3 Intro to the Tidyverse
https://learn.datacamp.com/courses/introduction-to-the-tidyverse
3.1 Data wrangling
The following info goes into how to select (filter), arrange, add, change (mutate) specific variables and observations. Install these two packages so codes below work properly:
install.packages("gapminder")
install.packages("dplyr")Loading the gapminder and dplyr packages
# Load the gapminder package
library(gapminder)
# Load the dplyr package
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Look at the gapminder dataset
gapminder## # A tibble: 1,704 × 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.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.
## # … with 1,694 more rowsFilter, Arrange, And Mutate Verbs
The filter verb extracts particular observations based on a condition. The ==is to compare two values.
# Filter for China in 2002
gapminder %>%
filter(country == "China",year == 2002)## # A tibble: 1 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 China Asia 2002 72.0 1280400000 3119.use arrange() to sort observations in ascending or descending order of a particular variable:
# Sort in ascending order of lifeExp
gapminder %>%
arrange(lifeExp)## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Rwanda Africa 1992 23.6 7290203 737.
## 2 Afghanistan Asia 1952 28.8 8425333 779.
## 3 Gambia Africa 1952 30 284320 485.
## 4 Angola Africa 1952 30.0 4232095 3521.
## 5 Sierra Leone Africa 1952 30.3 2143249 880.
## 6 Afghanistan Asia 1957 30.3 9240934 821.
## 7 Cambodia Asia 1977 31.2 6978607 525.
## 8 Mozambique Africa 1952 31.3 6446316 469.
## 9 Sierra Leone Africa 1957 31.6 2295678 1004.
## 10 Burkina Faso Africa 1952 32.0 4469979 543.
## # … with 1,694 more rows# Sort in descending order of lifeExp
gapminder %>%
arrange(desc(lifeExp))## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Japan Asia 2007 82.6 127467972 31656.
## 2 Hong Kong, China Asia 2007 82.2 6980412 39725.
## 3 Japan Asia 2002 82 127065841 28605.
## 4 Iceland Europe 2007 81.8 301931 36181.
## 5 Switzerland Europe 2007 81.7 7554661 37506.
## 6 Hong Kong, China Asia 2002 81.5 6762476 30209.
## 7 Australia Oceania 2007 81.2 20434176 34435.
## 8 Spain Europe 2007 80.9 40448191 28821.
## 9 Sweden Europe 2007 80.9 9031088 33860.
## 10 Israel Asia 2007 80.7 6426679 25523.
## # … with 1,694 more rowsmutate() changes or adds variables:
# Use mutate to change lifeExp to be in months
gapminder %>%
mutate(lifeExp = 12 * lifeExp)## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 346. 8425333 779.
## 2 Afghanistan Asia 1957 364. 9240934 821.
## 3 Afghanistan Asia 1962 384. 10267083 853.
## 4 Afghanistan Asia 1967 408. 11537966 836.
## 5 Afghanistan Asia 1972 433. 13079460 740.
## 6 Afghanistan Asia 1977 461. 14880372 786.
## 7 Afghanistan Asia 1982 478. 12881816 978.
## 8 Afghanistan Asia 1987 490. 13867957 852.
## 9 Afghanistan Asia 1992 500. 16317921 649.
## 10 Afghanistan Asia 1997 501. 22227415 635.
## # … with 1,694 more rows# Use mutate to create a new column called lifeExpMonths
gapminder %>%
mutate(lifeExpMonths = 12 * lifeExp) ## # A tibble: 1,704 × 7
## country continent year lifeExp pop gdpPercap lifeExpMonths
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779. 346.
## 2 Afghanistan Asia 1957 30.3 9240934 821. 364.
## 3 Afghanistan Asia 1962 32.0 10267083 853. 384.
## 4 Afghanistan Asia 1967 34.0 11537966 836. 408.
## 5 Afghanistan Asia 1972 36.1 13079460 740. 433.
## 6 Afghanistan Asia 1977 38.4 14880372 786. 461.
## 7 Afghanistan Asia 1982 39.9 12881816 978. 478.
## 8 Afghanistan Asia 1987 40.8 13867957 852. 490.
## 9 Afghanistan Asia 1992 41.7 16317921 649. 500.
## 10 Afghanistan Asia 1997 41.8 22227415 635. 501.
## # … with 1,694 more rows# Filter, mutate, and arrange the gapminder dataset
gapminder %>%
filter(year == 2007) %>%
mutate(lifeExpMonths = 12 * lifeExp) %>%
arrange(desc(lifeExpMonths))## # A tibble: 142 × 7
## country continent year lifeExp pop gdpPercap lifeExpMonths
## <fct> <fct> <int> <dbl> <int> <dbl> <dbl>
## 1 Japan Asia 2007 82.6 127467972 31656. 991.
## 2 Hong Kong, China Asia 2007 82.2 6980412 39725. 986.
## 3 Iceland Europe 2007 81.8 301931 36181. 981.
## 4 Switzerland Europe 2007 81.7 7554661 37506. 980.
## 5 Australia Oceania 2007 81.2 20434176 34435. 975.
## 6 Spain Europe 2007 80.9 40448191 28821. 971.
## 7 Sweden Europe 2007 80.9 9031088 33860. 971.
## 8 Israel Asia 2007 80.7 6426679 25523. 969.
## 9 France Europe 2007 80.7 61083916 30470. 968.
## 10 Canada Americas 2007 80.7 33390141 36319. 968.
## # … with 132 more rows3.2 Data visualization
Make sure to load the ggplot2 package. ggplot2 will allow the visualization of data into graphs.
library(ggplot2)
gapminder_1952 <- gapminder %>%
filter(year == 1952)
#Create a plot to compare population and life expectancy
ggplot(gapminder_1952, aes(x = pop, y = lifeExp)) + geom_point()
geom_point() stands for “geometric”, and the “point” tells R that it’s a scatter plot. aes() stands for aesthetic.
