Lab 11
This lab exercise is due 23:59 Monday 7 June (NZST).
- You should submit an R file (i.e. file extension
.R) containing R code that assigns the appropriate values to the appropriate symbols. - Your R file will be executed in order and checked against the values that have been assigned to the symbols using an automatic grading system. Marks will be fully deducted for non-identical results.
- Intermediate steps to achieve the final results will NOT be checked.
- Each question is worth 0.2 points.
- You should submit your R file on Canvas.
- Late assignments are NOT accepted unless prior arrangement for medical/compassionate reasons.
In this lab exercise, you are going to work with bus status data that was collected on 2017-03-09 from Auckland Transport. You shall use the following code snippet (and include them upfront in your R file) for this lab session:
library(fs)
library(lubridate)
library(tidyverse)
csv_files <- dir_ls("data/aklbus2017", glob = "*.csv")
head(csv_files)
#> data/aklbus2017/2017-03-09-05-14.csv
#> data/aklbus2017/2017-03-09-05-24.csv
#> data/aklbus2017/2017-03-09-05-34.csv
#> data/aklbus2017/2017-03-09-05-44.csv
#> data/aklbus2017/2017-03-09-05-54.csv
#> data/aklbus2017/2017-03-09-06-04.csv
Suppose that you have created an Rproj for this course. You need to
download and unzip aklbus2017.zip
here
to data/ under your Rproj folder.
Each file represents a snapshot of bus status information, where each
snapshot was taken approximately 10 minutes apart. Within each file,
each row of data gives the status of one bus. The delay is how many
seconds ahead or behind schedule the bus is (a positive value is behind
schedule and a negative value is ahead of schedule). The stop.id
identifies which bus stop the delay applies to. The stop.sequence is
the order of the bus stop within the bus route. The route identifies
the bus route.
- You’re required to use
csv_filesto import these data. NO marks will be given to this lab for using URL links or different file paths.
Question 1
Import all CSV files from the data/aklbus2017, with their unique
identifiers as "path".
You should end up with a tibble called aklbus.
aklbus
#> # A tibble: 43,793 x 5
#> path delay stop.id stop.sequence route
#> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 data/aklbus2017/2017-… 150 5050 28 15401-201702141…
#> 2 data/aklbus2017/2017-… 97 3093 6 83401-201702141…
#> 3 data/aklbus2017/2017-… 50 4810 13 98105-201702141…
#> 4 data/aklbus2017/2017-… 58 6838 5 36201-201702141…
#> 5 data/aklbus2017/2017-… -186 2745 11 37301-201702141…
#> 6 data/aklbus2017/2017-… 183 2635 14 03301-201702141…
#> 7 data/aklbus2017/2017-… -144 3360 4 10001-201702141…
#> 8 data/aklbus2017/2017-… -151 2206 20 38011-201702141…
#> 9 data/aklbus2017/2017-… -46 2419 38 38011-201702141…
#> 10 data/aklbus2017/2017-… -513 6906 42 38012-201702141…
#> # … with 43,783 more rows
Question 2
Transform aklbus:
- replace
routewith their first 5 digits as the route identifier - create a new column
datetimethat contains the recorded date-times, derived from thepath - remove the
pathcolumn - change the column positions as shown in
aklbus_time
You should end up with a tibble called aklbus_time.
aklbus_time
#> # A tibble: 43,793 x 5
#> datetime delay stop.id stop.sequence route
#> <dttm> <dbl> <dbl> <dbl> <chr>
#> 1 2017-03-09 05:14:00 150 5050 28 15401
#> 2 2017-03-09 05:14:00 97 3093 6 83401
#> 3 2017-03-09 05:14:00 50 4810 13 98105
#> 4 2017-03-09 05:14:00 58 6838 5 36201
#> 5 2017-03-09 05:14:00 -186 2745 11 37301
#> 6 2017-03-09 05:14:00 183 2635 14 03301
#> 7 2017-03-09 05:14:00 -144 3360 4 10001
#> 8 2017-03-09 05:14:00 -151 2206 20 38011
#> 9 2017-03-09 05:14:00 -46 2419 38 38011
#> 10 2017-03-09 05:14:00 -513 6906 42 38012
#> # … with 43,783 more rows
mean(aklbus_time$datetime)
#> [1] "2017-03-09 13:30:23 NZDT"
Question 3
Compute the percentage of on-time bus status (as ontime_prop) for each
route given that day. If the bus is ahead or behind schedule within 5
minutes, it is classified as on-time; otherwise late. Then arrange their
rows in ascending order by ontime_prop.
You should end up with a tibble called aklbus_ontime.
aklbus_ontime
#> # A tibble: 1,082 x 2
#> route ontime_prop
#> <chr> <dbl>
#> 1 00152 0
#> 2 00154 0
#> 3 00164 0
#> 4 00170 0
#> 5 00452 0
#> 6 00553 0
#> 7 00651 0
#> 8 00652 0
#> 9 00653 0
#> 10 00660 0
#> # … with 1,072 more rows
mean(aklbus_ontime$ontime_prop)
#> [1] 0.5356871
Question 4
Exclude these bus routes from aklbus_time, if their on-time
percentages are either 0 or 1 during the day. Then split the rest of bus
routes into separate tibbles in a list.
You should end up with a list of tibbles called aklbus_lst.
aklbus_lst[[220]]
#> # A tibble: 32 x 5
#> datetime delay stop.id stop.sequence route
#> <dttm> <dbl> <dbl> <dbl> <chr>
#> 1 2017-03-09 05:54:00 6 4935 13 06001
#> 2 2017-03-09 06:04:00 154 4890 18 06001
#> 3 2017-03-09 06:14:00 97 4872 27 06001
#> 4 2017-03-09 06:14:00 -104 4904 2 06001
#> 5 2017-03-09 06:24:00 208 4862 32 06001
#> 6 2017-03-09 06:24:00 121 4908 14 06001
#> 7 2017-03-09 06:34:00 802 4885 34 06001
#> 8 2017-03-09 06:34:00 197 4890 18 06001
#> 9 2017-03-09 06:34:00 -31 4904 2 06001
#> 10 2017-03-09 06:45:00 907 5640 42 06001
#> # … with 22 more rows
length(aklbus_lst)
#> [1] 817
Question 5
Get the dimension (the number of rows and colums) of each tibble in the
aklbus_lst.
You should end up with a list of integers called aklbus_size. Each
element of the list is an integer vector of length two.
head(aklbus_size)
#> [[1]]
#> [1] 64 5
#>
#> [[2]]
#> [1] 88 5
#>
#> [[3]]
#> [1] 12 5
#>
#> [[4]]
#> [1] 24 5
#>
#> [[5]]
#> [1] 5 5
#>
#> [[6]]
#> [1] 3 5
typeof(aklbus_size[[1]])
#> [1] "integer"