Detecting constants inside formulas
A smelly spreadsheet is distributed with the tidyxl package. It comes from the famous Enron subpoena, made available by Felienne Hermans.
How does it look at a glance? Here’s a screenshot of part of one sheet, showing the formulas rather than the cell values. It’s a financial plan, using formulas to forecast the rest of the year, and the plan for the following year.
What we want to see is whether the formulas have any embedded constants; ones that are hidden from our attention, but that are driving the forecasts. While we could read each formula, one by one, it would be a lot easier to visualise the ones containing constants. We can do this with xlex() and a graph plotting library like ggplot2.
The first step, after importing the spreadsheet, is to tokenize the formulas, using xlsx(). Let’s tokenize one formula to see what it looks like.
One formula
library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)
# The original filename was "barry_tycholiz__848__2002 Plan Worksheet CC107322.xlsx"
sheet <- tidy_xlsx(system.file("extdata/enron-constants.xlsx",
package = "tidyxl"),
"Detail Breakdown")$data[[1]]## Warning: 'tidy_xlsx()' is deprecated.
## Use 'xlsx_cells()' or 'xlsx_formats()' instead.## [1] "(C8/7)*12-48000"## root
## °-- ( paren_open
## ¦-- C8 ref
## ¦-- / operator
## °-- 7 number
## ¦-- ) paren_close
## ¦-- * operator
## ¦-- 12 number
## ¦-- - operator
## °-- 48000 numberThe formula is (C8/7)*12-48000, and xlex() separates it into its components. There are the parentheses, the operators (division, multiplication, subtraction), a reference to another cell (C8), and a few numeric constants: 7, 12, and 48000. What could they be?
The 7 is probably the 7th month, July, because of the column header “July YTD”. A year-to-date figure is being divided by 7, then multiplied by 12 to forecast the year-end figure. The 48000 is more mysterious – perhaps a future payment is expected.
Embedding these constants inside a formula is bad practice. Better practice would be to put the constants into their own cells, where they could be annotated with their meaning, and perhaps even named. Then formulas could refer to them, by name, e.g.
(Compensation/MonthsToDate)*12Months-FuturePaymentsMany formulas
The xlex() function isn’t vectorized (because it returns a data frame), so we map it over each of the formulas to create a ‘nest’ column of individual data frames.
tokens <-
sheet %>%
filter(!is.na(formula)) %>%
select(row, col, formula) %>%
mutate(tokens = map(formula, xlex)) %>%
select(-formula)
tokens## # A tibble: 154 x 3
## row col tokens
## <int> <int> <list>
## 1 8 4 <tibble [9 × 3]>
## 2 9 4 <tibble [7 × 3]>
## 3 10 4 <tibble [7 × 3]>
## 4 12 4 <tibble [7 × 3]>
## 5 13 4 <tibble [7 × 3]>
## 6 14 3 <tibble [4 × 3]>
## 7 14 4 <tibble [4 × 3]>
## 8 14 5 <tibble [4 × 3]>
## 9 14 6 <tibble [4 × 3]>
## 10 17 4 <tibble [7 × 3]>
## # ... with 144 more rowsThen we can unnest the data frames and filter for tokens that are constants, to find out which cells have constants in their formulas.
constants <-
tokens %>%
unnest(tokens) %>%
filter(type %in% c("error", "bool", "number", "text"))
constants## # A tibble: 201 x 5
## row col level type token
## <int> <int> <int> <chr> <chr>
## 1 8 4 1 number 7
## 2 8 4 0 number 12
## 3 8 4 0 number 48000
## 4 9 4 1 number 7
## 5 9 4 0 number 12
## 6 10 4 1 number 7
## 7 10 4 0 number 12
## 8 12 4 1 number 7
## 9 12 4 0 number 12
## 10 13 4 1 number 7
## # ... with 191 more rowsWhich constants are most common? Unsurprisingly, 12 and 7 are almost equally abundant, but there are also lots of 6s and 9s – two- and three-quarterly figures? Then there are some 150000s and the familiar 48000s, followed by some fractions that look like percentages, and then several one-offs.
## # A tibble: 24 x 2
## token n
## <chr> <int>
## 1 12 59
## 2 7 58
## 3 6 30
## 4 9 30
## 5 150000 4
## 6 48000 2
## 7 0.05 1
## 8 0.1 1
## 9 0.35 1
## 10 0.5 1
## 11 1.05 1
## 12 10 1
## 13 10000 1
## 14 12000 1
## 15 13000 1
## 16 15000 1
## 17 2000 1
## 18 25000 1
## 19 5000 1
## 20 5320 1
## 21 7314 1
## 22 7800 1
## 23 866 1
## 24 95000 1Visualising constants
A final step is to visualize the spreadsheet, highlighting cells that hide constants in their formulas. We already have a data frame of cells with constants, so we join it back to the full dataset, and pass the result into ggplot.
This time there doesn’t seem to be any particular pattern, which is perhaps suspicious in itself.
has_constants <-
constants %>%
distinct(row, col) %>%
mutate(has_constant = TRUE) %>%
right_join(sheet, by = c("row", "col")) %>%
filter(!is_blank) %>%
select(row, col, has_constant) %>%
replace_na(list(has_constant = FALSE))
has_constants## # A tibble: 412 x 3
## row col has_constant
## <int> <int> <lgl>
## 1 1 1 FALSE
## 2 2 1 FALSE
## 3 3 3 FALSE
## 4 3 4 FALSE
## 5 3 5 FALSE
## 6 3 6 FALSE
## 7 4 3 FALSE
## 8 4 4 FALSE
## 9 4 5 FALSE
## 10 4 6 FALSE
## # ... with 402 more rowshas_constants %>%
# filter(row <= 28) %>%
ggplot(aes(col, row, fill = has_constant)) +
geom_tile() +
scale_y_reverse() +
theme(legend.position = "top")