9
- 6          5  45.9
- 7          6  47.5
- 8          7  47.2
- 9          8  47.3
-10         10  46  
-11         11  50
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Looks like there is a linear relationship between menopausal age and number of deliveries, ignoring women with no childbirth. Let’s see if this is still true if we group all the women by number of childbirth.

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data <- data %>%
-  mutate(newgroup = case_when(
-    livebirths <= 0 ~ "0",
-    between(livebirths, 1, 2) ~ "1-2",
-    between(livebirths, 3, 5) ~ "3-5",
-    livebirths >= 6 ~ ">=6",
-    TRUE ~ NA_character_))
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-data %>% 
-  group_by(newgroup) %>% 
-  summarise(mean = mean(age_meno))
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# A tibble: 4 × 2
-  newgroup  mean
-  <chr>    <dbl>
-1 0         48.5
-2 1-2       44.2
-3 3-5       45.2
-4 >=6       47.4
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# Make categorical variables
-data$race <- factor(data$race, labels = c("Mexican American","Other Hispanic","White","Black","Asian","Multi-Racial")) %>% relevel(data$race, ref = "Black")
-data$drink <- factor(data$drink, labels = c("Ever", "Never"))
-data$smoke <- factor(data$smoke, labels = c("Ever", "Never"))
-data$pills <- factor(data$pills, labels = c("Ever", "Never"))
-data$education[data$education == 1] <- 2
-data$education <- factor(data$education, labels = c("Less Than High School Diploma",
-                                                    "High School Diploma","College","College Graduate Or Above"))
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Results

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Participant Characteristics

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The mean age of participants the survey was done is 62.7 (SD = 11.3) years old and ranged from 22 to 80. Of the 1189 female participants, most of them are non-Hispanic White (38.7%), have some college experience (33.3%), drink alcohol at some point in their life (85.4%), and never smoke (64.1%). Table 1 below presents specific breakdowns of each variable considered by this study.

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# Plot distribution of age
-library(ggplot2)
-ggplot(data=data) +
-  geom_bar(mapping=aes(x=age_sur)) +
-  geom_vline(xintercept = mean(data$age_sur), color = "red", linetype = "dashed", size = 1) +
-  labs(title = "Figure 1:", subtile = "Distribution of Participant Age at survey",
-       x = "Age", y = "Frequency", caption = "Created by Yinging Yu")
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Note that the frequency of 80 years old is extremely high because it includes 80 years of age and over.

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ggplot(data = data) + 
-  geom_point(mapping = aes(x = age_meno, y = livebirths)) + 
-  facet_wrap(~ race, nrow = 2) +
-  labs(title = "Figure 2:", 
-       subtitle = "Distribution of Menopausal Age and Livebirths by Race/Ethnicity", 
-       x = "Menopausal Age", y = "Number of Livebirths", 
-       caption = "Created by Yinging Yu")
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ggplot(data = data) + 
-  geom_smooth(mapping = aes(x = bmi, y = age_meno)) +
-  labs(title = "Figure 3:", subtitle = "Distribution of BMI and Menopausal Age", 
-       x = "BMI", y = "Menopausal Age",caption = "Created by Yinging Yu")
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library("table1")
-label(data$age_meno) <- "Menopause Age"
-label(data$livebirths) <- "Number of Childbirths"
-label(data$age_sur) <- "Age"
-label(data$race) <- "Race"
-label(data$education) <- "Education"
-label(data$drink) <- "Alcohol use"
-label(data$smoke) <- "Smoking status"
-label(data$pills) <- "Oral contraceptive use"
-label(data$bmi) <- "BMI"
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-units(data$bmi) <- "kg/m2"
-caption  <- "Table 1. Characteristics of the study population according to number of livebirths"
-footnote <- "Note: Continuous variables were displayed as mean (SD) and categorical variables were displayed as number (percentage)."
-mytable <- table1(~ age_sur + race + education + drink + smoke + pills + bmi | newgroup, data=data, footnote=footnote, caption=caption, overall=c(left="Total"))
-mytable
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Table 1. Characteristics of the study population according to number of livebirths
Total
(N=1189)		>=6
(N=64)		0
(N=6)		1-2
(N=577)		3-5
(N=542)

Note: Continuous variables were displayed as mean (SD) and categorical variables were displayed as number (percentage).

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Age
Mean (SD)62.7 (11.3)68.7 (10.3)60.2 (10.6)61.3 (11.1)63.5 (11.3)
Median [Min, Max]63.0 [22.0, 80.0]68.5 [37.0, 80.0]57.0 [51.0, 80.0]61.0 [22.0, 80.0]63.0 [25.0, 80.0]
Race
Black282 (23.7%)19 (29.7%)5 (83.3%)138 (23.9%)120 (22.1%)
Mexican American151 (12.7%)18 (28.1%)0 (0%)48 (8.3%)85 (15.7%)
Other Hispanic123 (10.3%)4 (6.3%)0 (0%)62 (10.7%)57 (10.5%)
White460 (38.7%)16 (25.0%)0 (0%)232 (40.2%)212 (39.1%)
Asian129 (10.8%)5 (7.8%)1 (16.7%)67 (11.6%)56 (10.3%)
Multi-Racial44 (3.7%)2 (3.1%)0 (0%)30 (5.2%)12 (2.2%)
Education
Less Than High School Diploma247 (20.8%)32 (50.0%)0 (0%)68 (11.8%)147 (27.1%)
High School Diploma305 (25.7%)10 (15.6%)3 (50.0%)139 (24.1%)153 (28.2%)
College396 (33.3%)14 (21.9%)2 (33.3%)217 (37.6%)163 (30.1%)
College Graduate Or Above241 (20.3%)8 (12.5%)1 (16.7%)153 (26.5%)79 (14.6%)
Alcohol use
Ever1015 (85.4%)53 (82.8%)3 (50.0%)503 (87.2%)456 (84.1%)
Never174 (14.6%)11 (17.2%)3 (50.0%)74 (12.8%)86 (15.9%)
Smoking status
Ever427 (35.9%)21 (32.8%)0 (0%)213 (36.9%)193 (35.6%)
Never762 (64.1%)43 (67.2%)6 (100%)364 (63.1%)349 (64.4%)
Oral contraceptive use
Ever830 (69.8%)32 (50.0%)5 (83.3%)418 (72.4%)375 (69.2%)
Never359 (30.2%)32 (50.0%)1 (16.7%)159 (27.6%)167 (30.8%)
BMI (kg/m2)
Mean (SD)30.6 (7.71)30.6 (6.15)31.7 (6.56)30.3 (7.64)30.8 (7.96)
Median [Min, Max]29.3 [15.5, 84.4]29.4 [18.4, 45.4]29.4 [26.1, 44.0]29.0 [15.5, 67.7]29.4 [16.2, 84.4]
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Regression Analysis

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Univariate and multivariate analyses were performed with simple and multiple linear regression, respectively. In univariate analysis, “umber of childbirth”, “age at survey”, and “smoking status” show statistically significance with our outcome variable. For race, only the subgroups “other Hispanic” and “Asian” are significant relative to the reference group. For education level, “high school diploma” and “college graduate or above” rose to the level of statistical significance.

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This association continued to appear after adjustment in the full model. After comparing between different combinations of covariates, the model with the lowest AIC score were picked as our final adjusted model shown in Table 2 below. In multivariable linear regression analyses, the number of childbirth was associated with the age of menopause, in which each additional childbirth is associated with a 0.34 (95% CI: 0.02, 0.66) increase of the menopausal age, while holding all other covariates constant. Future research will be needed to validify this association and explore the underlining biological mechanism.

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Slin1 = lm(age_meno ~ livebirths, data = data)
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-Mlin3 = lm(age_meno ~ livebirths + age_sur + race + education + pills + smoke, data = data)
-summary(Mlin3)
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-Call:
-lm(formula = age_meno ~ livebirths + age_sur + race + education + 
-    pills + smoke, data = data)
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-Residuals:
-    Min      1Q  Median      3Q     Max 
--24.655  -5.124   1.322   5.878  14.748 
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-Coefficients:
-                                    Estimate Std. Error t value Pr(>|t|)    
-(Intercept)                        28.737622   1.553534  18.498  < 2e-16 ***
-livebirths                          0.341821   0.162806   2.100 0.035980 *  
-age_sur                             0.220409   0.021450  10.276  < 2e-16 ***
-raceMexican American                0.675248   0.840104   0.804 0.421694    
-raceOther Hispanic                  2.472283   0.878333   2.815 0.004963 ** 
-raceWhite                          -0.819997   0.610227  -1.344 0.179286    
-raceAsian                           3.779387   0.901598   4.192 2.97e-05 ***
-raceMulti-Racial                    1.039540   1.304629   0.797 0.425723    
-educationHigh School Diploma       -0.549180   0.732069  -0.750 0.453299    
-educationCollege                    0.007979   0.701900   0.011 0.990931    
-educationCollege Graduate Or Above  2.650293   0.788800   3.360 0.000805 ***
-pillsNever                         -1.849122   0.530437  -3.486 0.000508 ***
-smokeNever                          1.588239   0.510617   3.110 0.001913 ** 
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-Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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-Residual standard error: 7.966 on 1176 degrees of freedom
-Multiple R-squared:  0.1496,    Adjusted R-squared:  0.1409 
-F-statistic: 17.24 on 12 and 1176 DF,  p-value: < 2.2e-16
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AIC(Mlin3)
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[1] 8323.974
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library(gtsummary)
-t1 <- tbl_regression(Slin1)%>%add_global_p()
-t2 <- tbl_regression(Mlin3)%>%add_global_p()
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-tbl_merge <- tbl_merge(tbls = list(t1,t2),
-          tab_spanner = c("**Unadjusted**","**Adjusted**"))
-tbl_merge
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Characteristic - Unadjusted - - Adjusted -
Beta95% CI1p-valueBeta95% CI1p-value
Number of Childbirths0.460.14, 0.780.0040.340.02, 0.660.036
Age0.220.18, 0.26<0.001
Race<0.001
    Black——
    Mexican American0.68-0.97, 2.3
    Other Hispanic2.50.75, 4.2
    White-0.82-2.0, 0.38
    Asian3.82.0, 5.5
    Multi-Racial1.0-1.5, 3.6
Education<0.001
    Less Than High School Diploma——
    High School Diploma-0.55-2.0, 0.89
    College0.01-1.4, 1.4
    College Graduate Or Above2.71.1, 4.2
Oral contraceptive use<0.001
    Ever——
    Never-1.8-2.9, -0.81
Smoking status0.002
    Ever——
    Never1.60.59, 2.6
1 CI = Confidence Interval
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Discussion

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Univariate and multivariate analyses were performed with simple and multiple linear regression, respectively. In univariate analysis, “umber of childbirth”, “age at survey”, and “smoking status” show statistically significance with our outcome variable. For race, only the subgroups “other Hispanic” and “Asian” are significant relative to the reference group. For education level, “high school diploma” and “college graduate or above” rose to the level of statistical significance.

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This association continued to appear after adjustment in the full model. In multivariable linear regression analyses, the number of childbirth was associated with the age of menopause, in which each additional childbirth is associated with a 0.34 (95% CI: 0.02, 0.66) increase of the menopausal age, while holding all other covariates constant. Future research will be needed to validify this association and explore the underlining biological mechanism.

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Function summary

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Package nameFunctions used
tidyrdrop_na
dplyrarrange mutate group_by summarise
ggplot2geom_bar geom_vline geom_point facet_wrap geom_smooth
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References

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-CDC. 2019. “NHANES 2017-2018.” 2019. https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2017. -
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-Greendale, G. A., N. P. Lee, and E. R. Arriola. 1999. “The Menopause.” Lancet (London, England) 353 (9152): 571–80. https://doi.org/10.1016/S0140-6736(98)05352-5. -
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-WHO. 2021. “Menopause.” 2021. https://www.who.int/news-room/fact-sheets/detail/menopause. -
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About

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My journey has been fueled by a deep-rooted passion for making a meaningful impact in the field of oncology. My undergraduate major in biology laid the foundation for my initial career aspirations. However, over time, I found myself increasingly drawn to the world of data science and bioinformatics. I realized the potential for data-driven insights to transform our understanding of complex biological processes.

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Currently, I’m pursuing a master’s degree in epidemiology, which has provided me with valuable knowledge and skills to better comprehend the broader context of disease patterns and public health. Through several hands-on experiences, I’ve become proficient in handling RNA sequencing data and utilizing numerous R packages related to genome-wide analysis. My goal now is to merge my biological expertise with data science and epidemiology to contribute to advancements in oncology research.

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I look forward to connecting with like-minded individuals who share my passion for driving positive change in the oncology field.

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Yingying Yu

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About

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Welcome to my site! My name is Yingying. I am a second-year MHS in Epidemiology student at the Johns Hopkins Bloomberg School of Public Health. My interests revolve around the convergence of cancer biology, data science, epidemiology, and omics data. Proficient in sequencing data analysis, I have experience in developing R packages, implementing version control on GitHub, and proficiently managing data using SQL.

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My enthusiasm lies in leveraging data and analytics to address health-related challenges. I am dedicated to data analysis and contributing to open-source software that elevates the advancements in oncology research.

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Previous/current research

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  • Chromosome segregation in human cells and its regulation by SAC
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Educational training

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MS in Epidemiology (2024) | Johns Hopkins University
-BA in Biology (2022) | New York University

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Effects on Natural Menopausal Age by Number of Childbirth

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NHANES
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Epidemiology
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Author
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Yingying Yu

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Published
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November 1, 2023

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Introduction

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Menopause is a natural biological event triggered by the decline in ovarian follicular function and a reduction in circulating blood estrogen levels (Greendale, Lee, and Arriola 1999). According to the World Health Organization (WHO 2021), the majority of women experience menopause in their forties and fifties, typically defined as twelve consecutive months without menstruation, with no other underlying cause. Notably, during pregnancy, women do not have their regular menstrual period for approximately nine months due to the profound impact on ovarian activity.

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Research Objective

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The objective of this study is to investigate whether the age at which natural menopause occurs is influenced by the number of childbirths among American women. This research utilizes data from the 2017-18 National Health and Nutrition Examination Survey (NHANES). Our hypothesis posits that an increased age at menopause is associated with a higher number of childbirths.

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Methodology

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Study population

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Performed by the National Center for Health Statistics (NCHS) within the Centers for Disease Control and Prevention (CDC), the NHANES is a program of cross-sectional studies collecting data regarding the health and nutritional status of Americans. This study is an analysis of the 2017-2018 data for female participants located in different counties across the nation, including demographics, socioeconomic, body measurements, reproductive health-related information gathered by interviews and physical examinations. The dataset can be download on the (NHANES CDC 2019) website.

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Measures

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The outcome of interest in this study is the age at natural menopause of a participant. Age at menopause was obtained from the self-reported question: ‘About how old were you when you had your last menstrual period?’ In this study, we defined age at menopause as the age in years when the woman had her last menstrual period.

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The primary covariate in this analysis is the number of childbirths. The number of childbirths was obtained from the self-reported questions: ‘How many of your deliveries resulted in a live birth?’ In this study, we used number of childbirths as a continuous variable (0–10 childbirths).

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After reviewing previous literatures, the following factors are included as additional covariates to address for potential confounding. Age in years was analyzed as continuous variable. Smoking status was dichotomized as “never” and “ever”. Alcohol use was dichotomized as “never” and “ever”. Educational level was categorized as “Less than high school diploma”, “High school diploma”, “College” and “College graduate or above”. Body mass index was analyzed as continues variable measured in kg/m2.

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Statistical Analysis

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Data of the outcome variable and covariates will be extracted from five sub-data files and combined into a single analytic dataset prior to analysis.

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# Load data
-library("foreign")
-BodyMeasure <- read.xport("data/BMX_J.XPT")
-Demographic <- read.xport("data/DEMO_J.XPT")
-Alcohol <- read.xport("data/ALQ_J.XPT")
-Smoking <- read.xport("data/SMQ_J.XPT")
-Reproductive <- read.xport("data/RHQ_J.XPT")
-BMI <- BodyMeasure[,c('SEQN','BMXBMI')] 
-Demo <- Demographic[, c('SEQN','RIDAGEYR','RIDRETH3','DMDEDUC2')]
-AlcUse <- Alcohol[,c('SEQN','ALQ111')]
-SmoUse <- Smoking[,c('SEQN','SMQ020')]
-FeHealth <- Reproductive[,c('SEQN','RHQ060','RHQ171','RHQ420')]
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-# Merge data
-data <- merge(BMI, Demo, by = "SEQN", all.x = T, all.y = F)
-data <- merge(AlcUse, data, by = "SEQN", all.x = T, all.y = F)
-data <- merge(SmoUse, data, by = "SEQN", all.x = F, all.y = T)
-data <- merge(FeHealth, data, by = "SEQN", all.x = T, all.y = F)
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-# Change column names
-colnames(data) <- c('ID','age_meno','livebirths','pills','smoke','drink','bmi',
-                    'age_sur','race','education')
-head(data,5)
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     ID age_meno livebirths pills smoke drink  bmi age_sur race education
-1 93705       50          2     1     1     1 31.7      66    4         2
-2 93708       48          3     1     2     2 23.7      66    6         1
-3 93709       NA         NA    NA     1    NA 38.9      75    4         4
-4 93714       52          1     1     1     1 39.9      54    4         4
-5 93719       NA         NA     2    NA    NA   NA      NA   NA        NA
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-Data Dictionary -
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The variables on the above data set are:

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NameMeaning
IDParticipant ID
age_menoMenopausal age
livebirthsNumber of deliveries
pillsOral contraceptive use
smokeSmoking status
drinkDrinking status
bmiBMI index
age_surAge at survey time
raceRace/ethnicity
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# drop NA and weird values
-library("tidyr")
-library("dplyr")
-data <- drop_na(data) 
-data <- data[data$age_meno != 999, ] # exclude those dont know when is their last period
-data <- data[data$pills != 9, ] # exclude those dont know whether they ever taken birth control pills
-data <- data[data$education != 9, ] # exclude those dont know their education level
-data <- arrange(data)
-head(data,5)
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     ID age_meno livebirths pills smoke drink  bmi age_sur race education
-1 93705       50          2     1     1     1 31.7      66    4         2
-2 93708       48          3     1     2     2 23.7      66    6         1
-3 93714       52          1     1     1     1 39.9      54    4         4
-4 93721       50          4     1     2     1 35.9      60    1         1
-5 93722       43          2     1     2     1 23.8      60    3         4
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# Quick look on menopausal age and number of delivery
-data %>% 
-  group_by(livebirths) %>% 
-  summarise(mean = mean(age_meno))
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# A tibble: 11 × 2
-   livebirths  mean
-        <dbl> <dbl>
- 1          0  48.5
- 2          1  43.9
- 3          2  44.4
- 4          3  45.2
- 5          4  44.9
- 6          5  45.9
- 7          6  47.5
- 8          7  47.2
- 9          8  47.3
-10         10  46  
-11         11  50
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Looks like there is a linear relationship between menopausal age and number of deliveries, ignoring women with no childbirth. Let’s see if this is still true if we group all the women by number of childbirth.

