The purrr package has a comprehensive set of tools for working with lists and vectors, and especially for iterating operations and analyses with its map(). This style of iteration is particularly powerful when used with the tibble package, because—unlike regular data frames—tibbles allow you to use lists as data frame columns as well as vectors. That means that complex objects, including models, plots and even other data frames can be stored inside a data frame list-column alongside other data.

The biggest downside of being able to do complex analysis on many list elements is that one little error can bring a lot of computation down. Another is that a warning or other message might get lost: if the 37th statistical model you fit of 45 has a problem, are you going to catch it?

purrr comes with tools for dealing with errors, warnings and other “side effects”, but it’s difficult to pair them effectively with purrr’s massively powerful iteration tools. And that’s where collateral comes in: it gives you ways to drop-in replacements for map() that use employ these side-effect capturing tools, as well as an array of other helpers to make navigating everything you’ve captured more pleasant.

Basic exploratory analysis

The aim of this vignette isn’t just to get you acquainted with collateral’s tools: it’s also to demonstrate the value of a tidy list-column workflow. We’ll be using the diamonds dataset, which comes with the ggplot2 package. Let’s take a look:

library(tibble)
#> Warning: package 'tibble' was built under R version 4.0.2
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(tidyr)
library(purrr)
library(ggplot2)

diamonds
#> # A tibble: 53,940 x 10
#>    carat cut       color clarity depth table price     x     y     z
#>    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#>  1 0.23  Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
#>  2 0.21  Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
#>  3 0.23  Good      E     VS1      56.9    65   327  4.05  4.07  2.31
#>  4 0.290 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
#>  5 0.31  Good      J     SI2      63.3    58   335  4.34  4.35  2.75
#>  6 0.24  Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
#>  7 0.24  Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
#>  8 0.26  Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
#>  9 0.22  Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
#> 10 0.23  Very Good H     VS1      59.4    61   338  4     4.05  2.39
#> # ... with 53,930 more rows

This dataset describes the prices and properties of about fifty thousand diamonds. We can see a number of categorical variables, including cut, color and clarity, and several continuous variables, like the price.

How is price distributed?

ggplot(diamonds) +
  geom_histogram(aes(x = price, y = stat(count)))
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(diamonds) +
  geom_histogram(aes(x = price, y = stat(count))) +
  scale_x_log10()
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Looking at a histogram with a logarithmic scale, we can see that it has multiple peaks. That probably means that there are several distinct groups in the data. Maybe there’s a relationship between price and one (or more) of the categorical variables.

ggplot(diamonds) +
  geom_histogram(aes(x = price, y = stat(count))) +
  facet_wrap(vars(cut), ncol = 1) +
  scale_x_log10() +
  ggtitle('Price vs. cut')
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(diamonds) +
  geom_histogram(aes(x = price, y = stat(count))) +
  facet_wrap(vars(color), ncol = 1) +
  scale_x_log10() +
  ggtitle('Price vs. color')
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Grouping the traditional way

It looks like there might be several relationships here, but let’s focus on cut and color for now. ggplot2 makes it very easy to visualise differences across a couple of grouping variables, but we may not always want to create a facetted plot. In fact, there may be lots of activities we want to do that don’t involve plotting at all.

This is a broad class of problem in data analysis called ‘split-apply-combine’: split data up into groups, apply operations to those groups and combine the results. One way of doing this with base R is to use split(), which takes in a data frame and some grouping variables and gives you back a list of identically-structured data frames broken up row-wise according to the values of the grouping variables.

