Building a complex table

Max Gordon

2023-10-29

Introduction

Tables are an essential part of publishing, well… anything. I therefore want to explore the options available for generating these in knitr. It is important to remember that there are two ways of generating tables in markdown:

  1. Markdown tables
  2. HTML tables

As the htmlTable-package is all about HTML tables we will work only with that output option. The core idea is that HTML is ubiquitous and that most word-processors will have to support copy-pasting tables and by providing simple simple CSS-formatting we are able to maximize this compatibility. Note CSS is today an extremely complex topic and it is no surprise that word-processors may have difficulty importing tables that have lots of advanced syntax, htmlTable tries to avoid all of that by putting the style close to each element, often at the cell-level.

Basics

I developed the htmlTable in order to get tables matching those available in top medical journals. After finding no HTML-alternative to the Hmisc::latex function on Stack Overflow I wrote a basic function allowing column spanners and row groups. Below is a basic example on these two:

library(htmlTable)

setHtmlTableTheme(theme = "Google docs")

output <-
  matrix(paste("Content", LETTERS[1:16]),
         ncol = 4, byrow = TRUE)

output |>
  htmlTable(header =  paste(c("1st", "2nd", "3rd", "4th"), "header"),
            rnames = paste(c("1st", "2nd", "3rd", "4th"), "row"),
            rgroup = c("Group A", "Group B"),
            n.rgroup = c(2, 2),
            cgroup = c("Cgroup 1", "Cgroup 2&dagger;"),
            n.cgroup = c(2, 2),
            caption = "Basic table with both column spanners (groups) and row groups",
            tfoot = "&dagger; A table footer commment")
Cgroup 1 Cgroup 2†
1st header 2nd header 3rd header 4th header
Group A
  1st row Content A Content B Content C Content D
  2nd row Content E Content F Content G Content H
Group B
  3rd row Content I Content J Content K Content L
  4th row Content M Content N Content O Content P
Basic table with both column spanners (groups) and row groups
† A table footer commment

We can modify all our tables by using the setHtmlTableTemplate() and we also don’t have to set the exact span of each group as it can be assumed from the data.

setHtmlTableTheme(pos.caption = "bottom")

output |>
  addHtmlTableStyle(css.rgroup = "font-style: italic") |>
  htmlTable(header =  paste(c("1st", "2nd", "3rd", "4th"), "header"),
            rnames = paste(c("1st", "2nd", "3rd", "4th"), "row"),
            rgroup = c("Group A", "Group B", ""),
            n.rgroup = c(1, 2),
            cgroup = c("Cgroup 1", "Cgroup 2&dagger;"),
            n.cgroup = 3,
            caption = "A slightly differnt table with a bottom caption",
            tfoot = "&dagger; A table footer commment")
Cgroup 1 Cgroup 2†
1st header 2nd header 3rd header 4th header
Group A
  1st row Content A Content B Content C Content D
Group B
  2nd row Content E Content F Content G Content H
  3rd row Content I Content J Content K Content L
4th row Content M Content N Content O Content P
A slightly differnt table with a bottom caption
† A table footer commment

The basic principles are:

Example based upon Swedish statistics

In order to make a more interesting example we will try to look at how the average age changes between Swedish counties the last 15 years. Goal: visualize migration patterns.

The dataset has been downloaded from Statistics Sweden and is attached to the htmlTable-package. We will start by reshaping our tidy dataset into a more table adapted format.

data(SCB)

# The SCB has three other columns and one value column
prepped_scb <- SCB |>
  dplyr::mutate(region = relevel(SCB$region, "Sweden")) |>
  dplyr::select(year, region, sex, values) |>
  tidyr::pivot_wider(names_from = c(region, sex), values_from = values)

