III. Graphics with ggplot2 (solution)

Import data

Loading the core tidyverse packages and the ‘readxl’ package for data import from .xlsx.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.0     ✔ readr     2.1.6
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.5     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(readxl)

Importing the climate data from climate.xlsx¹. (Change the path to the Excel file below so that it matches the path to the file saved on your own computer, or use Import Dataset in RStudio to obtain the relevant code.)

climate <- read_excel("climate.xlsx")
climate

# A tibble: 60 × 7
   station  year month    af  rain   sun device         
   <chr>   <dbl> <dbl> <dbl> <dbl> <dbl> <chr>          
 1 armagh   2016     1     5 132.   44.5 Campbell Stokes
 2 armagh   2016     2    10  62.6  71.3 Campbell Stokes
 3 armagh   2016     3     4  43.8 117.  Campbell Stokes
 4 armagh   2016     4     5  54   140.  Campbell Stokes
 5 armagh   2016     5     0  41.4 210.  Campbell Stokes
 6 armagh   2016     6     0  75.1 114.  Campbell Stokes
 7 armagh   2016     7     0  80.6 113.  Campbell Stokes
 8 armagh   2016     8     0  52.5 135.  Campbell Stokes
 9 armagh   2016     9     0  65.4  91.1 Campbell Stokes
10 armagh   2016    10     0  37.1  89.8 Campbell Stokes
# ℹ 50 more rows

Scatter plot I

ggplot(climate, aes(x = sun, y = rain, colour = station)) + 
  geom_point() +
  facet_wrap(~station) +
  theme_bw() +
  theme(legend.position = "none")

Graphic files

ggsave(file="weather.jpeg")

Saving 7 x 5 in image

ggsave(file="weather.png",width=10,height=8,units="cm")

Line plot

ggplot(climate, aes(x = month, y = rain, colour = station)) + geom_line()

climate <- mutate(climate, month = factor(month))

ggplot(climate, aes(x = month, y = rain, group = station, colour = station)) +
  geom_line() +
  geom_point() +
  theme(legend.position = "top") +
  geom_hline(yintercept = mean(climate$rain), linetype = "dashed") +
  labs(x = "Month", y = "Rainfall (mm)", colour = "Weather station")

Box plot(s)

ggplot(climate, aes(x = station, y = sun)) +
  geom_boxplot(fill = "lightgreen")

ggplot(climate, aes(x = station, y = sun)) +
  geom_boxplot(aes(fill = station)) +
  theme(legend.position = "none")

Histogram

ggplot(climate, aes(x = rain)) +
  geom_histogram(binwidth = 25, colour = "red", fill = "orange")

Bar chart I - a plot often used in papers!

summary_stats <- 
  climate %>%
  group_by(month) %>% 
  summarize(sun_avg = mean(sun), sun_sd = sd(sun))

ggplot(summary_stats, aes(x = month, y = sun_avg)) +
  geom_col(fill = "lightblue") +
  geom_errorbar(aes(ymin = sun_avg - sun_sd, ymax = sun_avg + sun_sd), width = 0.2)

Scatter plot II

p <- ggplot(summary_stats, aes(x = month, y = sun_avg)) +
  geom_point() +
  geom_errorbar(aes(ymin = sun_avg - sun_sd, ymax = sun_avg + sun_sd), width = 0.3)
p

p + coord_flip()

Bar chart II

ggplot(climate, aes(x = month, y = sun, fill = station)) +
  geom_col() +
  labs(x = "Month", y = "Sunshine (hours)", fill = "Weather station")

Bar chart III

ggplot(climate, aes(x = station, y = rain)) +
  geom_col()

annual_rain <- 
  climate %>%
  group_by(station) %>% 
  summarize(rain = sum(rain)) %>% 
  arrange(rain)

climate <- mutate(climate, station = factor(station, levels = annual_rain$station))

ggplot(climate, aes(x = station, y = rain)) +
  geom_col() +
  geom_label(mapping = aes(x = station, y = rain, label = rain), data = annual_rain, nudge_y = 60)

Scatter plot III

example_data <- tibble(X = 1:5, Y = 2*X, Z = letters[1:5])
example_data

# A tibble: 5 × 3
      X     Y Z    
  <int> <dbl> <chr>
1     1     2 a    
2     2     4 b    
3     3     6 c    
4     4     8 d    
5     5    10 e    

ggplot() +
  geom_point(mapping = aes(x = X, y = Y), data = example_data)

ggplot() +
  geom_text(mapping = aes(x = X, y = Y, label = Z), data = example_data)

ggplot() +
  geom_text(mapping = aes(x = X, y = Y, label = Z), data = example_data, nudge_y = 0.3)

End of solution

Footnotes

1. Contains public sector information licensed under the Open Government Licence v3.0.