diamonds %>% split(diamonds$cut)
#> $Fair
#> # A tibble: 1,610 x 10
#>    carat cut   color clarity depth table price     x     y     z
#>    <dbl> <ord> <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#>  1  0.22 Fair  E     VS2      65.1    61   337  3.87  3.78  2.49
#>  2  0.86 Fair  E     SI2      55.1    69  2757  6.45  6.33  3.52
#>  3  0.96 Fair  F     SI2      66.3    62  2759  6.27  5.95  4.07
#>  4  0.7  Fair  F     VS2      64.5    57  2762  5.57  5.53  3.58
#>  5  0.7  Fair  F     VS2      65.3    55  2762  5.63  5.58  3.66
#>  6  0.91 Fair  H     SI2      64.4    57  2763  6.11  6.09  3.93
#>  7  0.91 Fair  H     SI2      65.7    60  2763  6.03  5.99  3.95
#>  8  0.98 Fair  H     SI2      67.9    60  2777  6.05  5.97  4.08
#>  9  0.84 Fair  G     SI1      55.1    67  2782  6.39  6.2   3.47
#> 10  1.01 Fair  E     I1       64.5    58  2788  6.29  6.21  4.03
#> # ... with 1,600 more rows
#> 
#> $Good
#> # A tibble: 4,906 x 10
#>    carat cut   color clarity depth table price     x     y     z
#>    <dbl> <ord> <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#>  1  0.23 Good  E     VS1      56.9    65   327  4.05  4.07  2.31
#>  2  0.31 Good  J     SI2      63.3    58   335  4.34  4.35  2.75
#>  3  0.3  Good  J     SI1      64      55   339  4.25  4.28  2.73
#>  4  0.3  Good  J     SI1      63.4    54   351  4.23  4.29  2.7 
#>  5  0.3  Good  J     SI1      63.8    56   351  4.23  4.26  2.71
#>  6  0.3  Good  I     SI2      63.3    56   351  4.26  4.3   2.71
#>  7  0.23 Good  F     VS1      58.2    59   402  4.06  4.08  2.37
#>  8  0.23 Good  E     VS1      64.1    59   402  3.83  3.85  2.46
#>  9  0.31 Good  H     SI1      64      54   402  4.29  4.31  2.75
#> 10  0.26 Good  D     VS2      65.2    56   403  3.99  4.02  2.61
#> # ... with 4,896 more rows
#> 
#> $`Very Good`
#> # A tibble: 12,082 x 10
#>    carat cut       color clarity depth table price     x     y     z
#>    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#>  1  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
#>  2  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
#>  3  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
#>  4  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39
#>  5  0.3  Very Good J     SI1      62.7    59   351  4.21  4.27  2.66
#>  6  0.23 Very Good E     VS2      63.8    55   352  3.85  3.92  2.48
#>  7  0.23 Very Good H     VS1      61      57   353  3.94  3.96  2.41
#>  8  0.31 Very Good J     SI1      59.4    62   353  4.39  4.43  2.62
#>  9  0.31 Very Good J     SI1      58.1    62   353  4.44  4.47  2.59
#> 10  0.23 Very Good G     VVS2     60.4    58   354  3.97  4.01  2.41
#> # ... with 12,072 more rows
#> 
#> $Premium
#> # A tibble: 13,791 x 10
#>    carat cut     color clarity depth table price     x     y     z
#>    <dbl> <ord>   <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#>  1 0.21  Premium E     SI1      59.8    61   326  3.89  3.84  2.31
#>  2 0.290 Premium I     VS2      62.4    58   334  4.2   4.23  2.63
#>  3 0.22  Premium F     SI1      60.4    61   342  3.88  3.84  2.33
#>  4 0.2   Premium E     SI2      60.2    62   345  3.79  3.75  2.27
#>  5 0.32  Premium E     I1       60.9    58   345  4.38  4.42  2.68
#>  6 0.24  Premium I     VS1      62.5    57   355  3.97  3.94  2.47
#>  7 0.290 Premium F     SI1      62.4    58   403  4.24  4.26  2.65
#>  8 0.22  Premium E     VS2      61.6    58   404  3.93  3.89  2.41
#>  9 0.22  Premium D     VS2      59.3    62   404  3.91  3.88  2.31
#> 10 0.3   Premium J     SI2      59.3    61   405  4.43  4.38  2.61
#> # ... with 13,781 more rows
#> 
#> $Ideal
#> # A tibble: 21,551 x 10
#>    carat cut   color clarity depth table price     x     y     z
#>    <dbl> <ord> <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#>  1  0.23 Ideal E     SI2      61.5    55   326  3.95  3.98  2.43
#>  2  0.23 Ideal J     VS1      62.8    56   340  3.93  3.9   2.46
#>  3  0.31 Ideal J     SI2      62.2    54   344  4.35  4.37  2.71
#>  4  0.3  Ideal I     SI2      62      54   348  4.31  4.34  2.68
#>  5  0.33 Ideal I     SI2      61.8    55   403  4.49  4.51  2.78
#>  6  0.33 Ideal I     SI2      61.2    56   403  4.49  4.5   2.75
#>  7  0.33 Ideal J     SI1      61.1    56   403  4.49  4.55  2.76
#>  8  0.23 Ideal G     VS1      61.9    54   404  3.93  3.95  2.44
#>  9  0.32 Ideal I     SI1      60.9    55   404  4.45  4.48  2.72
#> 10  0.3  Ideal I     SI2      61      59   405  4.3   4.33  2.63
#> # ... with 21,541 more rows

