Type:	Package
Title:	Data Cleaning for Psychological Analyses
Version:	0.1.1
Description:	Useful for preparing and cleaning data. It includes functions to center data, reverse coding, dummy code and effect code data, and more.
License:	GPL (≥ 3)
Encoding:	UTF-8
RoxygenNote:	7.2.3
Imports:	dplyr, tidyr, tibble, data.table, rlang (≥ 0.1.2)
Suggests:	roxygen2, covr, misty, testthat (≥ 3.0.0)
URL:	https://jasonmoy28.github.io/psycCleaning/
Config/testthat/edition:	3
Depends:	R (≥ 2.10)
LazyData:	true
NeedsCompilation:	no
Packaged:	2023-11-04 20:21:58 UTC; Jasonmoy
Author:	Jason Moy [aut, cre]
Maintainer:	Jason Moy <jason.moyhj@gmail.com>
Repository:	CRAN
Date/Publication:	2023-11-05 06:30:02 UTC

Pipe operator

Description

Pipe operator

Usage

lhs %>% rhs

Value

no return value

Center with respect to grand mean

Description

This function will compute grand-mean-centered scores.

Usage

center_grand_mean(data, cols, keep_original = TRUE)

Arguments

Value

An object of the same type as .data. The output has the following properties: 1. Columns from .data will be preserved 2. Columns with scores that are grand-mean-centered.

Examples

center_grand_mean(iris,where(is.numeric))

Center with respect to group mean

Description

This function will compute group-mean-centered scores.

Usage

center_group_mean(data, cols, group, keep_original = TRUE)

Arguments

Value

An object of the same type as .data. The output has the following properties: 1. Columns from .data will be preserved 2. Columns with scores that are group-mean centered

Examples

center_group_mean(iris,where(is.numeric), group = Species)

Centering for multilevel analyses

Description

This function will group mean centered the scores at the level 1 and create a mean score for each group at L2.

Usage

center_mlm(data, cols, group, keep_original = TRUE)

Arguments

Value

An object of the same type as .data. The output has the following properties: 1. Columns from .data will be preserved 2. Columns with L1 scores that are group-mean centered. 3. Columns with L2 aggregated means.

Examples

center_mlm(iris,dplyr::ends_with('Length'),group = 'Species')

Composite column

Description

The function will perform a row-wise aggregation which then divided by the total number of columns.

Usage

composite_score(
  data,
  cols = dplyr::everything(),
  na.rm = FALSE,
  composite_col_name = "composited_column"
)

Arguments

Value

An object of the same type as .data. The output has the following properties: 1. Columns from .data will be preserved. 2. Columns with composited scores.

Examples

test_df = data.frame(col1 = c(1,2,3,4),col2 = c(1,2,3,4), col3 = c(1,2,NA,4))
composite_df = composite_score(data = test_df)

Dummy Coding

Description

Create dummy-coded columns, supporting tidyselect syntax to process multiple columns simultaneously.

Usage

dummy_coding(data, cols)

Arguments

Value

An object of the same type as .data. The output has the following properties: 1. Columns from .data will be preserved. 2. Columns that are dummy-coded.

Examples

dummy_coding(iris,Species)

Effect Coding

Description

Create effect-coded columns, supporting tidyselect syntax to process multiple columns simultaneously.

Usage

effect_coding(data, cols, factor = FALSE)

Arguments

Value

An object of the same type as .data. The output has the following properties: 1. Columns from .data will be preserved. 2. Columns that are effect-coded.

Examples

effect_coding(iris,Species)

Listwise deletion

Description

Perform listwise deletion (the entire rows is disregarded if the row has one 'NA' value)

Usage

listwise_deletion(data, cols = dplyr::everything())

Arguments

Value

An object of the same type as .data with rows revmoed if the row has one 'NA' value

Examples

test_df = data.frame(col1 = c(1,2,3),col2 = c(1,NA,3),col3 = c(1,2,NA))
listwise_deletion(test_df,col1:col2) # you can see that the row with NA in col3 is not deleted

mlbook_data

Description

Classic data-set from Snijders, Tom A.B., and Bosker, Roel J. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, second edition.

Usage

mlbook_data

Format

A data frame with 3758 rows and 34 variables:

schoolnr

School ID

pupilNR_new

Student Identifier (Level 1 units)

langPOST

Student language score

ses

Student socioeconomic score, grand-mean centered (in points, M = 0))

