CODEWITHSUNDEEP
rdataframedplyr
DR15
DR15

Reputation: 693

How to remove rows including sequential condition between two columns

I'm trying to remove the factors from my dataframe, but only those after a specific date. Here I made a toy example:

I have a test dataframe, and an inspection dataframe inspec. I would like to remove the letters that are in var1 that appear in inspec, but only the rows after the date in inspec. For example, consider

> test = data.frame(var1 = c("A", "B", "A", "B", "C","B", "A"), measure = c(6,7,8,6,10,1,0), date = as.Date(c("2021-01-02", "2021-01-03", "2021-01-04", "2021-01-05", "2021-01-06", "2021-01-07",  "2021-01-12")))
> test
  var1 measure       date
1    A       6 2021-01-02
2    B       7 2021-01-03
3    A       8 2021-01-04
4    B       6 2021-01-05
5    C      10 2021-01-06
6    B       1 2021-01-07
7    A       0 2021-01-12
> 
> inspec = data.frame(var1 = c("A", "C", "D", "A"), date = as.Date(c("2021-01-03", "2021-01-06", "2021-01-10", "2021-01-12")))
> inspec
  var1       date
1    A 2021-01-03
2    C 2021-01-06
3    D 2021-01-10
4    A 2021-01-12

Then, as result, I'd like to obtain:

> test
  var1 measure       date
1    A       6 2021-01-02
2    B       7 2021-01-03
3    B       6 2021-01-05
4    B       1 2021-01-07

Note that only the A in var1 that were inspected after the date indicated in the inspec dataframe were excluded. If I didnt' want to maintain the var1 before a inspec date, I could just use test= test[!(test$var1 %in% inspec$var1),]

Any hint on how can I do that?

Upvotes: 1

Views: 60

Answers (3)

M--
M--

Reputation: 28825

We can join on var1 and filter based on dates while the data is grouped by var1 and only keep the first match. See below;

library(dplyr)

test %>%
  left_join(inspec, by = "var1", suffix = c("", ".y")) %>%
  group_by(var1) %>% 
  filter(is.na(date.y) | date < first(date.y)) %>% 
  select(-date.y) %>% 
  group_by_all() %>% 
  slice(1)

#> # A tibble: 4 x 3
#> # Groups:   var1, measure, date [4]
#>   var1  measure date      
#>   <fct>   <dbl> <date>    
#> 1 A           6 2021-01-02
#> 2 B           1 2021-01-07
#> 3 B           6 2021-01-05
#> 4 B           7 2021-01-03

This is a variation of r2evans's answer.

Upvotes: 2

r2evans
r2evans

Reputation: 160437

base R

## reduce `inspec` to the earliest date
inspec$date <- as.Date(inspec$date)
tmpinspec <- inspec[ave(as.integer(inspec$date), inspec$var1, FUN = function(z) z == min(z)) > 0,]
tmpinspec
#   var1       date
# 1    A 2021-01-03
# 2    C 2021-01-06
# 3    D 2021-01-10

tmp <- merge(test, tmpinspec, by = "var1", all.x = TRUE, suffixes = c("", ".y"))
tmp
#   var1 measure       date     date.y
# 1    A       6 2021-01-02 2021-01-03
# 2    A       8 2021-01-04 2021-01-03
# 3    A       0 2021-01-12 2021-01-03
# 4    B       7 2021-01-03       <NA>
# 5    B       6 2021-01-05       <NA>
# 6    B       1 2021-01-07       <NA>
# 7    C      10 2021-01-06 2021-01-06

tmp <- tmp[with(tmp, is.na(date.y) | date < date.y),]
# tmp$date.y <- NULL
# tmp
  var1 measure       date
# 1    A       6 2021-01-02
# 4    B       7 2021-01-03
# 5    B       6 2021-01-05
# 6    B       1 2021-01-07

dplyr

library(dplyr)
group_by(inspec, var1) %>%
  slice_min(date) %>%
  left_join(test, ., by = "var1", suffix = c("", ".y")) %>%
  filter(is.na(date.y) | date < date.y) %>%
  select(-date.y)
#   var1 measure       date
# 1    A       6 2021-01-02
# 2    B       7 2021-01-03
# 3    B       6 2021-01-05
# 4    B       1 2021-01-07

Upvotes: 2

gsolomon10
gsolomon10

Reputation: 365

Using data.table, we can also use the merge and then filter:

library(data.table)
test <- setDT(test); inspec <- setDT(inspec)
test <- merge(test, inspec, by = "var1", all.x = TRUE, suffixes = c("", ".y"))

test <- test[date < date.y | !is.na(date.y), .(var1, measure, date)]

Upvotes: 0

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