# Applying Functions

## Apply Functions to matrixset Matrices

There are two ways to apply functions to the matrices of a matrixset object. The first one is through the apply_* family, which will be covered here.

The second is through mutate_matrix(), covered in the next section.

There are 3 functions in the apply_* family:

• apply_matrix(): The functions must take a matrix as input. In base R, this is similar to simply calling fun(matrix_object).
• apply_row(): The functions must take a vector as input. The vector will be a matrix row. In base R, this is akin to apply(matrix_object, 1, fun, simplify = FALSE).
• apply_column(): The functions must take a vector as input. The vector will be a matrix column. In base R, this is similar to apply(matrix_object, 2, fun, simplify = FALSE).

Each of these function will loop on the matrixset object’s matrices to apply the functions. In the case of apply_row() and apply_column(), an additional loop on the margin (row or column, as applicable) is executed, so that the functions are applied to each matrix and margin.

To see the functions in action, we will use the following object:

animals_ms
#> matrixset of 2 28 × 2 matrices
#>
#> matrix_set: msr
#> A 28 × 2 <dbl> matrix
#>                   body  brain
#> Mountain beaver   1.35   8.10
#>             ...    ...    ...
#>             Pig 192.00 180.00
#>
#> matrix_set: log_msr
#> A 28 × 2 <dbl> matrix
#>                 body brain
#> Mountain beaver 0.30  2.09
#>             ...  ...   ...
#>             Pig 5.26  5.19
#>
#>
#> row_info:
#> # A tibble: 28 × 3
#>    .rowname        is_extinct class
#>    <chr>           <lgl>      <chr>
#>  1 Mountain beaver FALSE      Rodent
#>  2 Cow             FALSE      Ruminant
#>  3 Grey wolf       FALSE      Canine
#>  4 Goat            FALSE      Ruminant
#>  5 Guinea pig      FALSE      Rodent
#>  6 Dipliodocus     TRUE       Dinosaurs
#>  7 Asian elephant  FALSE      Elephantidae
#>  8 Donkey          FALSE      Equidae
#>  9 Horse           FALSE      Equidae
#> 10 Potar monkey    FALSE      Primate
#> # ℹ 18 more rows
#>
#>
#> column_info:
#> # A tibble: 2 × 2
#>   .colname unit
#>   <chr>    <chr>
#> 1 body     kg
#> 2 brain    g

We will use the following custom printing functions for compactness purposes.

show_matrix <- function(x) {
if (nrow(x) > 4) {
storage.mode(newx) <- "character"
newx <- rbind(newx, rep("...", ncol(x)))
}  else newx <- x
newx
}
show_vector <- function(x) {
newx <- if (length(x) > 4) {
c(as.character(x[1:4]), "...")
} else x
newx
}
show_lst <- function(x) {
lapply(x, function(u) {
if (is.matrix(u)) show_matrix(u) else if (is.vector(u)) show_vector(u) else u
})
}

So now, let’s see the apply_matrix() in action.

