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)|>
  summarize(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)|>
  summarize(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

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
#Question: What is the average age of medalists and non-medalists by sex?

olympic_gymnasts|>
  group_by(sex, medalist)|>
  summarize(mean(age))
## `summarise()` has grouped output by 'sex'. You can override using the `.groups`
## argument.
## # A tibble: 4 × 3
## # Groups:   sex [2]
##   sex   medalist `mean(age)`
##   <chr> <lgl>          <dbl>
## 1 F     FALSE           19.2
## 2 F     TRUE            20.2
## 3 M     FALSE           24.6
## 4 M     TRUE            24.9

Discussion: Enter your discussion of results here. I looked at the difference in ages between medalists and non-medalists depending on their sex. This question required me to first group the data by both sexes and the medalist status, I did this by using the group_by function. Then, to find the mean age for each group, I used the summarize(mean(age)) function.The result of my function is a table looking at the difference in ages between the sexes depending on whether they are a medalist or not. This analysis could potentially show interesting patterns about the tendency of older/younger players to win and whether this pattern may differe between sexes.