Clone Assignment Repo

• First, go to the link for the assignment in order to create your personal private repository corresponding to the assignment. This link may be found at https://classroom.github.com/a/27agHlzr. Accepting the assignment creates a repository similar to lab02-[github_name].
• Next, navigate to the course GitHub website, and click on the repository corresponding to your account. For example, mine would be lab02-shawnsanto. Or, simply go to the repository you just created (there is a link provided when you created the repository).
• Click on the green Clone or download button in that repository, and copy the git URL (it should end in .git)
• Go to RStudio Cloud and into the STA 199 course workspace. Create a New Project from Git Repo and copy the git URL from your personal workspace. Make sure “Add packages from the base project” is checked.
• Click OK, and you should see the contents from your GitHub repo in the Files pane in RStudio.

Configure git

Remember that we first have to configure RStudio Cloud to talk to GitHub. An easy way to do this is to use the use_git_config() function from the usethis package. Type the following lines of code in the Console in RStudio filling in your name and email address.

library(usethis)
use_git_config(user.name="Your name", user.email="your-git-hub-email@email.edu")

Project name:

Currently your project is called Untitled Project. Update the name of your project to be “Lab 02 - Data wrangling and visualization”.

Packages

In this lab we will work with the tidyverse and fivethirtyeight packages. Load the packages into the Console (they have already been installed for you).

library(tidyverse)
library(fivethirtyeight)

YAML:

Open the R Markdown (Rmd) file in your project, change the author name to your name, and knit the document.

Stage, commit, and push changes:

• Go to the Git pane in RStudio.
• View the Diff and confirm that you are happy with the changes.
• Stage the files you want to commit.
• Add commit message “Updated name” in the Commit message box and hit Commit.
• Click on Push. This will prompt a dialog box where you first need to enter your GitHub user name and password. For the remainder of this lab, we won’t tell you when to commit – it is up to you to commit at appropriate intervals with meaningful commit messages. Be sure to commit at least three times during this lab.

Which major has the lowest unemployment rate?

In order to answer this question all we need to do is sort the data. We can use the arrange() function to do this, and sort it by the unemployment_rate variable. By default, arrange() sorts in ascending order, which is what we want here – we’re interested in the major with the lowest unemployment rate.

college_recent_grads %>%
  arrange(unemployment_rate)

This gives us what we wanted, but not in an ideal form. First, the name of the major barely fits on the page. Second, some of the variables are not that useful (e.g. major_code, major_category) and some we might want front and center are not easily viewed (e.g. unemployment_rate). We can use the select() function to choose which variables to display and in which order:

⊕Note how easily we expanded our code with adding another step to our pipeline, with the pipe operator: %>%.

college_recent_grads %>%
  arrange(unemployment_rate) %>%
  select(rank, major, unemployment_rate)

This is looking better, but do we really need all those decimal places in the unemployment variable? Not really!

There are two ways we can address this problem. One would be to round the unemployment_rate variable in the dataset or we can change the number of digits displayed, without touching the input data. Below are instructions for how you would do both of these:

• Round the variable itself: We create a new variable with the mutate() function. In this case, we’re overwriting the existing unemployment_rate variable, by rounding it to 4 decimal places.

college_recent_grads %>%
  arrange(unemployment_rate) %>%
  select(rank, major, unemployment_rate) %>%
  mutate(unemployment_rate = round(unemployment_rate, digits = 4))

• Change the displayed number of digits without touching data: We can add an option to our R Markdown document to change the displayed number of digits in the output. To do so, add a new chunk, and set:

options(digits = 2)

Note that the digits in options is scientific digits, and in round they are decimal places. If you’re thinking “Wouldn’t it be nice if they were consistent?”…so do we. You don’t need to do both of these, that would be redundant. The next exercise asks you to choose one.

1. Which of these options, changing the input data or altering the number of digits displayed without touching the input data, is the better option? Explain your reasoning. Implement the option you chose.

Which major has the highest percentage of women?

To answer such a question we need to arrange the data in descending order. For example, if earlier we were interested in the major with the highest unemployment rate, we would use the following:

⊕The desc() function specifies that we want unemployment_rate in descending order.

college_recent_grads %>%
  arrange(desc(unemployment_rate)) %>%
  select(rank, major, unemployment_rate)

2. Using what you’ve learned so far, arrange the data in descending order with respect to proportion of women in a major, and display only the major, the total number of people with that major, and proportion of women. Show only the top 3 majors by adding slice(1:3) at the end of the pipeline.

How do the distributions of median income compare across major categories?

⊕A percentile is a measure used in statistics indicating the value below which a given percentage of observations in a group of observations fall. For example, the 20th percentile is the value below which 20% of the observations may be found. (Source: Wikipedia

There are three types of incomes reported in this data frame: p25th, median, and p75th. These correspond to the 25th, 50th, and 75th percentiles of the income distribution of sampled individuals for a given major.

