Package {rextor}

Title:	Prepare 'WEXTOR' Data
Version:	1.1.0
Depends:	R (≥ 4.1.0)
Description:	Facilitate data preparation for data collected on 'WEXTOR' https://wextor.eu, created by Reips and Neuhaus (2002) <doi:10.3758/bf03195449>. Perform plausibility and other checks and make use of cool color palettes and themes for data visualization.
License:	GPL (≥ 3)
Date:	2026-05-06
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	dplyr, readr, ggplot2
Suggests:	knitr, rmarkdown, rlang, scales, tibble
VignetteBuilder:	knitr
URL:	https://iscience-kn.github.io/rextor/
NeedsCompilation:	no
Packaged:	2026-05-17 15:51:44 UTC; PCUser
Author:	Annika Tave Overlander [aut, cre], Ulf-Dietrich Reips [ths, cph]
Maintainer:	Annika Tave Overlander <annika-tave.overlander@uni.kn>
Repository:	CRAN
Date/Publication:	2026-05-21 09:20:07 UTC

BiFiX - Big Five raw data

Description

Raw data from the BiFiX study (submitted for publication in BRM)

Format

A data frame with 760 rows and 81 columns:

$validation_var

validation variable

.wx.1.id

ID

.wx.3.experimental_condition

Experimental condition

big_...

Big Five items (A - Agreeableness, C - Conscientiousness, E - Extraversion, N - Neuroticism, O - Openness)

...

Source

https://github.com/iscience-kn/BiFiX

Ask Rex

Description

This function lets you ask dino Rex for help. You can try to ask for specific functions and topics or just use the function as-is for some motivational words of encouragement. :)

Usage

ask_rex(topic = "?")

Arguments

Value

Returns advice and explanations on R; sometimes Rex asks follow-up questions

Examples

# Get motivation
ask_rex()

# Get specific information
ask_rex("mean")
ask_rex(topic = "ttest")

Check whether a plausibility condition is satisfied

Description

Returns a logical vector indicating whether a given check column equals "ok". If the column does not exist in the data, all cases are treated as passing.

Usage

check_ok(data, col)

Arguments

Details

Used inside the plausicheck() function.

Value

A logical vector of length nrow(data).

Examples

example_df <- data.frame(check_ip = c("ok", "nope", "ok", "OK"))
check_ok(example_df, "check_ip")

Get the mode of a vector or variable

Description

The mode is the number that appears the most often in a vector or variable. This measure is especially important for nominal variables since the mean or median cannot meaningfully be reported.

Usage

getmode(v)

Arguments

Value

Returns the mode of v, i.e., the value occurring most often

Examples

vec <- c("A", "A", "B")
getmode(vec)

Get percentage of a value

Description

Get percentage of a value

Usage

getperc(data, var, val)

Arguments

Value

Returns the percentage of value val in relation to all values of var

Examples

 getperc(data.frame(a = c("duck", "duck", "goose")),
  "a", 
  "duck")
 
 # Also works when val is indexed (alphabetically)
 getperc(data.frame(a = c("duck", "duck", "goose")),
  "a", 
  2)

Informed Consent Filter

Description

One of the best practices in data collection is including and informed consent question. With this function you can automatically filter out rows where participants did not explicitly consent to their data being used.

Usage

informed_consent(dataframe, varname = "informedconsent")

Arguments

Value

Returns data with cases filtered out that did not check the informed consent box

Examples

data <- data.frame(informedconsent = c("checked", "not checked", "checked"))
informed_only <- informed_consent(data) 

Check for duplicate IP addresses

Description

ip_check() identifies potential duplicate participations in a dataset by checking for repeated IP addresses. Cases with duplicated IPs are flagged as "possible duplicate", while unique IPs are labeled "ok". Keep in mind that in some cases, duplicate IP addresses should not lead to exclusion, for example when different participants (such as siblings or flatmates) plausibly use the same device.

Usage

ip_check(dataframe)

Arguments

Details

If WEXTOR-style prefixes (i.e. .wx.) are detected in the variable names, they are removed prior to performing the check.

