Building beautiful tables with the gt:: package in R

This post outlines how to quickly scrape and clean data from Wikipedia and build a beautiful table in R
R
web-scraping
gt
sports
Author

Mickey Rafa

Published

December 23, 2022

Introduction

Purpose

In this project, I wanted to experiment with the gt:: package to create a beautiful table using R. This code will walk through scraping a table from Wikipedia of the top international goal scorers in men’s soccer history.

Setup and data preparation

Required packages:

library(tidyverse)
library(janitor)
library(scales)
library(htmltools)
library(rvest)
library(gt)
library(countrycode)

Data.world has a dataset that consists of country names and URLs associated with .svg images of their national flags, which I’d like to include within the table for extra style.

flag_db <- read.csv("https://query.data.world/s/cnheo22w5mwowz2tfxrbb3z2i3mzya?dws=00000", header=TRUE, stringsAsFactors=FALSE) %>% 
  #Convert country names into 3-letter country codes
  mutate(Code = countrycode(sourcevar = Country, origin = "country.name", destination = "iso3c", warn = FALSE)) %>% 
  select(Country, flag_URL = ImageURL)

Scrape Wikipedia Data

url_goals <- 'https://en.wikipedia.org/wiki/List_of_men%27s_footballers_with_50_or_more_international_goals'
(raw <- url_goals %>%
  rvest::read_html() %>%
  rvest::html_nodes(xpath='//*[@id="mw-content-text"]/div[1]/table[2]') %>% 
  html_table() %>% 
  .[[1]] %>% 
  clean_names())
# A tibble: 80 × 10
    rank player      nation confederation goals caps  goalsper_match career_span
   <int> <chr>       <chr>  <chr>         <chr> <chr> <chr>          <chr>      
 1     1 Cristiano … Portu… UEFA          130   212   0.61           2003–      
 2     2 Lionel Mes… Argen… CONMEBOL      109   187   0.58           2005–      
 3     3 Ali Daei    Iran   AFC           108[… 148[… 0.73           1993–2006  
 4     4 Sunil Chhe… India  AFC           94    151[… 0.62           2005–2024  
 5     5 Mokhtar Da… Malay… AFC           89    142   0.63           1972–1985  
 6     6 Ali Mabkho… Unite… AFC           85    115   0.74           2009–      
 7     6 Romelu Luk… Belgi… UEFA          85    119   0.71           2010–      
 8     8 Ferenc Pus… Hunga… UEFA          84    89    0.94           1945–1962  
 9     9 Robert Lew… Poland UEFA          83    152   0.55           2008–      
10    10 Godfrey Ch… Zambia CAF           79    111   0.71           1968–1980  
# ℹ 70 more rows
# ℹ 2 more variables: date_of_50th_goal <chr>, ref <chr>

For data cleaning of this Wiki table, we need to: * Remove all footnotes within the table * Convert all chr columns to numeric values * Re-code one of the confederation values so that it’s clean for the by confederation table

(raw <- raw %>% 
  #Remove extra spaces in nation to clean this column
  mutate(nation = str_replace_all(str_trim(nation), "\\s+", " ")) %>%
  mutate(nation = case_when(
    str_detect(nation, "^Hungary\\s+Spain$") ~ "Hungary",
    #re-coding this as United Kingdom to bring in flag
    str_detect(nation, "^England\\s+England\\s+amateurs$") ~ "United Kingdom",
    TRUE ~ nation
  )) %>%
  mutate(confederation = case_when(
    confederation == "AFC / OFC[h]" ~ "AFC",
    TRUE ~ confederation)) %>% 
  #remove footnotes and transform columns to numeric values
  mutate_at(vars(player, career_span, goals, caps, goalsper_match), 
            ~ str_remove(., "\\[.*\\]")) %>% 
  mutate_at(vars(goals, caps, goalsper_match), 
            as.numeric) %>% 
  left_join(flag_db, by = c('nation' = 'Country')) %>% 
  select(flag_URL, everything()))
# A tibble: 80 × 11
   flag_URL          rank player nation confederation goals  caps goalsper_match
   <chr>            <int> <chr>  <chr>  <chr>         <dbl> <dbl>          <dbl>
 1 https://upload.…     1 Crist… Portu… UEFA            130   212           0.61
 2 https://upload.…     2 Lione… Argen… CONMEBOL        109   187           0.58
 3 https://upload.…     3 Ali D… Iran   AFC             108   148           0.73
 4 https://upload.…     4 Sunil… India  AFC              94   151           0.62
 5 https://upload.…     5 Mokht… Malay… AFC              89   142           0.63
 6 https://upload.…     6 Ali M… Unite… AFC              85   115           0.74
 7 https://upload.…     6 Romel… Belgi… UEFA             85   119           0.71
 8 https://upload.…     8 Feren… Hunga… UEFA             84    89           0.94
 9 https://upload.…     9 Rober… Poland UEFA             83   152           0.55
10 https://upload.…    10 Godfr… Zambia CAF              79   111           0.71
# ℹ 70 more rows
# ℹ 3 more variables: career_span <chr>, date_of_50th_goal <chr>, ref <chr>

