Reputation: 1245
I have a dataframe
. Some dates fall on the weekend. However I would like to change all weekend dates to the past Friday.
as.Date(aapl_earnings$Date, "%Y/%m/%d")
[1] "2018-04-30" "2018-01-31" "2017-11-01" "2017-07-31" "2017-05-01" "2017-01-30" "2016-10-24"
[8] "2016-07-25" "2016-04-25" "2016-01-25" "2015-10-26" "2015-07-20" "2015-04-26" "2015-01-26"
[15] "2014-10-19" "2014-07-21" "2014-04-22" "2014-01-26" "2013-10-27"
Upvotes: 0
Views: 154
Reputation: 388797
We can use a nested ifelse
here and check the day of the week using weekdays
and adjust the date accordingly.
dates <- weekdays(as.Date(x))
as.Date(ifelse(dates == "Saturday", x - 1,
ifelse(dates == "Sunday", x - 2, x)), origin = "1970-01-01")
#[1]"2018-04-30" "2018-01-31" "2017-11-01" "2017-07-31" "2017-05-01" "2017-01-30"
#[7]"2016-10-24" "2016-07-25" "2016-04-25" "2016-01-25" "2015-10-26" "2015-07-20"
#[13]"2015-04-24" "2015-01-26" "2014-10-17" "2014-07-21" "2014-04-22" "2014-01-24"
#[19]"2013-10-25"
Or we can also use case_when
from dplyr
which is more verbose.
library(dplyr)
aapl_earnings <- data.frame(Date = as.Date(x))
aapl_earnings %>%
mutate(date = weekdays(Date),
new_date = case_when(date == "Saturday" ~ Date - 1,
date == "Sunday" ~ Date - 2,
TRUE ~ Date)) %>%
select(-date)
# Date new_date
#1 2018-04-30 2018-04-30
#2 2018-01-31 2018-01-31
#3 2017-11-01 2017-11-01
#4 2017-07-31 2017-07-31
#5 2017-05-01 2017-05-01
#6 2017-01-30 2017-01-30
#7 2016-10-24 2016-10-24
#8 2016-07-25 2016-07-25
#9 2016-04-25 2016-04-25
#10 2016-01-25 2016-01-25
#11 2015-10-26 2015-10-26
#12 2015-07-20 2015-07-20
#13 2015-04-26 2015-04-24
#14 2015-01-26 2015-01-26
#15 2014-10-19 2014-10-17
#16 2014-07-21 2014-07-21
#17 2014-04-22 2014-04-22
#18 2014-01-26 2014-01-24
#19 2013-10-27 2013-10-25
data
x <- c("2018-04-30","2018-01-31","2017-11-01","2017-07-31","2017-05-01",
"2017-01-30","2016-10-24","2016-07-25","2016-04-25","2016-01-25","2015-10-26",
"2015-07-20","2015-04-26","2015-01-26" ,"2014-10-19","2014-07-21","2014-04-22",
"2014-01-26", "2013-10-27")
Upvotes: 2