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 the file
survey <- read.csv("StudentSurvey.csv")# Q1. Check the head of the dataset
# Used the head function.
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
# Used the dim function.
dim(survey)## [1] 362 17# Q3. Create a table of students' sex and HigherSAT
# Subset all rows and the vector of "Sex" and "HigherSAT" columns.
survey[,c("Sex", "HigherSAT")]## Sex HigherSAT
## 1 M Math
## 2 F Math
## 3 M Math
## 4 M Math
## 5 F Verbal
## 6 F Verbal
## 7 F Math
## 8 M Math
## 9 F Verbal
## 10 F Math
## 11 F Math
## 12 M Math
## 13 M Math
## 14 F Verbal
## 15 M Verbal
## 16 F Math
## 17 F Verbal
## 18 M Verbal
## 19 F Math
## 20 F Verbal
## 21 F Math
## 22 F Math
## 23 M Math
## 24 M Math
## 25 M Math
## 26 M Math
## 27 F Verbal
## 28 M Math
## 29 M Math
## 30 M Verbal
## 31 M Math
## 32 F Math
## 33 M Math
## 34 M Verbal
## 35 F Math
## 36 F Math
## 37 M Math
## 38 F Math
## 39 F Verbal
## 40 F Verbal
## 41 M Verbal
## 42 F Verbal
## 43 F Verbal
## 44 F Math
## 45 M Math
## 46 M Verbal
## 47 M Verbal
## 48 M Math
## 49 M Math
## 50 F Math
## 51 M Verbal
## 52 F Math
## 53 M Math
## 54 M Math
## 55 M Verbal
## 56 M Verbal
## 57 F Math
## 58 F Math
## 59 F Math
## 60 F Verbal
## 61 M Math
## 62 M Math
## 63 F Verbal
## 64 M Verbal
## 65 M Math
## 66 F Math
## 67 M Verbal
## 68 M Verbal
## 69 M Verbal
## 70 M Verbal
## 71 F Math
## 72 F Verbal
## 73 F Verbal
## 74 F Verbal
## 75 F Math
## 76 F Verbal
## 77 F
## 78 F Math
## 79 F Math
## 80 M Math
## 81 M Math
## 82 F Math
## 83 F Math
## 84 F Verbal
## 85 F Math
## 86 F Math
## 87 M Verbal
## 88 F Verbal
## 89 M Verbal
## 90 M Math
## 91 M Math
## 92 F Verbal
## 93 F Verbal
## 94 M Math
## 95 M Math
## 96 M Math
## 97 F Verbal
## 98 M Verbal
## 99 M Math
## 100 M Math
## 101 F Verbal
## 102 F Math
## 103 M Math
## 104 M Math
## 105 M Math
## 106 F Verbal
## 107 M Math
## 108 M Math
## 109 F Math
## 110 M Math
## 111 M Math
## 112 M Math
## 113 F Verbal
## 114 M Math
## 115 F Verbal
## 116 F Verbal
## 117 F Verbal
## 118 M
## 119 M Math
## 120 F Verbal
## 121 F Verbal
## 122 M Math
## 123 M Verbal
## 124 M Math
## 125 F Verbal
## 126 M Math
## 127 M Verbal
## 128 M Math
## 129 F Verbal
## 130 M Verbal
## 131 F Math
## 132 F Verbal
## 133 F Math
## 134 F Verbal
## 135 F Math
## 136 F Math
## 137 M Math
## 138 F Math
## 139 F Verbal
## 140 F Math
## 141 M Math
## 142 F Verbal
## 143 M Math
## 144 M Verbal
## 145 F Verbal
## 146 F Math
## 147 F Math
## 148 M Math
## 149 M Math
## 150 F Math
## 151 F Math
## 152 M Verbal
## 153 F Math
## 154 M Math
## 155 M Verbal
## 156 F Math
## 157 F Math
## 158 F Verbal
## 159 F Verbal
## 160 F Verbal
## 161 M Verbal
## 162 F Verbal
## 163 M Math
## 164 M Math
## 165 M Verbal
## 166 F Verbal
## 167 M Verbal
## 168 M Math
## 169 M Verbal
## 170 M Math
## 171 F Verbal
## 172 F Verbal
## 173 F Math
## 174 F Verbal
## 175 F Verbal
## 176 M Math
## 177 M Math
## 178 F Math
## 179 M Math
## 180 F Math
## 181 M Math
## 182 F Verbal
## 183 F Math
## 184 F Math
## 185 F Verbal
## 186 M Math
## 187 M Math
## 188 F
## 189 M Math
## 190 M Math
## 191 M Math
## 192 F Math
