I've been econometrics for a decade now, just trying to learn a bit of finance. I'm currently attempting to learn from this video, specifically at 8:40: https://www.youtube.com/watch?v=KYm01d2hr6g

Here is my code so far:

suppressWarnings(suppressMessages({
  library(tidytable)
  library(httr)
  library(jsonlite)
  library(ggplot2)
  library(lubridate)
  library(tidyquant)

  options("getSymbols.warning4.0"=FALSE)
  options("getSymbols.yahoo.warning"=FALSE)
}))

options(scipen = 999)


### Get balance sheet and income statement data
api_key = '' # API for financialmodelingprep.com

stock_ticker = 'INTC' # pick AMD, NVDA, INTC, etc.

url = 'https://financialmodelingprep.com/api/v3/balance-sheet-statement/'
api = GET(paste0(url, stock_ticker, '?apikey=', api_key))

df_income_statement = fromJSON(rawToChar(api$content)) |> 
  select(date, totalCurrentAssets, totalCurrentLiabilities, totalNonCurrentLiabilities) |> # need totalAssets?
  mutate(date = as.Date(date))


### Get stock price data
df_stock_price = getSymbols(Symbols = stock_ticker,
                            src= 'yahoo',
                            warnings = FALSE,
                            auto.assign = F) |>
  as.data.frame() |>
  tibble::rownames_to_column('date') |> 
  mutate(date = as.Date(date))


### Parameter values
# firm value
p_knot = df_income_statement |> 
  filter(date == min(date)) |> 
  pull(totalCurrentAssets)

p_n = df_income_statement |> 
  filter(date == max(date)) |> 
  pull(totalCurrentAssets)

n_max = df_income_statement |> 
  pull(date) |> 
  max()

n_min = df_income_statement |> 
  pull(date) |> 
  min()

n_diff = n_max - n_min

n = time_length(n_diff, 'years')

# expected return -- assuming past performance is future performance
mu = -((p_knot/p_n)^(1/n) - 1) * (p_knot/p_n)^(-1/n)

# volatility
sigma = df_stock_price |> 
  pull(paste0(stock_ticker, '.Close')) |> 
  sd()

# short term liability
STL = df_income_statement |> 
  filter(date == max(date)) |> 
  pull(totalCurrentLiabilities)

STL_max = STL |> max()

# long term liability
LTL = df_income_statement |> 
  filter(date == max(date)) |> 
  pull(totalNonCurrentLiabilities)

LTL_max = LTL |> max()

# 
default_point = STL_max   (LTL_max/2)

# probability default
distance_default_numerator = log(p_n / default_point)   (mu - sigma^2 / 2) * 1 # T = 1
distance_default_denominator = sigma * 1 # T = 1

# fraction
distance_to_default = distance_default_numerator / distance_default_denominator

# PD = NORMDIST(-DD)
probability_default = pnorm(-distance_to_default)

The problem is, with all companies I tried (as well as few other columns that seem to fit the purposes), probability_default will always result in 1, while the components of the probability_default fraction are above 1 when (I think) they shouldn't be. This leads me to believe that I'm using the wrong data/columns. Which ones am I supposed to use? Or where can I get such data for free?

edit: oops, I included the missing parameters -- missed copy/paste