AS - Day 2
Introduction to R - Session 1
Dr. J. Kavanagh
2023-09-14
Importing Data
Here is a clear and straightforward way to import data into
RStudio
read.csv('net-receipts-by-commodity.csv', stringsAsFactors = FALSE) -> receipts
Using some of the lessons from Day 1, explore the dataset
## year n
## 1 2003 30
## 2 2004 30
## 3 2005 29
## 4 2006 30
## 5 2007 28
## 6 2008 28
## 7 2009 28
## 8 2010 35
## 9 2011 35
## 10 2012 35
## 11 2013 36
## 12 2014 36
## 13 2015 36
## 14 2016 35
## 15 2017 35
## 16 2018 36
## 17 2019 36
## 18 2020 36
## Rows: 594
## Columns: 3
## $ commodity_and_head_of_duty <chr> "Alcohols Beer - Import", "Alcohols Beer - …
## $ year <int> 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2…
## $ net_receipts_.m <int> 183235512, 167879917, 12701490, 40340740, 2…
Tax Receipts from Duties on Beer
receipts %>% filter(commodity_and_head_of_duty == "Alcohols Beer - Home") -> receipts_alcohol_home
receipts %>% filter(commodity_and_head_of_duty == "Alcohols Beer - Import") -> receipts_alcohol_import
Plot of Domestic Beer Tax Receipts
receipts_alcohol_home %>% ggplot(aes(x=year, y=net_receipts_.m)) + geom_line()
Simplify the Column Names
colnames(receipts_alcohol_home) <- c("Type", "Year", "Tax")
colnames(receipts_alcohol_import) <- c("Type", "Year", "Tax")
Class Exercise
- Create a chart comparing the Imported and Domestic Tax Receipts of
Alcohol
Solution
rbind(receipts_alcohol_home, receipts_alcohol_import) -> receipts_alcohol_unified
receipts_alcohol_unified %>% ggplot(aes(x=Year, y=Tax, colour=Type, group=Type)) + geom_line()
Re-check the Data
receipts_alcohol_home %>% tail()
## Type Year Tax
## 12 Alcohols Beer - Home 2009 278308367
## 13 Alcohols Beer - Home 2008 309303625
## 14 Alcohols Beer - Home 2007 306918459
## 15 Alcohols Beer - Home 2006 349185923
## 16 Alcohols Beer - Home 2005 361930565
## 17 Alcohols Beer - Home 2004 378081957
receipts_alcohol_import %>% tail()
## Type Year Tax
## 14 Alcohols Beer - Import 2007 157883543
## 15 Alcohols Beer - Import 2006 111507924
## 16 Alcohols Beer - Import 2005 95377167
## 17 Alcohols Beer - Import 2004 80113005
## 18 Alcohols Beer - Import 2003 82770969
## 19 Alcohols Beer - Import 2003 372619049
Fixing the Data
In the receipts_alcohol_import dataframe, an error was made and 2003
was entered twice by the data creators. Therefore we need to fix the
data.
# This shows all the years
receipts_alcohol_import$Year
## [1] 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006
## [16] 2005 2004 2003 2003
# Using the square [] we can specify the date
receipts_alcohol_import$Year[19]
## [1] 2003
# Now we can change it
receipts_alcohol_import$Year[19] <- 2002
# Check the result
receipts_alcohol_import$Year
## [1] 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006
## [16] 2005 2004 2003 2002
Exploring Spatial Data & Visualisations
So we’re going to be re-using the tidyverse library and then an
additional HistData package to reproduce the Minard map of Napleon’s
campaign.
