Lab Worksheet 3

Problem 1: The data set AirPassengers built into R lists total numbers of international airline passengers, 1949 to 1960.

AirPassengers
##      Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 1949 112 118 132 129 121 135 148 148 136 119 104 118
## 1950 115 126 141 135 125 149 170 170 158 133 114 140
## 1951 145 150 178 163 172 178 199 199 184 162 146 166
## 1952 171 180 193 181 183 218 230 242 209 191 172 194
## 1953 196 196 236 235 229 243 264 272 237 211 180 201
## 1954 204 188 235 227 234 264 302 293 259 229 203 229
## 1955 242 233 267 269 270 315 364 347 312 274 237 278
## 1956 284 277 317 313 318 374 413 405 355 306 271 306
## 1957 315 301 356 348 355 422 465 467 404 347 305 336
## 1958 340 318 362 348 363 435 491 505 404 359 310 337
## 1959 360 342 406 396 420 472 548 559 463 407 362 405
## 1960 417 391 419 461 472 535 622 606 508 461 390 432

Is the dataset tidy? Explain why or why not.

Problem 2: The function data() lists all data sets that are available in R by default. Look through the list and identify a data set that is tidy. Explain why the data set is tidy.

I pick the data set… :

# R code goes here

Problem 3: In an in-class exercise, we made the following plot of the Sitka dataset:

# download the sitka data set:
head(sitka)
##   size Time tree treat
## 1 4.51  152    1 ozone
## 2 4.98  174    1 ozone
## 3 5.41  201    1 ozone
## 4 5.90  227    1 ozone
## 5 6.15  258    1 ozone
## 6 4.24  152    2 ozone
ggplot(sitka, aes(x=Time, y=size, group=tree)) + geom_line() + facet_wrap(~treat)

Now modify the plot so that the line for each tree is colored according to the maximum size of the tree.

# R code goes here

If that was easy…

Problem 4: The package nycflights13 contains information about all flights departing from one of the NY City airports in 2013. In particular, the data table flights lists on-time departure and arrival information for 336,776 individual flights:

library(nycflights13)
flights
## # A tibble: 336,776 × 19
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
## 1   2013     1     1      517            515         2      830
## 2   2013     1     1      533            529         4      850
## 3   2013     1     1      542            540         2      923
## 4   2013     1     1      544            545        -1     1004
## 5   2013     1     1      554            600        -6      812
## 6   2013     1     1      554            558        -4      740
## 7   2013     1     1      555            600        -5      913
## 8   2013     1     1      557            600        -3      709
## 9   2013     1     1      557            600        -3      838
## 10  2013     1     1      558            600        -2      753
## # ... with 336,766 more rows, and 12 more variables: sched_arr_time <int>,
## #   arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## #   origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## #   minute <dbl>, time_hour <dttm>

We would like to collect some information about arrival delays of United Airlines (UA) flights. Do the following: pick all UA departures with non-zero arrival delay and calculate the mean arrival delay for each of the corresponding flight numbers. Which flight had the longest mean arrival delay and how long was that delay?

# R code goes here.