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data <- data %>%
-  mutate(newgroup = case_when(
-    livebirths <= 0 ~ "0",
-    between(livebirths, 1, 2) ~ "1-2",
-    between(livebirths, 3, 5) ~ "3-5",
-    livebirths >= 6 ~ ">=6",
-    TRUE ~ NA_character_))
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-data %>% 
-  group_by(newgroup) %>% 
-  summarise(mean = mean(age_meno))
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# A tibble: 4 × 2
-  newgroup  mean
-  <chr>    <dbl>
-1 0         48.5
-2 1-2       44.2
-3 3-5       45.2
-4 >=6       47.4
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# Make categorical variables
-data$race <- factor(data$race, labels = c("Mexican American","Other Hispanic","White","Black","Asian","Multi-Racial")) %>% relevel(data$race, ref = "Black")
-data$drink <- factor(data$drink, labels = c("Ever", "Never"))
-data$smoke <- factor(data$smoke, labels = c("Ever", "Never"))
-data$pills <- factor(data$pills, labels = c("Ever", "Never"))
-data$education[data$education == 1] <- 2
-data$education <- factor(data$education, labels = c("Less Than High School Diploma",
-                                                    "High School Diploma","College","College Graduate Or Above"))
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Results

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Participant Characteristics

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The mean age of participants the survey was done is 62.7 (SD = 11.3) years old and ranged from 22 to 80. Of the 1189 female participants, most of them are non-Hispanic White (38.7%), have some college experience (33.3%), drink alcohol at some point in their life (85.4%), and never smoke (64.1%). Table 1 below presents specific breakdowns of each variable considered by this study.

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# Plot distribution of age
-library(ggplot2)
-ggplot(data=data) +
-  geom_bar(mapping=aes(x=age_sur)) +
-  geom_vline(xintercept = mean(data$age_sur), color = "red", linetype = "dashed", size = 1) +
-  labs(title = "Figure 1:", subtile = "Distribution of Participant Age at survey",
-       x = "Age", y = "Frequency", caption = "Created by Yinging Yu")
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Note that the frequency of 80 years old is extremely high because it includes 80 years of age and over.

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ggplot(data = data) + 
-  geom_point(mapping = aes(x = age_meno, y = livebirths)) + 
-  facet_wrap(~ race, nrow = 2) +
-  labs(title = "Figure 2:", 
-       subtitle = "Distribution of Menopausal Age and Livebirths by Race/Ethnicity", 
-       x = "Menopausal Age", y = "Number of Livebirths", 
-       caption = "Created by Yinging Yu")
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ggplot(data = data) + 
-  geom_smooth(mapping = aes(x = bmi, y = age_meno)) +
-  labs(title = "Figure 3:", subtitle = "Distribution of BMI and Menopausal Age", 
-       x = "BMI", y = "Menopausal Age",caption = "Created by Yinging Yu")
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library("table1")
-label(data$age_meno) <- "Menopause Age"
-label(data$livebirths) <- "Number of Childbirths"
-label(data$age_sur) <- "Age"
-label(data$race) <- "Race"
-label(data$education) <- "Education"
-label(data$drink) <- "Alcohol use"
-label(data$smoke) <- "Smoking status"
-label(data$pills) <- "Oral contraceptive use"
-label(data$bmi) <- "BMI"
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-units(data$bmi) <- "kg/m2"
-caption  <- "Table 1. Characteristics of the study population according to number of livebirths"
-footnote <- "Note: Continuous variables were displayed as mean (SD) and categorical variables were displayed as number (percentage)."
-mytable <- table1(~ age_sur + race + education + drink + smoke + pills + bmi | newgroup, data=data, footnote=footnote, caption=caption, overall=c(left="Total"))
-mytable
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Table 1. Characteristics of the study population according to number of livebirths
Total
(N=1189)		>=6
(N=64)		0
(N=6)		1-2
(N=577)		3-5
(N=542)

Note: Continuous variables were displayed as mean (SD) and categorical variables were displayed as number (percentage).

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Age
Mean (SD)62.7 (11.3)68.7 (10.3)60.2 (10.6)61.3 (11.1)63.5 (11.3)
Median [Min, Max]63.0 [22.0, 80.0]68.5 [37.0, 80.0]57.0 [51.0, 80.0]61.0 [22.0, 80.0]63.0 [25.0, 80.0]
Race
Black282 (23.7%)19 (29.7%)5 (83.3%)138 (23.9%)120 (22.1%)
Mexican American151 (12.7%)18 (28.1%)0 (0%)48 (8.3%)85 (15.7%)
Other Hispanic123 (10.3%)4 (6.3%)0 (0%)62 (10.7%)57 (10.5%)
White460 (38.7%)16 (25.0%)0 (0%)232 (40.2%)212 (39.1%)
Asian129 (10.8%)5 (7.8%)1 (16.7%)67 (11.6%)56 (10.3%)
Multi-Racial44 (3.7%)2 (3.1%)0 (0%)30 (5.2%)12 (2.2%)
Education
Less Than High School Diploma247 (20.8%)32 (50.0%)0 (0%)68 (11.8%)147 (27.1%)
High School Diploma305 (25.7%)10 (15.6%)3 (50.0%)139 (24.1%)153 (28.2%)
College396 (33.3%)14 (21.9%)2 (33.3%)217 (37.6%)163 (30.1%)
College Graduate Or Above241 (20.3%)8 (12.5%)1 (16.7%)153 (26.5%)79 (14.6%)
Alcohol use
Ever1015 (85.4%)53 (82.8%)3 (50.0%)503 (87.2%)456 (84.1%)
Never174 (14.6%)11 (17.2%)3 (50.0%)74 (12.8%)86 (15.9%)
Smoking status
Ever427 (35.9%)21 (32.8%)0 (0%)213 (36.9%)193 (35.6%)
Never762 (64.1%)43 (67.2%)6 (100%)364 (63.1%)349 (64.4%)
Oral contraceptive use
Ever830 (69.8%)32 (50.0%)5 (83.3%)418 (72.4%)375 (69.2%)
Never359 (30.2%)32 (50.0%)1 (16.7%)159 (27.6%)167 (30.8%)
BMI (kg/m2)
Mean (SD)30.6 (7.71)30.6 (6.15)31.7 (6.56)30.3 (7.64)30.8 (7.96)
Median [Min, Max]29.3 [15.5, 84.4]29.4 [18.4, 45.4]29.4 [26.1, 44.0]29.0 [15.5, 67.7]29.4 [16.2, 84.4]
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Regression Analysis

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Univariate and multivariate analyses were performed with simple and multiple linear regression, respectively. In univariate analysis, “umber of childbirth”, “age at survey”, and “smoking status” show statistically significance with our outcome variable. For race, only the subgroups “other Hispanic” and “Asian” are significant relative to the reference group. For education level, “high school diploma” and “college graduate or above” rose to the level of statistical significance.

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This association continued to appear after adjustment in the full model. After comparing between different combinations of covariates, the model with the lowest AIC score were picked as our final adjusted model shown in Table 2 below. In multivariable linear regression analyses, the number of childbirth was associated with the age of menopause, in which each additional childbirth is associated with a 0.34 (95% CI: 0.02, 0.66) increase of the menopausal age, while holding all other covariates constant. Future research will be needed to validify this association and explore the underlining biological mechanism.

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Slin1 = lm(age_meno ~ livebirths, data = data)
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-Mlin3 = lm(age_meno ~ livebirths + age_sur + race + education + pills + smoke, data = data)
-summary(Mlin3)
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-Call:
-lm(formula = age_meno ~ livebirths + age_sur + race + education + 
-    pills + smoke, data = data)
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-Residuals:
-    Min      1Q  Median      3Q     Max 
--24.655  -5.124   1.322   5.878  14.748 
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-Coefficients:
-                                    Estimate Std. Error t value Pr(>|t|)    
-(Intercept)                        28.737622   1.553534  18.498  < 2e-16 ***
-livebirths                          0.341821   0.162806   2.100 0.035980 *  
-age_sur                             0.220409   0.021450  10.276  < 2e-16 ***
-raceMexican American                0.675248   0.840104   0.804 0.421694    
-raceOther Hispanic                  2.472283   0.878333   2.815 0.004963 ** 
-raceWhite                          -0.819997   0.610227  -1.344 0.179286    
-raceAsian                           3.779387   0.901598   4.192 2.97e-05 ***
-raceMulti-Racial                    1.039540   1.304629   0.797 0.425723    
-educationHigh School Diploma       -0.549180   0.732069  -0.750 0.453299    
-educationCollege                    0.007979   0.701900   0.011 0.990931    
-educationCollege Graduate Or Above  2.650293   0.788800   3.360 0.000805 ***
-pillsNever                         -1.849122   0.530437  -3.486 0.000508 ***
-smokeNever                          1.588239   0.510617   3.110 0.001913 ** 
----
-Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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-Residual standard error: 7.966 on 1176 degrees of freedom
-Multiple R-squared:  0.1496,    Adjusted R-squared:  0.1409 
-F-statistic: 17.24 on 12 and 1176 DF,  p-value: < 2.2e-16
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AIC(Mlin3)
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[1] 8323.974
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library(gtsummary)
-t1 <- tbl_regression(Slin1)%>%add_global_p()
-t2 <- tbl_regression(Mlin3)%>%add_global_p()
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-tbl_merge <- tbl_merge(tbls = list(t1,t2),
-          tab_spanner = c("**Unadjusted**","**Adjusted**"))
-tbl_merge
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Characteristic - Unadjusted - - Adjusted -
Beta95% CI1p-valueBeta95% CI1p-value
Number of Childbirths0.460.14, 0.780.0040.340.02, 0.660.036
Age


0.220.18, 0.26<0.001
Race




<0.001
    Black


——
    Mexican American


0.68-0.97, 2.3
    Other Hispanic


2.50.75, 4.2
    White


-0.82-2.0, 0.38
    Asian


3.82.0, 5.5
    Multi-Racial


1.0-1.5, 3.6
Education




<0.001
    Less Than High School Diploma


——
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-0.55-2.0, 0.89
    College


0.01-1.4, 1.4
    College Graduate Or Above


2.71.1, 4.2
Oral contraceptive use




<0.001
    Ever


——
    Never


-1.8-2.9, -0.81
Smoking status




0.002
    Ever


——
    Never


1.60.59, 2.6
1 CI = Confidence Interval
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Discussion

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Univariate and multivariate analyses were performed with simple and multiple linear regression, respectively. In univariate analysis, “umber of childbirth”, “age at survey”, and “smoking status” show statistically significance with our outcome variable. For race, only the subgroups “other Hispanic” and “Asian” are significant relative to the reference group. For education level, “high school diploma” and “college graduate or above” rose to the level of statistical significance.

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This association continued to appear after adjustment in the full model. In multivariable linear regression analyses, the number of childbirth was associated with the age of menopause, in which each additional childbirth is associated with a 0.34 (95% CI: 0.02, 0.66) increase of the menopausal age, while holding all other covariates constant. Future research will be needed to validify this association and explore the underlining biological mechanism.

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Function summary

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Package nameFunctions used
tidyrdrop_na
dplyrarrange mutate group_by summarise
ggplot2geom_bar geom_vline geom_point facet_wrap geom_smooth
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References

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-CDC. 2019. “NHANES 2017-2018.” 2019. https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2017. -
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-Greendale, G. A., N. P. Lee, and E. R. Arriola. 1999. “The Menopause.” Lancet (London, England) 353 (9152): 571–80. https://doi.org/10.1016/S0140-6736(98)05352-5. -
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-WHO. 2021. “Menopause.” 2021. https://www.who.int/news-room/fact-sheets/detail/menopause. -
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BIOL103 - Bioinformatics in Medicine and Biology Final Project

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- This was my first time analysing sequencing data in R. The project analyzed 85 samples of high throughput RNA-seq data of six immune cells sorted from 13 COVID-19 patients with or without hematological cancer. -
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Yingying

