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.

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 %in% c(2008,2012,2016))
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) |>
summarise(
n=n(),
mean_age= mean(age)
)

## # A tibble: 3 × 3
##    year     n mean_age
##   <dbl> <int>    <dbl>
## 1  2008   994     21.6
## 2  2012   848     21.9
## 3  2016   861     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) |>
summarise (
n=n(),
mean_age = mean(age),
)
oly_year

## # A tibble: 29 × 3
##     year     n mean_age
##    <dbl> <int>    <dbl>
##  1  1896    73     24.3
##  2  1900    33     22.2
##  3  1904   317     25.1
##  4  1906    70     24.7
##  5  1908   240     23.2
##  6  1912   310     24.2
##  7  1920   206     26.7
##  8  1924   499     27.6
##  9  1928   561     25.6
## 10  1932   140     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

Which countries (team) have had the youngest average gymnast over all the years?

young_gymanists_teams <- olympic_gymnasts |>
  group_by(team) |>
  summarise( 
    n=n(),
    youngest_avg_gymnasts = mean(age), 
  ) |>
arrange(youngest_avg_gymnasts)

young_gymanists_teams

## # A tibble: 108 × 3
##    team                              n youngest_avg_gymnasts
##    <chr>                         <int>                 <dbl>
##  1 Ethnikos Gymnastikos Syllogos     1                  10  
##  2 Bolivia                           5                  15  
##  3 Guatemala                        18                  16.9
##  4 Peru                              4                  17  
##  5 Barbados                          7                  18  
##  6 Cyprus                            6                  18  
##  7 Namibia                           5                  18  
##  8 Mongolia                         49                  18.3
##  9 Greece                          105                  18.5
## 10 Malaysia                          9                  18.8
## # ℹ 98 more rows

Discussion: Enter your discussion of results here.

My question was “which countries (team) have had the youngest average gymnast over all the years?”To answer this question, I first used the ‘group by’ function to group the dataset by the teams, which were the countries. Next, using the pipe operator (‘and then’) (|>), I used summarise to go into the group, count the numbers of each team, and get the average age per team by using mean (age). Lastly, I used arrange to arrange the ages from youngest to oldest average gymnast age, so that I could look at the youngest average ages specifically. I did this investigation because I thought it would be interesting to see what ages are usually the average in each country. If I could further this investigation,I would try to link it to performance.