A tutorial on tidy cross-validation with R
Analyzing NetHack data, part 1: What kills the players
Analyzing NetHack data, part 2: What players kill the most
Building a shiny app to explore historical newspapers: a step-by-step guide
Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 1
Classification of historical newspapers content: a tutorial combining R, bash and Vowpal Wabbit, part 2
Dealing with heteroskedasticity; regression with robust standard errors using R
Easy time-series prediction with R: a tutorial with air traffic data from Lux Airport
Exporting editable plots from R to Powerpoint: making ggplot2 purrr with officer
Forecasting my weight with R
From webscraping data to releasing it as an R package to share with the world: a full tutorial with data from NetHack
Getting data from pdfs using the pdftools package
Getting the data from the Luxembourguish elections out of Excel
Going from a human readable Excel file to a machine-readable csv with {tidyxl}
How Luxembourguish residents spend their time: a small {flexdashboard} demo using the Time use survey data
Imputing missing values in parallel using {furrr}
Looking into 19th century ads from a Luxembourguish newspaper with R
Making sense of the METS and ALTO XML standards
Manipulate dates easily with {lubridate}
Manipulating strings with the {stringr} package
Maps with pie charts on top of each administrative division: an example with Luxembourg's elections data
Missing data imputation and instrumental variables regression: the tidy approach
Objects types and some useful R functions for beginners
Pivoting data frames just got easier thanks to `pivot_wide()` and `pivot_long()`
R or Python? Why not both? Using Anaconda Python within R with {reticulate}
Searching for the optimal hyper-parameters of an ARIMA model in parallel: the tidy gridsearch approach
Some fun with {gganimate}
The best way to visit Luxembourguish castles is doing data science + combinatorial optimization
The year of the GNU+Linux desktop is upon us: using user ratings of Steam Play compatibility to play around with regex and the tidyverse
Using Data Science to read 10 years of Luxembourguish newspapers from the 19th century
Using a genetic algorithm for the hyperparameter optimization of a SARIMA model
Using the tidyverse for more than data manipulation: estimating pi with Monte Carlo methods
What hyper-parameters are, and what to do with them; an illustration with ridge regression
{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

`purrr`

is a package that extends R’s functional programming capabilities. It brings a lot of new stuff to
the table and in this post I show you some of the most useful (at least to me) functions included in `purrr`

.

`map()`

```
library(purrr)
numbers <- list(11, 12, 13, 14)
map_dbl(numbers, sqrt)
```

`## [1] 3.316625 3.464102 3.605551 3.741657`

You might wonder why this might be preferred to a for loop? It’s a lot less verbose, and you do not need to
initialise any kind of structure to hold the result. If you google “create empty list in R” you will see that
this is very common. However, with the `map()`

family of functions, there is no need for an initial structure.
`map_dbl()`

returns an atomic list of real numbers, but if you use `map()`

you will get a list back. Try them all out!

```
# Create a helper function that returns TRUE if a number is even
is_even <- function(x){
!as.logical(x %% 2)
}
map_if(numbers, is_even, sqrt)
```

```
## [[1]]
## [1] 11
##
## [[2]]
## [1] 3.464102
##
## [[3]]
## [1] 13
##
## [[4]]
## [1] 3.741657
```

`map_at(numbers, c(1,3), sqrt)`

```
## [[1]]
## [1] 3.316625
##
## [[2]]
## [1] 12
##
## [[3]]
## [1] 3.605551
##
## [[4]]
## [1] 14
```

`map_if()`

and `map_at()`

have a further argument than `map()`

; in the case of `map_if()`

, a predicate function (
a function that returns `TRUE`

or `FALSE`

) and a vector of positions for `map_at()`

. This allows you to map your
function only when certain conditions are met, which is also something that a lot of people google for.

```
numbers2 <- list(1, 2, 3, 4)
map2(numbers, numbers2, `+`)
```

```
## [[1]]
## [1] 12
##
## [[2]]
## [1] 14
##
## [[3]]
## [1] 16
##
## [[4]]
## [1] 18
```

You can map two lists to a function which takes two arguments using `map_2()`

. You can even map an arbitrary number
of lists to any function using `pmap()`

.

By the way, try this in: ``+`(1,3)`

and see what happens.

```
possible_sqrt <- possibly(sqrt, otherwise = NA_real_)
numbers_with_error <- list(1, 2, 3, "spam", 4)
map(numbers_with_error, possible_sqrt)
```

```
## [[1]]
## [1] 1
##
## [[2]]
## [1] 1.414214
##
## [[3]]
## [1] 1.732051
##
## [[4]]
## [1] NA
##
## [[5]]
## [1] 2
```

Another very common issue is to keep running your loop even when something goes wrong. In most cases the loop simply stops
at the error, but you would like it to continue and see where it failed. Try to google “skip error in a loop”
or some variation of it and you’ll see that a lot of people really just want that.
This is possible by combining `map()`

and `possibly()`

. Most solutions involve the use of
`tryCatch()`

which I personally do not find very easy to use.

```
safe_sqrt <- safely(sqrt, otherwise = NA_real_)
map(numbers_with_error, safe_sqrt)
```

```
## [[1]]
## [[1]]$result
## [1] 1
##
## [[1]]$error
## NULL
##
##
## [[2]]
## [[2]]$result
## [1] 1.414214
##
## [[2]]$error
## NULL
##
##
## [[3]]
## [[3]]$result
## [1] 1.732051
##
## [[3]]$error
## NULL
##
##
## [[4]]
## [[4]]$result
## [1] NA
##
## [[4]]$error
## <simpleError in sqrt(x = x): non-numeric argument to mathematical function>
##
##
## [[5]]
## [[5]]$result
## [1] 2
##
## [[5]]$error
## NULL
```

