Working with Dates and Times in R
Arif Y.
04 April 2022
Libraries
library(tidyverse)
library(anytime)
library(lubridate)
library(ggridges)
library(microbenchmark)
library(fasttime)Data
releases <- read.csv("releases.csv")
logs <- read.csv("logs.csv")
akl_daily_raw <- read_csv("akl_daily_raw.csv")
akl_hourly_raw <- read.csv("akl_hourly_raw.csv")0. Course Description
Dates and times are abundant in data and essential for answering questions that start with when, how long, or how often. However, they can be tricky, as they come in a variety of formats and can behave in unintuitive ways. This course teaches you the essentials of parsing, manipulating, and computing with dates and times in R.
1. Dates and Times in R
R doesn’t know something is a date or time unless you tell it. In this chapter you’ll learn about some of the ways R stores dates and times by exploring how often R versions are released, and how quickly people download them. You’ll also get a sneak peek at what you’ll learn in the following chapters.
1.1 Specifying dates
As you saw in the video, R doesn’t know something is a date unless you tell it. If you have a character string that represents a date in the ISO 8601 standard you can turn it into a Date using the as.Date() function. Just pass the character string (or a vector of character strings) as the first argument.
# The date R 3.0.0 was released
x <- "2013-04-03"
# Examine structure of x
str(x)## chr "2013-04-03"# Use as.Date() to interpret x as a date
x_date <- as.Date(x)
# Examine structure of x_date
str(x_date)## Date[1:1], format: "2013-04-03"# Store April 10 2014 as a Date
april_10_2014 <- as.Date("2014-04-10")1.2 Automatic import
Sometimes you’ll need to input a couple of dates by hand using as.Date() but it’s much more common to have a column of dates in a data file.
Some functions that read in data will automatically recognize and parse dates in a variety of formats. In particular the import functions, like read_csv(), in the readr package will recognize dates in a few common formats.
There is also the anytime() function in the anytime package whose sole goal is to automatically parse strings as dates regardless of the format.
# Examine the structure of the date column
str(releases$date)## Factor w/ 104 levels "1997-12-04","1997-12-21",..: 1 2 3 4 5 6 6 7 8 9 ...# Various ways of writing Sep 10 2009
sep_10_2009 <- c("September 10 2009", "2009-09-10", "10 Sep 2009", "09-10-2009")
# Use anytime() to parse sep_10_2009
anytime(sep_10_2009)## [1] "2009-09-10 PST" "2009-09-10 PST" "2009-09-10 PST" "2009-09-10 PST"1.3 Plotting
f you plot a Date on the axis of a plot, you expect the dates to be in calendar order, and that’s exactly what happens with plot() or ggplot().
In this exercise you’ll make some plots with the R version releases data from the previous exercises using ggplot2. There are two big differences when a Date is on an axis:
- If you specify limits they must be Date objects.
- To control the behavior of the scale you use the scale_x_date() function.
releases <- releases %>% mutate(date= as.Date(date))
# Set the x axis to the date column
ggplot(releases, aes(x = date, y = type)) +
geom_line(aes(group = 1, color = factor(major)))
# Limit the axis to between 2010-01-01 and 2014-01-01
ggplot(releases, aes(x =date, y = type)) +
geom_line(aes(group = 1, color = factor(major))) +
xlim(as.Date("2010-01-01"), as.Date("2014-01-01"))## Warning: Removed 87 row(s) containing missing values (geom_path).
# Specify breaks every ten years and labels with "%Y"
ggplot(releases, aes(x = date, y = type)) +
geom_line(aes(group = 1, color = factor(major))) +
scale_x_date(date_breaks = "10 years", date_labels = "%Y")
1.4 Arithmetic and logical operators
Since Date objects are internally represented as the number of days since 1970-01-01 you can do basic math and comparisons with dates. You can compare dates with the usual logical operators (<, ==, > etc.), find extremes with min() and max(), and even subtract two dates to find out the time between them.
# Find the largest date
last_release_date <- max(releases$date)
# Filter row for last release
last_release <- filter(releases, date == last_release_date)
# Print last_release
last_release## X major minor patch date datetime time type
## 1 105 3 4 1 2017-06-30 2017-06-30T07:04:11Z 07:04:11.824142 patch# How long since last release?
Sys.Date() - last_release_date## Time difference of 1739 days1.5 Getting datetimes into R
Just like dates without times, if you want R to recognize a string as a datetime you need to convert it, although now you use as.POSIXct(). as.POSIXct() expects strings to be in the format YYYY-MM-DD HH:MM:SS.
The only tricky thing is that times will be interpreted in local time based on your machine’s set up. You can check your timezone with Sys.timezone(). If you want the time to be interpreted in a different timezone, you just set the tz argument of as.POSIXct(). You’ll learn more about time zones in Chapter 4.
# Use as.POSIXct to enter the datetime
as.POSIXct("2010-10-01 12:12:00")## [1] "2010-10-01 12:12:00 PST"# Use as.POSIXct again but set the timezone to `"America/Los_Angeles"`
as.POSIXct("2010-10-01 12:12:00", tz = "America/Los_Angeles")## [1] "2010-10-01 12:12:00 PDT"# Use readr to import rversions.csv
# Examine structure of datetime column
str(releases$datetime)## Factor w/ 105 levels "1997-12-04T08:47:58Z",..: 1 2 3 4 5 6 7 8 9 10 ...2. Parsing and Manipulating Dates and Times with lubridate
Dates and times come in a huge assortment of formats, so your first hurdle is often to parse the format you have into an R datetime. This chapter teaches you to import dates and times with the lubridate package. You’ll also learn how to extract parts of a datetime.
