In this post, we explore filling missing values with data.table in R and compare its speed to dplyr. We found that data.table outperforms dplyr in terms of efficiency, especially when working with large datasets, making it a valuable tool for data manipulation tasks.
How to Use data.table to Fill NA with the Previous Value in R Read More »
In this post, we explore how to find the first non-NA value in data.table, both for grouped and ungrouped data. We use practical examples, including a psychology research experiment, to demonstrate the process. This technique helps handle missing values in datasets and is useful for filling in missing data based on valid entries.
How to Find First Non-NA Value in data.table Read More »
Where did the USAID nutrition pack go? Using Python, we tracked down its exact location by extracting GPS coordinates from a photo. In this post, you’ll learn how to pull location data from images and plot it on a map—step by step. A small mystery solved with code and a curious eye.
How to Extract GPS Coordinates from a Photo: The USAID Mystery Read More »
In this post, we used R and ggplot2 to visualize correlations among BFI personality traits. We cleaned the data, computed the correlation matrix, and created a polished heatmap without grid lines. This approach provides a clear and visually appealing way to interpret relationships between personality dimensions in psychological data.
How to Make a Heatmap in R Read More »
Learn how to perform a two sample Z test in R to compare proportions and means between two groups. This short guide walks through examples using real numbers and shows both built-in functions and manual calculations. A useful starting point if you’re working with hypothesis testing in R and want clear, quick results.
Two-Sample Z Test in R: Short Guide to Proportions and Means Read More »
In this post, we explore how to use data.table to count rows by group in R. We cover using the .N operator and demonstrate how to group by one or more columns. This technique is quick and effective, making it a valuable tool for working with large datasets.
data.table Count Rows by Group Read More »
In this post, we explore how to count rows in R using nrow() for both data.frames and data.tables. We compare the performance of each method using a large dataset and discuss which one is quicker. Discover the speed differences and learn more about counting rows in R!
How to Get Number of Rows in R Using data.table Read More »
Filtering data is a a common step in data analysis. In this post, we explore how to filter in data.table in R, including subsetting by conditions and selecting specific groups. We also show how to save the filtered data as a new data frame for further analysis.
How to Filter in data.table in R Read More »
This post explores summing multiple columns in data.table, including group-wise summation and dynamic column selection with %in%. We also compare this approach with dplyr and base R. Whether you’re working with large datasets or need flexible column selection, these techniques will help you get the job done. Read on to learn more!
How to Sum Multiple Columns in data.table Read More »
Learn how to select columns in data.table using names, character vectors, and indices. This guide covers selecting single and multiple columns, using .SDcols, and indexing. With practical examples, you’ll see how data.table offers a fast and flexible way to manipulate large datasets in R.
How to Select Columns from data.table in R Read More »