# Set rownames to be year
rownames(prepped_scb) <- prepped_scb$year
prepped_scb$year <- NULL

# The dataset now has the rows
names(prepped_scb)
## [1] "Sweden_men"              "Sweden_women"           
## [3] "Stockholm county_men"    "Stockholm county_women" 
## [5] "Uppsala county_men"      "Uppsala county_women"   
## [7] "Norrbotten county_men"   "Norrbotten county_women"
# and the dimensions
dim(prepped_scb)
## [1] 15  8

The next step is to calculate two new columns:

To convey all these layers of information will create a table with multiple levels of column spanners:

County
Men   Women
Age Δint. Δext.   Age Δint. Δext. 
mx <- NULL
for (n in names(prepped_scb)) {
  tmp <- paste0("Sweden_", strsplit(n, "_")[[1]][2])
  mx <- cbind(mx,
              cbind(prepped_scb[[n]],
                    prepped_scb[[n]] - prepped_scb[[n]][1],
                    prepped_scb[[n]] - prepped_scb[[tmp]]))
}
rownames(mx) <- rownames(prepped_scb)
colnames(mx) <- rep(c("Age",
                      "&Delta;<sub>int</sub>",
                      "&Delta;<sub>std</sub>"),
                    times = ncol(prepped_scb))
mx <- mx[,c(-3, -6)]

# This automated generation of cgroup elements is
# somewhat of an overkill
cgroup <-
  unique(sapply(names(prepped_scb),
                function(x) strsplit(x, "_")[[1]][1],
                USE.NAMES = FALSE))
n.cgroup <-
  sapply(cgroup,
         function(x) sum(grepl(paste0("^", x), names(prepped_scb))),
         USE.NAMES = FALSE)*3
n.cgroup[cgroup == "Sweden"] <-
  n.cgroup[cgroup == "Sweden"] - 2

cgroup <-
  rbind(c(cgroup, rep(NA, ncol(prepped_scb) - length(cgroup))),
        Hmisc::capitalize(
          sapply(names(prepped_scb),
                 function(x) strsplit(x, "_")[[1]][2],
                 USE.NAMES = FALSE)))
n.cgroup <-
  rbind(c(n.cgroup, rep(NA, ncol(prepped_scb) - length(n.cgroup))),
        c(2,2, rep(3, ncol(cgroup) - 2)))

print(cgroup)
##      [,1]     [,2]               [,3]             [,4]                [,5] 
## [1,] "Sweden" "Stockholm county" "Uppsala county" "Norrbotten county" NA   
## [2,] "Men"    "Women"            "Men"            "Women"             "Men"
##      [,6]    [,7]  [,8]   
## [1,] NA      NA    NA     
## [2,] "Women" "Men" "Women"
print(n.cgroup)
##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,]    4    6    6    6   NA   NA   NA   NA
## [2,]    2    2    3    3    3    3    3    3

Next step is to output the table after rounding to the correct number of decimals. The txtRound function helps with this, as it uses the sprintf function instead of the round the resulting strings have the correct number of decimals, i.e. 1.02 will by round become 1 while we want it to retain the last decimal, i.e. be shown as 1.0.