2.1 Selecting the right parsing function
lubridate provides a set of functions for parsing dates of a known order. For example, ymd() will parse dates with year first, followed by month and then day. The parsing is flexible, for example, it will parse the m whether it is numeric (e.g. 9 or 09), a full month name (e.g. September), or an abbreviated month name (e.g. Sep).
All the functions with y, m and d in any order exist. If your dates have times as well, you can use the functions that start with ymd, dmy, mdy or ydm and are followed by any of _h, _hm or _hms.
To see all the functions available look at ymd() for dates and ymd_hms() for datetimes.
# Parse x
x <- "2010 September 20th" # 2010-09-20
ymd(x)## [1] "2010-09-20"# Parse y
y <- "02.01.2010" # 2010-01-02
dmy(y)## [1] "2010-01-02"# Parse z
z <- "Sep, 12th 2010 14:00" # 2010-09-12T14:00
mdy_hm(z)## [1] "2010-09-12 14:00:00 UTC"2.2 Specifying an order with parse_date_time()
What about if you have something in a really weird order like dym_msh? There’s no named function just for that order, but that is where parse_date_time() comes in. parse_date_time() takes an additional argument, orders, where you can specify the order of the components in the date.
For example, to parse “2010 September 20th” you could say parse_date_time(“2010 September 20th”, orders = “ymd”) and that would be equivalent to using the ymd() function from the previous exercise.
One advantage of parse_date_time() is that you can use more format characters. For example, you can specify weekday names with A, I for 12 hour time, am/pm indicators with p and many others. You can see a whole list on the help page ?parse_date_time
# Specify an order string to parse x
x <- "Monday June 1st 2010 at 4pm"
parse_date_time(x, orders = "ABdyIp")## [1] "2010-06-01 16:00:00 UTC"# Specify order to include both "mdy" and "dmy"
two_orders <- c("October 7, 2001", "October 13, 2002", "April 13, 2003",
"17 April 2005", "23 April 2017")
parse_date_time(two_orders, orders = c("mdy", "dmy"))## [1] "2001-10-07 UTC" "2002-10-13 UTC" "2003-04-13 UTC" "2005-04-17 UTC"
## [5] "2017-04-23 UTC"# Specify order to include "dOmY", "OmY" and "Y"
short_dates <- c("11 December 1282", "May 1372", "1253")
parse_date_time(short_dates, orders = c("dOmY", "OmY", "Y"))## [1] "1282-12-11 UTC" "1372-05-01 UTC" "1253-01-01 UTC"2.3 Import daily weather data
There are two data sets: akl_weather_daily.csv a set of once daily summaries for 10 years, and akl_weather_hourly_2016.csv observations every half hour for 2016. You’ll import the daily data in this exercise and the hourly weather in the next exercise.
# Print akl_daily_raw
head(akl_daily_raw)## # A tibble: 6 × 7
## date max_temp min_temp mean_temp mean_rh events cloud_cover
## <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 2007-9-1 60 51 56 75 <NA> 4
## 2 2007-9-2 60 53 56 82 Rain 4
## 3 2007-9-3 57 51 54 78 <NA> 6
## 4 2007-9-4 64 50 57 80 Rain 6
## 5 2007-9-5 53 48 50 90 Rain 7
## 6 2007-9-6 57 42 50 69 <NA> 1# Parse date
akl_daily <- akl_daily_raw %>% mutate(date = ymd(date))
# Print akl_daily
head(akl_daily)## # A tibble: 6 × 7
## date max_temp min_temp mean_temp mean_rh events cloud_cover
## <date> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 2007-09-01 60 51 56 75 <NA> 4
## 2 2007-09-02 60 53 56 82 Rain 4
## 3 2007-09-03 57 51 54 78 <NA> 6
## 4 2007-09-04 64 50 57 80 Rain 6
## 5 2007-09-05 53 48 50 90 Rain 7
## 6 2007-09-06 57 42 50 69 <NA> 1# Plot to check work
ggplot(akl_daily, aes(x = date, y = max_temp)) +
geom_line()## Warning: Removed 1 row(s) containing missing values (geom_path).
2.4 Import hourly weather data
The hourly data is a little different. The date information is spread over three columns year, month and mday, so you’ll need to use make_date() to combine them.
