The subtraction is straightforward in {base}, as shown below. The output shows the differences represented textually, however. Is your question on constructing the strings?
suppressPackageStartupMessages({
library(dplyr)
library(lubridate)
library(tidyr)
})
df1 <- data.frame(date1= c("2021-06-28","2021-06-28","2021-06-28","2021-06-28"),
date2 = c("2021-06-30","2021-06-30","2021-07-01","2021-07-01"),
Category = c("FDE","ABC","FDE","ABC"),
Week= c("Wednesday","Wednesday","Friday","Friday"),
DR1 = c(4,1,6,3),
DR01 = c(4,1,4,3), DR02= c(4,2,6,2),DR03= c(9,5,4,7),
DR04 = c(5,4,3,2),DR05 = c(5,4,5,4),
DR06 = c(2,4,3,2))
return_coef <- function(dmda, CategoryChosse) {
x<-df1 %>% select(starts_with("DR0"))
x<-cbind(df1, setNames(df1$DR1 - x, paste0(names(x), "_PV")))
PV<-select(x, date2,Week, Category, DR1, ends_with("PV"))
med<-PV %>%
group_by(Category,Week) %>%
summarize(across(ends_with("PV"), median))
SPV<-df1%>%
inner_join(med, by = c('Category', 'Week')) %>%
mutate(across(matches("^DR0\\d+$"), ~.x +
get(paste0(cur_column(), '_PV')),
.names = '{col}_{col}_PV')) %>%
select(date1:Category, DR01_DR01_PV:last_col())
SPV<-data.frame(SPV)
mat1 <- df1 %>%
filter(date2 == dmda, Category == CategoryChosse) %>%
select(starts_with("DR0")) %>%
pivot_longer(cols = everything()) %>%
arrange(desc(row_number())) %>%
mutate(cs = cumsum(value)) %>%
filter(cs == 0) %>%
pull(name)
(dropnames <- paste0(mat1,"_",mat1, "_PV"))
SPV <- SPV %>%
filter(date2 == dmda, Category == CategoryChosse) %>%
select(-any_of(dropnames))
datas<-SPV %>%
filter(date2 == ymd(dmda)) %>%
group_by(Category) %>%
summarize(across(starts_with("DR0"), sum)) %>%
pivot_longer(cols= -Category, names_pattern = "DR0(.+)", values_to = "val") %>%
mutate(name = readr::parse_number(name))
colnames(datas)[-1]<-c("Days","Numbers")
datas <- datas %>%
group_by(Category) %>%
slice((as.Date(dmda) - min(as.Date(df1$date1) [
df1$Category == first(Category)])):max(Days)+1) %>%
ungroup
mod <- nls(Numbers ~ b1*Days^2+b2,start = list(b1 = 0,b2 = 0),data = datas, algorithm = "port")
as.numeric(coef(mod)[2])
}
All<-cbind(df1 %>% select(date2, Category), coef = mapply(return_coef, df1$date2, df1$Category))
#> `summarise()` has grouped output by 'Category'. You can override using the `.groups` argument.
#> `summarise()` has grouped output by 'Category'. You can override using the `.groups` argument.
#> `summarise()` has grouped output by 'Category'. You can override using the `.groups` argument.
#> `summarise()` has grouped output by 'Category'. You can override using the `.groups` argument.
diffs <- df1[,5:11] - All[,3]
df2 <- df1[,1:4]
df2[,5:11] <- diffs
df2
#> date1 date2 Category Week DR1 DR01
#> 1 2021-06-28 2021-06-30 FDE Wednesday -6.360605e-08 -6.360605e-08
#> 2 2021-06-28 2021-06-30 ABC Wednesday -1.300449e-08 -1.300449e-08
#> 3 2021-06-28 2021-07-01 FDE Friday -1.020952e-07 -2.000000e+00
#> 4 2021-06-28 2021-07-01 ABC Friday -1.160027e-08 -1.160027e-08
#> DR02 DR03 DR04 DR05 DR06
#> 1 -6.360605e-08 5 0.9999999 0.9999999 -2
#> 2 1.000000e+00 4 3.0000000 3.0000000 3
#> 3 -1.020952e-07 -2 -3.0000001 -1.0000001 -3
#> 4 -1.000000e+00 4 -1.0000000 1.0000000 -1