# Easy Data Wrangling and Statistical Transformations

## Documentation for package ‘datawizard’ version 0.8.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A C D E F G K L M N P R S T U V W

## -- 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
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_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_restoretype | Restore the type of columns according to a reference data frame
data_rotate | Rotate a data frame
data_select | Find or get columns in a data frame based on search patterns
data_separate | Separate single variable into multiple variables
data_tabulate | Create frequency tables of variables
data_tabulate.data.frame | Create frequency tables of variables
data_tabulate.default | Create frequency tables 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
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_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.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