Easy Data Wrangling and Statistical Transformations


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Documentation for package ‘datawizard’ version 0.6.3

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

-- A --

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

-- C --

categorize Recode (or "cut") data into groups of values.
categorize.data.frame Recode (or "cut") data into groups of values.
categorize.numeric Recode (or "cut") 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
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_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_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_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
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

-- 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

-- R --

ranktransform (Signed) rank transformation
ranktransform.data.frame (Signed) rank transformation
ranktransform.numeric (Signed) rank transformation
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
rownames_as_column Tools for working with row names
row_to_colnames Tools for working with column names

-- S --

skewness 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_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