How to recode a value in a data frame based on two columns in a separate data frame?

Question

The problem I have is this. I have a data frame containing four columns, named CSES. I have a separate data frame named meta, containing two columns, code and name. If a value in any of the CSES columns match meta$code I want to replace it with the value in meta$name.

Currently my solution uses for loops and is fine; but is there a better way to do this that avoids the loops, preferably using the Tidyverse? Seems like a pretty standard question that should have a answer already but I didn't find any. I am thankful for any help. Cheers.

for(i in seq_along(meta$code)) {
  code <- meta$code[[i]]
  for(k in seq_along(CSES)) {
    col <- CSES[[k]]
    col[col == code] <- meta$name[[i]]
    CSES[[k]] <- col
  }
}

EDIT: I added input data and desired output below and clarification of desired output per request.

To clarify, it is desired that if the meta$code column matches a value in CSES the value in CSES should be changed to the meta$name value on the same row as the matching meta$code.

CSES, this is the data I want to recode:

# A tibble: 274,719 x 6
#   IMD5000_A IMD5000_B IMD5000_C IMD5000_D IMD5000_E IMD5000_F
#   <chr>     <chr>     <chr>     <chr>     <chr>     <chr>    
# 1 320001    320002    320003    320004    320005    320006   
# 2 320001    320002    320003    320004    320005    320006   
# 3 320001    320002    320003    320004    320005    320006   
# 4 320001    320002    320003    320004    320005    320006   
# 5 320001    320002    320003    320004    320005    320006   
# 6 320001    320002    320003    320004    320005    320006   
# 7 320001    320002    320003    320004    320005    320006   
# 8 320001    320002    320003    320004    320005    320006   
# 9 320001    320002    320003    320004    320005    320006   
# 10 320001    320002    320003    320004    320005    320006   
# … with 274,709 more rows

Meta:

# A tibble: 44 x 2
#   code    name 
#   <chr>   <chr>
# 1 7520001 SAP  
# 2 7520002 M    
# 3 7520003 FP   
# 4 7520004 KD   
# 5 7520005 MP   
# 6 7520006 C    
# 7 7520007 V    
# 8 7520008 SD   
# 9 7520009 Fi   
#10 7520010 Jl   
# … with 34 more rows

For example the value 7520001 in CSES should be changed to "SAP".

Answer 1

Example data:

set.seed(10)
CSES <- as.data.frame(matrix(sample(letters[1:10], 40, T), 10),
                      stringsAsFactors = F)
meta <- data.frame(code = letters[1:3], name = c('peach', 'pear', 'apple'),
                   stringsAsFactors = F)

CSES
#    V1 V2 V3 V4
# 1   f  g  i  f
# 2   d  f  g  a
# 3   e  b  h  b
# 4   g  f  d  i
# 5   a  d  e  e
# 6   c  e  h  h
# 7   c  a  i  i
# 8   c  c  c  j
# 9   g  d  h  g
# 10  e  i  d  f
meta
#   code  name
# 1    a peach
# 2    b  pear
# 3    c apple

Now update each column of CSES with the name in meta corresponding to the given code

base R

repl <- matrix(meta$name[match(as.matrix(CSES), meta$code)], nrow(CSES))

CSES[!is.na(repl)] <- repl[!is.na(repl)]

CSES
#       V1    V2    V3    V4
# 1      f     g     i     f
# 2      d     f     g peach
# 3      e  pear     h  pear
# 4      g     f     d     i
# 5  peach     d     e     e
# 6  apple     e     h     h
# 7  apple peach     i     i
# 8  apple apple apple     j
# 9      g     d     h     g
# 10     e     i     d     f

dplyr

library(dplyr)

CSES %>% 
  mutate_all(~ coalesce(meta$name[match(.x, meta$code)], .x))
#       V1    V2    V3    V4
# 1      f     g     i     f
# 2      d     f     g peach
# 3      e  pear     h  pear
# 4      g     f     d     i
# 5  peach     d     e     e
# 6  apple     e     h     h
# 7  apple peach     i     i
# 8  apple apple apple     j
# 9      g     d     h     g
# 10     e     i     d     f

data.table

library(data.table)
setDT(CSES)
setDT(meta)

for(n in names(CSES))
  CSES[meta, on = setNames('code', n), (n) := i.name]

CSES[]
#       V1    V2    V3    V4
#  1:     f     g     i     f
#  2:     d     f     g peach
#  3:     e  pear     h  pear
#  4:     g     f     d     i
#  5: peach     d     e     e
#  6: apple     e     h     h
#  7: apple peach     i     i
#  8: apple apple apple     j
#  9:     g     d     h     g
# 10:     e     i     d     f

Answer 2

Or in base R you could do this with sapply or, perhaps more efficiently, by resetting levels if your data frame is converted to factors.

meta = data.frame(code=c('a','c'), name=c('A', 'C'), stringsAsFactors = F)
CSES = data.frame(code = c('a', 'b', 'c', 'd'), c2 = c(1,2,3,4), stringsAsFactors = F)
print("---before----")
print(CSES)
f <- CSES$code %in% meta$code
CSES$code[f] <- sapply(CSES$code[f], function(x) meta$name[which(x==meta$code)])
print("---after using factors and levels----")
print(CSES)

With output [1] "---b

efore----"
  code c2
1    a  1
2    b  2
3    c  3
4    d  4
[1] "---after using factors and levels----"
  code c2
1    A  1
2    b  2
3    C  3
4    d  4

Using Factors looks like this

meta2 = data.frame(code=c('a','c'), name=c('A', 'C'))
CSES2 = data.frame(code = c('a', 'b', 'c', 'd'), c2 = c(1,2,3,4))
frepl <- function(s){
  if (s %in% meta2$code){levels(meta2$name)[meta2$code == s]} else {s}
}
levels(CSES2$code) <- sapply(levels(CSES2$code), function(s) frepl(s))
print("---after using factors and levels----")
print(CSES2)

with output

[1] "---after using factors and levels----"
  code c2
1    A  1
2    b  2
3    C  3
4    d  4