As it happens, the purrr::map() functions work quite well on the output of split():

diamonds %>%
  split(diamonds$cut) %>%
  map_dbl(~ mean(.$price))
#>      Fair      Good Very Good   Premium     Ideal 
#>  4358.758  3928.864  3981.760  4584.258  3457.542

But this starts to get really unwieldy as you add more grouping variables. In particular, “meta data”— data about the groups—are either stuck inside the original data frames or are encoded in the name of each data frame in the list. It’s also difficult to do a sequence of operations and keep everything associated with the original groupings:

diamonds_list = diamonds %>% split(list(diamonds$cut, diamonds$color))

map_dbl(diamonds_list, ~ mean(.$price))
#>      Fair.D      Good.D Very Good.D   Premium.D     Ideal.D      Fair.E 
#>    4291.061    3405.382    3470.467    3631.293    2629.095    3682.312 
#>      Good.E Very Good.E   Premium.E     Ideal.E      Fair.F      Good.F 
#>    3423.644    3214.652    3538.914    2597.550    3827.003    3495.750 
#> Very Good.F   Premium.F     Ideal.F      Fair.G      Good.G Very Good.G 
#>    3778.820    4324.890    3374.939    4239.255    4123.482    3872.754 
#>   Premium.G     Ideal.G      Fair.H      Good.H Very Good.H   Premium.H 
#>    4500.742    3720.706    5135.683    4276.255    4535.390    5216.707 
#>     Ideal.H      Fair.I      Good.I Very Good.I   Premium.I     Ideal.I 
#>    3889.335    4685.446    5078.533    5255.880    5946.181    4451.970 
#>      Fair.J      Good.J Very Good.J   Premium.J     Ideal.J 
#>    4975.655    4574.173    5103.513    6294.592    4918.186
map_dbl(diamonds_list, ~ cor(.$price, .$depth))
#>        Fair.D        Good.D   Very Good.D     Premium.D       Ideal.D 
#>  0.0085615605 -0.0554075394 -0.0372811241 -0.0137925475 -0.0002403403 
#>        Fair.E        Good.E   Very Good.E     Premium.E       Ideal.E 
#>  0.0250744630  0.0075789572 -0.0089509102 -0.0116591653 -0.0017429526 
#>        Fair.F        Good.F   Very Good.F     Premium.F       Ideal.F 
#>  0.0439661957  0.0058578333  0.0187700133  0.0264426847  0.0243688006 
#>        Fair.G        Good.G   Very Good.G     Premium.G       Ideal.G 
#>  0.0274192215 -0.0334169977  0.0256327220 -0.0031418182 -0.0192370909 
#>        Fair.H        Good.H   Very Good.H     Premium.H       Ideal.H 
#> -0.0837262712 -0.1247398747 -0.0078286322 -0.0030060456  0.0408014981 
#>        Fair.I        Good.I   Very Good.I     Premium.I       Ideal.I 
#>  0.0398190356 -0.2020227152 -0.1033508965 -0.0836557999  0.0077703288 
#>        Fair.J        Good.J   Very Good.J     Premium.J       Ideal.J 
#>  0.0192386809 -0.0709194473 -0.0789113617 -0.0156086436  0.0347510368

