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,name, 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

subset_df <- df %>% 
  select(name, sex, age, team, year, medalist)

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.

summary <- df2 %>%
  group_by(year) %>%
  summarize(mean_age = mean(age, na.rm = TRUE))

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(avg_age = mean(age, na.rm = TRUE))

oly_year

## # A tibble: 29 × 2
##     year avg_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_avg_age <- oly_year |> 
  filter(avg_age == min(avg_age))

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

Using olympic_gymnasts dataset, group by only males, and find the mean amount of the medalists for each year, call this dataset Male_medalists

Male_medalists <- olympic_gymnasts %>%
  filter(sex == "M") %>%
  group_by(year) %>%
  summarize(mean_medalists = mean(medalist, na.rm = TRUE))

Male_medalists

## # A tibble: 29 × 2
##     year mean_medalists
##    <dbl>          <dbl>
##  1  1896         0.425 
##  2  1900         0.0909
##  3  1904         0.142 
##  4  1906         0.6   
##  5  1908         0.404 
##  6  1912         0.639 
##  7  1920         0.811 
##  8  1924         0.0882
##  9  1928         0.0779
## 10  1932         0.321 
## # ℹ 19 more rows

Discussion: Enter your discussion of results here. I chose this question out of curiousity to see what the results would yield. i started the coding by filtering for just males because those were the sex i was interested in finding out a result for, then utilizing grouping and summarize i was able to determine the mean. this i believe could be utilized to see which year makes did the best compared to the other years.