Error: Problem with `mutate()` column in R - r

Question

This question is very similar to this one Error in UseMethod("select") : no applicable method for 'select' applied to an object of class "character", what is different is a few things in the return_coef function and it's also that I'm filtering the function by Id, date and Category, and in the answered question, it was just by date and Category.
You will see below that I can generate the coefficients for each Id/date/Category, but when I ask to do it for everyone at once, I get the following error:
Error: Problem with `mutate()` column `coef`.
i `coef = mapply(...)`.
x negative length vectors are not allowed
Executable code:
library(dplyr)
library(tidyverse)
library(lubridate)
library(data.table)
df1<- structure(
list(
Id = c(1, 1, 1, 1),
date1 = c("2022-01-06","2022-01-06","2022-01-06","2022-01-06"),
date2 = c("2022-01-02","2022-01-03","2022-01-09","2022-01-10"),
Week = c("Sunday","Monday","Sunday","Monday"),
Category = c("EFG", "ABC","EFG","ABC"),
DR1 = c(200, 300, 200, 200),
DRM01 = c(300, 300, 300, 300),
DRM02 = c(300, 300, 300, 300),
DRM03 = c(300,300,300,300),
DRM04 = c(300,300,300,300),
DRM05 = c(300,250,350,350)),row.names = c(NA, 4L), class = "data.frame")
return_coef <- function (df1, idd, dmda, CategoryChosse) {
selection = startsWith(names(df1), "DRM0")
df1[selection][is.na(df1[selection])] = 0
data1<-subset(df1,df1$date2<df1$date1)
dt1 <- as.data.table(data1)
cols <- grep("^DRM0", colnames(dt1), value = TRUE)
med <-
dt1[, (paste0(cols, "_PV")) := DR1 - .SD, .SDcols = cols
][, lapply(.SD, median), by = .(Id, Category, Week), .SDcols = paste0(cols, "_PV") ]
SPV<-df1%>%
inner_join(med, by = c('Id','Category', 'Week')) %>%
mutate(across(matches("^DRM0\\d+$"), ~.x +
get(paste0(cur_column(), '_PV')),
.names = '{col}_{col}_PV')) %>%
select(Id:Category, DRM01_DRM01_PV:last_col())%>%
data.frame()
mat1 <- df1 %>%
dplyr::filter(Id==idd, date2 == ymd(dmda), Category == CategoryChosse) %>%
select(starts_with("DRM0")) %>%
pivot_longer(cols = everything()) %>%
arrange(desc(row_number())) %>%
mutate(cs = cumsum(value)) %>%
dplyr::filter(cs == 0) %>%
pull(name)
(dropnames <- paste0(mat1,"_",mat1, "_PV"))
SPV <- SPV %>%
filter(Id==idd,date2 == ymd(dmda), Category == CategoryChosse) %>%
select(-any_of(dropnames))
if(length(grep("DRM0", names(SPV))) == 0) {
SPV[head(mat1,10)] <- NA_real_
}
datas <-SPV %>%
dplyr::filter(Id==idd,date2 == ymd(dmda)) %>%
group_by(Category) %>%
dplyr::summarize(dplyr::across(starts_with("DRM0"), sum)) %>%
pivot_longer(cols= -Category, names_pattern = "DRM0(.+)", values_to = "val") %>%
mutate(name = readr::parse_number(name))
colnames(datas)[-1]<-c("var1","var2")
datas$days <- datas[[as.name("var1")]]
datas$numbers <- datas[[as.name("var2")]]
datas <- datas %>%
group_by(Category) %>%
slice((as.Date(dmda) - min(as.Date(df1$date1) [
df1$Category == first(Category)])):max(days)+1) %>%
ungroup
m<-df1 %>%
group_by(Id,Category,Week) %>%
dplyr::summarize(dplyr::across(starts_with("DR1"), mean), .groups = 'drop')
m<-subset(m, Week == df1$Week[match(ymd(dmda), ymd(df1$date2))] & Category == CategoryChosse)$DR1
if (nrow(datas)<=2){
val<-as.numeric(m)
}
else{
mod <- nls(numbers ~ b1*days^2+b2,start = list(b1 = 0,b2 = 0),data = datas, algorithm = "port")
coef<-coef(mod)[2]
val<-as.numeric(coef(mod)[2])
}
return(val)
}
Find the coef one by one (It works):
return_coef(df1,"1","2022-01-09","EFG")
[1] 200
return_coef(df1,"1","2022-01-10","ABC")
[1] 250
Find all coef at once (Does not work)
subset_df1 <- subset(df1, date2 > date1)
All<-subset_df1%>%
transmute(
Id,date2,Category,
coef = mapply(return_coef, list(cur_data()), Id, as.Date(date2), Category))
Error: Problem with `mutate()` column `coef`.
i `coef = mapply(...)`.
x negative length vectors are not allowed
dput(head(df1))
structure(list(Id = c(1, 1, 1, 1), date1 = structure(c(1641427200,
1641427200, 1641427200, 1641427200), tzone = "UTC", class = c("POSIXct",
"POSIXt")), date2 = structure(c(1641081600, 1641168000, 1641686400,
1641772800), tzone = "UTC", class = c("POSIXct", "POSIXt")),
Week = c("Sunday", "Monday", "Sunday", "Monday"), Category = c("EFG",
"ABC", "EFG", "ABC"), DR1 = c(200, 300, 200, 200), DRM01 = c(300,
300, 300, 300), DRM02 = c(300, 300, 300, 300), DRM03 = c(300,
300, 300, 300), DRM04 = c(300, 300, 300, 300), DRM05 = c(300,
250, 350, 350)), row.names = c(NA, -4L), class = c("tbl_df",
"tbl", "data.frame"))

Answer 1

The function seems to be use df1 as the full data
library(dplyr)
subset_df1 %>%
rowwise %>%
mutate(coef = return_coef(df1, Id, date2, Category)) %>%
ungroup
-output
# A tibble: 2 × 12
Id date1 date2 Week Category DR1 DRM01 DRM02 DRM03 DRM04 DRM05 coef
<dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 2022-01-06 2022-01-09 Sunday EFG 200 300 300 300 300 350 200
2 1 2022-01-06 2022-01-10 Monday ABC 200 300 300 300 300 350 250
Update
In the new dataset, the columns 'date1', 'date2' are POSIXct, convert to Date class and it should work
df2 <- df1 %>%
mutate(across(c(date1, date2), as.Date))
subset_df2 <- subset(df2, date2 > date1)
subset_df2 %>%
rowwise %>%
mutate(coef = return_coef(df2, Id, date2, Category)) %>%
ungroup
# A tibble: 2 × 12
Id date1 date2 Week Category DR1 DRM01 DRM02 DRM03 DRM04 DRM05 coef
<dbl> <date> <date> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 2022-01-06 2022-01-09 Sunday EFG 200 300 300 300 300 350 200
2 1 2022-01-06 2022-01-10 Monday ABC 200 300 300 300 300 350 250
Or using pmap
library(purrr)
subset_df1 %>%
transmute(Id, date2, Category,
coeff = pmap_dbl(across(c(Id, date2, Category)),
~ return_coef(df1, ..1, ..2, ..3)))
-output
Id date2 Category coeff
3 1 2022-01-09 EFG 200
4 1 2022-01-10 ABC 250