Homework 3

Do not change anything in the following chunk

You will be working on olympic_gymnasts dataset. Do not change the code below:

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

olympic_gymnasts <- olympics %>% 
  filter(!is.na(age)) %>%             # only keep athletes with known age
  filter(sport == "Gymnastics") %>%   # keep only gymnasts
  mutate(
    medalist = case_when(             # add column for success in medaling
      is.na(medal) ~ FALSE,           # NA values go to FALSE
      !is.na(medal) ~ TRUE            # non-NA values (Gold, Silver, Bronze) go to TRUE
    )
  )

More information about the dataset can be found at

https://github.com/rfordatascience/tidytuesday/blob/master/data/2021/2021-07-27/readme.md

Question 1: Create a subset dataset with the following columns only: name, sex, age, team, year and medalist. Call it df.

library(tidyverse)

df <- olympic_gymnasts %>%
  select(name, sex, age, team, year, medalist)

head(df)

## # A tibble: 6 × 6
##   name                    sex     age team     year medalist
##   <chr>                   <chr> <dbl> <chr>   <dbl> <lgl>   
## 1 Paavo Johannes Aaltonen M        28 Finland  1948 TRUE    
## 2 Paavo Johannes Aaltonen M        28 Finland  1948 TRUE    
## 3 Paavo Johannes Aaltonen M        28 Finland  1948 FALSE   
## 4 Paavo Johannes Aaltonen M        28 Finland  1948 TRUE    
## 5 Paavo Johannes Aaltonen M        28 Finland  1948 FALSE   
## 6 Paavo Johannes Aaltonen M        28 Finland  1948 FALSE

Question 2: From df create df2 that only have year of 2008 2012, and 2016

df2 <- df %>%
  filter(year %in% c(2008, 2012, 2016))

head(df2)

## # A tibble: 6 × 6
##   name              sex     age team   year medalist
##   <chr>             <chr> <dbl> <chr> <dbl> <lgl>   
## 1 Nstor Abad Sanjun M        23 Spain  2016 FALSE   
## 2 Nstor Abad Sanjun M        23 Spain  2016 FALSE   
## 3 Nstor Abad Sanjun M        23 Spain  2016 FALSE   
## 4 Nstor Abad Sanjun M        23 Spain  2016 FALSE   
## 5 Nstor Abad Sanjun M        23 Spain  2016 FALSE   
## 6 Nstor Abad Sanjun M        23 Spain  2016 FALSE

Question 3 Group by these three years (2008,2012, and 2016) and summarize the mean of the age in each group.

df2 %>%
  group_by(year) %>%
  summarize(mean_age = mean(age, na.rm = TRUE))

## # A tibble: 3 × 2
##    year mean_age
##   <dbl>    <dbl>
## 1  2008     21.6
## 2  2012     21.9
## 3  2016     22.2

Question 4 Use olympic_gymnasts dataset, group by year, and find the mean of the age for each year, call this dataset oly_year. (optional after creating the dataset, find the minimum average age)

oly_year <- olympic_gymnasts %>%
  group_by(year) %>%
  summarize(mean_age = mean(age, na.rm = TRUE))

oly_year

## # A tibble: 29 × 2
##     year mean_age
##    <dbl>    <dbl>
##  1  1896     24.3
##  2  1900     22.2
##  3  1904     25.1
##  4  1906     24.7
##  5  1908     23.2
##  6  1912     24.2
##  7  1920     26.7
##  8  1924     27.6
##  9  1928     25.6
## 10  1932     23.9
## # ℹ 19 more rows

Question 5 This question is open ended. Create a question that requires you to use at least two verbs. Create a code that answers your question. Then below the chunk, reflect on your question choice and coding procedure

# Your R code here
top_teams_2016 <- olympic_gymnasts %>%
  filter(year == 2016, medalist == TRUE) %>%
  group_by(team) %>%
  summarize(num_medalists = n()) %>%
  arrange(desc(num_medalists))

top_teams_2016

## # A tibble: 12 × 2
##    team          num_medalists
##    <chr>                 <int>
##  1 Russia                   16
##  2 United States            16
##  3 China                    10
##  4 Japan                     7
##  5 Great Britain             6
##  6 Brazil                    3
##  7 Germany                   2
##  8 Ukraine                   2
##  9 Greece                    1
## 10 Netherlands               1
## 11 North Korea               1
## 12 Switzerland               1

Discussion: Enter your discussion of results here. The results show that countries like the USA, China, and Russia had the most medal-winning gymnasts in 2016. I filtered the data to focus on 2016 medalists, then grouped by team and counted. Using multiple verbs made it simple to see which teams dominated that year.