# Introduction

tidyHtmlTable acts as a wrapper function for the htmlTable function allowing columns to be mapped from the input data to specific htmlTable parameters in a manner similar to ggplot2.

# Some Examples

## Prepare Data

We’ll begin by turning the mtcars data into a tidy dataset. The pivot_longer function is called to collect 3 performance metrics into a pair of key and value columns.

library(magrittr)
library(tidyr)
library(dplyr)
library(htmlTable)
library(tibble)

td <- mtcars %>%
as_tibble(rownames = "rnames") %>%
pivot_longer(names_to = "per_metric",
cols = c(hp, mpg, qsec))

Now we will compute 4 summary statistics for each of the 3 performance metrics. This will be further grouped by number of cylinders and gears.

tidy_summary <- td %>%
group_by(cyl, gear, per_metric) %>%
summarise(Mean = round(mean(value), 1),
SD = round(sd(value), 1),
Min = round(min(value), 1),
Max = round(max(value), 1),
.groups = 'drop') %>%
pivot_longer(names_to = "summary_stat",
cols = c(Mean, SD, Min, Max)) %>%
ungroup() %>%
mutate(gear = paste(gear, "Gears"),
cyl = paste(cyl, "Cylinders"))

At this point, we are ready to implement the htmlTable function. Essentially, this constructs an html table using arguments similar to the htmlTable function. However, whereas htmlTable required the user to manually arrange the data and specify the column groups, headers, row names, row-groups, etc., each of these components of the table is mapped to a column within the input data.

## Output html table

### Example 1

tidy_summary  %>%
arrange(per_metric, summary_stat) %>%
cgroup = cyl,
rnames = summary_stat,
rgroup = per_metric)
4 Cylinders 6 Cylinders 8 Cylinders
3 Gears 4 Gears 5 Gears 3 Gears 4 Gears 5 Gears 3 Gears 5 Gears
hp
Max 97 109 113 110 123 175 245 335
Mean 97 76 102 107.5 116.5 175 194.2 299.5
Min 97 52 91 105 110 175 150 264
SD 20.1 15.6 3.5 7.5 33.4 50.2
mpg
Max 21.5 33.9 30.4 21.4 21 19.7 19.2 15.8
Mean 21.5 26.9 28.2 19.8 19.8 19.7 15.1 15.4
Min 21.5 21.4 26 18.1 17.8 19.7 10.4 15
SD 4.8 3.1 2.3 1.6 2.8 0.6
qsec
Max 20 22.9 16.9 20.2 18.9 15.5 18 14.6
Mean 20 19.6 16.8 19.8 17.7 15.5 17.1 14.6
Min 20 18.5 16.7 19.4 16.5 15.5 15.4 14.5
SD 1.5 0.1 0.6 1.1 0.8 0.1

### Example 2

tidy_summary  %>%
arrange(cyl, gear) %>%
cgroup = per_metric,
rnames = gear,
rgroup = cyl)
hp mpg qsec
Max Mean Min SD Max Mean Min SD Max Mean Min SD
4 Cylinders
3 Gears 97 97 97 21.5 21.5 21.5 20 20 20
4 Gears 109 76 52 20.1 33.9 26.9 21.4 4.8 22.9 19.6 18.5 1.5
5 Gears 113 102 91 15.6 30.4 28.2 26 3.1 16.9 16.8 16.7 0.1
6 Cylinders
3 Gears 110 107.5 105 3.5 21.4 19.8 18.1 2.3 20.2 19.8 19.4 0.6
4 Gears 123 116.5 110 7.5 21 19.8 17.8 1.6 18.9 17.7 16.5 1.1
5 Gears 175 175 175 19.7 19.7 19.7 15.5 15.5 15.5
8 Cylinders
3 Gears 245 194.2 150 33.4 19.2 15.1 10.4 2.8 18 17.1 15.4 0.8
5 Gears 335 299.5 264 50.2 15.8 15.4 15 0.6 14.6 14.6 14.5 0.1