Removing data after different start date based on id [duplicate]-CodePudding

I have a data set that includes a name, date and earliest_date, in which some name will have a earliest_date. Now I want to remove all the data after the earliest_date based on name. And ignore those that have NA in earliest_date. And sicne different name will have different earliest_date, I am pretty sure I can't use filter() with a set date. Any help will be much appericated.

Part of the data is below:

dput(mydata[1:10,])
structure(list(name = c("a", "b", "c", 
"d", "e", "f", "g", 
"a", "h", "i"), Date = structure(c(13214, 
17634, 15290, 18046, 16326, 18068, 10234, 12647, 15485, 15182
), class = "Date"), earliest_date = structure(c(12647, NA, NA, 
NA, NA, NA, NA, 12647, NA, 15552), class = "Date")), row.names = c(NA, 
10L), class = "data.frame")

Desired output: The first row will be removed as the Date recorded after earliest_date

dput(mydata[2:10,])
structure(list(name = c("b", "c", 
"d", "e", "f", "g", 
"a", "h", "i"), Date = structure(c(17634, 15290, 
18046, 16326, 18068, 10234, 12647, 15485, 15182), class = "Date"), 
    earliest_date = structure(c(NA, NA, NA, NA, NA, NA, 12647, 
    NA, 15552), class = "Date")), row.names = 2:10, class = "data.frame")

CodePudding user response：

This may helps

mydata %>%
  filter(is.na(earliest_date) | Date<=earliest_date)

  name       Date earliest_date
1    b 2018-04-13          <NA>
2    c 2011-11-12          <NA>
3    d 2019-05-30          <NA>
4    e 2014-09-13          <NA>
5    f 2019-06-21          <NA>
6    g 1998-01-08          <NA>
7    a 2004-08-17    2004-08-17
8    h 2012-05-25          <NA>
9    i 2011-07-27    2012-07-31

CodePudding user response：

Or try:

library(data.table)
setDT(mydata)[is.na(mydata$earliest_date) | mydata$Date<=earliest_date,]