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Merge and load in data from multiple gorilla downloads

Source: R/data_collator_gorilla.R
data_collator_gorilla.Rd

Reads in the raw data from one or more downloads from the gorilla experiment platform and collates them into a single list of data.tables. This is usually the first function to run in an analysis of gorilla data.

Usage

data_collator_gorilla(datafolder = NULL, ...)

Arguments

datafolder

The folder containing the gorilla downloads. If left as NULL, you will be asked to choose a folder.

...

Further arguments passed on to data_merger_gorilla(), for example gorilla_clean = TRUE to drop some of the less useful gorilla columns.

Value

A list with these items:

data_task

data.table of all the task (experiment) data, with added columns pid (anonymous participant id, a factor), lid (public id), and rt (numeric reaction time)

data_q

data.table of the questionnaire data

data_qtype

"long", "wide" or "none"

data_continuous

data.table of mouse or eye tracking data from the uploads folder, if there is any

uploads

table of the names and paths of any other uploaded files

task_names

the names of the tasks found

q_names

the names of the questionnaires found

Details

datafolder should be a folder that contains the downloads from gorilla. Each download can either be a .zip file, or an already unzipped folder that contains the gorilla .csv files. The data from all of the downloads are stacked together.

Note: any .zip files found in datafolder are unzipped into a folder with the same name, and the .zip file is then deleted.

Each download folder is processed by data_merger_gorilla(), and ... is passed on to that function (and from there to data_merger()).

The questionnaire data can be downloaded from gorilla in wide (one row per participant) or long format. This function works out which you have and reports it in data_qtype. The questionnaire functions in this package (gorilla_q_...) need long format.

The names of the tasks and questionnaires found are printed to the console, so that you can copy them into later analysis code.

See also

data_merger_gorilla(), data_merger(), pid_merge()

Examples

if (FALSE) { # \dontrun{
data <- data_collator_gorilla("gorilla_data")
head(data$data_task)
} # }