```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE) rm(list=ls()) # load the necessary libraries library(tidyverse) ``` # Prerequisite ```{r} # load the gapminder data gapminder <- read_csv("data/gapminder.csv") ``` # Review of factors - Create a new variable named **poor**, defined as any observation with a `gdpPercap` value below or equal to the 10th percentile (1st decile) of the distribution, assigning the value 1 if it is equal to or below the 10th percentile, and 0 otherwise. - After creating this variable, generate a new one named **poor_f** as a factor version. Set the correct levels and label them as *poor* for 1 and **not poor** for 0. ```{r} ## create variable poor using ifelse() and quantile() gapminder$poor <- ifelse(gapminder$gdpPercap <= quantile(gapminder$gdpPercap,probs = 0.1), 1,0) ## create the factor variable "poor_f" gapminder$poor_f <- factor(gapminder$poor, levels = c(0,1), labels = c("not poor","poor")) ``` # Density plot by group Please create the following plots 1. Distribution of `gdpPercap` using either a histogram or density plot. Inside the `geom_` function, define the `fill=` aesthetics with a color of your choice. 2. Group `gdpPercap` distribution per continent defining `group=` and `fill=` in the general aesthetics `aes()` 3. Repeat the previous plot but now take the logarithm `log()` of `gdpPercap.` Ypu can define this transformation directly within the general `aes()`. 4. Repeat the previous plot (3.) but now split each continent in multiple facets using `facet_wrap()`. ```{r} # 1. ggplot(gapminder, aes(x = gdpPercap)) + geom_density(fill="steelblue1") # 2. ggplot(gapminder, aes(x = gdpPercap, group=continent, fill=continent)) + geom_density() # 3. ggplot(gapminder, aes(x = log(gdpPercap), group=continent, fill=continent)) + geom_density() # 4. ggplot(gapminder, aes(x = log(gdpPercap), group=continent, fill=continent)) + geom_density() + facet_wrap(~ continent) ``` # Boxplot by group using `fill` Please plot the following box plots 1. Distribution of `lifeExp` for each continent 2. Distribution of `lifeExp` for each continent by poor status. Note that you do not need to define the `group` in the aesthetics because the levels are already defined in the `x` aesthetic. ```{r} # 1. ggplot(gapminder, aes(y = lifeExp, x = continent)) + geom_boxplot() # 2. ggplot(gapminder, aes(y = lifeExp, x = continent, fill=poor_f)) + geom_boxplot() ``` # Create a visual of your choice Generate a novel plot with the gapminder dataset, avoiding duplication of any previously created plots. Feel free to employ different aesthetics or geometries, such as `geom_line` or `geom_point`, ensuring the plot is informative. If applicable, incorporate colors for enhanced visualization. ```{r} ```
