- 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
-This quiz assumes that you have watched the videos, downloaded (to your examples folder) and worked through the exercises in exercises_slides-50-61.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 wwant to preview.
#Question: modify slide 51
-Create a plot with the mpg dataset
-add points with geom_point
-assign the variable displ to the x-axis
-assign the variable hwy to the y-axis
-add facet_wrap to split the data into panels based on the manufacturer
ggplot(data = mpg) +
geom_point(aes(x = displ, y = hwy)) +
facet_wrap(facets = vars(manufacturer))
#Question: Modify facet-ex-2
-Create a plot with the mpg dataset
-add bars with geom_bar
-assign the variable manufacturer to the y-axis
-add facet_grid tp split the data into panels based on the class
-let scales vary across columns
-let space taken up by panels vary by columns
ggplot(mpg) +
geom_bar(aes(y = manufacturer)) +
facet_grid(vars(class), scales = "free_y", space = "free_y")
#Question: Spend_time
To help you complete this question use:
-the patchwork slides and
-the vignette: https://patchwork.data-imaginist.com/articles/patchwork.html
Download the file spend_time.csv from moodle
-spend_time contains 10 years of data on how many hours Americans spend each day on 5 activities
-read it into spend_time
spend_time <- read_csv("spend_time.csv")
Start with spend_time
-extract observations for 2018
-THEN create a plot with that data
-ADD a barchart with geom_col
-assign activity to the x-axis
-assign avg_hours to the y-axis
-assign activity to fill
-ADD scale_y_continuous with breaks every hour from 0 to 6 hours
-ADD labs to
-set subtitle to Avg hours per day: 2018
-set x and y to NULL so they won’t be labeled
-assign the output to p1
-display p1
p1 <- spend_time %>% filter(year == "2018") %>%
ggplot() +
geom_col(aes(x = activity, y =avg_hours, fill = activity)) +
scale_y_continuous(breaks = seq(0, 6, by = 1)) +
labs(subtitle = "Avg hours per day: 2018", x = NULL, y = NULL)
p1
Start with spend_time
-THEN create a plot with it
-Add a barchart with geom_col
-assign year to the x-axis
-assign avg_hours to the y-axis
-assign activity to fill
-ADD labs to
-set subtitle to “Avg hours per day: 2010-2019”
-set x and y to NULL so they won;t be labeled
-assign the output to p2
-display p2
p2 <- spend_time %>%
ggplot() +
geom_col(aes(x = year, y = avg_hours, fill = activity)) +
labs(subtitle = "Avg hours per day: 2010-2019", x = NULL, y = NULL)
p2
Use patchwork to display p1 on top of p2
-assign the output to p_all
-display p_all
p_all <- p1/p2
p_all
Start with p_all
-AND set legend.position to ‘none’ to get rid of the legend
-assign the output to p_all_no_legend
-display p_all_no_legend
p_all_no_legend <- p_all & theme(legend.position = 'none')
p_all_no_legend
Start with p_all_no_legend
-see how annotate the composition here: https://patchwork.data-imaginist.com/reference/plot_annotation.html
-add plot_annotation set
-title to “How much time Americans spent on selected activities”
-caption to “Source: American Time of Use Survey, https://data.bls.gov/cgi-bin/surveymost?tu”
p_all_no_legend +
plot_annotation(title = "How much time Americans spent on selected activities",
caption = "Source: American Time of Use Survey, https://data.bls.gov/cgi-bin/surveymost?tu")
#patchwork 2
USE spend_time from last question patchwork slides
Start with spend_time
-extract observations for leisure/sports
-THEN create a plot with that data
-ADD points with geom_point
-assign year to the x-axis
-assign avg_hours to the y-axis
-ADD line with geom_smooth
-assign year to the x-axis
-assign avg_hours to the y-axis
-ADD breaks on for every year on x-axis with scale_x_continuous
-ADD labs to
-set subtitle to **Avg hours per day: leisure/sports
-set x and y to NULL so x and y axis won’t be labeled
-assign the output to p4
-display p4
p4 <- spend_time %>% filter(activity == "leisure/sports") %>%
ggplot() +
geom_point(aes(x = year, y = avg_hours)) +
geom_smooth(aes(x = year, y = avg_hours)) +
scale_x_continuous(breaks = seq(2010, 2019, by = 1)) +
labs(subtitle = "Avg hours per day: leisure/sports", x = NULL, y = NULL)
p4
Start with p4
-ADD coord_cartesian to change range on y axis to 0 to 6
-assign the output to p5
-display p5
p5 <- p4 + coord_cartesian(ylim = c(0,6))
p5
Start with spend_time
-create a plot with that data
-ADD points with geom_point
-assign year to the x-axis
-assign avg_hours to the y-axis
-assign activity to color
-assign activity to group
-ADD line with geom_smooth
-assign year to the x-axis
-assign avg_hours to the y-axis
-assign activity to color
-assign activity to group
-ADD breaks on for every year x axis with scale_x_continuous
-ADD coord_cartesian to change range on y axis to 0 to 6
-ADD labs to
-set x and y to NULL so they won’t be labeled
-assign the output to p6
display p6
p6 <- spend_time %>%
ggplot() +
geom_point(aes(x = year, y = avg_hours, color = activity, group = activity)) +
geom_smooth(aes(x = year, y =avg_hours, color = activity, group = activity)) +
scale_x_continuous(breaks = seq(2010, 2019, by = 1)) +
coord_cartesian(ylim = c(0, 6)) +
labs(x = NULL, y = NULL)
p6
Use patchwork to display p4 and p5 on top of p6
(p4 | p5) / p6
