Plotting inside gt table with gtExtras


This post use the gtExtras package to create table with plots inside cells such as density chart, line chart, barplot and even more. We’ll go through several examples of how to aggregate and manipulate data for this purpose, as well as choosing the chart type with gtExtras.

Table Data to Viz

Packages

For this post, we need to load the following library:

# install.packages("gtExtras")
library(gtExtras)


Data format

If you want to put charts in the cells of a table, you necessarily need to aggregate your data at some point. This is because you can’t really create any chart with a single value.

In this post, we’ll work with the iris dataset. This dataset has 5 columns: 4 quantitative and 1 qualitative (Species, with 3 distinct labels). A simple way to aggregate these data is to group by the Species column.

It implies that we will have a new dataset with 3 rows and as much column. Now that we have grouped by the dataset, we need to use an aggregation measure for the quantitative columns. And because we want to create chart for those cols, the aggregation will be the list of all values for the given species.

Here’s how to do it:

# load packages
library(gt)
library(gtExtras)
library(dplyr)

# load the dataset
data(iris)

# create aggregated dataset
agg_iris = iris %>%
  group_by(Species) %>%
  summarize(
    Sepal.L = list(Sepal.Length),
    Sepal.W = list(Sepal.Width),
    Petal.L = list(Petal.Length),
    Petal.W = list(Petal.Width)
    )

# display the table with default output with gt package
agg_iris %>%
  gt()
Species Sepal.L Sepal.W Petal.L Petal.W
setosa 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3.0, 3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2
versicolor 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7 3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1, 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8, 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3, 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8 4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3
virginica 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9 3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3.0, 2.5, 2.8, 3.2, 3.0, 3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3.0, 2.8, 3.0, 2.8, 3.8, 2.8, 2.8, 2.6, 3.0, 3.4, 3.1, 3.0, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0 6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5.0, 5.2, 5.4, 5.1 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8

Line chart

The gt_plt_sparkline() creates a line chart in table cells. It requires to add a line of code for each column you want to display. In our case, it means one line of code for each column.

library(gtExtras)

agg_iris %>%
  gt() %>%
  gt_plt_sparkline(Sepal.L) %>%
  gt_plt_sparkline(Sepal.W) %>%
  gt_plt_sparkline(Petal.L) %>%
  gt_plt_sparkline(Petal.W)
Species Sepal.L Sepal.W Petal.L Petal.W
setosa 5.0 3.3 1.4 0.20
versicolor 5.7 2.8 4.1 1.3
virginica 5.9 3.0 5.1 1.8

Distribution chart

The gt_plt_dist() creates a distribution chart chart in table cells. The type of chart depend on the type argument, as in the example below.

Let’s see what the available charts look like with this function:

library(gtExtras)

agg_iris %>%
  gt() %>%
  gt_plt_dist(
    Sepal.L,
    type = "density" 
  ) %>%
  gt_plt_dist( 
    Sepal.W,
    type = "boxplot"
  ) %>%
  gt_plt_dist( 
    Petal.L,
    type = "histogram"
  ) %>%
  gt_plt_dist(
    Petal.W,
    type = "rug_strip"
  )
Species Sepal.L Sepal.W Petal.L Petal.W
setosa
versicolor
virginica

Bar chart

The gt_plt_bar_pct() does not require aggregate data. The chart is actually a score bar that measures how close the value in the cell is to the maximum value in that column.

This means that the highest value in the table has its bar full.

library(gtExtras)

head(iris) %>%
  gt() %>%
  gt_plt_bar_pct(
    Sepal.Length,
    labels = TRUE
  ) %>%
  gt_plt_bar_pct(
    Sepal.Width,
    labels=FALSE,
    fill = "forestgreen"
  )
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
94.4%
1.4 0.2 setosa
90.7%
1.4 0.2 setosa
87%
1.3 0.2 setosa
85.2%
1.5 0.2 setosa
92.6%
1.4 0.2 setosa
100%
1.7 0.4 setosa

Summary chart

The gt_plt_summary() can, in just one single line of code, summarizes your entire dataset.

If you have correctly specify the data type in your dataframe, it will automatically aggregates columns and display the right chart for them!

library(gtExtras)

iris %>%
  gt_plt_summary()
.
150 rows x 5 cols
Column Plot Overview Missing Mean Median SD
Sepal.Length 4.37.9 0.0% 5.8 5.8 0.8
Sepal.Width 2.04.4 0.0% 3.1 3.0 0.4
Petal.Length 1.06.9 0.0% 3.8 4.3 1.8
Petal.Width 0.12.5 0.0% 1.2 1.3 0.8
Species setosa, versicolor and virginica
3 categories 0.0% — — —

Conclusion

This post explained how to add various plots in table cells using the gtExtras library. For more of this package, see the dedicated section or the table section.

Related chart types
Line plot
Area
Stacked area
Streamchart
Time Series



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