knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

The first step in data exploration usually consists of univariate, descriptive analysis of all variables of interest. Tidycomm offers three basic functions to quickly output relevant statistics:

`describe()`

for continuous variables`describe_cat()`

for categorical variables`tab_frequencies()`

for categorical variables

```
library(tidycomm)
```

For demonstration purposes, we will use sample data from the Worlds of Journalism 2012-16 study included in tidycomm.

WoJ

`describe()`

outputs several measures of central tendency and variability for all variables named in the function call:

WoJ %>% describe(autonomy_selection, autonomy_emphasis, work_experience)

If no variables are passed to `describe()`

, all numeric variables in the data are described:

WoJ %>% describe()

Data can be grouped before describing:

WoJ %>% dplyr::group_by(country) %>% describe(autonomy_emphasis, autonomy_selection)

`describe_cat()`

outputs a short summary of categorical variables (number of unique values, mode, N of mode) of all variables named in the function call:

WoJ %>% describe_cat(reach, employment, temp_contract)

If no variables are passed to `describe_cat()`

, all categorical variables (i.e., `character`

and `factor`

variables) in the data are described:

WoJ %>% describe_cat()

Data can be grouped before describing:

WoJ %>% dplyr::group_by(reach) %>% describe_cat(country, employment)

`tab_frequencies()`

outputs absolute and relative frequencies of all unique values of one or more categorical variables:

WoJ %>% tab_frequencies(employment)

Passing more than one variable will compute relative frequencies based on all combinations of unique values:

WoJ %>% tab_frequencies(employment, country)

You can also group your data before. This will lead to within-group relative frequencies:

WoJ %>% dplyr::group_by(country) %>% tab_frequencies(employment)

(Compare the columns `percent`

, `cum_n`

and `cum_percent`

with the output above.)

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