It is often the case that data is trapped inside pdfs, but thankfully there are ways to extract it from the pdfs. A very nice package for this task is pdftools (Github link) and this blog post will describe some basic functionality from that package. First, let’s find some ... [Read more...]

Yesterday I wrote this blog post which showed how one could use {furrr} and {mice} to impute missing data in parallel, thus speeding up the process tremendously. To make using this snippet of code easier, I quickly cobbled together an experimental package called {pmice} that you can install from Github: {{... [Read more...]

Today I saw this tweet on my timeline: For those of us that just can't wait until RStudio officially supports parallel purrr in #rstats, boy have I got something for you. Introducing `furrr`, parallel purrr through the use of futures. Go ahead, break things, you know you want to:https://... [Read more...]

I have added a new function to my {brotools} package, called describe(), which takes a data frame as an argument, and returns another data frame with descriptive statistics. It is very much inspired by the {skmir} package but also by assist::describe() (click on the packages to be redirected to ... [Read more...]

This is going to be the type of blog posts that would perhaps be better as a gist, but it is easier for me to use my blog as my own personal collection of gists. Plus, someone else might find this useful, so here it is! In this blog post ... [Read more...]

Sometimes you need to call an api to get some result from a web service, but sometimes this call might fail. You might get an error 500 for example, or maybe you’re making too many calls too fast. Regarding this last point, I really encourage you to read Ethics in ... [Read more...]

Disclaimer: the first part of this blog post draws heavily from Working with CSVs on the Command Line, which is a beautiful resource that lists very nice tips and tricks to work with CSV files before having to load them into R, or any other statistical software. I highly recommend ... [Read more...]

In this blog post, I am going to train a random forest on census data from the US to predict the probability that someone is looking for a job. To this end, I downloaded the US 1990 census data from the UCI Machine Learning Repository. Having a background in economics, I ... [Read more...]

This week I had the opportunity to teach R at my workplace, again. This course was the “advanced R” course, and unlike the one I taught at the end of last year, I had one more day (so 3 days in total) where I could show my colleagues the joys of ... [Read more...]

Shortly after my previous blog post, I saw this tweet on my timeline: The purrr resolution for 2018 - learn at least one purrr function per week - is officially launched with encouragement and inspiration from @statwonk and @hadleywickham. We start with modify_depth: https://t.co/dCMnSHP7Pl. Please join ... [Read more...]

Today, I had the opportunity to help someone over at the R for Data Science Slack group (read more about this group here) and I thought that the question asked could make for an interesting blog post, so here it is! Disclaimer: the way I’m doing things here is ... [Read more...]

This Stackoverflow question made me think about how to build formulae. For example, you might want to programmatically build linear model formulae and then map these models on data. For example, suppose the following (output suppressed):

data(mtcars)

lm(mpg ~ hp, data = mtcars)
lm(mpg ~I(hp^2), data = mtcars)
lm(mpg ~I(hp^3), data = mtcars)
lm(mpg ~I(hp^4), data = mtcars)
lm(mpg ~I(hp^5), data = mtcars)
lm(mpg ~I(hp^6), data = mtcars)

To avoid doing this, one can write a function that builds the formulae: {{... [Read more...]

End October I tweeted this: will teach #rstats soon again but this time following @drob 's suggestion of the tidyverse first as laid out here: https://t.co/js8SsUs8Nv— Bruno Rodrigues (@brodriguesco) October 24, 2017 and it generated some discussion. Some people believe that this is the right approach, and ... [Read more...]

I think what I enjoy the most about functional programming is the peace of mind that comes with it. With functional programming, there’s a lot of stuff you don’t need to think about. You can write functions that are general enough so that they solve a variety of ... [Read more...]

Model interpretation is essential in the social sciences. If one wants to know the effect of variable x on the dependent variable y, marginal effects are an easy way to get the answer. STATA includes a margins command that has been ported to R by Thomas J. Leeper of the ... [Read more...]

First thing’s first: maybe you shouldn’t care about tidyeval. Maybe you don’t need it. If you exclusively work interactively, I don’t think that learning about tidyeval is important. I can only speak for me, and explain to you why I personally ... [Read more...]

First thing’s first: maybe you shouldn’t care about tidyeval. Maybe you don’t need it. If you exclusively work interactively, I don’t think that learning about tidyeval is important. I can only speak for me, and explain to you why I personally ... [Read more...]

First thing’s first: maybe you shouldn’t care about tidyeval. Maybe you don’t need it. If you exclusively work interactively, I don’t think that learning about tidyeval is important. I can only speak for me, and explain to you why I personally ... [Read more...]

I was recently confronted to a situation that required going from a long dataset to a wide dataset, but with a small twist: there were two datasets, which I had to merge into one. You might wonder what kinda crappy twist that is, right? Well, let’s t... [Read more...]

I was recently confronted to a situation that required going from a long dataset to a wide dataset, but with a small twist: there were two datasets, which I had to merge into one. You might wonder what kinda crappy twist that is, right? Well, let’s take a look ... [Read more...]