Easy Data Wrangling and Statistical Transformations

Documentation for package ‘datawizard’ version 0.10.0

Help Pages

-- A --

adjust	Adjust data for the effect of other variable(s)
assign_labels	Assign variable and value labels
assign_labels.data.frame	Assign variable and value labels
assign_labels.numeric	Assign variable and value labels

-- C --

categorize	Recode (or "cut" / "bin") data into groups of values.
categorize.data.frame	Recode (or "cut" / "bin") data into groups of values.
categorize.numeric	Recode (or "cut" / "bin") data into groups of values.
center	Centering (Grand-Mean Centering)
center.data.frame	Centering (Grand-Mean Centering)
center.numeric	Centering (Grand-Mean Centering)
centre	Centering (Grand-Mean Centering)
change_code	Recode old values of variables into new values
change_scale	Rescale Variables to a New Range
coef_var	Compute the coefficient of variation
coef_var.numeric	Compute the coefficient of variation
coerce_to_numeric	Convert to Numeric (if possible)
colnames_to_row	Tools for working with column names
column_as_rownames	Tools for working with row names or row ids
contr.deviation	Deviation Contrast Matrix
convert_na_to	Replace missing values in a variable or a data frame.
convert_na_to.character	Replace missing values in a variable or a data frame.
convert_na_to.data.frame	Replace missing values in a variable or a data frame.
convert_na_to.numeric	Replace missing values in a variable or a data frame.
convert_to_na	Convert non-missing values in a variable into missing values.
convert_to_na.data.frame	Convert non-missing values in a variable into missing values.
convert_to_na.factor	Convert non-missing values in a variable into missing values.
convert_to_na.numeric	Convert non-missing values in a variable into missing values.

-- D --

data_addprefix	Rename columns and variable names
data_addsuffix	Rename columns and variable names
data_adjust	Adjust data for the effect of other variable(s)
data_arrange	Arrange rows by column values
data_codebook	Generate a codebook of a data frame.
data_duplicated	Extract all duplicates
data_extract	Extract one or more columns or elements from an object
data_extract.data.frame	Extract one or more columns or elements from an object
data_filter	Return filtered or sliced data frame, or row indices
data_find	Find or get columns in a data frame based on search patterns
data_group	Create a grouped data frame
data_join	Merge (join) two data frames, or a list of data frames
data_match	Return filtered or sliced data frame, or row indices
data_merge	Merge (join) two data frames, or a list of data frames
data_merge.data.frame	Merge (join) two data frames, or a list of data frames
data_merge.list	Merge (join) two data frames, or a list of data frames
data_modify	Create new variables in a data frame
data_modify.data.frame	Create new variables in a data frame
data_partition	Partition data
data_peek	Peek at values and type of variables in a data frame
data_peek.data.frame	Peek at values and type of variables in a data frame
data_read	Read (import) data files from various sources
data_relocate	Relocate (reorder) columns of a data frame
data_remove	Relocate (reorder) columns of a data frame
data_rename	Rename columns and variable names
data_rename_rows	Rename columns and variable names
data_reorder	Relocate (reorder) columns of a data frame
data_replicate	Expand (i.e. replicate rows) a data frame
data_restoretype	Restore the type of columns according to a reference data frame
data_rotate	Rotate a data frame
data_seek	Find variables by their names, variable or value labels
data_select	Find or get columns in a data frame based on search patterns
data_separate	Separate single variable into multiple variables
data_summary	Summarize data
data_summary.data.frame	Summarize data
data_tabulate	Create frequency and crosstables of variables
data_tabulate.data.frame	Create frequency and crosstables of variables
data_tabulate.default	Create frequency and crosstables of variables
data_to_long	Reshape (pivot) data from wide to long
data_to_wide	Reshape (pivot) data from long to wide
data_transpose	Rotate a data frame
data_ungroup	Create a grouped data frame
data_unique	Keep only one row from all with duplicated IDs
data_unite	Unite ("merge") multiple variables
data_write	Read (import) data files from various sources
degroup	Compute group-meaned and de-meaned variables
demean	Compute group-meaned and de-meaned variables
describe_distribution	Describe a distribution
describe_distribution.data.frame	Describe a distribution
describe_distribution.factor	Describe a distribution
describe_distribution.numeric	Describe a distribution
detrend	Compute group-meaned and de-meaned variables
distribution_coef_var	Compute the coefficient of variation
distribution_cv	Compute the coefficient of variation
distribution_mode	Compute mode for a statistical distribution

-- E --

efc	Sample dataset from the EFC Survey
empty_columns	Return or remove variables or observations that are completely missing
empty_rows	Return or remove variables or observations that are completely missing

-- F --

find_columns	Find or get columns in a data frame based on search patterns
format_text	Convenient text formatting functionalities