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December 16, 2021

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- - - - \ No newline at end of file diff --git a/docs/projects/2021-BMB-final-project/preview-image.png b/docs/projects/2021-BMB-final-project/preview-image.png deleted file mode 100644 index e73e18a..0000000 Binary files a/docs/projects/2021-BMB-final-project/preview-image.png and /dev/null differ diff --git a/docs/search.json b/docs/search.json deleted file mode 100644 index 9e30514..0000000 --- a/docs/search.json +++ /dev/null @@ -1,107 +0,0 @@ -[ - { - "objectID": "about.html", - "href": "about.html", - "title": "About", - "section": "", - "text": "My journey has been fueled by a deep-rooted passion for making a meaningful impact in the field of oncology. My undergraduate major in biology laid the foundation for my initial career aspirations. However, over time, I found myself increasingly drawn to the world of data science and bioinformatics. I realized the potential for data-driven insights to transform our understanding of complex biological processes.\nCurrently, I’m pursuing a master’s degree in epidemiology, which has provided me with valuable knowledge and skills to better comprehend the broader context of disease patterns and public health. Through several hands-on experiences, I’ve become proficient in handling RNA sequencing data and utilizing numerous R packages related to genome-wide analysis. My goal now is to merge my biological expertise with data science and epidemiology to contribute to advancements in oncology research.\nI look forward to connecting with like-minded individuals who share my passion for driving positive change in the oncology field." - }, - { - "objectID": "index.html", - "href": "index.html", - "title": "Yingying Yu", - "section": "", - "text": "About\nWelcome to my site! My name is Yingying. I am a second-year MHS in Epidemiology student at the Johns Hopkins Bloomberg School of Public Health. My interests revolve around the convergence of cancer biology, data science, epidemiology, and omics data. Proficient in sequencing data analysis, I have experience in developing R packages, implementing version control on GitHub, and proficiently managing data using SQL.\nMy enthusiasm lies in leveraging data and analytics to address health-related challenges. I am dedicated to data analysis and contributing to open-source software that elevates the advancements in oncology research.\n\n\nPrevious/current research\n\nChromosome segregation in human cells and its regulation by SAC\nIdentify potential marker metabolites using mass spectroscopy data\nIdentify novel isoforms of interferon-stimulated genes using RNA-seq data\nAssociation of parity and breastfeeding with breast cancer prognosis\n\n\n\nEducational training\nMS in Epidemiology (2024) | Johns Hopkins University\nBA in Biology (2022) | New York University" - }, - { - "objectID": "Example analysis.html", - "href": "Example analysis.html", - "title": "Example analysis", - "section": "", - "text": "Introduction\n Menopause is a natural biological event triggered by the decline in ovarian follicular function and a reduction in circulating blood estrogen levels (Greendale, Lee, and Arriola 1999). According to the World Health Organization (WHO 2021), the majority of women experience menopause in their forties and fifties, typically defined as twelve consecutive months without menstruation, with no other underlying cause. Notably, during pregnancy, women do not have their regular menstrual period for approximately nine months due to the profound impact on ovarian activity.\n\n\n\nResearch Objective\nThe objective of this study is to investigate whether the age at which natural menopause occurs is influenced by the number of childbirths among American women. This research utilizes data from the 2017-18 National Health and Nutrition Examination Survey (NHANES). Our hypothesis posits that an increased age at menopause is associated with a higher number of childbirths.\n\n\nNote:\nThis analysis is conducted for researchers, doctors, or anyone who is interested in the risk factors of menopause.\n\n\n\nMethodology\n\nStudy population\nPerformed by the National Center for Health Statistics (NCHS) within the Centers for Disease Control and Prevention (CDC), the NHANES is a program of cross-sectional studies collecting data regarding the health and nutritional status of Americans. This study is an analysis of the 2017-2018 data for female participants located in different counties across the nation, including demographics, socioeconomic, body measurements, reproductive health-related information gathered by interviews and physical examinations. The dataset can be download on the (NHANES CDC 2019) website.\n\n\nMeasures\nThe outcome of interest in this study is the age at natural menopause of a participant. Age at menopause was obtained from the self-reported question: ‘About how old were you when you had your last menstrual period?’ In this study, we defined age at menopause as the age in years when the woman had her last menstrual period.\nThe primary covariate in this analysis is the number of childbirths. The number of childbirths was obtained from the self-reported questions: ‘How many of your deliveries resulted in a live birth?’ In this study, we used number of childbirths as a continuous variable (0–10 childbirths).\nAfter reviewing previous literatures, the following factors are included as additional covariates to address for potential confounding. Age in years was analyzed as continuous variable. Smoking status was dichotomized as “never” and “ever”. Alcohol use was dichotomized as “never” and “ever”. Educational level was categorized as “Less than high school diploma”, “High school diploma”, “College” and “College graduate or above”. Body mass index was analyzed as continues variable measured in kg/m2.\n\n\nStatistical Analysis\nData of the outcome variable and covariates will be extracted from five sub-data files and combined into a single analytic dataset prior to analysis.\n\n\nCode\n# Load data\nlibrary(\"foreign\")\nBodyMeasure <- read.xport(\"data/BMX_J.XPT\")\nDemographic <- read.xport(\"data/DEMO_J.XPT\")\nAlcohol <- read.xport(\"data/ALQ_J.XPT\")\nSmoking <- read.xport(\"data/SMQ_J.XPT\")\nReproductive <- read.xport(\"data/RHQ_J.XPT\")\nBMI <- BodyMeasure[,c('SEQN','BMXBMI')] \nDemo <- Demographic[, c('SEQN','RIDAGEYR','RIDRETH3','DMDEDUC2')]\nAlcUse <- Alcohol[,c('SEQN','ALQ111')]\nSmoUse <- Smoking[,c('SEQN','SMQ020')]\nFeHealth <- Reproductive[,c('SEQN','RHQ060','RHQ171','RHQ420')]\n\n# Merge data\ndata <- merge(BMI, Demo, by = \"SEQN\", all.x = T, all.y = F)\ndata <- merge(AlcUse, data, by = \"SEQN\", all.x = T, all.y = F)\ndata <- merge(SmoUse, data, by = \"SEQN\", all.x = F, all.y = T)\ndata <- merge(FeHealth, data, by = \"SEQN\", all.x = T, all.y = F)\n\n# Change column names\ncolnames(data) <- c('ID','age_meno','livebirths','pills','smoke','drink','bmi',\n 'age_sur','race','education')\nhead(data,5)\n\n\n ID age_meno livebirths pills smoke drink bmi age_sur race education\n1 93705 50 2 1 1 1 31.7 66 4 2\n2 93708 48 3 1 2 2 23.7 66 6 1\n3 93709 NA NA NA 1 NA 38.9 75 4 4\n4 93714 52 1 1 1 1 39.9 54 4 4\n5 93719 NA NA 2 NA NA NA NA NA NA\n\n\n\n\n\n\n\n\nData Dictionary\n\n\n\nThe variables on the above data set are:\n\n\n\nName\nMeaning\n\n\n\n\nID\nParticipant ID\n\n\nage_meno\nMenopausal age\n\n\nlivebirths\nNumber of deliveries\n\n\npills\nOral contraceptive use\n\n\nsmoke\nSmoking status\n\n\ndrink\nDrinking status\n\n\nbmi\nBMI index\n\n\nage_sur\nAge at survey time\n\n\nrace\nRace/ethnicity\n\n\n\n\n\n\n\nCode\n# drop NA and weird values\nlibrary(\"tidyr\")\nlibrary(\"dplyr\")\ndata <- drop_na(data) \ndata <- data[data$age_meno != 999, ] # exclude those dont know when is their last period\ndata <- data[data$pills != 9, ] # exclude those dont know whether they ever taken birth control pills\ndata <- data[data$education != 9, ] # exclude those dont know their education level\ndata <- arrange(data)\nhead(data,5)\n\n\n ID age_meno livebirths pills smoke drink bmi age_sur race education\n1 93705 50 2 1 1 1 31.7 66 4 2\n2 93708 48 3 1 2 2 23.7 66 6 1\n3 93714 52 1 1 1 1 39.9 54 4 4\n4 93721 50 4 1 2 1 35.9 60 1 1\n5 93722 43 2 1 2 1 23.8 60 3 4\n\n\n\n\nCode\n# Quick look on menopausal age and number of delivery\ndata %>% \n group_by(livebirths) %>% \n summarise(mean = mean(age_meno)) \n\n\n# A tibble: 11 × 2\n livebirths mean\n <dbl> <dbl>\n 1 0 48.5\n 2 1 43.9\n 3 2 44.4\n 4 3 45.2\n 5 4 44.9\n 6 5 45.9\n 7 6 47.5\n 8 7 47.2\n 9 8 47.3\n10 10 46 \n11 11 50 \n\n\nLooks like there is a linear relationship between menopausal age and number of deliveries, ignoring women with no childbirth. Let’s see if this is still true if we group all the women by number of childbirth.\n\n\nCode\ndata <- data %>%\n mutate(newgroup = case_when(\n livebirths <= 0 ~ \"0\",\n between(livebirths, 1, 2) ~ \"1-2\",\n between(livebirths, 3, 5) ~ \"3-5\",\n livebirths >= 6 ~ \">=6\",\n TRUE ~ NA_character_))\n\ndata %>% \n group_by(newgroup) %>% \n summarise(mean = mean(age_meno))\n\n\n# A tibble: 4 × 2\n newgroup mean\n <chr> <dbl>\n1 0 48.5\n2 1-2 44.2\n3 3-5 45.2\n4 >=6 47.4\n\n\n\n\nCode\n# Make categorical variables\ndata$race <- factor(data$race, labels = c(\"Mexican American\",\"Other Hispanic\",\"White\",\"Black\",\"Asian\",\"Multi-Racial\")) %>% relevel(data$race, ref = \"Black\")\ndata$drink <- factor(data$drink, labels = c(\"Ever\", \"Never\"))\ndata$smoke <- factor(data$smoke, labels = c(\"Ever\", \"Never\"))\ndata$pills <- factor(data$pills, labels = c(\"Ever\", \"Never\"))\ndata$education[data$education == 1] <- 2\ndata$education <- factor(data$education, labels = c(\"Less Than High School Diploma\",\n \"High School Diploma\",\"College\",\"College Graduate Or Above\"))\n\n\n\n\n\nResults\n\nParticipant Characteristics\nThe mean age of participants the survey was done is 62.7 (SD = 11.3) years old and ranged from 22 to 80. Of the 1189 female participants, most of them are non-Hispanic White (38.7%), have some college experience (33.3%), drink alcohol at some point in their life (85.4%), and never smoke (64.1%). Table 1 below presents specific breakdowns of each variable considered by this study.\n\n\nCode\n# Plot distribution of age\nlibrary(ggplot2)\nggplot(data=data) +\n geom_bar(mapping=aes(x=age_sur)) +\n geom_vline(xintercept = mean(data$age_sur), color = \"red\", linetype = \"dashed\", size = 1) +\n labs(title = \"Figure 1:\", subtile = \"Distribution of Participant Age at survey\",\n x = \"Age\", y = \"Frequency\", caption = \"Created by Yinging Yu\")\n\n\n\n\n\n\n\n\n\n\n\nNote\n\n\n\nNote that the frequency of 80 years old is extremely high because it includes 80 years of age and over.\n\n\n\n\nCode\nggplot(data = data) + \n geom_point(mapping = aes(x = age_meno, y = livebirths)) + \n facet_wrap(~ race, nrow = 2) +\n labs(title = \"Figure 2:\", \n subtitle = \"Distribution of Menopausal Age and Livebirths by Race/Ethnicity\", \n x = \"Menopausal Age\", y = \"Number of Livebirths\", \n caption = \"Created by Yinging Yu\") \n\n\n\n\n\n\n\nCode\nggplot(data = data) + \n geom_smooth(mapping = aes(x = bmi, y = age_meno)) +\n labs(title = \"Figure 3:\", subtitle = \"Distribution of BMI and Menopausal Age\", \n x = \"BMI\", y = \"Menopausal Age\",caption = \"Created by Yinging Yu\") \n\n\n\n\n\n\n\nCode\nlibrary(\"table1\")\nlabel(data$age_meno) <- \"Menopause Age\"\nlabel(data$livebirths) <- \"Number of Childbirths\"\nlabel(data$age_sur) <- \"Age\"\nlabel(data$race) <- \"Race\"\nlabel(data$education) <- \"Education\"\nlabel(data$drink) <- \"Alcohol use\"\nlabel(data$smoke) <- \"Smoking status\"\nlabel(data$pills) <- \"Oral contraceptive use\"\nlabel(data$bmi) <- \"BMI\"\n\nunits(data$bmi) <- \"kg/m2\"\ncaption <- \"Table 1. Characteristics of the study population according to number of livebirths\"\nfootnote <- \"Note: Continuous variables were displayed as mean (SD) and categorical variables were displayed as number (percentage).\"\nmytable <- table1(~ age_sur + race + education + drink + smoke + pills + bmi | newgroup, data=data, footnote=footnote, caption=caption, overall=c(left=\"Total\"))\nmytable\n\n\n\nTable 1. Characteristics of the study population according to number of livebirths\n\n\n\n\nTotal(N=1189)\n>=6(N=64)\n0(N=6)\n1-2(N=577)\n3-5(N=542)\n\nNote: Continuous variables were displayed as mean (SD) and categorical variables were displayed as number (percentage).\n\n\n\n\nAge\n\n\n\n\n\n\n\nMean (SD)\n62.7 (11.3)\n68.7 (10.3)\n60.2 (10.6)\n61.3 (11.1)\n63.5 (11.3)\n\n\nMedian [Min, Max]\n63.0 [22.0, 80.0]\n68.5 [37.0, 80.0]\n57.0 [51.0, 80.0]\n61.0 [22.0, 80.0]\n63.0 [25.0, 80.0]\n\n\nRace\n\n\n\n\n\n\n\nBlack\n282 (23.7%)\n19 (29.7%)\n5 (83.3%)\n138 (23.9%)\n120 (22.1%)\n\n\nMexican American\n151 (12.7%)\n18 (28.1%)\n0 (0%)\n48 (8.3%)\n85 (15.7%)\n\n\nOther Hispanic\n123 (10.3%)\n4 (6.3%)\n0 (0%)\n62 (10.7%)\n57 (10.5%)\n\n\nWhite\n460 (38.7%)\n16 (25.0%)\n0 (0%)\n232 (40.2%)\n212 (39.1%)\n\n\nAsian\n129 (10.8%)\n5 (7.8%)\n1 (16.7%)\n67 (11.6%)\n56 (10.3%)\n\n\nMulti-Racial\n44 (3.7%)\n2 (3.1%)\n0 (0%)\n30 (5.2%)\n12 (2.2%)\n\n\nEducation\n\n\n\n\n\n\n\nLess Than High School Diploma\n247 (20.8%)\n32 (50.0%)\n0 (0%)\n68 (11.8%)\n147 (27.1%)\n\n\nHigh School Diploma\n305 (25.7%)\n10 (15.6%)\n3 (50.0%)\n139 (24.1%)\n153 (28.2%)\n\n\nCollege\n396 (33.3%)\n14 (21.9%)\n2 (33.3%)\n217 (37.6%)\n163 (30.1%)\n\n\nCollege Graduate Or Above\n241 (20.3%)\n8 (12.5%)\n1 (16.7%)\n153 (26.5%)\n79 (14.6%)\n\n\nAlcohol use\n\n\n\n\n\n\n\nEver\n1015 (85.4%)\n53 (82.8%)\n3 (50.0%)\n503 (87.2%)\n456 (84.1%)\n\n\nNever\n174 (14.6%)\n11 (17.2%)\n3 (50.0%)\n74 (12.8%)\n86 (15.9%)\n\n\nSmoking status\n\n\n\n\n\n\n\nEver\n427 (35.9%)\n21 (32.8%)\n0 (0%)\n213 (36.9%)\n193 (35.6%)\n\n\nNever\n762 (64.1%)\n43 (67.2%)\n6 (100%)\n364 (63.1%)\n349 (64.4%)\n\n\nOral contraceptive use\n\n\n\n\n\n\n\nEver\n830 (69.8%)\n32 (50.0%)\n5 (83.3%)\n418 (72.4%)\n375 (69.2%)\n\n\nNever\n359 (30.2%)\n32 (50.0%)\n1 (16.7%)\n159 (27.6%)\n167 (30.8%)\n\n\nBMI (kg/m2)\n\n\n\n\n\n\n\nMean (SD)\n30.6 (7.71)\n30.6 (6.15)\n31.7 (6.56)\n30.3 (7.64)\n30.8 (7.96)\n\n\nMedian [Min, Max]\n29.3 [15.5, 84.4]\n29.4 [18.4, 45.4]\n29.4 [26.1, 44.0]\n29.0 [15.5, 67.7]\n29.4 [16.2, 84.4]\n\n\n\n\n\n\n\n\nRegression Analysis\nUnivariate and multivariate analyses were performed with simple and multiple linear regression, respectively. In univariate analysis, “umber of childbirth”, “age at survey”, and “smoking status” show statistically significance with our outcome variable. For race, only the subgroups “other Hispanic” and “Asian” are significant relative to the reference group. For education level, “high school diploma” and “college graduate or above” rose to the level of statistical significance.\nThis association continued to appear after adjustment in the full model. After comparing between different combinations of covariates, the model with the lowest AIC score were picked as our final adjusted model shown in Table 2 below. In multivariable linear regression analyses, the number of childbirth was associated with the age of menopause, in which each additional childbirth is associated with a 0.34 (95% CI: 0.02, 0.66) increase of the menopausal age, while holding all other covariates constant. Future research will be needed to validify this association and explore the underlining biological mechanism.\n\n\nCode\nSlin1 = lm(age_meno ~ livebirths, data = data)\n\nMlin3 = lm(age_meno ~ livebirths + age_sur + race + education + pills + smoke, data = data)\nsummary(Mlin3)\n\n\n\nCall:\nlm(formula = age_meno ~ livebirths + age_sur + race + education + \n pills + smoke, data = data)\n\nResiduals:\n Min 1Q Median 3Q Max \n-24.655 -5.124 1.322 5.878 14.748 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 28.737622 1.553534 18.498 < 2e-16 ***\nlivebirths 0.341821 0.162806 2.100 0.035980 * \nage_sur 0.220409 0.021450 10.276 < 2e-16 ***\nraceMexican American 0.675248 0.840104 0.804 0.421694 \nraceOther Hispanic 2.472283 0.878333 2.815 0.004963 ** \nraceWhite -0.819997 0.610227 -1.344 0.179286 \nraceAsian 3.779387 0.901598 4.192 2.97e-05 ***\nraceMulti-Racial 1.039540 1.304629 0.797 0.425723 \neducationHigh School Diploma -0.549180 0.732069 -0.750 0.453299 \neducationCollege 0.007979 0.701900 0.011 0.990931 \neducationCollege Graduate Or Above 2.650293 0.788800 3.360 0.000805 ***\npillsNever -1.849122 0.530437 -3.486 0.000508 ***\nsmokeNever 1.588239 0.510617 3.110 0.001913 ** \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 7.966 on 1176 degrees of freedom\nMultiple R-squared: 0.1496, Adjusted R-squared: 0.1409 \nF-statistic: 17.24 on 12 and 1176 DF, p-value: < 2.2e-16\n\n\nCode\nAIC(Mlin3)\n\n\n[1] 8323.974\n\n\nCode\nlibrary(gtsummary)\nt1 <- tbl_regression(Slin1)%>%add_global_p()\nt2 <- tbl_regression(Mlin3)%>%add_global_p()\n\ntbl_merge <- tbl_merge(tbls = list(t1,t2),\n tab_spanner = c(\"**Unadjusted**\",\"**Adjusted**\"))\ntbl_merge \n\n\n\n\n\n\n \n \n \n Characteristic\n \n Unadjusted\n \n \n Adjusted\n \n \n \n Beta\n 95% CI1\n p-value\n Beta\n 95% CI1\n p-value\n \n \n \n Number of Childbirths\n0.46\n0.14, 0.78\n0.004\n0.34\n0.02, 0.66\n0.036\n Age\n\n\n\n0.22\n0.18, 0.26\n<0.001\n Race\n\n\n\n\n\n<0.001\n     Black\n\n\n\n—\n—\n\n     Mexican American\n\n\n\n0.68\n-0.97, 2.3\n\n     Other Hispanic\n\n\n\n2.5\n0.75, 4.2\n\n     White\n\n\n\n-0.82\n-2.0, 0.38\n\n     Asian\n\n\n\n3.8\n2.0, 5.5\n\n     Multi-Racial\n\n\n\n1.0\n-1.5, 3.6\n\n Education\n\n\n\n\n\n<0.001\n     Less Than High School Diploma\n\n\n\n—\n—\n\n     High School Diploma\n\n\n\n-0.55\n-2.0, 0.89\n\n     College\n\n\n\n0.01\n-1.4, 1.4\n\n     College Graduate Or Above\n\n\n\n2.7\n1.1, 4.2\n\n Oral contraceptive use\n\n\n\n\n\n<0.001\n     Ever\n\n\n\n—\n—\n\n     Never\n\n\n\n-1.8\n-2.9, -0.81\n\n Smoking status\n\n\n\n\n\n0.002\n     Ever\n\n\n\n—\n—\n\n     Never\n\n\n\n1.6\n0.59, 2.6\n\n \n \n \n \n 1 CI = Confidence Interval\n \n \n\n\n\n\n\n\n\nDiscussion\nUnivariate and multivariate analyses were performed with simple and multiple linear regression, respectively. In univariate analysis, “umber of childbirth”, “age at survey”, and “smoking status” show statistically significance with our outcome variable. For race, only the subgroups “other Hispanic” and “Asian” are significant relative to the reference group. For education level, “high school diploma” and “college graduate or above” rose to the level of statistical significance.