Trends line plot
Plot trends using line plots. For continuous y variables, plot the evolution of the mean. For binary y variables, plot the evolution of the proportion.
ggally_trends(data, mapping, ..., include_zero = FALSE)
data |
data set using |
mapping |
aesthetics being used |
... |
other arguments passed to |
include_zero |
Should 0 be included on the y-axis? |
Joseph Larmarange
# Small function to display plots only if it's interactive p_ <- GGally::print_if_interactive data(tips, package = "reshape") tips_f <- tips tips_f$day <- factor(tips$day, c("Thur", "Fri", "Sat", "Sun")) # Numeric variable p_(ggally_trends(tips_f, mapping = aes(x = day, y = total_bill))) p_(ggally_trends(tips_f, mapping = aes(x = day, y = total_bill, colour = time))) # Binary variable p_(ggally_trends(tips_f, mapping = aes(x = day, y = smoker))) p_(ggally_trends(tips_f, mapping = aes(x = day, y = smoker, colour = sex))) # Discrete variable with 3 or more categories p_(ggally_trends(tips_f, mapping = aes(x = smoker, y = day))) p_(ggally_trends(tips_f, mapping = aes(x = smoker, y = day, color = sex))) # Include zero on Y axis p_(ggally_trends(tips_f, mapping = aes(x = day, y = total_bill), include_zero = TRUE)) p_(ggally_trends(tips_f, mapping = aes(x = day, y = smoker), include_zero = TRUE)) # Change line size p_(ggally_trends(tips_f, mapping = aes(x = day, y = smoker, colour = sex), size = 3)) # Define weights with the appropriate aesthetic d <- as.data.frame(Titanic) p_(ggally_trends( d, mapping = aes(x = Class, y = Survived, weight = Freq, color = Sex), include_zero = TRUE ))
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