# Print akl_hourly_raw
head(akl_hourly_raw)## X year month mday time temperature weather conditions events humidity
## 1 1 2016 1 1 00:00:00 68 Clear Clear <NA> 68
## 2 2 2016 1 1 00:30:00 68 Clear Clear <NA> 68
## 3 3 2016 1 1 01:00:00 68 Clear Clear <NA> 73
## 4 4 2016 1 1 01:30:00 68 Clear Clear <NA> 68
## 5 5 2016 1 1 02:00:00 68 Clear Clear <NA> 68
## 6 6 2016 1 1 02:30:00 68 Clear Clear <NA> 68
## date_utc
## 1 2015-12-31T11:00:00Z
## 2 2015-12-31T11:30:00Z
## 3 2015-12-31T12:00:00Z
## 4 2015-12-31T12:30:00Z
## 5 2015-12-31T13:00:00Z
## 6 2015-12-31T13:30:00Zakl_hourly_raw <- akl_hourly_raw %>% mutate(temperature = as.numeric(temperature))
# Use make_date() to combine year, month and mday
akl_hourly <- akl_hourly_raw %>%
mutate(date = make_date(year = year, month = month, day = mday))
# Parse datetime_string
akl_hourly <- akl_hourly %>%
mutate(
datetime_string = paste(date, time, sep = "T"),
datetime = ymd_hms(datetime_string))## Warning: 861 failed to parse.# Print date, time and datetime columns of akl_hourly
akl_hourly %>% select(date, time, datetime)## date time datetime
## 1 2016-01-01 00:00:00 2016-01-01 00:00:00
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## 1910 <NA> 22:30:00 <NA>
## 1911 <NA> 23:00:00 <NA>
## 1912 <NA> 23:30:00 <NA>
## 1913 2016-02-02 00:00:00 2016-02-02 00:00:00
## 1914 2016-02-02 00:30:00 2016-02-02 00:30:00
## 1915 2016-02-02 01:00:00 2016-02-02 01:00:00
## 1916 2016-02-02 01:30:00 2016-02-02 01:30:00
## 1917 2016-02-02 02:00:00 2016-02-02 02:00:00
## 1918 2016-02-02 02:30:00 2016-02-02 02:30:00
## 1919 2016-02-02 03:00:00 2016-02-02 03:00:00
## 1920 2016-02-02 03:30:00 2016-02-02 03:30:00
## 1921 2016-02-02 04:00:00 2016-02-02 04:00:00
## 1922 2016-02-02 04:30:00 2016-02-02 04:30:00
## 1923 2016-02-02 05:00:00 2016-02-02 05:00:00
## 1924 2016-02-02 05:30:00 2016-02-02 05:30:00
## 1925 2016-02-02 06:00:00 2016-02-02 06:00:00
## 1926 2016-02-02 06:30:00 2016-02-02 06:30:00
## 1927 2016-02-02 07:00:00 2016-02-02 07:00:00
## 1928 2016-02-02 07:30:00 2016-02-02 07:30:00
## 1929 2016-02-02 08:00:00 2016-02-02 08:00:00
## 1930 2016-02-02 08:30:00 2016-02-02 08:30:00
## 1931 2016-02-02 09:00:00 2016-02-02 09:00:00
## 1932 2016-02-02 09:30:00 2016-02-02 09:30:00
## 1933 2016-02-02 10:00:00 2016-02-02 10:00:00
## 1934 2016-02-02 10:30:00 2016-02-02 10:30:00
## 1935 2016-02-02 11:00:00 2016-02-02 11:00:00
## 1936 2016-02-02 11:30:00 2016-02-02 11:30:00
## 1937 2016-02-02 12:00:00 2016-02-02 12:00:00
## 1938 2016-02-02 12:30:00 2016-02-02 12:30:00
## 1939 2016-02-02 13:00:00 2016-02-02 13:00:00
## 1940 2016-02-02 13:30:00 2016-02-02 13:30:00
## 1941 2016-02-02 14:00:00 2016-02-02 14:00:00
## 1942 2016-02-02 14:30:00 2016-02-02 14:30:00
## 1943 2016-02-02 15:00:00 2016-02-02 15:00:00
## 1944 2016-02-02 15:30:00 2016-02-02 15:30:00
## 1945 2016-02-02 16:00:00 2016-02-02 16:00:00
## 1946 2016-02-02 16:30:00 2016-02-02 16:30:00
## 1947 2016-02-02 17:00:00 2016-02-02 17:00:00
## 1948 2016-02-02 17:30:00 2016-02-02 17:30:00
## 1949 2016-02-02 18:00:00 2016-02-02 18:00:00
## 1950 2016-02-02 18:30:00 2016-02-02 18:30:00
## 1951 2016-02-02 19:00:00 2016-02-02 19:00:00
## 1952 2016-02-02 19:30:00 2016-02-02 19:30:00
## 1953 2016-02-02 20:00:00 2016-02-02 20:00:00
## 1954 2016-02-02 20:30:00 2016-02-02 20:30:00
## 1955 2016-02-02 21:00:00 2016-02-02 21:00:00
## 1956 2016-02-02 21:30:00 2016-02-02 21:30:00
## 1957 2016-02-02 22:00:00 2016-02-02 22:00:00
## 1958 2016-02-02 22:30:00 2016-02-02 22:30:00
## 1959 2016-02-02 23:00:00 2016-02-02 23:00:00
## 1960 2016-02-02 23:30:00 2016-02-02 23:30:00
## 1961 2016-02-03 00:00:00 2016-02-03 00:00:00
## 1962 2016-02-03 00:30:00 2016-02-03 00:30:00
## 1963 2016-02-03 01:00:00 2016-02-03 01:00:00
## 1964 2016-02-03 01:30:00 2016-02-03 01:30:00
## 1965 2016-02-03 02:00:00 2016-02-03 02:00:00
## 1966 2016-02-03 02:30:00 2016-02-03 02:30:00
## 1967 