Package index • datawizard

All functions

adjust() data_adjust(): Adjust data for the effect of other variable(s)
as.prop.table() as.data.frame(<datawizard_tables>) as.table(<datawizard_table>): Convert a crosstable to a frequency or a propensity table
assign_labels(): Assign variable and value labels
categorize(): Recode (or "cut" / "bin") data into groups of values.
center() centre(): Centering (Grand-Mean Centering)
coef_var() distribution_coef_var(): Compute the coefficient of variation
coerce_to_numeric(): Convert to Numeric (if possible)
row_to_colnames() colnames_to_row(): Tools for working with column names
contr.deviation(): Deviation Contrast Matrix
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.
data_arrange(): Arrange rows by column values
data_codebook() print_html(<data_codebook>) display(<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_group() data_ungroup(): Create a grouped data frame
data_match() data_filter(): Return filtered or sliced data frame, or row indices
data_merge() data_join(): 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_addprefix() data_addsuffix(): Add a prefix or suffix to column names
data_read() data_write(): Read (import) data files from various sources
data_relocate() data_reorder() data_remove(): Relocate (reorder) columns of a data frame
data_rename() data_rename_rows(): Rename columns and variable names
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() data_transpose(): Rotate a data frame
data_seek(): Find variables by their names, variable or value labels
data_separate(): Separate single variable into multiple variables
data_summary(): Summarize data
data_tabulate() print(<datawizard_table>) display(<datawizard_table>): Create frequency and crosstables of variables
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_unique(): Keep only one row from all with duplicated IDs
data_unite(): Unite ("merge") multiple variables
demean() degroup() detrend(): Compute group-meaned and de-meaned variables
describe_distribution(): Describe a distribution
distribution_mode(): Compute mode for a statistical distribution
efc: Sample dataset from the EFC Survey
data_select() extract_column_names() find_columns(): Find or get columns in a data frame based on search patterns
labels_to_levels(): Convert value labels into factor levels
makepredictcall(<dw_transformer>): Utility Function for Safe Prediction with datawizard transformers
mean_sd() median_mad(): Summary Helpers
means_by_group(): Summary of mean values by group
nhanes_sample: Sample dataset from the National Health and Nutrition Examination Survey
normalize() unnormalize(): Normalize numeric variable to 0-1 range
ranktransform(): (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
empty_columns() empty_rows() remove_empty_columns() remove_empty_rows() remove_empty(): Return or remove variables or observations that are completely missing
replace_nan_inf(): Convert infinite or NaN values into NA
rescale() change_scale(): Rescale Variables to a New Range
rescale_weights(): Rescale design weights for multilevel analysis
reshape_ci(): Reshape CI between wide/long formats
reverse() reverse_scale(): Reverse-Score Variables
row_count(): Count specific values row-wise
row_means() row_sums(): Row means or sums (optionally with minimum amount of valid values)
rownames_as_column() column_as_rownames() rowid_as_column(): Tools for working with row names or row ids
skewness() kurtosis() print(<parameters_kurtosis>) print(<parameters_skewness>) summary(<parameters_skewness>) summary(<parameters_kurtosis>): Compute Skewness and (Excess) Kurtosis
slide(): Shift numeric value range
smoothness(): Quantify the smoothness of a vector
standardize() standardise() unstandardize() unstandardise(): Standardization (Z-scoring)
standardize(<default>): Re-fit a model with standardized data
text_format() text_fullstop() text_lastchar() text_concatenate() text_paste() text_remove() text_wrap(): Convenient text formatting functionalities
to_factor(): Convert data to factors
to_numeric(): Convert data to numeric
visualisation_recipe(): Prepare objects for visualisation
weighted_mean() weighted_median() weighted_sd() weighted_mad(): Weighted Mean, Median, SD, and MAD
winsorize(): Winsorize data