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The files are 1. DATA-SCIENCE-AN-OVERVIEW-IN-R 2. INTRODUCTION-TO-PROBABILITY-USING-R 3. INTRODUCTION-TO-R-FOR-DATA-SCIENCE-PART-I 4. SELECTED-DESCRIPTIVE-AND-INFERENTIAL-STATISTICS-IN-R-FOR-UNDERGRADUATE-PROJECT 5. Test-for-the-Assumptions-of-Linear-Regression-Using-R From c74a8ec1b857dadff675b123d093563e75972533 Mon Sep 17 00:00:00 2001 From: Udokang Anietie Edem Date: Tue, 18 Jun 2024 01:20:21 +0100 Subject: [PATCH 4/5] Add files via upload --- .Rhistory | 0 .gitignore | 4 + Data-science-an-overview-in-r.Rmd | 36 ++ Introduction-to-probability-using-r.Rmd | 368 ++++++++++++++++++ Introduction-to-r-for-data-science-part-i.Rmd | 184 +++++++++ LICENSE | 242 ++++++------ ...in-Non-Linear-Regression-Model-Using-r.Rmd | 245 ++++++++++++ README.md | 19 +- ...tistics-in-r-for-undergraduate-project.Rmd | 247 ++++++++++++ ...sumptions-of-Linear-Regression-Using R.Rmd | 160 ++++++++ rugs-materials.Rproj | 13 + 11 files changed, 1381 insertions(+), 137 deletions(-) create mode 100644 .Rhistory create mode 100644 .gitignore create mode 100644 Data-science-an-overview-in-r.Rmd create mode 100644 Introduction-to-probability-using-r.Rmd create mode 100644 Introduction-to-r-for-data-science-part-i.Rmd create mode 100644 Parameter-Estimation-in-Non-Linear-Regression-Model-Using-r.Rmd create mode 100644 Selected-descriptive-and-inferential-statistics-in-r-for-undergraduate-project.Rmd create mode 100644 Test-for-the-Assumptions-of-Linear-Regression-Using R.Rmd create mode 100644 rugs-materials.Rproj diff --git a/.Rhistory b/.Rhistory new file mode 100644 index 0000000..e69de29 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..d44df33 --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +.Rproj.user +.Rhistory +.RData +.Ruserdata diff --git a/Data-science-an-overview-in-r.Rmd b/Data-science-an-overview-in-r.Rmd new file mode 100644 index 0000000..667fb91 --- /dev/null +++ b/Data-science-an-overview-in-r.Rmd @@ -0,0 +1,36 @@ +--- +title: "DATA SCIENCE: AN OVERVIEW IN R" +output: html_notebook +--- + +BEING AN ONLINE SHORT PRESENTATION IN OFFA-R-USERS-GROUP (ORUG) MEETING ON 31ST MAY, 2023 BY UDOKANG, ANIETIE EDEM (OGANIZER, ORUG) CHIF LECTURER, STATISTICS DEPARTMENT, THE FEDERAL POLYTECHNIC OFFA, NIGERIA +##Introduction +*Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. +*People prefer visualization to descriptions. +*Therefore, installing R to your systems shall be done today so that gradually we will get to some applications in subsequent meetings. +##What is Data Science? +*This is a process of collecting/recording, storing and analyzing of big data. +*Sources of data: Social media, Internet, Satellite images, e-commerce sites, healthcare surveys. +*Develop methods to effectively extract useful information for decision making on real life situation. +##What are the Difference between Statisticians and Data Scientists? +#Statisticians +Deal with small-scale data. Work on improving one simple model to best fit the data. +Only analysis data. +#Data Scientists +Work on massive data (big data). Try out different methods to create machine learning models, and then they choose the method that results in the best model. Go beyond data analysis to implement algorithms that process data automatically. The critical stage of data science is data cleaning. +##What is Data Cleaning? +When data is collected mostly in raw form it has to be arranged/transformed to have a reasonable structure that can be meaningful and useful. *Data cleaning usually takes care of • The missing values • The formatting of values • The structure of the data overall • Extracting information from complex values • Unit conversion. +An important tool of data science