Loop instead of iterative calculations in R - r

Question

In my example below I have quarterly data from 2021Q1 to 2022Q3 for variable X. I have forecasted growth rate of variable X (growth_x) from 2022Q4 to 2025Q4. I want to use the growth_x variable to calculate the variable X from 2022Q4 to 2025Q4 iteratively.I am manually calculating it below and still missing 2025Q4. Is it possible to write a function to do it? I am fairly new to writing loops. Any help will be greatly appreciated. Thank you in advance.
library(readxl)
library(dplyr)
library(lubridate)
# Quarterly Data
data <- data.frame(c("2021Q1","2021Q2","2021Q3","2021Q4",
"2022Q1","2022Q2","2022Q3","2022Q4",
"2023Q1","2023Q2","2023Q3","2023Q4",
"2024Q1","2024Q2","2024Q3","2024Q4",
"2025Q1","2025Q2","2025Q3","2025Q4"),
# Variable X - Actuals upto 2022Q3
c(804,511,479,462,
427,330,440,NA,
NA,NA,NA,NA,
NA,NA,NA,NA,
NA,NA,NA,NA),
# Forecasted Growth rates of X from 2022Q4
c(NA,NA,NA,NA,
NA,NA,NA,0.24,
0.49,0.65,0.25,0.71,
0.63,0.33,0.53,0.83,
0.87,0.19,0.99,0.16))
# Renaming the columns
data<-data%>%rename(yrqtr=1,x=2,growth_x=3)
# Creating Date Variable
data<-data%>%mutate(year=substr(yrqtr,1,4),
qtr=substr(yrqtr,5,6),
mon=ifelse(qtr=="Q1",3,
ifelse(qtr=="Q2",6,
ifelse(qtr=="Q3",9,12))),
date=make_date(year,mon,1))
# Computing Growth Rate from 2022Q3 to 2023Q3
Growth_2023_3<-data%>%mutate(forecast_x=(1+growth_x)*lag(x,4),
x=ifelse(date>"2022-09-01",forecast_x,x))%>%select(-forecast_x)
# Computing Growth Rate from 2023Q3 to 2024Q3
Growth_2024_3<-Growth_2023_3%>%mutate(forecast_x=(1+growth_x)*lag(x,4),
x=ifelse(date>"2023-09-01",forecast_x,x))%>%select(-forecast_x)
# Computing Growth Rate from 2024Q3 to 2025Q3
Growth_2025_3<-Growth_2024_3%>%mutate(forecast_x=(1+growth_x)*lag(x,4),
x=ifelse(date>"2024-09-01",forecast_x,x))%>%select(-forecast_x)

Answer 1

Does this do what you want?
n_years <- length(unique(data$year))
for(i in unique(data$year)[2:n_years]){
# Computing Growth Rate from 2022Q3 to 2023Q3
data <- data %>%
mutate(forecast_x=(1+growth_x)*lag(x,4),
x=ifelse(date > as.Date(paste0(i,"-09-01")),forecast_x,x))
}
As an aside, column names can be assigned at the time the data frame is created. For example:
# Quarterly Data
data <- data.frame(yrqtr = c("2021Q1","2021Q2"),
x = c(804,511),
growth_x = c(0.24,0.49))

Answer 2

If you want to avoid using a loop, you can use purrr::reduce().
library(tidyverse)
library(lubridate)
sol <- reduce(
.x = unique(data$year), # iterate over years
.init = data,
\(lhs, rhs) lhs %>%
mutate(x = ifelse(year == rhs & is.na(x), (1+growth_x)*lag(x,4), x))
)
sol
#> yrqtr x growth_x year qtr mon date
#> 1 2021Q1 804.0000 NA 2021 Q1 3 2021-03-01
#> 2 2021Q2 511.0000 NA 2021 Q2 6 2021-06-01
#> 3 2021Q3 479.0000 NA 2021 Q3 9 2021-09-01
#> 4 2021Q4 462.0000 NA 2021 Q4 12 2021-12-01
#> 5 2022Q1 427.0000 NA 2022 Q1 3 2022-03-01
#> 6 2022Q2 330.0000 NA 2022 Q2 6 2022-06-01
#> 7 2022Q3 440.0000 NA 2022 Q3 9 2022-09-01
#> 8 2022Q4 572.8800 0.24 2022 Q4 12 2022-12-01
#> 9 2023Q1 636.2300 0.49 2023 Q1 3 2023-03-01
#> 10 2023Q2 544.5000 0.65 2023 Q2 6 2023-06-01
#> 11 2023Q3 550.0000 0.25 2023 Q3 9 2023-09-01
#> 12 2023Q4 979.6248 0.71 2023 Q4 12 2023-12-01
#> 13 2024Q1 1037.0549 0.63 2024 Q1 3 2024-03-01
#> 14 2024Q2 724.1850 0.33 2024 Q2 6 2024-06-01
#> 15 2024Q3 841.5000 0.53 2024 Q3 9 2024-09-01
#> 16 2024Q4 1792.7134 0.83 2024 Q4 12 2024-12-01
#> 17 2025Q1 1939.2927 0.87 2025 Q1 3 2025-03-01
#> 18 2025Q2 861.7802 0.19 2025 Q2 6 2025-06-01
#> 19 2025Q3 1674.5850 0.99 2025 Q3 9 2025-09-01
#> 20 2025Q4 2079.5475 0.16 2025 Q4 12 2025-12-01