Remove rows with two conditions in R - r

Question

I have this following dataset:
df <- structure(list(Data = structure(c(1623888000, 1629158400, 1629158400
), class = c("POSIXct", "POSIXt"), tzone = "UTC"), Client = c("Client1",
"Client1", "Client1"), Fund = c("Fund1", "Fund1", "Fund2"), Nature = c("Application",
"Rescue", "Application"), Quantity = c(433.059697, 0, 171.546757
), Value = c(69800, -70305.67, 24875), `NAV Yesterday` = c(162.40991399996,
162.40991399996, 145.044589000056), `NAV in Application Date` = c(161.178702344125,
162.346370458944, 145.004198476337), `Var NAV` = c(0.00763879866215962,
0.00039140721678275, 0.000278547270652531), `Var * Value` = c(533.188146618741,
-27.5181466187465, 6.92886335748171), FinalValue = c(70333.1881466187,
-70333.1881466187, 24881.9288633575), `Rentability WRONG` = c(0.0210345899274819,
0.0210345899274819, 0.0210345899274819)), row.names = c(NA, -3L
), class = c("tbl_df", "tbl", "data.frame"))
What I need to do is:
If quantity = 0, then remove all rows with the same Fund name as that one, but remove only the rows that have Date < or = Date of the Quantity = 0 Fund

Answer 1

What I did here is:
I grouped the data by Fund
Arranged each group by Data
Created a column zero_point that assigns 1 to the row where Quantity == 0 and NA otherwise
Filled the fields in zero_point that come before the actual "zero point" with the same value.
filtered those rows out.
output <- df %>%
group_by(Fund) %>%
arrange(Data) %>%
mutate(zero_point = case_when(Quantity == 0 ~ 1)) %>%
fill(zero_point, .direction = "up") %>%
filter(is.na(zero_point))
(On the condition that there is only one instance where Quantity is 0 per Fund group)

Answer 2

You can try -
library(dplyr)
df %>%
filter({
#Row index where Quantity = 0
inds = which(Quantity == 0)
#Drop rows where Data value is less than Data value at Quantity = 0
#and Fund is same as present at Quantity = 0.
!(Data <= Data[inds] & Fund %in% Fund[inds])
})

Answer 3

Here's a thought:
df %>%
group_by(Fund) %>%
filter(!any(Quantity == 0) | Data <= Data[which.min(Quantity)])
# # A tibble: 3 x 12
# # Groups: Fund [2]
# Data Client Fund Nature Quantity Value `NAV Yesterday` `NAV in Applica~ `Var NAV` `Var * Value` FinalValue `Rentability WR~
# <dttm> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 2021-06-17 00:00:00 Clien~ Fund1 Appli~ 433. 69800 162. 161. 0.00764 533. 70333. 0.0210
# 2 2021-08-17 00:00:00 Clien~ Fund1 Rescue 0 -70306. 162. 162. 0.000391 -27.5 -70333. 0.0210
# 3 2021-08-17 00:00:00 Clien~ Fund2 Appli~ 172. 24875 145. 145. 0.000279 6.93 24882. 0.0210
I'm assuming you meant "Data <= Data of the Quantity = 0 Fund", therefore using Data instead of Date (not found) or NAV in Application Date.
This filters nothing in this sample data, I'm hoping the logic is correct.
Testing for equality with floating-point (numeric) can be problematic at times (see Why are these numbers not equal?, Is floating point math broken?, and https://en.wikipedia.org/wiki/IEEE_754). If you have some small near-zero numbers, then this will silently produce counter-intuitive results without warning or error. You might be more defensive to use something like:
df %>%
group_by(Fund) %>%
filter(all(abs(Quantity) > 0) | Data <= Data[which.min(Quantity)])
or even
df %>%
group_by(Fund) %>%
filter(all(abs(Quantity) > 0) |
row_number() == which.min(Quantity) |
Data < Data[which.min(Quantity)])
While the latter is a bit paranoid (and double-calculates which.min(.), it should not succumb to problems with equality tests.
The only time this will fail is if all(is.na(Quantity)); that is, which.min(c(NA,NA)) returns integer(0) which will cause an error in dplyr::filter. One might choose to add safeguard with something like filter(any(!is.na(Quantity)) & (...)).