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dint helps you with working with year-quarter, year-month and
year-isoweek dates. It stores them in an easily human readable integer
format, e.q 20141
for the first quarter of 2014 and so
forth. If you are already using such integers to represent dates, dint
can make many common operations easier for you.
dint is implemented in base R and comes with zero external dependencies. Even if you don’t work with such special dates directly, dint can still help you at formatting dates, labelling plot axes, or getting first / last days of calendar periods (quarters, months, isoweeks).
dint provides 4 different S3 classes that inherit from
date_xx
1.
date_yq
for year-quarter datesdate_yw
for year-month datesdate_yw
for year-isoweek dates. Please note that the
year for isoweeks does not necessarily correspond to the calendar year
wikipediadate_y
for storing years. This class exists for
consistency and provides little advantage over storing years as bare
integers. The main use of this class is in package development when you
want to write your own generics and methods for years.date_xx
vectors can be created using explicit
constructors…
date_yq(2015, 1)
#> [1] "2015-Q1"
date_ym(c(2015, 2016), c(1, 2))
#> [1] "2015-M01" "2016-M02"
date_yw(c(2008, 2009), 1)
#> [1] "2008-W01" "2009-W01"
..or through coercion of Dates
or
integers
You can coerce Dates
to any date_xx
subclass with as_date_**()
d <- as.Date("2018-05-12")
as_date_yq(d)
#> [1] "2018-Q2"
as_date_ym(d)
#> [1] "2018-M05"
as_date_yw(d)
#> [1] "2018-W19"
as_date_y(d)
#> [1] "2018"
Conversely, you can convert date_xx
back to R
Dates
as.POSIXct()
creates datetimes in UTC/GMT, so the result
might not always be as expected, depending on your local timezone.
All date_xx
support addition, subtraction and sequence
generation.
q <- date_yq(2014, 4)
q + 1
#> [1] "2015-Q1"
q - 1
#> [1] "2014-Q3"
seq(q - 2, q + 2)
#> [1] "2014-Q2" "2014-Q3" "2014-Q4" "2015-Q1" "2015-Q2"
m <- date_ym(2014, 12)
m + 1
#> [1] "2015-M01"
m - 1
#> [1] "2014-M11"
seq(m - 2, m + 2)
#> [1] "2014-M10" "2014-M11" "2014-M12" "2015-M01" "2015-M02"
w <- date_yw(2017, 33)
w + 1
#> [1] "2017-W34"
w - 1
#> [1] "2017-W32"
seq(w - 2, w + 2)
#> [1] "2017-W31" "2017-W32" "2017-W33" "2017-W34" "2017-W35"
You can access components of date_xx
(e.g the quarter of
a date_yq
) with accessor functions. You can also use these
functions to convert between date_xx
vectors.
q <- date_yq(2014, 4)
get_year(q)
#> [1] 2014
get_quarter(q)
#> [1] 4
get_month(q) # defaults to first month of quarter
#> [1] 10
get_isoweek(q)
#> [1] 40
m <- date_ym(2014, 12)
get_year(m)
#> [1] 2014
get_quarter(m)
#> [1] 4
get_month(m)
#> [1] 12
get_isoweek(m)
#> [1] 49
w <- date_yw(2014, 33)
get_year(w)
#> [1] 2014
get_quarter(w)
#> [1] 3
get_month(w)
#> [1] 8
get_isoweek(w)
#> [1] 33
If you use lubridate, you can just use the slightly less verbose lubridate accessors
suppressPackageStartupMessages(library(lubridate))
year(q)
#> [1] 2014
quarter(q)
#> [1] 4
month(q)
#> [1] 10
You can get the first and last days of calendar periods with dint
q <- date_yq(2015, 1)
first_of_quarter(q) # the same as as.Date(q), but more explicit
#> [1] "2015-01-01"
last_of_quarter(q)
#> [1] "2015-03-31"
These functions work with normal dates
d <- as.Date("2018-05-12")
first_of_year(d)
#> [1] "2018-01-01"
last_of_year(d)
#> [1] "2018-12-31"
first_of_quarter(d)
#> [1] "2018-04-01"
last_of_quarter(d)
#> [1] "2018-06-30"
first_of_month(d)
#> [1] "2018-05-01"
last_of_month(d)
#> [1] "2018-05-31"
first_of_isoweek(d)
#> [1] "2018-05-07"
last_of_isoweek(d)
#> [1] "2018-05-13"
# Alternativeley you can use these:
first_of_yq(2012, 2)
#> [1] "2012-04-01"
last_of_ym(2012, 2)
#> [1] "2012-02-29"
last_of_yw(2011, 52)
#> [1] "2012-01-01"
Formatting date_xx
vectors is easy and uses a subset of
the placeholders of base::strptime()
(+ %q
for
quarters).
dint implements scale_date_**()
and
date_**_breaks()
that provide nicely labeled axes for
ggplots by default
q <- data.frame(
time = seq(date_yq(2016, 1), date_yq(2016, 4)),
value = rnorm(4)
)
m <- data.frame(
time = seq(date_ym(2016, 8), date_ym(2016, 11)),
value = rnorm(4)
)
w <- data.frame(
time = seq(date_yw(2016, 48), date_yw(2017, 1)),
value = abs(rnorm(6))
)
w2 <- data.frame(
time = seq(date_yw(2016, 1), date_yw(2019, 1)),
value = abs(rnorm(157))
)
ggplot(q, aes(x = time, y = value)) +
geom_point()
ggplot(m, aes(x = time, y = value)) +
geom_line() +
scale_x_date_ym(labels = format_ym_shorter)
ggplot(w, aes(x = time, y = value)) +
geom_col() +
scale_x_date_yw(labels = format_yw_iso)
ggplot(w2, aes(x = time, y = value)) +
geom_smooth()
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
If you use R Date
vectors, you can still use the
formatting functions supplied by dint to generate nice axis labels.
x <- data.frame(
time = seq(as.Date("2016-01-01"), as.Date("2016-08-08"), by = "day"),
value = rnorm(221)
)
p <- ggplot(
x,
aes(
x = time,
y = value)
) + geom_point()
p + ggtitle("iso") + ggtitle("default")
p + scale_x_date(labels = format_yq_iso) + ggtitle("date_yq_iso")
p + scale_x_date(labels = format_ym_short) + ggtitle("date_ym_short")
p + scale_x_date(labels = format_yw_shorter) + ggtitle("date_yw_shorter")
date_xx
is just a superclass for all dint
date classes, you do not need to use it directly↩︎
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They may not be fully stable and should be used with caution. We make no claims about them.