Yuck. Nested data frames, which are supported by tibbles, make this much easier to handle:

nested_diamonds =
  diamonds %>%
  select(cut, color, clarity, depth, price) %>%
  nest(data = c(clarity, depth, price))

nested_diamonds
#> # A tibble: 35 x 3
#>    cut       color data                
#>    <ord>     <ord> <list>              
#>  1 Ideal     E     <tibble [3,903 x 3]>
#>  2 Premium   E     <tibble [2,337 x 3]>
#>  3 Good      E     <tibble [933 x 3]>  
#>  4 Premium   I     <tibble [1,428 x 3]>
#>  5 Good      J     <tibble [307 x 3]>  
#>  6 Very Good J     <tibble [678 x 3]>  
#>  7 Very Good I     <tibble [1,204 x 3]>
#>  8 Very Good H     <tibble [1,824 x 3]>
#>  9 Fair      E     <tibble [224 x 3]>  
#> 10 Ideal     J     <tibble [896 x 3]>  
#> # ... with 25 more rows

What are we looking at here? We still have our grouping variables as columns, but each unique combination only takes up one row—and we have another, data, that says it’s a tibble. The column isn’t a vector but a list, and if we look at the elements in it we can see the other columns:

nested_diamonds$data[[1]]
#> # A tibble: 3,903 x 3
#>    clarity depth price
#>    <ord>   <dbl> <int>
#>  1 SI2      61.5   326
#>  2 VVS2     62.9   554
#>  3 SI1      62.5  2757
#>  4 VVS2     62    2761
#>  5 SI2      62.2  2761
#>  6 VS2      60.7  2762
#>  7 SI1      62.3  2762
#>  8 SI1      60.9  2768
#>  9 VS1      61.7  2774
#> 10 SI1      62.7  2774
#> # ... with 3,893 more rows

nested_diamonds$data[[2]]
#> # A tibble: 2,337 x 3
#>    clarity depth price
#>    <ord>   <dbl> <int>
#>  1 SI1      59.8   326
#>  2 SI2      60.2   345
#>  3 I1       60.9   345
#>  4 VS2      61.6   404
#>  5 VVS1     60.7   553
#>  6 SI1      59.9  2760
#>  7 VVS1     60.9  2765
#>  8 VS2      62.7  2776
#>  9 VS2      61.1  2777
#> 10 SI1      60    2777
#> # ... with 2,327 more rows

Not only do we have our group identifiers associated with each data frame now, but we can create other outputs and keep them associated with the groups too:

nested_diamonds %>%
  mutate(
    mean_price = map_dbl(data, ~ mean(.$price)),
    pd_cor =     map_dbl(data, ~ cor(.$price, .$depth)))
#> # A tibble: 35 x 5
#>    cut       color data                 mean_price   pd_cor
#>    <ord>     <ord> <list>                    <dbl>    <dbl>
#>  1 Ideal     E     <tibble [3,903 x 3]>      2598. -0.00174
#>  2 Premium   E     <tibble [2,337 x 3]>      3539. -0.0117 
#>  3 Good      E     <tibble [933 x 3]>        3424.  0.00758
#>  4 Premium   I     <tibble [1,428 x 3]>      5946. -0.0837 
#>  5 Good      J     <tibble [307 x 3]>        4574. -0.0709 
#>  6 Very Good J     <tibble [678 x 3]>        5104. -0.0789 
#>  7 Very Good I     <tibble [1,204 x 3]>      5256. -0.103  
#>  8 Very Good H     <tibble [1,824 x 3]>      4535. -0.00783
#>  9 Fair      E     <tibble [224 x 3]>        3682.  0.0251 
#> 10 Ideal     J     <tibble [896 x 3]>        4918.  0.0348 
#> # ... with 25 more rows

