Comparing proportions across time with ggstream in R

Packages we need:

library(tidyverse)
library(ggstream)
library(magrittr)
library(bbplot)
library(janitor)

We can look at proportions of energy sources across 10 years in Ireland. Data source comes from: https://www.seai.ie/data-and-insights/seai-statistics/monthly-energy-data/electricity/

Before we graph the energy sources, we can tidy up the variable names with the janitor package. We next select column 2 to 12 which looks at the sources for electricity generation. Other rows are aggregates and not the energy-related categories we want to look at.

Next we pivot the dataset longer to make it more suitable for graphing.

We can extract the last two digits from the month dataset to add the year variable.

elec %<>% 
  janitor::clean_names()

elec[2:12,] -> elec

el <- elec %>% 
  pivot_longer(!electricity_generation_g_wh, 
               names_to = "month", values_to = "value") %>% 

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

el$year <- substrRight(el$month, 2)

el %<>% select(year, month, elec_type = electricity_generation_g_wh, elec_value = value) 

First we can use the geom_stream from the ggstream package. There are three types of plots: mirror, ridge and proportion.

First we will plot the proportion graph.

Select the different types of energy we want to compare, we can take the annual values, rather than monthly with the tried and trusted group_by() and summarise().

Optionally, we can add the bbc_style() theme for the plot and different hex colors with scale_fill_manual() and feed a vector of hex values into the values argument.

el %>% 
  filter(elec_type == "Oil" | 
           elec_type == "Coal" |
           elec_type == "Peat" | 
           elec_type == "Hydro" |
           elec_type == "Wind" |
           elec_type == "Natural Gas") %>% 
  group_by(year, elec_type) %>%
  summarise(annual_value = sum(elec_value, na.rm = TRUE)) %>% 
  ggplot(aes(x = year, 
             y = annual_value,
             group = elec_type,
             fill = elec_type)) +
  ggstream::geom_stream(type = "proportion") + 
  bbplot::bbc_style() +
  labs(title = "Comparing energy proportions in Ireland") +
  scale_fill_manual(values = c("#f94144",
                               "#277da1",
                               "#f9c74f",
                               "#f9844a",
                               "#90be6d",
                               "#577590"))

With ggstream::geom_stream(type = "mirror")

With ggstream::geom_stream(type = "ridge")

Without the ggstream package, we can recreate the proportion graph with slightly different looks

https://giphy.com/gifs/filmeditor-clueless-movie-l0ErMA0xAS1Urd4e4

el %>% 
  filter(elec_type == "Oil" | 
           elec_type == "Coal" |
           elec_type == "Peat" | 
           elec_type == "Hydro" |
           elec_type == "Wind" |
           elec_type == "Natural Gas") %>% 
  group_by(year, elec_type) %>%
  summarise(annual_value = sum(elec_value, na.rm = TRUE)) %>% 
  ggplot(aes(x = year, 
             y = annual_value,
             group = elec_type,
             fill = elec_type)) +
  geom_area(alpha=0.8 , size=1.5, colour="white") +
  bbplot::bbc_style() +
  labs(title = "Comparing energy proportions in Ireland") +
  theme(legend.key.size = unit(2, "cm")) + 
  scale_fill_manual(values = c("#f94144",
                               "#277da1",
                               "#f9c74f",
                               "#f9844a",
                               "#90be6d",
                               "#577590"))

Love You Hug GIF by Filmin - Find & Share on GIPHY

Create density plots with ggridges package in R

Packages we will need:

library(tidyverse)
library(ggridges)
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)

Happy Danny Devito GIF by It's Always Sunny in Philadelphia - Find & Share on GIPHY

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)")
Its Always Sunny In Philadelphia Thumbs Up GIF by HULU - Find & Share on GIPHY

Compare Irish census years with compareBars and csodata package in R

Packages we will need:

library(csodata)
library(janitor)
library(ggcharts)
library(compareBars)
library(tidyverse)

First, let’s download population data from the Irish census with the Central Statistics Office (CSO) API package, developed by Conor Crowley.

You can search for the data you want to analyse via R or you can go to the CSO website and browse around the site.

I prefer looking through the site because sometimes I stumble across a dataset I didn’t even think to look for!

Keep note of the code beside the red dot star symbol if you’re looking around for datasets.

Click here to check out the CRAN PDF for the CSO package.

