unpivotr

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unpivotr deals with non-tabular data, especially from spreadsheets. Use unpivotr when your source data has any of these ‘features’:

Multi-headered hydra
Meaningful formatting
Headers anywhere but at the top of each column
Non-text headers e.g. dates
Other stuff around the table
Several similar tables in one sheet
Sentinel values
Superscript symbols
Meaningful comments
Nested HTML tables

If that list makes your blood boil, you’ll enjoy the function names.

behead() deals with multi-headered hydra tables one layer of headers at a time, working from the edge of the table inwards. It’s a bit like using header = TRUE in read.csv(), but because it’s a function, you can apply it to as many layers of headers as you need. You end up with all the headers in columns.
spatter() is like tidyr::spread() but preserves mixed data types. You get into a mixed-data-type situation by delaying type coercion until after the table is tidy (rather than before, like read.csv() et al). And yes, it usually follows behead().

More positive, corrective functions:

justify() aligns column headers before behead()ing, and has deliberate moral overtones.
enhead() attaches a header to the body of the data, a la Frankenstein. The effect is the same as behead(), but is more powerful because you can choose exactly which header cells you want, paying attention to formatting (which behead() doesn’t understand).
isolate_sentinels() separates meaningful symbols like "N/A" or "confidential" from the rest of the data, giving them some time alone think about what they’ve done.
partition() takes a sheet with several tables on it, and slashes into pieces that each contain one table. You can then unpivot each table in turn with purrr::map() or similar.

Make cells tidy

Unpivotr uses data where each cells is represented by one row in a dataframe. Like this.

Gif of tidyxl converting cells into a tidy representation of one row per cell

What can you do with tidy cells? The best places to start are:

Spreadsheet Munging Strategies, a free, online cookbook using tidyxl and unpivotr
Screencasts on YouTube.
Worked examples on GitHub.

Otherwise the basic idea is:

Read the data with a specialist tool.
- For spreadsheets, use tidyxl.
- For plain text files, you might soon be able to use readr, but for now you’ll have to install a pull-request on that package with devtools::install_github("tidyverse/readr#760").
- For tables in html pages, use unpivotr::tidy_html()
- For data frames, use unpivotr::as_cells() – this should be a last resort, because by the time the data is in a conventional data frame, it is often too late – formatting has been lost, and most data types have been coerced to strings.
Either behead() straight away, else dplyr::filter() separately for the header cells and the data cells, and then recombine with enhead().
spatter() so that each column has one data type.

 library(unpivotr)
 library(tidyverse)
 #> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
 #> ✔ dplyr 1.1.4 ✔ readr 2.1.5
 #> ✔ forcats 1.0.0 ✔ stringr 1.5.1
 #> ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
 #> ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
 #> ✔ purrr 1.0.2 
 #> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
 #> ✖ dplyr::filter() masks stats::filter()
 #> ✖ dplyr::lag() masks stats::lag()
 #> ✖ tidyr::pack() masks unpivotr::pack()
 #> ✖ tidyr::unpack() masks unpivotr::unpack()
 #> i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
x <- purpose$`up-left left-up`
x # A pivot table in a conventional data frame. Four levels of headers, in two
 #> X2 X3 X4 X5 X6 X7
 #> 1 <NA> <NA> Female <NA> Male <NA>
 #> 2 <NA> <NA> 0 - 6 7 - 10 0 - 6 7 - 10
 #> 3 Bachelor's degree 15 - 24 7000 27000 <NA> 13000
 #> 4 <NA> 25 - 44 12000 137000 9000 81000
 #> 5 <NA> 45 - 64 10000 64000 7000 66000
 #> 6 <NA> 65+ <NA> 18000 7000 17000
 #> 7 Certificate 15 - 24 29000 161000 30000 190000
 #> 8 <NA> 25 - 44 34000 179000 31000 219000
 #> 9 <NA> 45 - 64 30000 210000 23000 199000
 #> 10 <NA> 65+ 12000 77000 8000 107000
 #> 11 Diploma 15 - 24 <NA> 14000 9000 11000
 #> 12 <NA> 25 - 44 10000 66000 8000 47000
 #> 13 <NA> 45 - 64 6000 68000 5000 58000
 #> 14 <NA> 65+ 5000 41000 1000 34000
 #> 15 No Qualification 15 - 24 10000 43000 12000 37000
 #> 16 <NA> 25 - 44 11000 36000 21000 50000
 #> 17 <NA> 45 - 64 19000 91000 17000 75000
 #> 18 <NA> 65+ 16000 118000 9000 66000
 #> 19 Postgraduate qualification 15 - 24 <NA> 6000 <NA> <NA>
 #> 20 <NA> 25 - 44 5000 86000 7000 60000
 #> 21 <NA> 45 - 64 6000 55000 6000 68000
 #> 22 <NA> 65+ <NA> 13000 <NA> 18000
 # rows and two columns.
 
