Check out part 1 of this blog where you can follow along how to scrape the data that we will use in this blog. It will create a dataset of the current MPs in the Irish Dail.
In this blog, we will use the ggparliament package, created by Zoe Meers.
With this dataset of the 33rd Dail, we will reduce it down to get the number of seats that each party holds.
If we don’t want to graph every party, we can lump most of the smaller parties into an “other” category. We can do this with the fct_lump_n() function from the forcats package. I want the top five biggest parties only in the graph. The rest will be colored as “Other”.
<fct> <int>
1 Fianna Fail 38
2 Sinn Fein 37
3 Fine Gael 35
4 Independent 19
5 Other 19
6 Green Party 12
Before we graph, I found the hex colors that represent each of the biggest Irish political party. We can create a new party color variables with the case_when() function and add each color.
If we view the dail_33_coord data.frame we can see that the parliament_data() function calculated new x and y coordinate variables for the semi-circle graph.
I don’t know what the theta variables is for… But there it is also … maybe to make circular shapes?
We feed the x and y coordinates into the ggplot() function and then add the geom_parliament_seat() layer to produce our graph!
Click here to check out the PDF for the ggparliament package
dail_33_coord %>%
ggplot(aes(x = x,
y = y,
colour = party_groups)) +
geom_parliament_seats(size = 20) -> dail_33_plot
And we can make it look more pretty with bbc_style() plot and colors.
library(tidyverse)
library(magrittr) # for pipes
library(broom) # add model variables
library(easystats) # diagnostic graphs
library(WDI) # World Bank data
library(democracyData) # Freedom House data
library(countrycode) # add ISO codes
library(bbplot) # pretty themes
library(ggthemes) # pretty colours
library(knitr) # pretty tables
library(kableExtra) # make pretty tables prettier
This blog will look at the augment() function from the broom package.
After we run a liner model, the augment() function gives us more information about how well our model can accurately preduct the model’s dependent variable.
It also gives us lots of information about how does each observation impact the model. With the augment() function, we can easily find observations with high leverage on the model and outlier observations.
For our model, we are going to use the “women in business and law” index as the dependent variable.
According to the World Bank, this index measures how laws and regulations affect women’s economic opportunity.
Overall scores are calculated by taking the average score of each index (Mobility, Workplace, Pay, Marriage, Parenthood, Entrepreneurship, Assets and Pension), with 100 representing the highest possible score.
Into the right-hand side of the model, our independent variables will be child mortality, military spending by the government as a percentage of GDP and Freedom House (democracy) Scores.
First we download the World Bank data and summarise the variables across the years.
We get the average across 60 ish years for three variables. I don’t want to run panel data regression, so I get a single score for each country. In total, there are 160 countries that have all observations. I use the countrycode() function to add Correlates of War codes. This helps us to filter out non-countries and regions that the World Bank provides. And later, we will use COW codes to merge the Freedom House scores.
The line is not flat at the beginning so that is not ideal..
We will look more into this later with the variables we create with augment() a bit further down this blog post.
None of our variables have a VIF score above 5, so that is always nice to see!
From the broom package, we can use the augment() function to create a whole heap of new columns about the variables in the model.
fem_bus_pred <- broom::augment(fem_bus_lm)
.fitted = this is the model prediction value for each country’s dependent variable score. Ideally we want them to be as close to the actual scores as possible. If they are totally different, this means that our independent variables do not do a good job explaining the variance in our “women in business” index.
.resid = this is actual dependent variable value minus the .fitted value.
We can look at the fitted values that the model uses to predict the dependent variable – level of women in business – and compare them to the actual values.
