Scenario: we got a table of id-value, and a matrix/tibble that contains the id, and we need the labels.

It may be useful when predicting the Key (or Ids) of in a classification model (like in Keras), and we need the labels as the final output.

There are two interesting things:

• The usage of apply based on column and rows at the same time.
• The creation of an empty tibble and how to fill it (append columns)

How to apply a function to a matrix/tibble

Scenario: we got a table of id-value, and a matrix/tibble that contains the id, and we need the labels.

It may be useful when predicting the Key (or Ids) in a classification model (like in Keras), and we need the labels as the final output.

There are two interesting things:

• The usage of apply based on column and rows at the same time.
• The creation of an empty tibble and how to fill it (append columns)

library(tidyverse)
# mapping table (id-value)
map_table=tibble(id=c(1,2,3), 
                 value=c("a", "b", "c")
                 )

map_table

## # A tibble: 3 x 2
##      id value
##   <dbl> <chr>
## 1     1 a    
## 2     2 b    
## 3     3 c

# given a key, retrun the label
get_label <- function(x) 
{
  res=filter(map_table, id==x)$value
  return(res)
}

# the data to get the label
X_data=tibble(v1=c(1,2,3), 
              v2=c(2,2,2),
              v3=c(3,2,1)
              )

X_data

## # A tibble: 3 x 3
##      v1    v2    v3
##   <dbl> <dbl> <dbl>
## 1     1     2     3
## 2     2     2     2
## 3     3     2     1

Option 1: as matrix

mat_res=apply(X_data, 1:2, get_label)

## Checking...
mat_res

##      v1  v2  v3 
## [1,] "a" "b" "c"
## [2,] "b" "b" "b"
## [3,] "c" "b" "a"

Option 2: as tibble (using 'for')

# creating a 1 column with NAs same length as nrow(X_data)
tib_res=tibble(V1=rep(NA, nrow(X_data))) 
for(i in 1:ncol(X_data))
{
  vec=X_data[,i]
  vec_lbl=sapply(t(vec), get_label) # if X_data is a matrid, no need to transpose with t()
  tib_res[,i]=vec_lbl
}

## Checking...
tib_res

## # A tibble: 3 x 3
##   V1    V2    V3   
##   <chr> <chr> <chr>
## 1 a     b     c    
## 2 b     b     b    
## 3 c     b     a

Option 3: as tibble (using 'mutate_all')

tib_res_2=mutate_all(X_data, .funs = get_label)
tib_res_2

## # A tibble: 3 x 3
##   v1    v2    v3   
##   <chr> <chr> <chr>
## 1 a     b     b    
## 2 b     b     b    
## 3 c     b     b

Finally...

Option 2, to my surprise, is faster than the option 1.
I didn't use the add_column because of the need of replacing the first dummy NA column.
Other approaches may include dictionaries.

Any improvement in the code is welcome.

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Thanks for reading 🚀

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