Data are stored in many different ways in tables or spreadsheets because no strict semantic or topographic standards for the organisation of tables are commonly accepted. In the R environment the tidy paradigm is a first step towards interoperability of data, in that it requires a certain arrangement of tables, where variables are recorded in columns and observations in rows (see https://tidyr.tidyverse.org/). Tables can be tidied (i.e., brought into a tidy arrangement) via packages such as tidyr, however, all functions that deal with reshaping tables to date require data that are already organised into topologically coherent, rectangular tables. This is often violated in practice, especially in data that are scraped off of the internet.

tabshiftr fills this gap in the toolchain towards more interoperable data via schema descriptions and a reorganise() function that is largely based on tidyr.


1) Install the official version from CRAN:


or the latest development version from github:


2) The vignette gives an introduction, provides an instruction on how to set up schema descriptions by going step by step through certain dimensions of disorganisation to show which table arrangements can be reorganised and how that works.


A disorganised table may look like the following table:


input <- read_csv(file = paste0(system.file("test_datasets", package = "tabshiftr"), "/table_mismatch_3.csv"),
                  col_names = FALSE, col_types = cols(.default = "c"))
X1 X2 X3 X4 X5 X6 X7
commodities harvested production . . . .
unit 1 . . . . . .
soybean 1111 1112 year 1 . . .
maize 1121 1122 year 1 . . .
soybean 1211 1212 year 2 . . .
maize 1221 1222 year 2 . . .
. . . . . . .
commodities harvested production commodities harvested production .
unit 2 . . unit 3 . . .
soybean 2111 2112 soybean 3111 3112 year 1
maize 2121 2122 maize 3121 3122 year 1
soybean 2211 2212 soybean 3211 3212 year 2
maize 2221 2222 maize 3221 3222 year 2

If we were to transform this data into tidy data by merely using the functions in tidyr (or the extended tidyverse in general), we’d potentially end up with a massive algorithm, especially for such complicated table arrangements. For other tables that may or may not be as complicated, we’d have to set up yet more algorithms and while a pipeline of tidy functions is relatively easy to set up, it would still become very laborious to repeat this for the dozens of potential table arrangements. In tabshiftr we solve that by describing the schema of the input table and providing this schema description to the reorganise() function. This requires us to use a vastly smaller set of code and makes it thus a lot more efficient to bring multiple heterogeneous data into an interoperable format.

A schema description can be started with any of the set* functions in this package and for the example table, we start the schema description by setting the clusters. This table contains two chunks of data at the rows 1 to 6 and 8 to 13 with a height of 6 cells and they are further organised into three similar groups (so-called clusters) with a width of 3 cells. In the second row of each cluster is another variable that is unique for each cluster and thus presumable the cluster identifier, we call it territories.


schema <- setCluster(id = "territories", top = c(1, 8, 8), left = c(1, 1, 4), 
                     width = 3, height = 6)

Next, we recognise that each cluster has variable names in the first row, so we set those as header. We decide to describe the positions in terms of values that are relative to the cluster positions, because that allows us to end up with a more concise schema description.

schema <- schema %>% 
  setHeader(rows = 1, relative = TRUE)

Each cluster contains the identifying variable commodities in the first column and the two observed variables harvested and production in the second and third column respectively. Moreover, the identifying variable year has values but no explicit name and is distinct from clusters (i.e., it doesn’t appear in each cluster). It thus has to be described with a position that is not relative to clusters, but to the spreadsheet. Before describing those variables though, we have to make sure that territories (the cluster ID) is set.

schema <- schema %>% 
  setIDVar(name = "territories", columns = 1, row = 2, relative = TRUE) %>% 
  setIDVar(name = "year", columns = 4, row = c(3:6), distinct = TRUE) %>% 
  setIDVar(name = "commodities", columns = 1, relative = TRUE) %>% 
  setObsVar(name = "harvested", unit = "ha", columns = 2, relative = TRUE) %>% 
  setObsVar(name = "production", unit = "t", columns = 3, relative = TRUE)

Input tables may contain many more data/variables than what we are interested in (for example if there were some metadata or another distinct variable in the empty cells in columns 5-7), but the schema description contains only those variables that shall be in the output table. Eventually, we end up with the following schema description.

#>   3 clusters
#>     origin: 1|1, 8|1, 8|4  (row|col)
#>     id    : territories
#>    variable      type       row   col   rel   dist 
#>   ------------- ---------- ----- ----- ----- ------  
#>    territories   id         2     1     T     F  
#>    year          id         3:6   4     F     T  
#>    commodities   id               1     T     F  
#>    harvested     observed         2     T     F  
#>    production    observed         3     T     F

Finally, the input table is reorganised simply by calling reorganise().

output <- reorganise(input = input, schema = schema)
territories year commodities harvested production
unit 1 year 1 maize 1121 1122
unit 1 year 1 soybean 1111 1112
unit 1 year 2 maize 1221 1222
unit 1 year 2 soybean 1211 1212
unit 2 year 1 maize 2121 2122
unit 2 year 1 soybean 2111 2112
unit 2 year 2 maize 2221 2222
unit 2 year 2 soybean 2211 2212
unit 3 year 1 maize 3121 3122
unit 3 year 1 soybean 3111 3112
unit 3 year 2 maize 3221 3222
unit 3 year 2 soybean 3211 3212


  • tabshiftr is still in development. So far it reliably reorganises 20 different types of tables, all of which are combinations of the dimensions of disorganisation outlined in the vignette. We suspect that there are further table arrangements, but they are not clear at this stage, issues submitted by users and contributors should be helpful.
  • Informative error management is work in process.
  • Moreover, the resulting schema descriptions can be useful for data archiving or database building and tabshiftr should at some point support that those schemas can be exported into data-formats that are used by downstream applications (xml, json, …), following proper (ISO) standards.

Contributions to those points and discussions on where tabshiftr should go are highly welcome!


This work was supported by funding to Carsten Meyer through the Flexpool mechanism of the German Centre for Integrative Biodiversity Research (Div) (FZT-118, DFG).