Now that I have the data scraped and cleaned, I’m interested in making some tables using the ::gt() package. Tables are great for communicating summary information, so I’ll first build a graphic of the top 12 international goal scorers of all time.

df_top_scorers <- raw %>% 
  slice(1:12)

Then, I’ll extract the min and max values from the table for conditional formatting of the table.

min_goals <- df_top_scorers$goals %>% min()
max_goals <- df_top_scorers$goals %>% max()

goals_palette <- col_numeric(c("lightgreen", "darkgreen"), 
                             domain = c(min_goals, max_goals), 
                             alpha = .75)

For the first table, I’ll put the columns and styling in place first. I’m going to build a table that includes the flag_url column – which is messy at first – and then I’ll use a function to render the flag graphics from those URLs. This blog post is excellent – it showed me how to do this step-by-step.

(tbl_top_scorers <- df_top_scorers %>% 
  select(rank, player, career_span, flag_URL, nation, goals, caps, goalsper_match) %>% 
  gt() %>% 
  #rename columns
  cols_label(rank = 'Rank',
             player = 'Name',
             career_span = 'Career Span',
             nation = 'Country',
             goals = 'Total Goals Scored',
             caps = 'Matches',
             goalsper_match = 'Goals per Match') %>% 
  #add table title
  tab_header(title = md("**Total Goals Scored in Men's International Soccer Matches**")) %>% 
  tab_source_note(source_note = "Data from Wikipedia") %>% 
  #apply new style to all column headers
  tab_style(
    locations = cells_column_labels(columns = everything()),
    style = list(
      #thick border
      cell_borders(sides = "bottom", weight = px(3)),
      #make text bold
      cell_text(weight = "bold")
    )
  ) %>% 
  #apply different style to title
  tab_style(locations = cells_title(groups = "title"),
            style = list(
              cell_text(weight = "bold", size = 24)
            )) %>% 
  data_color(columns = c(goals),
             colors = goals_palette) %>% 
  opt_all_caps() %>% 
  opt_table_font(
    font = list(
      google_font("Chivo"),
      default_fonts()
    )
  ) %>% 
  tab_options(
    #remove border between column headers and title
    column_labels.border.top.width = px(3),
    column_labels.border.top.color = "transparent",
    #remove border around the table
    table.border.top.color = "transparent",
    table.border.bottom.color = "transparent",
    #adjust font sizes and alignment
    source_notes.font.size = 12,
    heading.align = "left"
  ))
Total Goals Scored in Men’s International Soccer Matches
Rank Name Career Span flag_URL Country Total Goals Scored Matches Goals per Match
1 Cristiano Ronaldo 2003– https://upload.wikimedia.org/wikipedia/commons/5/5c/Flag_of_Portugal.svg Portugal 130 212 0.61
2 Lionel Messi 2005– https://upload.wikimedia.org/wikipedia/commons/1/1a/Flag_of_Argentina.svg Argentina 109 187 0.58
3 Ali Daei 1993–2006 https://upload.wikimedia.org/wikipedia/commons/c/ca/Flag_of_Iran.svg Iran 108 148 0.73
4 Sunil Chhetri 2005–2024 https://upload.wikimedia.org/wikipedia/en/4/41/Flag_of_India.svg India 94 151 0.62
5 Mokhtar Dahari 1972–1985 https://upload.wikimedia.org/wikipedia/commons/6/66/Flag_of_Malaysia.svg Malaysia 89 142 0.63