library(magrittr)
library(purrr)
out <- animals_ms %>%
apply_matrix(exp,
mean(.m, trim=.1),
foo=asinh,
pow = 2^.m,
reg = ~ {
is_alive <- !is_extinct
lm(.m ~ is_alive + class)
})
# out[[1]] %>% map(~ if (is.matrix(.x)) {head(.x, 5)} else .x)
show_lst(out[[1]])
#> $exp #> body brain #> Mountain beaver "3.85742553069697" "3294.46807528384" #> Cow "8.84981281719581e+201" "5.08821958272978e+183" #> Grey wolf "5996785676464821" "7.91025688556692e+51" #> Goat "1029402857448.45" "8.78750163583702e+49" #> "..." "..." #> #>$mean(.m, trim = 0.1)
#> [1] 335.1291
#>
#> $foo #> body brain #> Mountain beaver "1.10857244179685" "2.78880004092018" #> Cow "6.83518574234833" "6.74052075680554" #> Grey wolf "4.28598038575143" "5.47648105816811" #> Goat "4.01346111184316" "5.43809821197888" #> "..." "..." #> #>$pow
#>                 body                    brain
#> Mountain beaver "2.54912125463852"      "274.374006409291"
#> Cow             "9.52682052708738e+139" "2.16614819853189e+127"
#> Grey wolf       "86381301347.2935"      "9.39906129562518e+35"
#> Goat            "212075099.808884"      "4.15383748682786e+34"
#>                 "..."                   "..."
#>
#> $reg #> #> Call: #> lm(formula = .m ~ is_alive + class) #> #> Coefficients: #> body brain #> (Intercept) 36033.33 91.50 #> is_aliveTRUE -35997.00 28.00 #> classDinosaurs NA NA #> classElephantidae 4564.17 5038.00 #> classEquidae 317.72 417.50 #> classFeline 15.32 -28.20 #> classMacropodidae -1.33 -63.50 #> classPrimate 31.26 372.50 #> classRodent -35.44 -114.67 #> classRuminant 232.96 228.75 #> classSus 155.67 60.50 #> classTalpidae -36.21 -116.50 We have showcased several features of the apply_* functions: • Many functions can be supplied at once. • Functions can be supplied by bare function name. Note that this mean that only the current matrix will be used as input. You can also supply the function call or even, when submitting a one-sided formula, complex expressions. • The name of the function result can be controlled. • Object traits are accessible by bare name. The condition for this to work is that traits are unique across row and column annotation. You probably have noticed the use of .m. This is a pronoun that is accessible inside apply_matrix() and refers to the current matrix in the internal loop. Similar pronouns exists for apply_row() and apply_column(), and they are respecticely .i and .j. The returned object is a list of lists. The first layer is for each matrix and the second layer is for each function call. Let’s now showcase the row/column version with a apply_column() example: out <- animals_ms %>% apply_column(exp, mean(.j, trim=.1), foo=asinh, pow = 2^.j, reg = ~ { is_alive <- !is_extinct lm(.j ~ is_alive + class) }) out[[1]] %>% map(show_lst) #>$body
#> $body$exp
#> [1] "3.85742553069697"      "8.84981281719581e+201" "5996785676464821"
#> [4] "1029402857448.45"      "..."
#>
#> $body$mean(.j, trim = 0.1)
#> [1] 879.0059
#>
#> $body$foo
#> [1] "1.10857244179685" "6.83518574234833" "4.28598038575143" "4.01346111184316"
#> [5] "..."
#>
#> $body$pow
#> [1] "2.54912125463852"      "9.52682052708738e+139" "86381301347.2935"
#> [4] "212075099.808884"      "..."
#>
#> $body$reg
#>
#> Call:
#> lm(formula = .j ~ is_alive + class)
#>
#> Coefficients:
#>       (Intercept)       is_aliveTRUE     classDinosaurs  classElephantidae
#>          36033.33          -35997.00                 NA            4564.17
#>      classEquidae        classFeline  classMacropodidae       classPrimate
#>            317.72              15.32              -1.33              31.26
#>       classRodent      classRuminant           classSus      classTalpidae
#>            -35.44             232.96             155.67             -36.21
#>
#>
#>
#> $brain #>$brain$exp #> [1] "3294.46807528384" "5.08821958272978e+183" "7.91025688556692e+51" #> [4] "8.78750163583702e+49" "..." #> #>$brain$mean(.j, trim = 0.1) #> [1] 240.425 #> #>$brain$foo #> [1] "2.78880004092018" "6.74052075680554" "5.47648105816811" "5.43809821197888" #> [5] "..." #> #>$brain$pow #> [1] "274.374006409291" "2.16614819853189e+127" "9.39906129562518e+35" #> [4] "4.15383748682786e+34" "..." #> #>$brain$reg #> #> Call: #> lm(formula = .j ~ is_alive + class) #> #> Coefficients: #> (Intercept) is_aliveTRUE classDinosaurs classElephantidae #> 91.5 28.0 NA 5038.0 #> classEquidae classFeline classMacropodidae classPrimate #> 417.5 -28.2 -63.5 372.5 #> classRodent classRuminant classSus classTalpidae #> -114.7 228.7 60.5 -116.5 The idea is similar, but in the returned object, there is a third list layer: the first layer for the matrices, the second layer for the columns (it would be rows for apply_row()) and the third layer for the functions. Note as well the use of the .j pronoun instead of .m. ### Grouped Data The apply_* functions understand data grouping and will execute on the proper matrix/vector subsets. animals_ms %>% row_group_by(class) %>% apply_matrix(exp, mean(.m, trim=.1), foo=asinh, pow = 2^.m, reg = ~ { is_alive <- !is_extinct lm(.m ~ is_alive) }) #>$msr
