If you love using Excel, and it fits your needs, then by all means do your thing. However, there is also interest out there for moving Excel analyses into R. If you are one of those people, and your Excel data is “messy,” then this post is for you. We will be using the unpivotr package (GitHub) to tidy up some Excel cash flow spreadsheets.

### The problem

Often, cash flow spreadsheets contain valuable info about a company’s performance, but they generally come in a non-tidy format. For R users, especially those who use the tidyverse, this poses a real challenge, as most of our data analysis toolkit revolves around working with tidy data.

If you aren’t familiar with “tidy” data, check out this vignette. The basic 3 tenants of tidy data are:

1. Each variable forms a column.
2. Each observation forms a row.
3. Each type of observational unit forms a table.

Below is a typical cash flow statement for 1 year of performance, broken down by month. This does not fit the “tidy” data standards, but is incredibly common in the Financial world. Don’t worry too much about how hard this is to see, I’ll zoom in on relevant features as we go along. But do notice how columns C through Z are the months, and the left two columns represent main and sub-headers for the cash flow sheet.

Why doesn’t this fit the “tidy” principles? Here are just a few things we might change, and a picture to highlight the problems.

1. Cash Inflows (Income) in column A is a “main header” with “sub-headers” in column B rows 7-13. This violates principle 2, because each row is not a standalone observation. You need information from row 6 (the title of the category) to figure out what category Cash Collections belongs in. Ideally, Cash Inflows (Income) would be repeated in column A, rows 7-10 and in row 13 to uniquely identify that row.

2. Row 4 identifies 13 columns in this dataset (12 months and a total column). In the tidy world, this violates principle 1, because those 13 can actually be reduced to just 1 column, month, where we will just understand that TOTALS is the sum of the others. Some people would even drop the TOTALS column, as it is easily computed from the 12 months of data and may cause confusion when looking at monthly totals.

3. A general problem is that there are spaces in every other column. We can fix that easy enough.

4. A last major problem is that some “main headers” actually contain data in their rows, and some don’t. For instance, row 5 has a main header of Beginning Cash Balance and also has data for each month. Compare that to Cash Inflows (Income) which instead has subcategories. We will deal with all this as we go through the tidying process.

### Following along

If you want to follow along, feel free to fork this Github repo which contains the data and the RMarkdown script.

The data comes from an Excel cash flow template I found and filled in with random data just to test out.

### A tale of two packages

The main two packages that we need are readxl to extract the data itself from the sheet, and unpivotr, a relatively little known package that is incredibly powerful for this kind of work. We will additionally use dplyr and ggplot2 and purrr.

library(readxl)
library(dplyr)
library(ggplot2)
library(unpivotr)
library(purrr)

First, let’s load the data and see what we get.

wb <- "data/2018-02-16-tidying-excel-cash-flow-spreadsheets-in-r/untidy.xlsx"

rmarkdown:::print.paged_df(untidy_raw)

Hmm, that’s not helpful. Let’s try again, but this time let’s skip the first two rows and ignore the column names for now.

untidy <- read_excel(wb, skip = 2, col_names = FALSE)

rmarkdown:::print.paged_df(untidy)

At this point you can kind of match up the structure of the Excel sheet to the R tibble. What now? This is where unpivotr comes in. The basic idea is to:

1. Tokenize your Excel sheet into a tibble with three columns: row, column, and value.
3. Finally, you use that information to perform a series of “coordinate based” transformations that help tidy up your data (unpivotr performs most of the magic for us).

unpivotr comes equipped with a function that can help turn our imported spreadsheet into a tokenized version of itself, where each row of the resulting tibble corresponds to a cell identified by a row and col number. tidy_table() is just that function.

untidy_tokens <- tidy_table(untidy)
untidy_tokens
## # A tibble: 1,161 x 4
##      row   col chr                    lgl
##    <int> <int> <chr>                  <lgl>
##  1     1     1 <NA>                   NA
##  2     2     1 Beginning Cash Balance NA
##  3     3     1 Cash Inflows (Income): NA
##  4     4     1 <NA>                   NA
##  5     5     1 <NA>                   NA
##  6     6     1 <NA>                   NA
##  7     7     1 <NA>                   NA
##  8     8     1 <NA>                   NA
##  9     9     1 <NA>                   NA
## 10    10     1 <NA>                   NA
## # ... with 1,151 more rows

You might notice that “Beginning Cash Balance” corresponds to row 4 in the actual spreadsheet, not row 2, but remember that we skipped 2 rows on the import step, so this is actually correct.

You’ll also notice that two other columns have been created, chr and lgl. chr is the tokenized version of any column in the spreadsheet that contained character data, and lgl is the same but for logical data. If you look at untidy again, you’ll see that X__4 is a logical column, and just represents one of the blank columns used for spacing in the original data set, so that is the only reason we have a lgl column.

