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R4DS Study Group - Week 45

Pierrette Lo 2/12/2021

This week’s assignment

  • Chapter 16
library(tidyverse)
library(lubridate)
library(nycflights13)

Notes

{lubridate} cheat sheet: https://github.com/rstudio/cheatsheets/raw/master/lubridate.pdf

Note about current date:

  • today() is from the {lubridate} package
  • the base R version is Sys.Date()
  • I usually use Sys.Date() if I just want to append today’s date to a filename or similar, so I don’t need to load up a separate package just for that one function
  • use format() to format Sys.Date()

e.g.

Sys.Date()
## [1] "2021-02-26"

format(Sys.Date(), "%Y_%b_%d")
## [1] "2021_Feb_26"

paste0("myfilename_", Sys.Date(), ".csv")
## [1] "myfilename_2021-02-26.csv"

16.2.4 Creating dates and times

  1. What happens if you parse a string that contains invalid dates?

You will get an NA and a warning message.

ymd(c("2010-10-10", "bananas"))
## Warning: 1 failed to parse.

## [1] "2010-10-10" NA
  1. What does the tzone argument to today() do? Why is it important?

This argument specifies the time zone. The default (no argument specified) is to use the time zone that your computer is set to.

Use Sys.timezone() to get your current time zone (“PST” doesn’t work); use OlsonNames() to get a list of all possible time zones.

Sys.timezone()
## [1] "America/Los_Angeles"

now(tzone = "America/Los_Angeles")
## [1] "2021-02-26 10:25:34 PST"
  1. Use the appropriate {lubridate} function to parse each of the following dates:

“Parse” means to convert a character object to a date object. Use class() to confirm whether you have a character or a Date object.

d1 <- "January 1, 2010"

class(d1)
## [1] "character"

d1_parsed <- mdy(d1)

class(d1_parsed)
## [1] "Date"

d2 <- "2015-Mar-07"

ymd(d2)
## [1] "2015-03-07"

d3 <- "06-Jun-2017"

dmy(d3)
## [1] "2017-06-06"

d4 <- c("August 19 (2015)", "July 1 (2015)")

mdy(d4)
## [1] "2015-08-19" "2015-07-01"

d5 <- "12/30/14" # Dec 30, 2014

mdy(d5)
## [1] "2014-12-30"

16.3.4 Date-time components

  1. How does the distribution of flight times within a day change over the course of the year?

Start with code from the text to parse the departure and arrival times from numbers into date-time objects.

Remember these arithmetic operators? When dividing x by y,

  • %/% = the integer portion of the “answer”
  • %% = the remainder
make_datetime_100 <- function(year, month, day, time) {
  make_datetime(year, month, day, 
                hour = time %/% 100, # e.g. 517 %/% 100 = 5
                min = time %% 100)   # e.g. 517 %% 100 = 17
}

flights_dt <- flights %>% 
  filter(!is.na(dep_time), !is.na(arr_time)) %>% 
  mutate(
    dep_time = make_datetime_100(year, month, day, dep_time),
    arr_time = make_datetime_100(year, month, day, arr_time),
    sched_dep_time = make_datetime_100(year, month, day, sched_dep_time),
    sched_arr_time = make_datetime_100(year, month, day, sched_arr_time)
  ) %>% 
  select(origin, dest, ends_with("delay"), ends_with("time"))

flights_dt
## # A tibble: 328,063 x 9
##    origin dest  dep_delay arr_delay dep_time            sched_dep_time     
##    <chr>  <chr>     <dbl>     <dbl> <dttm>              <dttm>             
##  1 EWR    IAH           2        11 2013-01-01 05:17:00 2013-01-01 05:15:00
##  2 LGA    IAH           4        20 2013-01-01 05:33:00 2013-01-01 05:29:00
##  3 JFK    MIA           2        33 2013-01-01 05:42:00 2013-01-01 05:40:00
##  4 JFK    BQN          -1       -18 2013-01-01 05:44:00 2013-01-01 05:45:00
##  5 LGA    ATL          -6       -25 2013-01-01 05:54:00 2013-01-01 06:00:00
##  6 EWR    ORD          -4        12 2013-01-01 05:54:00 2013-01-01 05:58:00
##  7 EWR    FLL          -5        19 2013-01-01 05:55:00 2013-01-01 06:00:00
##  8 LGA    IAD          -3       -14 2013-01-01 05:57:00 2013-01-01 06:00:00
##  9 JFK    MCO          -3        -8 2013-01-01 05:57:00 2013-01-01 06:00:00
## 10 LGA    ORD          -2         8 2013-01-01 05:58:00 2013-01-01 06:00:00
## # ... with 328,053 more rows, and 3 more variables: arr_time <dttm>,
## #   sched_arr_time <dttm>, air_time <dbl>

Use the other code from the text (using update() to show distribution of flights across the course of the day) and plot by month:

flights_dt %>% 
  mutate(dep_hms = hms::as_hms(dep_time),
         month = month(dep_time, label = TRUE)) %>% 
  ggplot(aes(x = dep_hms, color = month)) +
    geom_freqpoly(binwidth = 300)

  1. Compare dep_time, sched_dep_time and dep_delay. Are they consistent? Explain your findings.

It looks like the cases where there is a discrepancy between dep_time and the expected value (sched_dep_time + dep_delay) involve incorrect dates.

Sorry, I ran out of time and have no further notes for this chapter. Please see the solutions manuals: