ETA: the point of the below, by the way, is to not have to iterate through my entire set of column vectors, just in case that was a proposed solution (just
you want to search through the whole data frame for any value that matches the value you're trying to replace. the same way you can run a logical test like replacing all missing values with 10..
data[ is.na( data ) ] <- 10
you can also replace all 4s with 10s.
data[ data == 4 ] <- 10
at least i think that's what you're after?
and let's say you wanted to ignore the first row (since it's all letters)
# identify which columns contain the values you might want to replace
data[ , 2:3 ]
# subset it with extended bracketing..
data[ , 2:3 ][ data[ , 2:3 ] == 4 ]
# ..those were the values you're going to replace
# now overwrite 'em with tens
data[ , 2:3 ][ data[ , 2:3 ] == 4 ] <- 10
# look at the final data
data
Just to provide a different answer, I thought I would write up a vector-math approach:
You can create a transformation matrix (really a data frame here, but will work the same), using a the vectorized 'ifelse' statement and multiply the transformation matrix and your original data, like so:
df.Rep <- function(.data_Frame, .search_Columns, .search_Value, .sub_Value){
.data_Frame[, .search_Columns] <- ifelse(.data_Frame[, .search_Columns]==.search_Value,.sub_Value/.search_Value,1) * .data_Frame[, .search_Columns]
return(.data_Frame)
}
To replace all values 4 with 10 in the data frame 'data' in columns 2 through 3, you would use the function like so:
# Either of these will work. I'm just showing options.
df.Rep(data, 2:3, 4, 10)
df.Rep(data, c("var1","var2"), 4, 10)
# name var1 var2
# 1 a 1 3
# 2 a 2 3
# 3 a 3 3
# 4 b 10 10
# 5 b 5 10
# 6 b 6 10
# 7 c 7 5
# 8 c 8 5
# 9 c 9 5
Just for continuity
data[,2:3][ data[,2:3] == 4 ] <- 10
But it looks ugly, So do it in 2 steps is better.
Basically data[, 2:3]==4
gave you the index for data[,2:3]
instead of data
:
R > data[, 2:3] ==4
var1 var2
[1,] FALSE FALSE
[2,] FALSE FALSE
[3,] FALSE FALSE
[4,] TRUE TRUE
[5,] FALSE TRUE
[6,] FALSE TRUE
[7,] FALSE FALSE
[8,] FALSE FALSE
[9,] FALSE FALSE
So you may try this:
R > data[,2:3][data[, 2:3] ==4]
[1] 4 4 4 4