2016-02-03 03:00:00 2016-02-03 03:00:00
## 1968 2016-02-03 03:30:00 2016-02-03 03:30:00
## 1969 2016-02-03 04:00:00 2016-02-03 04:00:00
## 1970 2016-02-03 04:30:00 2016-02-03 04:30:00
## 1971 2016-02-03 05:00:00 2016-02-03 05:00:00
## 1972 2016-02-03 05:30:00 2016-02-03 05:30:00
## 1973 2016-02-03 06:00:00 2016-02-03 06:00:00
## 1974 2016-02-03 06:30:00 2016-02-03 06:30:00
## 1975 2016-02-03 07:00:00 2016-02-03 07:00:00
## 1976 2016-02-03 07:30:00 2016-02-03 07:30:00
## 1977 2016-02-03 08:00:00 2016-02-03 08:00:00
## 1978 2016-02-03 08:30:00 2016-02-03 08:30:00
## 1979 2016-02-03 09:00:00 2016-02-03 09:00:00
## 1980 2016-02-03 09:30:00 2016-02-03 09:30:00
## 1981 2016-02-03 10:00:00 2016-02-03 10:00:00
## 1982 2016-02-03 10:30:00 2016-02-03 10:30:00
## 1983 2016-02-03 11:00:00 2016-02-03 11:00:00
## 1984 2016-02-03 11:30:00 2016-02-03 11:30:00
## 1985 2016-02-03 12:00:00 2016-02-03 12:00:00
## 1986 2016-02-03 12:30:00 2016-02-03 12:30:00
## 1987 2016-02-03 13:00:00 2016-02-03 13:00:00
## 1988 2016-02-03 13:30:00 2016-02-03 13:30:00
## 1989 2016-02-03 14:00:00 2016-02-03 14:00:00
## 1990 2016-02-03 14:30:00 2016-02-03 14:30:00
## 1991 2016-02-03 15:00:00 2016-02-03 15:00:00
## 1992 2016-02-03 15:30:00 2016-02-03 15:30:00
## 1993 2016-02-03 16:00:00 2016-02-03 16:00:00
## 1994 2016-02-03 16:30:00 2016-02-03 16:30:00
## 1995 2016-02-03 17:00:00 2016-02-03 17:00:00
## 1996 2016-02-03 17:30:00 2016-02-03 17:30:00
## 1997 2016-02-03 18:00:00 2016-02-03 18:00:00
## 1998 2016-02-03 18:30:00 2016-02-03 18:30:00
## 1999 2016-02-03 19:00:00 2016-02-03 19:00:00
## 2000 2016-02-03 19:30:00 2016-02-03 19:30:00
## 2001 2016-02-03 20:00:00 2016-02-03 20:00:00
## 2002 2016-02-03 20:30:00 2016-02-03 20:30:00
## 2003 2016-02-03 21:00:00 2016-02-03 21:00:00
## 2004 2016-02-03 21:30:00 2016-02-03 21:30:00
## 2005 2016-02-03 22:00:00 2016-02-03 22:00:00
## 2006 2016-02-03 22:30:00 2016-02-03 22:30:00
## 2007 2016-02-03 23:00:00 2016-02-03 23:00:00
## 2008 2016-02-03 23:30:00 2016-02-03 23:30:00
## 2009 2016-02-04 00:00:00 2016-02-04 00:00:00
## 2010 2016-02-04 00:30:00 2016-02-04 00:30:00
## 2011 2016-02-04 01:00:00 2016-02-04 01:00:00
## 2012 2016-02-04 01:30:00 2016-02-04 01:30:00
## 2013 2016-02-04 02:00:00 2016-02-04 02:00:00
## 2014 2016-02-04 02:30:00 2016-02-04 02:30:00
## 2015 2016-02-04 03:00:00 2016-02-04 03:00:00
## 2016 2016-02-04 03:30:00 2016-02-04 03:30:00
## 2017 2016-02-04 04:00:00 2016-02-04 04:00:00
## 2018 2016-02-04 04:30:00 2016-02-04 04:30:00
## 2019 2016-02-04 05:00:00 2016-02-04 05:00:00
## 2020 2016-02-04 05:30:00 2016-02-04 05:30:00
## 2021 2016-02-04 06:00:00 2016-02-04 06:00:00
## 2022 2016-02-04 06:30:00 2016-02-04 06:30:00
## 2023 2016-02-04 07:00:00 2016-02-04 07:00:00
## 2024 2016-02-04 07:30:00 2016-02-04 07:30:00
## 2025 2016-02-04 08:00:00 2016-02-04 08:00:00
## 2026 2016-02-04 08:30:00 2016-02-04 08:30:00
## 2027 2016-02-04 09:00:00 2016-02-04 09:00:00
## 2028 2016-02-04 09:30:00 2016-02-04 09:30:00
## 2029 2016-02-04 10:00:00 2016-02-04 10:00:00
## 2030 2016-02-04 10:30:00 2016-02-04 10:30:00
## 2031 2016-02-04 11:00:00 2016-02-04 11:00:00
## 2032 2016-02-04 11:30:00 2016-02-04 11:30:00
## 2033 2016-02-04 12:00:00 2016-02-04 12:00:00
## 2034 2016-02-04 12:30:00 2016-02-04 12:30:00
## 2035 2016-02-04 13:00:00 2016-02-04 13:00:00
## 2036 2016-02-04 13:30:00 2016-02-04 13:30:00
## 2037 2016-02-04 14:00:00 2016-02-04 14:00:00
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## 2079 2016-02-05 11:00:00 2016-02-05 11:00:00
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## 3781 <NA> 71.6 <NA>
## 3782 2016-03-18 16:30:00 2016-03-18 16:30:00
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## 17228 2016-12-26 23:30:00 2016-12-26 23:30:00# Plot to check work
ggplot(akl_hourly, aes(x = datetime, y = temperature)) +
geom_line()## Warning: Removed 861 row(s) containing missing values (geom_path).
