An Exercise in Data Manipulation and ggplot | SP
Updated 2024-01-21
What are we going to do here?
Use filter() to select rows based on some condition
Use mutate() to create a new variable
Use the pipe operator %>% to link various commands
Load the tidyverse and gapminder packages.
Note: If you haven’t installed these packages already, do that first.
Run install.packages("gapminder") and
install.packages("tidyverse").
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── 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 errorslibrary(gapminder)Inspect the gapminder dataset.
head(gapminder)## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.tail(gapminder)## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Zimbabwe Africa 1982 60.4 7636524 789.
## 2 Zimbabwe Africa 1987 62.4 9216418 706.
## 3 Zimbabwe Africa 1992 60.4 10704340 693.
## 4 Zimbabwe Africa 1997 46.8 11404948 792.
## 5 Zimbabwe Africa 2002 40.0 11926563 672.
## 6 Zimbabwe Africa 2007 43.5 12311143 470.glimpse(gapminder)## Rows: 1,704
## Columns: 6
## $ country <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
## $ year <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
## $ lifeExp <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
## $ pop <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …summary(gapminder)## country continent year lifeExp
## Afghanistan: 12 Africa :624 Min. :1952 Min. :23.60
## Albania : 12 Americas:300 1st Qu.:1966 1st Qu.:48.20
## Algeria : 12 Asia :396 Median :1980 Median :60.71
## Angola : 12 Europe :360 Mean :1980 Mean :59.47
## Argentina : 12 Oceania : 24 3rd Qu.:1993 3rd Qu.:70.85
## Australia : 12 Max. :2007 Max. :82.60
## (Other) :1632
## pop gdpPercap
## Min. :6.001e+04 Min. : 241.2
## 1st Qu.:2.794e+06 1st Qu.: 1202.1
## Median :7.024e+06 Median : 3531.8
## Mean :2.960e+07 Mean : 7215.3
## 3rd Qu.:1.959e+07 3rd Qu.: 9325.5
## Max. :1.319e+09 Max. :113523.1
## Create a new dataframe
Create a new dataframe called df1. Assign
gapminder to it. (This means we are creating a copy of the
gapminder dataset.)
<- is called the assignment operator.
df1 <- gapminderInspect the new dataframe.
head(df1)## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.tail(df1)## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Zimbabwe Africa 1982 60.4 7636524 789.
## 2 Zimbabwe Africa 1987 62.4 9216418 706.
## 3 Zimbabwe Africa 1992 60.4 10704340 693.
## 4 Zimbabwe Africa 1997 46.8 11404948 792.
## 5 Zimbabwe Africa 2002 40.0 11926563 672.
## 6 Zimbabwe Africa 2007 43.5 12311143 470.glimpse(df1)## Rows: 1,704
## Columns: 6
## $ country <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
## $ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
## $ year <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
## $ lifeExp <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
## $ pop <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
## $ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …summary(df1)## country continent year lifeExp
## Afghanistan: 12 Africa :624 Min. :1952 Min. :23.60
## Albania : 12 Americas:300 1st Qu.:1966 1st Qu.:48.20
## Algeria : 12 Asia :396 Median :1980 Median :60.71
## Angola : 12 Europe :360 Mean :1980 Mean :59.47
## Argentina : 12 Oceania : 24 3rd Qu.:1993 3rd Qu.:70.85
## Australia : 12 Max. :2007 Max. :82.60
## (Other) :1632
## pop gdpPercap
## Min. :6.001e+04 Min. : 241.2
## 1st Qu.:2.794e+06 1st Qu.: 1202.1
## Median :7.024e+06 Median : 3531.8
## Mean :2.960e+07 Mean : 7215.3
## 3rd Qu.:1.959e+07 3rd Qu.: 9325.5
## Max. :1.319e+09 Max. :113523.1
## Create a new variable.
Use the mutate() function to create a new variable
log(gdpPercap). Call it
log_income_per_capita.
