Package index • datawizard

Data Preparation

Main functions for cleaning and preparing data

data_to_long() reshape_longer(): Reshape (pivot) data from wide to long

data_to_wide() reshape_wider(): Reshape (pivot) data from long to wide

data_extract(): Extract one or more columns or elements from an object

data_match() data_filter(): Return filtered or sliced data frame, or row indices

data_select() extract_column_names() find_columns(): Find or get columns in a data frame based on search patterns

data_relocate() data_reorder() data_remove(): Relocate (reorder) columns of a data frame

data_arrange(): Arrange rows by column values

data_merge() data_join(): Merge (join) two data frames, or a list of data frames

data_partition(): Partition data

data_rotate() data_transpose(): Rotate a data frame

data_group() data_ungroup(): Create a grouped data frame

data_replicate(): Expand (i.e. replicate rows) a data frame

data_duplicated(): Extract all duplicates

data_unique(): Keep only one row from all with duplicated IDs

Data and Variable Transformations

Statistical Transformations

Functions for transforming variables

data_modify(): Create new variables in a data frame

data_separate(): Separate single variable into multiple variables

data_unite(): Unite ("merge") multiple variables

categorize(): Recode (or "cut" / "bin") data into groups of values.

recode_into(): Recode values from one or more variables into a new variable

recode_values(): Recode old values of variables into new values

adjust() data_adjust(): Adjust data for the effect of other variable(s)

demean() degroup() detrend(): Compute group-meaned and de-meaned variables

ranktransform(): (Signed) rank transformation

rescale_weights(): Rescale design weights for multilevel analysis

winsorize(): Winsorize data

Linear Transformers

Convenient functions for common linear transformations

center() centre(): Centering (Grand-Mean Centering)

slide(): Shift numeric value range

standardize() standardise() unstandardize() unstandardise(): Standardization (Z-scoring)

standardize(<default>): Re-fit a model with standardized data

reverse() reverse_scale(): Reverse-Score Variables

rescale() change_scale(): Rescale Variables to a New Range

normalize() unnormalize(): Normalize numeric variable to 0-1 range

makepredictcall(<dw_transformer>): Utility Function for Safe Prediction with datawizard transformers

Others

contr.deviation(): Deviation Contrast Matrix

Data Properties

Functions to compute statistical summaries of data properties and distributions

as.prop.table() as.data.frame(<datawizard_tables>) as.table(<datawizard_table>): Convert a crosstable to a frequency or a propensity table

data_codebook() print_html(<data_codebook>) display(<data_codebook>): Generate a codebook of a data frame.

data_summary(): Summarize data

data_tabulate() print(<datawizard_table>) display(<datawizard_table>): Create frequency and crosstables of variables

data_peek(): Peek at values and type of variables in a data frame

data_seek(): Find variables by their names, variable or value labels

means_by_group(): Summary of mean values by group

coef_var() distribution_coef_var(): Compute the coefficient of variation

describe_distribution(): Describe a distribution

distribution_mode(): Compute mode for a statistical distribution

skewness() kurtosis() print(<parameters_kurtosis>) print(<parameters_skewness>) summary(<parameters_skewness>) summary(<parameters_kurtosis>): Compute Skewness and (Excess) Kurtosis

smoothness(): Quantify the smoothness of a vector

row_count(): Count specific values row-wise

row_means() row_sums(): Row means or sums (optionally with minimum amount of valid values)

weighted_mean() weighted_median() weighted_sd() weighted_mad(): Weighted Mean, Median, SD, and MAD

mean_sd() median_mad(): Summary Helpers

Convert and Replace Data

Helpers for data replacements

assign_labels(): Assign variable and value labels

labels_to_levels(): Convert value labels into factor levels

coerce_to_numeric(): Convert to Numeric (if possible)

to_numeric(): Convert data to numeric

to_factor(): Convert data to factors

replace_nan_inf(): Convert infinite or NaN values into NA

convert_na_to(): Replace missing values in a variable or a data frame.

convert_to_na(): Convert non-missing values in a variable into missing values.

Import data

Helpers for importing data

data_read() data_write(): Read (import) data files from various sources

Helpers for Data Preparation

Primarily useful in the context of other ‘easystats’ packages

reshape_ci(): Reshape CI between wide/long formats

data_rename() data_rename_rows(): Rename columns and variable names

data_addprefix() data_addsuffix(): Add a prefix or suffix to column names

empty_columns() empty_rows() remove_empty_columns() remove_empty_rows() remove_empty(): Return or remove variables or observations that are completely missing

rownames_as_column() column_as_rownames() rowid_as_column(): Tools for working with row names or row ids

row_to_colnames() colnames_to_row(): Tools for working with column names

data_select() extract_column_names() find_columns(): Find or get columns in a data frame based on search patterns

data_restoretype(): Restore the type of columns according to a reference data frame

Helpers for Text Formatting

Primarily useful for ‘report’ package

text_format() text_fullstop() text_lastchar() text_concatenate() text_paste() text_remove() text_wrap(): Convenient text formatting functionalities

Visualization helpers

Primarily useful in the context of other ‘easystats’ packages

visualisation_recipe(): Prepare objects for visualisation

Data

Datasets useful for examples and tests

efc: Sample dataset from the EFC Survey

nhanes_sample: Sample dataset from the National Health and Nutrition Examination Survey