is data visualization. +##What is Data Visualization? +This is a way of bringing data to life that can convey trends and anomalies in the data more efficiently than a written description. Data visualization is a great way to communicate your predictions and conclusions to other people by using a useful tool as R software and its packages. *Data visualization can be described as the graphical representation of information and data. By using visual elements like charts, tables, graphs, maps, infographics and dashboards. Data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. +##Examples of Data Visualization: +*Examples in three categories; • Time series data visualization • Interactive data visualization • Static data visualization. ##Examples of Time Series Data Visualisation •Line Chart •Bar Chart •Scatter Chart •Area Chart •Map •Indicator • Pivot Table • Bullet Graph • Box plot • Matrix +##What is R ? +•R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. •R packages can be found in Comprehensive R Archive Network (CRAN) repository. It’s a huge repository of R packages that users can easily contribute to. +#Why R? +The following points is why R is attractive. i. R is mainly used when the data analysis tasks require standalone computing or analysis on individual servers. ii. R focuses on better, user-friendly data analysis, statistics and graphical models. iii. R has been used primarily in academics and research. However, it’s rapidly expanding into the enterprise market. iv. Statistical models can be written with only a few lines in R and the same piece of functionality can be written in several ways in R. *v. Once you know the basics, you can easily learn advanced techniques. +#Some R Packages for Data Cleaning: +• The Plyr Package • The Stringr Package • The tidyr package • The sqldf package • The janitor package • The splitstackshape package +#Some R Packages for Data Visualization: +• Colourpicker • Esquisse • ggplot2 • ggvis • ggforce • Lattice • Plotly • Patchwork • Quantmod • RGL +#TODAY WE HAVE LEARNT ABOUT DATA SCIENCE WITH R AND WHAT WE CAN USE TO ACHIEVE SUCH AS DATA CLEANING AND VISUALISATION. IN SUBSEQUENT MEETINGS WE WILL USE THE R PACKAGES TO RUN CODES TO ACTUALIZE WHAT WE LEARNT. #THANK YOU AND HAVE A NICE DAY diff --git a/Introduction-to-probability-using-r.Rmd b/Introduction-to-probability-using-r.Rmd new file mode 100644 index 0000000..cfbf0ac --- /dev/null +++ b/Introduction-to-probability-using-r.Rmd @@ -0,0 +1,368 @@ +--- +title: "INTRODUCTION TO PROBABILITY USING R" +output: + word_document: default + html_document: + df_print: paged +--- +BEING A SHORT TRAINING PRESENTED AT OFFA R USERS GROUP MEETING ON 13TH FEBRUARY, 2024 AT STATISTICAL LABORATORY, STATISTICS DEPARTMENT, THE FEDERAL POLYTECHNIC OFFA, NIGERIA +BY +UDOKANG, ANIETIE EDEM (OGANIZER, ORUG) +CHIF LECTURER, STATISTICS DEPARTMENT,THE FEDERAL POLYTECHNIC OFFA, NIGERIA + +#Introduction +This topic is carefully chosen because of the important of probability in Statistics, most especially when it has to do with inferential Statistics. +We deal with probability in many fields of Statistics such as Econometrics, Time Series, Biostatistics, Medical Statistics, Sampling Theory, Inference and Business Statistics. +Welcome on board as we discuss the elementary part of Statistics that has to do with the Concepts of Probability. +Before then, let’s get ourselves familiar with R Software. +#What is R? +R is a free statistical software created by statisticians Ross Ihaka and Robert Gentleman and supported by R Core Team and the R Foundation for Statistical Computing. +#Where can I Find R? +Visit: https://cran.r-project.org/ +Follow the instructions for free download and install in your system. +Display