# Describe

#### Roland Krasser

#### 2025-12-10

The explore package offers a simplified way to describe data.

- `describe_tbl()` describes the table (number of rows, columns, …)
- `describe_all()` returns a description of all variables as data frame
- `describe()` returns a description of of a specific variable (or a description of all variables if no specific variable is passed to the function)
- `describe_cat()` returns a description of of a specific variable (forcing to handle the variable as categorical)
- `describe_num()` returns a description of of a specific variable (forcing to handle the variable as numerical)

We use synthetic data in this example

    library(dplyr)
    library(explore)

    data <- create_data_buy(obs = 100)
    glimpse(data)
    #> Rows: 100
    #> Columns: 13
    #> $ period          <int> 202012, 202012, 202012, 202012, 202012, 202012, 202012…
    #> $ buy             <int> 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, …
    #> $ age             <int> 48, 68, 45, 50, 59, 60, 66, 56, 70, 47, 71, 40, 47, 92…
    #> $ city_ind        <int> 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, …
    #> $ female_ind      <int> 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, …
    #> $ fixedvoice_ind  <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, …
    #> $ fixeddata_ind   <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
    #> $ fixedtv_ind     <int> 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, …
    #> $ mobilevoice_ind <int> 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, …
    #> $ mobiledata_prd  <chr> "NO", "NO", "NO", "NO", "NO", "MOBILE STICK", "MOBILE …
    #> $ bbi_speed_ind   <int> 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, …
    #> $ bbi_usg_gb      <int> 79, 60, 82, 52, 54, 64, 52, 73, 36, 90, 78, 103, 52, 2…
    #> $ hh_single       <int> 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …

### Describe table

    data %>% describe_tbl()
    #> 100 observations with 13 variables
    #> 0 observations containing missings (NA)
    #> 0 variables containing missings (NA)
    #> 2 variables with no variance

### Describe all variables

    data %>% describe_all()
    #> # A tibble: 13 × 8
    #>    variable        type     na na_pct unique    min      mean    max
    #>    <chr>           <chr> <int>  <dbl>  <int>  <dbl>     <dbl>  <dbl>
    #>  1 period          int       0      0      1 202012 202012    202012
    #>  2 buy             int       0      0      2      0      0.53      1
    #>  3 age             int       0      0     41     24     52.6      92
    #>  4 city_ind        int       0      0      2      0      0.49      1
    #>  5 female_ind      int       0      0      2      0      0.53      1
    #>  6 fixedvoice_ind  int       0      0      2      0      0.08      1
    #>  7 fixeddata_ind   int       0      0      1      1      1         1
    #>  8 fixedtv_ind     int       0      0      2      0      0.43      1
    #>  9 mobilevoice_ind int       0      0      2      0      0.68      1
    #> 10 mobiledata_prd  chr       0      0      3     NA     NA        NA
    #> 11 bbi_speed_ind   int       0      0      2      0      0.6       1
    #> 12 bbi_usg_gb      int       0      0     56     10   1064.   100000
    #> 13 hh_single       int       0      0      2      0      0.29      1

    data %>%
      describe_all() %>%
      filter(unique == 1)
    #> # A tibble: 2 × 8
    #>   variable      type     na na_pct unique    min   mean    max
    #>   <chr>         <chr> <int>  <dbl>  <int>  <dbl>  <dbl>  <dbl>
    #> 1 period        int       0      0      1 202012 202012 202012
    #> 2 fixeddata_ind int       0      0      1      1      1      1

### Describe one variable

    data %>% describe(age)
    #> variable = age
    #> type     = integer
    #> na       = 0 of 100 (0%)
    #> unique   = 41
    #> min|max  = 24 | 92
    #> q05|q95  = 33.85 | 71
    #> q25|q75  = 45 | 60
    #> median   = 52.5
    #> mean     = 52.55

    data %>% describe(buy)
    #> variable = buy
    #> type     = integer
    #> na       = 0 of 100 (0%)
    #> unique   = 2
    #>        0 = 47 (47%)
    #>        1 = 53 (53%)