We don’t care about the lgl column, and at this point we also remove any cells that contain NA values. This solves problem 3.

tokens <- untidy_tokens %>%
select(-lgl) %>%
filter(!is.na(chr))

tokens
## # A tibble: 506 x 3
##      row   col chr
##    <int> <int> <chr>
##  1     2     1 Beginning Cash Balance
##  2     3     1 Cash Inflows (Income):
##  3    11     1 Available Cash Balance
##  4    12     1 Cash Outflows (Expenses):
##  5    35     1 Other Cash Out Flows:
##  6    43     1 Ending Cash Balance
##  7     4     2 Cash Collections
##  8     5     2 Credit Collections
##  9     6     2 Investment Income
## 10     7     2 Other:
## # ... with 496 more rows

### Separating out summary rows

Remember problem 4 from above about how some main headers contain data in their rows and some don’t? That is going to cause us problems later on if we don’t take care of it now. As it turns out, this happens in 3 places, "Beginning Cash Balance", "Available Cash Balance", and "Ending Cash Balance". These 3 rows all happen to be summary rows computed from other rows, and it makes sense to think about them separately anyways. For that reason, let’s pull them out. Luckily, this is incredibly simple thanks to our tokenized spreadsheet.

main_headers <- c(
"Beginning Cash Balance",
"Available Cash Balance",
"Ending Cash Balance"
)

pull(row)

# Only the three summary rows
# Also include row 1 so we keep the row headers (months)
tokens_summary <- tokens %>%

# Everything else
tokens_main <- tokens %>%

tokens_main
## # A tibble: 467 x 3
##      row   col chr
##    <int> <int> <chr>
##  1     3     1 Cash Inflows (Income):
##  2    12     1 Cash Outflows (Expenses):
##  3    35     1 Other Cash Out Flows:
##  4     4     2 Cash Collections
##  5     5     2 Credit Collections
##  6     6     2 Investment Income
##  7     7     2 Other:
##  8    10     2 Total Cash Inflows
## 10    14     2 Bank Service Charges
## # ... with 457 more rows

At this point, we need to identify the sets of row and column header cells that help put structure around our spreadsheet. In the actual workbook, row 4 corresponds to our column headers, and columns A and B correspond to our two sets of row headers. For now, we will just focus on the main tokens.

We just need to filter our dataset down to the cells that correspond to our row and column headers. These will be used later on to tell unpivotr how to tidy up.