of the R Console (A Tool to type command and see the result of the command) + +#What is Probability? +Probability can be defined as a quantity from 0 to 1used to measure the likelihood or the chance of an event occurring. +#Two Approaches of Probability +*Classical Approach -This probability approach assumes equally likely outcomes based on prior knowledge. + +*Relative Frequency – This approach is based on observed occurrences over a large number of trials. +Formula +P(A) = n(A)/n(S) +Or +P(A) = f/n +Where +P(A) = number of event A +n(S) = number all possible events = sample space +f = frequency of a sub group +n = Total frequency of all the groups = sample size + +##llustrative Examples (R-4.4.0) +#Example 1 (Classical Approach) +An unbiased coin has two sides Head (H) and Tail (T). This implies that each side has an equal chance of occurrence when toss or flip. What is the probability of having a head when the coin is tossed once? + +```{r} +outcomes <- c("Head", "Tail") +total_outcomes <- length(outcomes) +total_outcomes +``` +```{r} +head.outcome <- length(outcomes[outcomes == "Head"]) +head.outcome +``` +```{r} +prob.head <- head.outcome / total_outcomes +prob.head +``` +#Example 2 (Classical Approach) +Consider a situation that an unbiased coin is tossed twice or two of the coins are tossed twice. The sample space is HH, HT, TH and TT. +What is the probability of having two heads? +```{r} +outcomes <- c("HH", "HT", "TH", "TT") +total_outcomes <- length(outcomes) +total_outcomes +``` +```{r} +head.outcomes <- length(outcomes[outcomes == "HH"]) +head.outcomes +``` +```{r} +prob.head <- head.outcomes / total_outcomes +prob.head +``` +#Example 3 (Relative Frequency Approach) +The scores and grades students in an examination are 75 (A), 75 (A), 70 (AB), 70 (AB), 70 (AB), 70 (AB), 70(AB), 66(B), 66 (B) and 66(B). +What is the probability of a student having AB in the examination? +There are ten students in the sample space, out which three have AB. +```{r} +grades <- c("A", "A", "AB", "AB","AB","AB","AB","B","B","B") +total.grades <- length(grades) +total.grades +``` +```{r} +AB.grades <- length(grades[grades == "AB"]) +AB.grades +``` +```{r} +prob.AB <- AB.grades / total.grades +prob.AB +``` +We can as well put up a frequency distribution table and get the probabilities of 75 (A), AB (70) and B (66). +```{r} +Score<-c(75,75,70,70,70,70,70,66,66,66) +factor(Score) +table(Score) +prop.table(table(Score)) +``` +#Probability Properties with Illustrations and Examples +1. 0% replace_na(list(x = "x2", f = 4)) +``` + +#Filling missing values up with the preceding values +```{r} +xf<-tibble(x=c("x1","x2","x3","x4","x5","x6","x7","x8","x9","x10"),f=c(1,4,2, NA,NA,6,7,9,NA,NA),) +xf +xf1 <- xf %>% fill(f, .direction = 'up') +xf1 +``` + +#Filling missing values down with the preceding values +```{r} +xf2 <- xf1 %>% fill(f, .direction = 'down') +xf2 +``` +#Splitting columns using “separate” function +```{r} +GP<- tibble(id = 1:2, x = c("m-360", "f-580")) +GP +``` + +#GP is the data frame (df) representing population by gender +```{r} +df %>% separate(x, c("gender", "unit")) +``` +#Splitting columns using “strsplit” function +```{r} +Students<- data.frame(Programme = c("ND Statistics", "HND Computer", "NID Welding", "BTECH Civil"), Pop = c(120, 200, 180, 78) ) +Students +``` + +```{r} +Students[c("Programme","Department")]<- do.call(rbind, strsplit(Students$Programme, " ")) +print(Students) +``` + +#Uniting columns using “unite” function +```{r} +x<-c("B","P","S","A","T","O","J","C","BT","BR","K") +y<-c("Ben","Philip","Segun","Ani","Tom","Okon","Jide","Chidi","Betty","Bros","Ken") +df<-data.frame(x,y) +df +``` + +```{r} + z<-data.frame(x=c("B","P","S","A","T","O","J","C","BT","BR","K"),y=c("Ben","Philip","Segun","Ani","Tom","Okon","Jide","Chidi","Betty","Bros","Ken")) +df %>% unite("z", x:y, remove = FALSE) +``` +```{r} +df %>% unite("z", x:y, remove = TRUE) +``` + +#We appreciate your patient and interest in participating in the discussion today. The Offa-R-Users-Group (ORUG) is a place to learn and share knowledge in the use of R. I wish to see you next time. If you are a guest, find time to register as a member to actualize your goal in using R. +The ORUG (https://www.meetup.com/fedpofa-r-users-group/ ) is sponsored by R Consortium and AniKem_Consult. For any Enquiry Contact the Organizer (WhatsApp: +2349030912602, email: anietieeu@yahoo.com) + +#THANK YOU FOR PARTICIPATING IN THIS EVENT + + + + + diff --git a/LICENSE b/LICENSE index 0e259d4..354f1e0 100644 --- a/LICENSE +++ b/LICENSE @@ -1,121 +1,121 @@ -Creative Commons Legal Code - -CC0 1.0 Universal - - CREATIVE COMMONS CORPORATION IS NOT A LAW FIRM AND DOES NOT PROVIDE - LEGAL SERVICES. DISTRIBUTION OF THIS DOCUMENT DOES NOT CREATE AN - ATTORNEY-CLIENT RELATIONSHIP. CREATIVE COMMONS PROVIDES THIS - INFORMATION ON AN "AS-IS" BASIS. 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Affirmer understands and acknowledges that Creative Commons is not a + party to this document and has no duty or obligation with respect to + this CC0 or use of the Work. diff --git a/Parameter-Estimation-in-Non-Linear-Regression-Model-Using-r.Rmd b/Parameter-Estimation-in-Non-Linear-Regression-Model-Using-r.Rmd new file mode 100644 index 0000000..a257fb3 --- /dev/null +++ b/Parameter-Estimation-in-Non-Linear-Regression-Model-Using-r.Rmd @@ -0,0 +1,245 @@ +--- +title: "Parameter Estimation in Non-Linear Regression Model Using R" +output: html_notebook +--- + +Being a Short Training Material Presented at the Offa-R-Users-Group (ORUG) on 30th May, 2024. +By +Udokang, Anietie Edem +Organizer + +#Introduction# +The previous discussion on regression analysis in this group was based on linear relationship, where the parameters and explanatory variable(s) were both linear. Today we want to consider other situations whereby the parameters or explanatory or both, are not linear referred to as non-linear relationship which can as well be explained by a non-linear model. +It should be noted that for the parameters of a non-linear regression model to be estimated, the said model can to be transformed to a linear model using various transformation methods suitable to the model. Therefore, non-linear models will be presented in terms of linearity in parameters and explanatory variables and use it to compute the parameters of the transformed model which will be used to get the estimate of the initial parameters. +#1. Polynomial Non-Linear Regression Model# +This type of regression model can be described as non-linear regression model because its non-linearity in variables, though its parameters may be linear. The polynomial non-linear regression model may not require the model to be transformed to linear model but action should be taken on variables with the power greater than 1.There are two types of polynomial regression models which are polynomial in one variable and polynomial in k variables. +##i. Polynomial in One Variable## +Polynomial regression model with only one independent variables with different powers is polynomial regression model in one variable. +#The model:# +#The 2nd polynomial (quadratic) regression model in one variable +$y=a_{0}+a_{1}x+a_{2}x^{2}+e$ + +#The 3rd polynomial (cubic) regression model in one variable +$y=a_{0}+a_{1}x+a_{2}x^{2}+a_{3}x^{3}+e$ + +#The 4th polynomial (quatic) regression model in one variable +$y=a_{0}+a_{1}x+a_{2}x^{2}+a_{3}x^{3}+a_{4}x^{4}+e$ + +#The nth polynomial regression model in