Blog Archives

Getting {sparklyr}, {h2o}, {rsparkling} to work together and some fun with bash

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 I am going to show a little trick to...

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Keep trying that api call with purrr::possibly()

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 Web Scraping. In this blog post I will show you...

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Importing 30GB of data in R with sparklyr

February 15, 2018
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Importing 30GB of data in R with sparklyr

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 it! Also, if you find this interesting, read also...

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Predicting job search by training a random forest on an unbalanced dataset

February 10, 2018
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Predicting job search by training a random forest on an unbalanced dataset

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 am always quite interest by such datasets. I...

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Mapping a list of functions to a list of datasets with a list of columns as arguments

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 th...

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It’s lists all the way down, part 2: We need to go deeper

It’s lists all the way down, part 2: We need to go deeper

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 to learn and share. #rstats— Isabella R. Ghement (@IsabellaGhement) January 3,...

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It’s lists all the way down

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 totally not optimal, but I want to illustrate how to map...

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Building formulae

December 26, 2017
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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...

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Teaching the tidyverse to beginners

December 16, 2017
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Teaching the tidyverse to beginners

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 some others think that one should first present base R and then show how...

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Functional peace of mind

November 13, 2017
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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 problems. For example, imagine for a second that R does not...

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