Prepare Fruit dehiscence type data from TRY for use

The Fruit dehiscence type trait from TRY informs on whether fruits are dehiscent, meaning they open and release seeds by design, or indehiscent, meaning they do not do so.

If you intend to clean more than one or two traits, we recommend the use of the batch pre-processing script. Refer to the TRY main page for details.

If you have questions, suggestions, spot errors, or want to contribute, get in touch with us through planthub@idiv.de.

Author: David Schellenberger Costa

Requirements

To run the script, the following is needed:

• TRY data, available here

• the data.table library may need to be installed

Code

# load in libraries
library(data.table) # handle large datasets

# clear workspace
rm(list = ls())

Let’s get the TRY data

# set working directory (adapt this!)
setwd(paste0(.brd, "PlantHub"))

# read in data (adapt this!)
TRY <- fread("TRY_PlantHub.gz")

# select data of interest
TRYSubset <- TRY[TraitName == "Fruit dehiscence type"]

To get an overview of the data, we convert values to lowercase, sort them, and show them as a table.

# extract original data strings
oriVals <- TRYSubset$OrigValueStr # oriVals == original values

# change all to lowercase to ease later classification
oriVals <- tolower(oriVals)

# get an overview over the data by summarizing values and showing them in alphabetical order
valueOverview <- table(oriVals)
valueOverview[order(valueOverview)]

We first remove all remaining numeric values.

# remove purely numeric values and others that have no lowercase character included
oriVals[!grepl("[[:lower:]]", oriVals)] <- NA

The most important part of the cleaning process is the definition of the search strings to look for. We use regular expressions in some cases to be more inclusive (or exclusive).

# create a vector containing the search strings to look for
searchNames <- c("(present|^dehiscent)", "absent|indehiscent")

We can now search for the strings defined before and give names to the new categories.

# search for the strings defined before
searchResults <- sapply(searchNames, grepl, oriVals)

# name columns of searchResults matrix like corrected searchNames
colnames(searchResults) <- c("dehiscent", "indehiscent")

Let’s have a look at the results.

# show the number of matches to each category
colSums(searchResults)

# show the original entries for which no match was retrieved
sort(table(oriVals[rowSums(searchResults) < 1]))

# show the number of entries that weren't matched to any category
sum(rowSums(searchResults) < 1)

# show the number of entries that were matched to more that one category
sum(rowSums(searchResults) > 1)

As the categories are exclusive, we remove ambiguous entries.

# remove contradictory entries
searchResults[rowSums(searchResults) > 1, ] <- FALSE

We can now use the cleaned results data to create a new data vector.

# use the searchResults matrix to create new value strings by concatenating all data found
for (i in seq_along(searchResultsCols)) {
	searchResultsTemp <- searchResults[, colnames(searchResults) %in% searchResultsCols[[i]], drop = FALSE]
	newVals[, i] <- sapply(seq_len(nrow(searchResultsTemp)), function(x) {
		paste(searchResultsCols[[i]][searchResultsTemp[x, ]], collapse = ",")
	})
}
newVals[newVals == ""] <- NA

We can now transfer the data into the TRY data frame.

# integrate into TRY
TRY[TraitName == "Fruit dehiscence type", CleanedValueStr := newVals]

Let’s write the data to a file.

# write data
fwrite(TRY, file = paste0("TRY_processed_", Sys.Date(), ".gz"))