one variable +$y=a_{0}+a_{1}x+a_{2}x^{2}+a_{3}x^{3}+a_{4}x^{4}+\dots+a_{n}x^{n}+e$ + +The estimation of the parameters of the nth polynomial regression model takes the process of least square estimation of the linear regression. +The illustration will be done on quadratic and cubic polynomial regression which the same procedure will be followed to kth polynomial. + + +```{r} +#Illustrative Example +#Quadratic (2nd) Polynomial Regression +x<-c(25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40) +y<-c(65,63,62,61,60,57,56,55,56,58,60,62,63,64,65,66) +plot(x, y) +``` + + +```{r} +df <- data.frame(x, y) +m1= lm(y ~ x+I(x^2)) +m1 +``` + + +```{r} +summary(m1) +``` + + +```{r} +predict(m1) +``` + +#Plot of the actual values and the regression line +```{r} +library(ggplot2) +ggplot(df, aes(x, y)) +geom_point() +geom_line(aes(x, predict(m1)),col="red") + +ggtitle("Quadratic Polynomial Regression") +``` + +#Illustrative Example +#Cubic (3rd) Polynomial Regression +```{r} +x<-c(6,9,12,16,22,28,33,40,47,51,55,60) +y<-c(14,28,50,64,67,57,55,57,68,74,88,110) +plot(x, y) +``` + +```{r} +df <- data.frame(x, y) +m2= lm(y ~ x+I(x^2)+ I(x^3)) +m2 +``` + +```{r} +summary(m2) +``` + +```{r} +predict(m2) +``` + +```{r} +library(ggplot2) +ggplot(df, aes(x, y))+geom_point() +geom_line(aes(x, predict(m2)),col="blue") +ggtitle("Cubic Polynomial Regression") +``` + +##ii. Polynomial Regression Model in K Variables +The polynomial regression with number of explanatory variables more than 0ne ( ) is said to Polynomial Regression Model in K Variables. +#The polynomial regression model is + +#The 2nd polynomial (quadratic) regression model in two variables +$y=a_{0}+a_{1}x+a_{2}z+a_{3}x^{2}+a_{4}z^{2}+e$ + +#The 3rd polynomial (cubic) regression model in one variable +$y=a_{0}+a_{1}x+a_{2}z+a_{3}x^{2}+a_{4}z^{2}+a_{5}x^{3}+a_{6}z^{3}+e$ + +#The 4th polynomial (quatic) regression model in one variable +$y=a_{0}+a_{1}x+a_{2}z+a_{3}x^{2}+a_{4}z^{2}+a_{5}x^{3}+a_{6}z^{3}+a_{7}x^{4}+a_{8}z^{4}+e$ + +#The nth polynomial regression model in one variable +$y=a_{0}+a_{1}x+a_{2}z+a_{3}x^{2}+a_{4}z^{2}+a_{5}x^{3}+a_{6}z^{3}+\dots+a_{n-1}x^{n-1}+a_{n}z^{n}+e$ + +The estimation of the model parameters is done just like the linear in variable case, where least square method of estimation is used. +We will consider the quadratic case and the idea can be applied to cubic. +#Illustrative Example +#Quadratic (2nd) Polynomial Regression + + +```{r} +x<-c(25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40) +y<-c(65,63,62,61,60,57,56,55,56,58,60,62,63,64,65,66) +z<-c(23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38) +plot(x, y) +``` + +```{r} +plot(z, y) +``` + +```{r} +df <- data.frame(x, y,z) +m3= lm(y ~ x+I(x^2)+ z+I(z^2)) +m3 +``` + +```{r} +summary(m3) +``` + +```{r} +predict(m3) +``` + +#Plot of regression line with x +```{r} +library(ggplot2) +ggplot(df, aes(x, y)) +geom_point() +geom_line(aes(x, predict(m3)),col="red") + +ggtitle("Quadratic Polynomial Regression") +``` + +#Plot regression line with z +```{r} +#Plot regression line with z +ggplot(df, aes(z, y)) +geom_point() +geom_line(aes(z, predict(m3)),col="red") + +ggtitle("Quadratic Polynomial Regression") +``` + +##2. Exponential Non-Linear Regression Model with Natural Logarithm Transformation +This type of non-linear model can be transformed with with logarithm to base e to make it a linear model. +An example of such model is $y=ae^{bx}$ which can be transformed to linear model taking the natural log of both sides of the equation. +$log_{e}y=log_{e}a+bx$ +#let +$log_{e}y=y^*$ and $log_{e}a=b_{0}$ +#Therefore, the estimate of the response variable from the new model gives the regression line equation as +$y^*=b_{0}+bx$ +from $log_{e}a=b_{0}$ +Therefore, +$a=e^{b_{0}}$ +Then we can now estimate $y=ae^{bx}$ +```{r} +#Illustrative Example +x <- c(2,4,6,8,10,12,14,16,18) +y <- c(5,7,8,9,10,12,14,16,20) +df <- data.frame(x, y) +plot(x, y) +``` + +```{r} +m4= lm(log(y) ~ x) +m4 +``` + +```{r} +b0= 1.38460 #The constant term of the transformed regression model (b0 = loga ) +a=exp(b0) #The constant term of exponential regression model +a +``` + +```{r} +b= 0.09282 #The coefficient of the transformed model = the coefficient of the exponential model +predicty=a*exp(b*x) #To predict the exponential regression model +predicty +``` + +#Plot of regression line with actual values +```{r} +ggplot(df, aes(x, y)) +geom_point() +geom_line(aes(x, predicty),col="red") + +ggtitle("Exponential Regression") +``` +#This an example of exponential growth where b>1 (if 05, there is high correlation between a given explanatory variable and other explanatory variables, hence existence of multicollinearity +```{r} +library(car) +vif(lm1) +``` + +#There is existence of multicollinearity in IMP and EXP at severe level. But GVTREV has a moderate existence of autocorrelation which may require any action. +*Action:* Remove EXP with the highest VIF or find an appropriate way of combining the two of EXP and IMP. + +#5. Residuals must be Normally Distributed +##Q-Q Plot +```{r} +library(forecast) +resid<- resid(lm1) +qqnorm(resid) +``` + + +#This is near normality even though the points are not in a straight line but are close it, except one point which normality can be considered by these observation. + +#Let this be sunjected to a test of hypothesis using Shapiro-Wilk Test. + + ##Shapiro-Wilk Test. + +$H_{0}$: The residuals are normally distributed +Vs +$H_{1}$: The residuals are not normally distributed +```{r} +shapiro.test(residuals(lm1)) +``` + +# The Shapiro test statistic W = 0.95848, p-value = 0.5139>0.05, hence the residuals have a normal distribution. +*Action:* No action. +If the residuals are not normally distributed the original can be transformed or another model of non-linear form can be used. + +#This is end of today’s short training hosted by the Offa-R-Users-Group (ORUG) a place for learning and using R. I wish to you to be part of the training session either online or physical. If you are a guest, find time to register as a member to actualize your goal in using R. +The ORUG (https://www.meetup.com/fedpofa-r-users-group/ ) is sponsored by R Consortium and AniKem_Consult. For any Enquiry Contact the Organizer (WhatsApp: +2349030912602, email: anietieeu@yahoo.com) + +#BY NEXT QUARTER WE SHALL CONSIDER ACTIONS TO BE TAKEN WHEN THE ASSUMPTIONS ARE VIOLATED. + +THANKS FOR BEING PART OF THIS SHORT TRAINING. + + + diff --git a/rugs-materials.Rproj b/rugs-materials.Rproj new file mode 100644 index 0000000..3af27f6 --- /dev/null +++ b/rugs-materials.Rproj @@ -0,0 +1,13 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX From ce7f120957c99308ed914bfbb7f58c2729f5ef10 Mon Sep 17 00:00:00 2001 From: Udokang Anietie Edem Date: Thu, 27 Jun 2024 03:12:26 +0100 Subject: [PATCH 5/5] Delete Data-science-an-overview-in-r.Rmd --- Data-science-an-overview-in-r.Rmd | 36 ------------------------------- 1 file changed, 36 deletions(-) delete mode 100644 Data-science-an-overview-in-r.Rmd diff --git a/Data-science-an-overview-in-r.Rmd b/Data-science-an-overview-in-r.Rmd deleted file mode 100644 index 667fb91..0000000 --- a/Data-science-an-overview-in-r.Rmd +++ /dev/null @@ -1,36 +0,0 @@ ---- -title: "DATA SCIENCE: AN OVERVIEW IN R" -output: html_notebook ---- - -BEING AN ONLINE SHORT PRESENTATION IN OFFA-R-USERS-GROUP (ORUG) MEETING ON 31ST MAY, 2023 BY UDOKANG, ANIETIE EDEM (OGANIZER, ORUG) CHIF LECTURER, STATISTICS DEPARTMENT, THE FEDERAL POLYTECHNIC