Infographics Using R

R Daily Graph Series

Introduction to Infographics

Infographics are graphic visual representations of information, data, or knowledge intended to present information quickly and clearly. They can improve cognition by utilizing graphics to enhance the human visual system’s ability to see patterns and trends. Similar pursuits are information visualization, data visualization, statistical graphics, information design, or information architecture. Infographics have evolved in recent years to be for mass communication, and thus are designed with fewer assumptions about the readers’ knowledge base than other types of visualizations.

Infographics have been around for many years and recently the increase of a number of easy-to-use, free tools have made the creation of infographics available to a large segment of the population. Social media sites such as Facebook and Twitter have also allowed for individual infographics to be spread among many people around the world. Infographics are widely used in the age of short attention span.

In newspapers, infographics are commonly used to show the weather, as well as maps, site plans, and graphs for summaries of data. Some books are almost entirely made up of information graphics, such as David Macaulay’s The Way Things Work. The Snapshots in USA Today are also an example of simple infographics used to convey news and current events.

R Codes

R Codes for above inforgraphic:

# Clear our workspace: 

rm(list = ls())

# Load some R packages: 
library(tidyverse)
library(waffle) # remotes::install_github("hrbrmstr/waffle")
library(patchwork)
library(lubridate)
library(extrafont)
library(ggtext)
library(Cairo)

# Set theme for all plots: 

my_font <- "Roboto Condensed"

# Get the data: 

tuesdata <- tidytuesdayR::tt_load(2020, week = 49)
shelters <- tuesdata$shelters

# Extract year, month and unite it in a new column: 

shelters <- shelters %>% 
  mutate(year = year(occupancy_date),
         month = month(occupancy_date)) %>%
  unite("date", year:month , sep = "/", remove = FALSE) 

# Monthly occupancy plot: 

mo_plot <- shelters %>% 
  group_by(date) %>% 
  summarise(month_occupancy = sum(occupancy) / 1000) %>% 
  mutate(date = parse_date_time(date, "ym")) %>% 
  ggplot(aes(date, month_occupancy)) +
  geom_point(size = 2, color = "#371206") +
  geom_smooth(se = FALSE, color = "#F72C25", size = 1.5) +   
  labs(x = NULL, y = "Monthly Occupancy", subtitle = "**<span style='color:#F72C25;font-size:60px;'>50%</span><br>increase in monthly shelter occupancy<br>from Jan 2017 to Dec 2019.**") +
  theme(text = element_text(family = my_font), 
        plot.subtitle = element_markdown(color = "#292929"),
        plot.background = element_rect("#EBEBEB"),
        panel.background = element_blank(),
        axis.title = element_text(color = "#292929", face = "bold"),
        axis.text = element_text(color = "#292929", face = "bold"),
        panel.grid = element_blank()) 

# Waffle plot: 

wa_plot <- shelters %>% 
  group_by(sector) %>% 
  summarise(sum = n_distinct(shelter_name)) %>% 
  arrange(sum) %>% 
  ggplot(aes(fill = sector, values = sum)) +
  geom_waffle(n_rows = 9, size = 0.33, colour = "white", flip = FALSE, show.legend = FALSE) +
  scale_fill_manual(limits = c("Families", "Youth", "Co-ed", "Women", "Men"),
                    values = c("#FD8235", "#82869B", "#F7B32B", "#365181", "#F72C25")) +
  scale_y_continuous(breaks = seq(0, 9, by = 3)) +
  scale_x_continuous(breaks = seq(0, 10, by = 2)) +
  labs(title = "**Number of Shelters by Group**", 
       subtitle = "**Shelters in Toronto are sub-categorized by groups, these are:<br>
                  <span style='color:#FD8235;'>Families</span>,
                  <span style='color:#82869B;'>Youth</span>,
                  <span style='color:#F7B32B;'>Co-ed</span>,
                  <span style='color:#365181;'>Women</span> and
                  <span style='color:#F72C25;'>Men</span>.**") +
  theme(text = element_text(family = my_font), 
        plot.title = element_markdown(color = "#292929"), 
        plot.subtitle = element_markdown(color = "#292929"),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        plot.background = element_rect("#EBEBEB"),
        panel.background = element_blank(),
        axis.title = element_text(color = "#292929", face = "bold"),
        axis.text = element_text(color = "#292929", face = "bold")) 

# Circular bar plot: 

rate_plot <- shelters %>% 
  filter(!is.na(capacity)) %>% 
  group_by(month, year) %>% 
  summarise(n_distinct(shelter_name),
            year_occupancy = sum(occupancy), 
            year_capacity = sum(capacity),
            rate = year_occupancy / year_capacity) %>% 
  ggplot(aes(month, rate, fill = rate)) + 
  geom_bar(stat = "identity") +
  ylim(-1, 1) +
  coord_polar() +
  scale_fill_gradient(low = "#ffb950", high = "#a50104") +
  scale_x_discrete(breaks = 1:12, 
                   limits = as.character(1:12),
                   labels = month.abb) +
  facet_wrap(~ year, ncol = 3) +
  labs(x = NULL, y = NULL, 
       fill = "Occupancy Rate", 
       subtitle = "Monthly occupancy rate of shelters from
       <span style='color:#ffb950;'>low</span> to
       <span style='color:#a50104;'>high</span>.<br>
       From 2017 to 2019 monthly occupancy stayed above<br>90%.") +
  theme(text = element_text(family = my_font), 
        legend.position = "none",
        axis.text.y = element_blank(),
        axis.ticks = element_blank(),
        plot.background = element_rect("#EBEBEB"),
        panel.background = element_blank(),
        axis.title = element_text(color = "#292929", face = "bold"),
        axis.text = element_text(color = "#292929", face = "bold"),
        plot.subtitle = element_markdown(face = "bold", color = "#292929"),
        strip.text.x = element_text(face = "bold", color = "#292929"),
        strip.background = element_blank(),
        panel.spacing = unit(2, "lines")) 


# Combine plots: 

def_plot <- wa_plot / rate_plot / mo_plot

def_plot +
  plot_annotation(title = "Toronto Shelters: An Infographic Using R",
                  subtitle = "As of 2019 there were 62 shelters in Toronto.",
                  caption = "Data: open.toronto.ca", 
                  theme = theme(plot.title = element_text(color = "#292929", size = 22, face = "bold", family = my_font),
                                plot.subtitle = element_text(color = "#292929", size = 14, family = my_font),
                                plot.caption = element_text(color = "grey50", face = "bold.italic", family = my_font),
                                plot.background = element_rect("#EBEBEB"),
                                panel.background = element_rect("#EBEBEB")))

# Save the plot: 

ggsave("Shelters.png",
       width = 21,
       height = 30,
       units = "cm",
       dpi = 500,
       type = "cairo-png")