3. Why do we often choose the median, rather than the mean, to describe the typical income of a group of people?

In order to answer our question about the distributions of median income across major categories, we need to group the data by major_category. Then, we need a way to summarize the distributions of median income within these groups. This decision will depend on the shapes of these distributions. Let’s create a visualization to see the shapes of these distributions.

Function ggplot() will allow us to create our plots. The first argument is the data frame, and the next argument gives the mapping of the variables of the data to the aesthetic elements of the plot.

Let’s start simple and take a look at the distribution of all median incomes, without considering major categories.

ggplot(data = college_recent_grads, mapping = aes(x = median)) +
  geom_histogram()

Along with the plot, we get a message:

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

This is telling us that we might want to reconsider the binwidth we chose for our histogram – or more accurately, the binwidth we didn’t specify. It’s good practice to always think in the context of the data and try out a few binwidths before settling on your final choice. You might ask yourself: “What would be a meaningful difference in median incomes?” $1 is obviously too little, $10000 might be too high.

4. Plot two histograms of median incomes, one with a bin width of $1000 and one with a bin width of $5000. Select one and explain your reasoning for your choice. Argument binwidth is for the geom_histogram() function. So to specify a binwidth of $1000, you would use geom_histogram(binwidth = 1000). We can also calculate summary statistics for this distribution using the summarise() function:

college_recent_grads %>%
  summarise(min  = min(median), 
            max  = max(median),
            mean = mean(median), 
            med  = median(median),
            sd   = sd(median), 
            q1   = quantile(median, probs = 0.25),
            q3   = quantile(median, probs = 0.75))

5. Based on the shape of the histogram you created in the previous exercise, determine which of these summary statistics is useful for describing the distribution. Write up your description (remember shape, center, spread, any unusual observations) and include the summary statistic output as well.

Next, we facet the plot by major category.

ggplot(data = college_recent_grads, mapping = aes(x = median)) +
  geom_histogram() +
  facet_wrap( ~ major_category, ncol = 4)

6. Plot the distribution of median income using a histogram, faceted by major_category. Use the binwidth you chose in the earlier exercise. Now that we’ve seen the shapes of the distributions of median incomes for each major category, we should have a better idea for which summary statistic to use to quantify the typical median income.

7. Which major category has the highest typical (you’ll need to decide what this means) median income? Use the partial code below, filling it in with the appropriate statistic and function. Also note that we are looking for the highest statistic, so make sure to arrange your resulting data frame in the correct order.

college_recent_grads %>%
  group_by(major_category) %>%
  summarise(___ = ___(median)) %>%
  arrange(___)

8. Which major category is the least popular in this sample? To answer this question we use a new function called count(), which first groups the data and then counts the number of observations in each category (see below). Arrange the results so that the major with the lowest observations is in row 1 of the resulting data frame.

college_recent_grads %>%
  count(major_category)

All STEM fields aren’t the same

One section of the FiveThirtyEight story is titled “All STEM fields aren’t the same”. Let’s see if this is true. First, let’s create a new vector called stem_categories that lists the major categories that are considered STEM fields. Ask your TA what function c() does if you are not sure. Also, check the help with ?c.

stem_categories <- c("Biology & Life Science",
                     "Computers & Mathematics",
                     "Engineering",
                     "Physical Sciences")

We can use this to create a new variable in our data frame indicating whether a major is STEM or not.

college_recent_grads <- college_recent_grads %>%
  mutate(major_type = ifelse(major_category %in% stem_categories, "stem", "not stem"))

Let’s unpack this: with mutate() we create a new variable called major_type, which is defined as "stem" if the major_category is in the vector called stem_categories we created earlier, and as "not stem" otherwise. %in% is a logical operator. Other logical operators that are commonly used are

Here is a small example showing how the operator %in% works:

x <- c("red", "white", "blue")
y <- c("green", "red", "red", "orange", "white", "black", "purple", "blue")
y %in% x

#> [1] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE

We can use logical operators to filter() our data. Here will filter based on the conditions that the major type is STEM and the median salary is less than $36,000.

college_recent_grads %>%
  filter(
    major_type == "stem",
    median < 36000
  )

9. Which STEM majors have median salaries equal to or less than the median for all majors’ median earnings? Your output should only be a table that shows the major name, median, 25th percentile, and 75th percentile earning for that major, and should be sorted such that the major with the highest median earning is on top. Do not include narrative; the table itself will suffice.

What types of majors do women tend to choose?

10. Create a scatterplot of median income vs. proportion of women in that major. Set the point color as to whether the major is in a STEM field or not. Describe the association between these three variables. Make sure your figure is an appropriate size (make it have square dimensions).