Term Project
Garrett Kelsey
This is an extension of the tidytuesday assignment you have already done. Complete the questions below, using the screencast you chose for the tidytuesday assigment.
Import data
library(tidyverse)
theme_set(theme_light())
bob_ross <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-08-06/bob-ross.csv")bob_ross_gathered <- bob_ross %>%
janitor::clean_names() %>%
gather(element, present, -episode, -title) %>%
filter(present == 1) %>%
mutate(title = str_to_title(str_remove_all(title, '"')),
element = str_to_title(str_replace(element, "_", " "))) %>%
select(-present) %>%
extract(episode, c("season", "episode_number"), "S(.*)E(.*)", convert = TRUE, remove = FALSE) %>%
arrange(season, episode_number)Description of the data and definition of variables
This data is from the tidy tuesday project. Which is a weekly data project in R from the R4DS online learning community. During the week this screencast was made it was Bob Ross paintings. The row represents the binary presence of the element in the painting. There were 66 observations with variable like tree, clouds, mountain, lake, ect. These variables mean how often they showed up in a Bob Ross painting throught his 403 episodes of his tv show.
Visualize data
Hint: One graph of your choice.
bob_ross_gathered %>%
count(element, sort = TRUE) %>%
head(25) %>%
mutate(element = fct_reorder(element, n)) %>%
ggplot(aes(element, n)) +
geom_col() +
coord_flip()
What is the story behind the graph?
This bar graph shows how often each of the elements appear in Bob Ross’s paintings over the course of his television series. It also shows what the most common element to apear was, from this graph we can tell that the most commen element to apear in his paintings were tree or trees. This is an important graph to include because it organizes the data to show which element mas most common.