I have dates in an R dataframe column formatted as character strings as WK01Q32014.
I want to turn each date into a Date() object.
So I altered the format to make it look like 01-3-2014. I want to try to do something like as.Date("01-3-2014","%W-%Q-%Y") for example, but there is no format code for quarters that I know of.
Is there any way to do this using the lubridate, zoo, or any other libraries?
I dont know of any specific function, but here's a basic one:
convert_WQ_to_Date <- function(D) {
weeks <- as.integer(substr(D, 3, 4))
quarter <- as.integer(substr(D, 6, 6))
year <- substr(D, 7, 10)
days <- 7 * ((quarter - 1) * 13 + (weeks-1))
as.Date(sprintf("%s-01-01", year)) + days
}
Example
D <- c("WK01Q32014", "WK01Q12014", "WK05Q42014", "WK01Q22014", "WK02Q32014")
convert_WQ_to_Date(D)
[1] "2014-07-02" "2014-01-01" "2014-10-29" "2014-04-02" "2014-07-09"
The week, quarter and year does not uniquely define a date so we will have to add some assumption. Here we add the assumption that the first week is the first day of the quarter, the second week is 7 days later and so on,
Below, we extract the qtr-year part and use as.yearqtr in the zoo package to convert that to a yearqtr object and then use as.Date to convert that to a date which is the first of the quarter. We then extract the week, subtract 1 and multiply by 7 to get the days offset. Adding the first of the quarter to the offset gives the result:
library(zoo)
xx <- "01-3-2014" # week-quarter-year
qtr.start <- as.Date(as.yearqtr(sub("...", "", xx), "%q-%Y"))
days <- 7 * (as.numeric(sub("-.*", "", xx)) - 1)
qtr.start + days
## [1] "2014-07-01"
Assuming the traditional notion of each quarter starting respectively at the 1st January, 1st April, 1st July and 1st September (in line with the quarters function), just start at these dates and add 7 days for each week:
x <- c("01-3-2014","01-1-2014","05-4-2014","01-2-2014","02-3-2014")
y <- as.numeric(substr(x,6,9))
m <- as.numeric(substr(x,4,4))
d <- as.numeric(substr(x,1,2))
as.Date(paste(y,(m-1)*3+1,"01",sep="-")) + (7*(d-1))
#[1] "2014-07-01" "2014-01-01" "2014-10-29" "2014-04-01" "2014-07-08"
Related
Let's say we have this:
ex <- c('2012-41')
This represent the week 41 from the year 2012. How would I get the month from this?
Since a week can be between two months, I will be interested to get the month when that week started (here October).
Not duplicate to How to extract Month from date in R (do not have a standard date format like %Y-%m-%d).
you could try:
ex <- c('2019-10')
splitDate <- strsplit(ex, "-")
dateNew <- as.Date(paste(splitDate[[1]][1], splitDate[[1]][2], 1, sep="-"), "%Y-%U-%u")
monthSelected <- lubridate::month(dateNew)
3
I hope this helps!
This depends on the definition of week. See the discussion of %V and %W in ?strptime for two possible definitions of week. We use %V below but the function allows one to specify the other if desired. The function performs a sapply over the elements of x and for each such element it extracts the year into yr and forms a sequence of all dates for that year in sq. It then converts those dates to year-month and finds the first occurrence of the current component of x in that sequence, finally extracting the match's month.
yw2m <- function(x, fmt = "%Y-%V") {
sapply(x, function(x) {
yr <- as.numeric(substr(x, 1, 4))
sq <- seq(as.Date(paste0(yr, "-01-01")), as.Date(paste0(yr, "-12-31")), "day")
as.numeric(format(sq[which.max(format(sq, fmt) == x)], "%m"))
})
}
yw2m('2012-41')
## [1] 10
The following will add the week-of-year to an input of year-week formatted strings and return a vector of dates as character. The lubridate package weeks() function will add the dates corresponding to the end of the relevant week. Note for example I've added an additional case in your 'ex' variable to the 52nd week, and it returns Dec-31st
library(lubridate)
ex <- c('2012-41','2016-4','2018-52')
dates <- strsplit(ex,"-")
dates <- sapply(dates,function(x) {
year_week <- unlist(x)
year <- year_week[1]
week <- year_week[2]
start_date <- as.Date(paste0(year,'-01-01'))
date <- start_date+weeks(week)
#note here: OP asked for beginning of week.
#There's some ambiguity here, the above is end-of-week;
#uncommment here for beginning of week, just subtracted 6 days.
#I think this might yield inconsistent results, especially year-boundaries
#hence suggestion to use end of week. See below for possible solution
#date <- start_date+weeks(week)-days(6)
return (as.character(date))
})
Yields:
> dates
[1] "2012-10-14" "2016-01-29" "2018-12-31"
And to simply get the month from these full dates:
month(dates)
Yields:
> month(dates)
[1] 10 1 12
If I have a given date, how do I find the first and last days of the next month?
