Using ESS for Datascience

RStudio is a formidable IDE to work with and offers an environment to seamlessly work with multiple languages beyond R. It is especially convenient for tasks involving frequent visualisation of data frames and plots, and for use with Shiny app development. However, the text (i.e code) editing capabalities are still significantly lacking compared to the likes of Emacs and Vim. Besides this, it does not offer a seamless interface integrating task, time management and multi-language programming environments to the extent available within Org-mode via Emacs.

R notes and snippets

Long <-> Wide formats : example for gathering library("tidyverse") ## Defining a sample tribble with several duplicates a <- tribble( ~IDS, ~"client id 1", ~"client id 2", ~"client id 3", ~"client id 4", ~"old app", ~"new app", 123, 767, 888,"" , "", "yes" , "no", 222, 333, 455, 55, 677, "no", "yes", 222, 333, 343, 55,677, "no", "yes" ) ## Defining vector to form column names vec1 <- seq(1:4) vec2 <- "client id" vec3 <- str_glue("{vec2} {vec1}") ## Gathering and removing duplicates a %>% gather( key = "Client number", value = "client ID", vec3 ) %>% unique() Matrix Defining a matrix A matrix is a collection of elements of the same data type (numeric, character, or logical) arranged into a fixed number of rows and columns.

Course Certificates

List of course certificates completed on platforms like DataCamp, DataQuest, EdX etc.