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

tidy_summary  %>% 
  arrange(per_metric, summary_stat) %>% 
  addHtmlTableStyle(align = "r") %>% 
  tidyHtmlTable(header = gear,
                cgroup = cyl,
                rnames = summary_stat,
                rgroup = per_metric)

tidy_summary  %>% 
  arrange(cyl, gear) %>% 
  addHtmlTableStyle(align = "r") %>% 
  tidyHtmlTable(header = summary_stat,
                cgroup = per_metric,
                rnames = gear,
                rgroup = cyl)