htmlTable(txtRound(mx, 1),
          cgroup = cgroup,
          n.cgroup = n.cgroup,
          rgroup = c("First period",
                     "Second period",
                     "Third period"),
          n.rgroup = rep(5, 3),
          tfoot = txtMergeLines("&Delta;<sub>int</sub> correspnds to the change since start",
                                "&Delta;<sub>std</sub> corresponds to the change compared to national average"))
Sweden Stockholm county Uppsala county Norrbotten county
Men Women Men Women Men Women Men Women
Age Δint Age Δint Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd
First period
  1 38.9 0.0 41.5 0.0 37.3 0.0 -1.6 40.1 0.0 -1.4 37.2 0.0 -1.7 39.3 0.0 -2.2 39.7 0.0 0.8 41.9 0.0 0.4
  2 39.0 0.1 41.6 0.1 37.4 0.1 -1.6 40.1 0.0 -1.5 37.5 0.3 -1.5 39.4 0.1 -2.2 40.0 0.3 1.0 42.2 0.3 0.6
  3 39.2 0.3 41.7 0.2 37.5 0.2 -1.7 40.1 0.0 -1.6 37.6 0.4 -1.6 39.6 0.3 -2.1 40.2 0.5 1.0 42.5 0.6 0.8
  4 39.3 0.4 41.8 0.3 37.6 0.3 -1.7 40.2 0.1 -1.6 37.8 0.6 -1.5 39.7 0.4 -2.1 40.5 0.8 1.2 42.8 0.9 1.0
  5 39.4 0.5 41.9 0.4 37.7 0.4 -1.7 40.2 0.1 -1.7 38.0 0.8 -1.4 39.8 0.5 -2.1 40.7 1.0 1.3 43.0 1.1 1.1
Second period
  6 39.6 0.7 42.0 0.5 37.8 0.5 -1.8 40.3 0.2 -1.7 38.1 0.9 -1.5 40.0 0.7 -2.0 40.9 1.2 1.3 43.1 1.2 1.1
  7 39.7 0.8 42.0 0.5 37.9 0.6 -1.8 40.3 0.2 -1.7 38.3 1.1 -1.4 40.1 0.8 -1.9 41.1 1.4 1.4 43.4 1.5 1.4
  8 39.8 0.9 42.1 0.6 37.9 0.6 -1.9 40.2 0.1 -1.9 38.5 1.3 -1.3 40.4 1.1 -1.7 41.3 1.6 1.5 43.5 1.6 1.4
  9 39.8 0.9 42.1 0.6 37.8 0.5 -2.0 40.1 0.0 -2.0 38.6 1.4 -1.2 40.5 1.2 -1.6 41.5 1.8 1.7 43.8 1.9 1.7
  10 39.9 1.0 42.1 0.6 37.8 0.5 -2.1 40.1 0.0 -2.0 38.7 1.5 -1.2 40.5 1.2 -1.6 41.7 2.0 1.8 44.0 2.1 1.9
Third period
  11 39.9 1.0 42.1 0.6 37.8 0.5 -2.1 40.0 -0.1 -2.1 38.8 1.6 -1.1 40.6 1.3 -1.5 41.9 2.2 2.0 44.2 2.3 2.1
  12 40.0 1.1 42.1 0.6 37.8 0.5 -2.2 40.0 -0.1 -2.1 38.9 1.7 -1.1 40.6 1.3 -1.5 42.1 2.4 2.1 44.4 2.5 2.3
  13 40.1 1.2 42.2 0.7 37.9 0.6 -2.2 39.9 -0.2 -2.3 39.0 1.8 -1.1 40.7 1.4 -1.5 42.3 2.6 2.2 44.5 2.6 2.3
  14 40.2 1.3 42.2 0.7 37.9 0.6 -2.3 39.9 -0.2 -2.3 39.1 1.9 -1.1 40.8 1.5 -1.4 42.4 2.7 2.2 44.6 2.7 2.4
  15 40.2 1.3 42.2 0.7 38.0 0.7 -2.2 39.9 -0.2 -2.3 39.2 2.0 -1.0 40.9 1.6 -1.3 42.4 2.7 2.2 44.7 2.8 2.5
Δint correspnds to the change since start

Δstd corresponds to the change compared to national average

In order to increase the readability we may want to separate the Sweden columns from the county columns, one way is to use the align option with a |. Note that in 1.0 the function continues with the same alignment until the end, i.e. you no longer need count to have the exact right number of columns in your alignment argument.

