I am an #rstats and open science enthusiast. I currently work at Highmark Health, managing a team of researchers. Outside of my day job, I serve as a methodologist on several research teams, and develop accessible data tools. This site contains my blog and other open resources for learning data analysis.
With the advent of a more cohesive and complete dtplyr, I’ve been
wanting to write about how it can be used with tidyfast to use the
syntax of the tidyverse while relying on the speed and efficiency of
data.table. This workflow is already being adopted by some, including
Ivan
Leung,
who posted:
This post highlights six major themes of what I learned while creating the
tidyfast R package. This process
taught me about the tidyverse, data.table, R, and data science in
general.
This is a guest post by Jeremy Haynes, a doctoral student at Utah State University.
As I’ve spent time learning about different approaches to working with
data, I’ve seen several subtle, but important, differences in how to do
things. This very short post is presenting how one can perform
vectorized “if else” functions in R. The idea of “if else” basically is:
This short post is looking at data joins for both dplyr and data.table. There are a lot of moving parts when assessing these things, so the results here are just for this situation. It may differ in others. However, the results here are quite instructive.
As of late, I have used the data.table package to do some of my data
wrangling. It has been a fun adventure (the nerd type of fun). This was
made more meaningful with the renewed development of the dtplyr
package by Hadley Wickham and co. I introduce some of the different
behavior of data.table
here.