#> # A tibble: 11 × 2
#>    class        .vals
#>    <chr>        <list>
#>  1 Canine       <named list [5]>
#>  2 Dinosaurs    <named list [5]>
#>  3 Elephantidae <named list [5]>
#>  4 Equidae      <named list [5]>
#>  5 Feline       <named list [5]>
#>  6 Macropodidae <named list [5]>
#>  7 Primate      <named list [5]>
#>  8 Rodent       <named list [5]>
#>  9 Ruminant     <named list [5]>
#> 10 Sus          <named list [5]>
#> 11 Talpidae     <named list [5]>
#>
#> $log_msr #> # A tibble: 11 × 2 #> class .vals #> <chr> <list> #> 1 Canine <named list [5]> #> 2 Dinosaurs <named list [5]> #> 3 Elephantidae <named list [5]> #> 4 Equidae <named list [5]> #> 5 Feline <named list [5]> #> 6 Macropodidae <named list [5]> #> 7 Primate <named list [5]> #> 8 Rodent <named list [5]> #> 9 Ruminant <named list [5]> #> 10 Sus <named list [5]> #> 11 Talpidae <named list [5]> As one can see, the output format differs in situation of grouping. We still end up with a list with an element for each matrix, but each of these element is now a tibble. Each tibble has a column called .vals, where the function results are stored. This column is a list, one element per group. The group labels are given by the other columns of the tibble. For a given group, things are like the ungrouped version: further sub-lists for rows/columns - if applicable - and function values. ### Simplified Results Similar to the apply() function that has a simplify argument, the output structured can be simplified, baring two conditions: • Each function returns a vector, where a vector is every object for which is.vector returns TRUE. • Each vector must be of the same length $$\geq$$ 1. If the conditions are met, each apply_* function has two simplified version available: _dfl and dfw. Below is the _dfl flavor in action. We point out two things to notice: • For apply_column_dfl (and _dfw), a .column column stores the column ID (.row for apply_row_*). • We wrapped the lm result in a list so that the outcome is vector. animals_ms %>% apply_matrix_dfl(mean(.m, trim=.1), MAD=mad, reg = ~ { is_alive <- !is_extinct list(lm(.m ~ is_alive + class)) }) #>$msr
#> # A tibble: 1 × 3
#>   mean(.m, trim = 0.1)   MAD reg
#>                    <dbl> <dbl> <list>
#> 1                   335.  155. <mlm>
#>
#> $log_msr #> # A tibble: 1 × 3 #> mean(.m, trim = 0.1) MAD reg #> <dbl> <dbl> <list> #> 1 4.18 2.35 <mlm> animals_ms %>% apply_column_dfl(mean(.j, trim=.1), MAD=mad, reg = ~ { is_alive <- !is_extinct list(lm(.j ~ is_alive + class)) }) #>$msr
#> # A tibble: 2 × 4
#>   .colname mean(.j, trim = 0.1)   MAD reg
#>   <chr>                     <dbl> <dbl> <list>
#> 1 body                       879.  79.5 <lm>
#> 2 brain                      240. 193.  <lm>
#>
#> $log_msr #> # A tibble: 2 × 4 #> .colname mean(.j, trim = 0.1) MAD reg #> <chr> <dbl> <dbl> <list> #> 1 body 3.78 3.38 <lm> #> 2 brain 4.49 1.71 <lm> If using apply_column_dfw in this context, you wouldn’t notice a difference in output format. The difference between the two lies when the vectors are of length > 1. animals_ms %>% apply_row_dfl(rg = range(.i), qt = quantile(.i, probs = c(.25, .75))) #>$msr
#> # A tibble: 56 × 5
#>    .rowname        rg.name     rg qt.name     qt
#>    <chr>           <chr>    <dbl> <chr>    <dbl>
#>  1 Mountain beaver ..1       1.35 25%       3.04
#>  2 Mountain beaver ..2       8.1  75%       6.41
#>  3 Cow             ..1     423    25%     434.
#>  4 Cow             ..2     465    75%     454.
#>  5 Grey wolf       ..1      36.3  25%      57.1
#>  6 Grey wolf       ..2     120.   75%      98.7
#>  7 Goat            ..1      27.7  25%      49.5
#>  8 Goat            ..2     115    75%      93.2
#>  9 Guinea pig      ..1       1.04 25%       2.16
#> 10 Guinea pig      ..2       5.5  75%       4.38
#> # ℹ 46 more rows
#>
