# Blog Archives

## 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,...

## 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...

## 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...

## Teaching the tidyverse to beginners

December 16, 2017
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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...

## 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...

## Easy peasy STATA-like marginal effects with R

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 London School of Economics and Political Science. You can...

## Why I find tidyeval useful

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 ...

## Why I find tidyeval useful

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 ...

## Why I find tidyeval useful

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 ...

## tidyr::spread() and dplyr::rename_at() in action

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 at the data: data1; data2 ## # A tibble: 20 x 4 ##...