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In September 2019, YouGov survey asked 1,639 GB adults the following question:

In hindsight, do you think Britain was right/wrong to vote to leave EU?

The data from the survey are in data/brexit.csv.

brexit <- read_csv("data/brexit.csv")

In the course video we made the following visualisation.

brexit <- brexit %>%
  mutate(
    region = fct_relevel(region, "london", "rest_of_south", "midlands_wales", "north", "scot"),
    region = fct_recode(region, London = "london", `Rest of South` = "rest_of_south", `Midlands / Wales` = "midlands_wales", North = "north", Scotland = "scot")
  )

ggplot(brexit, aes(y = opinion, fill = opinion)) +
  geom_bar() +
  facet_wrap(~region, nrow = 1, labeller = label_wrap_gen(width = 12)) +
  guides(fill = FALSE) +
  labs(
    title = "Was Britain right/wrong to vote to leave EU?",
    subtitle = "YouGov Survey Results, 2-3 September 2019",
    caption = "Source: bit.ly/2lCJZVg",
    x = NULL, y = NULL
  ) +
  scale_fill_manual(values = c(
    "Wrong" = "#ef8a62",
    "Right" = "#67a9cf",
    "Don't know" = "gray"
  )) +
  theme_minimal()
## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

In this application exercise we tell different stories with the same data.

Exercise 1 - Free scales

Add scales = "free_x" as an argument to the facet_wrap() function. How does the visualisation change? How is the story this visualisation telling different than the story the original plot tells?

brexit <- brexit %>%
  mutate(region = factor(region)) %>%  # Pastikan region adalah faktor
  mutate(region = fct_relevel(region, 
                              "london", "rest_of_south", "midlands_wales", 
                              "north", "scot"))
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `region = fct_relevel(...)`.
## Caused by warning:
## ! 5 unknown levels in `f`: london, rest_of_south, midlands_wales, north, and scot
ggplot(brexit, aes(y = opinion, fill = opinion)) +
  geom_bar() +
  facet_wrap(~region,
    nrow = 1, labeller = label_wrap_gen(width = 12),
    scales = "free_x"  # tambahkan free_x ini
  ) +
  guides(fill = FALSE) +
  labs(
    title = "Was Britain right/wrong to vote to leave EU?",
    subtitle = "YouGov Survey Results, 2-3 September 2019",
    caption = "Source: bit.ly/2lCJZVg",
    x = NULL, y = NULL
  ) +
  scale_fill_manual(values = c(
    "Wrong" = "#ef8a62",
    "Right" = "#67a9cf",
    "Don't know" = "gray"
  )) +
  theme_minimal()

Exercise 2 - Comparing proportions across facets

First, calculate the proportion of wrong, right, and don’t know answers in each category and then plot these proportions (rather than the counts) and then improve axis labeling. How is the story this visualisation telling different than the story the original plot tells? Hint: You’ll need the scales package to improve axis labeling, which means you’ll need to load it on top of the document as well.

library(ggplot2)
library(dplyr)
library(forcats)
library(scales)
## Warning: package 'scales' was built under R version 4.3.3
## 
## Attaching package: 'scales'
## The following object is masked from 'package:purrr':
## 
##     discard
## The following object is masked from 'package:readr':
## 
##     col_factor
brexit <- brexit %>%
  mutate(region = as.factor(region)) %>%
  mutate(
    region = fct_relevel(region, "london", "rest_of_south", "midlands_wales", "north", "scot"),
    region = fct_recode(region, 
                        "London" = "london", 
                        "Rest of South" = "rest_of_south",  
                        "Midlands / Wales" = "midlands_wales", 
                        "North" = "north", 
                        "Scotland" = "scot")
  )
## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `region = fct_relevel(...)`.
## Caused by warning:
## ! 5 unknown levels in `f`: london, rest_of_south, midlands_wales, north, and scot
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
if (!"opinion" %in% colnames(brexit)) {
  stop("Error: The 'opinion' column is missing from the dataset.")
}

brexit_prop <- brexit %>%
  group_by(region, opinion) %>%
  summarise(count = n(), .groups = "drop") %>%
  group_by(region) %>%
  mutate(proportion = count / sum(count))

ggplot(brexit_prop, aes(x = opinion, y = proportion, fill = opinion)) +
  geom_col() +  # Gunakan geom_col() karena data sudah berupa agregat
  facet_wrap(~region, ncol = 2) +  # Menampilkan facet dengan 2 kolom agar lebih rapi
  scale_y_continuous(labels = percent_format()) +  
  labs(
    title = "Was Britain right/wrong to vote to leave EU?",
    subtitle = "YouGov Survey Results, 2-3 September 2019",
    caption = "Source: bit.ly/2lCJZVg",
    x = NULL, y = "Proportion",
    fill = "Opinion"
  ) +
  scale_fill_manual(values = c(
    "Wrong" = "#1a9850",
    "Right" = "#fee08b",
    "Don't know" = "#8c510a"
  )) +
  coord_flip() +
  theme_minimal() +
  theme(
    legend.position = "bottom",
    legend.title = element_text(face = "bold"),
    strip.text = element_text(face = "bold", size = 10)
  )

Exercise 3 - Comparing proportions across bars

Recreate the same visualisation from the previous exercise, this time dodging the bars for opinion proportions for each region, rather than faceting by region and then improve the legend. How is the story this visualisation telling different than the story the previous plot tells?

library(dplyr)
library(ggplot2)

brexit_prop <- brexit %>%
  group_by(region, opinion) %>%
  summarise(count = n(), .groups = "drop") %>%
  group_by(region) %>%  
  mutate(prop = count / sum(count))  

ggplot(brexit_prop, aes(x = region, y = prop, fill = opinion)) +
  geom_col(position = "dodge") +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(
    title = "Comparison of Brexit Opinions Across Regions",
    subtitle = "YouGov Survey Results, 2-3 September 2019",
    x = "Region", y = "Proportion",
    fill = "Opinion"
  ) +
  scale_fill_manual(values = c(
    "Wrong" = "#ef8a62",
    "Right" = "#67a9cf",
    "Don't know" = "gray"
  )) + 
  theme_minimal()