Tidyverse Functions

Preliminaries

Load packages

Check: Are the packages are already installed? If not, first run

install.packages()

library(tidyverse)
library(gapminder)

Make a copy of gapminder

df1 <- gapminder

Inspect the data

head(df1)

tail(df1)

dim(df1)

glimpse(df1)

summary(df1)

Filter function

Filter one country

Select one country: China

## Create a new data frame for China

df_China <- df1 %>% 
  filter(country == "China")

## Scatter plot and line plot

ggplot(df_China, 
       aes(x = year, y = pop)) + 
  geom_point() +
  geom_line() +
  labs(x = "",
       y = "Population",
       title = "China's population over time",
       caption = "Source: World Bank via gapminder")

Filter more than one country

Two countries: China, India

## Create a new dataframe

df2 <- df1 %>% 
  filter(country %in% c("China", "India"))

ggplot(df2, 
       aes(x = year, y = pop, color = country)) + 
  geom_line() +
  labs(x = "",
       y = "Population",
       title = "Population in China and India, 1952-2007",
       subtitle = "Wow, look at the increase over the period!",
       caption = "Source: World Bank via gapminder")

Mutate function

Create a new variable

## Create a new variable called log_gdp_per_capita

df3 <- df1 %>% 
  mutate(log_gdp_per_capita = log(gdpPercap))

df3

# A tibble: 1,704 × 7
   country     continent  year lifeExp      pop gdpPercap log_gdp_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

Add regression lines to a scatter plot

## Scatter plot

ggplot(data = df3, 
       aes(x = log_gdp_per_capita, y = lifeExp)) + 
  geom_point(color = "red")

## Add a regression line (straight line through the data points)

ggplot(data = df3, 
       aes(x = log_gdp_per_capita, y = lifeExp)) + 
  geom_point(color = "red") + 
  geom_smooth(method = "lm", se = FALSE)

## Plot a regression line for each continent

ggplot(data = df3, 
       aes(x = log_gdp_per_capita, y = lifeExp, color = continent)) + 
  geom_point() + 
  geom_smooth(method = "lm", se=FALSE)

Create plots using data for one year

df4 <- df3 %>% 
  filter(year == 2007)

ggplot(df4, 
       aes(x = log_gdp_per_capita, y = lifeExp)) +
  geom_point() +
  geom_smooth(method = lm, se = FALSE) +
  labs( title = "Life expectancy vs Income in 2007")

Exercise 1

Create a new variable called gdp, where gdp is the product of GDP per capita and population. Plot gdp over time for three countries, with a different color for each country. Provide suitable labels.

Exercise 2

Create a scatterplot of life expectancy vs gdp for all countries in 2002. Add a regression line through the data points. Provide suitable labels.

Same plot as before, but indicate each continent by a different color. Add a regression line for each continent. Provide suitable labels.

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ba260-tidyverse-commands-R.qmd