## 193 M Verbal
## 194 F Math
## 195 M Math
## 196 M Math
## 197 F Math
## 198 M Math
## 199 M Math
## 200 F Math
## 201 F Verbal
## 202 M Math
## 203 F Math
## 204 F Math
## 205 F Verbal
## 206 F Math
## 207 M Math
## 208 M Math
## 209 M Math
## 210 M Verbal
## 211 F Math
## 212 M Verbal
## 213 F Math
## 214 F Math
## 215 M Math
## 216 F Verbal
## 217 F Math
## 218 F Math
## 219 M Math
## 220 M Math
## 221 M Math
## 222 M Math
## 223 M Math
## 224 M Verbal
## 225 M Math
## 226 F Math
## 227 F Math
## 228 M Verbal
## 229 F Verbal
## 230 F Math
## 231 F Verbal
## 232 M Math
## 233 M Verbal
## 234 M Math
## 235 F Verbal
## 236 F Verbal
## 237 M Verbal
## 238 M Verbal
## 239 M Math
## 240 M Verbal
## 241 F Verbal
## 242 M Verbal
## 243 M Math
## 244 F Verbal
## 245 M Verbal
## 246 F Verbal
## 247 M Math
## 248 M Verbal
## 249 M Math
## 250 M Math
## 251 M Math
## 252 M Verbal
## 253 M Math
## 254 M Verbal
## 255 M Verbal
## 256 F Verbal
## 257 F Verbal
## 258 M Verbal
## 259 M Math
## 260 M Math
## 261 M Verbal
## 262 M Math
## 263 F Verbal
## 264 M
## 265 F Math
## 266 F Verbal
## 267 M Math
## 268 M Verbal
## 269 M Verbal
## 270 F Verbal
## 271 M Math
## 272 F Verbal
## 273 F Math
## 274 F Verbal
## 275 F Verbal
## 276 F Verbal
## 277 M Math
## 278 M Verbal
## 279 F Math
## 280 M Math
## 281 M Verbal
## 282 F Math
## 283 M Math
## 284 F Verbal
## 285 F Math
## 286 F Verbal
## 287 M Math
## 288 F Verbal
## 289 M Math
## 290 M Verbal
## 291 F Verbal
## 292 M Verbal
## 293 F Math
## 294 F Verbal
## 295 M Math
## 296 F
## 297 M Math
## 298 F Math
## 299 M Verbal
## 300 M Math
## 301 F Verbal
## 302 M Math
## 303 M Math
## 304 M Verbal
## 305 F Verbal
## 306 M Math
## 307 M Verbal
## 308 F
## 309 M Math
## 310 F Verbal
## 311 M Math
## 312 M Math
## 313 F Verbal
## 314 F Math
## 315 F Math
## 316 F Verbal
## 317 M Verbal
## 318 F Verbal
## 319 M Verbal
## 320 F Math
## 321 M Math
## 322 M Verbal
## 323 M Verbal
## 324 F Math
## 325 F Math
## 326 M Math
## 327 F Math
## 328 M Verbal
## 329 M Math
## 330 M Math
## 331 M Math
## 332 M Math
## 333 F Math
## 334 M Verbal
## 335 M Verbal
## 336 M Math
## 337 F Verbal
## 338 M Verbal
## 339 M
## 340 M Verbal
## 341 F Verbal
## 342 M Verbal
## 343 M Math
## 344 M Math
## 345 M Math
## 346 F Verbal
## 347 M Math
## 348 M Math
## 349 F Verbal
## 350 M Math
## 351 F Math
## 352 M Math
## 353 F Math
## 354 M Math
## 355 F Math
## 356 F Math
## 357 M Verbal
## 358 F Verbal
## 359 M Math
## 360 F Verbal
## 361 M Verbal
## 362 F Math# Q4. Display summary statistics for VerbalSAT
# Selected for only the verbalSAT and then used the summary function.
summary(survey$VerbalSAT)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 390.0 550.0 600.0 594.2 640.0 800.0# Q5. Find the average GPA of students
# Selected for only GPA and then used the mean function. Set to remove NA values and round to 2 decimal places.
round(mean(survey$GPA, na.rm = TRUE),2)## [1] 3.16# Q6. Create a new dataframe called column_df that contains students' weight
# and number of hours they exercise.
#Created a new dataframe by subsetting all rows and the vector of "Weight" and "Exercise" columns.
