Customizing X and Y axes
Exercise 1
The R code below will plot the bmi (x axis) against the total_intubation_time (y axis) as a scatterplot. Your challenge: - Make the Y axis start at exactly 0 (turn off multiplicative expansion) and end at 105 - make the X axis breaks at every 5 units of BMI from 30 to 65
laryngoscope %>%
ggplot(aes(x= bmi, y = total_intubation_time)) +
geom_point()laryngoscope %>%
ggplot(aes(x= bmi, y = total_intubation_time)) +
geom_point() +
scale_y_continuous(limits = ---,
expand = ---) +
scale_x_continuous(breaks = seq(---))laryngoscope %>%
ggplot(aes(x= bmi, y = total_intubation_time)) +
geom_point() +
scale_y_continuous(limits = c(0,105),
expand = expansion(mult = 0)) +
scale_x_continuous(breaks = seq(from = 30, to = 65, by=5))Exercise 2
You can use the expand argument to control the margins of a plot. These can be expanded additively or multiplicatively. The default for discrete scales is to add 0.6 of a category on each side. The default for continuous scales is to multiply by 5% of the range on each side.
In this exercise, try to - Make more room at the top for the legend (multiply by 20% of y on top only - leave bottom at 5%) - rename the legend title to “View Achieved”
laryngoscope %>%
ggplot(aes(x= asa, y = total_intubation_time, color = factor(view))) +
geom_jitter() +
theme(legend.position = c(0.5, 0.9)) +
scale_color_discrete(guide = guide_legend(nrow = 1))laryngoscope %>%
ggplot(aes(x= asa, y = total_intubation_time, color = factor(view))) +
geom_jitter() +
theme(legend.position = c(0.5, 0.9)) +
scale_color_discrete(guide = guide_legend(nrow = 1),
name = ---) +
scale_y_continuous(expand = expansion(add = c(--,--)))laryngoscope %>%
ggplot(aes(x= asa, y = total_intubation_time, color = factor(view))) +
geom_jitter() +
theme(legend.position = c(0.5, 0.9)) +
scale_color_discrete(guide = guide_legend(nrow = 1),
name = "View Achieved") +
scale_y_continuous(expand = expansion(mult = c(0.05,0.2)))Exercise 3
You can customize the appearance of dates on scales, with scale_(x|y)_date() functions.
The code below will plot the average NYC flight delay by date in 2013. Your challenge is to: - Set the x axis breaks to monthly - format the labels to abbreviated month and 2-digit year, with a 2-line format, using \n
nycflights13::flights %>%
mutate(date= lubridate::dmy(paste(day, month, year, sep = "-"))) %>%
group_by(date) %>%
summarize(mean_delay_min = mean(arr_delay, na.rm = TRUE)) %>%
ggplot(aes(x = date, y = mean_delay_min)) +
geom_point() +
scale_x_date(---)nycflights13::flights %>%
mutate(date= lubridate::dmy(paste(day, month, year, sep = "-"))) %>%
group_by(date) %>%
summarize(mean_delay_min = mean(arr_delay, na.rm = TRUE)) %>%
ggplot(aes(x = date, y = mean_delay_min)) +
geom_point() +
scale_x_date(date_breaks = "---",
date_labels = "---")nycflights13::flights %>%
mutate(date= lubridate::dmy(paste(day, month, year, sep = "-"))) %>%
group_by(date) %>%
summarize(mean_delay_min = mean(arr_delay, na.rm = TRUE)) %>%
ggplot(aes(x = date, y = mean_delay_min)) +
geom_point() +
scale_x_date(date_breaks = "1 month",
date_labels = "%b\n%y")Exercise 4
Edit the R code below to format the previous plot with breaks every 4 weeks, with labels using the digit week of the year (starting on Sunday).
nycflights13::flights %>%
mutate(date= lubridate::dmy(paste(day, month, year, sep = "-"))) %>%
group_by(date) %>%
summarize(mean_delay_min = mean(arr_delay, na.rm = TRUE)) %>%
ggplot(aes(x = date, y = mean_delay_min)) +
geom_point() nycflights13::flights %>%
mutate(date= lubridate::dmy(paste(day, month, year, sep = "-"))) %>%
group_by(date) %>%
summarize(mean_delay_min = mean(arr_delay, na.rm = TRUE)) %>%
ggplot(aes(x = date, y = mean_delay_min)) +
geom_point() +
scale_x_date(date_breaks = "--",
date_labels = "--")nycflights13::flights %>%
mutate(date= lubridate::dmy(paste(day, month, year, sep = "-"))) %>%
group_by(date) %>%
summarize(mean_delay_min = mean(arr_delay, na.rm = TRUE)) %>%
ggplot(aes(x = date, y = mean_delay_min)) +
geom_point() +
scale_x_date(date_breaks = "4 week",
date_labels = "%U")