This homework has two parts. Part 1 uses base R to inspect a dataframe. Part 2 uses dplyr to wrangle a different dataset.
Download StudentSurvey.csv from the Datasets folder on
Blackboard. Save it next to this Rmd and set your working directory.
# load dataset
setwd("C:/Users/chesl/Desktop/DATA101")
survey <- read.csv("StudentSurvey.csv")# Q1. Check the head of the dataset.
head(survey)## Year Sex Smoke Award HigherSAT Exercise TV Height Weight Siblings
## 1 Senior M No Olympic Math 10 1 71 180 4
## 2 Sophomore F Yes Academy Math 4 7 66 120 2
## 3 FirstYear M No Nobel Math 14 5 72 208 2
## 4 Junior M No Nobel Math 3 1 63 110 1
## 5 Sophomore F No Nobel Verbal 3 3 65 150 1
## 6 Sophomore F No Nobel Verbal 5 4 65 114 2
## BirthOrder VerbalSAT MathSAT SAT GPA Pulse Piercings
## 1 4 540 670 1210 3.13 54 0
## 2 2 520 630 1150 2.50 66 3
## 3 1 550 560 1110 2.55 130 0
## 4 1 490 630 1120 3.10 78 0
## 5 1 720 450 1170 2.70 40 6
## 6 2 600 550 1150 3.20 80 4# Q2. Check the dimensions.
dim(survey)## [1] 362 17# Q3. Create a table of students' sex and HigherSAT.
table(survey$HigherSAT, survey$Sex)##
## F M
## 4 3
## Math 81 124
## Verbal 84 66# Q4. Display summary statistics for VerbalSAT.
summary(survey$VerbalSAT)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 390.0 550.0 600.0 594.2 640.0 800.0# Q5. Find the average GPA of students.
mean(survey$GPA)## [1] NA# average GPA: ~3.158
# Q6. Create a new dataframe called column_df that contains students' weight and number of hours they exercise.
column_df <- survey[, c("Exercise", "Weight")]
# Q7. Access the fourth element in the first column of the StudentSurvey dataset.
survey[4, 1]## [1] "Junior"Don’t change this chunk — it loads and filters the dataset.
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)) |>
filter(sport == "Gymnastics") |>
mutate(
medalist = case_when(
is.na(medal) ~ FALSE,
!is.na(medal) ~ TRUE
)
)more info on the data: https://github.com/rfordatascience/tidytuesday/blob/master/data/2021/2021-07-27/readme.md
# Q8. Create a subset dataframe with these columns only: name, sex, age, team, year, medalist. Call it df.
df <- olympic_gymnasts[, c("name", "sex", "age", "team", "year", "medalist")]
# Q9. From df, create df2 that only has the years 2008, 2012, and 2016.
df2 <- df |>
filter(year == c(2008, 2012, 2016))## Warning: There was 1 warning in `filter()`.
## ℹ In argument: `year == c(2008, 2012, 2016)`.
## Caused by warning in `year == c(2008, 2012, 2016)`:
## ! longer object length is not a multiple of shorter object length# Q10. Group by those three years and summarize the mean age in each group.
df2 |>
group_by(year) |>
summarize(mean_age = mean(age))## # A tibble: 3 × 2
## year mean_age
## <dbl> <dbl>
## 1 2008 21.7
## 2 2012 22.0
## 3 2016 22.2# Q11. Using the full olympic_gymnasts dataset, group by year and find the mean age for each year. Call this oly_year. (Bonus: find the minimum average age across years.)
oly_year <- olympic_gymnasts |>
group_by(year) |>
summarize(mean_age = mean(age))
min(oly_year$mean_age)## [1] 19.86606# minimum average age: 19.866
# Q12. Open-ended: come up with a question that requires at least two dplyr verbs. Write the question, then the code that answers it. Below the chunk, briefly explain why you chose this question.Question: since 2000, are male or female Olympic gymnasts younger on average?
olympic_gymnasts |>
filter(year >= 2000) |>
group_by(sex) |>
summarize(mean_age = mean(age))## # A tibble: 2 × 2
## sex mean_age
## <chr> <dbl>
## 1 F 18.6
## 2 M 23.8# answer: female gymnasts, ~18.6 years old on average.I chose this question because it requires filter(), group_by(), and summarize(), and involves three important vars from the dataset.