DATES & TIMES
YOUR TURN #1
Q: Create date objects for today’s date by typing the date in text format and converting it with one of the lubridate converter functions.
library(lubridate)
ymd("2020-03-26")## [1] "2020-03-26"mdy("03-26-2020")## [1] "2020-03-26"Q: Try different formats of writing the date and compare the end results.
ymd("2020-03-26", tz = "UTC")## [1] "2020-03-26 UTC"Q: Use the appropriate lubridate function to parse each of the following dates:
d1 <- "January 1, 2010"
d2 <- "2015-Mar-07"
d3 <- "06-Jun-2017"
d4 <- c("August 19 (2015)", "July 1 (2015)")
d5 <- "12/30/14" # Dec 30, 2014mdy(d1)## [1] "2010-01-01"ymd(d2)## [1] "2015-03-07"dmy(d3)## [1] "2017-06-06"mdy(d4)## [1] "2015-08-19" "2015-07-01"mdy(d5)## [1] "2014-12-30"YOUR TURN #2
For this question use the flights data from the nycflights13 package.
library(nycflights13)
library(tidyverse)Q: Use make_datetime() to create a date-time variable for dep_time and arr_time.
Hint: use modular arithmetic %/% for hour and %% for minute.
flights_dt <- flights %>%
filter(!is.na(dep_time), !is.na(arr_time)) %>%
mutate(dep_hour = dep_time %/% 100,
dep_min = dep_time %% 100,
dep_time = make_datetime(year, month, day, dep_hour, dep_min),
arr_hour = arr_time %/% 100,
arr_min = arr_time %% 100,
arr_time = make_datetime(year, month, day, arr_hour, arr_min))
flights_dt## # A tibble: 328,063 x 23
## year month day dep_time sched_dep_time dep_delay
## <int> <int> <int> <dttm> <int> <dbl>
## 1 2013 1 1 2013-01-01 05:17:00 515 2
## 2 2013 1 1 2013-01-01 05:33:00 529 4
## 3 2013 1 1 2013-01-01 05:42:00 540 2
## 4 2013 1 1 2013-01-01 05:44:00 545 -1
## 5 2013 1 1 2013-01-01 05:54:00 600 -6
## 6 2013 1 1 2013-01-01 05:54:00 558 -4
## 7 2013 1 1 2013-01-01 05:55:00 600 -5
## 8 2013 1 1 2013-01-01 05:57:00 600 -3
## 9 2013 1 1 2013-01-01 05:57:00 600 -3
## 10 2013 1 1 2013-01-01 05:58:00 600 -2
## # … with 328,053 more rows, and 17 more variables: arr_time <dttm>,
## # 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>, dep_hour <dbl>, dep_min <dbl>,
## # arr_hour <dbl>, arr_min <dbl>YOUR TURN #3
For this question use the flights_dt data created in the last your turn.
Q: Use an accessor function to calculate the average departure delay by minute within the hour. Use ggplot2 to plot your results.
flights_dt %>%
mutate(minute = minute(dep_time)) %>%
group_by(minute) %>%
summarise(
avg_delay = mean(arr_delay, na.rm = TRUE),
n = n()) %>%
ggplot(aes(minute, avg_delay)) +
geom_line()
YOUR TURN #4
Q: Inspect the budget data set from the classdata package
library(classdata)
budget %>% glimpse()## Observations: 5,855
## Variables: 5
## $ ReleaseDate <date> 2009-12-17, 2011-05-20, 2019-04-23, 2015-04-22, 201…
## $ Movie <chr> "Avatar", "Pirates of the Caribbean: On Stranger Tid…
## $ ProductionBudget <dbl> 425000000, 410600000, 400000000, 330600000, 31700000…
## $ DomesticGross <dbl> 760507625, 241063875, 858373000, 459005868, 62018138…
## $ WorldwideGross <dbl> 2789705275, 1045663875, 2795473000, 1403013963, 1316…Q: Make sure the variable Release Date is a date format.
