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## Introduction

`expss` package provides tabulation functions with support for 'SPSS'-style labels, multiple / nested banners, weights, multiple-response variables and significance testing. There are facilities for nice output of tables in 'knitr', R notebooks, 'shiny' and 'Jupyter' notebooks. Proper methods for labelled variables add value labels support to base R functions and to some functions from other packages. Additionally, the package offers useful functions for data processing in marketing research / social surveys - popular data transformation functions from 'SPSS' Statistics ('RECODE', 'COUNT', 'COMPUTE', 'DO IF', etc.) and 'Excel' ('COUNTIF', 'VLOOKUP', etc.). Package is intended to help people to move data processing from 'Excel'/'SPSS' to R. See examples below. You can get help about
`expss` package provides tabulation functions with support for 'SPSS'-style labels, multiple / nested banners, weights, multiple-response variables and significance testing. There are facilities for nice output of tables in 'knitr', R notebooks, 'Shiny' and 'Jupyter' notebooks. Proper methods for labelled variables add value labels support to base R functions and to some functions from other packages. Additionally, the package offers useful functions for data processing in marketing research / social surveys - popular data transformation functions from 'SPSS' Statistics ('RECODE', 'COUNT', 'COMPUTE', 'DO IF', etc.) and 'Excel' ('COUNTIF', 'VLOOKUP', etc.). Package is intended to help people to move data processing from 'Excel'/'SPSS' to R. See examples below. You can get help about
any function by typing `?function_name` in the R console.

### Links
Expand Down Expand Up @@ -42,24 +42,31 @@ mtcars = apply_labels(mtcars,
```

For quick cross-tabulation there are `fre` and `cro` family of function. For simplicity we demonstrate here only `cro_cpct` which caluclates column percent. Documentation for other functions, such as `cro_cases` for counts, `cro_rpct` for row percent, `cro_tpct` for table percent and `cro_fun` for custom summary functions can be seen by typing `?cro` and `?cro_fun` in the console.
For quick cross-tabulation there are `fre` and `cro` family of function. For simplicity we demonstrate here only `cro_cpct` which caluclates column percent. Documentation for other functions, such as `cro_cases` for counts, `cro_rpct` for row percent, `cro_tpct` for table percent and `cro_fun` for custom summary functions can be seen by typing `?cro` and `?cro_fun` in the console.

```{r}
# 'cro' examples
# multiple banners
# Table with multiple banners (column %).
mtcars %>%
calculate(cro_cpct(cyl, list(total(), am, vs))) %>%
htmlTable(caption = "Table with multiple banners (column %).")
calculate(cro_cpct(cyl, list(total(), am, vs)))
# nested banners
# Table with nested banners (column %).
mtcars %>%
calculate(cro_cpct(cyl, list(total(), am %nest% vs))) %>%
htmlTable(caption = "Table with nested banners (column %).")
calculate(cro_cpct(cyl, list(total(), am %nest% vs)))
```
We have more sophisticated interface for table construction with `magrittr` piping. Table construction consists of at least of three functions chained with pipe operator: `%>%`. At first we need to specify variables for which statistics will be computed with `tab_cells`. Secondary, we calculate statistics with one of the `tab_stat_*` functions. And last, we finalize table creation with `tab_pivot`, e. g.: `dataset %>% tab_cells(variable) %>% tab_stat_cases() %>% tab_pivot()`. After that we can optionally sort table with `tab_sort_asc`, drop empty rows/columns with `drop_rc` and transpose with `tab_transpose`. Resulting table is just a `data.frame` so we can use usual R operations on it. Detailed documentation for table creation can be seen via `?tables`. For significance testing see `?significance`.
Generally, tables automatically translated to HTML for output in knitr or Jupyter notebooks. However, if we want HTML output in the R notebooks or in the RStudio viewer we need to set options for that: `expss_output_rnotebook()` or `expss_output_viewer()`.

```{r}
# simple example
mtcars %>%
tab_cells(cyl) %>%
tab_cols(total(), am) %>%
tab_stat_cpct() %>%
tab_pivot()
# if we need caption then we use 'htmlTable'
mtcars %>%
tab_cells(mpg, disp, hp, wt, qsec) %>%
tab_cols(total(), am) %>%
Expand All @@ -68,13 +75,14 @@ mtcars %>%
tab_pivot() %>%
htmlTable(caption = "Table with summary statistics and significance marks.")
# Table with the same summary statistics. Statistics labels in columns.
mtcars %>%
tab_cells(mpg, disp, hp, wt, qsec) %>%
tab_cols(total(label = "#Total| |"), am) %>%
tab_stat_fun(Mean = w_mean, "Std. dev." = w_sd, "Valid N" = w_n, method = list) %>%
tab_pivot() %>%
htmlTable(caption = "Table with the same summary statistics. Statistics labels in columns.")
tab_pivot()
# Different statistics for different variables.
mtcars %>%
tab_cols(total(), vs) %>%
tab_cells(mpg) %>%
Expand All @@ -84,19 +92,19 @@ mtcars %>%
tab_stat_cpct(total_row_position = "none", label = "col %") %>%
tab_stat_rpct(total_row_position = "none", label = "row %") %>%
tab_stat_tpct(total_row_position = "none", label = "table %") %>%
tab_pivot(stat_position = "inside_rows") %>%
htmlTable(caption = "Different statistics for different variables.")
tab_pivot(stat_position = "inside_rows")
# Table with split by rows and with custom totals.
mtcars %>%
tab_cells(cyl) %>%
tab_cols(total(), vs) %>%
tab_rows(am) %>%
tab_stat_cpct(total_row_position = "above",
total_label = c("number of cases", "row %"),
total_statistic = c("u_cases", "u_rpct")) %>%
tab_pivot() %>%
htmlTable(caption = "Table with split by rows and with custom totals.")
tab_pivot()
# Linear regression by groups.
mtcars %>%
tab_cells(dtfrm(mpg, disp, hp, wt, qsec)) %>%
tab_cols(total(label = "#Total| |"), am) %>%
Expand All @@ -109,8 +117,7 @@ mtcars %>%
)
}
) %>%
tab_pivot() %>%
htmlTable(caption = "Linear regression by groups.")
tab_pivot()
```

## Example of data processing with multiple-response variables
Expand Down Expand Up @@ -218,8 +225,6 @@ w = apply_labels(w,
p22 = overall_liking_scale
)
cro(w$c1r) %>% htmlTable(caption = "Distribution of preferences." )
```
Are there any significant differences between preferences? Yes, difference is significant.
```{r}
Expand All @@ -229,36 +234,40 @@ w %>% tab_cols(total(), age_cat) %>%
tab_mis_val(3) %>%
tab_stat_cases() %>%
tab_last_sig_cases() %>%
tab_pivot() %>%
htmlTable()
tab_pivot()
```
Further we calculate distribution of answers in the survey questions.
```{r}
# lets specify repeated parts of table creation chains
banner = w %>% tab_cols(total(), age_cat, c1r)
# column percent with significance
tab_cpct_sig = . %>% tab_stat_cpct() %>% tab_last_sig_cpct(sig_labels = paste0("<b>",LETTERS, "</b>"))
tab_cpct_sig = . %>% tab_stat_cpct() %>%
tab_last_sig_cpct(sig_labels = paste0("<b>",LETTERS, "</b>"))
# means with siginifcance
tab_means_sig = . %>% tab_stat_mean_sd_n(labels = c("<b><u>Mean</u></b>", "sd", "N")) %>%
tab_last_sig_means(sig_labels = paste0("<b>",LETTERS, "</b>"), keep = "means")
tab_last_sig_means(
sig_labels = paste0("<b>",LETTERS, "</b>"),
keep = "means")
# Preferences
banner %>%
tab_cells(c1r) %>%
tab_cpct_sig() %>%
tab_pivot() %>%
htmlTable(caption = "Preferences")
tab_pivot()
# Overall liking
banner %>%
tab_cells(h22) %>%
tab_means_sig() %>%
tab_cpct_sig() %>%
tab_cells(p22) %>%
tab_means_sig() %>%
tab_cpct_sig() %>%
tab_pivot() %>%
htmlTable(caption = "Overall liking")
tab_pivot()
# Likes
banner %>%
tab_cells(h_likes) %>%
tab_means_sig() %>%
Expand All @@ -268,20 +277,17 @@ banner %>%
tab_means_sig() %>%
tab_cells(mrset(p1_1 %to% p1_6)) %>%
tab_cpct_sig() %>%
tab_pivot() %>%
htmlTable(caption = "Likes")
tab_pivot()
# below more complciated table were we compare likes side by side
# Likes - side by side comparison
w %>%
tab_cols(total(label = "#Total| |"), c1r) %>%
tab_cells(list(unvr(mrset(h1_1 %to% h1_6)))) %>%
tab_stat_cpct(label = var_lab(h1_1)) %>%
tab_cells(list(unvr(mrset(p1_1 %to% p1_6)))) %>%
tab_stat_cpct(label = var_lab(p1_1)) %>%
tab_pivot(stat_position = "inside_columns") %>%
htmlTable(caption = "Likes - side by side comparison")
tab_pivot(stat_position = "inside_columns")
```

Expand All @@ -308,7 +314,6 @@ boxplot(mpg ~ am, data = mtcars)
```

There is a special function for variables labels support - `use_labels`. By now variables labels support available only for expression which will be evaluated inside data.frame.

```{r}
# table with dimension names
use_labels(mtcars, table(am, vs))
Expand Down Expand Up @@ -772,3 +777,5 @@ fre(w$a) # Frequency of fruits
cro_cpct(w$b, w$a) # Column percent of cost by fruits
cro_mean(dtfrm(w$b, w$c), w$a) # Mean cost and price by fruits
```


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