creating an if/then and if/else loop for multiple columns in R for annuitizing dollar values?-CodePudding

I have multiple columns in R with different costs in dollars and the year of the transaction, like so:

dollar <- data.frame(
  variable_costs = c(1200, 4000, 5000, 200),
  direct_costs = c(200, 1000, 500, 2000),
  total_costs = c(1400, 5000, 5500, 2200),
  year = c(2014, 2014, 2015, 2017)
)

I want to annuitize this based on inflation (if year = 2014, then account for all 7 years of inflation up until 2021 (i.e. multiply value by 2.5% (2014 to 2015 inflation), then multiply that value by 3.2% (2016 to 2017 inflation), and so forth). Final columns I am looking for would look like this:

annuitized_dollar <- data.frame(
  variable_costs_2021 = c(1324, 4802, 5402, 284),
  direct_costs_2021 = c(244, 1233, 591, 2281),
  total_costs_2021 = c(1623, 5942, 6123, 2561)
)

Would someone be able to help in creating an if/then or if/else loop for this purpose? It's not a huge dataset of ~6700 rows.

Thank you very much.

CodePudding user response：

First you should provide the values for inflation for every year.

I created some fake values:

set.seed(1)
inflation<-setNames(round(abs(rnorm(7, 0.03, sd=0.03)),3), 2014:2020)

inflation
2014  2015  2016  2017  2018  2019  2020 
0.053 0.032 0.030 0.049 0.028 0.025 0.014

-First, create a vector of cummulative inflations:

library(purrr)
cummulative_inflation<-accumulate(rev(inflation 1), ~.x*.y)

    2020     2019     2018     2017     2016     2015     2014 
1.014000 1.039350 1.068452 1.120806 1.154430 1.191372 1.254515

Then, we can use dplyr::mutate(across(tidy_selection) with rowwise and use the vector of cummulative inflations subseted by the 'year' variable.

library(dplyr)

dollar %>%
    rowwise %>%
    mutate(across(ends_with('costs'),
                  ~ .x * (cummulative_inflation[as.character(year)])))

# A tibble: 4 × 4
# Rowwise: 
  variable_costs direct_costs total_costs  year
           <dbl>        <dbl>       <dbl> <dbl>
1          1505.         251.       1756.  2014
2          5018.        1255.       6273.  2014
3          5957.         596.       6553.  2015
4           224.        2242.       2466.  2017

CodePudding user response：

here is a base R solution that might be easier to understand at the beginning

Greetings

dollar <- data.frame(
  variable_costs = c(1200, 4000, 5000, 200),
  direct_costs = c(200, 1000, 500, 2000),
  total_costs = c(1400, 5000, 5500, 2200),
  year = c(2014, 2014, 2015, 2017)
)

multi_df <- data.frame("V1"=seq(0.04, 0.16, 0.04))
multi_df$V2 <- multi_df$V1   0.005
multi_df$V3 <- multi_df$V1   (multi_df$V2 - 0.04) - 0.04

annuitized_dollar <- dollar
annuitized_dollar[, 1]<- annuitized_dollar[, 1] * ( 1   multi_df[, 1])
annuitized_dollar[, 2] <- annuitized_dollar[, 2] * ( 1   multi_df[, 2])
annuitized_dollar[, 3]<- annuitized_dollar[, 3] * ( 1   multi_df[, 3])