2.5 What can you extract?
As you saw in the video, components of a datetime can be extracted by lubridate functions with the same name like year(), month(), day(), hour(), minute() and second(). They all work the same way just pass in a datetime or vector of datetimes. There are also a few useful functions that return other aspects of a datetime like if it occurs in the morning am(), during daylight savings dst(), in a leap_year(), or which quarter() or semester() it occurs in.
release_time <- releases$datetime
head((release_time))## [1] 1997-12-04T08:47:58Z 1997-12-21T13:09:22Z 1998-01-10T00:31:55Z
## [4] 1998-03-14T19:25:55Z 1998-05-02T07:58:17Z 1998-06-14T12:56:20Z
## 105 Levels: 1997-12-04T08:47:58Z 1997-12-21T13:09:22Z ... 2017-06-30T07:04:11Z# Examine the head() of the months of release_time
head(month(release_time))## [1] 12 12 1 3 5 6# Extract the month of releases
month(release_time) %>% table()## .
## 1 2 3 4 5 6 7 8 9 10 11 12
## 5 6 8 18 5 16 4 7 2 15 6 13# Extract the year of releases
year(release_time) %>% table()## .
## 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
## 2 10 9 6 6 5 5 4 4 4 4 6 5 4 6 4
## 2013 2014 2015 2016 2017
## 4 4 5 5 3# How often is the hour before 12 (noon)?
mean(hour(release_time) < 12)## [1] 1# How often is the release in am?
mean(am(release_time))## [1] 12.6 Adding useful labels
In the previous exercise you found the month of releases:
- head(month(release_time))
and received numeric months in return. Sometimes it’s nicer (especially for plotting or tables) to have named months. Both the month() and wday() (day of the week) functions have additional arguments label and abbr to achieve just that.
Set label = TRUE to have the output labelled with month (or weekday) names, and abbr = FALSE for those names to be written in full rather than abbreviated.
# Use wday() to tabulate release by day of the week
wday(releases$datetime) %>% table()## Warning: tz(): Don't know how to compute timezone for object of class factor;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.## .
## 1 2 3 4 5 6 7
## 3 29 9 12 18 31 3# Add label = TRUE to make table more readable
wday(releases$datetime, label = TRUE) %>% table()## Warning: tz(): Don't know how to compute timezone for object of class factor;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.## .
## Sun Mon Tue Wed Thu Fri Sat
## 3 29 9 12 18 31 3# Create column wday to hold week days
releases$wday <- wday(releases$datetime, label = TRUE)## Warning: tz(): Don't know how to compute timezone for object of class factor;
## returning "UTC". This warning will become an error in the next major version of
## lubridate.# Plot barchart of weekday by type of release
ggplot(releases, aes(wday)) +
geom_bar() +
facet_wrap(~ type, ncol = 1, scale = "free_y")
2.7 Extracting for plotting
Extracting components from a datetime is particularly useful when exploring data. Earlier in the chapter you imported daily data for weather in Auckland, and created a time series plot of ten years of daily maximum temperature. While that plot gives you a good overview of the whole ten years, it’s hard to see the annual pattern.
# Add columns for year, yday and month
akl_daily <- akl_daily %>%
mutate(
year = year(date),
yday = yday(date),
month = month(date, label = TRUE))
# Plot max_temp by yday for all years
ggplot(akl_daily, aes(x = yday, y = max_temp)) +
geom_line(aes(group = year), alpha = 0.5)## Warning: Removed 1 row(s) containing missing values (geom_path).
# Examine distribution of max_temp by month
ggplot(akl_daily, aes(x = max_temp, y = month, height = ..density..)) +
geom_density_ridges(stat = "density")## Warning: Removed 10 rows containing non-finite values (stat_density).
2.8 Extracting for filtering and summarizing
Another reason to extract components is to help with filtering observations or creating summaries. For example, if you are only interested in observations made on weekdays (i.e. not on weekends) you could extract the weekdays then filter out weekends, e.g. wday(date) %in% 2:6.
# Create new columns hour, month and rainy
akl_hourly <- akl_hourly %>%
mutate(
hour = hour(datetime),
month = month(datetime, label = TRUE),
rainy = weather == "Precipitation")
# Filter for hours between 8am and 10pm (inclusive)
akl_day <- akl_hourly %>%
filter(hour >= 8, hour <= 22)
# Summarise for each date if there is any rain
rainy_days <- akl_day %>%
group_by(month, date) %>%
summarise(
any_rain = any(rainy))## `summarise()` has grouped output by 'month'. You can override using the
## `.groups` argument.# Summarise for each month, the number of days with rain
rainy_days %>%
summarise(
days_rainy = sum(any_rain))## # A tibble: 12 × 2
## month days_rainy
## <ord> <int>
## 1 Jan 15
## 2 Feb 12
## 3 Mar 9
## 4 Apr 14
## 5 May 21
## 6 Jun 17
## 7 Jul 22
## 8 Aug 15
## 9 Sep 24
## 10 Oct 19
## 11 Nov 18
## 12 Dec 112.9 Rounding datetimes
As you saw in the video, round_date() rounds a date to the nearest value, floor_date() rounds down, and ceiling_date() rounds up. All three take a unit argument which specifies the resolution of rounding. You can specify “second”, “minute”, “hour”, “day”, “week”, “month”, “bimonth”, “quarter”, “halfyear”, or “year”. Or, you can specify any multiple of those units, e.g. “5 years”, “3 minutes” etc.
