lubridate - select first non-Monday of every week. - r

Question

Having a tibble of financial data, I would like to filter it by only selecting the first non-Monday of every week. Usually it will be a Tuesday, but sometimes it can be a Wednesday if Tuesday is a Holiday.
Here is my code that works in most cases
XLF <- quantmod::getSymbols("XLF", from = "2000-01-01", auto.assign = FALSE)
library(tibble)
library(lubridate)
library(dplyr)
xlf <- as_tibble(XLF) %>% rownames_to_column(var = "date") %>%
select(date, XLF.Adjusted)
xlf$date <- ymd(xlf$date)
# We create Month, Week number and Days of the week columns
# Then we remove all the Mondays
xlf <- xlf %>% mutate(Year = year(date), Month = month(date),
IsoWeek = isoweek(date), WDay = wday(date)) %>%
filter(WDay != 2)
# Creating another tibble just for ease of comparison
xlf2 <- xlf %>%
group_by(Year, IsoWeek) %>%
filter(row_number() == 1) %>%
ungroup()
That said, there are some issues that I have not been able to solve so far.
The issue is for instance that it is skipping "2002-12-31" which is a Tuesday because it is considered as part of the first ISO week of 2003.
There are a few similar issues.
My question is how could I select of the first non-Monday of every week without such issues while staying in the tidyverse (ie. not having to use xts / zoo class)?

Answer 1

You can create a consistently increasing week number yourself. Perhaps not the most elegant solution but it works fine for me.
as_tibble(XLF) %>%
rownames_to_column(var = "date")%>%
select(date, XLF.Adjusted)%>%
mutate(date = ymd(date),
Year = year(date),
Month = month(date),
WDay = wday(date),
WDay_label = wday(date, label = T))%>%
# if the weekday number is higher in the line above or
# if the date in the previous line is more than 6 days ago
# the week number should be incremented
mutate(week_increment = (WDay < lag(WDay) | difftime(date, lag(date), unit = 'days') > 6))%>%
# the previous line causes the first element to be NA due to
# the fact that the lag function can't find a line above
# we correct this here by setting the first element to TRUE
mutate(week_increment = ifelse(row_number() == 1,
TRUE,
week_increment))%>%
# we can sum the boolean elements in a cumulative way to get a week number
mutate(week_number = cumsum(week_increment))%>%
filter(WDay != 2)%>%
group_by(Year, week_number) %>%
filter(row_number() == 1)