The data is too cramped up to the left of the plot so use “log scales” to distribute the data better.
*Log Scale
# Scatter plot comparing pop and gdpPercap, with both axes on a log scale
ggplot(gapminder_1952, aes(x = pop, y = gdpPercap)) + geom_point() +scale_x_log10() + scale_y_log10()
the newly added “log_scales” has created a better looking graph by adjusting the scale of the x-axis.
Additional Aesthetic Functions
Adding color to a scatter plot can be used to show which continent each point in a scatter plot represents. Then, adding size can be used to show the magnitude of each observations:
# Add the color, and size aesthetics to represent the continents and the country's gdpPercap
ggplot(gapminder_1952, aes(x = pop, y = lifeExp, color = continent, size = gdpPercap)) +
geom_point() +
scale_x_log10()
Use facet_wrap(~ ) function to divide a graph into subplots based on one of its variables:
# Scatter plot comparing pop and lifeExp, faceted by continent
ggplot(gapminder_1952, aes(x = pop, y = lifeExp)) + geom_point() + scale_x_log10() + facet_wrap(~ continent)
3.3 Grouping and summarizing
Summarize Verb
The function summarize() combines many observations from the same variable into one:
# Filter for 1957 then summarize the median life expectancy and the maximum GDP per capita
gapminder %>%
filter(year == 1957) %>%
summarize(medianLifeExp = median(lifeExp), maxGdpPercap = max(gdpPercap))## # A tibble: 1 × 2
## medianLifeExp maxGdpPercap
## <dbl> <dbl>
## 1 48.4 113523.In this case, the code summarized the median life expectancy and maximum GDP/capita of all the countries and conitents from 1957.
Group_by Verb
This function group_by() is basically an advanced version of the filter() function. If the filter() along with the summarize() functions can summarize observations of variable(s) one of each filtered type at a time, then group_by() function can run the same calculations for all the observation units:
# Find median life expectancy and maximum GDP per capita in each year
gapminder %>%
group_by(year) %>%
summarize(medianLifeExp = median(lifeExp), maxGdpPercap = max(gdpPercap))## # A tibble: 12 × 3
## year medianLifeExp maxGdpPercap
## <int> <dbl> <dbl>
## 1 1952 45.1 108382.
## 2 1957 48.4 113523.
## 3 1962 50.9 95458.
## 4 1967 53.8 80895.
## 5 1972 56.5 109348.
## 6 1977 59.7 59265.
## 7 1982 62.4 33693.
## 8 1987 65.8 31541.
## 9 1992 67.7 34933.
## 10 1997 69.4 41283.
## 11 2002 70.8 44684.
## 12 2007 71.9 49357.Visualizing the dataset
Assigning the dataset to a variable then plug it in to the ggplot2 to make a graph:
# Summarize medianGdpPercap within each continent within each year: by_year_continent
by_year_continent <- gapminder %>%
group_by(continent, year) %>%
summarize(medianGdpPercap = median(gdpPercap))## `summarise()` has grouped output by 'continent'. You can override using the
## `.groups` argument.# Plot the change in medianGdpPercap in each continent over time
ggplot(by_year_continent, aes(x = year, y = medianGdpPercap, color = continent)) + geom_point() + expand_limits(y = 0)
3.4 Types of visualizations
Line Plot
A line plot is useful for visualizing trends over time. Use geom_line() to create a line plot instead of geom_point() to create a scatter plot. The function expand_limits(y = 0) tells the y-axis to start from 0:
# Create a line plot showing the change in medianGdpPercap by continent over time
ggplot(by_year_continent, aes(x = year, y = medianGdpPercap, color = continent)) + geom_line() + expand_limits(y = 0)
Bar Plot
A bar plot is useful for visualizing summary statistics. use geom_col() to create a bar plot:
# Filter for observations in the Oceania continent in 1952
oceania_1952 <- gapminder %>%
filter(continent == "Oceania", year == 1952)
# Create a bar plot of gdpPercap by country
ggplot(oceania_1952, aes(x = country, y = gdpPercap)) + geom_col()
Histogram
A histogram is useful for examining the distribution of a numeric variable. Every bar represents a bin of a variable, and the height of the bar represents how many observations fall into that bin. Use geom_histogram() to create a histogram:
gapminder_1952 <- gapminder %>%
filter(year == 1952) %>%
mutate(pop_by_mil = pop / 1000000)
# Create a histogram of population (pop_by_mil)
ggplot(gapminder_1952, aes(x = pop_by_mil)) + geom_histogram(bins = 50)
Boxplot
A boxplot is useful for comparing a distribution of values across several groups.
# Create a boxplot comparing gdpPercap among continents
ggplot(gapminder_1952, aes(x = continent, y = gdpPercap)) + geom_boxplot() + scale_y_log10()
How To Add A Title To A Graph
Use ggtitle() function to add a title to a graph:
# Add a title to this graph: "Comparing GDP per capita across continents"
ggplot(gapminder_1952, aes(x = continent, y = gdpPercap)) +
geom_boxplot() +
scale_y_log10() +
ggtitle("Comparing GDP per capita across continents")