We will also use the pipe operator %>% to link
commands. Read the following command as: Take df1 and then
… do something …
df1 %>%
mutate(log_income_per_capita = log(gdpPercap))## # A tibble: 1,704 × 7
## country continent year lifeExp pop gdpPercap log_income_per_capita
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779. 6.66
## 2 Afghanistan Asia 1957 30.3 9240934 821. 6.71
## 3 Afghanistan Asia 1962 32.0 10267083 853. 6.75
## 4 Afghanistan Asia 1967 34.0 11537966 836. 6.73
## 5 Afghanistan Asia 1972 36.1 13079460 740. 6.61
## 6 Afghanistan Asia 1977 38.4 14880372 786. 6.67
## 7 Afghanistan Asia 1982 39.9 12881816 978. 6.89
## 8 Afghanistan Asia 1987 40.8 13867957 852. 6.75
## 9 Afghanistan Asia 1992 41.7 16317921 649. 6.48
## 10 Afghanistan Asia 1997 41.8 22227415 635. 6.45
## # ℹ 1,694 more rows# Look at the last column!
# Store the new variable in a new dataframe called df2.
df2 <- df1 %>%
mutate(log_income_per_capita = log(gdpPercap))
head(df2)## # A tibble: 6 × 7
## country continent year lifeExp pop gdpPercap log_income_per_capita
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779. 6.66
## 2 Afghanistan Asia 1957 30.3 9240934 821. 6.71
## 3 Afghanistan Asia 1962 32.0 10267083 853. 6.75
## 4 Afghanistan Asia 1967 34.0 11537966 836. 6.73
## 5 Afghanistan Asia 1972 36.1 13079460 740. 6.61
## 6 Afghanistan Asia 1977 38.4 14880372 786. 6.67Draw plots using ggplot()
ggplot(data = df2, aes(x = gdpPercap, y = lifeExp)) +
geom_point() 
# Add a title
ggplot(data = df2, aes(x = gdpPercap, y = lifeExp)) +
geom_point() +
labs(title = "Life expectancy vs income") 
# Show each continent in a different color
# Note that "color = continent" is inside aes()
# A legend is automatically created
ggplot(data = df2, aes(x = gdpPercap, y = lifeExp, color = continent)) +
geom_point() 
# Change the X-axis variable to log_gdpPercap
ggplot(data = df2, aes(x = log_income_per_capita, y = lifeExp)) +
geom_point(color = "red") 
# add a regression line (straight line through the data points)
ggplot(data = df2, aes(x = log_income_per_capita, y = lifeExp)) +
geom_point(color = "red") +
geom_smooth(method = "lm", se = FALSE) ## `geom_smooth()` using formula = 'y ~ x'
# Plot a regression line for each continent
ggplot(data = df2, aes(x = log_income_per_capita, y = lifeExp, color = continent)) +
geom_point() +
geom_smooth(method = "lm", se=FALSE) ## `geom_smooth()` using formula = 'y ~ x'
Look at data for 3 countries.
Create a list of the countries using
c("country 1", "country 2", "country 3").Use filter() to select the countries.
Use
%in%to specify that the country must be in the above list.
Note: You must use quotation marks around strings (the name of each country, in this case.)
df2 %>%
filter(country %in% c("Argentina", "Brazil", "India")) ## # A tibble: 36 × 7
## country continent year lifeExp pop gdpPercap log_income_per_capita
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Argentina Americas 1952 62.5 17876956 5911. 8.68
## 2 Argentina Americas 1957 64.4 19610538 6857. 8.83
## 3 Argentina Americas 1962 65.1 21283783 7133. 8.87
## 4 Argentina Americas 1967 65.6 22934225 8053. 8.99
## 5 Argentina Americas 1972 67.1 24779799 9443. 9.15
## 6 Argentina Americas 1977 68.5 26983828 10079. 9.22
## 7 Argentina Americas 1982 69.9 29341374 8998. 9.10
## 8 Argentina Americas 1987 70.8 31620918 9140. 9.12
## 9 Argentina Americas 1992 71.9 33958947 9308. 9.14
## 10 Argentina Americas 1997 73.3 36203463 10967. 9.30
## # ℹ 26 more rows# Store the results in a new dataframe df3
df3 <- df2 %>%
filter(country %in% c("Argentina", "Brazil", "India"))
head(df3)## # A tibble: 6 × 7
