# How to apply a function to a matrix/tibble

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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)

library(tibble) # 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 #### 1 1 a ## 2 2 b ## 3 3 c # given a key, retrun the label get_label <- function(x) { id_flag=map_table$id==x res=as.character(map_table[id_flag, 2]) 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 ## ## 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

# 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 #### 1 a b c ## 2 b b b ## 3 c b a

Option 2, to my surprise, is faster.

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.

Thanks for reading ????

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