The third column in the table is the difference between the predicted and actual values.
fem_bus_pred %>%
mutate(across(where(is.numeric), ~round(., 2))) %>%
arrange(mean_fem_bus) %>%
select(Country = country,
`Fem in bus (Actual)` = mean_fem_bus,
`Fem in bus (Predicted)` = .fitted,
`Fem in bus (Difference)` = .resid,
`Mortality rate` = mean_mortality,
`Freedom House` = mean_fh,
`Military spending GDP` = mean_mil_gdp) %>%
kbl(full_width = F)
Country
Leverage of country
Fem in bus (Actual)
Fem in bus (Predicted)
Austria
0.02
88.92
88.13
Belgium
0.02
92.13
87.65
Costa Rica
0.02
79.80
87.84
Denmark
0.02
96.36
87.74
Finland
0.02
94.23
87.74
Iceland
0.02
96.36
88.90
Ireland
0.02
95.80
88.18
Luxembourg
0.02
94.32
88.33
Sweden
0.02
96.45
87.81
Switzerland
0.02
83.81
87.78
And we can graph them out:
fem_bus_pred %>%
mutate(fh_category = cut(mean_fh, breaks = 5,
labels = c("full demo ", "high", "middle", "low", "no demo"))) %>% ggplot(aes(x = .fitted, y = mean_fem_bus)) +
geom_point(aes(color = fh_category), size = 4, alpha = 0.6) +
geom_smooth(method = "loess", alpha = 0.2, color = "#20948b") +
bbplot::bbc_style() +
labs(x = '', y = '', title = "Fitted values versus actual values")
In addition to the predicted values generated by the model, other new columns that the augment function adds include:
.hat = this is a measure of the leverage of each variable.
.cooksd = this is the Cook’s Distance. It shows how much actual influence the observation had on the model. Combines information from .residual and .hat.
.sigma = this is the estimate of residual standard deviation if that observation is dropped from model
.std.resid = standardised residuals
If we look at the .hat observations, we can examine the amount of leverage that each country has on the model.
fem_bus_pred %>%
mutate(dplyr::across(where(is.numeric), ~round(., 2))) %>%
arrange(desc(.hat)) %>%
select(Country = country,
`Leverage of country` = .hat,
`Fem in bus (Actual)` = mean_fem_bus,
`Fem in bus (Predicted)` = .fitted) %>%
kbl(full_width = F) %>%
kable_material_dark()
Next, we can look at Cook’s Distance. This is an estimate of the influence of a data point. According to statisticshowto website, Cook’s D is a combination of each observation’s leverage and residual values; the higher the leverage and residuals, the higher the Cook’s distance.
If a data point has a Cook’s distance of more than three times the mean, it is a possible outlier
Any point over 4/n, where n is the number of observations, should be examined
To find the potential outlier’s percentile value using the F-distribution. A percentile of over 50 indicates a highly influential point
Next we will pivot the variables to long format. The new names variable will be survey_question and the responses (Strongly agree, Somewhat agree etc) will go to the new response variable!
And we use the hex colours from the original graph … very brown… I used this hex color picker website to find the right hex numbers: https://imagecolorpicker.com/en
And last, we use the geom_bar() – with position = "stack" and stat = "identity" arguments – to create the bar chart.
To add the numbers, write geom_text() function with label = frequency within aes() and then position = position_stack() with hjust and vjust to make sure you’re happy with where the numbers are
gun_reordered %>%
filter(!is.na(response)) %>%
mutate(frequency = round(freq * 100), 0) %>%
ggplot(aes(x = survey_question_reorder,
y = frequency, fill = response)) +
geom_bar(position = "stack",
stat = "identity") +
coord_flip() +
scale_fill_manual(values = brown_palette) +
geom_text(aes(label = frequency), size = 10,
color = "black",
position = position_stack(vjust = 0.5)) +
bbplot::bbc_style() +
labs(title = "Broad support for barring people with mental illnesses
\n from obtaining guns, expanded background checks",
subtitle = "% who",
caption = "Note: No answer resposes not shown.\n Source: Survey of U.S. adults conducted April 5-11 2021.") +
scale_x_discrete(labels = c(
"Allowing people to carry conealed \n guns without a person",
"Shortening waiting periods for people \n who want to buy guns leagally",
"Allowing reachers and school officials \n to carry guns in K-12 school",
"Allowing people to carry \n concealed guns in more places",
"Banning assault-style weapons",
"Banning high capacity ammunition \n magazines that hold more than 10 rounds",
"Creating a federal government \n database to track all gun sales",
"Making private gun sales \n subject to background check",
"Preventing people with mental \n illnesses from purchasing guns"
))
Unfortunately this does not have diverving stacks from the middle of the graph
We can make a diverging stacked bar chart using function likert() from the HH package.