We can even do complex things like build regression models on the groups:

diamonds_models =
  nested_diamonds %>%
  mutate(
    price_mod = map(data, ~ lm(.$price ~ .$depth)),
    price_summary = map(price_mod, summary),
    price_rsq = map_dbl(price_summary, 'r.squared'))

diamonds_models
#> # A tibble: 35 x 6
#>    cut       color data                 price_mod price_summary  price_rsq
#>    <ord>     <ord> <list>               <list>    <list>             <dbl>
#>  1 Ideal     E     <tibble [3,903 x 3]> <lm>      <smmry.lm>    0.00000304
#>  2 Premium   E     <tibble [2,337 x 3]> <lm>      <smmry.lm>    0.000136  
#>  3 Good      E     <tibble [933 x 3]>   <lm>      <smmry.lm>    0.0000574 
#>  4 Premium   I     <tibble [1,428 x 3]> <lm>      <smmry.lm>    0.00700   
#>  5 Good      J     <tibble [307 x 3]>   <lm>      <smmry.lm>    0.00503   
#>  6 Very Good J     <tibble [678 x 3]>   <lm>      <smmry.lm>    0.00623   
#>  7 Very Good I     <tibble [1,204 x 3]> <lm>      <smmry.lm>    0.0107    
#>  8 Very Good H     <tibble [1,824 x 3]> <lm>      <smmry.lm>    0.0000613 
#>  9 Fair      E     <tibble [224 x 3]>   <lm>      <smmry.lm>    0.000629  
#> 10 Ideal     J     <tibble [896 x 3]>   <lm>      <smmry.lm>    0.00121   
#> # ... with 25 more rows

(These are pretty lousy models, but they’ll do for our purposes!)

Nesting and handling side effects with purrr

We can carry on like this, adding analyses to the groups, but it’s also a pretty reckless way to operate. There could be anything going on in these list columns, and without being able to see them from the outside it’d be easy to miss a problem that turns up. In addition, we might hit an error and be unsure which group is causing it.

For example, if we remove all of the rows in one of the data groups, we’ll get this error:

nested_diamonds$data[[5]] = nested_diamonds$data[[5]] %>% filter(price < 300)

diamonds_models =
  nested_diamonds %>%
  mutate(
    price_mod = map(data, ~ lm(.$price ~ .$depth)),
    price_summary = map(price_mod, summary),
    price_rsq = map_dbl(price_summary, 'r.squared'))

diamonds_models
#> Error in mutate_impl(.data, dots) : Evaluation error: 0 (non-NA) cases.

We can see that there’s a problem here, but we have no idea which group is causing it without inspecting them (at least, we wouldn’t if we hadn’t just set this up!).

purrr tries to tackle this with two functions: safely() and quietly(). The former catches errors; the latter catches warnings, messages and other output. You use it like this:

safe_lm = safely(lm)
safe_summary = safely(summary)

purrr_models =
  nested_diamonds %>%
  mutate(price_mod = map(data, ~ safe_lm(.$price ~ .$depth)))

purrr_models
#> # A tibble: 35 x 4
#>    cut       color data                 price_mod       
#>    <ord>     <ord> <list>               <list>          
#>  1 Ideal     E     <tibble [3,903 x 3]> <named list [2]>
#>  2 Premium   E     <tibble [2,337 x 3]> <named list [2]>
#>  3 Good      E     <tibble [933 x 3]>   <named list [2]>
#>  4 Premium   I     <tibble [1,428 x 3]> <named list [2]>
#>  5 Good      J     <tibble [307 x 3]>   <named list [2]>
#>  6 Very Good J     <tibble [678 x 3]>   <named list [2]>
#>  7 Very Good I     <tibble [1,204 x 3]> <named list [2]>
#>  8 Very Good H     <tibble [1,824 x 3]> <named list [2]>
#>  9 Fair      E     <tibble [224 x 3]>   <named list [2]>
#> 10 Ideal     J     <tibble [896 x 3]>   <named list [2]>
#> # ... with 25 more rows

