AE 06: Join data sets + data wrangling

Code styling + workflow

• Avoid long lines of code.
  • We should be able to see all of your code.
• Name code chunks.
• Knit, commit, and push regularly.
  • Think about it like clicking to save regularly as you type a report.

Questions?

Clone a repo + start a new project

Go to the appex-06-[GITHUB USERNAME] repo, clone it into your Math118 folder, and start a new project in RStudio.

Note

For this Application Exercise, you are provided with lots of starter code/hints. In order to allow the whole document to run, we have set each R chunk headers to eval = FALSE. As you complete each exercise, change the R chunk header to eval = TRUE before you knit in order to run your changes.

Practice with data joins and wrangling

You may need to install the scales library. If so, run install.packages("scales") in your console.

library(tidyverse)
library(scales)
# From Kaggle: https://www.kaggle.com/datasets/kaggle/kaggle-survey-2017/221
datascience <- read_csv("data/kaggle_survey_subset.csv", show_col_types = F) 
conversion <- read_csv("data/kaggle_conversionRates.csv", show_col_types = F) 

We will continue working with the Kaggle survey data about data science. You may recall that each respondent provided their compensation amount in their home currency. This application exercise will join data sets in order to convert the currency to USD. Take a look at the conversion data by typing View(conversion) in your Console.

Exercise 1

We wish to add to the datascience data the conversion rate from the original CompensationCurrency to the USD.

Write code that joins together the datascience dataset and the conversion dataset by the variable they have in common. Store it by saving over the current datascience data frame. The code below will help you get started.

<- datascience %>%
  left_join(______, by = ______)  

Exercise 2

Now create a new variable called compensationUSD that converts the original CompensationAmount into USD. This is achieved by multiplying the CompensationAmount by the exchangeRate. Store it by saving over the current datascience data frame.

<- datascience %>%
  __________

Exercise 3

Create a new data frame called compensation_summary that calculates median compensation in USD for each Major.

Recall that the function for calculating the median is median() in R.

# code here                      

Exercise 4

Take the compensation_summary data frame and order the results in descending order of median USD compensation. Is anything surprising?

# code here

Exercise 5

Recreate the following graph using the compensation_summary data frame you created! The code should help you get started, just fill in the necessary information!

ggplot(data = ________, 
       aes(y = fct_reorder(______, _______), x = _______)) +
  geom_col() +
  labs(
    x = "Compensation (USD)",
    y = "",
    title = "Median compensation of data scientists by major",
    subtitle = "from 2017 Kaggle Survey",
  ) +
  theme_minimal()

Submit

Once you’re finished, knit, commmit, and then push to GitHub!