You can use the lapply/Map function to loop over the columns of ‘data’ and apply the same code as before to each one. Then, you can bind the results together using cbind. Here is an example: library(GSVA) # assuming data is a list of data frames named "name1", "name2", ... out <- do.call(cbind, Map(function(x) { Sig <- unique(x$name) set.seed(8, sample.kind = "Rounding") core <- gsva(expr=as.matrix(data6), gset.idx.list=list(Sig), method="ssgsea") core2 <- as.data.frame(t(core)) colnames(core2)[1] <- names(x)$name core2 }, data, names(data))) out This will create a new data frame out where each row corresponds to one of the original lists (data$name1, data$name2, etc.

Calculating Intermittent Average: A Deep Dive into Moving Averages and Data Manipulation When working with time series data, it’s not uncommon to encounter intervals of zeros or missing values. In such cases, calculating the average of the numbers between these zero-filled gaps can be a valuable metric. This blog post delves into the process of calculating intermittent averages, exploring two common approaches: zero-padding and circularity. Understanding Moving Averages A moving average is a mathematical technique used to smooth out data points over a specific window size.

Importing and Restoring SQLite Databases from iPhone Apps using Core Data Introduction Core Data is a powerful tool for managing data in iOS apps. It provides a flexible and efficient way to store, manage, and retrieve data. However, sometimes it’s necessary to import or restore backed-up SQLite databases into an app that uses Core Data. In this article, we will explore the process of importing and restoring SQLite databases from iPhone apps using Core Data.

Understanding the Problem with Shiny Modal Windows and Syntax Highlighting When building interactive web applications using Shiny, it’s essential to consider how different components interact with each other. In this article, we’ll delve into a common issue that arises when trying to display code within a modal window in Shiny. The problem is caused by the timing of reading JavaScript scripts, specifically those used for syntax highlighting. We’ll explore why this timing difference makes a difference and provide a solution to overcome it.

R- Matching a Cell to Another to Retrieve a Value for a Different Row In this article, we will explore how to match values in one column of a data frame with another column and retrieve the corresponding value from a different row. Recreating Your Data Before we begin, it’s essential to recreate your data using stri_split_lines or stri_split_regex. The provided example uses the latter function. # Load required libraries library(stringr) # Create the master data frame a_d_f <- NULL # Define the data master_data <- " 1 1_04 Amp_d6 2.

R Shiny renderDataTable Issue ===================================================== Table of Contents Introduction The Problem Understanding the Code The Solution Explanation and Breakdown Example Use Case Introduction In this blog post, we will be exploring a common issue with the renderDataTable function in Shiny when used in conjunction with R’s DT package. Specifically, we will look at how to correctly render a dynamic table of data with buttons that can be clicked multiple times.

Stratified Sampling with Restrictions: Fixed Total Size Evenly Partitioned Among Groups In this article, we will explore the concept of stratified sampling and its application in R programming. Specifically, we will delve into how to perform stratified sampling with restrictions, where a fixed total size is evenly partitioned among groups, while ensuring that the number of samples taken from each group does not exceed its size. Introduction Stratified sampling is a type of sampling technique used in statistics and data analysis.

Understanding the Challenges of Reading CSV Files with Dask As a data scientist, working with large datasets is an essential part of our daily tasks. In this article, we will explore how to parallelize reading from a CSV file using Dask, a powerful library for parallel computing in Python. Dask is built on top of the existing libraries you know and love, such as Pandas, NumPy, and Scikit-learn. It provides a flexible way to scale up your computations by harnessing the power of multiple CPU cores or even distributed computing architectures like Apache Spark.

The issue lies in the way you’re using strsplit and its interaction with the character encoding of your R console. When running locally, it’s likely that your R console uses the system locale, which includes a specific character encoding (e.g., UTF-8). However, on an Ubuntu server, the default locale might be different, potentially affecting how characters are interpreted. To resolve this issue: Check Your Console Encoding: Before you start debugging, check what character encoding your R console uses by running getlocale() in your console or terminal.

Understanding SQL Data Compare and Its Limitations SQL Data Compare is a powerful tool for identifying differences between two databases and migrating those changes to the target database. While it offers numerous benefits, such as ease of use and flexibility, there are also some limitations that users should be aware of. One common question that arises when using SQL Data Compare is whether it’s possible to randomize a column’s value before moving data over.