This is great! We bulldozed our way through the error, so we still have the other 34 models! In lieu of a list of model objects, price_mod is now a list of lists: each element of the column is a list with two elements: $result and $error (quietly() returns four components).

purrr_models$price_mod[[1]]
#> $result
#> 
#> Call:
#> .f(formula = ..1)
#> 
#> Coefficients:
#> (Intercept)      .$depth  
#>    3046.968       -7.285  
#> 
#> 
#> $error
#> NULL

purrr_models$price_mod[[5]]
#> $result
#> 
#> Call:
#> .f(formula = ..1)
#> 
#> Coefficients:
#> (Intercept)      .$depth  
#>       12310         -124  
#> 
#> 
#> $error
#> NULL

purrr_models %>% mutate(mod_result = map(price_mod, 'result'))
#> # A tibble: 35 x 5
#>    cut       color data                 price_mod        mod_result
#>    <ord>     <ord> <list>               <list>           <list>    
#>  1 Ideal     E     <tibble [3,903 x 3]> <named list [2]> <lm>      
#>  2 Premium   E     <tibble [2,337 x 3]> <named list [2]> <lm>      
#>  3 Good      E     <tibble [933 x 3]>   <named list [2]> <lm>      
#>  4 Premium   I     <tibble [1,428 x 3]> <named list [2]> <lm>      
#>  5 Good      J     <tibble [307 x 3]>   <named list [2]> <lm>      
#>  6 Very Good J     <tibble [678 x 3]>   <named list [2]> <lm>      
#>  7 Very Good I     <tibble [1,204 x 3]> <named list [2]> <lm>      
#>  8 Very Good H     <tibble [1,824 x 3]> <named list [2]> <lm>      
#>  9 Fair      E     <tibble [224 x 3]>   <named list [2]> <lm>      
#> 10 Ideal     J     <tibble [896 x 3]>   <named list [2]> <lm>      
#> # ... with 25 more rows

We can now extract the result element using map(), and there’s a NULL value for the group that failed. But there are still some big problems here:

  1. We have to remember to wrap each function in safely() or quietly() ahead of time; and
  2. We have to check each element of the list column, or carefully extract the results, to locate the problem.

Collateral: capture, identify and isolate side effects

The idea of collateral is to both make these functions easier to use and to make them more powerful. Instead of wrapping our functions ourselves, we use map() variants:

library(collateral)

nested_diamonds$data[[5]] = nested_diamonds$data[[5]] %>% filter(price < 300)

collat_models =
  nested_diamonds %>%
  mutate(price_mod = map_safely(data, ~ lm(.x$price ~ .x$depth)))

print(collat_models)
#> # A tibble: 35 x 4
#>    cut       color data                 price_mod
#>    <ord>     <ord> <list>               <collat> 
#>  1 Ideal     E     <tibble [3,903 x 3]> R _      
#>  2 Premium   E     <tibble [2,337 x 3]> R _      
#>  3 Good      E     <tibble [933 x 3]>   R _      
#>  4 Premium   I     <tibble [1,428 x 3]> R _      
#>  5 Good      J     <tibble [0 x 3]>     _ E      
#>  6 Very Good J     <tibble [678 x 3]>   R _      
#>  7 Very Good I     <tibble [1,204 x 3]> R _      
#>  8 Very Good H     <tibble [1,824 x 3]> R _      
#>  9 Fair      E     <tibble [224 x 3]>   R _      
#> 10 Ideal     J     <tibble [896 x 3]>   R _      
#> # ... with 25 more rows

Now price_mod is a list of class collat, and scanning down it we can see which group hit an error immediately, all rows but the fifth are labelled R, for result, but the fifth is labelled E, for error.

If you’re working in a terminal that supports colour, this stands out even more:

collat_models output, as seen in a terminal with colour

This collateral list-column is actually completely identical to the manually wrapped-and-mapped solution from purrr, other than the class name. The fancy tibble printing is totally separate from the content of the column and is handled using the pillar package (which provides extensions for tibble output), so if you’re already familiar with the purrr workflow you get immediate benefits.

