README.md

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’:

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

More positive, corrective functions:

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:

Otherwise the basic idea is:

  1. 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.
  2. Either behead() straight away, else dplyr::filter() separately for the header cells and the data cells, and then recombine with enhead().
  3. spatter() so that each column has one data type.
library(unpivotr)
library(tidyverse)
#> ── Attaching packages ────────────────────────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 3.2.1     ✔ purrr   0.3.2
#> ✔ tibble  2.1.3     ✔ dplyr   0.8.3
#> ✔ tidyr   0.8.3     ✔ stringr 1.4.0
#> ✔ readr   1.3.1     ✔ forcats 0.4.0
#> ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
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 x 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>             
#> # … with 122 more rows

rectify(y) # useful for reviewing the melted form as though in a spreadsheet
#> # A tibble: 22 x 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
#> # … with 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 x 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   
#> # … with 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.

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.



nacnudus/unpivotr documentation built on March 26, 2020, 2:39 a.m.