You can search for keywords with cso_search_toc(). I want total population counts for the whole country.

cso_search_toc("total population")

We can download the variables we want by entering the code into the cso_get_data() function

irish_pop <- cso_get_data("EY007")
View(irish_pop)

The EY007 code downloads population census data in both 2011 and 2016 at every age.

It needs a little bit of tidying to get it ready for graphing.

irish_pop %<>%  
  clean_names()

First, we can be lazy and use the clean_names() function from the janitor package.

GIF by The Good Place - Find & Share on GIPHY

Next we can get rid of the rows that we don’t want with select().

Then we use the pivot_longer() function to turn the data.frame from wide to long and to turn the x2011 and x2016 variables into one year variable.

irish_pop %>% 
  filter(at_each_year_of_age == "Population") %>% 
  filter(sex == 'Both sexes') %>% 
  filter(age_last_birthday != "All ages") %>% 
  select(!statistic) %>% 
  select(!sex) %>% 
  select(!at_each_year_of_age) -> irish_wide

irish_wide %>% 
  pivot_longer(!age_last_birthday,
    names_to = "year", 
    values_to = "pop_count",
    values_drop_na = TRUE) %>% 
    mutate(year = as.factor(year)) -> irish_long

No we can create our pyramid chart with the pyramid_chart() from the ggcharts package. The first argument is the age category for both the 2011 and 2016 data. The second is the actual population counts for each year. Last, enter the group variable that indicates the year.

irish_long %>%   
  pyramid_chart(age_last_birthday, pop_count, year)

One problem with the pyramid chart is that it is difficult to discern any differences between the two years without really really examining each year.

One way to more easily see the differences with the compareBars function

The compareBars package created by David Ranzolin can help to simplify comparative bar charts! It’s a super simple function to use that does a lot of visualisation leg work under the hood!

First we need to pivot the data.frame back to wide format and then input the age, and then the two groups – x2011 and x2016 – in the compareBars() function.

We can add more labels and colors to customise the graph also!

irish_long %>% 
  pivot_wider(names_from = year, values_from = pop_count) %>% 
  compareBars(age_last_birthday, x2011, x2016, orientation = "horizontal",
              xLabel = "Population",
              yLabel = "Year",
              titleLabel = "Irish Populations",
              subtitleLabel = "Comparing 2011 and 2016",
              fontFamily = "Arial",
              compareVarFill1 = "#FE6D73",
              compareVarFill2 = "#17C3B2") 

We can see that under the age of four-ish, 2011 had more at the time. And again, there were people in their twenties in 2011 compared to 2016.

However, there are more older people in 2016 than in 2011.

Similar to above it is a bit busy! So we can create groups for every five age years categories and examine the broader trends with fewer horizontal bars.

First we want to remove the word “years” from the age variable and convert it to a numeric class variable. We can easily do this with the parse_number() function from the readr package

irish_wide %<>% 
mutate(age_num = readr::parse_number(as.character(age_last_birthday))) 

Next we can group the age years together into five year categories, zero to 5 years, 6 to 10 years et cetera.

We use the cut() function to divide the numeric age_num variable into equal groups. We use the seq() function and input age 0 to 100, in increments of 5.

irish_wide$age_group = cut(irish_wide$age_num, seq(0, 100, 5))

Next, we can use group_by() to calculate the sum of each population number in each five year category.

And finally, we use the distinct() function to remove the duplicated rows (i.e. we only want to keep the first row that gives us the five year category’s population count for each category.

irish_wide %<>% 
  group_by(age_group) %>% 
  mutate(five_year_2011 = sum(x2011)) %>% 
  mutate(five_year_2016 = sum(x2016)) %>% 
  distinct(five_year_2011, five_year_2016, .keep_all = TRUE)

Next plot the bar chart with the five year categories

compareBars(irish_wide, age_group, five_year_2011, five_year_2016, orientation = "horizontal",
              xLabel = "Population",
              yLabel = "Year",
              titleLabel = "Irish Populations",
              subtitleLabel = "Comparing 2011 and 2016",
              fontFamily = "Arial",
              compareVarFill1 = "#FE6D73",
              compareVarFill2 = "#17C3B2") 

irish_wide2 %>% 
  select(age_group, five_year_2011, five_year_2016) %>% 
  pivot_longer(!age_group,
             names_to = "year", 
             values_to = "pop_count",
             values_drop_na = TRUE) %>% 
  mutate(year = as.factor(year)) -> irishlong2

irishlong2 %>%   
  pyramid_chart(age_group, pop_count, year)

The Good Place Yes GIF by NBC - Find & Share on GIPHY