The function uses duplicated() to flag repeated IP addresses. Only subsequent occurrences are marked as duplicates; the first instance of each IP is treated as valid.

Value

A data frame with an additional column check_ip, indicating whether each case has a unique IP ("ok") or is a potential duplicate ("possible duplicate").

Examples

data <- read_WEXTOR(path_to_file("BiFiX_data_raw.csv"))
# The example data does not contain real IPs (data protection), so we will use simulate ones
data$ip <- sample(1:1000, nrow(data), replace = TRUE)

new_data <- ip_check(data)

Add variable name prefix (with substitution)

Description

This function strips the old prefix from the variable names of the dataframe. It then adds the new prefix where the old one was and changes the dataframe's names to the new ones.

Usage

namepref(dataframe, pref_old, pref_new)

Arguments

Value

Returns dataframe with those variables renamed that start with the old prefix.

Examples

bla <- tibble::tibble(x_ar = 1:5, y_ar = 6:10)
blo <- namepref(bla, "x_", "z_")
names(bla)
names(blo)

Add variable name prefix (without substitution)

Description

This function adds the new prefix and changes the dataframe's names to the new ones.

Usage

namepref0(dataframe, pref_new)

Arguments

Value

Returns dataframe with all variables renamed to start with the new prefix.

Examples

bla <- tibble::tibble(x_ar = 1:5, y_ar = 6:10)
blo <- namepref0(bla, "var")
names(bla)
names(blo)

Get file path to example data file

Description

This function makes the example data from the BiFiX study (submitted for publication in BRM) easy to access.

Usage

path_to_file(path = NULL)

Arguments

Value

Gives the path to the example file included in the package. Meant for use in read_WEXTOR().

Source

This function is adapted from palmerpenguins (which is adapted from readxl::readxl_example()).

Examples

path_to_file()
path_to_file("BiFiX_data_raw.csv")
head(read.csv(path_to_file("BiFiX_data_raw.csv")))

Plausibility check on WEXTOR study data

Description

plausicheck() performs basic plausibility checks on a study dataset to identify potentially invalid or suspicious participation. The function can check whether participants visited a minimum number of pages, whether the recorded session length appears plausible, and whether IP addresses indicate duplicate participation.

Usage

plausicheck(dataframe, min_pages, check_sess_length = TRUE, check_ip = TRUE)

Arguments

Details

If WEXTOR prefixes are detected in the variable names, they are removed before the plausibility checks are applied.

Value

A data frame with additional plausibility check variables. The final variable check_plausibility indicates whether all selected checks were passed ("all ok") or whether the case should be excluded ("exclude"). Keep in mind that researchers are advised to make sure that the "exclude"-cases were correctly identified and are indeed of poorer data quality to avoi unnecessary data loss.

Examples

data <- read_WEXTOR(path_to_file("BiFiX_data_raw.csv"))
# The example data does not contain real IPs (data protection), so we will use simulate ones
data$ip <- sample(1:1000, nrow(data), replace = TRUE)

plausi_data <- plausicheck(dataframe = data,
  min_pages = 6,
  check_sess_length = TRUE,
  check_ip = TRUE)

Read 'WEXTOR' Data

Description

With this function, you can easily read in your 'WEXTOR' generated data. By default, it will be exported as a "CSV" file, which stands for "comma-separated values". R has many available options of reading in this type of data, which can make it hard to navigate which one to use - especially for beginners.

Usage

read_WEXTOR(filepath, keep_validation = TRUE)

Arguments

Details

In the read_WEXTOR() function, you just need to provide the filepath, i.e. the location of the CSV data file that you downloaded from 'WEXTOR'. If you want, you can already decided whether to keep the first column containing a so-called validation variable, but you don't have to. With no other input, rextor will keep the variable for you. You can explicitly set keep_validation to FALSE if you like your data neat and do not need this extra measure. The usage is explained in the examples down below using the open source BiFiX data with psychological Big Five personality traits.

This function prepares the 'WEXTOR' data so that it is readable by both R and you as a human. It will give you your original data and also make the start and end time of each participation easier to read and work with later (by default, 'WEXTOR' will return these values as date and time separately, cluttering your dataset).

