Installation

To get the latest in-development features, install the development version from GitHub:

if(!requireNamespace("devtools", quietly = TRUE)) {
 install.packages("devtools")
}
devtools::install_github("le-huynh/chva.extras")

This package is also accessible for download via Zenodo at 10.5281/zenodo.14910967.

Functions

See Package index for full list of functions.

1. Convert files to Parquet format
   

• get_parquet_arrow(): Convert multiple files to Parquet format.
  
• get_parquet_by_chunk(): Convert file to Parquet format by chunks.

2. Support handy workflow

• check_overview(): Provide an overview of datasets.
  
• check_unique_value(): Count unique values of a specified column.
  
• get_dataset(): Get full working datasets as a named list of Dataset R6 objects.
  
• recode_values(): Recode values based on grouping logic.

3. Compute indices used in climate health research

• Humidity variables: cal_absolute_humidity(), cal_dewpoint_temperature(), cal_mixing_ratio(), cal_relative_humidity(), cal_specific_humidity().
  
• Comfort indices: cal_apparent_temperature(), cal_humidex(), cal_temperature_humidity_index().
  
• cal_saturation_vapor_pressure()

4. Support visualization

• plot_contour_dlnm(): Generate a filled contour plot for DLNM output with custom color levels.

For a comprehensive overview of the package’s functions, check out the package website at
le-huynh.github.io/chva.extras/.

Installation

lehuynh R package is available on CRAN via:

install.packages("lehuynh")

To get the latest in-development features, install the development version from GitHub:

if(!requireNamespace("devtools", quietly = TRUE)) {
 install.packages("devtools")
}
devtools::install_github("le-huynh/lehuynh")

Additionally, this package is accessible for download via Zenodo at 10.5281/zenodo.11522849.

Functions

• ggsave_elsevier( ): Save a plot using ggplot2::ggsave( ). Plot size follows instructions of Elsevier journals
• import_data( ): Imports multiple data files of the same format from a specified directory. The output is a named list of imported objects
• import_excel( ): Imports an Excel file with multiple sheets and returns a named list of imported sheets
• lehuynh_theme( ): Personal ggplot2 theme (white background, black axis, black text, etc.)
• MinMaxScaling( ): Min-max normalization (min = 0, max = 1)
• new_project( ): Create a project for reproducible purpose
• ngrams_filter(): Filter and generate N-Grams from text data
• plot_networkD3(): Plot network using NetworkD3
• ppc_brms( ): Plot fitted versus observed values for brmsfit Objects
• tidytuesday( ): Create a new folder for #tidytuesday challenge
• tsi( ): Calculate TSI (Trophic state index) (Carlson, 1977)

Templates

• draft_elsevier: Creating an Rmarkdown manuscript for Elsevier journals (example)
• lehuynh_Bayes_brms: Basic steps to fit, check, and interpret a Bayesian model via brms package
• lehuynh_EA: Basic steps to do Exploratory Analysis

For a comprehensive overview of the package’s functions, check out the package website at
le-huynh.github.io/lehuynh/.

Installation

To get the latest in-development features, install the development version from GitHub:

if(!requireNamespace("devtools", quietly = TRUE)) {
 install.packages("devtools")
}
devtools::install_github("yen-kim/omics4drug")

This package is also accessible for download via Zenodo at 10.5281/zenodo.17117623.

Functions

See Package index for full list of functions.

1. Data Processing and Quality Control

• get_count_phosphosite(): Counts and visualizes the number of unique phosphosites per sample or group, often based on a probability threshold.
  
• get_count_protein(): Counts and visualizes the number of unique protein groups per sample or group.
  
• get_cv(): Calculates and visualizes the coefficient of variation (CV) for a given dataset, useful for assessing data variability and quality.
• get_sty(): Calculates and visualizes the count and percentage of phosphorylation sites (Serine (S), Threonine (T), Tyrosine (Y)).

2. Data Normalization

• get_norm_phos(): Normalizes phosphosite intensity data to account for variations between samples.
• get_norm_prot(): Normalizes protein group intensity data.

3. Functional and Pathway Enrichment Analysis

• get_GO(): Performs Gene Ontology (GO) enrichment analysis to identify biological processes, molecular functions, or cellular components that are overrepresented in your data.
• get_KEGG(): Performs KEGG pathway enrichment analysis to determine which biological pathways are significantly impacted.

4. Kinase and Drug Prediction

• get_KSEA(): Performs Kinase Substrate Enrichment Analysis (KSEA) to predict the activity of kinases based on the phosphorylation of their substrates.
• get_inhibitor(): Predicts which drugs might target the kinases identified in your analysis, using an external database.

5. Others

• get_annotation(): Map Gene Identifiers

For a comprehensive overview of the package’s functions, check out the package website at
yen-kim.github.io/omics4drug.