mx |>
  txtRound(digits = 1) |>
  addHtmlTableStyle(align = "rrrr|r",
                    spacer.celltype = "double_cell") |>
  htmlTable(cgroup = cgroup,
            n.cgroup = n.cgroup,
            rgroup = c("First period",
                       "Second period",
                       "Third period"),
            n.rgroup = rep(5, 3),
            tfoot = txtMergeLines("&Delta;<sub>int</sub> correspnds to the change since start",
                                  "&Delta;<sub>std</sub> corresponds to the change compared to national average"))
Sweden Stockholm county Uppsala county Norrbotten county
Men Women Men Women Men Women Men Women
Age Δint Age Δint Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd
First period
  1 38.9 0.0 41.5 0.0 37.3 0.0 -1.6 40.1 0.0 -1.4 37.2 0.0 -1.7 39.3 0.0 -2.2 39.7 0.0 0.8 41.9 0.0 0.4
  2 39.0 0.1 41.6 0.1 37.4 0.1 -1.6 40.1 0.0 -1.5 37.5 0.3 -1.5 39.4 0.1 -2.2 40.0 0.3 1.0 42.2 0.3 0.6
  3 39.2 0.3 41.7 0.2 37.5 0.2 -1.7 40.1 0.0 -1.6 37.6 0.4 -1.6 39.6 0.3 -2.1 40.2 0.5 1.0 42.5 0.6 0.8
  4 39.3 0.4 41.8 0.3 37.6 0.3 -1.7 40.2 0.1 -1.6 37.8 0.6 -1.5 39.7 0.4 -2.1 40.5 0.8 1.2 42.8 0.9 1.0
  5 39.4 0.5 41.9 0.4 37.7 0.4 -1.7 40.2 0.1 -1.7 38.0 0.8 -1.4 39.8 0.5 -2.1 40.7 1.0 1.3 43.0 1.1 1.1
Second period
  6 39.6 0.7 42.0 0.5 37.8 0.5 -1.8 40.3 0.2 -1.7 38.1 0.9 -1.5 40.0 0.7 -2.0 40.9 1.2 1.3 43.1 1.2 1.1
  7 39.7 0.8 42.0 0.5 37.9 0.6 -1.8 40.3 0.2 -1.7 38.3 1.1 -1.4 40.1 0.8 -1.9 41.1 1.4 1.4 43.4 1.5 1.4
  8 39.8 0.9 42.1 0.6 37.9 0.6 -1.9 40.2 0.1 -1.9 38.5 1.3 -1.3 40.4 1.1 -1.7 41.3 1.6 1.5 43.5 1.6 1.4
  9 39.8 0.9 42.1 0.6 37.8 0.5 -2.0 40.1 0.0 -2.0 38.6 1.4 -1.2 40.5 1.2 -1.6 41.5 1.8 1.7 43.8 1.9 1.7
  10 39.9 1.0 42.1 0.6 37.8 0.5 -2.1 40.1 0.0 -2.0 38.7 1.5 -1.2 40.5 1.2 -1.6 41.7 2.0 1.8 44.0 2.1 1.9
Third period
  11 39.9 1.0 42.1 0.6 37.8 0.5 -2.1 40.0 -0.1 -2.1 38.8 1.6 -1.1 40.6 1.3 -1.5 41.9 2.2 2.0 44.2 2.3 2.1
  12 40.0 1.1 42.1 0.6 37.8 0.5 -2.2 40.0 -0.1 -2.1 38.9 1.7 -1.1 40.6 1.3 -1.5 42.1 2.4 2.1 44.4 2.5 2.3
  13 40.1 1.2 42.2 0.7 37.9 0.6 -2.2 39.9 -0.2 -2.3 39.0 1.8 -1.1 40.7 1.4 -1.5 42.3 2.6 2.2 44.5 2.6 2.3
  14 40.2 1.3 42.2 0.7 37.9 0.6 -2.3 39.9 -0.2 -2.3 39.1 1.9 -1.1 40.8 1.5 -1.4 42.4 2.7 2.2 44.6 2.7 2.4
  15 40.2 1.3 42.2 0.7 38.0 0.7 -2.2 39.9 -0.2 -2.3 39.2 2.0 -1.0 40.9 1.6 -1.3 42.4 2.7 2.2 44.7 2.8 2.5
Δint correspnds to the change since start