\nThis association continued to appear after adjustment in the full model. In multivariable linear regression analyses, the number of childbirth was associated with the age of menopause, in which each additional childbirth is associated with a 0.34 (95% CI: 0.02, 0.66) increase of the menopausal age, while holding all other covariates constant. Future research will be needed to validify this association and explore the underlining biological mechanism.\n\n\n\nFunction summary\n\n\n\n\n\n\n\nPackage name\nFunctions used\n\n\n\n\ntidyr\ndrop_na\n\n\ndplyr\narrange mutate group_by summarise\n\n\nggplot2\ngeom_bar geom_vline geom_point facet_wrap geom_smooth\n\n\n\n\n\n\n\n\n\n\n\nReferences\n\nCDC. 2019. “NHANES 2017-2018.” 2019. https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2017.\n\n\nGreendale, G. A., N. P. Lee, and E. R. Arriola. 1999. “The Menopause.” Lancet (London, England) 353 (9152): 571–80. https://doi.org/10.1016/S0140-6736(98)05352-5.\n\n\nWHO. 2021. “Menopause.” 2021. https://www.who.int/news-room/fact-sheets/detail/menopause." - }, - { - "objectID": "Example analysis.html#study-population", - "href": "Example analysis.html#study-population", - "title": "Example analysis", - "section": "Study population", - "text": "Study population\nPerformed by the National Center for Health Statistics (NCHS) within the Centers for Disease Control and Prevention (CDC), the NHANES is a program of cross-sectional studies collecting data regarding the health and nutritional status of Americans. This study is an analysis of the 2017-2018 data for female participants located in different counties across the nation, including demographics, socioeconomic, body measurements, reproductive health-related information gathered by interviews and physical examinations. The dataset can be download here." - }, - { - "objectID": "Example analysis.html#measures", - "href": "Example analysis.html#measures", - "title": "Example analysis", - "section": "Measures", - "text": "Measures\nThe outcome of interest in this study is the age at natural menopause of a participant. Age at menopause was obtained from the self-reported question: ‘About how old were you when you had your last menstrual period?’ In this study, we defined age at menopause as the age in years when the woman had her last menstrual period.\nThe primary covariate in this analysis is the number of childbirths. The number of childbirths was obtained from the self-reported questions: ‘How many of your deliveries resulted in a live birth?’ In this study, we used number of childbirths as a continuous variable (0–10 childbirths).\nAfter reviewing previous literatures, the following factors are included as additional covariates to address for potential confounding. Age in years was analyzed as continuous variable. Smoking status was dichotomized as “never” and “ever”. Alcohol use was dichotomized as “never” and “ever”. Educational level was categorized as “Less than high school diploma”, “High school diploma”, “College” and “College graduate or above”. Body mass index was analyzed as continues variable measured in kg/m2." - }, - { - "objectID": "Example analysis.html#statistical-analysis", - "href": "Example analysis.html#statistical-analysis", - "title": "Example analysis", - "section": "Statistical Analysis", - "text": "Statistical Analysis\nData of the outcome variable and covariates will be extracted from five sub-data files and combined into a single analytic dataset prior to analysis.\n\n# Load data\nlibrary(\"foreign\")\nBodyMeasure <- read.xport(\"data/BMX_J.XPT\")\nDemographic <- read.xport(\"data/DEMO_J.XPT\")\nAlcohol <- read.xport(\"data/ALQ_J.XPT\")\nSmoking <- read.xport(\"data/SMQ_J.XPT\")\nReproductive <- read.xport(\"data/RHQ_J.XPT\")\nBMI <- BodyMeasure[,c('SEQN','BMXBMI')] \nDemo <- Demographic[, c('SEQN','RIDAGEYR','RIDRETH3','DMDEDUC2')]\nAlcUse <- Alcohol[,c('SEQN','ALQ111')]\nSmoUse <- Smoking[,c('SEQN','SMQ020')]\nFeHealth <- Reproductive[,c('SEQN','RHQ060','RHQ171','RHQ420')]\n\n# Merge data\ndata <- merge(BMI, Demo, by = \"SEQN\", all.x = T, all.y = F)\ndata <- merge(AlcUse, data, by = \"SEQN\", all.x = T, all.y = F)\ndata <- merge(SmoUse, data, by = \"SEQN\", all.x = F, all.y = T)\ndata <- merge(FeHealth, data, by = \"SEQN\", all.x = T, all.y = F)\n\n# Change column names\ncolnames(data) <- c('ID','age_meno','livebirths','pills','smoke','drink','bmi',\n 'age_sur','race','education')\nhead(data,5)\n\n ID age_meno livebirths pills smoke drink bmi age_sur race education\n1 93705 50 2 1 1 1 31.7 66 4 2\n2 93708 48 3 1 2 2 23.7 66 6 1\n3 93709 NA NA NA 1 NA 38.9 75 4 4\n4 93714 52 1 1 1 1 39.9 54 4 4\n5 93719 NA NA 2 NA NA NA NA NA NA\n\n\n\n# drop NA and weird values\nlibrary(\"tidyr\")\ndata <- drop_na(data)\ndata <- data[data$age_meno != 999, ] # exclude those dont know when is their last period\ndata <- data[data$pills != 9, ] # exclude those dont know whether they ever taken birth control pills\ndata <- data[data$education != 9, ] # exclude those dont know their education level\nhead(data,5)\n\n ID age_meno livebirths pills smoke drink bmi age_sur race education\n1 93705 50 2 1 1 1 31.7 66 4 2\n2 93708 48 3 1 2 2 23.7 66 6 1\n3 93714 52 1 1 1 1 39.9 54 4 4\n4 93721 50 4 1 2 1 35.9 60 1 1\n5 93722 43 2 1 2 1 23.8 60 3 4\n\n\n\n# Make categorical variables\ndata$race <- factor(data$race, labels = c(\"Mexican American\", \"Other Hispanic\",\n \"White\",\"Black\",\"Asian\",\"Multi-Racial\")) %>% relevel(data$race, ref = \"Black\")\ndata$drink <- factor(data$drink, labels = c(\"Ever\", \"Never\"))\ndata$smoke <- factor(data$smoke, labels = c(\"Ever\", \"Never\"))\ndata$pills <- factor(data$pills, labels = c(\"Ever\", \"Never\"))\ndata$education[data$education == 1] <- 2\ndata$education <- factor(data$education, labels = c(\"Less Than High School Diploma\",\"High School Diploma\",\"College\",\"College Graduate Or Above\"))" - }, - { - "objectID": "index.html#second-year-mhs-student-in-epidemiology-at-johns-hopkins-univeristy", - "href": "index.html#second-year-mhs-student-in-epidemiology-at-johns-hopkins-univeristy", - "title": "Yingying Yu", - "section": "", - "text": "Yingying possesses an ingrained passion for creating a substantial influence in the realm of oncology. With a foundation in cell and molecular biology, she discovered a growing attraction to the domain of data science and bioinformatics. This shift occurred as she recognized the capacity of data-driven insights to revolutionize comprehension of intricate biological processes." - }, - { - "objectID": "index.html#her-previouscurrent-research", - "href": "index.html#her-previouscurrent-research", - "title": "Yingying Yu", - "section": "Her previous/current research", - "text": "Her previous/current research\n\nChromosome segregation in human cells and its regulation by Spindle Assembly Checkpoint (SAC)\nIdentify potential marker metabolites using mass spectroscopy data\nIdentify novel isoforms of interferon-stimulated genes using RNA-seq data\nAssociation of parity and breastfeeding with breast cancer prognosis" - }, - { - "objectID": "index.html#educational-training", - "href": "index.html#educational-training", - "title": "Yingying Yu", - "section": "Educational training", - "text": "Educational training\nMS in Epidemiology (2024) | Johns Hopkins University\nBA in Biology (2022) | New York University" - }, - { - "objectID": "index.html#working-experience", - "href": "index.html#working-experience", - "title": "Yingying Yu", - "section": "Working experience", - "text": "Working experience\nBioinformatics intern | Shanghai Luming Biological Technology\nBusiness development intern | BGI Groups\nSummer research fellow | NIH NCI" - }, - { - "objectID": "projects.html", - "href": "projects.html", - "title": "Projects", - "section": "", - "text": "Effects on Natural Menopausal Age by Number of Childbirth\n\n\n\n\n\n\n\nNHANES\n\n\nEpidemiology\n\n\n\n\n\n\n\n\n\n\n\nNov 1, 2023\n\n\nYingying Yu\n\n\n\n\n\n\nNo matching items" - }, - { - "objectID": "index.html#previouscurrent-research", - "href": "index.html#previouscurrent-research", - "title": "Yingying Yu", - "section": "Previous/current research", - "text": "Previous/current research\n\nChromosome segregation in human cells and its regulation by SAC\nIdentify potential marker metabolites using mass spectroscopy data\nIdentify novel isoforms of interferon-stimulated genes using RNA-seq data\nAssociation of parity and breastfeeding with breast cancer prognosis" - }, - { - "objectID": "index.html#about", - "href": "index.html#about", - "title": "Yingying Yu", - "section": "", - "text": "Welcome to my site! My name is Yingying. I am a second-year MHS in Epidemiology student at the Johns Hopkins Bloomberg School of Public Health. My interests revolve around the convergence of cancer biology, data science, epidemiology, and omics data. Proficient in sequencing data analysis, I have experience in developing R packages, implementing version control on GitHub, and proficiently managing data using SQL.\nMy enthusiasm lies in leveraging data and analytics to address health-related challenges. I am dedicated to data analysis and contributing to open-source software that elevates the advancements in oncology research." - }, - { - "objectID": "index.html#programming-skills", - "href": "index.html#programming-skills", - "title": "Yingying Yu", - "section": "Programming skills", - "text": "Programming skills\nR, Python, SQL" - }, - { - "objectID": "projects/140.777.Project1/Example analysis.html", - "href": "projects/140.777.Project1/Example analysis.html", - "title": "Effects on Natural Menopausal Age by Number of Childbirth", - "section": "", - "text": "Introduction\n Menopause is a natural biological event triggered by the decline in ovarian follicular function and a reduction in circulating blood estrogen levels (Greendale, Lee, and Arriola 1999). According to the World Health Organization (WHO 2021), the majority of women experience menopause in their forties and fifties, typically defined as twelve consecutive months without menstruation, with no other underlying cause. Notably, during pregnancy, women do not have their regular menstrual period for approximately nine months due to the profound impact on ovarian activity.\n\n\n\nResearch Objective\nThe objective of this study is to investigate whether the age at which natural menopause occurs is influenced by the number of childbirths among American women. This research utilizes data from the 2017-18 National Health and Nutrition Examination Survey (NHANES). Our hypothesis posits that an increased age at menopause is associated with a higher number of childbirths.\n\n\n\nMethodology\n\nStudy population\nPerformed by the National Center for Health Statistics (NCHS) within the Centers for Disease Control and Prevention (CDC), the NHANES is a program of cross-sectional studies collecting data regarding the health and nutritional status of Americans. This study is an analysis of the 2017-2018 data for female participants located in different counties across the nation, including demographics, socioeconomic, body measurements, reproductive health-related information gathered by interviews and physical examinations. The dataset can be download on the (NHANES CDC 2019) website.\n\n\nMeasures\nThe outcome of interest in this study is the age at natural menopause of a participant. Age at menopause was obtained from the self-reported question: ‘About how old were you when you had your last menstrual period?’ In this study, we defined age at menopause as the age in years when the woman had her last menstrual period.\nThe primary covariate in this analysis is the number of childbirths. The number of childbirths was obtained from the self-reported questions: ‘How many of your deliveries resulted in a live birth?’ In this study, we used number of childbirths as a continuous variable (0–10 childbirths).\nAfter reviewing previous literatures, the following factors are included as additional covariates to address for potential confounding. Age in years was analyzed as continuous variable. Smoking status was dichotomized as “never” and “ever”. Alcohol use was dichotomized as “never” and “ever”. Educational level was categorized as “Less than high school diploma”, “High school diploma”, “College” and “College graduate or above”. Body mass index was analyzed as continues variable measured in kg/m2.\n\n\nStatistical Analysis\nData of the outcome variable and covariates will be extracted from five sub-data files and combined into a single analytic dataset prior to analysis.\n\n\nCode\n# Load data\nlibrary(\"foreign\")\nBodyMeasure <- read.xport(\"data/BMX_J.XPT\")\nDemographic <- read.xport(\"data/DEMO_J.XPT\")\nAlcohol <- read.xport(\"data/ALQ_J.XPT\")\nSmoking <- read.xport(\"data/SMQ_J.XPT\")\nReproductive <- read.xport(\"data/RHQ_J.XPT\")\nBMI <- BodyMeasure[,c('SEQN','BMXBMI')] \nDemo <- Demographic[, c('SEQN','RIDAGEYR','RIDRETH3','DMDEDUC2')]\nAlcUse <- Alcohol[,c('SEQN','ALQ111')]\nSmoUse <- Smoking[,c('SEQN','SMQ020')]\nFeHealth <- Reproductive[,c('SEQN','RHQ060','RHQ171','RHQ420')]\n\n# Merge data\ndata <- merge(BMI, Demo, by = \"SEQN\", all.x = T, all.y = F)\ndata <- merge(AlcUse, data, by = \"SEQN\", all.x = T, all.y = F)\ndata <- merge(SmoUse, data, by = \"SEQN\", all.x = F, all.y = T)\ndata <- merge(FeHealth, data, by = \"SEQN\", all.x = T, all.y = F)\n\n# Change column names\ncolnames(data) <- c('ID','age_meno','livebirths','pills','smoke','drink','bmi',\n 'age_sur','race','education')\nhead(data,5)\n\n\n ID age_meno livebirths pills smoke drink bmi age_sur race education\n1 93705 50 2 1 1 1 31.7 66 4 2\n2 93708 48 3 1 2 2 23.7 66 6 1\n3 93709 NA NA NA 1 NA 38.9 75 4 4\n4 93714 52 1 1 1 1 39.9 54 4 4\n5 93719 NA NA 2 NA NA NA NA NA NA\n\n\n\n\n\n\n\n\nData Dictionary\n\n\n\nThe variables on the above data set are:\n\n\n\nName\nMeaning\n\n\n\n\nID\nParticipant ID\n\n\nage_meno\nMenopausal age\n\n\nlivebirths\nNumber of deliveries\n\n\npills\nOral contraceptive use\n\n\nsmoke\nSmoking status\n\n\ndrink\nDrinking status\n\n\nbmi\nBMI index\n\n\nage_sur\nAge at survey time\n\n\nrace\nRace/ethnicity\n\n\n\n\n\n\n\nCode\n# drop NA and weird values\nlibrary(\"tidyr\")\nlibrary(\"dplyr\")\ndata <- drop_na(data) \ndata <- data[data$age_meno != 999, ] # exclude those dont know when is their last period\ndata <- data[data$pills != 9, ] # exclude those dont know whether they ever taken birth control pills\ndata <- data[data$education != 9, ] # exclude those dont know their education level\ndata <- arrange(data)\nhead(data,5)\n\n\n ID age_meno livebirths pills smoke drink bmi age_sur race education\n1 93705 50 2 1 1 1 31.7 66 4 2\n2 93708 48 3 1 2 2 23.7 66 6 1\n3 93714 52 1 1 1 1 39.9 54 4 4\n4 93721 50 4 1 2 1 35.9 60 1 1\n5 93722 43 2 1 2 1 23.8 60 3 4\n\n\n\n\nCode\n# Quick look on menopausal age and number of delivery\ndata %>% \n group_by(livebirths) %>% \n summarise(mean = mean(age_meno)) \n\n\n# A tibble: 11 × 2\n livebirths mean\n <dbl> <dbl>\n 1 0 48.5\n 2 1 43.9\n 3 2 44.4\n 4 3 45.2\n 5 4 44.9\n 6 5 45.9\n 7 6 47.5\n 8 7 47.2\n 9 8 47.3\n10 10 46 \n11 11 50 \n\n\nLooks like there is a linear relationship between menopausal age and number of deliveries, ignoring women with no childbirth. Let’s see if this is still true if we group all the women by number of childbirth.\n\n\nCode\ndata <- data %>%\n mutate(newgroup = case_when(\n livebirths <= 0 ~ \"0\",\n between(livebirths, 1, 2) ~ \"1-2\",\n between(livebirths, 3, 5) ~ \"3-5\",\n livebirths >= 6 ~ \">=6\",\n TRUE ~ NA_character_))\n\ndata %>% \n group_by(newgroup) %>% \n summarise(mean = mean(age_meno))\n\n\n# A tibble: 4 × 2\n newgroup mean\n <chr> <dbl>\n1 0 48.5\n2 1-2 44.2\n3 3-5 45.2\n4 >=6 47.4\n\n\n\n\nCode\n# Make categorical variables\ndata$race <- factor(data$race, labels = c(\"Mexican American\",\"Other Hispanic\",\"White\",\"Black\",\"Asian\",\"Multi-Racial\")) %>% relevel(data$race, ref = \"Black\")\ndata$drink <- factor(data$drink, labels = c(\"Ever\", \"Never\"))\ndata$smoke <- factor(data$smoke, labels = c(\"Ever\", \"Never\"))\ndata$pills <- factor(data$pills, labels = c(\"Ever\", \"Never\"))\ndata$education[data$education == 1] <- 2\ndata$education <- factor(data$education, labels = c(\"Less Than High School Diploma\",\n \"High School Diploma\",\"College\",\"College Graduate Or Above\"))\n\n\n\n\n\nResults\n\nParticipant Characteristics\nThe mean age of participants the survey was done is 62.7 (SD = 11.3) years old and ranged from 22 to 80. Of the 1189 female participants, most of them are non-Hispanic White (38.7%), have some college experience (33.3%), drink alcohol at some point in their life (85.4%), and never smoke (64.1%). Table 1 below presents specific breakdowns of each variable considered by this study.\n\n\nCode\n# Plot distribution of age\nlibrary(ggplot2)\nggplot(data=data) +\n geom_bar(mapping=aes(x=age_sur)) +\n geom_vline(xintercept = mean(data$age_sur), color = \"red\", linetype = \"dashed\", size = 1) +\n labs(title = \"Figure 1:\", subtile = \"Distribution of Participant Age at survey\",\n x = \"Age\", y = \"Frequency\", caption = \"Created by Yinging Yu\")\n\n\n\n\n\n\n\n\n\n\n\nNote\n\n\n\nNote that the frequency of 80 years old is extremely high because it includes 80 years of age and over.