Press "Enter" to skip to content

Category: R

Building a Model with shiny and tinyAML

Published 2023-05-01 by Kevin Feasel

Steven Sanderson has a series on using the tidyAML Model Builder. Part 1 builds a simple model:

The first reactive expression, data, reads in the data file uploaded by the user or selects a built-in dataset, depending on which option the user chooses. If the user uploads a file, the read.csv() function is used to read the data file into a data frame. If the user selects a built-in dataset, the get() function is used to retrieve the data frame associated with that dataset. In both cases, the column names of the data frame are used to update the choices in the predictor_col select input, so that the user can select which column to use as the predictor variable.

Part 2 builds on it by adding new regression algorithms:

Yesterday I spoke about building tidymodels models using my package {tidyAML} and {shiny}. I have made an update to it, and will continue to make updates to it this week.

I have added all of the supported engines for regression problems only, NOT classification yet, that will be tomorrow’s work. I will then add a drop down for users to pick which backend function they want to use from {parsnp} like linear_reg().

Comments closed

Knitting R-Markdown Files into Google Docs

Published 2023-04-28 by Kevin Feasel

Benjamin Smith makes a Google Doc:

RMarkdown is a powerful framework for writing a documents that contain a mixture of text, code, and the output of the code. Popular output formats for RMarkdown Documents (.Rmd) include HTML, PDF and Word Documents. It is also possible to output RMarkdown documents as part of a static website using blogdown package and is (still!) possible to publish RMarkdown documents to WordPress sites as well (like this one)!

Recently, I started to look into the possibility of outputting an .Rmd file as a Google Doc, but I was unable to locate any out-of-box solutions. After looking into the issue I developed a small function that makes it possible!

Click through for that function.

Comments closed

Creating a Clickable Word Cloud with Shiny

Published 2023-04-28 by Kevin Feasel

Mandy Norrbo builds a word cloud:

Word clouds are a visual representation of text data where words are arranged in a cluster, with the size of each word reflecting its frequency or importance in the data set. Word clouds are a great way of displaying the most prominent topics or keywords in free text data obtained from websites, social media feeds, reviews, articles and more. If you want to learn more about working with unstructured text data, we recommend attending our Text Mining in R course

Usually, a word cloud will be used solely as an output. But what if you wanted to use a word cloud as an input? For example, let’s say we visualised the most common words in reviews for a hotel. Imagine we could then click on a specific word in the word cloud, and it would then show us only the reviews which mention that specific word. Useful, right?

Read on to see how you can create one of these.

Comments closed

R in 10 Minutes

Published 2023-04-24 by Kevin Feasel

Holger von Jouanne-Diedrich gives us a quick primer on R:

R is a powerful programming language and environment for statistical computing and graphics. In this post, we will provide a quick introduction to R using the famous iris dataset.

We will cover loading data, exploring the dataset, basic data manipulation, and plotting. By the end, you should have a good understanding of how to get started with R, so read on!

Click through for the intro.

Comments closed

Styling Excel Tables in R

Published 2023-04-14 by Kevin Feasel

Steven Sanderson wants to spice things up:

The styledtable package in R, which allows users to create styled tables in R Markdown documents. The package can help to create tables with various formatting options such as bold text, colored cells, and borders. It also has functionality on how to port these to Excel itself.

The package offers a simple syntax that allows users to specify formatting options using HTML and CSS. The resulting table can be customized by changing the CSS file or by using the ‘styler’ function to apply custom styles to individual cells or rows.

Read on for more information on what the package does and a few examples of how it works.

Comments closed

Comparing Data Visualization in Excel and R

Published 2023-04-14 by Kevin Feasel

Amieroh Abrahams builds some graphs:

In Excel it is challenging to eye-ball which changes have been made to a graph, especially if these were minor changes. With R (and some easy to use version control systems), you can see exactly which files were changed. Also, in Excel, a user would usually draw a graph on a single Excel document, and if the same graph is required on a different data set, it is common to copy-and-paste a bunch of manipulations and configurations to another document. Such repeated human interaction is prone to introducing errors, as well as consuming a large amount of time. With R we can avoid this by creating functions, which can be used to run the same code on different data sets simply by changing the input, thereby producing reliable outputs and saving us a lot of time.

Click through for the article. One big thing in Excel’s defense that I did not see here was that it’s a lot easier to perform specific story-telling in Excel visuals. For example, highlight just these two data points, or annotate this segment of the visual. You can do those things in ggplot2 but it’s considerably more difficult than “right-click the data point and format.”

Comments closed

Reading Multi-Sheet Excel Files in R

Published 2023-04-10 by Kevin Feasel

Steven Sanderson does a bit of Excel file reading:

Reading in an Excel file with multiple sheets can be a daunting task, especially for users who are not familiar with the process. In this blog post, we will walk through a sample function that can be used to read in an Excel file with multiple sheets using the R programming language.

Click through for the process, which makes use of the lapply() function and the readxl package.

Comments closed

Testing Performance of File Formats in R

Published 2023-03-29 by Kevin Feasel

Steven Sanderson performs some tests:

We can save the generated matrix in different file formats using different functions in R. Here are the functions we will use for each file format:

  • CSV: write.csv()
  • RDS: saveRDS()
  • FST: write_fst()
  • Arrow: write_feather()

Steve then has a follow-up around compressed data:

In this post I create a square matrix and then convert it to a data.frame (2,000 rows by 2,000 columns) and then saved it as a gz compressed csv file. The benchmark compares different R packages and functions, including base R, data.table, vroom, and readr, and measures their relative speeds based on the time it takes to read in the .csv.gz file.

There’s not a direct comparison between the two posts, as the second matrix is larger than the first, though even with that caveat in mind, this post lets you see how much extra processing occurs to gunzip the data before reading it.

Comments closed

Alt Text in R

Published 2023-03-24 by Kevin Feasel

Nicola Rennie looks at different ways to incorporate alt text in R-based images:

Alt text (short for alternative text) is text that describes the appearance and purpose of an image. Alt text has multiple purposes, the main one being that it aids visually impaired users to better understand your content when the alt text is read aloud by screen readers. Alt text is also used in place of an image if it fails to load, which means that users with poor internet connection are more likely to be able to engage with your content.

The ggplot2 example was an interesting one, as I hadn’t ever added alt text to an image there.

Comments closed