I have a vector of dates e.g.
dates <- c('2013-01-01', '2013-04-02', '2013-06-10', '2013-09-30')
And a dataframe which contains a date column e.g.
df <- data.frame(
'date' = c('2013-01-04', '2013-01-22', '2013-10-01', '2013-10-10'),
'a' = c(1,2,3,4),
'b' = c('a', 'b', 'c', 'd')
)
And I would would like to subset the dataframe so it only contains rows where the date is less than 5 days after any of the dates in the 'dates' vector.
i.e. The initial dataframe looks like this
date a b
2013-01-04 1 a
2013-01-22 2 b
2013-10-01 3 c
2013-10-10 4 d
After the query I would only be left with the first and third row (since 2013-01-04 is within 5 days of 2013-01-01 and 2013-10-01 is within 5 days of 2013-09-30)
Does anyone know of the best way to do this?
Thanks in advance
This is easy (and very fast) to do with a data.table
roll:
library(data.table)
dt = data.table(df)
# convert to Date (or IDate) to have numbers instead of strings for dates
# also set the key for dates for the join
dt[, date := as.Date(date)]
dates = data.table(date = as.Date(dates), key = 'date')
# join with a roll of 5 days, throwing out dates that don't match
dates[dt, roll = 5, nomatch = 0]
# date a b
#1: 2013-01-04 1 a
#2: 2013-10-01 3 c
broken down into steps:
# Rows Selected: Iterate over each row in the DF,
# and check if its `date` value is within 5 from any value in the `dates` vector
rows <- sapply(df$date, function(x) any( abs(x-dates) <= 5))
# Use that result to subset your data.frame
df[rows, ]
# date a b
# 1 2013-01-04 1 a
# 3 2013-10-01 3 c
Importantly, make sure your date values are actual Date
s and not character
s looking like dates
dates <- as.Date(dates)
df$date <- as.Date(df$date)
First make sure that df$date
is of class date. Then:
df[df$date %in% sapply(dates, function(x) x:(x+5)),]
date a b
1 2013-01-04 1 a
3 2013-10-01 3 c
For some reason I feel like this may be a more proper method:
df[df$date %in% mapply(`:`, from=dates, to=dates+5),]