column_df <- survey[,c("Weight", "Exercise")]
column_df## Weight Exercise
## 1 180 10.0
## 2 120 4.0
## 3 208 14.0
## 4 110 3.0
## 5 150 3.0
## 6 114 5.0
## 7 128 10.0
## 8 235 13.0
## 9 NA 3.0
## 10 115 12.0
## 11 140 12.0
## 12 200 10.0
## 13 162 12.0
## 14 135 6.0
## 15 193 9.0
## 16 110 10.0
## 17 99 3.0
## 18 165 7.0
## 19 120 2.0
## 20 154 14.0
## 21 110 10.0
## 22 145 14.0
## 23 195 20.0
## 24 200 7.0
## 25 167 12.0
## 26 175 10.0
## 27 155 6.0
## 28 185 14.0
## 29 190 12.0
## 30 165 10.0
## 31 175 8.0
## 32 126 0.0
## 33 187 10.0
## 34 170 6.0
## 35 158 5.0
## 36 119 24.0
## 37 205 2.0
## 38 129 10.0
## 39 145 6.0
## 40 130 5.0
## 41 215 5.0
## 42 135 12.0
## 43 145 2.0
## 44 98 7.0
## 45 150 15.0
## 46 159 5.0
## 47 174 7.0
## 48 160 15.0
## 49 165 8.0
## 50 161 14.0
## 51 160 14.0
## 52 130 4.0
## 53 175 15.0
## 54 255 4.0
## 55 160 15.0
## 56 160 3.0
## 57 95 3.0
## 58 115 15.0
## 59 120 20.0
## 60 135 3.0
## 61 180 6.0
## 62 155 12.0
## 63 110 4.0
## 64 215 20.0
## 65 175 15.0
## 66 140 10.0
## 67 195 10.0
## 68 185 4.0
## 69 185 9.0
## 70 209 12.0
## 71 145 2.0
## 72 140 15.0
## 73 146 10.0
## 74 130 7.0
## 75 140 3.0
## 76 130 4.0
## 77 140 15.0
## 78 160 8.0
## 79 120 5.0
## 80 150 10.0
## 81 155 15.0
## 82 128 4.0
## 83 143 5.0
## 84 155 6.0
## 85 119 18.0
## 86 138 16.0
## 87 240 4.0
## 88 160 3.0
## 89 191 20.0
## 90 165 5.0
## 91 200 10.0
## 92 125 2.0
## 93 140 4.0
## 94 206 14.0
## 95 275 7.0
## 96 142 12.0
## 97 140 14.0
## 98 145 3.0
## 99 128 5.0
## 100 165 15.0
## 101 140 5.0
## 102 130 4.0
## 103 170 8.0
## 104 160 14.0
## 105 165 5.0
## 106 145 12.0
## 107 155 15.0
## 108 155 3.0
## 109 113 12.0
## 110 155 12.0
## 111 173 6.0
## 112 195 2.0
## 113 120 3.0
## 114 225 24.0
## 115 160 3.0
## 116 120 6.0
## 117 138 12.0
## 118 260 18.0
## 119 150 2.0
## 120 135 12.0
## 121 165 5.0
## 122 142 3.0
## 123 210 3.0
## 124 171 21.0
## 125 150 12.0
## 126 188 3.0
## 127 195 20.0
## 128 230 8.0
## 129 140 2.0
## 130 200 15.0
## 131 140 6.0
## 132 180 2.0
## 133 160 1.0
## 134 135 13.0
## 135 140 12.0
## 136 155 10.0
## 137 235 10.0
## 138 140 10.0
## 139 130 3.0
## 140 125 2.0
## 141 222 10.0
## 142 128 4.0
## 143 183 8.0
## 144 175 18.0
## 145 125 5.0
## 146 156 4.0
## 147 145 20.0
## 148 195 14.0
## 149 185 8.0
## 150 150 4.0
## 151 140 5.0
## 152 150 14.0
## 153 150 4.0
## 154 220 4.0
## 155 195 15.0
## 156 140 12.0
## 157 135 10.0
## 158 138 6.0
## 159 170 12.0
## 160 145 10.0
## 161 135 14.0
## 162 140 5.0
## 163 155 18.0
## 164 155 10.0
## 165 155 8.0
## 166 135 8.0
## 167 165 3.0
## 168 160 5.0
## 169 175 4.0
## 170 183 8.0
## 171 140 3.0
## 172 140 12.0
## 173 110 5.0
## 174 125 5.0
## 175 125 15.0
## 176 180 10.0
## 177 195 10.0
## 178 128 10.0
## 179 172 14.0
## 