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With reactablefmtr, we can easily apply color tiles to columns within a table by using color_tiles() within cell of reactable::colDef().

By default a normalized orange-white-blue color scale is applied to each column.

df <- gapminder %>%  
 filter(year == 2007) %>%
 select(-year) %>% 
 relocate(lifeExp, .after = last_col())

df %>%
  reactable(
    defaultColDef = colDef(
      cell = color_tiles(.)
      )
  )

color_tiles() customization options

Parameter Description Default Value
data name of data set NULL
colors color palette c(‘#15607A’,‘#FFFFFF’,‘#FA8C00’)
color_ref column containing color assignments NULL
color_by column containing value assignments NULL
opacity opaqueness of color palette 1
bias the spacing between colors 1
number_fmt the format of the values NULL
text_size the size of the text NULL
text_color the color of the text ‘black’
text_color_ref column containing text color assignments NULL
show_text show or hide text TRUE
brighten_text auto-adjust text color based on color of cell TRUE
brighten_text_color color of the auto-adjusted text color ‘white’
bold_text bold format text FALSE
span show as row-wise instead of column-wise FALSE
box_shadow add a box shadow around tiles FALSE
tooltip enable hover tooltip FALSE
animation animation of color transitions on sort ‘background 1s ease’

Format numbers

Numbers can be formatted within the number_fmt argument in color_tiles(). One method of formatting the numbers is by utilizing the formatters from the scales package, and the numbers can be formatted in the same way as they are in ggplot2.

df %>%
  reactable(
    columns = list(
      pop = colDef(
        cell = color_tiles(., number_fmt = scales::label_number_si(accuracy = 0.1))
      ),
      gdpPercap = colDef(
        cell = color_tiles(., number_fmt = scales::comma)
      ),
      lifeExp = colDef(
        cell = color_tiles(., number_fmt = scales::label_number(accuracy = 1))
      )
    )
  )

Custom color palettes

If we want to show a different color palette than the default, we can call them within the colors argument like so:

library(RColorBrewer)
library(viridis)

df %>%
  reactable(
    defaultSorted = 'pop',
    defaultSortOrder = 'desc',
    columns = list(
      pop = colDef(
        cell = color_tiles(., number_fmt = scales::label_number_si(accuracy = 0.1), colors = viridis::viridis(5))
      ),
      gdpPercap = colDef(
        cell = color_tiles(., number_fmt = scales::comma, colors = RColorBrewer::brewer.pal(7, 'Greens'))
      ),
      lifeExp = colDef(
        cell = color_tiles(., number_fmt = scales::label_number(accuracy = 1), colors = c('tomato','white','dodgerblue'))
      )
    )
  )

Add a legend

add_legend() customization options

Parameter Description Default Value
data name of dataset NULL
col_name name of column containing numeric values NULL
bins number of bins to be displayed 5
colors color palette c(‘#15607A’,‘#FFFFFF’,‘#FA8C00’)
bias opaqueness of color palette 1
labels show or hide value labels TRUE
number_fmt the format of the values NULL
title the title above the legend NULL
footer the footer below the legend NULL
align align to the left or right of the table ‘right’

If you would like to add a legend for the color palette used within color_tiles(), you can do so by including add_legend() below the table and listing the color palette used within color_tiles(). If no color palette is defined by the user within add_legend(), it will show the default blue-to-orange color palette used in color_tiles().

df %>%
  reactable(
    columns = list(
      lifeExp = colDef(
        cell = color_tiles(., number_fmt = scales::comma)
      )
    )
  ) %>% 
  add_legend(df, col_name = 'lifeExp')
  • 83
  • 76
  • 72
  • 57
  • 40


You can add a title and a footer to the legend with title and footer respectively:

df %>%
  reactable(
    columns = list(
      lifeExp = colDef(
        cell = color_tiles(., number_fmt = scales::label_number(accuracy = 1))
        )
      )
  ) %>% 
  add_legend(df, col_name = 'lifeExp', title = 'Life Expectancy (yrs)', footer = 'Reported as of 2007')
Life Expectancy (yrs)
  • 83
  • 76
  • 72
  • 57
  • 40
Reported as of 2007


