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(mean_age = mean(age))

## # 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)|>
  summarise(mean_age = mean(age))

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

min(oly_year$mean_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

young_metal_teams<- olympic_gymnasts|>
  filter(medalist == TRUE)|>
  group_by(team)|>
  summarise(mean_age = mean(age))|>
  arrange(mean_age)

young_metal_teams

## # A tibble: 42 × 2
##    team                          mean_age
##    <chr>                            <dbl>
##  1 Ethnikos Gymnastikos Syllogos     10  
##  2 Romania                           17.9
##  3 Pistoja/Firenze                   19  
##  4 Unified Team                      19.0
##  5 Russia                            19.8
##  6 China                             20.7
##  7 East Germany                      21.1
##  8 Spain                             21.2
##  9 United States                     21.3
## 10 Canada                            22  
## # ℹ 32 more rows

Discussion: For my question I wanted to see which teams have younger medal winning gymnasts on average. I first filtered the dataset so it only included athletes who won a medal. Then I grouped the data by team and calculated the average age using summarise. After that I arranged the results so the teams with the lowest average age appear first. This made it easier to see which teams tend to have younger successful gymnasts.

Homework 3

Elijah Peaks