IQ_verb

Student verbal IQ, grand-mean centered (in points, M = 0)

sex

Student binary gender, 1 = female, 0 = not female

Minority

Student minority status, 1 = minoritized, 0 = not minoritized

denomina

School-level religious denominations, 5 categories

female_dum

Dummy coded sex

female_eff

Effect-coded sex

female_CMC

Group-mean-centered of female_eff

fempct_agg

Aggregated mean female_dum for each school

Zfempct_agg

Z-scored aggregated mean female_dum for each school

ses_CMC

Group-mean-centered SES

Zses_CMC

Z-scored group-mean-centered SES

ses_agg

Aggregated mean SES for each school

Zses_agg

Z-scored aggregated mean SES for each school

Source

https://www.stats.ox.ac.uk/~snijders/mlbook.htm

Recode values of a data frame

Description

Recode values of a data frame

Usage

recode_item(data, cols, code_from = NULL, code_to = NULL, retain_code = NULL)

Arguments

Value

An object of the same type as .data. The output has the following properties: 1. Columns except the recoded columns from .data will be preserved 2. Recoded columns

Examples

pre_recoded_df = tibble::tibble(x1 = 1:5, x2 = 5:1)
recoded_df = recode_item(pre_recoded_df, cols = dplyr::contains('x'),
                        code_from = 1:5,
                        code_to = 5:1)

Count the number of missing values

Description

It counts the number of missing (i.e.,'NA') values in each column.

Usage

summarize_missing_values(
  data,
  cols = dplyr::everything(),
  group = NULL,
  verbose = TRUE,
  return_result = FALSE
)

Arguments

Value

An object of the same type as .data. that specified the number of NA values of the columns (only when 'return_result = TRUE')

Examples

df1 = data.frame(col1 = c(1,2,3),col2 = c(1,NA,3),col3 = c(1,2,NA))
summarize_missing_values(df1,everything())

Tidy eval helpers

Description

• sym() creates a symbol from a string and syms() creates a list of symbols from a character vector.

• enquo() and enquos() delay the execution of one or several function arguments. enquo() returns a single quoted expression, which is like a blueprint for the delayed computation. enquos() returns a list of such quoted expressions.

• expr() quotes a new expression locally. It is mostly useful to build new expressions around arguments captured with enquo() or enquos(): expr(mean(!!enquo(arg), na.rm = TRUE)).

• as_name() transforms a quoted variable name into a string. Supplying something else than a quoted variable name is an error.

  That's unlike as_label() which also returns a single string but supports any kind of R object as input, including quoted function calls and vectors. Its purpose is to summarise that object into a single label. That label is often suitable as a default name.

  If you don't know what a quoted expression contains (for instance expressions captured with enquo() could be a variable name, a call to a function, or an unquoted constant), then use as_label(). If you know you have quoted a simple variable name, or would like to enforce this, use as_name().

To learn more about tidy eval and how to use these tools, visit https://www.tidyverse.org and the Metaprogramming section of Advanced R.

Value

no return value

Grand mean z-score

Description

This function will compute z-scores with respect to the grand mean.

Usage

z_scored_grand_mean(data, cols, keep_original = TRUE)

Arguments

Value

An object of the same type as .data. The output has the following properties: 1. Columns from .data will be preserved 2. Columns with scores that are z-scored

Examples

z_scored_grand_mean(iris,where(is.numeric))

Z scored with with respect to the group mean

Description

This function will compute group-mean-centered scores, and then z-scored the group-mean-centered scores with respect to the grand mean.

Usage

z_scored_group_mean(data, cols, group, keep_original = TRUE)

Arguments

Value

return a dataframe with the columns z-scored (replace existing columns)

Examples

z_scored_group_mean(iris, dplyr::ends_with("Petal.Width"), "Species")

Z-scored for multilevel analyses

Description

This function will group mean centered the scores at the level 1 and create an aggregated mean score for each group at L2. After that, the group-mean-centered L1 scores and mean L2 scores will be z-scored with respect to the grand mean. Please see 'center_mlm' if you want to use the version without the z-scoring.

Usage

z_scored_mlm(data, cols, group, keep_original = TRUE)

Arguments

Value

An object of the same type as .data. The output has the following properties: 1. Columns from .data will be preserved 2. Columns with L1 scores that are group-mean centered then grand-mean z-scored. 3. Columns with L2 aggregated means that are z-scored

Examples

z_scored_mlm(iris,dplyr::ends_with('Length'),group = 'Species')

Z-scored for multilevel analyses

Description

This is a specialized function for mean centering categorical variables. There are two cases where this function should be used instead of the generic 'center_mlm'. 1. This function should be used when you need group mean centering for non-dummy-coded variables at L1. Variables at L2 are always dummy-coded as they represent the percentage of subjects in that group. 2. This function should be used whenever you want to z-score the aggregated L2 means

Usage

z_scored_mlm_categorical(
  data,
  cols,
  dummy_coded = NA,
  group,
  keep_original = TRUE
)

Arguments

Value

An object of the same type as .data. The output has the following properties: 1. Columns from .data will be preserved 2. Columns with L1 scores that are group-mean centered 3. Columns with L2 aggregated means (i.e., percentage) that are z-scored

Examples

z_scored_mlm_categorical(mlbook_data,cols='female_eff',dummy_coded='female_dum','schoolnr')