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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Graphing Pew survey responses with ggplot in R

Packages we will need:


We are going to look at a few questions from the 2019 US Pew survey on relations with foreign countries.

Data can be found by following this link:

We are going to make bar charts to plot out responses to the question asked to American participaints: Should the US cooperate more or less with some key countries? The countries asked were China, Russia, Germany, France, Japan and the UK.

Before we dive in, we can find some nice hex colors for the bar chart. There are four possible responses that the participants could give: cooperate more, cooperate less, cooperate the same as before and refuse to answer / don’t know.

pal <- c("Cooperate more" = "#0a9396",
         "Same as before" = "#ee9b00",
         "Don't know" = "#005f73",
         "Cooperate less" ="#ae2012")

We first select the questions we want from the full survey and pivot the dataframe to long form with pivot_longer(). This way we have a single column with all the different survey responses. that we can manipulate more easily with dplyr functions.

Then we summarise the data to count all the survey reponses for each of the four countries and then calculate the frequency of each response as a percentage of all answers.

Then we mutate the variables so that we can add flags. The geom_flag() function from the ggflags packages only recognises ISO2 country codes in lower cases.

After that we change the factors level for the four responses so they from positive to negative views of cooperation

pew %>% 
  select(id = case_id, Q2a:Q2f) %>% 
  pivot_longer(!id, names_to = "survey_question", values_to = "response")  %>% 
  group_by(survey_question, response) %>% 
  summarise(n = n()) %>%
  mutate(freq = n / sum(n)) %>% 
  ungroup() %>% 
  mutate(response_factor = as.factor(response)) %>% 
  mutate(country_question = ifelse(survey_question == "Q2a", "fr",
ifelse(survey_question == "Q2b", "gb",
ifelse(survey_question == "Q2c", "ru",
ifelse(survey_question == "Q2d", "cn",
ifelse(survey_question == "Q2e", "de",
ifelse(survey_question == "Q2f", "jp", survey_question))))))) %>% 
  mutate(response_string = ifelse(response_factor == 1, "Cooperate more",
ifelse(response_factor == 2, "Cooperate less",
ifelse(response_factor == 3, "Same as before",
ifelse(response_factor == 9, "Don't know", response_factor))))) %>% 
  mutate(response_string = fct_relevel(response_string, c("Cooperate less","Same as before","Cooperate more", "Don't know"))) -> pew_clean

We next use ggplot to plot out the responses.

We use the position = "stack" to make all the responses “stack” onto each other for each country. We use stat = "identity" because we are not counting each reponses. Rather we are using the freq variable we calculated above.

pew_clean %>%
  ggplot() +
  geom_bar(aes(x = forcats::fct_reorder(country_question, freq), y = freq, fill = response_string), color = "#e5e5e5", size = 3, position = "stack", stat = "identity") +
  geom_flag(aes(x = country_question, y = -0.05 , country = country_question), color = "black", size = 20) -> pew_graph

And last we change the appearance of the plot with the theme function

pew_graph + 
coord_flip() + 
  scale_fill_manual(values = pal) +
  ggthemes::theme_fivethirtyeight() + 
  ggtitle("Should the US cooperate more or less with the following country?") +
  theme(legend.title = element_blank(),
        legend.position = "top",
        legend.key.size = unit(2, "cm"),
        text = element_text(size = 25),
        legend.text = element_text(size = 20),
        axis.text = element_blank())