For this we want to turn the dataset back to wider with a column for each of the responses (strongly agree, somewhat agree etc) and find the frequency of each response for each of the questions on different gun control measures.
Then with the likert() function, we take the survey question variable and with the ~tilda~ make it the product of each response. Because they are the every other variable in the dataset we can use the shorthand of the period / fullstop.
We use positive.order = TRUE because we want them in a nice descending order to response, not in alphabetical order or something like that
gun_reordered %<>%
filter(!is.na(response)) %>%
select(survey_question, response, freq) %>%
pivot_wider(names_from = response, values_from = freq ) %>%
ungroup() %>%
HH::likert(survey_question ~., positive.order = TRUE,
main = "Broad support for barring people with mental illnesses
\n from obtaining guns, expanded background checks")
With this function, it is difficult to customise … but it is very quick to make a diverging stacked bar chart.
If we return to ggplot2, which is more easy to customise … I found a solution on Stack Overflow! Thanks to this answer! The solution is to put two categories on one side of the centre point and two categories on the other!
ggplot(data = gun_final, aes(x = survey_question_reorder,
fill = response)) +
geom_bar(data = subset(gun_final, response %in% c("Strongly favor",
"Somewhat favor")),
aes(y = -frequency), position="stack", stat="identity") +
geom_bar(data = subset(gun_final, !response %in% c("Strongly favor",
"Somewhat favor")),
aes(y = frequency), position="stack", stat="identity") +
coord_flip() +
scale_fill_manual(values = brown_palette) +
geom_text(data = gun_final, aes(y = frequency, label = frequency), size = 10, color = "black", position = position_stack(vjust = 0.5)) +
bbplot::bbc_style() +
labs(title = "Broad support for barring people with mental illnesses
\n from obtaining guns, expanded background checks",
subtitle = "% who",
caption = "Note: No answer resposes not shown.\n Source: Survey of U.S. adults conducted April 5-11 2021.") +
scale_x_discrete(labels = c(
"Allowing people to carry conealed \n guns without a person",
"Shortening waiting periods for people \n who want to buy guns leagally",
"Allowing reachers and school officials \n to carry guns in K-12 school",
"Allowing people to carry \n concealed guns in more places",
"Banning assault-style weapons",
"Banning high capacity ammunition \n magazines that hold more than 10 rounds",
"Creating a federal government \n database to track all gun sales",
"Making private gun sales \n subject to background check",
"Preventing people with mental \n illnesses from purchasing guns"
))
Next to complete in PART 2 of this graph, I need to figure out how to add lines to graphs and add the frequency in the correct place
In this blog, we will graph out some of the key features of Ireland’ foreign policy and so we can have a quick overview of the key relationships and trends.
Data on alliance portfolios comes from the Correlates of War and is used to calculate similarity of foreign policy positions (see Altfeld & Mesquita, 1979).
The assumption is that similar alliance portfolios are the result of similar foreign policy positions.
With increasing in level of commitment, the strength of alliance commitments can be:
no commitment
entente
neutrality or nonaggression pact
defense pact
We will map out alliance similarity. This will use the measurement calculated with Cohen’s Kappa. Check out Hage’s (2011) article to read more about the different ways to measure alliance similarity.
Next we can look at UN similarity.
The UN voting variable calculates three values:
1 = Yes
2 = Abstain
3 = No
Based on these data, if two countries in a similar way on the same UN resolutions, this is a measure of the degree to which dyad members’ foreign policy positions are similar.
Last we are going to look at globalization scores. The data comes from the the KOF Globalisation Index. This measures the economic, social and political dimensions of globalisation. Globalisation in the economic, social and political fields has been on the rise since the 1970s, receiving a particular boost after the end of the Cold War.
And compare Ireland to other EU countries on financial KOF index scores. We will put Ireland in green and the rest of the countries as grey to make it pop.
Ireland appears to follow the general EU trends and is not an outlier for financial globalisation scores.
For this blog post, we will look at UN peacekeeping missions and compare across regions.