You can, of course, pull out the results you would’ve gotten from a regular map (assuming you didn’t hit any errors), and you’ll probably want to do that in order to continue operating on those results. You can also extract other side effects, although keep in mind that:

  1. The regular typed map variants, like map_chr, are excellent for extracting side effects quickly.
  2. Unlike other kinds of output, errors aren’t just simple character vectors. Generally, you’ll want the error$message.
  3. Sometimes an operation might deliver more than one warning, message or output per row. That means that you may need to concatenate several warnings together—using the paste function with the collapse option, for example.
collat_models %>%
  mutate(
    # this returns a list of `lm` objects
    mod_result = map(price_mod, 'result'),
    # this returns a character vector
    mod_error = map_chr(price_mod, ~ paste(.x$error$message, collapse = ' ')))
#> # A tibble: 35 x 6
#>    cut       color data                 price_mod mod_result mod_error         
#>    <ord>     <ord> <list>               <collat>  <list>     <chr>             
#>  1 Ideal     E     <tibble [3,903 x 3]> R _       <lm>       ""                
#>  2 Premium   E     <tibble [2,337 x 3]> R _       <lm>       ""                
#>  3 Good      E     <tibble [933 x 3]>   R _       <lm>       ""                
#>  4 Premium   I     <tibble [1,428 x 3]> R _       <lm>       ""                
#>  5 Good      J     <tibble [0 x 3]>     _ E       <NULL>     "0 (non-NA) cases"
#>  6 Very Good J     <tibble [678 x 3]>   R _       <lm>       ""                
#>  7 Very Good I     <tibble [1,204 x 3]> R _       <lm>       ""                
#>  8 Very Good H     <tibble [1,824 x 3]> R _       <lm>       ""                
#>  9 Fair      E     <tibble [224 x 3]>   R _       <lm>       ""                
#> 10 Ideal     J     <tibble [896 x 3]>   R _       <lm>       ""                
#> # ... with 25 more rows

Filtering on, and summarising, side effects

collateral also comes with some additional helpers. You can get a quick summary of the side effects (which also invisibly returns a named vector for non-interactive use):

summary(collat_models$price_mod)
#> 35 elements in total.
#> 34 elements returned results, and
#> 1 element encountered errors.

You can also use the tally_*() functions in conjunction with dplyr::summarise() to get a count of each component (even if the top-level data frame grouped again!) or has_*() to dplyr::filter() out the rows that didn’t make it (or the ones that did):

collat_models %>%
  group_by(color) %>%
  summarise(
    n_res = tally_results(price_mod),
    n_err = tally_errors(price_mod))
#> # A tibble: 7 x 3
#>   color n_res n_err
#>   <ord> <int> <int>
#> 1 D         5     0
#> 2 E         5     0
#> 3 F         5     0
#> 4 G         5     0
#> 5 H         5     0
#> 6 I         5     0
#> 7 J         4     1

collat_models %>%
  filter(has_errors(price_mod))
#> # A tibble: 1 x 4
#>   cut   color data             price_mod
#>   <ord> <ord> <list>           <collat> 
#> 1 Good  J     <tibble [0 x 3]> _ E

collat_models %>%
  filter(!has_results(price_mod))
#> # A tibble: 1 x 4
#>   cut   color data             price_mod
#>   <ord> <ord> <list>           <collat> 
#> 1 Good  J     <tibble [0 x 3]> _ E

Together, these tools make debugging problems drastically easier, even if you need to run a thousand statistical models or analyse 500 datasets.

Further tools

Occasionally, you may find that an operation could return either errors, warnings or both. For example, log outputs a warning when it’s given negative numbers but throws an error if its input is non-numeric. Regression functions are particularly prone to this.

For these purposes, use map_peacefully, which works in the same way as map_safely and map_quietly but returns the side effects from both. If you aren’t sure which collateral mapper to reach for, start with this one!