Retrieve subsequent rows when condition is met from two different columns - r

Question

I have a data frame with data that looks like this:
Part Number Vendor Name Position Repair
123 ABC 1 2
NA <NA> 2 4
NA <NA> 3 1
NA <NA> 4 5
NA <NA> 5 6
NA <NA> 6 3
123 XYZ 1 4
NA <NA> 2 5
NA <NA> 3 7
NA <NA> 4 1
NA <NA> 5 2
NA <NA> 6 3
NA <NA> 7 6
I have a part number and vendor name grouped. Whenever position column > 3 and Repair ==1, retrieve subsequent rows.
Suppose in the given example for Part number =123 and vendor name=ABC, the repair==1 is at third position [position=3]-> All the rows that belong to part=123 and vendor name =ABC should be excluded.
Part=123 and vendor name=XYZ, the repair ==1 is at the fourth position. So retrieve 4th,5th,6th and 7th rows.
Condition to be considered is consider rows where Position >3 and Repair ==1, retrieve all subsequent rows.
Sample data:
Input <- structure(list(`Part Number` = c(123, NA, NA, NA, NA, NA, 123,
NA, NA, NA, NA, NA, NA), `Vendor Name` = c("ABC", NA, NA, NA,
NA, NA, "XYZ", NA, NA, NA, NA, NA, NA), Position = c(1, 2, 3,
4, 5, 6, 1, 2, 3, 4, 5, 6, 7), Repair = c(2, 4, 1, 5, 6, 3, 4,
5, 7, 1, 2, 3, 6)), .Names = c("Part Number", "Vendor Name", "Position",
"Repair"), row.names = c(NA, -13L), class = c("tbl_df", "tbl",
"data.frame"))
I've tried the following but it hasn't resulted in what I wanted:
output_table <- Input %>% group_by(`Part Number`,`Vendor Name`) %>%
mutate(rn=row_number()) %>% filter(rn>=which(pivot$Repair==1)) #Here I'm able to filter subsequent rows where repair==1 but how to exclude the rows which doesn't fall under the mentioned conditions.
output_table <- Input[Input$Position >3 & Input$Repair==1,] # gives me rows matching the condition but I need subsequent rows once the condition is met

Answer 1

Your format seems like it is geared towards presentation (reports) vice for data processing. Any processing like this should really be done before you do things like remove repeating rows for visual-grouping.
Ultimately, the only part you need here within group_by is the use of cumany. The rest of the mutating code is to accommodate the NA fields.
Input %>%
# assuming order is "safe to assume"
mutate_at(vars(`Part Number`, `Vendor Name`), zoo::na.locf) %>%
group_by(`Part Number`,`Vendor Name`) %>%
filter(cumany(Position > 3 & Repair == 1)) %>%
# return the first two columns to NA
mutate(toprow = row_number() == 1L) %>%
ungroup() %>%
mutate_at(vars(`Part Number`, `Vendor Name`), ~ if_else(toprow, ., .[NA])) %>%
select(-toprow)
# # A tibble: 4 x 4
# `Part Number` `Vendor Name` Position Repair
# <dbl> <chr> <dbl> <dbl>
# 1 123 XYZ 4 1
# 2 NA <NA> 5 2
# 3 NA <NA> 6 3
# 4 NA <NA> 7 6
If you are doing more processing on the data, I'd suggest you don't undo "dragging the labels down", instead just doing:
Input %>%
# assuming order is "safe to assume"
mutate_at(vars(`Part Number`, `Vendor Name`), zoo::na.locf) %>%
group_by(`Part Number`,`Vendor Name`) %>%
filter(cumany(Position > 3 & Repair == 1)) %>%
ungroup()
# # A tibble: 4 x 4
# `Part Number` `Vendor Name` Position Repair
# <dbl> <chr> <dbl> <dbl>
# 1 123 XYZ 4 1
# 2 123 XYZ 5 2
# 3 123 XYZ 6 3
# 4 123 XYZ 7 6

Answer 2

With dplyr and tidyr you can do this as follows:
library(dplyr)
library(tidyr)
Input %>%
fill(`Part Number`, `Vendor Name`) %>% # fill down missing values
group_by(`Part Number`, `Vendor Name`) %>% # group by `Part Number` & `Vendor Name`
filter( cumsum(Position>3 & Repair==1) >= 1) # select only rows where the cumulative sum of true/false condition >= 1
Output for that should be what you are looking for:
# A tibble: 4 x 4
`Part Number` `Vendor Name` Position Repair
<dbl> <chr> <dbl> <dbl>
1 123 XYZ 4 1
2 123 XYZ 5 2
3 123 XYZ 6 3
4 123 XYZ 7 6