For example,
today <- as.Date("2009-04-04")
I want to find
# first date in next month
"2009-05-01"
# last date in next month
"2009-05-31"
You can do this with base R:
today <- as.Date("2009-04-04")
first <- function(x) {
x <- as.POSIXlt(x)
x$mon[] <- x$mon + 1
x$mday[] <- 1
x$isdst[] <- -1L
as.Date(x)
}
first(today)
#[1] "2009-05-01"
first(first(today)) - 1
#[1] "2009-05-31"
lubridate has some useful tools for this purpose.
library(lubridate)
today <- ymd("2009-04-12")
# First day of next month
first <- ceiling_date(today, unit = "month")
# Last day of next month
last <- ceiling_date(first, unit= "month") -1
first
#"2009-05-01"
last
#"2009-05-31"
Here are some solutions. We use today from the question to test. In both cases the input may be a Date class vector.
1) Base R Define function fom to give the first of the month of its Date
argument. Using that we can get the date of the first and last of the next month as follows. We use the facts that 31 and 62 days after the first of the month is necessarily a date in the next month and month after the next month.
fom <- function(x) as.Date(cut(x, "month"))
fom(fom(today) + 31)
## [1] "2009-05-01"
fom(fom(today) + 62) - 1
## [1] "2009-05-31"
2) yearmon yearmon class objects internally represent a year and month as the year plus 0 for January, 1/12 for Febrary, 2/12 for March and so on. Using as.Date.yearmon the frac argument specifies the fraction of the way through the month to output. The default is frac = 0 and results in the first of the month being output and frac = 1 means the end of the month.
library(zoo)
as.Date(as.yearmon(today) + 1/12)
## [1] "2009-05-01"
as.Date(as.yearmon(today) + 1/12, frac = 1)
## [1] "2009-05-31"
Is there a way in R to get the 1st and the last day for a specified month.
Eg.
Input: September 2018 or any other format to specify month and year
Expected output:
1st day function (Input) -> 01-Sep-2018 or any other valid date format
Last day function (Input) -> 30-Sep-2018 or any other valid date format
We can create a function in base R
get_first_and_last_date <- function(month_year) {
start_date = as.Date(paste0("01 ", month_year), "%d %b %Y")
end_date = (seq(start_date, length.out = 2, by = "month") - 1)[2]
c(start_date, end_date)
}
get_first_and_last_date('Dec 2018')
#[1] "2018-12-01" "2018-12-31"
get_first_and_last_date('Sep 2016')
#[1] "2016-09-01" "2016-09-30"
Whatever format you enter make sure it is consistent throughout. Here I have considered the input would always be a month name and complete year.
Using the lubridate library:
require(lubridate)
d <- as.Date('2018-09-01')
last_day <- d
day(last_day) <- days_in_month(last_day)
For a base R solution, we can define a helper method to add months. Then, the last day of a given month can be computed by adding one month to the first of the month and subtracting one day:
add.months <- function(date, n) seq(date, by=paste (n, "months"), length=2 [2]
d <- as.Date('2018-09-01') # first of the month
last_day <- add.months(d, 1) - 1 # last of the month
Credit for the add.months helper function is given to this SO question.
I have a column of strings in my data set formatted as year week (e.g. '201401' is equivalent to 7th April 2014, or the first fiscal week of the year)
I am trying to convert these to a proper date so I can manipulate them later, however I always receive the dame date for a given year, specifically the 14th of April.
e.g.
test_set <- c('201401', '201402', '201403')
as.Date(test_set, '%Y%U')
gives me:
[1] "2014-04-14" "2014-04-14" "2014-04-14"
Try something like this:
> test_set <- c('201401', '201402', '201403')
>
> extractDate <- function(dateString, fiscalStart = as.Date("2014-04-01")) {
+ week <- substr(dateString, 5, 6)
+ currentDate <- fiscalStart + 7 * as.numeric(week) - 1
+ currentDate
+ }
>
> extractDate(test_set)
[1] "2014-04-07" "2014-04-14" "2014-04-21"
Basically, I'm extracting the weeks from the start of the year, converting it to days and then adding that number of days to the start of the fiscal year (less 1 day to make things line up).
Not 100% sure what is your desired output but this may work
as.Date(paste0(substr(test_set, 1, 4), "-04-07")) +
(as.numeric(substr(test_set, 5, 6)) - 1) * 7
# [1] "2014-04-07" "2014-04-14" "2014-04-21"
I want to correct source activity based on the difference between reference and measurement date and source half life (measured in years). Say I have
ref_date <- as.Date('06/01/08',format='%d/%m/%y')
and a column in my data.frame with the same date format, e.g.,
today <- as.Date(Sys.Date(), format='%d/%m/%y')
I can find the number of years between these dates using the lubridate package
year(today)-year(ref_date)
[1] 5
Is there a function I can use to get a floating point answer today - ref_date = 5.2y, for example?
Yes, of course, use difftime() with an as numeric:
R> as.numeric(difftime(as.Date("2003-04-05"), as.Date("2001-01-01"),
+ unit="weeks"))/52.25
[1] 2.2529
R>
Note that we do have to switch to weeks scaled by 52.25 as there is a bit of ambiguity
there in terms of counting years---a February 29 comes around every 4 years but not every 100th etc.
