In colinbousige/tutor: Interactive tutorials for learning R

# rmarkdown::run("plots.Rmd")
library(learnr)
library(tidyverse)
library(tutor)
library(patchwork)
df <- read_table(tutor_examples("exo_fit.txt"))

Exercise 1

We will work with the well known table mtcars included in R:

str(mtcars)

Basic stuff 1

Modify the following code to add a color depending on the gear column:

mtcars %>% # we work on the mtcars dataset, send it to ggplot
    ggplot(aes(x = wt, y = mpg))+ # define the x and y variables of the plot, and also the color
        geom_point() # plot with points

mtcars %>%
    ggplot(aes(x = wt, y = mpg, color=gear)) +
    geom_point()

grade_code()

Basic stuff 2

What happens if you use factor(gear) instead?

mtcars %>% # we work on the mtcars dataset, send it to ggplot
    ggplot(aes(x = wt, y = mpg, color = gear))+ # define the x and y variables of the plot, and also the color
        geom_point() # plot with points

mtcars %>%
    ggplot(aes(x = wt, y = mpg, color = factor(gear))) +
    geom_point()

grade_code()

Faceting 1

Modify the following code to place each carb in a different facet. Also add a color, but remove the legend.

mtcars %>% # we work on the mtcars dataset, send it to ggplot
    ggplot(aes(x = wt, y = mpg))+ # define the x and y variables of the plot, and also the color
        geom_point() +   # plot with points
        facet____(___) + # add a faceting
        theme(___)       # remove the legend

mtcars %>%
    ggplot(aes(x = wt, y = mpg, color = factor(gear)))+
    geom_point() +
    facet_wrap(~carb)+
    theme(legend.position = "none")

grade_code()

Faceting 2

Modify the following code to arrange mpg vs wt plots on a grid showing gear vs carb. Also add a color depending on cyl. Also, try adding a free x scale range, or a free y scale range, or free x and y scale ranges.

mtcars %>% # we work on the mtcars dataset, send it to ggplot
    ggplot(aes(x = ___, y = ___))+ # define the x and y variables of the plot, and also the color
        geom_point() +   # plot with points
        facet____(___) # add a faceting

mtcars %>%
    ggplot(aes(x = wt, y = mpg, color = factor(cyl)))+
    geom_point() +
    facet_grid(paste("gear = ", gear) ~ paste("carb = ", carb), scales = "free")

grade_code()

Exercise 2

We will look at data loaded into df.

df

Using ggplot, plot y as a function of x with points and save it into Py:

Py <- df %>% 
    ___
Py

Py <- df %>% 
    ggplot(aes(x = x, y = y)) + 
    geom_point()
Py

grade_code()

Add a straight line in Py resulting from a linear fit:

Py <- Py +
    geom_smooth(___)
Py

Py <- Py +
    geom_smooth(method = "lm")
Py

grade_code()

Using ggplot, plot z as a function of x with a red line and save it into Pz:

Pz <- df %>% 
    ___
Pz

Pz <- df %>% 
    ggplot(aes(x = x, y = z)) + 
    geom_line(color = "red")
Pz

grade_code()

Using ggplot, plot a histogram of w with transparent blue bars surrounded by a red line, and save it into Pw. You can play with the number of bins too.

Pw <- df %>% 
    ___
Pw

Pw <- df %>% 
    ggplot(aes(x = w)) + 
    geom_histogram(fill = "blue", alpha=.5, color="red", bins=50)
Pw

grade_code()

Using ggplot, plot a density of u with a transparent blue area surrounded by a red line, and save it into Pu. Play with the bw parameter so that you see many peaks.

Pu <- df %>% 
    ___
Pu

Pu <- df %>% 
    ggplot(aes(x = u)) + 
    geom_density(fill = "blue", alpha=.5, color="red", bw=.02)
Pu

grade_code()

Using patchwork, gather the previous plots on a 2x2 grid.

Py
Pz
Pw
Pu

library(patchwork)
Py+Pz+Pw+Pu

grade_code()

Using patchwork, gather the previous plots on a grid with 3 plots in the 1st row, and one large plot in the 2nd row. Using plot_annotation(), add tags such as (a), (b)...

Py
Pz
Pw
Pu

library(patchwork)
(Py + Pz + Pw) / Pu + plot_annotation(tag_levels = "a", tag_prefix="(", tag_suffix=")")

grade_code()

colinbousige/tutor documentation built on Jan. 29, 2023, 7:35 p.m.