summary(budget$ReleaseDate)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## "1915-02-08" "2000-04-21" "2007-02-11" "2004-07-14" "2012-12-25" "2022-12-16"
## NA's
## "163"budget %>%
arrange(desc(ReleaseDate)) %>%
head()## ReleaseDate Movie ProductionBudget DomesticGross WorldwideGross
## 1 2022-12-16 Heaven and Hell 4.00e+07 0 0
## 2 2021-01-15 355 7.50e+07 0 0
## 3 2020-08-28 Unhinged 2.90e+07 0 0
## 4 2020-07-17 Tenet 2.24e+08 0 0
## 5 2020-02-21 Call of the Wild 8.20e+07 0 0
## 6 2020-02-14 Sonic The Hedgehog 9.00e+07 0 0Q: Plot a histogram of the variable
budget %>%
ggplot(aes(x = ReleaseDate)) +
geom_histogram(binwidth=365)
Q: Merge (join) budget and box office data (by movie name)
box_budget <- box %>% left_join(budget, by = "Movie")
head(box_budget)## Rank Rank.Last.Week Movie Distributor Gross Change
## 1 1 1 Joker Warner Bros. 55861403 -42
## 2 2 NA The Addams Family United Artists 30300007 NA
## 3 2 NA The Addams Family United Artists 30300007 NA
## 4 3 NA Gemini Man Paramount Pictures 20552372 NA
## 5 4 2 Abominable Universal 6072235 -49
## 6 5 3 Downton Abbey Focus Features 4881075 -39
## Thtrs. Per.Thtr. Total.Gross Week Date ReleaseDate ProductionBudget
## 1 4374 12771 193590190 2 2019-10-11 2019-10-02 5.5e+07
## 2 4007 7562 30300007 1 2019-10-11 1991-11-22 3.0e+07
## 3 4007 7562 30300007 1 2019-10-11 2019-10-11 2.4e+07
## 4 3642 5643 20552372 1 2019-10-11 <NA> NA
## 5 3496 1737 47873585 3 2019-10-11 <NA> NA
## 6 3019 1617 82668665 4 2019-10-11 <NA> NA
## DomesticGross WorldwideGross
## 1 208909478 563609478
## 2 113502246 191502246
## 3 37784650 37784650
## 4 NA NA
## 5 NA NA
## 6 NA NAQ: Is the time between the release of a movie and the date is equal to the number of weeks in theaters?
box_budget %>%
filter(!is.na(ReleaseDate)) %>%
mutate(test_week = (ReleaseDate %--% Date) %/% dweeks(1)) %>%
select(Movie, ReleaseDate, Date, Week, test_week) %>%
head()## Movie ReleaseDate Date Week test_week
## 1 Joker 2019-10-02 2019-10-11 2 1
## 2 The Addams Family 1991-11-22 2019-10-11 1 1455
## 3 The Addams Family 2019-10-11 2019-10-11 1 0
## 4 Hustlers 2019-09-12 2019-10-11 5 4
## 5 It: Chapter Two 2019-09-04 2019-10-11 6 5
## 6 Ad Astra 2019-09-19 2019-10-11 4 3box_budget %>%
filter(!is.na(ReleaseDate)) %>%
mutate(test_week = ceiling((ReleaseDate %--% Date) / dweeks(1))) %>%
select(Movie, ReleaseDate, Date, Week, test_week) %>%
head()## Movie ReleaseDate Date Week test_week
## 1 Joker 2019-10-02 2019-10-11 2 2
## 2 The Addams Family 1991-11-22 2019-10-11 1 1455
## 3 The Addams Family 2019-10-11 2019-10-11 1 0
## 4 Hustlers 2019-09-12 2019-10-11 5 5
## 5 It: Chapter Two 2019-09-04 2019-10-11 6 6
## 6 Ad Astra 2019-09-19 2019-10-11 4 4