So you have to define that. difftime() handles all time units up to weeks. Months cannot be done for the same reason of the non-constant 'numerator'.
The lubridate package contains a built-in function, time_length, which can help perform this task.
time_length(difftime(as.Date("2003-04-05"), as.Date("2001-01-01")), "years")
[1] 2.257534
time_length(difftime(as.Date("2017-03-01"), as.Date("2012-03-01")),"years")
[1] 5.00274
Documentation for the lubridate package can be found here.
Inspired by Bryan F, time_length() would work better if using interval object
time_length(interval(as.Date("2003-04-05"), as.Date("2001-01-01")), "years")
[1] -2.257534
time_length(difftime(as.Date("2017-03-01"), as.Date("2012-03-01")),"years")
[1] 5.00274
time_length(interval(as.Date("2017-03-01"), as.Date("2012-03-01")),"years")
[1] -5
You can see if you use interval() to get the time difference and then pass it to time_length(), time_length() would take into account the fact that not all months and years have the same number of days, e.g., the leap year.
Not an exact answer to your question, but the answer from Dirk Eddelbuettel in some situations can produce small errors.
Please, consider the following example:
as.numeric(difftime(as.Date("2012-03-01"), as.Date("2017-03-01"), unit="weeks"))/52.25
[1] -4.992481
The correct answer here should be at least 5 years.
The following function (using lubridate package) will calculate a number of full years between two dates:
# Function to calculate an exact full number of years between two dates
year.diff <- function(firstDate, secondDate) {
yearsdiff <- year(secondDate) - year(firstDate)
monthsdiff <- month(secondDate) - month(firstDate)
daysdiff <- day(secondDate) - day(firstDate)
if ((monthsdiff < 0) | (monthsdiff == 0 & daysdiff < 0)) {
yearsdiff <- yearsdiff - 1
}
yearsdiff
}
You can modify it to calculate a fractional part depending on how you define the number of days in the last (not finished) year.
You can use the function AnnivDates() of the package BondValuation:
R> library('BondValuation')
R> DateIndexes <- unlist(
+ suppressWarnings(
+ AnnivDates("2001-01-01", "2003-04-05", CpY=1)$DateVectors[2]
+ )
+ )
R> names(DateIndexes) <- NULL
R> DateIndexes[length(DateIndexes)] - DateIndexes[1]
[1] 2.257534
Click here for documentation of the package BondValuation.
To get the date difference in years (floating point) you can convert the dates to decimal numbers of Year and calculate then their difference.
#Example Dates
x <- as.Date(c("2001-01-01", "2003-04-05"))
#Convert Date to decimal year:
date2DYear <- function(x) {
as.numeric(format(x,"%Y")) + #Get Year an add
(as.numeric(format(x,"%j")) - 0.5) / #Day of the year divided by
as.numeric(format(as.Date(paste0(format(x,"%Y"), "-12-31")),"%j")) #days of the year
}
diff(date2DYear(x)) #Get the difference in years
#[1] 2.257534
I subtract 0.5 from the day of the year as it is not known if you are at the beginning or the end of the day and %j starts with 1.
I think the difference between 2012-03-01 and 2017-03-01 need not to be 5 Years, as 2012 has 366 days and 2017 365 and 2012-03-01 is on the 61 day of the year and 2017-03-01 on the 60.
x <- as.Date(c("2012-03-01", "2017-03-01"))
diff(date2DYear(x))
#[1] 4.997713
Note that using time_length and interval from lubridate need not come to the same result when you make a cumulative time difference.
library(lubridate)
x <- as.Date(c("2012-01-01", "2012-03-01", "2012-12-31"))
time_length(interval(x[1], x[3]), "years")
#[1] 0.9972678
time_length(interval(x[1], x[2]), "years") +
time_length(interval(x[2], x[3]), "years")
#[1] 0.9995509 #!
diff(date2DYear(x[c(1,3)]))
#[1] 0.9972678
diff(date2DYear(x[c(1,2)])) + diff(date2DYear(x[c(2,3)]))
#[1] 0.9972678
x <- as.Date(c("2013-01-01", "2013-03-01", "2013-12-31"))
time_length(interval(x[1], x[3]), "years")
#[1] 0.9972603
time_length(interval(x[1], x[2]), "years") +
time_length(interval(x[2], x[3]), "years")
#[1] 0.9972603
diff(date2DYear(x[c(1,3)]))
#[1] 0.9972603
diff(date2DYear(x[c(1,2)])) + diff(date2DYear(x[c(2,3)]))
#[1] 0.9972603
Since you are already using lubridate package, you can obtain number of years in floating point using a simple trick:
find number of seconds in one year:
seconds_in_a_year <- as.integer((seconds(ymd("2010-01-01")) - seconds(ymd("2009-01-01"))))
now obtain number of seconds between the 2 dates you desire
seconds_between_dates <- as.integer(seconds(date1) - seconds(date2))
your final answer for number of years in floating points will be
years_between_dates <- seconds_between_dates / seconds_in_a_year