Selecting variable column names for further IRR calculation in R - r

Question

I have a table of cash flows for various projects over time (years) and want to calculate the IRR for each project. I can't seem to select the appropriate columns, which vary, for each project. The table structure is as follows:
structure(list(`Portfolio Company` = c("Ventures II", "Pal III",
"River Fund II", "Ventures III"),
minc = c(2007, 2008, 2008, 2012),
maxc = c(2021, 2021, 2021, 2020),
num_pers = c(14, 13, 13, 8),
`2007` = c(-660000, NA, NA, NA),
`2008` = c(-525000, -954219, -1427182.55, NA),
`2009` = c(-351991.03, -626798, -1694353.41, NA),
`2010` = c(-299717.06, -243248, -1193954, NA),
`2011` = c(-239257.08, 465738, -288309, NA),
`2012` = c(-9057.31000000001, -369011, 128509.63, -480000),
`2013` = c(-237233.9, -131111, 53718, -411734.58),
`2014` = c(-106181.76, -271181, 887640, -600000),
`2015` = c(-84760.51, 441808, 906289, -900000),
`2016` = c(2770719.21, -377799, 166110, -150000),
`2017` = c(157820.08, -12147, 1425198, -255000),
`2018` = c(204424.36,-1626110, 361270, -180000),
`2019` = c(563463.62, 119577, 531555, 3300402.62),
`2020` = c(96247.29, 7057926, 2247027, 36111.6),
`2021` = c(614848.68, 1277996, 258289, NA)),
class = c("grouped_df", "tbl_df", "tbl", "data.frame"),
row.names = c(NA, -4L),
groups = structure(list(`Portfolio Company` =c("Ventures II","Ventures III","Pal III", "River Fund II"),
.rows = structure(list(1L, 4L, 2L, 3L),
ptype = integer(0),
class = c("vctrs_list_of", "vctrs_vctr", "list"))),
class = c("tbl_df", "tbl", "data.frame"),
row.names = c(NA, -4L), .drop = TRUE))
Each project (Portfolio Company) has a different start and end date which is captured by the minc and maxc columns. I would like to use the text in minc and maxc to select from minc:maxc for each project to perform the IRR calculation. I get a variety of errors including: object maxc not found, incorrect arg ... Have tried about 20 combinations of !!sym, as.String (from NLP package) ... none works.
This is the code that created the table and the problematic select code:
sum_fund_CF <- funds %>% group_by(`TX_YR`, `Portfolio Company`) %>%
summarise(CF=sum(if_else(is.na(Proceeds),0,Proceeds)-if_else(is.na(Investment),0,Investment))) %>% ungroup() #organizes source data and calculates cash flows
sum_fund_CF <- sum_fund_CF %>%
group_by(`Portfolio Company`) %>% mutate(minc=min(`TX_YR`),maxc=max(`TX_YR`),num_pers=maxc-minc) %>%
pivot_wider(names_from = TX_YR, values_from = `CF`) #creates the table and finds first year and last year of cash flow, and num of periods between them
sum_fund_CF %>% group_by(`Portfolio Company`)%>% select(!!sym(as.String(maxc))):!!sym(as.String(max))) #want to select appropriate columns for each record to do the IRR analysis ... IRR() ... need a string of cash flows and no NA.
I'm sure it's something simple, but this has me perplexed. Thanks !