By default, the color palette is broken into 5 distinct bins. However, we can increase or decrease the number of color bins we would like to show in the legend by providing a number within bins:

df %>%
  reactable(
    columns = list(
      lifeExp = colDef(
        cell = color_tiles(., number_fmt = scales::label_number(accuracy = 1))
        )
      )
  ) %>% 
  add_legend(df, col_name = 'lifeExp', title = 'Life Expectancy (yrs)', footer = 'Reported as of 2007', bins = 9)
Life Expectancy (yrs)
  • 83
  • 79
  • 76
  • 74
  • 72
  • 65
  • 57
  • 49
  • 40
Reported as of 2007


If you are using a different color palette than the default one provided, you can specify the color palette in the same way that you did within color_tiles():

df %>%
  reactable(
    columns = list(
      lifeExp = colDef(
        cell = color_tiles(., number_fmt = scales::label_number(accuracy = 1), colors = viridis::viridis(5))
        )
      )
  ) %>% 
  add_legend(df, col_name = 'lifeExp', title = 'Life Expectancy (yrs)', footer = 'Reported as of 2007', colors = viridis::viridis(5))
Life Expectancy (yrs)
  • 83
  • 76
  • 72
  • 57
  • 40
Reported as of 2007


If the data within your column is not evenly distributed, you can set the color bias to lean more towards the higher values or lower values within the column with bias. Changing the bias within the legend is a good visual gauge of how the bias affects the distribution of colors within the column:

df %>%
  reactable(
    columns = list(
      lifeExp = colDef(
        cell = color_tiles(., number_fmt = scales::label_number(accuracy = 1), colors = viridis::viridis(5), bias = 3)
        )
      )
  ) %>% 
  add_legend(df, col_name = 'lifeExp', title = 'Life Expectancy (yrs)', footer = 'Reported as of 2007', colors = viridis::viridis(5), bias = 3)
Life Expectancy (yrs)
  • 83
  • 76
  • 72
  • 57
  • 40
Reported as of 2007


Add a box shadow

Box shadows can be added to the tiles to create a ‘3-D’ effect via box_shadow.

df %>%
  reactable(
    columns = list(
      pop = colDef(
        cell = color_tiles(., number_fmt = scales::label_number_si(accuracy = 0.1), box_shadow = TRUE)
      ),
      gdpPercap = colDef(
        cell = color_tiles(., number_fmt = scales::comma, box_shadow = TRUE)
      ),
      lifeExp = colDef(
        cell = color_tiles(., number_fmt = scales::label_number(accuracy = 1), box_shadow = TRUE)
      )
    )
  )

Assign colors from another column

Colors can be conditionally assigned to values based on another column by using color_ref.

In the example below, we assigned a blue color to Compact cars, a red color to Sporty cars, and a gold color to Vans using dplyr::case_when().

Then within color_tiles(), we reference the name of the conditional column we just created to apply the colors to the values in MPG.city and MPG.highway.

df_continents <- df %>%
  mutate(
    continent_cols = dplyr::case_when(
      continent == 'Africa' ~ 'orange',
      continent == 'Americas' ~ 'pink',
      continent == 'Asia' ~ 'violet',
      continent == 'Europe' ~ 'gold',
      continent == 'Oceania' ~ 'skyblue',
      TRUE ~ 'grey'
    )
  )

df_continents %>%
  reactable(
    defaultSorted = 'continent',
    defaultSortOrder = 'desc',
    columns = list(
      continent_cols = colDef(show = FALSE),
      pop = colDef(
        cell = color_tiles(., number_fmt = scales::label_number_si(accuracy = 0.1), color_ref = 'continent_cols')
      ),
      gdpPercap = colDef(
        cell = color_tiles(., number_fmt = scales::comma, color_ref = 'continent_cols')
      ),
      lifeExp = colDef(
        cell = color_tiles(., number_fmt = scales::label_number(accuracy = 1), color_ref = 'continent_cols')
      )
    )
  )

We can further apply the conditional colors to the entire dataset by setting the style within defaultColDef:

df_continents %>%
  reactable(
    defaultSorted = 'continent',
    defaultSortOrder = 'desc',
    defaultColDef = colDef(
        cell = color_tiles(., color_ref = 'continent_cols')
        ),
    columns = list(
      continent_cols = colDef(
        show = FALSE
      )
    )
  )