Despite the criticisms about some operations, the empirical record for UN peacekeeping records has been robust in the academic literature
“In short, peacekeeping intervenes in the most difficult cases, dramatically increases the chances that peace will last, and does so by altering the incentives of the peacekept, by alleviating their fear and mistrust of each other, by preventing and controlling accidents and misbehavior by hard-line factions, and by encouraging political inclusion” (Goldstone, 2008: 178).
The data on the current and previous PKOs (peacekeeping operations) will come from the Wikipedia page. But the variables do not really lend themselves to analysis as they are.
Once we have the url, we scrape all the tables on the Wikipedia page in a few lines
We then bind the completed and current mission data.frames
rbind(pko_complete, pko_current) -> pko
Then we clean the variable names with the function from the janitor package.
pko_df <- pko %>%
janitor::clean_names()
Next we’ll want to create some new variables.
We can make a new row for each country that is receiving a peacekeeping mission. We can paste all the countries together and then use the separate function from the tidyr package to create new variables.
pko_countries %>%
ggplot(mapping = aes(x = decade,
y = duration,
fill = decade)) +
geom_boxplot(alpha = 0.4) +
geom_jitter(aes(color = decade),
size = 6, alpha = 0.8, width = 0.15) +
coord_flip() +
geom_curve(aes(x = "1950s", y = 60, xend = "1940s", yend = 72),
arrow = arrow(length = unit(0.1, "inch")), size = 0.8, color = "black",
curvature = -0.4) +
annotate("text", label = "First Mission to Kashmir",
x = "1950s", y = 49, size = 8, color = "black") +
geom_curve(aes(x = "1990s", y = 46, xend = "1990s", yend = 32),
arrow = arrow(length = unit(0.1, "inch")), size = 0.8, color = "black",curvature = 0.3) +
annotate("text", label = "Most Missions after the Cold War",
x = "1990s", y = 60, size = 8, color = "black") +
bbplot::bbc_style() + ggtitle("Duration of Peacekeeping Missions")
Years
Following the end of the Cold War, there were renewed calls for the UN to become the agency for achieving world peace, and the agency’s peacekeeping dramatically increased, authorizing more missions between 1991 and 1994 than in the previous 45 years combined.
We can use a waffle plot to see which decade had the most operation missions. Waffle plots are often seen as more clear than pie charts.
To get the data ready for a waffle chart, we just need to count the number of peacekeeping missions (i.e. the number of rows) in each decade. Then we fill the groups (i.e. decade) and enter the n variable we created as the value.
library(democracyData)
library(tidyverse)
library(magrittr) # for pipes
library(ggstream) # proportion plots
library(ggthemes) # nice ggplot themes
library(forcats) # reorder factor variables
library(ggflags) # add flags
library(peacesciencer) # more great polisci data
library(countrycode) # add ISO codes to countries
This blog will highlight some quick datasets that we can download with this nifty package.
To install the democracyData package, it is best to do this via the github of Xavier Marquez:
remotes::install_github("xmarquez/democracyData", force = TRUE)
library(democracyData)
We can download the dataset from the Democracy and Dictatorship Revisited paper by Cheibub Gandhi and Vreeland (2010) with the redownload_pacl() function. It’s all very simple!
pacl <- redownload_pacl()
This gives us over 80 variables, with information on things such as regime type, geographical data, the name and age of the leaders, and various democracy variables.
We are going to focus on the different regimes across the years.
The six-fold regime classification Cheibub et al (2010) present is rooted in the dichotomous classification of regimes as democracy and dictatorship introduced in Przeworski et al. (2000). They classify according to various metrics, primarily by examining the way in which governments are removed from power and what constitutes the “inner sanctum” of power for a given regime. Dictatorships can be distinguished according to the characteristics of these inner sanctums. Monarchs rely on family and kin networks along with consultative councils; military rulers confine key potential rivals from the armed forces within juntas; and, civilian dictators usually create a smaller body within a regime party—a political bureau—to coopt potential rivals. Democracies highlight their category, depending on how the power of a given leadership ends
We can change the regime variable from numbers to a factor variables, describing the type of regime that the codebook indicates:
Before we make the graph, we can give traffic light hex colours to the types of democracy. This goes from green (full democracy) to more oranges / reds (autocracies):
This describes political conditions in every country (including tenures and personal characteristics of world leaders, the types of political institutions and political regimes in effect, election outcomes and election announcements, and irregular events like coups)
Again, to download this dataset with the democracyData package, it is very simple:
reign <- download_reign()
I want to compare North and South Korea since their independence from Japan and see the changes in regimes and democracy scores over the years.