`safely()`

is very similar to `possibly()`

but it returns a list of lists. An element is thus a list of the result
and the accompagnying error message. If there is no error, the error component is `NULL`

if there is an error, it
returns the error message.

```
safe_result_list <- map(numbers_with_error, safe_sqrt)
transpose(safe_result_list)
```

```
## $result
## $result[[1]]
## [1] 1
##
## $result[[2]]
## [1] 1.414214
##
## $result[[3]]
## [1] 1.732051
##
## $result[[4]]
## [1] NA
##
## $result[[5]]
## [1] 2
##
##
## $error
## $error[[1]]
## NULL
##
## $error[[2]]
## NULL
##
## $error[[3]]
## NULL
##
## $error[[4]]
## <simpleError in sqrt(x = x): non-numeric argument to mathematical function>
##
## $error[[5]]
## NULL
```

Here we transposed the above list. This means that we still have a list of lists, but where the first list holds
all the results (which you can then access with `safe_result_list$result`

) and the second list holds all the errors
(which you can access with `safe_result_list$error`

). This can be quite useful!

```
transposed_list <- transpose(safe_result_list)
transposed_list %>%
at_depth(2, is_null)
```

`## Warning: at_depth() is deprecated, please use `modify_depth()` instead`

```
## $result
## $result[[1]]
## [1] FALSE
##
## $result[[2]]
## [1] FALSE
##
## $result[[3]]
## [1] FALSE
##
## $result[[4]]
## [1] FALSE
##
## $result[[5]]
## [1] FALSE
##
##
## $error
## $error[[1]]
## [1] TRUE
##
## $error[[2]]
## [1] TRUE
##
## $error[[3]]
## [1] TRUE
##
## $error[[4]]
## [1] FALSE
##
## $error[[5]]
## [1] TRUE
```

Sometimes working with lists of lists can be tricky, especially when we want to apply a function to the sub-lists. This
is easily done with `at_depth()`

!

```
name_element <- c("sqrt()", "ok?")
set_names(transposed_list, name_element)
```

```
## $`sqrt()`
## $`sqrt()`[[1]]
## [1] 1
##
## $`sqrt()`[[2]]
## [1] 1.414214
##
## $`sqrt()`[[3]]
## [1] 1.732051
##
## $`sqrt()`[[4]]
## [1] NA
##
## $`sqrt()`[[5]]
## [1] 2
##
##
## $`ok?`
## $`ok?`[[1]]
## NULL
##
## $`ok?`[[2]]
## NULL
##
## $`ok?`[[3]]
## NULL
##
## $`ok?`[[4]]
## <simpleError in sqrt(x = x): non-numeric argument to mathematical function>
##
## $`ok?`[[5]]
## NULL
```

`reduce(numbers, `*`)`

`## [1] 24024`

`reduce()`

applies the function `*`

iteratively to the list of numbers. There’s also `accumulate()`

:

`accumulate(numbers, `*`)`

`## [1] 11 132 1716 24024`

which keeps the intermediary results.

This function is very general, and you can reduce anything:

Matrices:

```
mat1 <- matrix(rnorm(10), nrow = 2)
mat2 <- matrix(rnorm(10), nrow = 2)
mat3 <- matrix(rnorm(10), nrow = 2)
```

```
list_mat <- list(mat1, mat2, mat3)
reduce(list_mat, `+`)
```

```
## [,1] [,2] [,3] [,4] [,5]
## [1,] -2.48530177 1.0110049 0.4450388 1.280802 1.3413979
## [2,] 0.07596679 -0.6872268 -0.6579242 1.615237 0.8231933
```

even data frames:

```
df1 <- as.data.frame(mat1)
df2 <- as.data.frame(mat2)
df3 <- as.data.frame(mat3)
list_df <- list(df1, df2, df3)
reduce(list_df, dplyr::full_join)
```

```
## Joining, by = c("V1", "V2", "V3", "V4", "V5")
## Joining, by = c("V1", "V2", "V3", "V4", "V5")
```

```
## V1 V2 V3 V4 V5
## 1 -0.6264538 -0.8356286 0.32950777 0.48742905 0.5757814
## 2 0.1836433 1.5952808 -0.82046838 0.73832471 -0.3053884
## 3 -0.8969145 1.5878453 -0.08025176 0.70795473 1.9844739
## 4 0.1848492 -1.1303757 0.13242028 -0.23969802 -0.1387870
## 5 -0.9619334 0.2587882 0.19578283 0.08541773 -1.2188574
## 6 -0.2925257 -1.1521319 0.03012394 1.11661021 1.2673687
```

Hope you enjoyed this list of useful functions! If you enjoy the content of my blog, you can follow me on twitter.