In base R, we can filter rows where a column is between two values using bracket notation or the subset() function along with logical operators like >=, <=, &, and !. The key is creating a logical test that checks if values are within our desired range.
For example, to filter rows where the column "value" is between 5 and 8
Joint event by R-Ladies Cologne (@cosima_meyer and
Gabe Winter) and R-Ladies Bergen (Jonelle and me): we're thrilled to be hosting Mine Çetinkaya-Rundel who will teach us about Quarto 🥳 👩💻
This is a kick-off event for a brand-new book club, where we'll be going through the book "Building reproducible analytical pipelines with R". Come, join us!
Compare traditional lm() with robust rlm() using a dataset. Blue vs. red residuals visually unveil how each model handles outliers. Dive in, experiment with your data, and empower your coding journey! 💻
🔬📊 Mastering Data Grouping with R's ave() Function 📊🔬
Are you tired of manually calculating statistics for different groups in your data analysis projects? Look no further! R's ave() function is here to revolutionize your data grouping experience. 🚀
Struggling with weird variable names in R? make.names to the rescue! This function wrangles your names into R-approved format (letters, numbers, periods, underscores). Bonus: set unique = TRUE for no duplicates! Try it on funky characters & data frames! 🪄 Master make.names and become an R name-wrangling pro! #DataScience#R#RStats#RProgramming #Coding#Programming
Unleash Excel date power in R! Convert formats to proper dates effortlessly. With as.Date() & convertToDateTime(), transform data for smoother analysis. Dive into R, empower your data journey! Try it yourself & elevate your analysis game!
Learn how to set a data frame column as the index for faster data access and streamlined operations.
In R, utilize the setDT() function from #datatable or column_to_rownames() from #tibble to seamlessly set your desired column as the index. Try it out with your datasets and experience the boost in productivity!
file_path <- "data.csv"
if (file.exists(file_path)) {
print("The file exists!")
} else {
print("The file does not exist.")
}
In this example, we check if the file named "data.csv" exists. Depending on the outcome, it will print either "The file exists!" or "The file does not exist."
I'll give you a quick rundown on creating horizontal boxplots in R using both base R and ggplot2. We'll work with the "palmerpenguins" dataset to keep things interesting!
Looking to do some data filtering in base R? Well then of course I have a post for you! Many times people in a corporate environment are strapped from installing packages or it is cumbersome to get it done, this is why I like to focus on base R solutions.
Imagine you have a bunch of data points and you want to know how many belong to different categories. This is where grouped counting comes in. We've got three fantastic methods for you to explore, each with its own flair: aggregate(), dplyr, and data.table.
🚀 Unleash the power of regression in R! 🔍 Follow this quick guide: load data, visualize with a scatter plot, fit a power regression model using nls, add visual flair, and embrace uncertainty with prediction intervals. Ready to code? Dive in! 💻✨