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
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:
|unit 2||.||.||unit 3||.||.||.|
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
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
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.
schema #> 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
|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|
Contributions to those points and discussions on where tabshiftr should go are highly welcome!