How best to transform quarterly data into monthly - r

Question

Below is the sample data. I receive the data in a form such as this. Each row is a quarter and then the months are columns inside of it. Trying to do some month over month calculation but am thinking that I transform the data frame in order to do so. I am thinking that I would do a pivot_longer but not seeing anything online that is of a similar vein. Below is the desired result
year<-c(2018,2018,2018,2018,2019,2019,2019,2019,2020,2020,2020,2020)
qtr<-c(1,2,3,4,1,2,3,4,1,2,3,4)
avgemp <-c(3,5,7,9,11,13,15,17,19,21,23,25)
month1emp<-c(2,4,6,8,10,12,14,16,18,20,22,24)
month2emp<-c(3,5,7,9,11,13,15,17,19,21,23,25)
month3emp<-c(4,6,8,10,12,14,16,18,20,22,24,26)
sample<-data.frame(year,qtr,month1emp,month2emp,month3emp)
Desired Result
year qtr month employment
2018 1 1 2
2018 1 2 3
2018 1 3 4
2018 2 4 4
2018 2 4 5
2018 2 4 6
and so on. At 2019, the month value would restart and go from 1 to 12.

Answer 1

We could use pivot_longer on the 'month' columns, specify the names_pattern to capture the digits ((\\d+)) followed by the emp for the 'month' and the .value columns
library(dplyr)
library(tidyr)
sample %>%
pivot_longer(cols = starts_with('month'),
names_to = c("month", ".value"), names_pattern = ".*(\\d+)(emp)")%>%
rename(employment = emp)
-output
# A tibble: 36 x 4
year qtr month employment
<dbl> <dbl> <chr> <dbl>
1 2018 1 1 2
2 2018 1 2 3
3 2018 1 3 4
4 2018 2 1 4
5 2018 2 2 5
6 2018 2 3 6
7 2018 3 1 6
8 2018 3 2 7
9 2018 3 3 8
10 2018 4 1 8
# … with 26 more rows
If we need to increment the 'month' based on 'qtr' value
sample %>%
pivot_longer(cols = starts_with('month'),
names_to = c("month", ".value"), names_pattern = ".*(\\d+)(emp)")%>%
rename(employment = emp) %>%
mutate(month = as.integer(month) + c(0, 3, 6, 9)[qtr])
# A tibble: 36 x 4
year qtr month employment
<dbl> <dbl> <dbl> <dbl>
1 2018 1 1 2
2 2018 1 2 3
3 2018 1 3 4
4 2018 2 4 4
5 2018 2 5 5
6 2018 2 6 6
7 2018 3 7 6
8 2018 3 8 7
9 2018 3 9 8
10 2018 4 10 8
# … with 26 more rows

Answer 2

Base R solution:
# Create a vector of boolean values,
# denoting whether or not the columns should
# be unpivoted: unpivot_cols => boolean vector
unpivot_cols <- startsWith(
names(df),
"month"
)
# Reshape the data.frame, calculate
# the month value: rshpd_df => data.frame
rshpd_df <- transform(
reshape(
df,
direction = "long",
varying = names(df)[unpivot_cols],
ids = NULL,
timevar = "month",
times = seq_len(sum(unpivot_cols)),
v.names = "employment",
new.row.names = seq_len(
nrow(df) * ncol(df)
)
),
month = ((12 / 4) * (qtr - 1)) + month
)
# Order the data.frame by year and month:
# ordered_df => data.frame
ordered_df <- with(
rshpd_df,
rshpd_df[order(year, month),]
)