Tag: democracy

Grouping, counting words and making wordclouds

library(tidytext)
library(wordcloud)
library(knitr)
library(kableExtra)

How to make wordclouds in R!

First, download stop words (such as and, the, of) to filter out of the dataset

data("stop_words")

Then we will will unnest tokens and count the occurences of each word in each decade. 

tokens <- democracy_aid %>%
  select(description, year) %>% 
  mutate(decade = substr(year, 1, 3)) %>% 
  mutate(decade = paste0(decade, "0s")) %>% 
  group_by(decade) %>% 
  unnest_tokens(word, activity_description) %>% 
  count(word, sort = TRUE) %>% 
  ungroup() %>% 
  anti_join(stop_words) 

nums <- tokens %>% filter(str_detect(word, "^[0-9]")) %>% select(word) %>% unique()

tokens %<>%
  anti_join(nums, by = "word")

And with the kable() function we can make a HTML table that I copy and paste to this blog. Below I rewrite the HTML to change the headings

tokens %>% 
    group_by(decade) %>% 
    top_n(n = 10,
          wt = n)  %>%
    arrange(decade, desc(n)) %>%
    arrange(desc(n)) %>%
    knitr::kable("html")
decade word n
2010s rights 4541
2010s local 3981
2010s youth 3778
2010s promote 3679
2010s democratic 3618
2010s public 3444
2010s national 3060
2010s political 3020
2010s human 3009
2010s organization 2711
2000s rights 2548
2000s human 1745
2000s local 1544
2000s conduct 1381
2000s political 1257
2000s training 1217
2000s promote 1142
2000s public 1121
2000s democratic 1071
2000s national 988

Create a vector of colors:

my_colors <- c("#0450b4", "#046dc8", "#1184a7","#15a2a2", "#6fb1a0", 
               "#b4418e", "#d94a8c", "#ea515f", "#fe7434", "#fea802")
Always Sunny Reaction GIF - Find & Share on GIPHY 
tokens %<>% 
  mutate(word = ifelse(grepl("democr", word), "democracy", 
                ifelse(grepl("politi", word), "politics", 
                ifelse(grepl("institut", word), "institution", 
                ifelse(grepl("govern", word), "government", 
                ifelse(grepl("organiz", word), "organization", 
                ifelse(grepl("elect", word), "election", word))))))) 

wordcloud(tokens$word, tokens$n, random.order = FALSE, max.words = 50, colors = my_colors)
2010s Decade Word Count
2010s rights 4541
2010s local 3981
2010s youth 3778
2010s promote 3679
2010s democratic 3618
2010s public 3444
2010s national 3060
2010s political 3020
2010s human 3009
2010s organization 2711
2000s Decade Word Count
2000s rights 2548
2000s human 1745
2000s local 1544
2000s conduct 1381
2000s political 1257
2000s training 1217
2000s promote 1142
2000s public 1121
2000s democratic 1071
2000s national 988

And if we compare civic versus politically-oriented aid, we can see that more money goes towards projects that have political or electoral aims rather than civic or civil society education goals

tokens %>% 
  group_by(year) %>% 
  top_n(n = 20,
        wt = n) %>% 
  mutate(word = case_when(word == "party" ~ "political",
                          word == "parties" ~ "political",
                          word == "election" ~ "political",
                          word == "electoral" ~ "political",
                          word == "civil" ~ "civic", 
                          word == "civic" ~ "civic",
                          word == "social" ~ "civic",
                          word == "education" ~ "civic",
                          word == "society" ~ "civic", 
                          TRUE ~ as.character(word))) %>% 
  filter(word == "political" | word == "civic") %>% 
  ggplot(aes(x = year, y = n, group = word)) + 
  geom_line(aes(color = word ), size = 2.5,alpha = 0.6)  +
  geom_point(aes(color = word ), fill = "white", 
             shape = 21, size = 3, stroke = 2) +
  bbplot::bbc_style() + 
  scale_x_discrete(limits = c(2001:2019)) +
  theme(axis.text.x= element_text(size = 15,
                                  angle = 45)) +
  scale_color_discrete(name = "Aid type", labels = c("Civic grants", "Political grants"))

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Graph linear model plots with sjPlots in R

This blog post will look at the plot_model() function from the sjPlot package. This plot can help simply visualise the coefficients in a model.

Packages we need:

library(sjPlot)
library(kable)

We can look at variables that are related to citizens’ access to public services.

This dependent variable measures equal access access to basic public services, such as access to security, primary education, clean water, and healthcare and whether they are distributed equally or unequally according to socioeconomic position.

Higher scores indicate a more equal society.

I will throw some variables into the model and see what relationships are statistically significant.

The variables in the model are

  • level of judicial constraint on the executive branch,
  • freedom of information (such as freedom of speech and uncensored media),
  • level of democracy,
  • level of regime corruption and
  • strength of civil society.

So first, we run a simple linear regression model with the lm() function:

summary(my_model <- lm(social_access ~ judicial_constraint +
        freedom_information +
        democracy_score + 
        regime_corruption +
        civil_society_strength, 
        data = df))

We can use knitr package to produce a nice table or the regression coefficients with kable().

I write out the independent variable names in the caption argument

I also choose the four number columns in the col.names argument. These numbers are:

  • beta coefficient,
  • standard error,
  • t-score
  • p-value

I can choose how many decimals I want for each number columns with the digits argument.

And lastly, to make the table, I can set the type to "html". This way, I can copy and paste it into my blog post directly.

my_model %>% 
tidy() %>%
kable(caption = "Access to public services by socio-economic position.", 
col.names = c("Predictor", "B", "SE", "t", "p"),
digits = c(0, 2, 3, 2, 3), "html")
Access to public services by socio-economic position
Predictor B SE t p
(Intercept) 1.98 0.380 5.21 0.000
Judicial constraints -0.03 0.485 -0.06 0.956
Freedom information -0.60 0.860 -0.70 0.485
Democracy Score 2.61 0.807 3.24 0.001
Regime Corruption -2.75 0.381 -7.22 0.000
Civil Society Strength -1.67 0.771 -2.17 0.032
Kristin Cavallari GIF by E! - Find & Share on GIPHY

Higher democracy scores are significantly and positively related to equal access to public services for different socio-economic groups.

There is no statistically significant relationship between judicial constraint on the executive.

But we can also graphically show the coefficients in a plot with the sjPlot package.

There are many different arguments you can add to change the colors of bars, the size of the font or the thickness of the lines. 

p <-  plot_model(my_model, 
      line.size = 8, 
      show.values = TRUE,
      colors = "Set1",
      vline.color = "#d62828",
      axis.labels = c("Civil Society Strength",  "Regime Corruption", "Democracy Score", "Freedom information", "Judicial constraints"), title = "Equal access to public services distributed by socio-economic position")

p + theme_sjplot(base_size = 20)

So how can we interpret this graph?

If a bar goes across the vertical red line, the coefficient is not significant. The further the bar is from the line, the higher the t-score and the more significant the coefficient!