Δstd corresponds to the change compared to national average

If we still feel that we want more separation it is always possible to add colors.

mx |>
  txtRound(digits = 1) |>
  addHtmlTableStyle(align = "rrrr|r",
                    align.header = "c",
                    col.columns = c(rep("#E6E6F0", 4),
                          rep("none", ncol(mx) - 4))) |>
  htmlTable(cgroup = cgroup,
            n.cgroup = n.cgroup,
            rgroup = c("First period",
                       "Second period",
                       "Third period"),
            n.rgroup = rep(5, 3),
            tfoot = txtMergeLines("&Delta;<sub>int</sub> correspnds to the change since start",
                                  "&Delta;<sub>std</sub> corresponds to the change compared to national average"))
Sweden Stockholm county Uppsala county Norrbotten county
Men Women Men Women Men Women Men Women
Age Δint Age Δint Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd
First period
  1 38.9 0.0 41.5 0.0 37.3 0.0 -1.6 40.1 0.0 -1.4 37.2 0.0 -1.7 39.3 0.0 -2.2 39.7 0.0 0.8 41.9 0.0 0.4
  2 39.0 0.1 41.6 0.1 37.4 0.1 -1.6 40.1 0.0 -1.5 37.5 0.3 -1.5 39.4 0.1 -2.2 40.0 0.3 1.0 42.2 0.3 0.6
  3 39.2 0.3 41.7 0.2 37.5 0.2 -1.7 40.1 0.0 -1.6 37.6 0.4 -1.6 39.6 0.3 -2.1 40.2 0.5 1.0 42.5 0.6 0.8
  4 39.3 0.4 41.8 0.3 37.6 0.3 -1.7 40.2 0.1 -1.6 37.8 0.6 -1.5 39.7 0.4 -2.1 40.5 0.8 1.2 42.8 0.9 1.0
  5 39.4 0.5 41.9 0.4 37.7 0.4 -1.7 40.2 0.1 -1.7 38.0 0.8 -1.4 39.8 0.5 -2.1 40.7 1.0 1.3 43.0 1.1 1.1
Second period
  6 39.6 0.7 42.0 0.5 37.8 0.5 -1.8 40.3 0.2 -1.7 38.1 0.9 -1.5 40.0 0.7 -2.0 40.9 1.2 1.3 43.1 1.2 1.1
  7 39.7 0.8 42.0 0.5 37.9 0.6 -1.8 40.3 0.2 -1.7 38.3 1.1 -1.4 40.1 0.8 -1.9 41.1 1.4 1.4 43.4 1.5 1.4
  8 39.8 0.9 42.1 0.6 37.9 0.6 -1.9 40.2 0.1 -1.9 38.5 1.3 -1.3 40.4 1.1 -1.7 41.3 1.6 1.5 43.5 1.6 1.4
  9 39.8 0.9 42.1 0.6 37.8 0.5 -2.0 40.1 0.0 -2.0 38.6 1.4 -1.2 40.5 1.2 -1.6 41.5 1.8 1.7 43.8 1.9 1.7
  10 39.9 1.0 42.1 0.6 37.8 0.5 -2.1 40.1 0.0 -2.0 38.7 1.5 -1.2 40.5 1.2 -1.6 41.7 2.0 1.8 44.0 2.1 1.9
Third period
  11 39.9 1.0 42.1 0.6 37.8 0.5 -2.1 40.0 -0.1 -2.1 38.8 1.6 -1.1 40.6 1.3 -1.5 41.9 2.2 2.0 44.2 2.3 2.1
  12 40.0 1.1 42.1 0.6 37.8 0.5 -2.2 40.0 -0.1 -2.1 38.9 1.7 -1.1 40.6 1.3 -1.5 42.1 2.4 2.1 44.4 2.5 2.3
  13 40.1 1.2 42.2 0.7 37.9 0.6 -2.2 39.9 -0.2 -2.3 39.0 1.8 -1.1 40.7 1.4 -1.5 42.3 2.6 2.2 44.5 2.6 2.3
  14 40.2 1.3 42.2 0.7 37.9 0.6 -2.3 39.9 -0.2 -2.3 39.1 1.9 -1.1 40.8 1.5 -1.4 42.4 2.7 2.2 44.6 2.7 2.4
  15 40.2 1.3 42.2 0.7 38.0 0.7 -2.2 39.9 -0.2 -2.3 39.2 2.0 -1.0 40.9 1.6 -1.3 42.4 2.7 2.2 44.7 2.8 2.5
Δint correspnds to the change since start

