Programming with R

How-To Tutorials Tips - R Programming for Humans

How to reorder arrange bars with in each Facet of ggplot

One of the problems that we usually face with ggplot is that rearranging the bars in ascending or descending order. If that problem is solved using reorder() or fct_reorder(), the next problem is when we have facets and ordering bars within each facet. Recently I came acrosss this function reorder_within() from the package tidytext (Thanks to Julia Silge and Tyler Rinker - who created this solution originally) Example Code: library(tidyr) library(ggplot2) iris_gathered <- gather(iris, metric, value, -Species) ggplot(iris_gathered, aes(reorder(Species, value), value)) + geom_bar(stat = 'identity') + facet_wrap(~ metric) As you can see above, the bars in the last facet isn’t ordered properly.

Kannada MNIST Prediction Classification using H2O AutoML in R

Kannada MNIST dataset is another MNIST-type Digits dataset for Kannada (Indian) Language. All details of the dataset curation has been captured in the paper titled: “Kannada-MNIST: A new handwritten digits dataset for the Kannada language.” by Vinay Uday Prabhu. The github repo of the author can be found here. The objective of this post is to demonstrate how to use’s automl function to quickly get a (better) baseline. Thsi also proves a point how these automl tools help democratizing Machine Learning Model Building process.

Handling Missing Values in R using tidyr

In this post, We’ll see 3 functions from tidyr that’s useful for handling Missing Values (NAs) in the dataset. Please note: This post isn’t going to be about Missing Value Imputation. tidyr According to the documentation of tidyr, The goal of tidyr is to help you create tidy data. Tidy data is data where: + Every column is variable. + Every row is an observation.. + Every cell is a single value.

Functional Programming + Iterative Web Scraping in R

Web Scraping in R Web scraping needs no introduction among Data enthusiasts. It’s one of the most viable and most essential ways of collecting Data when the data itself isn’t available. Knowing web scraping comes very handy when you are in shortage of data or in need of Macroeconomics indicators or simply no data available for a particular project like a Word2vec / Language with a custom text dataset.