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))

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))

## # 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))

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

# Your R code here
 oly_weight <- olympic_gymnasts |>
  group_by(sex) |>
summarize(mean_weight = mean(weight, na.rm = TRUE))

oly_weight

## # A tibble: 2 × 2
##   sex   mean_weight
##   <chr>       <dbl>
## 1 F            47.8
## 2 M            63.3

Discussion: Enter your discussion of results here. I created a code that created a dataset named oly_weight. I wanted to compare the average weight between the female and male gymnasts. I first grouped the data by sex, and then calculated the mean weight for the two genders. When I first ran my code, I did not use ‘na.rm = TRUE,’ to remove the NA’s, which made both my results NA. Once I added that function in, It allowed me to see the weight differences between them. On average, the male gymnasts weigh around 63 kilograms, and female gymnasts weigh around 48 kilograms if you round up.