Δstd corresponds to the change compared to national average

If we add a color to the row group and restrict the rgroup spanner we may even have a more visual aid.

mx |>
  txtRound(digits = 1) |>
  addHtmlTableStyle(align = "rrrr|r",
                    align.header = "c",
                    col.columns = c(rep("#E6E6F0", 4),
                          rep("none", ncol(mx) - 4)),
                    col.rgroup = c("none", "#FFFFCC")) |>
  htmlTable(cgroup = cgroup,
            n.cgroup = n.cgroup,
            # I use the &nbsp; - the no breaking space as I don't want to have a
            # row break in the row group. This adds a little space in the table
            # when used together with the cspan.rgroup=1.
            rgroup = c("1st&nbsp;period",
                       "2nd&nbsp;period",
                       "3rd&nbsp;period"),
            n.rgroup = rep(5, 3),
            tfoot = txtMergeLines("&Delta;<sub>int</sub> correspnds to the change since start",
                                  "&Delta;<sub>std</sub> corresponds to the change compared to national average"),
            cspan.rgroup = 1)
Sweden Stockholm county Uppsala county Norrbotten county
Men Women Men Women Men Women Men Women
Age Δint Age Δint Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd
1st period
  1 38.9 0.0 41.5 0.0 37.3 0.0 -1.6 40.1 0.0 -1.4 37.2 0.0 -1.7 39.3 0.0 -2.2 39.7 0.0 0.8 41.9 0.0 0.4
  2 39.0 0.1 41.6 0.1 37.4 0.1 -1.6 40.1 0.0 -1.5 37.5 0.3 -1.5 39.4 0.1 -2.2 40.0 0.3 1.0 42.2 0.3 0.6
  3 39.2 0.3 41.7 0.2 37.5 0.2 -1.7 40.1 0.0 -1.6 37.6 0.4 -1.6 39.6 0.3 -2.1 40.2 0.5 1.0 42.5 0.6 0.8
  4 39.3 0.4 41.8 0.3 37.6 0.3 -1.7 40.2 0.1 -1.6 37.8 0.6 -1.5 39.7 0.4 -2.1 40.5 0.8 1.2 42.8 0.9 1.0
  5 39.4 0.5 41.9 0.4 37.7 0.4 -1.7 40.2 0.1 -1.7 38.0 0.8 -1.4 39.8 0.5 -2.1 40.7 1.0 1.3 43.0 1.1 1.1
2nd period
  6 39.6 0.7 42.0 0.5 37.8 0.5 -1.8 40.3 0.2 -1.7 38.1 0.9 -1.5 40.0 0.7 -2.0 40.9 1.2 1.3 43.1 1.2 1.1
  7 39.7 0.8 42.0 0.5 37.9 0.6 -1.8 40.3 0.2 -1.7 38.3 1.1 -1.4 40.1 0.8 -1.9 41.1 1.4 1.4 43.4 1.5 1.4
  8 39.8 0.9 42.1 0.6 37.9 0.6 -1.9 40.2 0.1 -1.9 38.5 1.3 -1.3 40.4 1.1 -1.7 41.3 1.6 1.5 43.5 1.6 1.4
  9 39.8 0.9 42.1 0.6 37.8 0.5 -2.0 40.1 0.0 -2.0 38.6 1.4 -1.2 40.5 1.2 -1.6 41.5 1.8 1.7 43.8 1.9 1.7
  10 39.9 1.0 42.1 0.6 37.8 0.5 -2.1 40.1 0.0 -2.0 38.7 1.5 -1.2 40.5 1.2 -1.6 41.7 2.0 1.8 44.0 2.1 1.9
3rd period
  11 39.9 1.0 42.1 0.6 37.8 0.5 -2.1 40.0 -0.1 -2.1 38.8 1.6 -1.1 40.6 1.3 -1.5 41.9 2.2 2.0 44.2 2.3 2.1
  12 40.0 1.1 42.1 0.6 37.8 0.5 -2.2 40.0 -0.1 -2.1 38.9 1.7 -1.1 40.6 1.3 -1.5 42.1 2.4 2.1 44.4 2.5 2.3
  13 40.1 1.2 42.2 0.7 37.9 0.6 -2.2 39.9 -0.2 -2.3 39.0 1.8 -1.1 40.7 1.4 -1.5 42.3 2.6 2.2 44.5 2.6 2.3
  14 40.2 1.3 42.2 0.7 37.9 0.6 -2.3 39.9 -0.2 -2.3 39.1 1.9 -1.1 40.8 1.5 -1.4 42.4 2.7 2.2 44.6 2.7 2.4
  15 40.2 1.3 42.2 0.7 38.0 0.7 -2.2 39.9 -0.2 -2.3 39.2 2.0 -1.0 40.9 1.6 -1.3 42.4 2.7 2.2 44.7 2.8 2.5
Δint correspnds to the change since start

