# Tips & tricks

#### Roland Krasser

#### 2025-12-10

    library(dplyr)
    library(explore)

## Count with percent

A classic `count()` returns the number of observations.

    data <- use_data_penguins()
    data %>% count(island)
    #> # A tibble: 3 × 2
    #>   island        n
    #>   <fct>     <int>
    #> 1 Biscoe      168
    #> 2 Dream       124
    #> 3 Torgersen    52

To add percent values, simply use `count_pct()` from {explore}.

    data %>% count_pct(island)
    #> # A tibble: 3 × 4
    #>   island        n total   pct
    #>   <fct>     <int> <int> <dbl>
    #> 1 Biscoe      168   344  48.8
    #> 2 Dream       124   344  36.0
    #> 3 Torgersen    52   344  15.1

## Add id

    data %>% glimpse()
    #> Rows: 344
    #> Columns: 8
    #> $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
    #> $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
    #> $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
    #> $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
    #> $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
    #> $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
    #> $ sex               <fct> male, female, female, NA, female, male, female, male…
    #> $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

To add an id variable, simply use `add_var_id()` from {explore}.

    data %>% add_var_id() %>% glimpse()
    #> Rows: 344
    #> Columns: 9
    #> $ id                <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1…
    #> $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
    #> $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
    #> $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
    #> $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
    #> $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
    #> $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
    #> $ sex               <fct> male, female, female, NA, female, male, female, male…
    #> $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

## User defined report

Create a user defined report (RMarkdown template) to explore your own data.

    create_notebook_explore(
      output_dir = tempdir(),
      output_file = "notebook-explore.Rmd")

## Data Dictionary

Create a Data Dictionary of a data set (Markdown File data_dict.md)

    iris  %>%  data_dict_md(output_dir = tempdir())

Add title, detailed descriptions and change default filename

    description <- data.frame(
                      variable = c("Species"),
                      description = c("Species of Iris flower"))
    data_dict_md(iris,
                 title = "iris flower data set",
                 description =  description,
                 output_file = "data_dict_iris.md",
                 output_dir = tempdir())

## Color

You can make your explore-plot more colorful

    data <- use_data_penguins()
    data |> explore(flipper_length_mm, color = "lightskyblue")

    data |>
      drop_obs_with_na() |>
      explore(flipper_length_mm, bill_length_mm,
              target = sex, color = c("deeppink", "blue"))

You can even mix your own colors

    colors <- mix_color("blue", n = 5)
    colors
    #> [1] "#AAAAFF" "#5555FF" "#0000FF" "#0000AA" "#000055"

    show_color(colors)

    colors <- mix_color("gold", "red", n = 4)
    colors
    #> [1] "#FFD700" "#FF8F00" "#FF4700" "#FF0000"

    show_color(colors)

Or use some of the predefined colors in {explore}

    get_color()
    #> $a1
    #> greylight       red      blue     black  greydark
    #> "#a3a9b0" "#d32c1c" "#5dbcd2" "#000000" "#868e96"
    #>
    #> $apple
    #>     green    yellow    orange       red    violet      blue
    #> "#61bb46" "#fdb827" "#f5821f" "#e03a3e" "#963d97" "#009ddc"
    #>
    #> $google
    #>      blue     green    yellow       red
    #> "#4285f4" "#34a853" "#fbbc05" "#ea4335"
    #>
    #> $mario
    #>      mario      luigi      peach       toad     bowser donkeykong
    #>  "#e0102f"  "#08a936"  "#f096be"  "#17419a"  "#f8be10"  "#742607"
    #>
    #> $python
    #>    yellow      blue      grey
    #> "#ffde57" "#4584b6" "#646464"
    #>
    #> $r
    #>      blue
    #> "#2065b8"
    #>
    #> $redbull
    #>    yellow       red      blue  bluedark
    #> "#ffc906" "#cc1e4a" "#223971" "#121f45"
    #>
    #> $slack
    #>      blue       red    yellow     green    violet
    #> "#36c5f0" "#e01e5a" "#ecb22e" "#2eb67d" "#4a154b"
    #>
    #> $ubuntu
    #>    orange  greydark greylight    violet
    #> "#dd4814" "#333333" "#aea79f" "#77216f"

    colors <- get_color("google")
    show_color(colors)

## Period yyyymm

This is how to calculate with periods (format yyyymm)

    yyyymm_calc(202410, add_month = 3)
    #> [1] 202501

    yyyymm_calc(c(202408, 202409, 202410), add_month = -1, add_year = -1)
    #> [1] 202307 202308 202309