#> $log_msr #> # A tibble: 56 × 5 #> .rowname rg.name rg qt.name qt #> <chr> <chr> <dbl> <chr> <dbl> #> 1 Mountain beaver ..1 0.300 25% 0.748 #> 2 Mountain beaver ..2 2.09 75% 1.64 #> 3 Cow ..1 6.05 25% 6.07 #> 4 Cow ..2 6.14 75% 6.12 #> 5 Grey wolf ..1 3.59 25% 3.89 #> 6 Grey wolf ..2 4.78 75% 4.49 #> 7 Goat ..1 3.32 25% 3.68 #> 8 Goat ..2 4.74 75% 4.39 #> 9 Guinea pig ..1 0.0392 25% 0.456 #> 10 Guinea pig ..2 1.70 75% 1.29 #> # ℹ 46 more rows animals_ms %>% apply_row_dfw(rg = range(.i), qt = quantile(.i, probs = c(.25, .75))) #>$msr
#> # A tibble: 28 × 5
#>    .rowname        rg ..1 rg ..2 qt 25% qt 75%
#>    <chr>              <dbl>    <dbl>    <dbl>    <dbl>
#>  1 Mountain beaver     1.35      8.1     3.04     6.41
#>  2 Cow               423       465     434.     454.
#>  3 Grey wolf          36.3     120.     57.1     98.7
#>  4 Goat               27.7     115      49.5     93.2
#>  5 Guinea pig          1.04      5.5     2.16     4.38
#>  6 Dipliodocus        50     11700    2962.    8788.
#>  7 Asian elephant   2547      4603    3061     4089
#>  8 Donkey            187.      419     245.     361.
#>  9 Horse             521       655     554.     622.
#> 10 Potar monkey       10       115      36.2     88.8
#> # ℹ 18 more rows
#>
#> $log_msr #> # A tibble: 28 × 5 #> .rowname rg ..1 rg ..2 qt 25% qt 75% #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Mountain beaver 0.300 2.09 0.748 1.64 #> 2 Cow 6.05 6.14 6.07 6.12 #> 3 Grey wolf 3.59 4.78 3.89 4.49 #> 4 Goat 3.32 4.74 3.68 4.39 #> 5 Guinea pig 0.0392 1.70 0.456 1.29 #> 6 Dipliodocus 3.91 9.37 5.28 8.00 #> 7 Asian elephant 7.84 8.43 7.99 8.29 #> 8 Donkey 5.23 6.04 5.43 5.84 #> 9 Horse 6.26 6.48 6.31 6.43 #> 10 Potar monkey 2.30 4.74 2.91 4.13 #> # ℹ 18 more rows We can observe three things: 1. dfl stands for long and stacks the elements of the function output into different rows, adding a column to identify the different elements. 2. dfw stands for wide and put the elements of the function output into different columns. 3. Element names are made unique if necessary. ### Knowing the current context It may happen that you need to get information about the current group. For this reason, the following context functions are made available: • current_n_row() and current_n_column(). They each give the number of rows and columns, respectively, of the current matrix. They are the context equivalent of nrow() and ncol(). • current_row_info() and current_column_info(). They give access to the current row/column annotation data frame. The are the context equivlent of row_info() and column_info(). • row_pos() and column_pos(). They give the current row/column indices. The indices are the the ones before matrix subsetting. • row_rel_pos() and column_rel_pos(). They give the row/column indices relative to the current matrix. They are equivalent to seq_len(current_n_row())/seq_len(current_n_column()). For instance, a simple way of knowing the number of animals per group could be animals_ms %>% row_group_by(class) %>% apply_matrix_dfl(n=current_n_row()) %>% .$msr
#> # A tibble: 11 × 2
#>    class            n
#>    <chr>        <int>
#>  1 Canine           1
#>  2 Dinosaurs        3
#>  3 Elephantidae     2
#>  4 Equidae          2
#>  5 Feline           2
#>  6 Macropodidae     1
#>  7 Primate          5
#>  8 Rodent           6
#>  9 Ruminant         4
#> 10 Sus              1
#> 11 Talpidae         1

### With common row and column annotation trait

The context functions can also be of use when one or more traits are shared (in name) between rows and columns.

Here’s a pseudo-code example:

# ms_object %>%
#     apply_matrix( ~ {
#       ctrt <- current_column_info()$common_trait # rtrt <- current_row_info()$common_trait
#
#       do something with ctrt and rtrt
#     })

### Pronouns, or dealing with ambiguous variables

It may happen that a variable in the calling environment shares its name with a trait of a matrixset object.

You can make it explicit which version of the variable you are using the pronouns .data (the trait annotation version) and .env.

## Quasi quotation

reg_expr <- expr({
is_alive <- !is_extinct
list(lm(.j ~ is_alive + class))
})

animals_ms %>%
apply_column_dfl(mean(.j, trim=.1),
#> $msr #> # A tibble: 2 × 4 #> .colname mean(.j, trim = 0.1) MAD reg #> <chr> <dbl> <dbl> <list> #> 1 body 879. 79.5 <lm> #> 2 brain 240. 193. <lm> #> #>$log_msr
#>   .colname mean(.j, trim = 0.1)   MAD reg
#> 2 brain                      4.49  1.71 <lm>