\n\n\n\n\nCode\nggplot(data = data) + \n geom_point(mapping = aes(x = age_meno, y = livebirths)) + \n facet_wrap(~ race, nrow = 2) +\n labs(title = \"Figure 2:\", \n subtitle = \"Distribution of Menopausal Age and Livebirths by Race/Ethnicity\", \n x = \"Menopausal Age\", y = \"Number of Livebirths\", \n caption = \"Created by Yinging Yu\") \n\n\n\n\n\n\n\nCode\nggplot(data = data) + \n geom_smooth(mapping = aes(x = bmi, y = age_meno)) +\n labs(title = \"Figure 3:\", subtitle = \"Distribution of BMI and Menopausal Age\", \n x = \"BMI\", y = \"Menopausal Age\",caption = \"Created by Yinging Yu\") \n\n\n\n\n\n\n\nCode\nlibrary(\"table1\")\nlabel(data$age_meno) <- \"Menopause Age\"\nlabel(data$livebirths) <- \"Number of Childbirths\"\nlabel(data$age_sur) <- \"Age\"\nlabel(data$race) <- \"Race\"\nlabel(data$education) <- \"Education\"\nlabel(data$drink) <- \"Alcohol use\"\nlabel(data$smoke) <- \"Smoking status\"\nlabel(data$pills) <- \"Oral contraceptive use\"\nlabel(data$bmi) <- \"BMI\"\n\nunits(data$bmi) <- \"kg/m2\"\ncaption <- \"Table 1. Characteristics of the study population according to number of livebirths\"\nfootnote <- \"Note: Continuous variables were displayed as mean (SD) and categorical variables were displayed as number (percentage).\"\nmytable <- table1(~ age_sur + race + education + drink + smoke + pills + bmi | newgroup, data=data, footnote=footnote, caption=caption, overall=c(left=\"Total\"))\nmytable\n\n\n\nTable 1. Characteristics of the study population according to number of livebirths\n\n\n\n\nTotal(N=1189)\n>=6(N=64)\n0(N=6)\n1-2(N=577)\n3-5(N=542)\n\nNote: Continuous variables were displayed as mean (SD) and categorical variables were displayed as number (percentage).\n\n\n\n\nAge\n\n\n\n\n\n\n\nMean (SD)\n62.7 (11.3)\n68.7 (10.3)\n60.2 (10.6)\n61.3 (11.1)\n63.5 (11.3)\n\n\nMedian [Min, Max]\n63.0 [22.0, 80.0]\n68.5 [37.0, 80.0]\n57.0 [51.0, 80.0]\n61.0 [22.0, 80.0]\n63.0 [25.0, 80.0]\n\n\nRace\n\n\n\n\n\n\n\nBlack\n282 (23.7%)\n19 (29.7%)\n5 (83.3%)\n138 (23.9%)\n120 (22.1%)\n\n\nMexican American\n151 (12.7%)\n18 (28.1%)\n0 (0%)\n48 (8.3%)\n85 (15.7%)\n\n\nOther Hispanic\n123 (10.3%)\n4 (6.3%)\n0 (0%)\n62 (10.7%)\n57 (10.5%)\n\n\nWhite\n460 (38.7%)\n16 (25.0%)\n0 (0%)\n232 (40.2%)\n212 (39.1%)\n\n\nAsian\n129 (10.8%)\n5 (7.8%)\n1 (16.7%)\n67 (11.6%)\n56 (10.3%)\n\n\nMulti-Racial\n44 (3.7%)\n2 (3.1%)\n0 (0%)\n30 (5.2%)\n12 (2.2%)\n\n\nEducation\n\n\n\n\n\n\n\nLess Than High School Diploma\n247 (20.8%)\n32 (50.0%)\n0 (0%)\n68 (11.8%)\n147 (27.1%)\n\n\nHigh School Diploma\n305 (25.7%)\n10 (15.6%)\n3 (50.0%)\n139 (24.1%)\n153 (28.2%)\n\n\nCollege\n396 (33.3%)\n14 (21.9%)\n2 (33.3%)\n217 (37.6%)\n163 (30.1%)\n\n\nCollege Graduate Or Above\n241 (20.3%)\n8 (12.5%)\n1 (16.7%)\n153 (26.5%)\n79 (14.6%)\n\n\nAlcohol use\n\n\n\n\n\n\n\nEver\n1015 (85.4%)\n53 (82.8%)\n3 (50.0%)\n503 (87.2%)\n456 (84.1%)\n\n\nNever\n174 (14.6%)\n11 (17.2%)\n3 (50.0%)\n74 (12.8%)\n86 (15.9%)\n\n\nSmoking status\n\n\n\n\n\n\n\nEver\n427 (35.9%)\n21 (32.8%)\n0 (0%)\n213 (36.9%)\n193 (35.6%)\n\n\nNever\n762 (64.1%)\n43 (67.2%)\n6 (100%)\n364 (63.1%)\n349 (64.4%)\n\n\nOral contraceptive use\n\n\n\n\n\n\n\nEver\n830 (69.8%)\n32 (50.0%)\n5 (83.3%)\n418 (72.4%)\n375 (69.2%)\n\n\nNever\n359 (30.2%)\n32 (50.0%)\n1 (16.7%)\n159 (27.6%)\n167 (30.8%)\n\n\nBMI (kg/m2)\n\n\n\n\n\n\n\nMean (SD)\n30.6 (7.71)\n30.6 (6.15)\n31.7 (6.56)\n30.3 (7.64)\n30.8 (7.96)\n\n\nMedian [Min, Max]\n29.3 [15.5, 84.4]\n29.4 [18.4, 45.4]\n29.4 [26.1, 44.0]\n29.0 [15.5, 67.7]\n29.4 [16.2, 84.4]\n\n\n\n\n\n\n\n\nRegression Analysis\nUnivariate and multivariate analyses were performed with simple and multiple linear regression, respectively. In univariate analysis, “umber of childbirth”, “age at survey”, and “smoking status” show statistically significance with our outcome variable. For race, only the subgroups “other Hispanic” and “Asian” are significant relative to the reference group. For education level, “high school diploma” and “college graduate or above” rose to the level of statistical significance.\nThis association continued to appear after adjustment in the full model. After comparing between different combinations of covariates, the model with the lowest AIC score were picked as our final adjusted model shown in Table 2 below. In multivariable linear regression analyses, the number of childbirth was associated with the age of menopause, in which each additional childbirth is associated with a 0.34 (95% CI: 0.02, 0.66) increase of the menopausal age, while holding all other covariates constant. Future research will be needed to validify this association and explore the underlining biological mechanism.\n\n\nCode\nSlin1 = lm(age_meno ~ livebirths, data = data)\n\nMlin3 = lm(age_meno ~ livebirths + age_sur + race + education + pills + smoke, data = data)\nsummary(Mlin3)\n\n\n\nCall:\nlm(formula = age_meno ~ livebirths + age_sur + race + education + \n pills + smoke, data = data)\n\nResiduals:\n Min 1Q Median 3Q Max \n-24.655 -5.124 1.322 5.878 14.748 \n\nCoefficients:\n Estimate Std. Error t value Pr(>|t|) \n(Intercept) 28.737622 1.553534 18.498 < 2e-16 ***\nlivebirths 0.341821 0.162806 2.100 0.035980 * \nage_sur 0.220409 0.021450 10.276 < 2e-16 ***\nraceMexican American 0.675248 0.840104 0.804 0.421694 \nraceOther Hispanic 2.472283 0.878333 2.815 0.004963 ** \nraceWhite -0.819997 0.610227 -1.344 0.179286 \nraceAsian 3.779387 0.901598 4.192 2.97e-05 ***\nraceMulti-Racial 1.039540 1.304629 0.797 0.425723 \neducationHigh School Diploma -0.549180 0.732069 -0.750 0.453299 \neducationCollege 0.007979 0.701900 0.011 0.990931 \neducationCollege Graduate Or Above 2.650293 0.788800 3.360 0.000805 ***\npillsNever -1.849122 0.530437 -3.486 0.000508 ***\nsmokeNever 1.588239 0.510617 3.110 0.001913 ** \n---\nSignif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 7.966 on 1176 degrees of freedom\nMultiple R-squared: 0.1496, Adjusted R-squared: 0.1409 \nF-statistic: 17.24 on 12 and 1176 DF, p-value: < 2.2e-16\n\n\nCode\nAIC(Mlin3)\n\n\n[1] 8323.974\n\n\nCode\nlibrary(gtsummary)\nt1 <- tbl_regression(Slin1)%>%add_global_p()\nt2 <- tbl_regression(Mlin3)%>%add_global_p()\n\ntbl_merge <- tbl_merge(tbls = list(t1,t2),\n tab_spanner = c(\"**Unadjusted**\",\"**Adjusted**\"))\ntbl_merge \n\n\n\n\n\n\n \n \n \n Characteristic\n \n Unadjusted\n \n \n Adjusted\n \n \n \n Beta\n 95% CI1\n p-value\n Beta\n 95% CI1\n p-value\n \n \n \n Number of Childbirths\n0.46\n0.14, 0.78\n0.004\n0.34\n0.02, 0.66\n0.036\n Age\n\n\n\n0.22\n0.18, 0.26\n<0.001\n Race\n\n\n\n\n\n<0.001\n     Black\n\n\n\n—\n—\n\n     Mexican American\n\n\n\n0.68\n-0.97, 2.3\n\n     Other Hispanic\n\n\n\n2.5\n0.75, 4.2\n\n     White\n\n\n\n-0.82\n-2.0, 0.38\n\n     Asian\n\n\n\n3.8\n2.0, 5.5\n\n     Multi-Racial\n\n\n\n1.0\n-1.5, 3.6\n\n Education\n\n\n\n\n\n<0.001\n     Less Than High School Diploma\n\n\n\n—\n—\n\n     High School Diploma\n\n\n\n-0.55\n-2.0, 0.89\n\n     College\n\n\n\n0.01\n-1.4, 1.4\n\n     College Graduate Or Above\n\n\n\n2.7\n1.1, 4.2\n\n Oral contraceptive use\n\n\n\n\n\n<0.001\n     Ever\n\n\n\n—\n—\n\n     Never\n\n\n\n-1.8\n-2.9, -0.81\n\n Smoking status\n\n\n\n\n\n0.002\n     Ever\n\n\n\n—\n—\n\n     Never\n\n\n\n1.6\n0.59, 2.6\n\n \n \n \n \n 1 CI = Confidence Interval\n \n \n\n\n\n\n\n\n\nDiscussion\nUnivariate and multivariate analyses were performed with simple and multiple linear regression, respectively. In univariate analysis, “umber of childbirth”, “age at survey”, and “smoking status” show statistically significance with our outcome variable. For race, only the subgroups “other Hispanic” and “Asian” are significant relative to the reference group. For education level, “high school diploma” and “college graduate or above” rose to the level of statistical significance.\nThis association continued to appear after adjustment in the full model. In multivariable linear regression analyses, the number of childbirth was associated with the age of menopause, in which each additional childbirth is associated with a 0.34 (95% CI: 0.02, 0.66) increase of the menopausal age, while holding all other covariates constant. Future research will be needed to validify this association and explore the underlining biological mechanism.\n\n\n\nFunction summary\n\n\n\n\n\n\n\nPackage name\nFunctions used\n\n\n\n\ntidyr\ndrop_na\n\n\ndplyr\narrange mutate group_by summarise\n\n\nggplot2\ngeom_bar geom_vline geom_point facet_wrap geom_smooth\n\n\n\n\n\n\n\n\n\n\n\nReferences\n\nCDC. 2019. “NHANES 2017-2018.” 2019. https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2017.\n\n\nGreendale, G. A., N. P. Lee, and E. R. Arriola. 1999. “The Menopause.” Lancet (London, England) 353 (9152): 571–80. https://doi.org/10.1016/S0140-6736(98)05352-5.\n\n\nWHO. 2021. “Menopause.” 2021. https://www.who.int/news-room/fact-sheets/detail/menopause." - } -] \ No newline at end of file diff --git a/docs/site_libs/bootstrap/bootstrap-icons.css b/docs/site_libs/bootstrap/bootstrap-icons.css deleted file mode 100644 index 94f1940..0000000 --- a/docs/site_libs/bootstrap/bootstrap-icons.css +++ /dev/null @@ -1,2018 +0,0 @@ -@font-face { - font-display: block; - font-family: "bootstrap-icons"; - src: -url("./bootstrap-icons.woff?2ab2cbbe07fcebb53bdaa7313bb290f2") format("woff"); -} - -.bi::before, -[class^="bi-"]::before, -[class*=" bi-"]::before { - display: inline-block; - font-family: bootstrap-icons !important; - font-style: normal; - font-weight: normal !important; - font-variant: normal; - text-transform: none; - line-height: 1; - vertical-align: -.125em; - -webkit-font-smoothing: antialiased; - 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t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let 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0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new RegExp(`(^|\\s)${this._getBasicClassPrefix()}\\S+`,"g"),i=t.getAttribute("class").match(e);null!==i&&i.length>0&&i.map((t=>t.trim())).forEach((e=>t.classList.remove(e)))}_getBasicClassPrefix(){return"bs-tooltip"}_handlePopperPlacementChange(t){const{state:e}=t;e&&(this.tip=e.elements.popper,this._cleanTipClass(),this._addAttachmentClass(this._getAttachment(e.placement)))}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null)}static jQueryInterface(t){return this.each((function(){const e=un.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(un);const fn={...un.Default,placement:"right",offset:[0,8],trigger:"click",content:"",template:''},pn={...un.DefaultType,content:"(string|element|function)"},mn={HIDE:"hide.bs.popover",HIDDEN:"hidden.bs.popover",SHOW:"show.bs.popover",SHOWN:"shown.bs.popover",INSERTED:"inserted.bs.popover",CLICK:"click.bs.popover",FOCUSIN:"focusin.bs.popover",FOCUSOUT:"focusout.bs.popover",MOUSEENTER:"mouseenter.bs.popover",MOUSELEAVE:"mouseleave.bs.popover"};class gn extends un{static get Default(){return fn}static get NAME(){return"popover"}static get Event(){return mn}static get DefaultType(){return pn}isWithContent(){return this.getTitle()||this._getContent()}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),".popover-header"),this._sanitizeAndSetContent(t,this._getContent(),".popover-body")}_getContent(){return this._resolvePossibleFunction(this._config.content)}_getBasicClassPrefix(){return"bs-popover"}static jQueryInterface(t){return this.each((function(){const e=gn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}g(gn);const _n="scrollspy",bn={offset:10,method:"auto",target:""},vn={offset:"number",method:"string",target:"(string|element)"},yn="active",wn=".nav-link, .list-group-item, .dropdown-item",En="position";class An extends B{constructor(t,e){super(t),this._scrollElement="BODY"===this._element.tagName?window:this._element,this._config=this._getConfig(e),this._offsets=[],this._targets=[],this._activeTarget=null,this._scrollHeight=0,j.on(this._scrollElement,"scroll.bs.scrollspy",(()=>this._process())),this.refresh(),this._process()}static get Default(){return bn}static get NAME(){return _n}refresh(){const 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this._scrollElement.scrollHeight||Math.max(document.body.scrollHeight,document.documentElement.scrollHeight)}_getOffsetHeight(){return this._scrollElement===window?window.innerHeight:this._scrollElement.getBoundingClientRect().height}_process(){const t=this._getScrollTop()+this._config.offset,e=this._getScrollHeight(),i=this._config.offset+e-this._getOffsetHeight();if(this._scrollHeight!==e&&this.refresh(),t>=i){const t=this._targets[this._targets.length-1];this._activeTarget!==t&&this._activate(t)}else{if(this._activeTarget&&t0)return this._activeTarget=null,void this._clear();for(let e=this._offsets.length;e--;)this._activeTarget!==this._targets[e]&&t>=this._offsets[e]&&(void 0===this._offsets[e+1]||t`${e}[data-bs-target="${t}"],${e}[href="${t}"]`)),i=V.findOne(e.join(","),this._config.target);i.classList.add(yn),i.classList.contains("dropdown-item")?V.findOne(".dropdown-toggle",i.closest(".dropdown")).classList.add(yn):V.parents(i,".nav, 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a/docs/site_libs/quarto-html/quarto-syntax-highlighting-dark.css +++ /dev/null @@ -1,189 +0,0 @@ -/* quarto syntax highlight colors */ -:root { - --quarto-hl-al-color: #f07178; - --quarto-hl-an-color: #d4d0ab; - --quarto-hl-at-color: #00e0e0; - --quarto-hl-bn-color: #d4d0ab; - --quarto-hl-bu-color: #abe338; - --quarto-hl-ch-color: #abe338; - --quarto-hl-co-color: #f8f8f2; - --quarto-hl-cv-color: #ffd700; - --quarto-hl-cn-color: #ffd700; - --quarto-hl-cf-color: #ffa07a; - --quarto-hl-dt-color: #ffa07a; - --quarto-hl-dv-color: #d4d0ab; - --quarto-hl-do-color: #f8f8f2; - --quarto-hl-er-color: #f07178; - --quarto-hl-ex-color: #00e0e0; - --quarto-hl-fl-color: #d4d0ab; - --quarto-hl-fu-color: #ffa07a; - --quarto-hl-im-color: #abe338; - --quarto-hl-in-color: #d4d0ab; - --quarto-hl-kw-color: #ffa07a; - --quarto-hl-op-color: #ffa07a; - --quarto-hl-ot-color: #00e0e0; - --quarto-hl-pp-color: #dcc6e0; - --quarto-hl-re-color: #00e0e0; - --quarto-hl-sc-color: #abe338; - --quarto-hl-ss-color: #abe338; - --quarto-hl-st-color: #abe338; - --quarto-hl-va-color: #00e0e0; - --quarto-hl-vs-color: #abe338; - --quarto-hl-wa-color: #dcc6e0; -} - -/* other quarto variables */ -:root { - --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; -} - -code span.al { - background-color: #2a0f15; - font-weight: bold; - color: #f07178; -} - -code span.an { - color: #d4d0ab; -} - -code span.at { - color: #00e0e0; -} - -code span.bn { - color: #d4d0ab; -} - -code span.bu { - color: #abe338; -} - -code span.ch { - color: #abe338; -} - -code span.co { - font-style: italic; - color: #f8f8f2; -} - -code span.cv { - color: #ffd700; -} - -code span.cn { - color: #ffd700; -} - -code span.cf { - font-weight: bold; - color: #ffa07a; -} - -code span.dt { - color: #ffa07a; -} - -code span.dv { - color: #d4d0ab; -} - -code span.do { - color: #f8f8f2; -} - -code span.er { - color: #f07178; - text-decoration: underline; -} - -code span.ex { - font-weight: bold; - color: #00e0e0; -} - -code span.fl { - color: #d4d0ab; -} - -code span.fu { - color: #ffa07a; -} - -code span.im { - color: #abe338; -} - -code span.in { - color: #d4d0ab; -} - -code span.kw { - font-weight: bold; - color: #ffa07a; -} - -pre > code.sourceCode > span { - color: #f8f8f2; -} - -code span { - color: #f8f8f2; -} - -code.sourceCode > span { - color: #f8f8f2; -} - -div.sourceCode, -div.sourceCode pre.sourceCode { - color: #f8f8f2; -} - -code span.op { - color: #ffa07a; -} - -code span.ot { - color: #00e0e0; -} - -code span.pp { - color: #dcc6e0; -} - -code span.re { - background-color: #f8f8f2; - color: #00e0e0; -} - -code span.sc { - color: #abe338; -} - -code span.ss { - color: #abe338; -} - -code span.st { - color: #abe338; -} - -code span.va { - color: #00e0e0; -} - -code span.vs { - color: #abe338; -} - -code span.wa { - color: #dcc6e0; -} - -.prevent-inlining { - content: " code.sourceCode > span { - color: #003B4F; -} - -code span { - color: #003B4F; -} - -code.sourceCode > span { - color: #003B4F; -} - -div.sourceCode, -div.sourceCode pre.sourceCode { - color: #003B4F; -} - -code span.ot { - color: #003B4F; - font-style: inherit; -} - -code span.at { - color: #657422; - font-style: inherit; -} - -code span.ss { - color: #20794D; - font-style: inherit; -} - -code span.an { - color: #5E5E5E; - font-style: inherit; -} - -code span.fu { - color: #4758AB; - font-style: inherit; -} - -code span.st { - color: #20794D; - font-style: inherit; -} - -code span.cf { - color: #003B4F; - font-style: inherit; -} - -code span.op { - color: #5E5E5E; - font-style: inherit; -} - -code span.er { - color: #AD0000; - font-style: inherit; -} - -code span.bn { - color: #AD0000; - font-style: inherit; -} - -code span.al { - color: #AD0000; - font-style: inherit; -} - -code span.va { - color: #111111; - font-style: inherit; -} - -code span.bu { - font-style: inherit; -} - -code span.ex { - font-style: inherit; -} - -code span.pp { - color: 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-}; - -window.document.addEventListener("DOMContentLoaded", function (_event) { - // Recompute the position of margin elements anytime the body size changes - if (window.ResizeObserver) { - const resizeObserver = new window.ResizeObserver( - throttle(layoutMarginEls, 50) - ); - resizeObserver.observe(window.document.body); - } - - const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); - const sidebarEl = window.document.getElementById("quarto-sidebar"); - const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); - const marginSidebarEl = window.document.getElementById( - "quarto-margin-sidebar" - ); - // function to determine whether the element has a previous sibling that is active - const prevSiblingIsActiveLink = (el) => { - const sibling = el.previousElementSibling; - if (sibling && sibling.tagName === "A") { - return sibling.classList.contains("active"); - } else { - return false; - } - }; - - // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) - function fireSlideEnter(e) { - const event = window.document.createEvent("Event"); - event.initEvent("slideenter", true, true); - window.document.dispatchEvent(event); - } - const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); - tabs.forEach((tab) => { - tab.addEventListener("shown.bs.tab", fireSlideEnter); - }); - - // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) - document.addEventListener("tabby", fireSlideEnter, false); - - // Track scrolling and mark TOC links as active - // get table of contents and sidebar (bail if we don't have at least one) - const tocLinks = tocEl - ? [...tocEl.querySelectorAll("a[data-scroll-target]")] - : []; - const makeActive = (link) => tocLinks[link].classList.add("active"); - const removeActive = (link) => tocLinks[link].classList.remove("active"); - const removeAllActive = () => - [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); - - // activate the anchor for a section associated with this TOC entry - tocLinks.forEach((link) => { - link.addEventListener("click", () => { - if (link.href.indexOf("#") !