README.md
In rextor: Prepare 'WEXTOR' Data

rextor

rextor is an R package to facilitate data preparation for data collected on WEXTOR. It offers functions to read in the data, help visualize the data with color themes and scales that can help show data with a light-on-dark color scheme, as well as a helper function. Here, the dinosaur Rex will offer some words of wisdom and advice.

You can install the development version of rextor from GitHub with:

# install.packages("devtools")
devtools::install_github("iscience-kn/rextor")

Or the official CRAN version via:

install.packages("rextor")

You can ask Rex for specific topics, such as t.tests and they will ask you to specify the exact type of statistic you are looking for. If you are generally having a hard time, just ask Rex for nothing in particular and they will give you some encouragement to help you to try again!

library(rextor)

set.seed(1)
ask_rex()
#> 
#>                                      __
#>                                     / _)
#>                              .-^^^-/ /
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#>                           <__.|_|-|_|
#>                           
#>                           Small progress beats perfect code.

If you are not yet familiar with WEXTOR: It is an online experiment building tool which relies on standards of web-based experimentation and methodological best practices.

That means, generating studies to collect data is easy but analyzing that data in R might be tricky. WEXTOR data comes in a specific csv^comma-separated-values format and figuring out how to properly read that data into R can be difficult to do when starting out your R journey.

Luckily, with rextor, reading in that data becomes easy! We will use an openly available data set on personality data collected in three different conditions^For more information on the data, see github.com/iscience-kn/BiFiX. This dataset is also included in the rextor package in the WEXTOR-exported format (without real IP addresses for data anonymization). To read in this data from the package, you need to use the path_to_file() function as shown below. Because some of the automatically generated variable names are not ideal for usage in R, we can also identify any variable name that starts with “.wx.” and have those names start instead with “v_” for “variable”. Another option is to remove those prefixes altogether to be left with just easily legible variable names, which we will do.

raw_data <- read_WEXTOR(path_to_file("BiFiX_data_raw.csv"), 
                        keep_validation = FALSE)

# personality <- namepref(raw_data, ".wx.", "v_")
personality <- removepref(raw_data)

This way, the data frame personality is now nicely formatted with useful variable names ready to be edited further. For example, we recommend conducting dropout analysis as a routine part of analyzing online data and offer the dropR package to facilitate such analyses!