R

Mapping the states in Malaysia

I have written two blog posts about making map in R: Making maps with R (my first attempt ever!) My first interactive map with {leaflet} This post is sort of a continuation to the first blog post.

Using UMAP preprocessing for image classification

UMAP Uniform manifold approximation and projection or in short UMAP is a type of dimension reduction techniques. So, basically UMAP will project a set of features into a smaller space.

Fitted vs predict in R

There are two functions in R that seems almost similar yet different: fitted() predict() First let’s prepare some data first. # Packages library(dplyr) # Data set.seed(123) dat <- iris %>% mutate(twoGp = sample(c("Gp1", "Gp2"), 150, replace = T), #create two group factor twoGp = as.

My first interactive map with {leaflet}

I have tried creating a map with ggplot2 previously. In this post, I will try to create an interactive map using leaflet package in R.

Variable selection for imputation model in {mice}

Some note I have written a short post about missing data and multiple imputation in mice package previously. This post will add to that previous post. Imputation model Imputation model is the model that we use for our imputation approach.

Making maps with R (my first attempt ever!)

As written in the title of the post, this is my first try ever in making a map with R. I found a great data on the distribution of the clinics in Malaysia.

Some COVID-19 plots for Southeast Asian countries

Recently, I found a GitHub repo containing a global COVID-19 dataset. I thought, why not try to do some plotting for Southeast Asian countries. So, I downloaded the data and limited the data to Southeast Asian countries only (Brunei, Indonesia, Malaysia, Philippines, Singapore, Thailand and Vietnam).

R