Imputing missing values in parallel using {furrr}
{pmice}, an experimental package for missing data imputation in parallel using {mice} and {furrr}
Building formulae
Functional peace of mind
Get basic summary statistics for all the variables in a data frame
Getting {sparklyr}, {h2o}, {rsparkling} to work together and some fun with bash
Importing 30GB of data into R with sparklyr
Introducing brotools
It's lists all the way down
It's lists all the way down, part 2: We need to go deeper
Keep trying that api call with purrr::possibly()
Lesser known dplyr 0.7* tricks
Lesser known dplyr tricks
Lesser known purrr tricks
Make ggplot2 purrr
Mapping a list of functions to a list of datasets with a list of columns as arguments
Predicting job search by training a random forest on an unbalanced dataset
Teaching the tidyverse to beginners
Why I find tidyeval useful
tidyr::spread() and dplyr::rename_at() in action
Easy peasy STATA-like marginal effects with R
Functional programming and unit testing for data munging with R available on Leanpub
How to use jailbreakr
My free book has a cover!
Work on lists of datasets instead of individual datasets by using functional programming
Method of Simulated Moments with R
New website!
Nonlinear Gmm with R - Example with a logistic regression
Simulated Maximum Likelihood with R
Bootstrapping standard errors for difference-in-differences estimation with R
Careful with tryCatch
Data frame columns as arguments to dplyr functions
Export R output to a file
I've started writing a 'book': Functional programming and unit testing for data munging with R
Introduction to programming econometrics with R
Merge a list of datasets together
Object Oriented Programming with R: An example with a Cournot duopoly
R, R with Atlas, R with OpenBLAS and Revolution R Open: which is fastest?
Read a lot of datasets at once with R
Unit testing with R
Update to Introduction to programming econometrics with R
Using R as a Computer Algebra System with Ryacas

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:

```
create_form = function(power){
rhs = substitute(I(hp^pow), list(pow=power))
rlang::new_formula(quote(mpg), rhs)
}
```

If you are not familiar with `substitute()`

, try the following to understand what it does:

`substitute(y ~ x, list(x = 1))`

`## y ~ 1`

Then using `rlang::new_formula()`

I build a formula by providing the left hand side, which is `quote(mpg)`

here, and the right hand side, which I built using `substitute()`

. Now I can create a list of formulae:

```
library(tidyverse)
list_formulae = map(seq(1, 6), create_form)
str(list_formulae)
```

```
## List of 6
## $ :Class 'formula' language mpg ~ I(hp^1L)
## .. ..- attr(*, ".Environment")=<environment: 0x561228f1b310>
## $ :Class 'formula' language mpg ~ I(hp^2L)
## .. ..- attr(*, ".Environment")=<environment: 0x561228f34198>
## $ :Class 'formula' language mpg ~ I(hp^3L)
## .. ..- attr(*, ".Environment")=<environment: 0x561228e0efc0>
## $ :Class 'formula' language mpg ~ I(hp^4L)
## .. ..- attr(*, ".Environment")=<environment: 0x561228e0d088>
## $ :Class 'formula' language mpg ~ I(hp^5L)
## .. ..- attr(*, ".Environment")=<environment: 0x561228e0b5b0>
## $ :Class 'formula' language mpg ~ I(hp^6L)
## .. ..- attr(*, ".Environment")=<environment: 0x561228e07c98>
```

As you can see, `power`

got replaced by 1, 2, 3,… and each element of the list is a nice formula. Exactly what `lm()`

needs. So now it’s easy to map `lm()`

to this list of formulae:

```
data(mtcars)
map(list_formulae, lm, data = mtcars)
```

```
## [[1]]
##
## Call:
## .f(formula = .x[[i]], data = ..1)
##
## Coefficients:
## (Intercept) I(hp^1)
## 30.09886 -0.06823
##
##
## [[2]]
##
## Call:
## .f(formula = .x[[i]], data = ..1)
##
## Coefficients:
## (Intercept) I(hp^2)
## 24.3887252 -0.0001649
##
##
## [[3]]
##
## Call:
## .f(formula = .x[[i]], data = ..1)
##
## Coefficients:
## (Intercept) I(hp^3)
## 2.242e+01 -4.312e-07
##
##
## [[4]]
##
## Call:
## .f(formula = .x[[i]], data = ..1)
##
## Coefficients:
## (Intercept) I(hp^4)
## 2.147e+01 -1.106e-09
##
##
## [[5]]
##
## Call:
## .f(formula = .x[[i]], data = ..1)
##
## Coefficients:
## (Intercept) I(hp^5)
## 2.098e+01 -2.801e-12
##
##
## [[6]]
##
## Call:
## .f(formula = .x[[i]], data = ..1)
##
## Coefficients:
## (Intercept) I(hp^6)
## 2.070e+01 -7.139e-15
```

This is still a new topic for me there might be more elegant ways to do that, using tidyeval to remove the hardcoding of the columns in `create_form()`

. I might continue exploring this.