row_headers <- tokens_main %>%

# Only columns A and B (1 and 2)
filter(col <= 2) %>%

# A quick rename

# Split into two tibbles by the column (a nice base R function)
# A list of two tibbles is returned
split(.$col) %>% # Name the elements of the list for easy access set_names(c("main_headers", "sub_headers")) row_headers ##$main_headers
## # A tibble: 3 x 3
##   <int> <int> <chr>
## 1     3     1 Cash Inflows (Income):
## 2    12     1 Cash Outflows (Expenses):
## 3    35     1 Other Cash Out Flows:
##
## $sub_headers ## # A tibble: 32 x 3 ## row col header ## <int> <int> <chr> ## 1 4 2 Cash Collections ## 2 5 2 Credit Collections ## 3 6 2 Investment Income ## 4 7 2 Other: ## 5 10 2 Total Cash Inflows ## 6 13 2 Advertising ## 7 14 2 Bank Service Charges ## 8 15 2 Insurance ## 9 16 2 Interest ## 10 17 2 Inventory Purchases ## # ... with 22 more rows And now column headers… col_headers <- tokens_main %>% # Only the first row (where the month names are) filter(row == 1) %>% rename(header = chr) col_headers ## # A tibble: 13 x 3 ## row col header ## <int> <int> <chr> ## 1 1 3 April ## 2 1 5 May ## 3 1 7 June ## 4 1 9 July ## 5 1 11 Aug ## 6 1 13 Sept ## 7 1 15 Oct ## 8 1 17 Nov ## 9 1 19 Dec ## 10 1 21 Jan ## 11 1 23 Feb ## 12 1 25 Mar ## 13 1 27 TOTALS ### Coordinate tidying With these in hand, we get to the fun (magic?) part. The key piece of unpivotr (at least to me) seems to be a handful of functions that perform transformations on your data based on map directions. This might be something like North with N(), South with S(), or combinations like North-NorthWest with NNW(). Internally, these are a mix of dplyr and data.table join functions. It takes a bit to wrap your head around the purpose of them, but I’ll try and use some pictures to help. For each of our 3 header groups, we need to tell unpivotr how to join our main data cells (where the numbers are) to the actual headers. For instance, for the column header, all we have to do is go directly North from any data point to run into the corresponding header. We tell unpivotr this like so: tokens_main %>% N(header = col_headers) %>% # passing in our col_headers tibble we extracted earlier rmarkdown:::print.paged_df() unpivotr identified any data cell that had a corresponding column header, and created a new dataset for us that is essentially an inner join of the column headers and the original tokens. Notice that columns A and B from the original sheet do not have column headers, so they don’t show up here. Next, we need to take care of our row headers. To do so, we need two transformations. For the sub-headers, it is as simple as the column headers, we just go directly west from any cell to run into the sub-header. For the main headers we need to do something slightly more complicated. For each data cell, we need to run all the way to the west wall of the sheet, and then run north from there to run into, for example Cash Inflows (Income). unpivotr gives us the power to do this in the WNW() (West-NorthWest) function. It searches West and NorthWest for the first matching row header. Combining this with the W() function that is needed for the sub-headers, we get: tokens_main %>% W(header = row_headers$sub_headers) %>%
WNW(header = row_headers$main_headers) %>% rmarkdown:::print.paged_df() Notice how each cell in the chr column now has extra columns that identify the corresponding main and sub headers that go with it. Click Next a few times on the interative data frame to get down to actual numbers. You should see that each cell has the correct main and sub header added on. At this point, the actual sub headers are also still in the chr column, mapped to their corresponding main header and sub header (itself). When we combine this row header step with the column header step, those will be removed as well. So let’s do that. All together now: tokens_main_tidy <- tokens_main %>% N(col_headers) %>% WNW(row_headers$main_headers) %>%
W(row_headers$sub_headers) %>% # Also rename to give us cleaner names rename(main_header = header.data, month = i.header, sub_header = header.header) rmarkdown:::print.paged_df(tokens_main_tidy) Nice! We successfully tidied the main section of the worksheet. ### Tidying the summary rows We can also quickly tidy up the summary rows that we removed earlier. Now that we know the steps, it’s a quick task to find the row and column headers and perform the corresponding transformation steps. row_headers_summary <- tokens_summary %>% filter(col == 1) %>% rename(header = chr) col_headers_summary <- col_headers tokens_summary_tidy <- tokens_summary %>% W(row_headers_summary) %>% N(col_headers_summary) %>% rename(main_header = header.data, month = header.header) tokens_summary_tidy ## # A tibble: 36 x 5 ## row col chr main_header month ## <int> <int> <chr> <chr> <chr> ## 1 2 3 0 Beginning Cash Balance April ## 2 11 3 -4407 Available Cash Balance April ## 3 43 3 -39895 Ending Cash Balance April ## 4 2 5 -39895 Beginning Cash Balance May ## 5 11 5 -34824 Available Cash Balance May ## 6 43 5 -43080 Ending Cash Balance May ## 7 2 7 -43080 Beginning Cash Balance June ## 8 11 7 -40529 Available Cash Balance June ## 9 43 7 -39830 Ending Cash Balance June ## 10 2 9 -39830 Beginning Cash Balance July ## # ... with 26 more rows ### Visualizing yearly cash flows So what can you do with this? Well, now that we have a tidy dataset we can use any of our standard tools, like ggplot2, and analyze the yearly cash flow statement. One thing we can do is divide up the sheet into it’s 3 main headers (that aren’t summary headers), Cash Inflows (Income):, Cash Outflows (Expenses):, and Other Cash Out Flows: and then create plots of their sub headers over the year. # The year goes from April -> March, we will need to create an ordered factor using this ordered_months <- c("April", "May", "June", "July", "Aug", "Sept", "Oct", "Nov", "Dec", "Jan", "Feb", "Mar") # We are going to iterate over the 3 Main headers (that aren't summary headers) main_headers <- unique(tokens_main_tidy$main_header)
main_headers
## [1] "Cash Inflows (Income):"    "Cash Outflows (Expenses):"
## [3] "Other Cash Out Flows:"
# For each main header, we are going to plot all of the sub-headers
p <- x %>%
ggplot(aes(x = month, y = chr, group = sub_header)) +
geom_line() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
print(p)
}

# We need to manipulate the data a little more to get it ready for plotting

# Remove any total rows
filter(month != "TOTALS") %>%

# To use the month column as our x-axis, we need it to be an ordered factor
# Additionally, the chr column need to be converted over to numeric
mutate(month = factor(month, levels = ordered_months, ordered = TRUE),
chr   = as.numeric(chr))

tokens_plot_ready
## # A tibble: 384 x 6
##    <int> <int> <dbl> <chr>                     <ord> <chr>
##  1     4     3  2227 Cash Inflows (Income):    April Cash Collections
##  2     5     3 -4712 Cash Inflows (Income):    April Credit Collections
##  3     6     3 -2412 Cash Inflows (Income):    April Investment Income
##  4     7     3   490 Cash Inflows (Income):    April Other:
##  5    10     3 -4407 Cash Inflows (Income):    April Total Cash Inflows
##  6    13     3  -324 Cash Outflows (Expenses): April Advertising
##  7    14     3  3221 Cash Outflows (Expenses): April Bank Service Charges
##  8    15     3   960 Cash Outflows (Expenses): April Insurance
##  9    16     3   936 Cash Outflows (Expenses): April Interest
## 10    17     3  2522 Cash Outflows (Expenses): April Inventory Purchases
## # ... with 374 more rows

Using purrr, we can walk over the 3 main headers, producing a plot at each one. walk is like map except it is mainly called for its “side effects” (like producing a plot) rather than for something like manipulating data and returning it to the user.

walk(main_headers, ~yearly_plot(tokens_plot_ready, .x))

### Conclusion

I really think that unpivotr is a powerful package. Using map directions to tidy these data sets is a pretty neat idea! That’s all for now!