Package index • iNZightTools

Data wrangling

Functions for performing common data wrangling procedures.

Data Import

Functions for working with data files.

smart_read(): Read a data file

read_dictionary() print(<dictionary>) `[`(<dictionary>) apply_dictionary() has_dictionary() get_dictionary(): Data Dictionaries

read_meta(): Read CSV with iNZight metadata

read_text(): Read text as data

load_linked(): Import linked data into an inzdf object

load_rda(): Load object(s) from an Rdata file

save_rda(): Save an object with, optionally, a (valid) name

sheets(): List available sheets within a file

inzdf(): iNZight data frame object

Dataset operations

Methods for working with entire datasets at once. For users coming from the iNZight GUI, these operations would be found in the Dataset menu.

aggregate_data() aggregate_dt(): Aggregate data by categorical variables

append_rows(): Append rows to a dataset

remove_rows(): Remove rows from data by row numbers

filter(<inzdf_db>): Filter

filter_cat(): Filter data by levels of categorical variables

filter_num(): Filter data by levels of numeric variables

join_data(): Join data with another dataset

separate_var(): Separate columns

reshape_data(): Reshaping dataset from wide to long or from long to wide

combine_vars(): Combine variables into one categorical variable

select_vars(): Select variables from a dataset

sort_vars(): Sort data by variables

select: Select

validation_details(): Details of Validation Rule Results

validation_summary(): Validation Confrontation Summary

Variable operations

Methods for working with one or several variables (columns) in a dataset. iNZight GUI users will be familiar with these operations from the Variables menu.

collapse_cat(): Collapse data by values of a categorical variable

convert_to_cat(): Convert variables to categorical variables

convert_to_date(): Convert variables to dates

convert_to_datetime(): Convert variables to date-time

create_vars(): Create new variables

delete_vars(): Delete variables

extract_dt_comp(): Extract date component from a date-time variable

extract_part(): Extract part of a datetimes variable (DEPRECATED)

form_class_intervals(): Form Class Intervals

missing_to_cat(): Convert missing values to categorical variables

rank_vars(): Rank the data of numeric variables

rename_levels(): Rename the levels of a categorical variable

rename_vars(): Rename column names

reorder_levels(): Reorder the levels of a categorical variable

standardize_vars(): Standardize the data of a numeric variable

transform_vars(): Transform data of numeric variables

Working with data in R

Code writing

Functions for accessing and working with code returned by iNZightTools functions.

code(): Get Data's Code

print_code(): Tidy-printing of the code attached to an object

tidy_all_code(): iNZight Tidy Code

Working with dataset objects in R

Many of these functions are only useful in scripts, and are provided more for GUIs that R users.

add_suffix(): Add suffix to string

create_varname(): Create variable name

make_names(): Make unique variable names

Helper functions

A set of helper functions for checking variable types and reducing code elsewhere.

is_cat(): Is factor check

is_dt(): Is datetime check

is_num(): Is numeric check

is_preview(): Is Preview

is_survey(): Check if object is a survey object (either standard or replicate design)

is_svydesign(): Check if object is a survey object (created by svydesign())

is_svyrep(): Check if object is a replicate survey object (created by svrepdesign())

vartype(): Get variable type name

vartypes(): Get all variable types from data object

`%notin%`: Anti value matching

`%||%`: NULL or operator

survey_IQR(): Interquartile range function for surveys

Other wrapper functions

fitModel(): Fit models

fitDesign(): Fit a survey design

newdevice(): Open a New Graphics Device

random_sample(): Random sampling without replacement