Term Project
Mitchell Prive
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) recent_grads <- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/recent-grads.csv")recent_grads
## # A tibble: 173 x 21
## Rank Major_code Major Total Men Women Major_category ShareWomen
## <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 1 2419 PETR… 2339 2057 282 Engineering 0.121
## 2 2 2416 MINI… 756 679 77 Engineering 0.102
## 3 3 2415 META… 856 725 131 Engineering 0.153
## 4 4 2417 NAVA… 1258 1123 135 Engineering 0.107
## 5 5 2405 CHEM… 32260 21239 11021 Engineering 0.342
## 6 6 2418 NUCL… 2573 2200 373 Engineering 0.145
## 7 7 6202 ACTU… 3777 2110 1667 Business 0.441
## 8 8 5001 ASTR… 1792 832 960 Physical Scie… 0.536
## 9 9 2414 MECH… 91227 80320 10907 Engineering 0.120
## 10 10 2408 ELEC… 81527 65511 16016 Engineering 0.196
## # … with 163 more rows, and 13 more variables: Sample_size <dbl>,
## # Employed <dbl>, Full_time <dbl>, Part_time <dbl>,
## # Full_time_year_round <dbl>, Unemployed <dbl>, Unemployment_rate <dbl>,
## # Median <dbl>, P25th <dbl>, P75th <dbl>, College_jobs <dbl>,
## # Non_college_jobs <dbl>, Low_wage_jobs <dbl>Description of the data and definition of variables
The data consists of 173 rows and 21 columns. Each row is a different college major and the rows dive into the specifics of that topic. It takes a look at the economic earnings that are paired with each major. Unemployment rate is a pretty self explanaotry row but the row median isn’t. After watching more of the video, you learn that this row is representing the median salary for said major.
Visualize data
Hint: One graph of your choice.
recent_grads %>%
mutate(Major_category = fct_reorder(Major_category, Median)) %>%
ggplot(aes(Major_category, Median)) +
geom_boxplot() +
coord_flip()
What is the story behind the graph?
This is a box plot showing the earnings from each major. I used the function coord_flip to rotate the graph 90 degrees to read the x-axis. I then reorderd the graph by the median column. This makes it easy to see the majors from lowest to highest earning.