6 Ali Mabkhout 2009– https://upload.wikimedia.org/wikipedia/commons/c/cb/Flag_of_the_United_Arab_Emirates.svg United Arab Emirates 85 115 0.74
6 Romelu Lukaku 2010– https://upload.wikimedia.org/wikipedia/commons/9/92/Flag_of_Belgium_%28civil%29.svg Belgium 85 119 0.71
8 Ferenc Puskás 1945–1962 https://upload.wikimedia.org/wikipedia/commons/c/c1/Flag_of_Hungary.svg Hungary 84 89 0.94
9 Robert Lewandowski 2008– https://upload.wikimedia.org/wikipedia/en/1/12/Flag_of_Poland.svg Poland 83 152 0.55
10 Godfrey Chitalu 1968–1980 https://upload.wikimedia.org/wikipedia/commons/0/06/Flag_of_Zambia.svg Zambia 79 111 0.71
10 Neymar 2010– https://upload.wikimedia.org/wikipedia/en/0/05/Flag_of_Brazil.svg Brazil 79 128 0.62
12 Hussein Saeed 1977–1990 https://upload.wikimedia.org/wikipedia/commons/f/f6/Flag_of_Iraq.svg Iraq 78 137 0.57
Data from Wikipedia 
(tbl_top_scorers <- tbl_top_scorers %>% 
    text_transform(
    #Apply a function to a column
    locations = cells_body(c(flag_URL)),
    fn = function(x) {
      #Return an image of set dimensions
      web_image(
        url = x,
        height = 12
      )
    }
  ) %>% 
  #Hide column header flag_URL and reduce width
  cols_width(c(flag_URL) ~ px(30)) %>% 
  cols_label(flag_URL = ""))
Total Goals Scored in Men’s International Soccer Matches
Rank Name Career Span Country Total Goals Scored Matches Goals per Match
1 Cristiano Ronaldo 2003– Portugal 130 212 0.61
2 Lionel Messi 2005– Argentina 109 187 0.58
3 Ali Daei 1993–2006 Iran 108 148 0.73
4 Sunil Chhetri 2005–2024 India 94 151 0.62
5 Mokhtar Dahari 1972–1985 Malaysia 89 142 0.63
6 Ali Mabkhout 2009– United Arab Emirates 85 115 0.74
6 Romelu Lukaku 2010– Belgium 85 119 0.71
8 Ferenc Puskás 1945–1962 Hungary 84 89 0.94
9 Robert Lewandowski 2008– Poland 83 152 0.55
10 Godfrey Chitalu 1968–1980 Zambia 79 111 0.71
10 Neymar 2010– Brazil 79 128 0.62
12 Hussein Saeed 1977–1990 Iraq 78 137 0.57
Data from Wikipedia

Top scorers by confederation

Next, I’d like to see who are the top-5 all-time goal scorers by confederation.

(df_scorers_by_confed <- raw %>% 
  filter(!is.na(goals)) %>% 
  group_by(confederation) %>% 
  slice(1:5))
# A tibble: 25 × 11
# Groups:   confederation [5]
   flag_URL          rank player nation confederation goals  caps goalsper_match
   <chr>            <int> <chr>  <chr>  <chr>         <dbl> <dbl>          <dbl>
 1 https://upload.…     3 Ali D… Iran   AFC             108   148           0.73
 2 https://upload.…     4 Sunil… India  AFC              94   151           0.62
 3 https://upload.…     5 Mokht… Malay… AFC              89   142           0.63
 4 https://upload.…     6 Ali M… Unite… AFC              85   115           0.74
 5 https://upload.…    12 Husse… Iraq   AFC              78   137           0.57
 6 https://upload.…    10 Godfr… Zambia CAF              79   111           0.71
 7 https://upload.…    19 Kinna… Malawi CAF              71   117           0.61
 8 https://upload.…    25 Hossa… Egypt  CAF              69   177           0.39
 9 https://upload.…    31 Didie… Ivory… CAF              65   105           0.62
10 https://upload.…    45 Moham… Egypt  CAF              57   100           0.57
# ℹ 15 more rows
# ℹ 3 more variables: career_span <chr>, date_of_50th_goal <chr>, ref <chr>
#re-run the min_goals and max_goals functions
min_goals <- df_scorers_by_confed$goals %>% min(na.rm = TRUE)
max_goals <- df_scorers_by_confed$goals %>% max(na.rm = TRUE)

