Create a transpose table from data frame - r

Question

I Have a big data base of below sample data . I am trying to create a function which can filter Dept from Dept column one by one and the from filtered subset data create a table for all filtered managers like below.
Managers can be many or less for dept , so that it can create table dynamically for filtered managers. because sometimes managers can be 1-23 or more for dept.
I tried with the help filtering and transposing data and then binding it but that is not working properly for me as i am medium proficient in dplyr only.
df <- data.frame(Dept = c("CA","HR","CA","HR","HR","CA","HR","HR","CA","CA","HR","CA","CA"),
Manager = c("AKASH","MANU","AMAN","SANU","NISH","KAMAL","VEER","SANIL","SAMEER","KANU","NUKUL","KUNAL","RAMIT"),
PF = c("Yes","No","Yes","Yes","Yes","No","No","Yes","No","Yes","Yes","Yes","No"),
Yearlybonus=c(6946,5871,0,7173,2161,3008,0,3025,4323,4196,0,5594,2313),
Quaterlybonus=c(2683,3846,0,2391,6716,6012,5479,3869,3764,0,4632,0,2371),
monthlybonus=c(4453,6466,2811,6845,4377,2617,0,7631,7761,2944,6270,3534,5856))

Answer 1

using data.table for large dataset
This solution does not accumulate transformed data as list. Instead, it uses a function for filtering data
library('data.table')
setDT(df)
df[, `:=`(Yearlybonus = as.character(Yearlybonus),
Quaterlybonus = as.character(Quaterlybonus),
monthlybonus = as.character(monthlybonus))]
This function is a pure function, because it sends a copy of data (df) to function everytime. If you have less memory space, you could refactor this function to use the data (df) from global scope. The refactored code will look like the ones in commented lines.
# myfun <- function(x) {
myfun <- function(df, x) {
# wide to long
# y <- melt( df[Dept == x],
df <- melt( df[Dept == x],
id.vars = c('Manager'),
measure.vars = c('PF', 'Yearlybonus', 'Quaterlybonus', 'monthlybonus'),
variable.name = 'T' )
# long to wide
# y <- dcast(y, T ~ Manager, value.var = 'value')
df <- dcast(df, T ~ Manager, value.var = 'value')
# add dept column
#y[, Dept := x ]
df[, Dept := x ]
# set column order in memory
#nm <- names(y)
nm <- names(df)
nm <- c('Dept', nm[nm != 'Dept'])
#setcolorder(y, nm)
setcolorder(df, nm)
#return(y[])
return(df[]) }
}
# create index for speed
setkey(df, Dept)
# myfun(x = 'CA')
myfun(df = df, x = 'CA')
# Dept T AKASH AMAN KAMAL KANU KUNAL RAMIT SAMEER
#1: CA PF Yes Yes No Yes Yes No No
#2: CA Yearlybonus 6946 0 3008 4196 5594 2313 4323
#3: CA Quaterlybonus 2683 0 6012 0 0 2371 3764
#4: CA monthlybonus 4453 2811 2617 2944 3534 5856 7761
# myfun(x = 'HR')
myfun(df = df, x = 'HR')
# Dept T MANU NISH NUKUL SANIL SANU VEER
#1: HR PF No Yes Yes Yes Yes No
#2: HR Yearlybonus 5871 2161 0 3025 7173 0
#3: HR Quaterlybonus 3846 6716 4632 3869 2391 5479
#4: HR monthlybonus 6466 4377 6270 7631 6845 0

Answer 2

How about this:
library(tidyr)
library(dplyr)
library(purrr)
map(unique(df$Dept), ~df %>% filter(Dept == .x) %>%
mutate(across(Yearlybonus:monthlybonus, ~as.character(.x))) %>%
pivot_longer(PF:monthlybonus, names_to="T", values_to="vals") %>%
pivot_wider(names_from="Manager", values_from="vals"))
# [[1]]
# # A tibble: 4 x 9
# Dept T AKASH AMAN KAMAL SAMEER KANU KUNAL RAMIT
# <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
# 1 CA PF Yes Yes No No Yes Yes No
# 2 CA Yearlybonus 6946 0 3008 4323 4196 5594 2313
# 3 CA Quaterlybonus 2683 0 6012 3764 0 0 2371
# 4 CA monthlybonus 4453 2811 2617 7761 2944 3534 5856
#
# [[2]]
# # A tibble: 4 x 8
# Dept T MANU SANU NISH VEER SANIL NUKUL
# <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
# 1 HR PF No Yes Yes No Yes Yes
# 2 HR Yearlybonus 5871 7173 2161 0 3025 0
# 3 HR Quaterlybonus 3846 2391 6716 5479 3869 4632
# 4 HR monthlybonus 6466 6845 4377 0 7631 6270