Answer 1

You can modify your definition of IRR accordingly. I followed this article on how to calculate IRR using the jrvFinance package.
The filter function from the dplyr package is used after group_by, to select the years indicated by the minc and maxc columns.
library(tidyverse)
library(janitor)
#>
#> Attaching package: 'janitor'
#> The following objects are masked from 'package:stats':
#>
#> chisq.test, fisher.test
library(jrvFinance)
data <- structure(list(`Portfolio Company` = c("Ventures II", "Pal III",
"River Fund II", "Ventures III"),
minc = c(2007, 2008, 2008, 2012),
maxc = c(2021, 2021, 2021, 2020),
num_pers = c(14, 13, 13, 8),
`2007` = c(-660000, NA, NA, NA),
`2008` = c(-525000, -954219, -1427182.55, NA),
`2009` = c(-351991.03, -626798, -1694353.41, NA),
`2010` = c(-299717.06, -243248, -1193954, NA),
`2011` = c(-239257.08, 465738, -288309, NA),
`2012` = c(-9057.31000000001, -369011, 128509.63, -480000),
`2013` = c(-237233.9, -131111, 53718, -411734.58),
`2014` = c(-106181.76, -271181, 887640, -600000),
`2015` = c(-84760.51, 441808, 906289, -900000),
`2016` = c(2770719.21, -377799, 166110, -150000),
`2017` = c(157820.08, -12147, 1425198, -255000),
`2018` = c(204424.36,-1626110, 361270, -180000),
`2019` = c(563463.62, 119577, 531555, 3300402.62),
`2020` = c(96247.29, 7057926, 2247027, 36111.6),
`2021` = c(614848.68, 1277996, 258289, NA)),
class = c("grouped_df", "tbl_df", "tbl", "data.frame"),
row.names = c(NA, -4L),
groups = structure(list(`Portfolio Company` =c("Ventures II","Ventures III","Pal III", "River Fund II"),
.rows = structure(list(1L, 4L, 2L, 3L),
ptype = integer(0),
class = c("vctrs_list_of", "vctrs_vctr", "list"))),
class = c("tbl_df", "tbl", "data.frame"),
row.names = c(NA, -4L), .drop = TRUE))
clean_data <- data %>%
clean_names() %>%
ungroup() %>%
pivot_longer(cols = -1:-4,
names_to = "year",
values_to = "cashflow") %>%
mutate(year = str_replace(year, "x", ""),
year = as.numeric(year))
clean_data %>%
print(n = 20)
#> # A tibble: 60 x 6
#> portfolio_company minc maxc num_pers year cashflow
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Ventures II 2007 2021 14 2007 -660000
#> 2 Ventures II 2007 2021 14 2008 -525000
#> 3 Ventures II 2007 2021 14 2009 -351991.
#> 4 Ventures II 2007 2021 14 2010 -299717.
#> 5 Ventures II 2007 2021 14 2011 -239257.
#> 6 Ventures II 2007 2021 14 2012 -9057.
#> 7 Ventures II 2007 2021 14 2013 -237234.
#> 8 Ventures II 2007 2021 14 2014 -106182.
#> 9 Ventures II 2007 2021 14 2015 -84761.
#> 10 Ventures II 2007 2021 14 2016 2770719.
#> 11 Ventures II 2007 2021 14 2017 157820.
#> 12 Ventures II 2007 2021 14 2018 204424.
#> 13 Ventures II 2007 2021 14 2019 563464.
#> 14 Ventures II 2007 2021 14 2020 96247.
#> 15 Ventures II 2007 2021 14 2021 614849.
#> 16 Pal III 2008 2021 13 2007 NA
#> 17 Pal III 2008 2021 13 2008 -954219
#> 18 Pal III 2008 2021 13 2009 -626798
#> 19 Pal III 2008 2021 13 2010 -243248
#> 20 Pal III 2008 2021 13 2011 465738
#> # ... with 40 more rows
clean_data %>%
group_by(portfolio_company) %>%
filter(between(year, min(minc), max(maxc))) %>%
summarise(irr = irr(cashflow,
cf.freq = 1))
#> # A tibble: 4 x 2
#> portfolio_company irr
#> <chr> <dbl>
#> 1 Pal III 0.111
#> 2 River Fund II 0.0510
#> 3 Ventures II 0.0729
#> 4 Ventures III 0.0251
Created on 2022-01-04 by the reprex package (v2.0.1)

Answer 2

Another way to do it using jvrFinance::irr().
library(jrvFinance)
library(tidyverse)
df %>%
rowwise() %>%
summarise(irr = irr(na.omit(c_across(matches('^\\d')))), .groups = 'drop')
#> # A tibble: 4 × 2
#> `Portfolio Company` irr
#> <chr> <dbl>
#> 1 Ventures II 0.0729
#> 2 Pal III 0.111
#> 3 River Fund II 0.0510
#> 4 Ventures III 0.0251
Created on 2022-01-04 by the reprex package (v2.0.1)