OFFA, NIGERIA -##Introduction -*Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. -*People prefer visualization to descriptions. -*Therefore, installing R to your systems shall be done today so that gradually we will get to some applications in subsequent meetings. -##What is Data Science? -*This is a process of collecting/recording, storing and analyzing of big data. -*Sources of data: Social media, Internet, Satellite images, e-commerce sites, healthcare surveys. -*Develop methods to effectively extract useful information for decision making on real life situation. -##What are the Difference between Statisticians and Data Scientists? -#Statisticians -Deal with small-scale data. Work on improving one simple model to best fit the data. -Only analysis data. -#Data Scientists -Work on massive data (big data). Try out different methods to create machine learning models, and then they choose the method that results in the best model. Go beyond data analysis to implement algorithms that process data automatically. The critical stage of data science is data cleaning. -##What is Data Cleaning? -When data is collected mostly in raw form it has to be arranged/transformed to have a reasonable structure that can be meaningful and useful. *Data cleaning usually takes care of • The missing values • The formatting of values • The structure of the data overall • Extracting information from complex values • Unit conversion. -An important tool of data science is data visualization. -##What is Data Visualization? -This is a way of bringing data to life that can convey trends and anomalies in the data more efficiently than a written description. Data visualization is a great way to communicate your predictions and conclusions to other people by using a useful tool as R software and its packages. *Data visualization can be described as the graphical representation of information and data. By using visual elements like charts, tables, graphs, maps, infographics and dashboards. Data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. -##Examples of Data Visualization: -*Examples in three categories; • Time series data visualization • Interactive data visualization • Static data visualization. ##Examples of Time Series Data Visualisation •Line Chart •Bar Chart •Scatter Chart •Area Chart •Map •Indicator • Pivot Table • Bullet Graph • Box plot • Matrix -##What is R ? -•R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. •R packages can be found in Comprehensive R Archive Network (CRAN) repository. It’s a huge repository of R packages that users can easily contribute to. -#Why R? -The following points is why R is attractive. i. R is mainly used when the data analysis tasks require standalone computing or analysis on individual servers. ii. R focuses on better, user-friendly data analysis, statistics and graphical models. iii. R has been used primarily in academics and research. However, it’s rapidly expanding into the enterprise market. iv. Statistical models can be written with only a few lines in R and the same piece of functionality can be written in several ways in R. *v. Once you know the basics, you can easily learn advanced techniques. -#Some R Packages for Data Cleaning: -• The Plyr Package • The Stringr Package • The tidyr package • The sqldf package • The janitor package • The splitstackshape package -#Some R Packages for Data Visualization: -• Colourpicker • Esquisse • ggplot2 • ggvis • ggforce • Lattice • Plotly • Patchwork • Quantmod • RGL -#TODAY WE HAVE LEARNT ABOUT DATA SCIENCE WITH R AND WHAT WE CAN USE TO ACHIEVE SUCH AS DATA CLEANING AND VISUALISATION. IN SUBSEQUENT MEETINGS WE WILL USE THE R PACKAGES TO RUN CODES TO ACTUALIZE WHAT WE LEARNT. #THANK YOU AND HAVE A NICE DAY