Styling tables

library(gapminder)
library(dplyr)
library(tidyr)
library(stringr)
library(purrr)
library(gt)
library(mmtable2)

Formatting tables

mmtable2 outputs tables using the gt package’s format.

This means you can alter formatting using many existing gt styling commands.

gm_table_formatted <- 
gapminder_mm %>% 
  filter(var != "Life expectancy") %>% 
  mmtable(cells = value) +
  header_top(year) +
  header_left(country) +
  header_top_left(var)  +
  header_left_top(continent)  +
  cells_format(cell_predicate = T, style = list(cell_text(align = "right"))) +
  header_format(header = year, style = list(cell_text(align = "right"))) +
  header_format("all_cols", style = list(cell_text(weight = "bolder"))) +
  header_format("all_rows", style = list(cell_text(weight = "bolder"))) +
  header_format(continent,scope = "table",style = list(cell_borders(sides = "top",color = "grey"))) 

print(gm_table_formatted)
GDP Population
1992 1997 2002 2007 1992 1997 2002 2007
Africa Botswana 8.0 8.6 11.0 12.6 1.3 1.5 1.6 1.6
Gabon 13.5 14.7 12.5 13.2 1.0 1.1 1.3 1.5
Americas Canada 26.3 29.0 33.3 36.3 28.5 30.3 31.9 33.4
United States 32.0 35.8 39.1 43.0 256.9 272.9 287.7 301.1
Asia Kuwait 34.9 40.3 35.1 47.3 1.4 1.8 2.1 2.5
Singapore 24.8 33.5 36.0 47.1 3.2 3.8 4.2 4.6
Europe Ireland 17.6 24.5 34.1 40.7 3.6 3.7 3.9 4.1
Norway 34.0 41.3 44.7 49.4 4.3 4.4 4.5 4.6
Oceania Australia 23.4 27.0 30.7 34.4 17.5 18.6 19.5 20.4
New Zealand 18.4 21.1 23.2 25.2 3.4 3.7 3.9 4.1

Merged header columns

Table headers can be merged with header_merged_cols(). This supports an aributrary number of column headers.

style_list <- list(cell_borders(sides = "top",color = "lightgrey",weight = px(3)))
gm_df <- gapminder_mm %>% filter(var != "Life expectancy")
style_list3 = list(cell_text(align = "left"))

gm_table_merged <- 
 gm_df %>% 
  mmtable(cells = value) +
  header_left(year) +
  header_top(country) +
  header_left_top(var)  +
  header_top_left(continent)  +
  header_format(var,scope = "table",style = style_list) +
  header_format(continent,style_list3 ) +
  header_merged_cols()

 print(gm_table_merged)
 Botswana Gabon Canada United States Kuwait Singapore Ireland Norway Australia New Zealand
GDP 1992 8.0 13.5 26.3 32.0 34.9 24.8 17.6 34.0 23.4 18.4
1997 8.6 14.7 29.0 35.8 40.3 33.5 24.5 41.3 27.0 21.1
2002 11.0 12.5 33.3 39.1 35.1 36.0 34.1 44.7 30.7 23.2
2007 12.6 13.2 36.3 43.0 47.3 47.1 40.7 49.4 34.4 25.2
Population 1992 1.3 1.0 28.5 256.9 1.4 3.2 3.6 4.3 17.5 3.4
1997 1.5 1.1 30.3 272.9 1.8 3.8 3.7 4.4 18.6 3.7
2002 1.6 1.3 31.9 287.7 2.1 4.2 3.9 4.5 19.5 3.9
2007 1.6 1.5 33.4 301.1 2.5 4.6 4.1 4.6 20.4 4.1

Alternative pipe syntax

Adding the add_ prefix to functions allows use of %>% in place of +.

