Classwork 5

Graph Tables; Add Labels; Make Notes

Below is the packages, theme, and scale settings used in this classwork:

library(tidyverse)
library(skimr)
library(ggthemes)
library(hrbrthemes)
library(ggrepel)
library(socviz)
library(DT)

theme_set(theme_fivethirtyeight()+
          theme(strip.background =element_rect(fill="lightgray"),
                axis.title.x = 
                  element_text(angle = 0,
                               size = rel(1.33),
                               margin = margin(10,0,0,0)),
                axis.title.y = 
                  element_text(angle = 90,
                               size = rel(1.33),
                               margin = margin(0,10,0,0))
                )
          )
scale_colour_discrete <- function(...) scale_color_tableau(...)
scale_fill_discrete <- function(...) scale_fill_tableau(...)

Recreate the R code necessary to generate the following graphs.

Question 1

gss_sm <- socviz::gss_sm

DT::datatable(gss_sm) # displaying an interactive data.frame

rel_by_region <- gss_sm |>
    group_by( bigregion, religion ) |>
    summarize( N = n() ) |>
    mutate( freq = N / sum(N),
            pct = round( (freq*100), 0) )

p <- ggplot(rel_by_region, 
            aes(x = pct, 
                y = religion, 
                fill = religion))
p + geom_col(position = "dodge2") +
    labs(x = NULL, 
         y = "Percent", 
         fill = "Religion") +
    guides(fill = FALSE) + 
    facet_grid(~ bigregion)

Question 2

organdata <- socviz::organdata

DT::datatable(organdata)

Q2a

by_country <- organdata |> group_by(consent_law, country) |>
  summarize_if(is.numeric, lst(mean, sd), na.rm = TRUE) |>
  ungroup()

p <- ggplot(data = by_country,
            mapping = aes(x = donors_mean, y = reorder(country, donors_mean),
                          color = consent_law))
p + geom_point(size=3) +
    labs(x = "Donor Procurement Rate",
         y = "", color = "Consent Law") +
    theme(legend.position="top")

Q2b

by_country <- organdata |> group_by(consent_law, country) |>
  summarize_if(is.numeric, lst(mean, sd), na.rm = TRUE) |>
  ungroup()

p <- ggplot(data = by_country,
            mapping = aes(x = donors_mean,
                          y = reorder(country, donors_mean)))

p + geom_point(size=3) +
    facet_wrap(~ consent_law, scales = "free_y", ncol = 1) +
    labs(x= "Donor Procurement Rate",
         y= "") 

Q2c

p <- ggplot(data = by_country, mapping = aes(x = reorder(country,
              donors_mean), y = donors_mean))

p + geom_pointrange(mapping = aes(ymin = donors_mean - donors_sd,
       ymax = donors_mean + donors_sd)) +
     labs(x= "", y= "Donor Procurement Rate") + coord_flip()

Q2d

p <- ggplot(data = organdata,
            mapping = aes(x = roads,
                          y = donors,
                          color = world))
p + geom_point() +
    scale_y_continuous(breaks = c(5, 15, 25),
                       labels = c("Five", "Fifteen", "Twenty Five"))

Q2e

p <- ggplot(data = organdata,
            mapping = aes(x = roads,
                          y = donors,
                          color = world))
p + geom_point() +
    scale_color_discrete(labels =
                             c("Corporatist", "Liberal",
                               "Social Democratic", "Unclassified")) +
    labs(x = "Road Deaths",
         y = "Donor Procurement",
        color = "Welfare State")

Q2f

organdata |> 
  ggplot(mapping = aes(x = roads,
                       y = donors,
                       color = consent_law)) + 
  geom_point() +
  facet_wrap(~ consent_law, ncol = 1) +
  guides(color = "none") + 
  labs(x = "Road Deaths",
       y = "Donor Procurement")

Q2g

organdata |> 
  ggplot(mapping = aes(x = roads,
                       y = donors,
                       color = consent_law)) + 
  geom_point() +
  labs(title = "By Consent Law",
    x = "Road Deaths",
    y = "Donor Procurement", 
    color = "Legal Regime:") + 
  theme(legend.position = "bottom", 
        plot.title = element_text(color = "darkred",
                                  face = "bold"))

Question 3

mtcars <- datasets::mtcars
mtcars <- mtcars %>%   # A native pipe (|>) does not work here.
  mutate(car = rownames(.))
rownames(mtcars) <- 1:nrow(mtcars)

DT::datatable(mtcars)

dat <- filter(mtcars, 
              wt > 5 & mpg < 15)

p <- ggplot() + 
  geom_point(data = mtcars, 
             aes(wt, mpg), 
             color = 'red')
p1 <- p + geom_text_repel(data = dat, 
                          aes(wt, mpg, 
                              label = car),
                          box.padding = 0.5)
p1

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