Next, we can easily download Freedom House or Polity 5 scores.
The Freedom House Scores default dataset ranges from 1972 to 2020, covering around 195 countries (depending on the year)
fh <- download_fh()
Alternatively, we can look at Polity Scores. This default dataset countains around 190 ish countries (again depending on the year and the number of countries in existance at that time) and covers a far longer range of years; from 1880 to 2018.
polityiv <- redownload_polityIV()
Alternatively, to download democracy scores, we can also use the peacesciencer dataset. Click here to read more about this package:
We may to use specific hex colours for our graphs. I always prefer these deeper colours, rather than the pastel defaults that ggplot uses. I take them from coolors.co website!
While North Korea has been consistently ruled by the Kim dynasty, South Korea has gone through various types of government and varying levels of democracy!
References
Cheibub, J. A., Gandhi, J., & Vreeland, J. R. (2010). . Public choice, 143(1), 67-101.
Przeworski, A., Alvarez, R. M., Alvarez, M. E., Cheibub, J. A., Limongi, F., & Neto, F. P. L. (2000). Democracy and development: Political institutions and well-being in the world, 1950-1990 (No. 3). Cambridge University Press.
If we look at the table, some of the surveys started in Feb but ended in March. We want to extract the final section (i.e. the March section) and use that.
So we use grepl() to find rows that have both Feb AND March, and just extract the March section. If it only has one of those months, we leave it as it is.
Following that, we use the parse_number() function from tidyr package to extract the first number found in the string and create a day_number varible (with integer class now)
We want to take these two variables we created and combine them together with the unite() function from tidyr again! We want to delete the variables after we unite them. But often I want to keep the original variables, so usually I change the argument remove to FALSE.
We indicate we want to have nothing separating the vales with the sep = "" argument
And we convert this new date into Date class with as.Date() function.
Here is a handy cheat sheet to help choose the appropriate % key so the format recognises the dates. I will never memorise these values, so I always need to refer to this site.
We have days as numbers (1, 2, 3) and abbreviated 3 character month (Jan, Feb, Mar), so we choose %d and %b
After than, we need to clean the actual numbers, remove the percentage signs and convert from character to number class. We use the str_extract() and the regex code to extract the number and not keep the percentage sign.
Some of the different polls took place on the same day. So we will take the average poll favourability value for each candidate on each day with the group_by() function
We can create variables to help us filter different groups of candidates. If we want to only look at the largest candidates, we can makes an important variable and then filter
We can lump the candidates that do not have data from every poll (i.e. the smaller candidate) and add them into the “other_undecided” category with the fct_lump_min() function from the forcats package
I want to only look at the main two candidates from the main parties that have been polling in the 40% range – Lee and Yoon – as well as the data for Ahn (who recently dropped out and endorsed Yoon).
Last, with the annotate() functions, we can also add an annotation arrow and text to add some more information about Ahn Cheol-su the candidate dropping out.
annotate("text", x = as.Date("2022-02-11"), y = 13, label = "Ahn dropped out just as the polling blackout began", size = 10, color = "white") +
annotate(geom = "curve", x = as.Date("2022-02-25"), y = 13, xend = as.Date("2022-03-01"), yend = 10,
curvature = -.3, arrow = arrow(length = unit(2, "mm")), size = 1, color = "white")
We will just have to wait until next Wednesday / Thursday to see who is the winner ~
library(tidyverse) # of course
library(ggridges) # density plots
library(GGally) # correlation matrics
library(stargazer) # tables
library(knitr) # more tables stuff
library(kableExtra) # more and more tables
library(ggrepel) # spread out labels
library(ggstream) # streamplots
library(bbplot) # pretty themes
library(ggthemes) # more pretty themes
library(ggside) # stack plots side by side
library(forcats) # reorder factor levels
Before jumping into any inferentional statistical analysis, it is helpful for us to get to know our data. For me, that always means plotting and visualising the data and looking at the spread, the mean, distribution and outliers in the dataset.