== -1) { - const anchor = link.href.split("#")[1]; - const heading = window.document.querySelector( - `[data-anchor-id=${anchor}]` - ); - if (heading) { - // Add the class - heading.classList.add("reveal-anchorjs-link"); - - // function to show the anchor - const handleMouseout = () => { - heading.classList.remove("reveal-anchorjs-link"); - heading.removeEventListener("mouseout", handleMouseout); - }; - - // add a function to clear the anchor when the user mouses out of it - heading.addEventListener("mouseout", handleMouseout); - } - } - }); - }); - - const sections = tocLinks.map((link) => { - const target = link.getAttribute("data-scroll-target"); - if (target.startsWith("#")) { - return window.document.getElementById(decodeURI(`${target.slice(1)}`)); - } else { - return window.document.querySelector(decodeURI(`${target}`)); - } - }); - - const sectionMargin = 200; - let currentActive = 0; - // track whether we've initialized state the first time - let init = false; - - const updateActiveLink = () => { - // The index from bottom to top (e.g. reversed list) - let sectionIndex = -1; - if ( - window.innerHeight + window.pageYOffset >= - window.document.body.offsetHeight - ) { - sectionIndex = 0; - } else { - sectionIndex = [...sections].reverse().findIndex((section) => { - if (section) { - return window.pageYOffset >= section.offsetTop - sectionMargin; - } else { - return false; - } - }); - } - if (sectionIndex > -1) { - const current = sections.length - sectionIndex - 1; - if (current !== currentActive) { - removeAllActive(); - currentActive = current; - makeActive(current); - if (init) { - window.dispatchEvent(sectionChanged); - } - init = true; - } - } - }; - - const inHiddenRegion = (top, bottom, hiddenRegions) => { - for (const region of hiddenRegions) { - if (top <= region.bottom && bottom >= region.top) { - return true; - } - } - return false; - }; - - const categorySelector = "header.quarto-title-block .quarto-category"; - const activateCategories = (href) => { - // Find any categories - // Surround them with a link pointing back to: - // #category=Authoring - try { - const categoryEls = window.document.querySelectorAll(categorySelector); - for (const categoryEl of categoryEls) { - const categoryText = categoryEl.textContent; - if (categoryText) { - const link = `${href}#category=${encodeURIComponent(categoryText)}`; - const linkEl = window.document.createElement("a"); - linkEl.setAttribute("href", link); - for (const child of categoryEl.childNodes) { - linkEl.append(child); - } - categoryEl.appendChild(linkEl); - } - } - } catch { - // Ignore errors - } - }; - function hasTitleCategories() { - return window.document.querySelector(categorySelector) !== null; - } - - function offsetRelativeUrl(url) { - const offset = getMeta("quarto:offset"); - return offset ? offset + url : url; - } - - function offsetAbsoluteUrl(url) { - const offset = getMeta("quarto:offset"); - const baseUrl = new URL(offset, window.location); - - const projRelativeUrl = url.replace(baseUrl, ""); - if (projRelativeUrl.startsWith("/")) { - return projRelativeUrl; - } else { - return "/" + projRelativeUrl; - } - } - - // read a meta tag value - function getMeta(metaName) { - const metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; - } - - async function findAndActivateCategories() { - const currentPagePath = offsetAbsoluteUrl(window.location.href); - const response = await fetch(offsetRelativeUrl("listings.json")); - if (response.status == 200) { - return response.json().then(function (listingPaths) { - const listingHrefs = []; - for (const listingPath of listingPaths) { - const pathWithoutLeadingSlash = listingPath.listing.substring(1); - for (const item of listingPath.items) { - if ( - item === currentPagePath || - item === currentPagePath + "index.html" - ) { - // Resolve this path against the offset to be sure - // we already are using the correct path to the listing - // (this adjusts the listing urls to be rooted against - // whatever root the page is actually running against) - const relative = offsetRelativeUrl(pathWithoutLeadingSlash); - const baseUrl = window.location; - const resolvedPath = new URL(relative, baseUrl); - listingHrefs.push(resolvedPath.pathname); - break; - } - } - } - - // Look up the tree for a nearby linting and use that if we find one - const nearestListing = findNearestParentListing( - offsetAbsoluteUrl(window.location.pathname), - listingHrefs - ); - if (nearestListing) { - activateCategories(nearestListing); - } else { - // See if the referrer is a listing page for this item - const referredRelativePath = offsetAbsoluteUrl(document.referrer); - const referrerListing = listingHrefs.find((listingHref) => { - const isListingReferrer = - listingHref === referredRelativePath || - listingHref === referredRelativePath + "index.html"; - return isListingReferrer; - }); - - if (referrerListing) { - // Try to use the referrer if possible - activateCategories(referrerListing); - } else if (listingHrefs.length > 0) { - // Otherwise, just fall back to the first listing - activateCategories(listingHrefs[0]); - } - } - }); - } - } - if (hasTitleCategories()) { - findAndActivateCategories(); - } - - const findNearestParentListing = (href, listingHrefs) => { - if (!href || !listingHrefs) { - return undefined; - } - // Look up the tree for a nearby linting and use that if we find one - const relativeParts = href.substring(1).split("/"); - while (relativeParts.length > 0) { - const path = relativeParts.join("/"); - for (const listingHref of listingHrefs) { - if (listingHref.startsWith(path)) { - return listingHref; - } - } - relativeParts.pop(); - } - - return undefined; - }; - - const manageSidebarVisiblity = (el, placeholderDescriptor) => { - let isVisible = true; - let elRect; - - return (hiddenRegions) => { - if (el === null) { - return; - } - - // Find the last element of the TOC - const lastChildEl = el.lastElementChild; - - if (lastChildEl) { - // Converts the sidebar to a menu - const convertToMenu = () => { - for (const child of el.children) { - child.style.opacity = 0; - child.style.overflow = "hidden"; - } - - nexttick(() => { - const toggleContainer = window.document.createElement("div"); - toggleContainer.style.width = "100%"; - toggleContainer.classList.add("zindex-over-content"); - toggleContainer.classList.add("quarto-sidebar-toggle"); - toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom - toggleContainer.id = placeholderDescriptor.id; - toggleContainer.style.position = "fixed"; 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- clone.style.display = null; - toggleContents.append(clone); - } - toggleContents.style.height = "0px"; - const positionToggle = () => { - // position the element (top left of parent, same width as parent) - if (!elRect) { - elRect = el.getBoundingClientRect(); - } - toggleContainer.style.left = `${elRect.left}px`; - toggleContainer.style.top = `${elRect.top}px`; - toggleContainer.style.width = `${elRect.width}px`; - }; - positionToggle(); - - toggleContainer.append(toggleContents); - el.parentElement.prepend(toggleContainer); - - // Process clicks - let tocShowing = false; - // Allow the caller to control whether this is dismissed - // when it is clicked (e.g. sidebar navigation supports - // opening and closing the nav tree, so don't dismiss on click) - const clickEl = placeholderDescriptor.dismissOnClick - ? toggleContainer - : toggleTitle; - - const closeToggle = () => { - if (tocShowing) { - toggleContainer.classList.remove("expanded"); - toggleContents.style.height = "0px"; - tocShowing = false; - } - }; - - // Get rid of any expanded toggle if the user scrolls - window.document.addEventListener( - "scroll", - throttle(() => { - closeToggle(); - }, 50) - ); - - // Handle positioning of the toggle - window.addEventListener( - "resize", - throttle(() => { - elRect = undefined; - positionToggle(); - }, 50) - ); - - window.addEventListener("quarto-hrChanged", () => { - elRect = undefined; - }); - - // Process the click - clickEl.onclick = () => { - if (!tocShowing) { - toggleContainer.classList.add("expanded"); - toggleContents.style.height = null; - tocShowing = true; - } else { - closeToggle(); - } - }; - }); - }; - - // Converts a sidebar from a menu back to a sidebar - const convertToSidebar = () => { - for (const child of el.children) { - child.style.opacity = 1; - child.style.overflow = null; - } - - const placeholderEl = window.document.getElementById( - placeholderDescriptor.id - ); - if (placeholderEl) { - placeholderEl.remove(); - } - - el.classList.remove("rollup"); - }; - - if (isReaderMode()) { - convertToMenu(); - isVisible = false; - } else { - // Find the top and bottom o the element that is being managed - const elTop = el.offsetTop; - const elBottom = - elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; - - if (!isVisible) { - // If the element is current not visible reveal if there are - // no conflicts with overlay regions - if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToSidebar(); - isVisible = true; - } - } else { - // If the element is visible, hide it if it conflicts with overlay regions - // and insert a placeholder toggle (or if we're in reader mode) - if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { - convertToMenu(); - isVisible = false; - } - } - } - } - }; - }; - - const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); - for (const tabEl of tabEls) { - const id = tabEl.getAttribute("data-bs-target"); - if (id) { - const columnEl = document.querySelector( - `${id} .column-margin, .tabset-margin-content` - ); - if (columnEl) - tabEl.addEventListener("shown.bs.tab", function (event) { - const el = event.srcElement; - if (el) { - const visibleCls = `${el.id}-margin-content`; - // walk up until we find a parent tabset - let panelTabsetEl = el.parentElement; - while (panelTabsetEl) { - if (panelTabsetEl.classList.contains("panel-tabset")) { - break; - } - panelTabsetEl = panelTabsetEl.parentElement; - } - - if (panelTabsetEl) { - const prevSib = panelTabsetEl.previousElementSibling; - if ( - prevSib && - prevSib.classList.contains("tabset-margin-container") - ) { - const childNodes = prevSib.querySelectorAll( - ".tabset-margin-content" - ); - for (const childEl of childNodes) { - if (childEl.classList.contains(visibleCls)) { - childEl.classList.remove("collapse"); - } else { - childEl.classList.add("collapse"); - } - } - } - } - } - - layoutMarginEls(); - }); - } - } - - // Manage the visibility of the toc and the sidebar - const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { - id: "quarto-toc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { - id: "quarto-sidebarnav-toggle", - titleSelector: ".title", - dismissOnClick: false, - }); - let tocLeftScrollVisibility; - if (leftTocEl) { - tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { - id: "quarto-lefttoc-toggle", - titleSelector: "#toc-title", - dismissOnClick: true, - }); - } - - // Find the first element that uses formatting in special columns - const conflictingEls = window.document.body.querySelectorAll( - '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' - ); - - // Filter all the possibly conflicting elements into ones - // the do conflict on the left or ride side - const arrConflictingEls = Array.from(conflictingEls); - const leftSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return false; - } - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - className.startsWith("column-") && - !className.endsWith("right") && - !className.endsWith("container") && - className !== "column-margin" - ); - }); - }); - const rightSideConflictEls = arrConflictingEls.filter((el) => { - if (el.tagName === "ASIDE") { - return true; - } - - const hasMarginCaption = Array.from(el.classList).find((className) => { - return className == "margin-caption"; - }); - if (hasMarginCaption) { - return true; - } - - return Array.from(el.classList).find((className) => { - return ( - className !== "column-body" && - !className.endsWith("container") && - className.startsWith("column-") && - !className.endsWith("left") - ); - }); - }); - - const kOverlapPaddingSize = 10; - function toRegions(els) { - return els.map((el) => { - const boundRect = el.getBoundingClientRect(); - const top = - boundRect.top + - document.documentElement.scrollTop - - kOverlapPaddingSize; - return { - top, - bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, - }; - }); - } - - let hasObserved = false; - const visibleItemObserver = (els) => { - let visibleElements = [...els]; - const intersectionObserver = new IntersectionObserver( - (entries, _observer) => { - entries.forEach((entry) => { - if (entry.isIntersecting) { - if (visibleElements.indexOf(entry.target) === -1) { - visibleElements.push(entry.target); - } - } else { - visibleElements = visibleElements.filter((visibleEntry) => { - return visibleEntry !== entry; - }); - } - }); - - if (!hasObserved) { - hideOverlappedSidebars(); - } - hasObserved = true; - }, - {} - ); - els.forEach((el) => { - intersectionObserver.observe(el); - }); - - return { - getVisibleEntries: () => { - return visibleElements; - }, - }; - }; - - const rightElementObserver = visibleItemObserver(rightSideConflictEls); - const leftElementObserver = visibleItemObserver(leftSideConflictEls); - - const hideOverlappedSidebars = () => { - marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); - sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); - if (tocLeftScrollVisibility) { - tocLeftScrollVisibility( - toRegions(leftElementObserver.getVisibleEntries()) - ); - } - }; - - window.quartoToggleReader = () => { - // Applies a slow class (or removes it) - // to update the transition speed - const slowTransition = (slow) => { - const manageTransition = (id, slow) => { - const el = document.getElementById(id); - if (el) { - if (slow) { - el.classList.add("slow"); - } else { - el.classList.remove("slow"); - } - } - }; - - manageTransition("TOC", slow); - manageTransition("quarto-sidebar", slow); - }; - const readerMode = !isReaderMode(); - setReaderModeValue(readerMode); - - // If we're entering reader mode, slow the transition - if (readerMode) { - slowTransition(readerMode); - } - highlightReaderToggle(readerMode); - hideOverlappedSidebars(); - - // If we're exiting reader mode, restore the non-slow transition - if (!readerMode) { - slowTransition(!readerMode); - } - }; - - const highlightReaderToggle = (readerMode) => { - const els = document.querySelectorAll(".quarto-reader-toggle"); - if (els) { - els.forEach((el) => { - if (readerMode) { - el.classList.add("reader"); - } else { - el.classList.remove("reader"); - } - }); - } - }; - - const setReaderModeValue = (val) => { - if (window.location.protocol !== "file:") { - window.localStorage.setItem("quarto-reader-mode", val); - } else { - localReaderMode = val; - } - }; - - const isReaderMode = () => { - if (window.location.protocol !