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
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}
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
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 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

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 how you can keep trying to make this api call using `purrr::possibly()`

.

For this, let’s use this function that will simulate an api call:

```
get_data = function(){
number = rbinom(1, 1, 0.9)
ifelse(number == 0, "OK", stop("Error: too many calls!"))
}
```

This function simply returns a random draw from a binomial distribution. If this number equals 0 with probability 0.1, the function returns “OK”, if not, it throws an error. Because the probability of success is only 10%, your api call might be unsuccessful:

`get_data()`

```
Error in ifelse(number == 0, "OK", stop("Error: too many calls!")) :
Error: too many calls!
```

How to keep trying until it works? For this, we’re going to use `purrr::possibly()`

; this function
takes another function as argument and either returns the result, or another output in case of error,
that the user can define:

`possibly_get_data = purrr::possibly(get_data, otherwise = NULL)`

Let’s try it:

```
set.seed(12)
possibly_get_data()
```

`## NULL`

With `set.seed(12)`

, the function returns a number different from 0, and thus throws an error: but
because we’re wrapping the function around `purrr::possibly()`

, the function now returns `NULL`

. The
first step is done; now we can use this to our advantage:

```
definitely_get_data = function(func, n_tries, sleep, ...){
possibly_func = purrr::possibly(func, otherwise = NULL)
result = NULL
try_number = 1
while(is.null(result) && try_number <= n_tries){
print(paste("Try number: ", try_number))
try_number = try_number + 1
result = possibly_func(...)
Sys.sleep(sleep)
}
return(result)
}
```

`definitely_get_data()`

is a function that takes any function as argument, as well as a user provided
number of tries (as well as `...`

to pass further arguments to `func()`

). Remember, if `func()`

fails,
it will return `NULL`

; the while loop ensures that while the result is `NULL`

, and the number of tries
is below what you provided, the function will keep getting called. I didn’t talk about `sleep`

; this
argument is provided to `Sys.sleep()`

which introduces a break between calls that is equal to `sleep`

seconds. This ensures you don’t make too many calls too fast. Let’s try it out:

```
set.seed(123)
definitely_get_data(get_data, 10, 1)
```

```
## [1] "Try number: 1"
## [1] "Try number: 2"
## [1] "Try number: 3"
## [1] "Try number: 4"
## [1] "Try number: 5"
```

`## [1] "OK"`

It took 5 tries to get the result! However, if after 10 tries `get_data()`

fails to return
what you need it will stop (but you can increase the number of tries…).

If you found this blog post useful, you might want to follow me on twitter for blog post updates.