-- G --

get_columns

Find or get columns in a data frame based on search patterns

-- K --

kurtosis	Compute Skewness and (Excess) Kurtosis
kurtosis.numeric	Compute Skewness and (Excess) Kurtosis

-- L --

labels_to_levels	Convert value labels into factor levels
labels_to_levels.data.frame	Convert value labels into factor levels
labels_to_levels.factor	Convert value labels into factor levels

-- M --

makepredictcall.dw_transformer	Utility Function for Safe Prediction with 'datawizard' transformers
means_by_group	Summary of mean values by group
means_by_group.data.frame	Summary of mean values by group
means_by_group.numeric	Summary of mean values by group
mean_sd	Summary Helpers
median_mad	Summary Helpers

-- N --

nhanes_sample	Sample dataset from the National Health and Nutrition Examination Survey
normalize	Normalize numeric variable to 0-1 range
normalize.data.frame	Normalize numeric variable to 0-1 range
normalize.numeric	Normalize numeric variable to 0-1 range

-- P --

print.parameters_kurtosis	Compute Skewness and (Excess) Kurtosis
print.parameters_skewness	Compute Skewness and (Excess) Kurtosis
print_html.data_codebook	Generate a codebook of a data frame.

-- R --

ranktransform	(Signed) rank transformation
ranktransform.data.frame	(Signed) rank transformation
ranktransform.numeric	(Signed) rank transformation
recode_into	Recode values from one or more variables into a new variable
recode_values	Recode old values of variables into new values
recode_values.data.frame	Recode old values of variables into new values
recode_values.numeric	Recode old values of variables into new values
remove_empty	Return or remove variables or observations that are completely missing
remove_empty_columns	Return or remove variables or observations that are completely missing
remove_empty_rows	Return or remove variables or observations that are completely missing
replace_nan_inf	Convert infinite or 'NaN' values into 'NA'
rescale	Rescale Variables to a New Range
rescale.data.frame	Rescale Variables to a New Range
rescale.numeric	Rescale Variables to a New Range
rescale_weights	Rescale design weights for multilevel analysis
reshape_ci	Reshape CI between wide/long formats
reshape_longer	Reshape (pivot) data from wide to long
reshape_wider	Reshape (pivot) data from long to wide
reverse	Reverse-Score Variables
reverse.data.frame	Reverse-Score Variables
reverse.numeric	Reverse-Score Variables
reverse_scale	Reverse-Score Variables
rowid_as_column	Tools for working with row names or row ids
rownames_as_column	Tools for working with row names or row ids
row_means	Row means (optionally with minimum amount of valid values)
row_to_colnames	Tools for working with column names

-- S --

skewness	Compute Skewness and (Excess) Kurtosis
skewness.numeric	Compute Skewness and (Excess) Kurtosis
slide	Shift numeric value range
slide.data.frame	Shift numeric value range
slide.numeric	Shift numeric value range
smoothness	Quantify the smoothness of a vector
standardise	Standardization (Z-scoring)
standardize	Standardization (Z-scoring)
standardize.data.frame	Standardization (Z-scoring)
standardize.default	Re-fit a model with standardized data
standardize.factor	Standardization (Z-scoring)
standardize.numeric	Standardization (Z-scoring)
standardize_models	Re-fit a model with standardized data
summary.parameters_kurtosis	Compute Skewness and (Excess) Kurtosis
summary.parameters_skewness	Compute Skewness and (Excess) Kurtosis

-- T --

text_concatenate	Convenient text formatting functionalities
text_format	Convenient text formatting functionalities
text_fullstop	Convenient text formatting functionalities
text_lastchar	Convenient text formatting functionalities
text_paste	Convenient text formatting functionalities
text_remove	Convenient text formatting functionalities
text_wrap	Convenient text formatting functionalities
to_factor	Convert data to factors
to_factor.data.frame	Convert data to factors
to_factor.numeric	Convert data to factors
to_numeric	Convert data to numeric
to_numeric.data.frame	Convert data to numeric

-- U --

unnormalize	Normalize numeric variable to 0-1 range
unnormalize.data.frame	Normalize numeric variable to 0-1 range
unnormalize.grouped_df	Normalize numeric variable to 0-1 range
unnormalize.numeric	Normalize numeric variable to 0-1 range
unstandardise	Standardization (Z-scoring)
unstandardize	Standardization (Z-scoring)
unstandardize.data.frame	Standardization (Z-scoring)
unstandardize.numeric	Standardization (Z-scoring)

-- V --

visualisation_recipe

Prepare objects for visualisation

-- W --

weighted_mad	Weighted Mean, Median, SD, and MAD
weighted_mean	Weighted Mean, Median, SD, and MAD
weighted_median	Weighted Mean, Median, SD, and MAD
weighted_sd	Weighted Mean, Median, SD, and MAD
winsorize	Winsorize data
winsorize.numeric	Winsorize data