Create density plots with ggridges package in R

Packages we will need:

library(ggimage)  # to add png images
library(bbplot)   # for pretty graph themes

We will plot out the favourability opinion polls for the three main political parties in Ireland from 2016 to 2020. Data comes from Louwerse and Müller (2020)

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Before we dive into the ggridges plotting, we have a little data cleaning to do. First, we extract the last four “characters” from the date string to create a year variable.

I took this quick function from a StackOverflow response:

substrRight <- function(x, n){
  substr(x, nchar(x)-n+1, nchar(x))}

polls_csv$year <- substrRight(polls_csv$Date, 4)

Next, pivot the data from wide to long format.

More information of pivoting data with dplyr can be found here. I tend to check it at least once a month as the arguments refuse to stay in my head.

I only want to take the main parties in Ireland to compare in the plot.

polls <- polls_csv %>%
  select(year, FG:SF) %>% 
  pivot_longer(!year, names_to = "party", values_to = "opinion_poll")

I went online and found the logos for the three main parties (sorry, Labour) and saved them in the working directory I have for my RStudio. That way I can call the file with the prefix “~/**.png” rather than find the exact location they are saved on the computer.

polls %>% 
  filter(party == "FF" | party == "FG" | party == "SF" ) %>% 
  mutate(image = ifelse(party=="FF","~/ff.png",
 ifelse(party=="FG","~/fg.png", "~/sf.png"))) -> polls_three

Now we are ready to plot out the density plots for each party with the geom_density_ridges() function from the ggridges package.

We will add a few arguments into this function.

We add an alpha = 0.8 to make each density plot a little transparent and we can see the plots behind.

The scale = 2 argument pushes all three plots togheter so they are slightly overlapping. If scale =1, they would be totally separate and 3 would have them overlapping far more.

The rel_min_height = 0.01 argument removes the trailing tails from the plots that are under 0.01 density. This is again for aesthetics and just makes the plot look slightly less busy for relatively normally distributed densities

The geom_image takes the images and we place them at the beginning of the x axis beside the labels for each party.

Last, we use the bbplot package BBC style ggplot theme, which I really like as it makes the overall graph look streamlined with large font defaults.

polls_three %>% 
  ggplot(aes(x = opinion_poll, y = as.factor(party))) +  
  geom_density_ridges(aes(fill = party), 
                      alpha = 0.8, 
                      scale = 2,
                      rel_min_height = 0.01) + 
  ggimage::geom_image(aes(y = party, x= 1, image = image), asp = 0.9, size = 0.12) + 
  facet_wrap(~year) + 
  bbplot::bbc_style() +
  scale_fill_manual(values = c("#f2542d", "#edf6f9", "#0e9594")) +
  theme(legend.position = "none") + 
  labs(title = "Favourability Polls for the Three Main Parties in Ireland", subtitle = "Data from Irish Polling Indicator (Louwerse & Müller, 2020)")
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Make a timeline graph with dates in ggplot2

We will use the geom_segment layer from ggplot2 to make a timeline graph!

This layer takes

  • x and xend for the start of the segment lines
  • y and yend inputs for the end of the segment lines

For our timeline, the x will be the start of each Irish Taoiseach’s term.

The xend will be the end of their term, when they get kicked out of office.

Taoisigh (plural of Taoiseach) are Irish prime ministers and are in charge of the executive branch when their party is in change.

For Ireland, that means that basically every Taoiseach has been the leader of one of the two main parties – Fianna Fail or Fine Gael.

Not very exciting.

Also they have all been men.

This is also not very exciting.

We have a bit more to go with increasing the diversity in Ireland’s top job.

The y argument is the Taoiseach number in office. Although there have been fifteen men that have held the office of Taoiseach, this does not mean that they only held office for one time only.

Ireland has a parliamentary system so when a party loses an election, the former Taoiseach can become the leader of the opposition and hope in the future they can become Taoiseach again. Some men have been Taoiseach two or three times in non-consecutive terms.