r_3_4_1 <- ymd_hms("2016-05-03 07:13:28 UTC")
# Round down to day
floor_date(r_3_4_1, unit ="day")## [1] "2016-05-03 UTC"# Round to nearest 5 minutes
round_date(r_3_4_1, unit = "5 minutes")## [1] "2016-05-03 07:15:00 UTC"# Round up to week
ceiling_date(r_3_4_1, unit = "week")## [1] "2016-05-08 UTC"# Subtract r_3_4_1 rounded down to day
r_3_4_1 - round_date(r_3_4_1, unit = "day")## Time difference of 7.224444 hours# Create day_hour, datetime rounded down to hour
akl_hourly <- akl_hourly %>% mutate( day_hour = floor_date(datetime, unit = "hour"))
# Count observations per hour
akl_hourly %>% count(day_hour) ## day_hour n
## 1 2016-01-01 00:00:00 2
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## 7986 2016-12-22 00:00:00 1
## 7987 2016-12-22 01:00:00 2
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## 7999 2016-12-22 13:00:00 2
## 8000 2016-12-22 14:00:00 2
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## 8009 2016-12-22 23:00:00 2
## 8010 2016-12-23 00:00:00 1
## 8011 2016-12-23 01:00:00 2
## 8012 2016-12-23 02:00:00 2
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## 8017 2016-12-23 07:00:00 2
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## 8032 2016-12-23 22:00:00 2
## 8033 2016-12-23 23:00:00 2
## 8034 2016-12-24 00:00:00 2
## 8035 2016-12-24 01:00:00 1
## 8036 2016-12-24 02:00:00 2
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## 8047 2016-12-24 13:00:00 2
## 8048 2016-12-24 14:00:00 2
## 8049 2016-12-24 15:00:00 2
## 8050 2016-12-24 16:00:00 1
## 8051 2016-12-24 17:00:00 2
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## 8084 2016-12-26 02:00:00 1
## 8085 2016-12-26 03:00:00 2
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## 8105 2016-12-26 23:00:00 1
## 8106 2016-12-27 00:00:00 2
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## 8114 2016-12-27 08:00:00 1
## 8115 2016-12-27 09:00:00 2
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## 8122 2016-12-27 16:00:00 1
## 8123 2016-12-27 17:00:00 2
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## 8144 2016-12-28 14:00:00 1
## 8145 2016-12-28 15:00:00 2
## 8146 2016-12-28 16:00:00 1
## 8147 2016-12-28 17:00:00 2
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## 8224 2016-12-31 22:00:00 2
## 8225 2016-12-31 23:00:00 2
## 8226 <NA> 861# Find day_hours with n != 2
akl_hourly %>% count(day_hour) %>% filter(n != 2) %>% arrange(desc(n))## day_hour n
## 1 <NA> 861
## 2 2016-04-24 02:00:00 4
## 3 2016-09-19 00:00:00 4
## 4 2016-03-18 16:00:00 1
## 5 2016-06-20 09:00:00 1
## 6 2016-09-01 23:00:00 1
## 7 2016-09-02 04:00:00 1
## 8 2016-09-02 06:00:00 1
## 9 2016-09-02 10:00:00 1
## 10 2016-09-02 13:00:00 1
## 11 2016-09-02 16:00:00 1
## 12 2016-09-02 17:00:00 1
## 13 2016-09-03 02:00:00 1
## 14 2016-09-03 11:00:00 1
## 15 2016-09-03 16:00:00 1
## 16 2016-09-03 17:00:00 1
## 17 2016-09-04 12:00:00 1
## 18 2016-09-04 16:00:00 1
## 19 2016-09-04 17:00:00 1
## 20 2016-09-04 20:00:00 1
## 21 2016-09-05 02:00:00 1
## 22 2016-09-05 08:00:00 1
## 23 2016-09-05 13:00:00 1
## 24 2016-09-06 01:00:00 1
## 25 2016-09-06 05:00:00 1
## 26 2016-09-06 15:00:00 1
## 27 2016-09-06 20:00:00 1
## 28 2016-09-07 08:00:00 1
## 29 2016-09-07 09:00:00 1
## 30 2016-09-07 13:00:00 1
## 31 2016-09-07 19:00:00 1
## 32 2016-09-08 09:00:00 1
## 33 2016-09-08 10:00:00 1
## 34 2016-09-08 11:00:00 1
## 35 2016-09-08 15:00:00 1
## 36 2016-09-08 22:00:00 1
## 37 2016-09-10 00:00:00 1
## 38 2016-09-10 04:00:00 1
## 39 2016-09-10 05:00:00 1
## 40 2016-09-10 08:00:00 1
## 41 2016-09-10 11:00:00 1
## 42 2016-09-11 04:00:00 1
## 43 2016-09-11 05:00:00 1
## 44 2016-09-11 12:00:00 1
## 45 2016-09-12 04:00:00 