Before we plot anything, a simple package that creates tables in the stargazer package. We can examine descriptive statistics of the variables in one table.
Click here to read this practically exhaustive cheat sheet for the stargazer package by Jake Russ. I refer to it at least once a week.
I want to summarise a few of the stats, so I write into the summary.stat() argument the number of observations, the mean, median and standard deviation.
The kbl() and kable_classic() will change the look of the table in R (or if you want to copy and paste the code into latex with the type = "latex" argument).
In HTML, they do not appear.
To find out more about the knitr kable tables, click here to read the cheatsheet by Hao Zhu.
Choose the variables you want, put them into a data.frame and feed them into the stargazer() function
stargazer(my_df_summary,
covariate.labels = c("Corruption index",
"Civil society strength",
'Rule of Law score',
"Physical Integerity Score",
"GDP growth"),
summary.stat = c("n", "mean", "median", "sd"),
type = "html") %>%
kbl() %>%
kable_classic(full_width = F, html_font = "Times", font_size = 25)
Statistic
N
Mean
Median
St. Dev.
Corruption index
179
0.477
0.519
0.304
Civil society strength
179
0.670
0.805
0.287
Rule of Law score
173
7.451
7.000
4.745
Physical Integerity Score
179
0.696
0.807
0.284
GDP growth
163
0.019
0.020
0.032
Next, we can create a barchart to look at the different levels of variables across categories. We can look at the different regime types (from complete autocracy to liberal democracy) across the six geographical regions in 2018 with the geom_bar().
my_df %>%
filter(year == 2018) %>%
ggplot() +
geom_bar(aes(as.factor(region),
fill = as.factor(regime)),
color = "white", size = 2.5) -> my_barplot
This type of graph also tells us that Sub-Saharan Africa has the highest number of countries and the Middle East and North African (MENA) has the fewest countries.
However, if we want to look at each group and their absolute percentages, we change one line: we add geom_bar(position = "fill"). For example we can see more clearly that over 50% of Post-Soviet countries are democracies ( orange = electoral and blue = liberal democracy) as of 2018.
We can also check out the density plot of democracy levels (as a numeric level) across the six regions in 2018.
With these types of graphs, we can examine characteristics of the variables, such as whether there is a large spread or normal distribution of democracy across each region.
Click here to read more about the GGally package and click here to read their CRAN PDF.
We can use the ggside package to stack graphs together into one plot.
There are a few arguments to add when we choose where we want to place each graph.
For example, geom_xsideboxplot(aes(y = freedom_house), orientation = "y") places a boxplot for the three Freedom House democracy levels on the top of the graph, running across the x axis. If we wanted the boxplot along the y axis we would write geom_ysideboxplot(). We add orientation = "y" to indicate the direction of the boxplots.
Next we indiciate how big we want each graph to be in the panel with theme(ggside.panel.scale = .5) argument. This makes the scatterplot take up half and the boxplot the other half. If we write .3, the scatterplot takes up 70% and the boxplot takes up the remainning 30%. Last we indicade scale_xsidey_discrete() so the graph doesn’t think it is a continuous variable.
We add Darjeeling Limited color palette from the Wes Anderson movie.
Click here to learn about adding Wes Anderson theme colour palettes to graphs and plots.
The next plot will look how variables change over time.
We can check out if there are changes in the volume and proportion of a variable across time with the geom_stream(type = "ridge") from the ggstream package.
In this instance, we will compare urban populations across regions from 1800s to today.
Click here to read more about the ggstream package and click here to read their CRAN PDF.
We can also look at interquartile ranges and spread across variables.
We will look at the urbanization rate across the different regions. The variable is calculated as the ratio of urban population to total country population.
Before, we will create a hex color vector so we are not copying and pasting the colours too many times.
If we want to look more closely at one year and print out the country names for the countries that are outliers in the graph, we can run the following function and find the outliers int he dataset for the year 1990:
In the next blog post, we will look at t-tests, ANOVAs (and their non-parametric alternatives) to see if the difference in means / medians is statistically significant and meaningful for the underlying population.
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.
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.
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)
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 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.
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)")
R Functions and Packages for Political Science Analysis 