## country continent year lifeExp pop gdpPercap log_income_per_capita
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Argentina Americas 1952 62.5 17876956 5911. 8.68
## 2 Argentina Americas 1957 64.4 19610538 6857. 8.83
## 3 Argentina Americas 1962 65.1 21283783 7133. 8.87
## 4 Argentina Americas 1967 65.6 22934225 8053. 8.99
## 5 Argentina Americas 1972 67.1 24779799 9443. 9.15
## 6 Argentina Americas 1977 68.5 26983828 10079. 9.22tail(df3)## # A tibble: 6 × 7
## country continent year lifeExp pop gdpPercap log_income_per_capita
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 India Asia 1982 56.6 708000000 856. 6.75
## 2 India Asia 1987 58.6 788000000 977. 6.88
## 3 India Asia 1992 60.2 872000000 1164. 7.06
## 4 India Asia 1997 61.8 959000000 1459. 7.29
## 5 India Asia 2002 62.9 1034172547 1747. 7.47
## 6 India Asia 2007 64.7 1110396331 2452. 7.80glimpse(df3)## Rows: 36
## Columns: 7
## $ country <fct> "Argentina", "Argentina", "Argentina", "Argentin…
## $ continent <fct> Americas, Americas, Americas, Americas, Americas…
## $ year <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, …
## $ lifeExp <dbl> 62.485, 64.399, 65.142, 65.634, 67.065, 68.481, …
## $ pop <int> 17876956, 19610538, 21283783, 22934225, 24779799…
## $ gdpPercap <dbl> 5911.3151, 6856.8562, 7133.1660, 8052.9530, 9443…
## $ log_income_per_capita <dbl> 8.684624, 8.833004, 8.872510, 8.993794, 9.153033…Draw plots for the 3 countries. Use a different color for each country.
Note: “color = country” is inside
aes()!
ggplot(df3, aes(x = year, y = gdpPercap, color = country)) +
geom_line() 
ggplot(df3, aes(x = year, y = log_income_per_capita, color = country)) +
geom_line() +
labs(x = "", ## remove "year" on the X-axis
y = "log(income per capita)", ## specify a new lable for the Y-axis
title = "Log of income per capita over time")
Look at data for all countries, but for a certain year.
- Start with
df2(notdf3. Why?) - Use
filter()to pick the year. Use==to specify one value.
df2 %>%
filter(year == 2007) # no quotations needed for a numeric variable## # A tibble: 142 × 7
## country continent year lifeExp pop gdpPercap log_income_per_capita
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 2007 43.8 31889923 975. 6.88
## 2 Albania Europe 2007 76.4 3600523 5937. 8.69
## 3 Algeria Africa 2007 72.3 33333216 6223. 8.74
## 4 Angola Africa 2007 42.7 12420476 4797. 8.48
## 5 Argentina Americas 2007 75.3 40301927 12779. 9.46
## 6 Australia Oceania 2007 81.2 20434176 34435. 10.4
## 7 Austria Europe 2007 79.8 8199783 36126. 10.5
## 8 Bahrain Asia 2007 75.6 708573 29796. 10.3
## 9 Bangladesh Asia 2007 64.1 150448339 1391. 7.24
## 10 Belgium Europe 2007 79.4 10392226 33693. 10.4
## # ℹ 132 more rows# Save in a new dataframe
df4 <- df2 %>%
filter(year == 2007)
head(df4)## # A tibble: 6 × 7
## country continent year lifeExp pop gdpPercap log_income_per_capita
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 2007 43.8 31889923 975. 6.88
## 2 Albania Europe 2007 76.4 3600523 5937. 8.69
## 3 Algeria Africa 2007 72.3 33333216 6223. 8.74
## 4 Angola Africa 2007 42.7 12420476 4797. 8.48
## 5 Argentina Americas 2007 75.3 40301927 12779. 9.46
## 6 Australia Oceania 2007 81.2 20434176 34435. 10.4glimpse(df4)## Rows: 142
## Columns: 7
## $ country <fct> "Afghanistan", "Albania", "Algeria", "Angola", "…
## $ continent <fct> Asia, Europe, Africa, Africa, Americas, Oceania,…
## $ year <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, …
## $ lifeExp <dbl> 43.828, 76.423, 72.301, 42.731, 75.320, 81.235, …
## $ pop <int> 31889923, 3600523, 33333216, 12420476, 40301927,…
## $ gdpPercap <dbl> 974.5803, 5937.0295, 6223.3675, 4797.2313, 12779…
## $ log_income_per_capita <dbl> 6.882007, 8.688964, 8.736066, 8.475794, 9.455588…Theend