180 150 6.0
## 181 188 4.0
## 182 110 14.0
## 183 125 5.0
## 184 135 16.0
## 185 130 14.0
## 186 230 10.0
## 187 198 11.0
## 188 127 4.0
## 189 150 12.0
## 190 175 5.0
## 191 160 8.0
## 192 140 8.0
## 193 200 3.0
## 194 130 8.0
## 195 210 20.0
## 196 210 10.0
## 197 145 3.0
## 198 156 4.0
## 199 207 8.0
## 200 150 15.0
## 201 137 14.0
## 202 153 14.0
## 203 175 12.0
## 204 136 6.0
## 205 104 3.0
## 206 122 12.0
## 207 192 5.0
## 208 218 8.0
## 209 170 1.0
## 210 220 10.0
## 211 150 7.0
## 212 135 5.0
## 213 155 10.0
## 214 135 12.0
## 215 140 15.0
## 216 120 10.0
## 217 135 7.0
## 218 135 NA
## 219 165 15.0
## 220 180 10.0
## 221 165 15.0
## 222 175 12.0
## 223 200 10.0
## 224 138 12.0
## 225 265 5.0
## 226 105 14.0
## 227 130 3.0
## 228 165 12.0
## 229 145 15.0
## 230 138 3.0
## 231 193 25.0
## 232 170 5.0
## 233 155 6.0
## 234 155 27.0
## 235 140 8.0
## 236 130 3.0
## 237 180 17.0
## 238 140 40.0
## 239 210 5.0
## 240 150 0.0
## 241 115 27.0
## 242 225 3.0
## 243 170 3.0
## 244 120 11.0
## 245 170 2.0
## 246 130 9.0
## 247 182 12.0
## 248 138 15.0
## 249 180 2.0
## 250 230 20.0
## 251 222 12.0
## 252 145 5.0
## 253 150 10.0
## 254 190 2.0
## 255 195 12.0
## 256 115 5.0
## 257 140 3.0
## 258 155 12.0
## 259 150 8.0
## 260 155 8.0
## 261 130 5.0
## 262 210 6.0
## 263 105 8.0
## 264 192 10.0
## 265 220 18.0
## 266 134 12.0
## 267 160 10.0
## 268 135 10.0
## 269 140 5.0
## 270 135 7.0
## 271 145 3.0
## 272 140 7.0
## 273 NA 3.0
## 274 138 4.0
## 275 NA 8.0
## 276 130 7.0
## 277 210 7.0
## 278 233 3.0
## 279 180 7.0
## 280 185 14.0
## 281 189 5.0
## 282 127 14.0
## 283 135 5.0
## 284 140 5.0
## 285 162 2.0
## 286 215 1.0
## 287 200 15.0
## 288 120 5.0
## 289 165 5.0
## 290 175 15.0
## 291 160 5.0
## 292 145 20.0
## 293 180 3.0
## 294 170 7.0
## 295 235 20.0
## 296 155 10.0
## 297 140 6.0
## 298 145 6.0
## 299 175 10.0
## 300 210 9.0
## 301 130 1.5
## 302 195 20.0
## 303 165 8.0
## 304 180 21.0
## 305 145 10.0
## 306 163 8.0
## 307 160 10.0
## 308 150 18.0
## 309 170 15.0
## 310 110 8.0
## 311 170 6.0
## 312 145 12.0
## 313 160 10.0
## 314 130 7.0
## 315 155 5.0
## 316 NA 6.0
## 317 160 5.0
## 318 151 5.0
## 319 180 30.0
## 320 130 5.0
## 321 185 12.0
## 322 198 0.0
## 323 175 12.0
## 324 123 0.0
## 325 145 15.0
## 326 190 18.0
## 327 130 12.0
## 328 185 2.0
## 329 165 12.0
## 330 165 3.0
## 331 150 3.0
## 332 160 4.0
## 333 142 12.0
## 334 165 25.0
## 335 175 3.0
## 336 175 6.0
## 337 190 7.0
## 338 180 7.0
## 339 185 6.0
## 340 NA 6.0
## 341 135 13.0
## 342 195 25.0
## 343 175 8.0
## 344 165 5.0
## 345 135 11.0
## 346 140 18.0
## 347 182 10.0
## 348 155 6.0
## 349 180 2.0
## 350 170 5.0
## 351 135 5.0
## 352 165 6.0
## 353 137 10.0
## 354 147 4.0
## 355 150 5.0
## 356 155 17.0
## 357 160 7.0
## 358 130 2.0
## 359 180 8.0
## 360 150 1.0
## 361 205 14.0
## 362 115 12.0# Q7. Access the fourth element in the first column of the StudentSurvey dataset.
#Subset just row 4, column 1.
