Posts

Basic plotting with Matplotlib and Seaborn

This post is continuation of my previous post about Python. For those interested: Basic data wrangling with Python Basic plotting with matplotlib and seaborn Comparison of ggplot in R versus in Python There are several packages or libraries available in Python for plotting and visualization.
Last updated on Aug 7, 2024 3 min read Python, Visualisation

Basic data wrangling with Python

Python is one of the most popular programming language and software. In this post, I will demonstrate how to do a basic data wrangling with Python. This is going to be one of the several series of post related to Python (hopefully).
Last updated on Jul 18, 2024 7 min read Python, Data

What makes data "good enough" for a statistical analysis?

A few days earlier, someone asked me to help her with the data analysis. However, the data that she gave me was so bad that it was completely impossible to run the analysis unless a serious data cleaning was done first.
Last updated on Feb 29, 2024 3 min read Data, General

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.
Last updated on Feb 23, 2023 6 min read R, Epidemiology, Map

Visualising augmented images in Keras

Data augmentation Data augmentation is been used in deep learning for many reasons. One of the reason is to reduce overfitting and makes the model more robust. Data augmentation can be done relatively easy in keras package in R.
Last updated on Dec 28, 2022 2 min read

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.
Last updated on Mar 16, 2022 6 min read R, Machine Learning

Explore data using PCA

Principal component analysis (PCA) PCA is a dimension reduction techniques. So, if we have a large number of predictors, instead of using all the predictors for modelling or other analysis, we can compressed all the information from the variables and create a new set of variables.
Last updated on Feb 10, 2022 5 min read Machine Learning

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.
Last updated on Jan 9, 2022 4 min read applied statistics, R

A short note on variable selection

Variable selection Variable or feature selection is one of the important step whether in machine learning or statistical analysis. This post is geared more to the machine learning side.
Last updated on Jan 8, 2022 3 min read Machine Learning, variable selection

Stepwise selection after multiple imputation

Some note I have written two post previously about multiple imputation using mice package: A short note on multiple imputation Variable selection for imputation model in {mice} This post probably my last post about multiple imputation using mice package.
Last updated on Jan 4, 2022 8 min read applied statistics, variable selection