Project 1

This is the dataset you will be working with:

olympics <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-07-27/olympics.csv')

triathlon <- olympics %>% 
  filter(!is.na(height)) %>%             # only keep athletes with known height
  filter(sport == "Triathlon") %>%       # keep only triathletes
  mutate(
    medalist = case_when(                # add column to track medalist vs not
      is.na(medal) ~ "non-medalist",
      !is.na(medal) ~ "medalist"         # any medals (Gold, Silver, Bronze) count
    )
  )

triathlon is a subset of olympics and contains only the data for triathletes. More information about the original olympics dataset can be found at https://github.com/rfordatascience/tidytuesday/tree/master/data/2021/2021-07-27/readme.md and https://www.sports-reference.com/olympics.html.

For this project, use triathlon to answer the following questions about athletes competing in this sport:

1. In how many events total did male and female triathletes compete for each country?
2. Are there height differences among triathletes between sexes or over time?
3. Are there height differences among triathletes that have medaled or not, again also considering athlete sex?

You should make one plot per question.

Hints:

• We recommend you use a bar plot for question 1, a boxplot for question 2, and a sina plot overlaid on top of violins for question 3. However, you are free to use any of the plots we have discussed in class so far.
• For question 2, you will have to convert year into a factor.
• For question 3, consider why a boxplot or simple violin plot is not a good idea and mention this in the approach section.
• For all questions, you can use either faceting or color coding or both. Pick whichever you prefer.
• Adjust fig.width and fig.height in the chunk headers to customize figure sizing and figure aspect ratios.

You can delete these instructions from your project. Please also delete text such as Your approach here or # Q1: Your R code here.

Introduction: Your introduction here.

Approach: Your approach here.

Analysis:

# Q1: Your R code here

# Q2: Your R code here

# Q3: Your R code here

Discussion: Your discussion of results here.