Installation

You can install the development version of neartools from GitHub with:

# install.packages("devtools")
devtools::install_github("Bolin-Wu/neartools", force = TRUE)

Core Functionality

1. Data Exploration & Variable Discovery Quickly locate variables or examine column metadata across multiple datasets in your global environment.

library(neartools)

# Load example data into Global Environment
data("fake_snacn_ph_fu")
data("fake_snacn_ph_wave3")

# Search for variables starting with 'ph' across SNAC-N datasets
get_vars_by_pattern(data_pattern = "^fake_snacn_ph", var_pattern = "^ph")

# Get comprehensive column metadata (labels, NA percentages, etc.)
get_all_colnames(df_name = c("fake_snacn_ph_fu", "fake_snacn_ph_wave3"))

2. Bulk Data Import & Export Manage multiple files simultaneously, including specialized conversions for SPSS data.

# Import all supported files in a folder
import_bulk(data_dir = "path/to/data", file_type = "all")

# Convert all .sav (SPSS) files in a directory to .csv
export_sav_to_csv(target_dir = "path/to/spss_files")

3. Data Cleaning Utilities Standardize and clean data frames with specialized utility functions:

• Uniqueness Checks: get_unique_join() performs joins while ensuring uniqueness to prevent row inflation.
• Missing Values: fix_empty_string() converts empty strings to NA across a dataframe.
• Date Parsing: get_date_digit() extracts standardized date components from numeric strings.
• Label Management: get_label_df() creates a tidy tibble of variable labels and types.

Template Generation

Generate standardized R Markdown templates for HTML, PDF, or Word reports:

get_pretty_template(type = "html", output_dir = "reports")

Changelog

Please check NEWS.md for history updates.