library(tidyverse)
library(gapminder)
ggplot(gapminder,aes(x="year",y="pop")) + geom_point()
Create subsets data
library(tidyverse)
library(gapminder)
ggplot(gapminder,aes(x="year",y="pop")) + geom_point()
%in% to see if the variable contains various countries of your choice.source("my_script.R",echo = TRUE)Below we make a subset of the data, whose country is China
C <- filter(gapminder,
country == "China")Do a ?filter to learn how else to modify the 2nd parameter using & , | and more.
Use a filter to reduce the size of the data and then label points on a scatterplot using geom_text_repel
library(ggrepel)
hi_pop_countries <- filter(gapminder,
pop > 500000000)
ggplot(hi_pop_countries,
aes(x = year, y = gdpPercap)) +
geom_point() +
geom_text_repel(aes(label = country))
Here’s another filter and preview of boxplots & the reorder function.
hi_pop_countries <- filter(gapminder,
pop > 50000000)
ggplot(hi_pop_countries,
aes(x = lifeExp, y = country)) + geom_boxplot()
Isn’t this better? Use a plot & reorder for different variable.
ggplot(hi_pop_countries,
aes(x = lifeExp, y = reorder(country,lifeExp))) +
geom_boxplot()
And here’s a histogram:
TCU <- filter(gapminder,country %in% c("Taiwan","China","United States"))
TCU |> ggplot(aes(x = lifeExp, fill = country)) + geom_histogram(bins = 10)
The data() command lists all dataset included with R and the Tidyverse. Note that the storms data is in dplyr.
Use filter and varioius geoms geom_point(), geom_histogram(), geom_boxplot(). to compare storms across time.
Complete these exercises. Append your answers to the quarto doc for Assignment 2. Submit as a .html. Copy the questions into your .qmd file and insert your responses after each one.
Create three plots using filter and varioius geoms geom_point(), geom_histogram(), and geom_boxplot() to compare storms across time.