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 rowsQuestion 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 rowsQuestion 3 Group by these three years (2008,2012, and 2016) and summarize the mean of the age in each group.
mean(df2$age)## [1] 21.88383Question 4 Use olympic_gymnasts dataset, group by year, and find the mean of the age for each year, call this dataset oly_year. (optional after creating the dataset, find the minimum average age)
oly_year<-olympic_gymnasts|>
group_by(year)|>
summarize(mean_age=mean(age,na.rm=TRUE))
oly_year## # A tibble: 29 × 2
## year mean_age
## <dbl> <dbl>
## 1 1896 24.3
## 2 1900 22.2
## 3 1904 25.1
## 4 1906 24.7
## 5 1908 23.2
## 6 1912 24.2
## 7 1920 26.7
## 8 1924 27.6
## 9 1928 25.6
## 10 1932 23.9
## # ℹ 19 more rowsQuestion 5 This question is open ended. Create a question that requires you to use at least two verbs. Create a code that answers your question. Then below the chunk, reflect on your question choice and coding procedure
Display summary statistics of height and Create a table of age and country
summary(olympic_gymnasts$height)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 127.0 158.0 164.0 162.9 169.0 188.0 6924table(olympic_gymnasts$age,olympic_gymnasts$team)##
## Algeria Argentina Armenia Australia Austria Azerbaijan Bangladesh Barbados
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## 43 0 0 0 0 0
## 44 0 0 0 0 0
## 45 0 0 0 0 0
## 49 0 0 0 0 0
##
## Vietnam West Germany Yemen Yugoslavia
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## 13 0 0 0 0
## 14 0 12 0 0
## 15 0 29 0 6
## 16 0 24 0 0
## 17 0 30 0 6
## 18 0 29 0 7
## 19 5 12 0 18
## 20 2 28 0 38
## 21 0 48 4 25
## 22 0 16 0 34
## 23 2 48 0 14
## 24 4 24 0 91
## 25 0 22 0 37
## 26 0 32 0 51
## 27 0 16 0 23
## 28 1 8 0 46
## 29 0 0 0 23
## 30 0 8 0 24
## 31 0 8 0 17
## 32 0 0 0 8
## 33 0 0 0 8
## 34 0 0 0 25
## 35 0 0 0 23
## 36 0 0 0 8
## 37 0 0 0 8
## 38 0 0 0 16
## 39 0 0 0 0
## 40 0 0 0 0
## 41 0 0 0 0
## 42 0 0 0 0
## 43 0 0 0 0
## 44 0 0 0 0
## 45 0 0 0 0
## 49 0 0 0 0Discussion: I used “summary” to make a summary statistic of the dataset, then I used the $ sign to access a specific column in the dataset (which is height). For the table part, I used “table” to make a table, then I put the dataset name and used the dollar sign to grab age from the dataset, then I did the same thing for the team.