gm_table_piped <- 
gapminder_mm %>% 
  filter(var != "Life expectancy") %>% 
  mmtable(cells = value, use_default_formats = T) %>% 
  add_header_top(year) %>% 
  add_header_left(country) %>% 
  add_header_top_left(var)  %>% 
  add_header_left_top(continent)  %>% 
  add_cells_format(cell_predicate = T, style = list(cell_text(align = "right"))) %>% 
  add_header_format(header = year, style = list(cell_text(align = "right"))) %>% 
  add_header_format("all_cols", style = list(cell_text(weight = "bolder"))) %>% 
  add_header_format("all_rows", style = list(cell_text(weight = "bolder"))) %>% 
  add_header_format(continent,scope = "table",style = style_list) 

print(gm_table_piped)
GDP Population
1992 1997 2002 2007 1992 1997 2002 2007
Africa Botswana 8.0 8.6 11.0 12.6 1.3 1.5 1.6 1.6
Gabon 13.5 14.7 12.5 13.2 1.0 1.1 1.3 1.5
Americas Canada 26.3 29.0 33.3 36.3 28.5 30.3 31.9 33.4
United States 32.0 35.8 39.1 43.0 256.9 272.9 287.7 301.1
Asia Kuwait 34.9 40.3 35.1 47.3 1.4 1.8 2.1 2.5
Singapore 24.8 33.5 36.0 47.1 3.2 3.8 4.2 4.6
Europe Ireland 17.6 24.5 34.1 40.7 3.6 3.7 3.9 4.1
Norway 34.0 41.3 44.7 49.4 4.3 4.4 4.5 4.6
Oceania Australia 23.4 27.0 30.7 34.4 17.5 18.6 19.5 20.4
New Zealand 18.4 21.1 23.2 25.2 3.4 3.7 3.9 4.1

Working with colors

mmtable2 uses the style features of gt. This gives you a lot of control over the format of your table. As an example, a rainbow histogram is constructed below.

Data generation

The code below generates a set of random numbers and bins them in 10s.

library(mmtable2)
library(tidyverse)

set.seed(123)
df <- 
tibble(data = rnorm(100,100,30) %>% round()) %>% 
  mutate(rounded = floor(data/10)*10) %>% 
  mutate(rounded_chr = paste0(rounded,"'s"))  %>% 
  arrange(rounded,desc(data))

df %>% glimpse
#> Rows: 100
#> Columns: 3
#> $ data        <dbl> 31, 49, 41, 54, 69, 69, 69, 68, 68, 66, 66, 63, 62, 62, 79…
#> $ rounded     <dbl> 30, 40, 40, 50, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 70…
#> $ rounded_chr <chr> "30's", "40's", "40's", "50's", "60's", "60's", "60's", "6…

Table construction

The code below constructs a table with these values, and then adds a list of formatting commands.

# Generate random data 
df <- 
tibble(data = rnorm(100,100,30) %>% round()) %>% 
  mutate(rounded = floor(data/10)*10) %>% 
  mutate(rounded_chr = paste0(rounded,"'s"))  %>% 
  arrange(rounded,desc(data))

# Create table  
table <- 
df %>% 
  mmtable(cells = data) +
  header_top_left(rounded_chr) + 
  header_format(rounded_chr, 
                list(gt::cell_fill(color = "black"),
                     gt::cell_text(color = "white")))

# Format table
colors <-  c("#ff0000","#ffa500","#ffff00","#008000",
             "#0000ff","#4b0082","#ee82ee")
color_list <- 
  map2(unique(df$rounded_chr),rep(colors,2), 
       function(nums,colours){
        cells_format(cell_predicate =nchar(data) %in% c(2,3) & 
                     rounded_chr == nums, 
                     gt::cell_fill(color = colours))
         }
       )

print(append(list(table),color_list) %>% reduce(`+`))
100’s 110’s 120’s 130’s 140’s 150’s 160’s 190’s 30’s 50’s 60’s 70’s 80’s 90’s
99
109 89 99
109 89 98
109 89 98
108 87 96
107 119 79 87 94
106 118 69 78 86 94
104 118 129 57 69 76 85 93
103 117 128 56 68 75 85 93
103 116 124 56 67 74 85 92
103 116 123 55 64 73 84 92
102 114 123 138 53 62 72 83 92
102 113 122 134 164 52 62 71 82 90
101 111 121 133 157 163 51 61 71 81 90
101 110 121 132 143 155 160 197 38 50 61 70 80 90