1
## 46 2016-09-13 01:00:00 1
## 47 2016-09-27 11:00:00 1
## 48 2016-09-27 16:00:00 1
## 49 2016-09-27 17:00:00 1
## 50 2016-09-28 00:00:00 1
## 51 2016-09-28 15:00:00 1
## 52 2016-09-29 00:00:00 1
## 53 2016-09-29 09:00:00 1
## 54 2016-09-30 06:00:00 1
## 55 2016-09-30 16:00:00 1
## 56 2016-09-30 20:00:00 1
## 57 2016-10-03 10:00:00 1
## 58 2016-10-28 22:00:00 1
## 59 2016-10-28 23:00:00 1
## 60 2016-11-05 02:00:00 1
## 61 2016-11-14 10:00:00 1
## 62 2016-11-15 15:00:00 1
## 63 2016-11-16 11:00:00 1
## 64 2016-11-19 06:00:00 1
## 65 2016-11-19 19:00:00 1
## 66 2016-11-21 17:00:00 1
## 67 2016-11-23 21:00:00 1
## 68 2016-11-27 01:00:00 1
## 69 2016-12-01 13:00:00 1
## 70 2016-12-01 14:00:00 1
## 71 2016-12-08 10:00:00 1
## 72 2016-12-08 23:00:00 1
## 73 2016-12-10 05:00:00 1
## 74 2016-12-14 12:00:00 1
## 75 2016-12-14 16:00:00 1
## 76 2016-12-14 17:00:00 1
## 77 2016-12-14 21:00:00 1
## 78 2016-12-14 23:00:00 1
## 79 2016-12-22 00:00:00 1
## 80 2016-12-23 00:00:00 1
## 81 2016-12-24 01:00:00 1
## 82 2016-12-24 16:00:00 1
## 83 2016-12-26 02:00:00 1
## 84 2016-12-26 23:00:00 1
## 85 2016-12-27 08:00:00 1
## 86 2016-12-27 16:00:00 1
## 87 2016-12-28 14:00:00 1
## 88 2016-12-28 16:00:00 1
## 89 2016-12-30 18:00:00 1
## 90 2016-12-31 01:00:00 13. Arithmetic with Dates and Times
Getting datetimes into R is just the first step. Now that you know how to parse datetimes, you need to learn how to do calculations with them. In this chapter, you’ll learn the different ways of representing spans of time with lubridate and how to leverage them to do arithmetic on datetimes. By the end of the chapter, you’ll have calculated how long it’s been since the first man stepped on the moon, generated sequences of dates to help schedule reminders, calculated when an eclipse occurs, and explored the reigns of monarch’s of England (and which ones might have seen Halley’s comet!).
3.1 How long has it been?
To get finer control over a difference between datetimes use the base function difftime(). For example instead of time1 - time2, you use difftime(time1, time2).
difftime() takes an argument units which specifies the units for the difference. Your options are “secs”, “mins”, “hours”, “days”, or “weeks”
# The date of landing and moment of step
date_landing <- mdy("July 20, 1969")
moment_step <- mdy_hms("July 20, 1969, 02:56:15", tz = "UTC")
# How many days since the first man on the moon?
difftime(today(), date_landing, units = "days")## Time difference of 19251 days# How many seconds since the first man on the moon?
difftime(now(), moment_step, units = "secs")## Time difference of 1663324458 secs3.2 Adding or subtracting a time span to a datetime
A common use of time spans is to add or subtract them from a moment in time. For, example to calculate the time one day in the future from mar_11
# Add a period of one week to mon_2pm
mon_2pm <- dmy_hm("27 Aug 2018 14:00")
mon_2pm + weeks(1)## [1] "2018-09-03 14:00:00 UTC"# Add a duration of 81 hours to tue_9am
tue_9am <- dmy_hm("28 Aug 2018 9:00")
tue_9am + hours(81)## [1] "2018-08-31 18:00:00 UTC"# Subtract a period of five years from today()
today() - years(5)## [1] "2017-04-04"# Subtract a duration of five years from today()
today() - dyears(5)## [1] "2017-04-03 18:00:00 UTC"# A period of 2 weeks
weeks(2)## [1] "14d 0H 0M 0S"dweeks(2)## [1] "1209600s (~2 weeks)"3.3 Arithmetic with timespans
You can add and subtract timespans to create different length timespans, and even multiply them by numbers. For example, to create a duration of three days and three hours you could do: ddays(3) + dhours(3), or 3ddays(1) + 3dhours(1) or even 3*(ddays(1) + dhours(1)).