y <- as_cells(x) # 'Tokenize' or 'melt' the data frame into one row per cell
y
 #> # A tibble: 132 ×ばつ 4
 #> row col data_type chr 
 #> <int> <int> <chr> <chr> 
 #> 1 1 1 chr <NA> 
 #> 2 2 1 chr <NA> 
 #> 3 3 1 chr Bachelor's degree
 #> 4 4 1 chr <NA> 
 #> 5 5 1 chr <NA> 
 #> 6 6 1 chr <NA> 
 #> 7 7 1 chr Certificate 
 #> 8 8 1 chr <NA> 
 #> 9 9 1 chr <NA> 
 #> 10 10 1 chr <NA> 
 #> # i 122 more rows
 
 rectify(y) # useful for reviewing the melted form as though in a spreadsheet
 #> # A tibble: 22 ×ばつ 7
 #> `row/col` `1(A)` `2(B)` `3(C)` `4(D)` `5(E)` `6(F)`
 #> <int> <chr> <chr> <chr> <chr> <chr> <chr> 
 #> 1 1 <NA> <NA> Female <NA> Male <NA> 
 #> 2 2 <NA> <NA> 0 - 6 7 - 10 0 - 6 7 - 10
 #> 3 3 Bachelor's degree 15 - 24 7000 27000 <NA> 13000 
 #> 4 4 <NA> 25 - 44 12000 137000 9000 81000 
 #> 5 5 <NA> 45 - 64 10000 64000 7000 66000 
 #> 6 6 <NA> 65+ <NA> 18000 7000 17000 
 #> 7 7 Certificate 15 - 24 29000 161000 30000 190000
 #> 8 8 <NA> 25 - 44 34000 179000 31000 219000
 #> 9 9 <NA> 45 - 64 30000 210000 23000 199000
 #> 10 10 <NA> 65+ 12000 77000 8000 107000
 #> # i 12 more rows
 
y %>%
 behead("up-left", "sex") %>% # Strip headers
 behead("up", "life-satisfication") %>% # one
 behead("left-up", "qualification") %>% # by
 behead("left", "age-band") %>% # one.
 select(-row, -col, -data_type, count = chr) %>% # cleanup
 mutate(count = as.integer(count))
 #> # A tibble: 80 ×ばつ 5
 #> count sex `life-satisfication` qualification `age-band`
 #> <int> <chr> <chr> <chr> <chr> 
 #> 1 7000 Female 0 - 6 Bachelor's degree 15 - 24 
 #> 2 12000 Female 0 - 6 Bachelor's degree 25 - 44 
 #> 3 10000 Female 0 - 6 Bachelor's degree 45 - 64 
 #> 4 NA Female 0 - 6 Bachelor's degree 65+ 
 #> 5 27000 Female 7 - 10 Bachelor's degree 15 - 24 
 #> 6 137000 Female 7 - 10 Bachelor's degree 25 - 44 
 #> 7 64000 Female 7 - 10 Bachelor's degree 45 - 64 
 #> 8 18000 Female 7 - 10 Bachelor's degree 65+ 
 #> 9 NA Male 0 - 6 Bachelor's degree 15 - 24 
 #> 10 9000 Male 0 - 6 Bachelor's degree 25 - 44 
 #> # i 70 more rows

Note the compass directions in the code above, which hint to behead() where to find the header cell for each data cell.

"up-left" means the header (Female, Male) is positioned up and to the left of the columns of data cells it describes.
"up" means the header (0 - 6, 7 - 10) is positioned directly above the columns of data cells it describes.
"left-up" means the header (Bachelor's degree, Certificate, etc.) is positioned to the left and upwards of the rows of data cells it describes.
"left" means the header (15 - 24, 25 - 44, etc.) is positioned directly to the left of the rows of data cells it describes.

Installation

 # install.packages("devtools") # If you don't already have devtools
devtools::install_github("nacnudus/unpivotr", build_vignettes = TRUE)

The version 0.4.0 release had somee breaking changes. See NEWS.md for details. The previous version can be installed as follow:

devtools::install_version("unpivotr", version = "0.3.1", repos = "http://cran.us.r-project.org")

Similar projects

unpivotr is inspired by Databaker, a collaboration between the United Kingdom Office of National Statistics and The Sensible Code Company. unpivotr.

jailbreaker attempts to extract non-tabular data from spreadsheets into tabular structures automatically via some clever algorithms. unpivotr differs by being less magic, and equipping you to express what you want to do.