goals_palette <- col_numeric(c("lightgreen", "darkgreen"), 
                             domain = c(min_goals, max_goals), 
                             alpha = .75)
(tbl_scorers_by_confed <- df_scorers_by_confed %>% 
  select(rank, player, career_span, flag_URL, nation, goals, caps, goalsper_match, confederation) %>% 
  gt(groupname_col = "confederation") %>% 
    cols_label(rank = 'Global Rank',
             player = 'Name',
             career_span = 'Career Span',
             nation = 'Country',
             goals = 'Total Goals Scored',
             caps = 'Matches',
             goalsper_match = 'Goals per Match') %>% 
  #add table title
  tab_header(title = md("**Total Goals Scored in Men's International Soccer Matches**")) %>% 
  tab_source_note(source_note = "Data from Wikipedia") %>% 
  #apply new style to all column headers
  tab_style(
    locations = cells_column_labels(columns = everything()),
    style = list(
      #thick border
      cell_borders(sides = "bottom", weight = px(3)),
      #make text bold
      cell_text(weight = "bold")
    )
  ) %>% 
  #apply different style to title
  tab_style(locations = cells_title(groups = "title"),
            style = list(
              cell_text(weight = "bold", size = 24)
            )) %>% 
  data_color(columns = c(goals),
             colors = goals_palette) %>% 
  opt_all_caps() %>% 
  opt_table_font(
    font = list(
      google_font("Chivo"),
      default_fonts()
    )
  ) %>% 
  tab_options(
    #remove border between column headers and title
    column_labels.border.top.width = px(3),
    column_labels.border.top.color = "transparent",
    #remove border around the table
    table.border.top.color = "transparent",
    table.border.bottom.color = "transparent",
    #adjust font sizes and alignment
    source_notes.font.size = 12,
    heading.align = "left"
  ) %>% 
  text_transform(
    #Apply a function to a column
    locations = cells_body(c(flag_URL)),
    fn = function(x) {
      #Return an image of set dimensions
      web_image(
        url = x,
        height = 12
      )
    }
  ) %>% 
  #Hide column header flag_URL and reduce width
  cols_width(c(flag_URL) ~ px(30)) %>% 
  cols_label(flag_URL = ""))
Total Goals Scored in Men’s International Soccer Matches
Global Rank Name Career Span Country Total Goals Scored Matches Goals per Match
AFC
3 Ali Daei 1993–2006 Iran 108 148 0.73
4 Sunil Chhetri 2005–2024 India 94 151 0.62
5 Mokhtar Dahari 1972–1985 Malaysia 89 142 0.63
6 Ali Mabkhout 2009– United Arab Emirates 85 115 0.74
12 Hussein Saeed 1977–1990 Iraq 78 137 0.57
CAF
10 Godfrey Chitalu 1968–1980 Zambia 79 111 0.71
19 Kinnah Phiri 1973–1981 Malawi 71 117 0.61
25 Hossam Hassan 1985–2006 Egypt 69 177 0.39
31 Didier Drogba 2002–2014 Ivory Coast 65 105 0.62
45 Mohamed Salah 2011– Egypt 57 100 0.57
CONCACAF
22 Stern John 1995–2012 Trinidad and Tobago 70 115 0.61
27 Carlos Ruiz 1998–2016 Guatemala 68 133 0.51
45 Carlos Pavón 1993–2010 Honduras 57 101 0.56
45 Clint Dempsey 2004–2018 United States 57 141 0.40
45 Landon Donovan 2000–2014 United States 57 157 0.36
CONMEBOL
2 Lionel Messi 2005– Argentina 109 187 0.58
10 Neymar 2010– Brazil 79 128 0.62
13 Pelé 1957–1971 Brazil 77 92 0.84
25 Luis Suárez 2007– Uruguay 69 142 0.49
35 Ronaldo 1994–2011 Brazil 62 98 0.63
UEFA
1 Cristiano Ronaldo 2003– Portugal 130 212 0.61
6 Romelu Lukaku 2010– Belgium 85 119 0.71
8 Ferenc Puskás 1945–1962 Hungary 84 89 0.94
9 Robert Lewandowski 2008– Poland 83 152 0.55
14 Vivian Woodward 1903–1914 United Kingdom 75 53 1.42
Data from Wikipedia