- Load the R package we will use.
- Quiz questions
-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
- 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.
Question: modify slide 34
-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 64
ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting, colour = waiting < 64))
Modify intro-slide 35
-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 purple to all the points
ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting),
colour = 'purple')
Modify intro-slide 36
-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))
Modify geom-ex-1
See how shapes and sizes of points can be specified here: 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 asterisk -set the point size to 8 -set the point transparency 0.7
ggplot(faithful) +
geom_point(aes(x = eruptions, y = waiting),
shape = "asterisk", size = 8, transparency= 0.7)
Modify geom-ex-2
-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 = 0 > 3.2))
Modify stat-slide-40
-Create a plot with the mpg dataset
-add geom_bar() to create a bar chart of the variable manufacturer
data("mpg")
# variable definitions
# ?mpg
# mpg %>% glimpse()
ggplot(mpg) +
geom_bar(aes(x = manufacturer))
Modify stat-slide-41
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')
Modify stat-slide-43
-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))))
Modify answer to stat-ex-2
for reference see: https://ggplot2.tidyverse.org/reference/stat_summary.html?q=stat%20_%20summary#examples
Use stat_summary() to add a dot at the median of each group
-color the dot blueviolet -make the shape of the dot cross -make the dot size 9
ggplot(mpg) +
geom_jitter(aes(x = class, y = hwy), width = 0.2)
stat_summary(aes(x = class, y = hwy), geom = "point",
fun = "median", color = "blueviolet",
shape = "cross", size = 9)
mapping: x = ~class, y = ~hwy
geom_point: na.rm = FALSE
stat_summary: fun.data = NULL, fun = median, fun.max = NULL, fun.min = NULL, fun.args = list(), na.rm = FALSE, orientation = NA
position_identity