argument "" is missing, with no default when using map_dfc in R - r

Question

I am trying to modify Monte Carlo code from Reproduceable Finance with R to handle withdrawals and inflation of these withdrawals.
to do so I have changed a simulation accumulate function to subtract a withdrawl amount that has been increased do to inflation prior to calling the function.
simulation_accum_withdrawls <- function(init_value, N, mean, stdev, wAmt) {
tibble(c(init_value, 1 + rnorm(N, mean, stdev),wAmt)) %>%
`colnames<-`("returns") %>%
mutate(growth =
accumulate(returns, function(x, y, wAmt) x * y - wAmt)) %>%
select(growth)
}
the function is run from the following code
monte_carlo_sim <-
map_dfc(starts, simulation_accum_withdrawls,
N = sim_duration_months,
mean = geo_mean_port_return,
stdev = geo_stddev_port_return,
wAmt = inflated_withdrawls)
which throws off the following error.
Error in map():
ℹ In index: 1.
Caused by error in mutate():
! Problem while computing growth = accumulate(returns, function(x, y, wAmt) x * y - wAmt).
Caused by error in fn():
! argument "wAmt" is missing, with no default
Backtrace:
purrr::map_dfc(...)
purrr::accumulate(returns, function(x, y, wAmt) x * y - wAmt)
purrr:::reduce_impl(...)
fn(out, elt, ...)
Error in map(.x, .f, ...) :
The inflated_withdrawls data frame does exist and is not empty i.e. see below
str(inflated_withdrawls)
'data.frame': 38 obs. of 1 variable:
$ wAmt: num 1805391 0 0 0 0 ...
Any advice or insight on how to debug this would be appreciated.
Thanks
Here is an executable example per the request.
library(tidyverse)
library(highcharter)
library(tidyquant)
library(timetk)
library(broom)
library(highcharter)
library(purrr)
library(knitr)
library(readxl)
accum_inflation <- function(init_value, inflation=0.0) {
tibble(c(init_value, 1 + inflation)) %>%
`colnames<-`("acc_inf") %>%
mutate(acc_inf =
accumulate(acc_inf, function(x, y) x * y ))
}
accum_wd <- function(wAmt, acc_inf=0.0) {
growth = wAmt * acc_inf
}
simulation_accum_1 <- function(init_value, N, mean, stdev,inf_wd) {
tibble(c(init_value, 1 + rnorm(N, mean, stdev), inf_wd)) %>%
`colnames<-`("returns") %>%
mutate(growth =
accumulate(returns, function(x, y) x * y - inf_wd)) %>%
select(growth)
}
simulation_accum_withdrawls <- function(init_value, N, mean, stdev, wAmt=0) {
tibble(c(init_value, 1 + rnorm(N, mean, stdev),wAmt)) %>%
`colnames<-`("returns") %>%
mutate(growth =
accumulate(returns, function(x, y, wAmt) x * y + wAmt)) %>%
select(growth)
}
test_df <- data.frame(wAmt =c(100000,-180000,-180000,-180000),
date=seq.Date(as.Date("2024-01-01"), by = "year", length.out = 4)
)
test_df <- test_df %>% pad_by_time(date, .by = "month", .pad_value = 0)
inf_df <- rnorm(nrow(test_df),0.03/12,0.042/12)
inflated_returns <- accum_inflation(1, inf_df)
edatep1 <- ymd(edate)+months(1)
test_df<- rows_append(test_df,tibble(wAmt = 0, date = edatep1))
inflated_withdrawls <- accum_wd(test_df[1], inflated_returns)
nav=1000000
sim_duration_months = 40
total_sims = 10
geo_mean_port_return = 0.00443402454379282
geo_stddev_port_return = 0.0237813751473552
sims <- total_sims
starts <-
rep(nav, sims) %>%
set_names(paste("sim", 1:sims, sep = ""))
monte_carlo_sim <-
map_dfc(starts, simulation_accum_withdrawls,
N = sim_duration_months,
mean = geo_mean_port_return,
stdev = geo_stddev_port_return,
wAmt = inflated_withdrawls)