Δstd corresponds to the change compared to national average

If you want to further add to the visual hints you can use specific HTML-code and insert it into the cells. Here we will color the Δstd according to color. By default htmlTable does not escape HTML characters.

cols_2_clr <- grep("&Delta;<sub>std</sub>", colnames(mx))
# We need a copy as the formatting causes the matrix to loos
# its numerical property
out_mx <- txtRound(mx, 1)

min_delta <- min(mx[,cols_2_clr])
span_delta <- max(mx[,cols_2_clr]) - min(mx[,cols_2_clr])
for (col in cols_2_clr) {
  out_mx[, col] <- mapply(function(val, strength)
    paste0("<span style='font-weight: 900; color: ",
           colorRampPalette(c("#009900", "#000000", "#990033"))(101)[strength],
           "'>",
           val, "</span>"),
    val = out_mx[,col],
    strength = round((mx[,col] - min_delta)/span_delta*100 + 1),
    USE.NAMES = FALSE)
}

out_mx |>
  addHtmlTableStyle(align = "rrrr|r",
                    align.header = "cccc|c",
                    pos.rowlabel = "bottom",
                    col.rgroup = c("none", "#FFFFCC"),
                    col.columns = c(rep("#EFEFF0", 4),
                                    rep("none", ncol(mx) - 4))) |>
  htmlTable(caption = "Average age in Sweden counties over a period of
                     15 years. The Norbotten county is typically known
                     for having a negative migration pattern compared to
                     Stockholm, while Uppsala has a proportionally large
                     population of students.",
            rowlabel = "Year",
            cgroup = cgroup,
            n.cgroup = n.cgroup,
            rgroup = c("1st&nbsp;period",
                       "2nd&nbsp;period",
                       "3rd&nbsp;period"),
            n.rgroup = rep(5, 3),
            tfoot = txtMergeLines("&Delta;<sub>int</sub> corresponds to the change since start",
                                  "&Delta;<sub>std</sub> corresponds to the change compared to national average"),
            cspan.rgroup = 1)
Sweden Stockholm county Uppsala county Norrbotten county
Men Women Men Women Men Women Men Women
Year Age Δint Age Δint Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd Age Δint Δstd
