The Problem Real life data is often riddled with missing values - or NAs - where no data value are stored for the variable in observation. Missing data such as this can have a significant effect on the conclusions which can be drawn from the data.... [Read more...]

The Problem I wish to write a function that can mutate a column of my data using a function which is passed as an input. For example, I want to write a function which can modify an existing tbl_spark using dplyr::mutate() by adding a column that i... [Read more...]

Introduction Welcome to my series of blog posts about my data manipulation package, {poorman}. For those of you that don’t know, {poorman} is aiming to be a replication of {dplyr} but using only {base} R, and therefore be completely dependency fre... [Read more...]

Introduction Welcome to my series of blog posts about my data manipulation package, {poorman}. For those of you that don’t know, {poorman} is aiming to be a replication of {dplyr} but using only {base} R, and therefore be completely dependency free. What’s nice about this series is that ... [Read more...]

Introduction Welcome to my series of blog posts about my data manipulation package, {poorman}. For those of you that don’t know, {poorman} is aiming to be a replication of {dplyr} but using only {base} R, and therefore be completely dependency free. What’s nice about this series is that ... [Read more...]

Introduction Welcome to my series of blog posts about my data manipulation package, {poorman}. For those of you that don’t know, {poorman} is aiming to be a replication of {dplyr} but using only {base} R, and therefore be completely dependency free. What’s nice about this series is that ... [Read more...]

Introduction Hello all and welcome to another edition of the poorman series of blog posts. In this series I am discussing my progress in writing a base R equivalent of dplyr. What’s nice about this series is that if you’re not into poorman and would prefer just to ... [Read more...]

Introduction The first official release of poorman (v 0.1.9) is now on CRAN! You can now install poorman directly from CRAN with the following code:

install.packages("poorman")

In this blog post I want to address some common questions that I have received since I started writing the package. What is poorman? poorman ... [Read more...]

Introduction In my last post I discussed performing split-apply-combine operations on R data.frames and how this functionality was used within poorman - a base R replica of dplyr. In this post I’d like to talk about replicating dplyr’s join and filter join functionality, again using nothing but ... [Read more...]

Introduction In my last post we looked at how we can recreate base equivalents of the dplyr functions select(), filter(), mutate() and arrange(), amongst others. I wrote these functions and presented them in a new package called poorman. In this post I will be discussing new functionality that I have ... [Read more...]

Introduction This week I was working on a package which included a function factory. A function factory is a function which returns a function. The problem I faced was that when I was running R CMD check on my package, the check informed me my package had several issues which ... [Read more...]

Introduction In one of my latest posts, I discussed the idea of turning base R’s get and set operators ([, [[, [ [Read more...]

Ever wondered which R functions are actually passed to internal C code? Well, wonder no more as it turns out there is an unexported named list within the methods package providing instructions for turning builtin and special functions into generic functions. Wrapping this list with names() gives us the list ... [Read more...]

Introduction Did you know that the magrittr pipe, %__%, can be used for more than just data.frames and tibbles? In this blog post, we look at how we can create get and set functions for list elements. Getting List Elements First, let’s create a simple list.

z1 <- list(a = pi, b = 2.718, c = 0.57721)
z1
# $a
# [1] 3.141593
# 
# $b
# [1] 2.718
# 
# $c
# [1] 0.57721

Let’s ... [Read more...]

Introduction In my last post we looked at how to slice a data.table by group to obtain the rows for which a particular column in that group is at its maximum value using the excellent data.table package. In this post, we will be taking a look at how ... [Read more...]

[This article was first published on Random R Ramblings, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your [Read more...]

Introduction In my last post we looked at how to slice a data.frame by group to obtain the rows for which a particular column in that group is at its maximum value sing base R. In this post, we will be taking a look at how to perform this ... [Read more...]

Introduction Let’s say we wish to group some data by a variable, then for each group we wish to find the row of the maximum value of another variable, and then finally extract the entire row. This is a fairly common task and in fact I’ve had to ... [Read more...]

Introduction Let’s say you want to write a function with optional functionality which is dependent on the installation of a package that your colleague may not have installed. For example, let’s say you want to have an option to return a data.table (or a tibble) instead of ... [Read more...]

Recently I have been working on a very large legacy project which utilises the excellent data.table package throughout. What this has resulted in is an R CMD check containing literally thousands of NOTEs similar to the following:

❯ checking R code for possible problems ... NOTE
  my_fn: no visible binding for global variable ‘mpg’

There are several reasons why you might see these NOTEs and, ... [Read more...]