Data visualization, part 1. Code for Quiz 7.
Replace all the ???s. These are answers on your moodle quiz.
Run all the individual code chunks to make sure the answers in this file correspond with your quiz answers
After you check all your code chunks run then you can knit it. It won’t knit until the ??? are replaced
The quiz assumes you have watched the videos had worked through the exercises in exercises_slides-1-49.Rmd
3- Pick one of your plots to save as your preview plot. Use the ggsave command at the end of the chunk of the plot that you want to preview.
Create a plot with the faithful dataset add points with geom_point
assign the variable eruptions to the x-axis
assign the variable waiting to the y-axis
colour the points according to whether waiting is smaller or greater than 57
ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting, colour = waiting> 57))
Create a plot with the faithful dataset
add points with geom_point assign the variable eruptions to the x-axis
assign the variable waiting to the y-axis
assign the colour dodgerblue to all the points
ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting),
colour = 'dodgerblue ')
Create a plot with the faithful dataset
use geom_histogram() to plot the distribution of waiting time assign the variable waiting to the x-axis
ggplot(faithful) +
geom_histogram(aes(x = waiting))
See how shapes and sizes of points can be specified https://ggplot2.tidyverse.org/articles/ggplot2-specs.html#sec:shape-spec
Create a plot with the faithful dataset
add points with geom_point
assign the variable eruptions to the x-axis
assign the variable waiting to the y-axis
set the shape of the points to triangle
set the point size to 7
set the point transparency to .5
ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting),
shape="triangle",size=7,alpha=0.5)
Create a plot with the faithful dataset
use geom_histogram() to plot the distribution of the eruptions (time)
fill in the histogram based on whether eruptions are greater than or less than 3.2 minutes
ggplot(faithful) +
geom_histogram(aes(x = eruptions, fill=eruptions>3.2))
Create a plot with the mpg dataset
add geom_bar() to create a bar chart of the variable manufacturer
ggplot(mpg) +
geom_bar(aes(x = manufacturer))
change code to count and to plot the variable manufacturer instead of class
mpg_counted <- mpg %>%
count(manufacturer , name = 'count')
ggplot(mpg_counted) +
geom_bar(aes(x =manufacturer , y = count), stat = 'identity')
change code to plot bar chart of each manufacturer as a percent of total
change class to manufacturer
ggplot(mpg) +
geom_bar(aes(x = manufacturer, y = after_stat(100 * count / sum(count))))
Use stat_summary() to add a dot at the median of each group
color the dot purple3
make the shape of the dot diamond
make the dot size 4
ggplot(mpg) +
geom_jitter(aes(x = class, y = hwy), width = 0.2) +
stat_summary(aes(x = class, y = hwy), geom ="point",fun="median", color="purple3",shape ="diamond", size=4)