install.packages("tidyverse", "ggthemes", "HistData", "lubridate", "gridExtra")
First Import the data from the HistData packaage
data("Minard.troops", "Minard.cities", "Minard.temp")
## long lat survivors direction group
## 1 24.0 54.9 340000 A 1
## 2 24.5 55.0 340000 A 1
## 3 25.5 54.5 340000 A 1
## 4 26.0 54.7 320000 A 1
## 5 27.0 54.8 300000 A 1
## 6 28.0 54.9 280000 A 1
## 7 28.5 55.0 240000 A 1
## 8 29.0 55.1 210000 A 1
## 9 30.0 55.2 180000 A 1
## 10 30.3 55.3 175000 A 1
## 11 32.0 54.8 145000 A 1
## 12 33.2 54.9 140000 A 1
## 13 34.4 55.5 127100 A 1
## 14 35.5 55.4 100000 A 1
## 15 36.0 55.5 100000 A 1
## 16 37.6 55.8 100000 A 1
## 17 37.7 55.7 100000 R 1
## 18 37.5 55.7 98000 R 1
## 19 37.0 55.0 97000 R 1
## 20 36.8 55.0 96000 R 1
## 21 35.4 55.3 87000 R 1
## 22 34.3 55.2 55000 R 1
## 23 33.3 54.8 37000 R 1
## 24 32.0 54.6 24000 R 1
## 25 30.4 54.4 20000 R 1
## 26 29.2 54.3 20000 R 1
## 27 28.5 54.2 20000 R 1
## 28 28.3 54.3 20000 R 1
## 29 27.5 54.5 20000 R 1
## 30 26.8 54.3 12000 R 1
## 31 26.4 54.4 14000 R 1
## 32 25.0 54.4 8000 R 1
## 33 24.4 54.4 4000 R 1
## 34 24.2 54.4 4000 R 1
## 35 24.1 54.4 4000 R 1
## 36 24.0 55.1 60000 A 2
## 37 24.5 55.2 60000 A 2
## 38 25.5 54.7 60000 A 2
## 39 26.6 55.7 40000 A 2
## 40 27.4 55.6 33000 A 2
## 41 28.7 55.5 33000 A 2
## 42 28.7 55.5 33000 R 2
## 43 29.2 54.2 30000 R 2
## 44 28.5 54.1 30000 R 2
## 45 28.3 54.2 28000 R 2
## 46 24.0 55.2 22000 A 3
## 47 24.5 55.3 22000 A 3
## 48 24.6 55.8 6000 A 3
## 49 24.6 55.8 6000 R 3
## 50 24.2 54.4 6000 R 3
## 51 24.1 54.4 6000 R 3
## long lat survivors direction group
## 1 24.0 54.9 340000 A 1
## 2 24.5 55.0 340000 A 1
## 3 25.5 54.5 340000 A 1
## 4 26.0 54.7 320000 A 1
## 5 27.0 54.8 300000 A 1
## 6 28.0 54.9 280000 A 1
## 7 28.5 55.0 240000 A 1
## 8 29.0 55.1 210000 A 1
## 9 30.0 55.2 180000 A 1
## 10 30.3 55.3 175000 A 1
## 11 32.0 54.8 145000 A 1
## 12 33.2 54.9 140000 A 1
## 13 34.4 55.5 127100 A 1
## 14 35.5 55.4 100000 A 1
## 15 36.0 55.5 100000 A 1
## 16 37.6 55.8 100000 A 1
## 17 37.7 55.7 100000 R 1
## 18 37.5 55.7 98000 R 1
## 19 37.0 55.0 97000 R 1
## 20 36.8 55.0 96000 R 1
## 21 35.4 55.3 87000 R 1
## 22 34.3 55.2 55000 R 1
## 23 33.3 54.8 37000 R 1
## 24 32.0 54.6 24000 R 1
## 25 30.4 54.4 20000 R 1
## 26 29.2 54.3 20000 R 1
## 27 28.5 54.2 20000 R 1
## 28 28.3 54.3 20000 R 1
## 29 27.5 54.5 20000 R 1
## 30 26.8 54.3 12000 R 1
## 31 26.4 54.4 14000 R 1
## 32 25.0 54.4 8000 R 1
## 33 24.4 54.4 4000 R 1
## 34 24.2 54.4 4000 R 1
## 35 24.1 54.4 4000 R 1
## 36 24.0 55.1 60000 A 2
## 37 24.5 55.2 60000 A 2
## 38 25.5 54.7 60000 A 2
## 39 26.6 55.7 40000 A 2
## 40 27.4 55.6 33000 A 2
## 41 28.7 55.5 33000 A 2
## 42 28.7 55.5 33000 R 2
## 43 29.2 54.2 30000 R 2
## 44 28.5 54.1 30000 R 2
## 45 28.3 54.2 28000 R 2
## 46 24.0 55.2 22000 A 3
## 47 24.5 55.3 22000 A 3
## 48 24.6 55.8 6000 A 3
## 49 24.6 55.8 6000 R 3
## 50 