== "file:") { - return window.localStorage.getItem("quarto-reader-mode") === "true"; - } else { - return localReaderMode; - } - }; - let localReaderMode = null; - - const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); - const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; - - // Walk the TOC and collapse/expand nodes - // Nodes are expanded if: - // - they are top level - // - they have children that are 'active' links - // - they are directly below an link that is 'active' - const walk = (el, depth) => { - // Tick depth when we enter a UL - if (el.tagName === "UL") { - depth = depth + 1; - } - - // It this is active link - let isActiveNode = false; - if (el.tagName === "A" && el.classList.contains("active")) { - isActiveNode = true; - } - - // See if there is an active child to this element - let hasActiveChild = false; - for (child of el.children) { - hasActiveChild = walk(child, depth) || hasActiveChild; - } - - // Process the collapse state if this is an UL - if (el.tagName === "UL") { - if (tocOpenDepth === -1 && depth > 1) { - el.classList.add("collapse"); - } else if ( - depth <= tocOpenDepth || - hasActiveChild || - prevSiblingIsActiveLink(el) - ) { - el.classList.remove("collapse"); - } else { - el.classList.add("collapse"); - } - - // untick depth when we leave a UL - depth = depth - 1; - } - return hasActiveChild || isActiveNode; - }; - - // walk the TOC and expand / collapse any items that should be shown - - if (tocEl) { - walk(tocEl, 0); - updateActiveLink(); - } - - // Throttle the scroll event and walk peridiocally - window.document.addEventListener( - "scroll", - throttle(() => { - if (tocEl) { - updateActiveLink(); - walk(tocEl, 0); - } - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 5) - ); - window.addEventListener( - "resize", - throttle(() => { - if (!isReaderMode()) { - hideOverlappedSidebars(); - } - }, 10) - ); - hideOverlappedSidebars(); - highlightReaderToggle(isReaderMode()); -}); - -// grouped tabsets -window.addEventListener("pageshow", (_event) => { - function getTabSettings() { - const data = localStorage.getItem("quarto-persistent-tabsets-data"); - if (!data) { - localStorage.setItem("quarto-persistent-tabsets-data", "{}"); - return {}; - } - if (data) { - return JSON.parse(data); - } - } - - function setTabSettings(data) { - localStorage.setItem( - "quarto-persistent-tabsets-data", - JSON.stringify(data) - ); - } - - function setTabState(groupName, groupValue) { - const data = getTabSettings(); - data[groupName] = groupValue; - setTabSettings(data); - } - - function toggleTab(tab, active) { - const tabPanelId = tab.getAttribute("aria-controls"); - const tabPanel = document.getElementById(tabPanelId); - if (active) { - tab.classList.add("active"); - tabPanel.classList.add("active"); - } else { - tab.classList.remove("active"); - tabPanel.classList.remove("active"); - } - } - - function toggleAll(selectedGroup, selectorsToSync) { - for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { - const active = selectedGroup === thisGroup; - for (const tab of tabs) { - toggleTab(tab, active); - } - } - } - - function findSelectorsToSyncByLanguage() { - const result = {}; - const tabs = Array.from( - document.querySelectorAll(`div[data-group] a[id^='tabset-']`) - ); - for (const item of tabs) { - const div = item.parentElement.parentElement.parentElement; - const group = div.getAttribute("data-group"); - if (!result[group]) { - result[group] = {}; - } - const selectorsToSync = result[group]; - const value = item.innerHTML; - if (!selectorsToSync[value]) { - selectorsToSync[value] = []; - } - selectorsToSync[value].push(item); - } - return result; - } - - function setupSelectorSync() { - const selectorsToSync = findSelectorsToSyncByLanguage(); - Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { - Object.entries(tabSetsByValue).forEach(([value, items]) => { - items.forEach((item) => { - item.addEventListener("click", (_event) => { - setTabState(group, value); - toggleAll(value, selectorsToSync[group]); - }); - }); - }); - }); - return selectorsToSync; - } - - const selectorsToSync = setupSelectorSync(); - for (const [group, selectedName] of 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this._bitapSearch.searchIn(e)}}],[{key:"type",get:function(){return"fuzzy"}},{key:"multiRegex",get:function(){return/^"(.*)"$/}},{key:"singleRegex",get:function(){return/^(.*)$/}}]),n}(z),H=function(e){a(n,e);var t=l(n);function n(e){return r(this,n),t.call(this,e)}return o(n,[{key:"search",value:function(e){for(var t,n=0,r=[],i=this.pattern.length;(t=e.indexOf(this.pattern,n))>-1;)n=t+i,r.push([t,n-1]);var o=!!r.length;return{isMatch:o,score:o?0:1,indices:r}}}],[{key:"type",get:function(){return"include"}},{key:"multiRegex",get:function(){return/^'"(.*)"$/}},{key:"singleRegex",get:function(){return/^'(.*)$/}}]),n}(z),Q=[K,H,B,J,V,U,q,G],X=Q.length,Y=/ +(?=(?:[^\"]*\"[^\"]*\")*[^\"]*$)/;function Z(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return e.split("|").map((function(e){for(var n=e.trim().split(Y).filter((function(e){return e&&!!e.trim()})),r=[],i=0,o=n.length;i1&&void 0!==arguments[1]?arguments[1]:{},i=n.isCaseSensitive,o=void 0===i?I.isCaseSensitive:i,c=n.includeMatches,a=void 0===c?I.includeMatches:c,s=n.minMatchCharLength,u=void 0===s?I.minMatchCharLength:s,h=n.ignoreLocation,l=void 0===h?I.ignoreLocation:h,f=n.findAllMatches,d=void 0===f?I.findAllMatches:f,v=n.location,g=void 0===v?I.location:v,y=n.threshold,p=void 0===y?I.threshold:y,m=n.distance,k=void 0===m?I.distance:m;r(this,e),this.query=null,this.options={isCaseSensitive:o,includeMatches:a,minMatchCharLength:u,findAllMatches:d,ignoreLocation:l,location:g,threshold:p,distance:k},this.pattern=o?t:t.toLowerCase(),this.query=Z(this.pattern,this.options)}return o(e,[{key:"searchIn",value:function(e){var t=this.query;if(!t)return{isMatch:!1,score:1};var n=this.options,r=n.includeMatches;e=n.isCaseSensitive?e:e.toLowerCase();for(var i=0,o=[],c=0,a=0,s=t.length;a-1&&(n.refIndex=e.idx),t.matches.push(n)}}))}function ve(e,t){t.score=e.score}function ge(e,t){var n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},r=n.includeMatches,i=void 0===r?I.includeMatches:r,o=n.includeScore,c=void 0===o?I.includeScore:o,a=[];return i&&a.push(de),c&&a.push(ve),e.map((function(e){var n=e.idx,r={item:t[n],refIndex:n};return a.length&&a.forEach((function(t){t(e,r)})),r}))}var ye=function(){function e(n){var i=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},o=arguments.length>2?arguments[2]:void 0;r(this,e),this.options=t(t({},I),i),this.options.useExtendedSearch,this._keyStore=new S(this.options.keys),this.setCollection(n,o)}return o(e,[{key:"setCollection",value:function(e,t){if(this._docs=e,t&&!(t instanceof $))throw new Error("Incorrect 'index' type");this._myIndex=t||F(this.options.keys,this._docs,{getFn:this.options.getFn,fieldNormWeight:this.options.fieldNormWeight})}},{key:"add",value:function(e){k(e)&&(this._docs.push(e),this._myIndex.add(e))}},{key:"remove",value:function(){for(var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:function(){return!1},t=[],n=0,r=this._docs.length;n1&&void 0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return fe(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),ge(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=re(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(le(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/docs/site_libs/quarto-search/quarto-search.js b/docs/site_libs/quarto-search/quarto-search.js deleted file mode 100644 index f5d852d..0000000 --- a/docs/site_libs/quarto-search/quarto-search.js +++ /dev/null @@ -1,1140 +0,0 @@ -const kQueryArg = "q"; -const kResultsArg = "show-results"; - -// If items don't provide a URL, then both the navigator and the onSelect -// function aren't called (and therefore, the default implementation is used) -// -// We're using this sentinel URL to signal to those handlers that this -// item is a more item (along with the type) and can be handled appropriately -const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; - -window.document.addEventListener("DOMContentLoaded", function (_event) { - // Ensure that search is available on this page. If it isn't, - // should return early and not do anything - var searchEl = window.document.getElementById("quarto-search"); - if (!searchEl) return; - - const { autocomplete } = window["@algolia/autocomplete-js"]; - - let quartoSearchOptions = {}; - let language = {}; - const searchOptionEl = window.document.getElementById( - "quarto-search-options" - ); - if (searchOptionEl) { - const jsonStr = searchOptionEl.textContent; - quartoSearchOptions = JSON.parse(jsonStr); - language = quartoSearchOptions.language; - } - - // note the search mode - if (quartoSearchOptions.type === "overlay") { - searchEl.classList.add("type-overlay"); - } else { - searchEl.classList.add("type-textbox"); - } - - // Used to determine highlighting behavior for this page - // A `q` query param is expected when the user follows a search - // to this page - const currentUrl = new URL(window.location); - const query = currentUrl.searchParams.get(kQueryArg); - const showSearchResults = currentUrl.searchParams.get(kResultsArg); - const mainEl = window.document.querySelector("main"); - - // highlight matches on the page - if (query !== null && mainEl) { - // perform any highlighting - highlight(escapeRegExp(query), mainEl); - - // fix up the URL to remove the q query param - const replacementUrl = new URL(window.location); - replacementUrl.searchParams.delete(kQueryArg); - window.history.replaceState({}, "", replacementUrl); - } - - // function to clear highlighting on the page when the search query changes - // (e.g. if the user edits the query or clears it) - let highlighting = true; - const resetHighlighting = (searchTerm) => { - if (mainEl && highlighting && query !== null && searchTerm !== query) { - clearHighlight(query, mainEl); - highlighting = false; - } - }; - - // Clear search highlighting when the user scrolls sufficiently - const resetFn = () => { - resetHighlighting(""); - window.removeEventListener("quarto-hrChanged", resetFn); - window.removeEventListener("quarto-sectionChanged", resetFn); - }; - - // Register this event after the initial scrolling and settling of events - // on the page - window.addEventListener("quarto-hrChanged", resetFn); - window.addEventListener("quarto-sectionChanged", resetFn); - - // Responsively switch to overlay mode if the search is present on the navbar - // Note that switching the sidebar to overlay mode requires more coordinate (not just - // the media query since we generate different HTML for sidebar overlays than we do - // for sidebar input UI) - const detachedMediaQuery = - quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; - - // If configured, include the analytics client to send insights - const plugins = configurePlugins(quartoSearchOptions); - - let lastState = null; - const { setIsOpen, setQuery, setCollections } = autocomplete({ - container: searchEl, - detachedMediaQuery: detachedMediaQuery, - defaultActiveItemId: 0, - panelContainer: "#quarto-search-results", - panelPlacement: quartoSearchOptions["panel-placement"], - debug: false, - openOnFocus: true, - plugins, - classNames: { - form: "d-flex", - }, - translations: { - clearButtonTitle: language["search-clear-button-title"], - detachedCancelButtonText: language["search-detached-cancel-button-title"], - submitButtonTitle: language["search-submit-button-title"], - }, - initialState: { - query, - }, - getItemUrl({ item }) { - return item.href; - }, - onStateChange({ state }) { - // Perhaps reset highlighting - resetHighlighting(state.query); - - // If the panel just opened, ensure the panel is positioned properly - if (state.isOpen) { - if (lastState && !lastState.isOpen) { - setTimeout(() => { - positionPanel(quartoSearchOptions["panel-placement"]); - }, 150); - } - } - - // Perhaps show the copy link - showCopyLink(state.query, quartoSearchOptions); - - lastState = state; - }, - reshape({ sources, state }) { - return sources.map((source) => { - try { - const items = source.getItems(); - - // Validate the items - validateItems(items); - - // group the items by document - const groupedItems = new Map(); - items.forEach((item) => { - const hrefParts = item.href.split("#"); - const baseHref = hrefParts[0]; - const isDocumentItem = hrefParts.length === 1; - - const items = groupedItems.get(baseHref); - if (!items) { - groupedItems.set(baseHref, [item]); - } else { - // If the href for this item matches the document - // exactly, place this item first as it is the item that represents - // the document itself - if (isDocumentItem) { - items.unshift(item); - } else { - items.push(item); - } - groupedItems.set(baseHref, items); - } - }); - - const reshapedItems = []; - let count = 1; - for (const [_key, value] of groupedItems) { - const firstItem = value[0]; - reshapedItems.push({ - ...firstItem, - type: kItemTypeDoc, - }); - - const collapseMatches = quartoSearchOptions["collapse-after"]; - const collapseCount = - typeof collapseMatches === "number" ? collapseMatches : 1; - - if (value.length > 1) { - const target = `search-more-${count}`; - const isExpanded = - state.context.expanded && - state.context.expanded.includes(target); - - const remainingCount = value.length - collapseCount; - - for (let i = 1; i < value.length; i++) { - if (collapseMatches && i === collapseCount) { - reshapedItems.push({ - target, - title: isExpanded - ? language["search-hide-matches-text"] - : remainingCount === 1 - ? `${remainingCount} ${language["search-more-match-text"]}` - : `${remainingCount} ${language["search-more-matches-text"]}`, - type: kItemTypeMore, - href: kItemTypeMoreHref, - }); - } - - if (isExpanded || !collapseMatches || i < collapseCount) { - reshapedItems.push({ - ...value[i], - type: kItemTypeItem, - target, - }); - } - } - } - count += 1; - } - - return { - ...source, - getItems() { - return reshapedItems; - }, - }; - } catch (error) { - // Some form of error occurred - return { - ...source, - getItems() { - return [ - { - title: error.name || "An Error Occurred While Searching", - text: - error.message || - "An unknown error occurred while attempting to perform the requested search.", - type: kItemTypeError, - }, - ]; - }, - }; - } - }); - }, - navigator: { - navigate({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - window.location.assign(itemUrl); - } - }, - navigateNewTab({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - const windowReference = window.open(itemUrl, "_blank", "noopener"); - if (windowReference) { - windowReference.focus(); - } - } - }, - navigateNewWindow({ itemUrl }) { - if (itemUrl !== offsetURL(kItemTypeMoreHref)) { - window.open(itemUrl, "_blank", "noopener"); - } - }, - }, - getSources({ state, setContext, setActiveItemId, refresh }) { - return [ - { - sourceId: "documents", - getItemUrl({ item }) { - if (item.href) { - return offsetURL(item.href); - } else { - return undefined; - } - }, - onSelect({ - item, - state, - setContext, - setIsOpen, - setActiveItemId, - refresh, - }) { - if (item.type === kItemTypeMore) { - toggleExpanded(item, state, setContext, setActiveItemId, refresh); - - // Toggle more - setIsOpen(true); - } - }, - getItems({ query }) { - if (query === null || query === "") { - return []; - } - - const limit = quartoSearchOptions.limit; - if (quartoSearchOptions.algolia) { - return algoliaSearch(query, limit, quartoSearchOptions.algolia); - } else { - // Fuse search options - const fuseSearchOptions = { - isCaseSensitive: false, - shouldSort: true, - minMatchCharLength: 2, - limit: limit, - }; - - return readSearchData().then(function (fuse) { - return fuseSearch(query, fuse, fuseSearchOptions); - }); - } - }, - templates: { - noResults({ createElement }) { - const hasQuery = lastState.query; - - return createElement( - "div", - { - class: `quarto-search-no-results${ - hasQuery ? "" : " no-query" - }`, - }, - language["search-no-results-text"] - ); - }, - header({ items, createElement }) { - // count the documents - const count = items.filter((item) => { - return item.type === kItemTypeDoc; - }).length; - - if (count > 0) { - return createElement( - "div", - { class: "search-result-header" }, - `${count} ${language["search-matching-documents-text"]}` - ); - } else { - return createElement( - "div", - { class: "search-result-header-no-results" }, - `` - ); - } - }, - footer({ _items, createElement }) { - if ( - quartoSearchOptions.algolia && - quartoSearchOptions.algolia["show-logo"] - ) { - const libDir = quartoSearchOptions.algolia["libDir"]; - const logo = createElement("img", { - src: offsetURL( - `${libDir}/quarto-search/search-by-algolia.svg` - ), - class: "algolia-search-logo", - }); - return createElement( - "a", - { href: "http://www.algolia.com/" }, - logo - ); - } - }, - - item({ item, createElement }) { - return renderItem( - item, - createElement, - state, - setActiveItemId, - setContext, - refresh - ); - }, - }, - }, - ]; - }, - }); - - window.quartoOpenSearch = () => { - setIsOpen(false); - setIsOpen(true); - focusSearchInput(); - }; - - // Remove the labeleledby attribute since it is pointing - // to a non-existent label - if (quartoSearchOptions.type === "overlay") { - const inputEl = window.document.querySelector( - "#quarto-search .aa-Autocomplete" - ); - if (inputEl) { - inputEl.removeAttribute("aria-labelledby"); - } - } - - // If the main document scrolls dismiss the search results - // (otherwise, since they're floating in the document they can scroll with the document) - window.document.body.onscroll = () => { - setIsOpen(false); - }; - - if (showSearchResults) { - setIsOpen(true); - focusSearchInput(); - } -}); - -function configurePlugins(quartoSearchOptions) { - const autocompletePlugins = []; - const algoliaOptions = quartoSearchOptions.algolia; - if ( - algoliaOptions && - algoliaOptions["analytics-events"] && - algoliaOptions["search-only-api-key"] && - algoliaOptions["application-id"] - ) { - const apiKey = algoliaOptions["search-only-api-key"]; - const appId = algoliaOptions["application-id"]; - - // Aloglia insights may not be loaded because they require cookie consent - // Use deferred loading so events will start being recorded when/if consent - // is granted. - const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { - if ( - window.aa && - window["@algolia/autocomplete-plugin-algolia-insights"] - ) { - window.aa("init", { - appId, - apiKey, - useCookie: true, - }); - - const { createAlgoliaInsightsPlugin } = - window["@algolia/autocomplete-plugin-algolia-insights"]; - // Register the insights client - const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ - insightsClient: window.aa, - onItemsChange({ insights, insightsEvents }) { - const events = insightsEvents.map((event) => { - const maxEvents = event.objectIDs.slice(0, 20); - return { - ...event, - objectIDs: maxEvents, - }; - }); - - insights.viewedObjectIDs(...events); - }, - }); - return algoliaInsightsPlugin; - } - }); - - // Add the plugin - autocompletePlugins.push(algoliaInsightsDeferredPlugin); - return autocompletePlugins; - } -} - -// For plugins that may not load immediately, create a wrapper -// plugin and forward events and plugin data once the plugin -// is initialized. This is useful for cases like cookie consent -// which may prevent the analytics insights event plugin from initializing -// immediately. -function deferredLoadPlugin(createPlugin) { - let plugin = undefined; - let subscribeObj = undefined; - const wrappedPlugin = () => { - if (!plugin && subscribeObj) { - plugin = createPlugin(); - if (plugin && plugin.subscribe) { - plugin.subscribe(subscribeObj); - } - } - return plugin; - }; - - return { - subscribe: (obj) => { - subscribeObj = obj; - }, - onStateChange: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onStateChange) { - plugin.onStateChange(obj); - } - }, - onSubmit: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onSubmit) { - plugin.onSubmit(obj); - } - }, - onReset: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.onReset) { - plugin.onReset(obj); - } - }, - getSources: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.getSources) { - return plugin.getSources(obj); - } else { - return Promise.resolve([]); - } - }, - data: (obj) => { - const plugin = wrappedPlugin(); - if (plugin && plugin.data) { - plugin.data(obj); - } - }, - }; -} - -function validateItems(items) { - // Validate the first item - if (items.length > 0) { - const item = items[0]; - const missingFields = []; - if (item.href == undefined) { - missingFields.push("href"); - } - if (!item.title == undefined) { - missingFields.push("title"); - } - if (!item.text == undefined) { - missingFields.push("text"); - } - - if (missingFields.length === 1) { - throw { - name: `Error: Search index is missing the ${missingFields[0]} field.