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.

df<- olympic_gymnasts|>
  select(name, sex, age, team, year, medalist)
df
## # A tibble: 25,528 × 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   
##  7 Paavo Johannes Aaltonen M        28 Finland  1948 FALSE   
##  8 Paavo Johannes Aaltonen M        28 Finland  1948 TRUE    
##  9 Paavo Johannes Aaltonen M        32 Finland  1952 FALSE   
## 10 Paavo Johannes Aaltonen M        32 Finland  1952 TRUE    
## # ℹ 25,518 more rows

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

df2 <- df|>
  filter(year==2008 | year==2012 | year==2016)
df2
## # A tibble: 2,703 × 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   
##  7 Katja Abel        F        25 Germany  2008 FALSE   
##  8 Katja Abel        F        25 Germany  2008 FALSE   
##  9 Katja Abel        F        25 Germany  2008 FALSE   
## 10 Katja Abel        F        25 Germany  2008 FALSE   
## # ℹ 2,693 more rows

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(avg_age = mean(age))
## # A tibble: 3 × 2
##    year avg_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(avg_age = mean(age))
min(oly_year$avg_age)
## [1] 19.86606

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

What was the highest BMI of any male Olympic Gymnast?

# Your R code here
oly_bodies <- olympic_gymnasts |>
  filter(sex=='M') |>
  mutate(BMI=weight/(height*0.01)^2)
oly_bodies
## # A tibble: 16,484 × 17
##       id name     sex     age height weight team  noc   games  year season city 
##    <dbl> <chr>    <chr> <dbl>  <dbl>  <dbl> <chr> <chr> <chr> <dbl> <chr>  <chr>
##  1    17 Paavo J… M        28    175     64 Finl… FIN   1948…  1948 Summer Lond…
##  2    17 Paavo J… M        28    175     64 Finl… FIN   1948…  1948 Summer Lond…
##  3    17 Paavo J… M        28    175     64 Finl… FIN   1948…  1948 Summer Lond…
##  4    17 Paavo J… M        28    175     64 Finl… FIN   1948…  1948 Summer Lond…
##  5    17 Paavo J… M        28    175     64 Finl… FIN   1948…  1948 Summer Lond…
##  6    17 Paavo J… M        28    175     64 Finl… FIN   1948…  1948 Summer Lond…
##  7    17 Paavo J… M        28    175     64 Finl… FIN   1948…  1948 Summer Lond…
##  8    17 Paavo J… M        28    175     64 Finl… FIN   1948…  1948 Summer Lond…
##  9    17 Paavo J… M        32    175     64 Finl… FIN   1952…  1952 Summer Hels…
## 10    17 Paavo J… M        32    175     64 Finl… FIN   1952…  1952 Summer Hels…
## # ℹ 16,474 more rows
## # ℹ 5 more variables: sport <chr>, event <chr>, medal <chr>, medalist <lgl>,
## #   BMI <dbl>
max(oly_bodies$BMI, na.rm=TRUE)
## [1] 30.82483

Discussion: Enter your discussion of results here.

I first used filter to select only the male athletes, then used mutate to add the BMI column using the BMI formula. Then, I used the max() summary function to find the max value in the BMI column. This question may have been a little simple. I could have made it more interesting if I grouped by country and summarized BMI on a per-country basis.