Classwork 6

Visualization Practice

Author

Byeong-Hak Choe

Published

February 24, 2025

Modified

April 6, 2025

Question 1

The following data is for Question 1:

gapminder <- gapminder::gapminder

Q1a

Replicate the following ggplot.
- Use the color #0072B2 for dots.

Click to Check the Answer!

# Set the data and filter to include only observations from 2007 and exclude Oceania
ggplot(data = filter(gapminder, year == 2007,
                     continent != 'Oceania'),

       # Set the aesthetics (x-axis and y-axis) to life expectancy and reorder countries by life expectancy
       aes(x = lifeExp,
           y = fct_reorder(country, lifeExp))) +

  # Add a layer of points to the plot, setting the color to blue and size to 1.75
  geom_point(color = "#0072B2", size = 1.75) +

  # Add a layer of text labels to the plot, setting the label to life expectancy, hjust to -.25, and size to 2
  geom_text(aes(label = lifeExp), hjust = -.25,
            size = 2) +

  # Facet the plot by continent, with y-scales free
  facet_wrap(.~continent, scales = "free_y") +

  # Set the x-axis label to NULL (no name) and limit the x-axis to 35-90
  scale_x_continuous(
    name = NULL,
    lim = c(35, 90)
  ) +

  # Set the y-axis label to NULL (no name)
  scale_y_discrete(name = NULL) +

  # Add a title to the plot
  labs(title = 'Life expectancy in 2007') +

  # Set the theme to minimal
  theme_minimal() +

  # Customize theme elements: set the y-axis text size, the plot title size and position, and the facet strip text size and font face
  theme(
    axis.text.y = element_text(size = rel(.75)),
    plot.title = element_text(size = rel(1.5),
                              hjust = 0.5,
                              face = 'bold'),
    strip.text = element_text(size = rel(1.25),
                              face = 'bold')
  )

Q1b

Make a simple comment on the visualization result.
- Any comment that is not made up is okay.

Question 2

The following data set is for Question 2:

electricity <- read_csv(
  'https://bcdanl.github.io/data/electricity-usa-chn.csv')

Q2a

Replicate the following ggplot.

Click to Check the Answer!

ggplot(data = electricity,
       aes(x = year, y = value)) + 
  geom_line(aes(color = energy),
            linewidth = 1.25) +
  geom_hline(yintercept = 0) +  
  geom_vline(xintercept = min(electricity$year)) +
  facet_wrap(.~ iso3c, 
             labeller = as_labeller(c('CHN' = "China",
                                      "USA" = "United States"))) +
  scale_y_comma() +
  scale_color_viridis_d(option = "C") +
  guides(color = guide_legend(label.position = "bottom",
                             keywidth = 3,
                             nrow = 1)) +
  labs(x = "Year", y = "Electricity\nGeneration\n(TWh)",
       color = "Energy") +
  hrbrthemes::theme_ipsum() +
  theme(axis.title.y = element_text(size = rel(1.25),
                                    angle = 0),
        axis.title.x = element_text(size = rel(1.25),
                                    angle = 0),
        strip.background = element_rect(fill = 'grey90',
                                        colour = NA),
        legend.position = 'top')

Q2b

Replicate the following ggplot.

Click to Check the Answer!

electricity2 <- electricity |> 
  group_by(iso3c, year) |> 
  mutate(tot = sum(value),
         prop = value / tot)


ggplot(data = electricity2,
       aes(x = year, y = prop)) + 
  geom_line(aes(color = energy),
            linewidth = 1.25) +
  geom_hline(yintercept = 0) +  
  geom_vline(xintercept = min(electricity$year)) +
  facet_wrap(.~ iso3c, 
             labeller = as_labeller(c('CHN' = "China",
                                      "USA" = "United States"))) +
  scale_y_percent() +
  scale_color_viridis_d(option = "D") +
  guides(color = guide_legend(label.position = "bottom",
                              keywidth = 3,
                              nrow = 1)) +
  labs(x = "Year", y = "Percentage of\nElectricity\nGeneration\n(TWh)",
       color = "Energy") +
  hrbrthemes::theme_ipsum() +
  theme(axis.title.y = element_text(size = rel(1.25),
                                    angle = 0),
        axis.title.x = element_text(size = rel(1.25),
                                    angle = 0),
        strip.background = element_rect(fill = 'grey90',
                                        colour = NA),
        legend.position = 'top')

Question 3

The following data is for Question 3:

n_tweets_long <- read_csv(
  'https://bcdanl.github.io/data/n_tweets_long.csv')

Q3a

Replicate the following ggplot.
- The following describes the type values:
  - n_ot_us: Number of US tweets
  - n_ot_wrld: Number of worldwide tweets
  - n_rt_lk_us: Number of US retweets & likes
  - n_rt_lk_wrld: Number of worldwide retweets & likes
- Use the colors, maroon and #428bca properly.

Click to Check the Answer!

n_tweets_long1 <- n_tweets_long |> 
  filter(type %in% c("n_ot_us", "n_ot_wrld") ) |> 
  mutate(type = ifelse(type == "n_ot_us", "US", "Worldwide"))

n_tweets_long2 <- n_tweets_long |> 
  filter(type %in% c("n_rt_lk_us", "n_rt_lk_wrld") ) |> 
  mutate(type = ifelse(type == "n_rt_lk_us", "US", "Worldwide"))

p2 <- ggplot(mapping = aes(x = year, y = n)) +
  geom_col(n_tweets_long1,
           mapping = aes(fill = type),
           position = 'dodge', alpha = .67) + 
  geom_line(n_tweets_long2,  
            mapping = aes(color = type),
            size = 1.5) +  
  geom_point(data = n_tweets_long2,  
             size = 2,  
             color = 'black')  +  
  scale_x_continuous(breaks = seq(2012, 2017, 1)) +  
  scale_y_continuous(label = scales::comma) +  
  scale_color_manual(values = c('maroon', '#428bca')) +  
  scale_fill_manual(values = c('maroon', '#428bca')) +   
  guides(fill = guide_legend(reverse = TRUE, 
                             label.position = "bottom",
                             keywidth = 2,
                             nrow = 2,
                             order = 1),
         color = guide_legend(reverse = TRUE,  
                             label.position = "bottom",
                             keywidth = 2,
                             nrow = 2,
                             order = 2)) +
  labs(x = "Year", 
       y = "Number of Tweets, Retweets & Likes\n (in thousand)",  
       fill = "Tweets",  
       color = "Retweets and likes",  
       caption = 'Source: Choe, "Social Media Campaigns, Lobbying, and Climate Change Legislation:\n Evidence from #climatechange/#globalwarming and Energy Lobbies" (2023)') +  
  theme_ipsum() + 
  theme(
  axis.title.y = element_text(
    size = rel(1.5),
    margin = margin(t = 0, r = 20, b = 0, l = 0) 
  ),
  axis.title.x = element_text(
    size = rel(1.5),
    margin = margin(t = 10, r = 0, b = 0, l = 0) 
  ),
  axis.text.x = element_text(
    size = rel(1.25) 
  ),
  axis.text.y = element_text(
    size = rel(1.25) 
  ),
  legend.position = 'top', 
  legend.title = element_text(
    size = rel(1),
    face = 'bold',
    hjust = .5 
  ),
  legend.text = element_text(
    size = rel(1) 
  ),
  legend.spacing.x = unit(1.25, "cm"), 
  plot.caption = element_text(
    size = rel(1.25),
    hjust = .5 
  )
)


p2

Q3b.

Make a simple comment on the visualization result.

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