`, - message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, - }; - } else if (missingFields.length > 1) { - const missingFieldList = missingFields - .map((field) => { - return `${field}`; - }) - .join(", "); - - throw { - name: `Error: Search index is missing the following fields: ${missingFieldList}.`, - message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, - }; - } - } -} - -let lastQuery = null; -function showCopyLink(query, options) { - const language = options.language; - lastQuery = query; - // Insert share icon - const inputSuffixEl = window.document.body.querySelector( - ".aa-Form .aa-InputWrapperSuffix" - ); - - if (inputSuffixEl) { - let copyButtonEl = window.document.body.querySelector( - ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" - ); - - if (copyButtonEl === null) { - copyButtonEl = window.document.createElement("button"); - copyButtonEl.setAttribute("class", "aa-CopyButton"); - copyButtonEl.setAttribute("type", "button"); - copyButtonEl.setAttribute("title", language["search-copy-link-title"]); - copyButtonEl.onmousedown = (e) => { - e.preventDefault(); - e.stopPropagation(); - }; - - const linkIcon = "bi-clipboard"; - const checkIcon = "bi-check2"; - - const shareIconEl = window.document.createElement("i"); - shareIconEl.setAttribute("class", `bi ${linkIcon}`); - copyButtonEl.appendChild(shareIconEl); - inputSuffixEl.prepend(copyButtonEl); - - const clipboard = new window.ClipboardJS(".aa-CopyButton", { - text: function (_trigger) { - const copyUrl = new URL(window.location); - copyUrl.searchParams.set(kQueryArg, lastQuery); - copyUrl.searchParams.set(kResultsArg, "1"); - return copyUrl.toString(); - }, - }); - clipboard.on("success", function (e) { - // Focus the input - - // button target - const button = e.trigger; - const icon = button.querySelector("i.bi"); - - // flash "checked" - icon.classList.add(checkIcon); - icon.classList.remove(linkIcon); - setTimeout(function () { - icon.classList.remove(checkIcon); - icon.classList.add(linkIcon); - }, 1000); - }); - } - - // If there is a query, show the link icon - if (copyButtonEl) { - if (lastQuery && options["copy-button"]) { - copyButtonEl.style.display = "flex"; - } else { - copyButtonEl.style.display = "none"; - } - } - } -} - -/* Search Index Handling */ -// create the index -var fuseIndex = undefined; -async function readSearchData() { - // Initialize the search index on demand - if (fuseIndex === undefined) { - // create fuse index - const options = { - keys: [ - { name: "title", weight: 20 }, - { name: "section", weight: 20 }, - { name: "text", weight: 10 }, - ], - ignoreLocation: true, - threshold: 0.1, - }; - const fuse = new window.Fuse([], options); - - // fetch the main search.json - const response = await fetch(offsetURL("search.json")); - if (response.status == 200) { - return response.json().then(function (searchDocs) { - searchDocs.forEach(function (searchDoc) { - fuse.add(searchDoc); - }); - fuseIndex = fuse; - return fuseIndex; - }); - } else { - return Promise.reject( - new Error( - "Unexpected status from search index request: " + response.status - ) - ); - } - } - return fuseIndex; -} - -function inputElement() { - return window.document.body.querySelector(".aa-Form .aa-Input"); -} - -function focusSearchInput() { - setTimeout(() => { - const inputEl = inputElement(); - if (inputEl) { - inputEl.focus(); - } - }, 50); -} - -/* Panels */ -const kItemTypeDoc = "document"; -const kItemTypeMore = "document-more"; -const kItemTypeItem = "document-item"; -const kItemTypeError = "error"; - -function renderItem( - item, - createElement, - state, - setActiveItemId, - setContext, - refresh -) { - switch (item.type) { - case kItemTypeDoc: - return createDocumentCard( - createElement, - "file-richtext", - item.title, - item.section, - item.text, - item.href - ); - case kItemTypeMore: - return createMoreCard( - createElement, - item, - state, - setActiveItemId, - setContext, - refresh - ); - case kItemTypeItem: - return createSectionCard( - createElement, - item.section, - item.text, - item.href - ); - case kItemTypeError: - return createErrorCard(createElement, item.title, item.text); - default: - return undefined; - } -} - -function createDocumentCard(createElement, icon, title, section, text, href) { - const iconEl = createElement("i", { - class: `bi bi-${icon} search-result-icon`, - }); - const titleEl = createElement("p", { class: "search-result-title" }, title); - const titleContainerEl = createElement( - "div", - { class: "search-result-title-container" }, - [iconEl, titleEl] - ); - - const textEls = []; - if (section) { - const sectionEl = createElement( - "p", - { class: "search-result-section" }, - section - ); - textEls.push(sectionEl); - } - const descEl = createElement("p", { - class: "search-result-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - textEls.push(descEl); - - const textContainerEl = createElement( - "div", - { class: "search-result-text-container" }, - textEls - ); - - const containerEl = createElement( - "div", - { - class: "search-result-container", - }, - [titleContainerEl, textContainerEl] - ); - - const linkEl = createElement( - "a", - { - href: offsetURL(href), - class: "search-result-link", - }, - containerEl - ); - - const classes = ["search-result-doc", "search-item"]; - if (!section) { - classes.push("document-selectable"); - } - - return createElement( - "div", - { - class: classes.join(" "), - }, - linkEl - ); -} - -function createMoreCard( - createElement, - item, - state, - setActiveItemId, - setContext, - refresh -) { - const moreCardEl = createElement( - "div", - { - class: "search-result-more search-item", - onClick: (e) => { - // Handle expanding the sections by adding the expanded - // section to the list of expanded sections - toggleExpanded(item, state, setContext, setActiveItemId, refresh); - e.stopPropagation(); - }, - }, - item.title - ); - - return moreCardEl; -} - -function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { - const expanded = state.context.expanded || []; - if (expanded.includes(item.target)) { - setContext({ - expanded: expanded.filter((target) => target !== item.target), - }); - } else { - setContext({ expanded: [...expanded, item.target] }); - } - - refresh(); - setActiveItemId(item.__autocomplete_id); -} - -function createSectionCard(createElement, section, text, href) { - const sectionEl = createSection(createElement, section, text, href); - return createElement( - "div", - { - class: "search-result-doc-section search-item", - }, - sectionEl - ); -} - -function createSection(createElement, title, text, href) { - const descEl = createElement("p", { - class: "search-result-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - - const titleEl = createElement("p", { class: "search-result-section" }, title); - const linkEl = createElement( - "a", - { - href: offsetURL(href), - class: "search-result-link", - }, - [titleEl, descEl] - ); - return linkEl; -} - -function createErrorCard(createElement, title, text) { - const descEl = createElement("p", { - class: "search-error-text", - dangerouslySetInnerHTML: { - __html: text, - }, - }); - - const titleEl = createElement("p", { - class: "search-error-title", - dangerouslySetInnerHTML: { - __html: ` ${title}`, - }, - }); - const errorEl = createElement("div", { class: "search-error" }, [ - titleEl, - descEl, - ]); - return errorEl; -} - -function positionPanel(pos) { - const panelEl = window.document.querySelector( - "#quarto-search-results .aa-Panel" - ); - const inputEl = window.document.querySelector( - "#quarto-search .aa-Autocomplete" - ); - - if (panelEl && inputEl) { - panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; - if (pos === "start") { - panelEl.style.left = `${Math.round(inputEl.left)}px`; - } else { - panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; - } - } -} - -/* Highlighting */ -// highlighting functions -function highlightMatch(query, text) { - if (text) { - const start = text.toLowerCase().indexOf(query.toLowerCase()); - if (start !== -1) { - const startMark = ""; - const endMark = ""; - - const end = start + query.length; - text = - text.slice(0, start) + - startMark + - text.slice(start, end) + - endMark + - text.slice(end); - const startInfo = clipStart(text, start); - const endInfo = clipEnd( - text, - startInfo.position + startMark.length + endMark.length - ); - text = - startInfo.prefix + - text.slice(startInfo.position, endInfo.position) + - endInfo.suffix; - - return text; - } else { - return text; - } - } else { - return text; - } -} - -function clipStart(text, pos) { - const clipStart = pos - 50; - if (clipStart < 0) { - // This will just return the start of the string - return { - position: 0, - prefix: "", - }; - } else { - // We're clipping before the start of the string, walk backwards to the first space. - const spacePos = findSpace(text, pos, -1); - return { - position: spacePos.position, - prefix: "", - }; - } -} - -function clipEnd(text, pos) { - const clipEnd = pos + 200; - if (clipEnd > text.length) { - return { - position: text.length, - suffix: "", - }; - } else { - const spacePos = findSpace(text, clipEnd, 1); - return { - position: spacePos.position, - suffix: spacePos.clipped ? "…" : "", - }; - } -} - -function findSpace(text, start, step) { - let stepPos = start; - while (stepPos > -1 && stepPos < text.length) { - const char = text[stepPos]; - if (char === " " || char === "," || char === ":") { - return { - position: step === 1 ? stepPos : stepPos - step, - clipped: stepPos > 1 && stepPos < text.length, - }; - } - stepPos = stepPos + step; - } - - return { - position: stepPos - step, - clipped: false, - }; -} - -// removes highlighting as implemented by the mark tag -function clearHighlight(searchterm, el) { - const childNodes = el.childNodes; - for (let i = childNodes.length - 1; i >= 0; i--) { - const node = childNodes[i]; - if (node.nodeType === Node.ELEMENT_NODE) { - if ( - node.tagName === "MARK" && - node.innerText.toLowerCase() === searchterm.toLowerCase() - ) { - el.replaceChild(document.createTextNode(node.innerText), node); - } else { - clearHighlight(searchterm, node); - } - } - } -} - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string -} - -// highlight matches -function highlight(term, el) { - const termRegex = new RegExp(term, "ig"); - const childNodes = el.childNodes; - - // walk back to front avoid mutating elements in front of us - for (let i = childNodes.length - 1; i >= 0; i--) { - const node = childNodes[i]; - - if (node.nodeType === Node.TEXT_NODE) { - // Search text nodes for text to highlight - const text = node.nodeValue; - - let startIndex = 0; - let matchIndex = text.search(termRegex); - if (matchIndex > -1) { - const markFragment = document.createDocumentFragment(); - while (matchIndex > -1) { - const prefix = text.slice(startIndex, matchIndex); - markFragment.appendChild(document.createTextNode(prefix)); - - const mark = document.createElement("mark"); - mark.appendChild( - document.createTextNode( - text.slice(matchIndex, matchIndex + term.length) - ) - ); - markFragment.appendChild(mark); - - startIndex = matchIndex + term.length; - matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); - if (matchIndex > -1) { - matchIndex = startIndex + matchIndex; - } - } - if (startIndex < text.length) { - markFragment.appendChild( - document.createTextNode(text.slice(startIndex, text.length)) - ); - } - - el.replaceChild(markFragment, node); - } - } else if (node.nodeType === Node.ELEMENT_NODE) { - // recurse through elements - highlight(term, node); - } - } -} - -/* Link Handling */ -// get the offset from this page for a given site root relative url -function offsetURL(url) { - var offset = getMeta("quarto:offset"); - return offset ? offset + url : url; -} - -// read a meta tag value -function getMeta(metaName) { - var metas = window.document.getElementsByTagName("meta"); - for (let i = 0; i < metas.length; i++) { - if (metas[i].getAttribute("name") === metaName) { - return metas[i].getAttribute("content"); - } - } - return ""; -} - -function algoliaSearch(query, limit, algoliaOptions) { - const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; - - const applicationId = algoliaOptions["application-id"]; - const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; - const indexName = algoliaOptions["index-name"]; - const indexFields = algoliaOptions["index-fields"]; - const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); - const searchParams = algoliaOptions["params"]; - const searchAnalytics = !!algoliaOptions["analytics-events"]; - - return getAlgoliaResults({ - searchClient, - queries: [ - { - indexName: indexName, - query, - params: { - hitsPerPage: limit, - clickAnalytics: searchAnalytics, - ...searchParams, - }, - }, - ], - transformResponse: (response) => { - if (!indexFields) { - return response.hits.map((hit) => { - return hit.map((item) => { - return { - ...item, - text: highlightMatch(query, item.text), - }; - }); - }); - } else { - const remappedHits = response.hits.map((hit) => { - return hit.map((item) => { - const newItem = { ...item }; - ["href", "section", "title", "text"].forEach((keyName) => { - const mappedName = indexFields[keyName]; - if ( - mappedName && - item[mappedName] !== undefined && - mappedName !== keyName - ) { - newItem[keyName] = item[mappedName]; - delete newItem[mappedName]; - } - }); - newItem.text = highlightMatch(query, newItem.text); - return newItem; - }); - }); - return remappedHits; - } - }, - }); -} - -function fuseSearch(query, fuse, fuseOptions) { - return fuse.search(query, fuseOptions).map((result) => { - const addParam = (url, name, value) => { - const anchorParts = url.split("#"); - const baseUrl = anchorParts[0]; - const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; - anchorParts[0] = baseUrl + sep + name + "=" + value; - return anchorParts.join("#"); - }; - - return { - title: result.item.title, - section: result.item.section, - href: addParam(result.item.href, kQueryArg, query), - text: highlightMatch(query, result.item.text), - }; - }); -} diff --git a/docs/site_libs/table1-1.0/table1_defaults.css b/docs/site_libs/table1-1.0/table1_defaults.css deleted file mode 100644 index bea58a8..0000000 --- a/docs/site_libs/table1-1.0/table1_defaults.css +++ /dev/null @@ -1,102 +0,0 @@ -/* Default */ -.Rtable1 table, table.Rtable1 { - font-family: "Arial", Arial, sans-serif; - font-size: 10pt; - border-collapse: collapse; - padding: 0px; - margin: 0px; - /* margin-bottom: 10pt; */ -} -.Rtable1 td { - white-space:nowrap; -} -.Rtable1 th, .Rtable1 td { - border: 0; - text-align: center; - padding: 0.5ex 1.5ex; - margin: 0px; -} -.Rtable1 thead>tr:first-child>th { - border-top: 2pt solid black; -} -.Rtable1 thead>tr:last-child>th { - border-bottom: 1pt solid black; -} -.Rtable1 tbody>tr:last-child>td { - border-bottom: 2pt solid black; -} -.Rtable1 th.grouplabel { - padding-left: 0; - padding-right: 0; -} -.Rtable1 th.grouplabel>div { - margin-left: 1.5ex; - margin-right: 1.5ex; - border-bottom: 1pt solid black; -} -.Rtable1 th.grouplabel:last-child>div { - margin-right: 0; -} -.Rtable1 .rowlabel { - text-align: left; - padding-left: 2.5ex; -} -.Rtable1 .firstrow.rowlabel { - padding-left: 0.5ex; - font-weight: bold; -} - -/* Zebra stripes */ -.Rtable1-zebra tbody tr:nth-child(odd) { - background-color: #eee; -} - -/* Times font */ -table.Rtable1-times { - font-family: "Times New Roman", Times, serif; -} - -/* Shade style */ -.Rtable1-shade th { - background-color: #ccc; -} - -/* Grid style */ -.Rtable1-grid th, .Rtable1-grid td { - border-left: 1pt solid black; - border-right: 1pt solid black; -} -.Rtable1-grid thead>tr:first-child>th { - border-top: 1pt solid black; -} -.Rtable1-grid thead>tr:last-child>th { - border-bottom: 1pt solid black; -} -.Rtable1-grid tbody>tr:last-child>td { - border-bottom: 1pt solid black; -} -.Rtable1-grid .firstrow, .Rtable1-grid .firstrow ~ td { - border-top: 1pt solid black; -} -.Rtable1-grid th.grouplabel>div { - margin-left: 0; - margin-right: 0; - border-bottom: 0; -} - -/* Center style */ -.Rtable1-center td.rowlabel, .Rtable1-center td.firstrow.rowlabel { - font-weight: bold; - text-align: center; - padding: 0.5ex 1.5ex; -} - -/* Footnote */ -.Rtable1 .Rtable1-footnote { - font-size: smaller; - padding: 0px; - margin: 0px; - text-align: left; - white-space: normal; -} - diff --git a/docs/styles.css b/docs/styles.css deleted file mode 100644 index 2ddf50c..0000000 --- a/docs/styles.css +++ /dev/null @@ -1 +0,0 @@ -/* css styles */