1st period
  1 38.9 0.0 41.5 0.0 37.3 0.0 -1.6 40.1 0.0 -1.4 37.2 0.0 -1.7 39.3 0.0 -2.2 39.7 0.0 0.8 41.9 0.0 0.4
  2 39.0 0.1 41.6 0.1 37.4 0.1 -1.6 40.1 0.0 -1.5 37.5 0.3 -1.5 39.4 0.1 -2.2 40.0 0.3 1.0 42.2 0.3 0.6
  3 39.2 0.3 41.7 0.2 37.5 0.2 -1.7 40.1 0.0 -1.6 37.6 0.4 -1.6 39.6 0.3 -2.1 40.2 0.5 1.0 42.5 0.6 0.8
  4 39.3 0.4 41.8 0.3 37.6 0.3 -1.7 40.2 0.1 -1.6 37.8 0.6 -1.5 39.7 0.4 -2.1 40.5 0.8 1.2 42.8 0.9 1.0
  5 39.4 0.5 41.9 0.4 37.7 0.4 -1.7 40.2 0.1 -1.7 38.0 0.8 -1.4 39.8 0.5 -2.1 40.7 1.0 1.3 43.0 1.1 1.1
2nd period
  6 39.6 0.7 42.0 0.5 37.8 0.5 -1.8 40.3 0.2 -1.7 38.1 0.9 -1.5 40.0 0.7 -2.0 40.9 1.2 1.3 43.1 1.2 1.1
  7 39.7 0.8 42.0 0.5 37.9 0.6 -1.8 40.3 0.2 -1.7 38.3 1.1 -1.4 40.1 0.8 -1.9 41.1 1.4 1.4 43.4 1.5 1.4
  8 39.8 0.9 42.1 0.6 37.9 0.6 -1.9 40.2 0.1 -1.9 38.5 1.3 -1.3 40.4 1.1 -1.7 41.3 1.6 1.5 43.5 1.6 1.4
  9 39.8 0.9 42.1 0.6 37.8 0.5 -2.0 40.1 0.0 -2.0 38.6 1.4 -1.2 40.5 1.2 -1.6 41.5 1.8 1.7 43.8 1.9 1.7
  10 39.9 1.0 42.1 0.6 37.8 0.5 -2.1 40.1 0.0 -2.0 38.7 1.5 -1.2 40.5 1.2 -1.6 41.7 2.0 1.8 44.0 2.1 1.9
3rd period
  11 39.9 1.0 42.1 0.6 37.8 0.5 -2.1 40.0 -0.1 -2.1 38.8 1.6 -1.1 40.6 1.3 -1.5 41.9 2.2 2.0 44.2 2.3 2.1
  12 40.0 1.1 42.1 0.6 37.8 0.5 -2.2 40.0 -0.1 -2.1 38.9 1.7 -1.1 40.6 1.3 -1.5 42.1 2.4 2.1 44.4 2.5 2.3
  13 40.1 1.2 42.2 0.7 37.9 0.6 -2.2 39.9 -0.2 -2.3 39.0 1.8 -1.1 40.7 1.4 -1.5 42.3 2.6 2.2 44.5 2.6 2.3
  14 40.2 1.3 42.2 0.7 37.9 0.6 -2.3 39.9 -0.2 -2.3 39.1 1.9 -1.1 40.8 1.5 -1.4 42.4 2.7 2.2 44.6 2.7 2.4
  15 40.2 1.3 42.2 0.7 38.0 0.7 -2.2 39.9 -0.2 -2.3 39.2 2.0 -1.0 40.9 1.6 -1.3 42.4 2.7 2.2 44.7 2.8 2.5
Average age in Sweden counties over a period of 15 years. The Norbotten county is typically known for having a negative migration pattern compared to Stockholm, while Uppsala has a proportionally large population of students.
Δint corresponds to the change since start

Δstd corresponds to the change compared to national average

Although a graph most likely does the visualization task better, tables are good at conveying detailed information. It is in my mind without doubt easier in the latest version to find the pattern in the data.

Lastly I would like to thank Stephen Few, ThinkUI, and LabWrite for inspiration.