Value

The WEXTOR data as an R data object.

Examples

data <- read_WEXTOR(path_to_file("BiFiX_data_raw.csv"))

# If you don't need the validation variable, try
data_noval<-read_WEXTOR(path_to_file("BiFiX_data_raw.csv"),
                       keep_validation = FALSE)

Remove 'WEXTOR' variable name prefixes (automatic)

Description

This function strips the old prefix from the server variable names of the dataframe. It identifies such variables that start with ".wx.#." where # stands for any number, or simply ".wx." and then removes these prefixes for more easily legible and usable variable names in your data!

Usage

removepref(dataframe)

Arguments

Value

Returns dataframe with those variables renamed that start with the old prefix.

Examples

bli <- tibble::tibble(.wx.1.ar = 1:5, y_ar = 6:10, .wx.z = 11:15)
blu <- removepref(bli)
names(bli)
names(blu)

Coloring Scale for Data Visualization

Description

This scale is very versatile and powerful. It offers several color palettes to choose from and automatically recognizes whether to use a discrete or continuous scale depending on the data type of the mapping object. It can also be used for both color and fill aesthetics by defining aesthetic accordingly directly inside the function!

Usage

scale_rextor(pal = "cute", direction = 1, aesthetic = "color", ...)

Arguments

Value

ggplot plot object

Examples

library(ggplot2)
ggplot(iris, aes(Species, Petal.Width, color = Petal.Width)) + 
geom_jitter() + 
theme_wob() + 
scale_rextor()

data <- data.frame(Time = rep(c("Time 1", "Time 2", "Time 3", "Time 4", 
                                "Time 5", "Time 6", "Time 7", "Time 8"), 
                                each = 2),
                                cont = 1:16,
                                Value = c(8, 5, 10, 3, 8, 5, 10, 3,  8, 5, 10, 3,  8, 5, 10, 3))

ggplot(data, aes(x = Time, y = Value, color = Time, fill = Time)) +
  geom_boxplot(alpha = 0.95) +
    theme_wob() +
    scale_rextor(pal = "neon") +
    scale_rextor(pal = "neon", aesthetic = "fill")
    
ggplot(data, aes(x = cont, y = Value, color = cont, fill = cont)) +
  geom_jitter(alpha = 0.8, size = 8) +
  theme_minimal() +
  scale_rextor(pal = "wextor") +
  scale_rextor(pal = "wextor", aesthetic = "fill")

Filter data frame by seriousness check

Description

Filter your data frame by your item for the "seriousness check". If the data comes from 'WEXTOR' with no previous changes to the automatically created variable, you will be able to accept all defaults in this function and simply input the data frame.

Usage

serious_check(dataframe, varname = "seriousness", keep = "participate")

Arguments

Value

Filtered data

Examples

data <- read_WEXTOR(path_to_file("BiFiX_data_raw.csv"))
serious_only <- serious_check(data)

Session Length Check

Description

This function takes a dataframe as input as well as the minimum number of webpages in your web experiment or study, that a participant should have seen in order to be considered a complete (or plausible) participation. It flags the most extreme 10% of complete participations in the data as very slow or very fast.

Usage

sess_length_check(dataframe, min_pages = 6)

Arguments

Value

A dataframe with added variables sess_length_clean for only completed session lengths, otherwise NA and check_sess_length for a flag of the most extreme 10% of time needed to finish participation

Examples

data <- read_WEXTOR(path_to_file("BiFiX_data_raw.csv"))
new_data <- sess_length_check(data)

Theme White on Black

Description

This theme option is built to be used with a ggplot2 plot. It offers several options for a dark theme, i.e. dark background with light writing.

Usage

theme_wob(max_font_size = 14, contrast = "high")

Arguments

Value

Plot theme.

Examples

# Load the data
data <- read_WEXTOR(path_to_file("BiFiX_data_raw.csv"))

# Create any plot
library(ggplot2)
ggplot(data, aes(age, color = gender, fill = gender)) + 
  geom_density(alpha = .5) + 
  theme_wob(contrast = "rex")