When we are adding the labels with the geom_text() layer, I created an order variable which indicates the first time each man took the office of Taoiseach.

This is so I only have the name of each man only once in the graph. If we don’t do this step, if a man held office more than once, their name appears every time on the graph and the plot becomes a crowded mess.

I add the ifelse statement so that the first name appears after the segment line and therefore text does not take up too much room on the left edge of the graph.

Last we use the scale_color_manual() function with nice hex colors for each of the political parties.

time_line <- df %>% 
 ggplot(aes(x = as.Date(start), y = number, color = party_factor)) +
 geom_segment(aes(xend = as.Date(end), yend = number, color =  party_factor), size = 6) +
 geom_text(aes(label = order, hjust = ifelse(taoiseach_number < 2, -0.7, 1.1)), size = 8, show.legend = FALSE) +
 scale_color_manual(values = c("Fine Gael" = "#004266", "Fianna Fáil" = "#FCB322", "Cumann na nGaedheal" = "#D62828"))

I increase the limits of the graph to accommodate the name labels. Most of the time, these extra bits of code in ggplot2 depend on the type of data you have and what fits on the graph plane nicely!

So this stages is often only finished after trial-and-error.

I add a snazzy theme_fivethirtyeight() theme from ggthemes package.

Last, with the theme() function, we can remove most of the elements of the graph to make the graph cleaner.

time_line <- time_line + 
  expand_limits(x = as.Date("1915-01-01")) +
  theme_fivethirtyeight() +
  theme(legend.position = "top",
        legend.title = element_blank(),
        legend.direction = "vertical",
        axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        text = element_text(size = 20)) +
  labs(title = "Taoiseach Terms in Ireland",
 subtitle = "From 1922 to 2021") 

We can also create the pie chart to see which party has held power longest in Ireland.

With dplyr we can subtract the start date from the end date and add all the Taoiseach durations (in days) together with the cumsum() argument.

We then choose the highest duration value for each party with the slice(which.max()) functions.

I was lazy and I just re-wrote the values in a new data.frame and called it counts.

df %>%
  group_by(party_factor) %>% 
  dplyr::summarise(max_count = cumsum(duration_number)) %>%  
  slice(which.max(max_count)) %>% 
  select(party_factor, max_count) %>% 

counts <- data.frame(group = c("Cumann na nGaedheal", "Fine Gael" ,"Fianna Fáil"), 
                     value = c(3381, 10143, 22539))

Create proportion values for our pie-chart graph. To do this divide value by the sum of the values and multiply by 100.

data <- counts %>% 
  arrange(desc(group)) %>%
  dplyr::mutate(prop = value / sum(value) * 100) 

Change the numeric variables to factors.

data$duration <- as.factor(data$value)
data$party_factor <- as.factor(data$group)

We use the coord_polar() to create the piechart. To learn more, check out the r-graph-gallery page about creating pie-charts:

pie_chart <- ggplot(data, aes(x = ", y = prop, fill = group)) + geom_bar(stat = "identity", width = 1, color = "white") + coord_polar("y", start = 0) +

theme(legend.position = "none") + scale_fill_manual(values = c("Fine Gael" = "#004266", "Fianna Fáil" = "#FCB322", "Cumann na nGaedheal" = "#D62828")) +
 labs(title = "Which party held the office of Taoiseach longest?", subtitle = "From 1922 to 2021")

We can tidy up the plot and get rid of theme elements we don’t want with theme_void()

pie_chart <- pie_chart + theme_void() + theme(legend.title = element_blank(), legend.position = none, text = element_text(size = 40))

I want to add both graphs together so I can save the pie chart with a transparent background with the ggsave() function. I also make sure the lines are not jagged with the type = "cairo" from with Cairo package.

ggsave(pie_chart, file="pie_chart.png", type="cairo", bg = "transparent", width = 50, height = 50, units = "cm")

And we can use to add them together and create a single chart

And viola!