# Time of North American Eclipse 2017
eclipse_2017 <- ymd_hms("2017-08-21 18:26:40")
# Duration of 29 days, 12 hours, 44 mins and 3 secs
synodic <- ddays(29) + dhours(12) + dminutes(44) + dseconds(3)
# 223 synodic months
saros <- 223*synodic
# Add saros to eclipse_2017
eclipse_2017 + saros## [1] "2035-09-02 02:09:49 UTC"3.4 Generating sequences of datetimes
By combining addition and multiplication with sequences you can generate sequences of datetimes. For example, you can generate a sequence of periods from - 1 day up to 10 days with, - 1:10 * days(1)
Then by adding this sequence to a specific datetime, you can construct a sequence of datetimes from 1 day up to 10 days into the future today() + 1:10 * days(1)
# Add a period of 8 hours to today
today_8am <- today() + dhours(8)
# Sequence of two weeks from 1 to 26
every_two_weeks <- 1:26 * weeks(2)
# Create datetime for every two weeks for a year
today_8am + every_two_weeks## [1] "2022-04-18 08:00:00 UTC" "2022-05-02 08:00:00 UTC"
## [3] "2022-05-16 08:00:00 UTC" "2022-05-30 08:00:00 UTC"
## [5] "2022-06-13 08:00:00 UTC" "2022-06-27 08:00:00 UTC"
## [7] "2022-07-11 08:00:00 UTC" "2022-07-25 08:00:00 UTC"
## [9] "2022-08-08 08:00:00 UTC" "2022-08-22 08:00:00 UTC"
## [11] "2022-09-05 08:00:00 UTC" "2022-09-19 08:00:00 UTC"
## [13] "2022-10-03 08:00:00 UTC" "2022-10-17 08:00:00 UTC"
## [15] "2022-10-31 08:00:00 UTC" "2022-11-14 08:00:00 UTC"
## [17] "2022-11-28 08:00:00 UTC" "2022-12-12 08:00:00 UTC"
## [19] "2022-12-26 08:00:00 UTC" "2023-01-09 08:00:00 UTC"
## [21] "2023-01-23 08:00:00 UTC" "2023-02-06 08:00:00 UTC"
## [23] "2023-02-20 08:00:00 UTC" "2023-03-06 08:00:00 UTC"
## [25] "2023-03-20 08:00:00 UTC" "2023-04-03 08:00:00 UTC"4. Problems in practice
You now know most of what you need to tackle data that includes dates and times, but there are a few other problems you might encounter in practice. In this final chapter you’ll learn a little more about these problems by returning to some of the earlier data examples and learning how to handle time zones, deal with times when you don’t care about dates, parse dates quickly, and output dates and times.
4.1 Setting the timezone
If you import a datetime and it has the wrong timezone, you can set it with force_tz(). Pass in the datetime as the first argument and the appropriate timezone to the tzone argument. Remember the timezone needs to be one from OlsonNames().
# Game2: CAN vs NZL in Edmonton
game2 <- mdy_hm("June 11 2015 19:00")
# Game3: CHN vs NZL in Winnipeg
game3 <- mdy_hm("June 15 2015 18:30")
# Set the timezone to "America/Edmonton"
game2_local <- force_tz(game2, tzone = "America/Edmonton")
game2_local## [1] "2015-06-11 19:00:00 MDT"# Set the timezone to "America/Winnipeg"
game3_local <- force_tz(game3, tzone = "America/Winnipeg")
game3_local## [1] "2015-06-15 18:30:00 CDT"# How long does the team have to rest?
as.period(game2_local %--% game3_local)## [1] "3d 22H 30M 0S"4.2 Viewing in a timezone
To view a datetime in another timezone use with_tz(). The syntax of with_tz() is the same as force_tz(), passing a datetime and set the tzone argument to the desired timezone. Unlike force_tz(), with_tz() isn’t changing the underlying moment of time, just how it is displayed.
# What time is game2_local in NZ?
with_tz(game2_local, tzone = "Pacific/Auckland")## [1] "2015-06-12 13:00:00 NZST"# What time is game2_local in Corvallis, Oregon?
with_tz(game2_local, tzone = "America/Los_Angeles")## [1] "2015-06-11 18:00:00 PDT"# What time is game3_local in NZ?
with_tz(game3_local, tzone = "Pacific/Auckland")## [1] "2015-06-16 11:30:00 NZST"4.3 Timezones in the weather data
Did you ever notice that in the hourly Auckland weather data there was another datetime column, date_utc? Take a look:
- tibble::glimpse(akl_hourly)
# Examine datetime and date_utc columns
head(akl_hourly$datetime)## [1] "2016-01-01 00:00:00 UTC" "2016-01-01 00:30:00 UTC"
## [3] "2016-01-01 01:00:00 UTC" "2016-01-01 01:30:00 UTC"
## [5] "2016-01-01 02:00:00 UTC" "2016-01-01 02:30:00 UTC"head(akl_hourly$date_utc)## [1] 2015-12-31T11:00:00Z 2015-12-31T11:30:00Z 2015-12-31T12:00:00Z
## [4] 2015-12-31T12:30:00Z 2015-12-31T13:00:00Z 2015-12-31T13:30:00Z
## 17226 Levels: 2015-12-31T11:00:00Z 2015-12-31T11:30:00Z ... 2016-12-31T10:30:00Z# Force datetime to Pacific/Auckland
akl_hourly <- akl_hourly %>%
mutate(
datetime = force_tz(datetime, tzone = "Pacific/Auckland"))
# Reexamine datetime
head(akl_hourly$datetime)## [1] "2016-01-01 00:00:00 NZDT" "2016-01-01 00:30:00 NZDT"
## [3] "2016-01-01 01:00:00 NZDT" "2016-01-01 01:30:00 NZDT"
## [5] "2016-01-01 02:00:00 NZDT" "2016-01-01 02:30:00 NZDT"The End.
Thanks DataCamp
- My Favorite Team - 