24.2 54.4 6000 R 3
## 51 24.1 54.4 6000 R 3
## long lat survivors direction group
## 1 24.0 54.9 340000 A 1
## 2 24.5 55.0 340000 A 1
## 3 25.5 54.5 340000 A 1
## 4 26.0 54.7 320000 A 1
## 5 27.0 54.8 300000 A 1
## 6 28.0 54.9 280000 A 1
## 7 28.5 55.0 240000 A 1
## 8 29.0 55.1 210000 A 1
## 9 30.0 55.2 180000 A 1
## 10 30.3 55.3 175000 A 1
## 11 32.0 54.8 145000 A 1
## 12 33.2 54.9 140000 A 1
## 13 34.4 55.5 127100 A 1
## 14 35.5 55.4 100000 A 1
## 15 36.0 55.5 100000 A 1
## 16 37.6 55.8 100000 A 1
## 17 37.7 55.7 100000 R 1
## 18 37.5 55.7 98000 R 1
## 19 37.0 55.0 97000 R 1
## 20 36.8 55.0 96000 R 1
## 21 35.4 55.3 87000 R 1
## 22 34.3 55.2 55000 R 1
## 23 33.3 54.8 37000 R 1
## 24 32.0 54.6 24000 R 1
## 25 30.4 54.4 20000 R 1
## 26 29.2 54.3 20000 R 1
## 27 28.5 54.2 20000 R 1
## 28 28.3 54.3 20000 R 1
## 29 27.5 54.5 20000 R 1
## 30 26.8 54.3 12000 R 1
## 31 26.4 54.4 14000 R 1
## 32 25.0 54.4 8000 R 1
## 33 24.4 54.4 4000 R 1
## 34 24.2 54.4 4000 R 1
## 35 24.1 54.4 4000 R 1
## 36 24.0 55.1 60000 A 2
## 37 24.5 55.2 60000 A 2
## 38 25.5 54.7 60000 A 2
## 39 26.6 55.7 40000 A 2
## 40 27.4 55.6 33000 A 2
## 41 28.7 55.5 33000 A 2
## 42 28.7 55.5 33000 R 2
## 43 29.2 54.2 30000 R 2
## 44 28.5 54.1 30000 R 2
## 45 28.3 54.2 28000 R 2
## 46 24.0 55.2 22000 A 3
## 47 24.5 55.3 22000 A 3
## 48 24.6 55.8 6000 A 3
## 49 24.6 55.8 6000 R 3
## 50 24.2 54.4 6000 R 3
## 51 24.1 54.4 6000 R 3
Step 1
## plot path of troops, and another layer for city names
plot_troops <- ggplot(Minard.troops, aes(long, lat)) +
geom_path(aes(linewidth = survivors, colour = direction, group = group),
lineend = "round", linejoin = "round")
Step 2
plot_cities <- geom_text(aes(label = city), size = 4, data = Minard.cities)
## Combine these, and add scale information, labels, etc.
# Set the x-axis limits for longitude explicitly, to coincide with those for temperature
breaks <- c(1, 2, 3) * 10^5
Step 3
# Create a new gg object
plot_minard <- plot_troops + plot_cities +
scale_size("Survivors", range = c(1, 10),
breaks = breaks, labels = scales::comma(breaks)) +
scale_color_manual("Direction",
values = c("grey50", "red"),
labels=c("Advance", "Retreat")) +
coord_cartesian(xlim = c(24, 38)) +
xlab(NULL) +
ylab("Latitude") +
ggtitle("Napoleon's March on Moscow") +
theme_bw() +
theme(legend.position=c(.8, .2), legend.box="horizontal")
Step 4
## plot temperature vs. longitude, with labels for dates
plot_temp <- ggplot(Minard.temp, aes(long, temp)) +
geom_path(color="grey", size=1.5) +
geom_point(size=2) +
geom_text(aes(label=date)) +
xlab("Longitude") + ylab("Temperature") +
coord_cartesian(xlim = c(24, 38)) +
theme_bw()
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 1 rows containing missing values (`geom_text()`).
Step 5
grid.arrange(plot_minard, plot_temp, nrow=2, heights=c(3,1))
## Warning: Removed 1 rows containing missing values (`geom_text()`).
