Chapter 5 tidying the raw data into the tidy data using pivot_longer() and separate() functions in the tidyr package

library(tidyverse) iris %>% pivot_longer(cols = -Species, names_to = “Part,” values_to = “Value”) %>% separate(col = “Part,” into = c(“Part,” “Measure”))

# Code chunk 3 for HW1 # transforming our data using group_by() and summarize() functions in the dplyr package # Because we created the Part variable in our tidy data, # we can easily calculate the mean of the Value by Species and Part iris %>% pivot_longer(cols = -Species, names_to = “Part,” values_to = “Value”) %>% separate(col = “Part,” into = c(“Part,” “Measure”)) %>% group_by(Species, Part) %>% summarize(m = mean(Value))

# Code chunk 4 for HW1 # visualizing our data using ggplot() function in the ggplot2 package iris %>% pivot_longer(cols = -Species, names_to = “Part,” values_to = “Value”) %>% separate(col = “Part,” into = c(“Part,” “Measure”)) %>% ggplot(aes(x = Value, color = Part)) + geom_boxplot()