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 <- olympics %>%
select(name, sex, age, team, year, medal)
head(df)## # A tibble: 6 × 6
## name sex age team year medal
## <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 A Dijiang M 24 China 1992 <NA>
## 2 A Lamusi M 23 China 2012 <NA>
## 3 Gunnar Nielsen Aaby M 24 Denmark 1920 <NA>
## 4 Edgar Lindenau Aabye M 34 Denmark/Sweden 1900 Gold
## 5 Christine Jacoba Aaftink F 21 Netherlands 1988 <NA>
## 6 Christine Jacoba Aaftink F 21 Netherlands 1988 <NA># Q9. From df, create df2 that only has the years 2008, 2012, and 2016.
df2 <- df %>%
filter(year%in%c(2008, 2012, 2016))
df2## # A tibble: 40,210 × 6
## name sex age team year medal
## <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 A Lamusi M 23 China 2012 <NA>
## 2 Ragnhild Margrethe Aamodt F 27 Norway 2008 Gold
## 3 Andreea Aanei F 22 Romania 2016 <NA>
## 4 Jamale (Djamel-) Aarrass (Ahrass-) M 30 France 2012 <NA>
## 5 Abdelhak Aatakni M 24 Morocco 2012 <NA>
## 6 Moonika Aava F 28 Estonia 2008 <NA>
## 7 Nstor Abad Sanjun M 23 Spain 2016 <NA>
## 8 Nstor Abad Sanjun M 23 Spain 2016 <NA>
## 9 Nstor Abad Sanjun M 23 Spain 2016 <NA>
## 10 Nstor Abad Sanjun M 23 Spain 2016 <NA>
## # ℹ 40,200 more rows# Q10. Group by those three years and summarize the mean age in each group.
df2 %>%
group_by(year) %>%
summarize(avg_age=(round(mean(age, na.rm=TRUE),2)))## # A tibble: 3 × 2
## year avg_age
## <dbl> <dbl>
## 1 2008 25.7
## 2 2012 26.0
## 3 2016 26.2# 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 <- olympics %>%
group_by(year) %>%
summarize(avg_age=(round(mean(age, na.rm=TRUE),2))) %>%
arrange(avg_age)
oly_year## # A tibble: 35 × 2
## year avg_age
## <dbl> <dbl>
## 1 1896 23.6
## 2 1980 23.7
## 3 1976 23.8
## 4 1984 23.9
## 5 1988 24.1
## 6 1968 24.2
## 7 1972 24.3
## 8 1992 24.3
## 9 1994 24.4
## 10 1996 24.9
## # ℹ 25 more rows# 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.
olympics %>%
group_by(medal) %>%
summarize(avg_age=(round(mean(age, na.rm=TRUE),2))) %>%
arrange(desc(avg_age))## # A tibble: 4 × 2
## medal avg_age
## <chr> <dbl>
## 1 Silver 26
## 2 Gold 25.9
## 3 Bronze 25.9
## 4 <NA> 25.5Your question and reflection: Question: What is the average age of olympic medalists by medal color (or no medal)?
This question allowed me to use the group_by, summarize, and arrange dplyr functions.
I chose this question because I am curious about if older athletes are more or less likely to win goal than younger athletes. I found almost no difference, which I thought was interesting.