**rdata.lu Blog | Data science with R**, and kindly contributed to R-bloggers)

You probably have encountered situations similar to this one:

```
result = vector("list", length(some_numbers))
for(i in seq_along(some_numbers)){
result[[i]] = some_function(some_numbers[[i]])
}
print(result)
```

First I initialize `result`

, an empty list of size equal to the length of `some_numbers`

which will contains the results of applying `some_function()`

to each element of `some_numbers`

. Then, using a for loop, I apply the function. This is what I get back:

`NaNs producedError in sqrt(x) : non-numeric argument to mathematical function`

Let’s take a look at `some_numbers`

and `some_function()`

:

`print(some_numbers)`

```
## [[1]]
## [1] -1.9
##
## [[2]]
## [1] 20
##
## [[3]]
## [1] "-88"
##
## [[4]]
## [1] -42
```

`some_function`

```
## function(x){
## if(x == 0) res = 0
## if(x < 0) res = -sqrt(-x)
## if(x > 0) res = sqrt(x)
## return(res)
## }
```

So the function simply returns the square root of `x`

(or minus the square root of `-x`

if `x`

is negative), but the number in third position of the list `some_numbers`

is actually a character. This type of mistakes can commonly happen. The `result`

list looks like this:

`print(result)`

```
[[1]]
[1] -1.378405
[[2]]
[1] 4.472136
[[3]]
NULL
[[4]]
NULL
```

As you see, even though the fourth element could have been computed, the error made the whole loop stop. In such a simple example, you could correct this and then run your function. But what if the list you want to apply your function to is very long and the computation take a very, very long time? Perhaps you simply want to skip these errors and get back to them later. One way of doing that is using `tryCatch()`

:

```
result = vector("list", length(some_numbers))
for(i in seq_along(some_numbers)){
result[[i]] = tryCatch(some_function(some_numbers[[i]]),
error = function(e) paste("something wrong here"))
}
print(result)
```

```
## [[1]]
## [1] -1.378405
##
## [[2]]
## [1] 4.472136
##
## [[3]]
## [1] "something wrong here"
##
## [[4]]
## [1] -6.480741
```

This works, but it’s verbose and easy to mess up. My advice here is that if you want to skip errors in loops you don’t write loops! This is quite easy with the `purrr`

package:

```
library(purrr)
result = map(some_numbers, some_function)
```

There’s several advantages here already; no need to initialize an empty structure to hold your result, and no need to think about indices, which can sometimes get confusing. This however does not work either; there’s still the problem that we have a character inside `some_numbers`

:

`Error in sqrt(x) : non-numeric argument to mathematical function`

However, `purrr`

contains some very amazing functions for error handling, `safely()`

and `possibly()`

. Let’s try `possibly()`

first:

`possibly_some_function = possibly(some_function, otherwise = "something wrong here")`

`possibly()`

takes a function as argument as well as `otherwise`

; this is where you define a return value in case something is wrong. `possibly()`

then returns a new function that skips errors:

```
result = map(some_numbers, possibly_some_function)
print(result)
```

```
## [[1]]
## [1] -1.378405
##
## [[2]]
## [1] 4.472136
##
## [[3]]
## [1] "something wrong here"
##
## [[4]]
## [1] -6.480741
```

When you use `possibly()`

on a function, you’re politely telling R “would you kindly apply the function wherever possible, and if not, tell me where there was an issue”. What about `safely()`

?

```
safely_some_function = safely(some_function)
result = map(some_numbers, safely_some_function)
str(result)
```

```
## List of 4
## $ :List of 2
## ..$ result: num -1.38
## ..$ error : NULL
## $ :List of 2
## ..$ result: num 4.47
## ..$ error : NULL
## $ :List of 2
## ..$ result: NULL
## ..$ error :List of 2
## .. ..$ message: chr "invalid argument to unary operator"
## .. ..$ call : language -x
## .. ..- attr(*, "class")= chr [1:3] "simpleError" "error" "condition"
## $ :List of 2
## ..$ result: num -6.48
## ..$ error : NULL
```

The major difference with `possibly()`

is that `safely()`

returns a more complex object: it returns a list of lists. There are as many lists as there are elements in `some_numbers`

. Let’s take a look at the first one:

`print(result[[1]])`

```
## $result
## [1] -1.378405
##
## $error
## NULL
```

`result[[1]]`

is a list with a `result`

and an `error`

. If there was no error, we get a value in `result`

and `NULL`

in `error`

. If there was an `error`

, this is what we see:

`print(result[[3]])`

```
## $result
## NULL
##
## $error
##
```

Because lists of lists are not easy to handle, I like to use `possibly()`

, but if you use `safely()`

you might want to know about `transpose()`

, which is another function from `purrr`

:

```
result2 = transpose(result)
str(result2)
```

```
## List of 2
## $ result:List of 4
## ..$ : num -1.38
## ..$ : num 4.47
## ..$ : NULL
## ..$ : num -6.48
## $ error :List of 4
## ..$ : NULL
## ..$ : NULL
## ..$ :List of 2
## .. ..$ message: chr "invalid argument to unary operator"
## .. ..$ call : language -x
## .. ..- attr(*, "class")= chr [1:3] "simpleError" "error" "condition"
## ..$ : NULL
```

`result2`

is now a list of two lists: a `result`

list holding all the results, and an `error`

list holding all the error message. You can get results with:

`result2$result`

```
## [[1]]
## [1] -1.378405
##
## [[2]]
## [1] 4.472136
##
## [[3]]
## NULL
##
## [[4]]
## [1] -6.480741
```

I hope you enjoyed this blog post, and that these functions will make your life easier!

Don’t hesitate to follow us on twitter @rdata_lu and to subscribe to our youtube channel.

You can also contact us if you have any comments or suggestions. See you for the next post!

**leave a comment**for the author, please follow the link and comment on their blog:

**rdata.lu Blog | Data science with R**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...