We are pleased to announce the release of bulkreadr
version 1.1.2. This version introduces a powerful new utility to
streamline multi-sheet Excel workflows:
write_excel_sheets_to_csv():Reads every sheet from an Excel workbook and exports each as a separate CSV file into a specified directory. This automates the tedious task of manually saving multiple sheets, ensures consistent naming, and supports reproducible reporting pipelines.
We are pleased to announce the release of bulkreadr
version 1.1.1. This version introduces significant enhancements and
features aimed at improving the functionality and user experience of the
package.
fill_missing_values()
Functionality: The fill_missing_values() function
has been significantly improved to support various imputation methods,
empowering users to handle missing data with greater precision and
flexibility. In addition to the previously supported “mean” imputation
method, the function now accommodates the following strategies:Minimum Value (Min): Imputes missing entries with the
minimum value observed within each respective column.
Maximum Value (Max): Fills missing data points with the
maximum value found in each column. Mean: Continues to offer the average
value imputation, replacing missing values with the mean of the
available data in each column.
Median: Imputes missing entries by employing the median
value of each column, providing a robust alternative to mean imputation,
especially in the presence of outliers.
Harmonic Mean: Offers a sophisticated option for
imputing missing values using the harmonic mean, ideal for data
distributions where this approach is more representative.
Geometric Mean: Completes our enhanced range of
imputation methods by allowing for the replacement of missing values
with the geometric mean, suited for datasets where the product of values
is of interest.
These enhancements are designed to provide users with a comprehensive
toolkit for data imputation, ensuring that
fill_missing_values() can be effectively tailored to meet
the unique demands of diverse datasets and analysis requirements.
To better serve our users and provide detailed documentation, we have
launched the official bulkreadr package website. The
website offers comprehensive guides, function references, and examples
to help users maximize the package’s potential. Visit us at https://gbganalyst.github.io/bulkreadr
for more information.
We believe these updates will significantly enhance your data analysis workflows and look forward to your feedback.
This update includes the following new features:
generate_dictionary(): This function is designed to
automatically create a comprehensive data dictionary from labelled
datasets. The generated dictionary provides detailed insights into each
variable, aiding in better data understanding and management.
look_for(): This enhances the capability to
efficiently search within labelled datasets. It allows users to quickly
find variable names and their descriptions by searching for specific
keywords. This feature streamlines data exploration and analysis,
particularly in large datasets with extensive variables.
These enhancements aim to improve the user experience in data
management and exploration within bulkreadr. We hope these
new features will assist our users in more effectively navigating and
understanding their labelled datasets.
This update includes the following new features and improvements:
Developed read_stata_data() to import Stata data
file (.dta) into an R data frame, converting labeled
variables into factors.
Reduced dependency packages to optimize efficiency.
This update includes the following new features and improvements:
Developed bulkreadr vignette
Developed read_spss_data() to seamlessly import data
from an SPSS data (.sav or .zsav) files and
converting labelled variables into factors, a crucial step that enhances
the ease of data manipulation and analysis within the R programming
environment.
Added more unit tests
This update includes the following new features and improvements:
Improved error handling by adding meaningful error messages for
all functions within bulkreadr package. This will make it
easier for users to identify and troubleshoot issues that may arise
during their use of the package.
Added package-level documentation. The user can now use
?bulkreadr::bulkreadr for basic package-level
documentation.
Added inspect_na() to summarize missingness in data
frame columns and fill_missing_values() to impute missing
values in a dataframe.
The development version of bulkreadr is now on Githhub.