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Multiply specific values in columns of a dataframe columnwise with a vector

 原文  2015-07-05 01:06:28  8   3    r

Question

I am an only very occasional R user and this is the first time I am asking a question regarding R here or anywhere else online, so I apologize beforehand if anything remains unclear.

I have a numeric dataframe with about 100 columns in each of which there is the same number (number 10 in this example) that needs to be multiplied with a value from a numeric vector, which is specific to each column. I am completely stuck and would appreciate any help.

Here is a simplified example: 

df

                    V1            V2          V3
1                   0             0           2  
2                   1             0           2  
3                   0             0           1  
4                   0             0           2  
5                   0             0           1  
6                  10             0           1  
7                   0             0           1  
8                   0             0           2  
9                   0            10           2  
10                  0             0           2  
11                 10             0           1  
12                  0             0          10  
13                  1             2           1  
14                  0             0           2  
15                  0             0           0  
16                  0             1           2  
17                  1             0          10  
18                  1             1           1  
19                  0             0           1  
20                  0             0           2

The corresponding vector would look as follows:

V 

v1                 v2                 v3  
0.01256117         0.03037231         0.55444079

So, the values "10" of df column V1 would need to be multiplied by value v1 of vector V, the values "10" of df column V2 by the value v2 of vector V, etc.

Any help is very much appreciated!

3 answers

solution1
 3  2015-07-05 01:44:43

Here is one simple approach: 

# sample data
df<-data.frame(v1=c(1:10,10), v2= c(5:13,10,14), v3=8:18)
vec=c(0.1, 0.2, 0.3) # sample vector to multiply by
df
#   v1 v2 v3
#1   1  5  8
#2   2  6  9
#3   3  7 10
#4   4  8 11
#5   5  9 12
#6   6 10 13
#7   7 11 14
#8   8 12 15
#9   9 13 16
#10 10 10 17
#11 10 14 18

df2 <- t(t(df==10) * vec * t(df))
df[df==10] <- 0
df + df2
#   v1 v2 v3
#1   1  5  8
#2   2  6  9
#3   3  7  3
#4   4  8 11
#5   5  9 12
#6   6  2 13
#7   7 11 14
#8   8 12 15
#9   9 13 16
#10  1  2 17
#11  1 14 18

solution2
 2  2015-07-05 05:56:38

Here is a variation 

df1 <- (df!=10)*df + ((df==10)*df) * vec1[col(df)]
df1
#          V1        V2       V3
#1  0.0000000 0.0000000 2.000000
#2  1.0000000 0.0000000 2.000000
#3  0.0000000 0.0000000 1.000000
#4  0.0000000 0.0000000 2.000000
#5  0.0000000 0.0000000 1.000000
#6  0.1256117 0.0000000 1.000000
#7  0.0000000 0.0000000 1.000000
#8  0.0000000 0.0000000 2.000000
#9  0.0000000 0.3037231 2.000000
#10 0.0000000 0.0000000 2.000000
#11 0.1256117 0.0000000 1.000000
#12 0.0000000 0.0000000 5.544408
#13 1.0000000 2.0000000 1.000000
#14 0.0000000 0.0000000 2.000000
#15 0.0000000 0.0000000 0.000000
#16 0.0000000 1.0000000 2.000000
#17 1.0000000 0.0000000 5.544408
#18 1.0000000 1.0000000 1.000000
#19 0.0000000 0.0000000 1.000000
#20 0.0000000 0.0000000 2.000000

For big datasets, it may also be better to use lapply/Map etc 

f1 <- function(x,y) {i <- x==10
                    x[i] <- x[i]*y 
                     x}
df2 <- data.frame(Map(f1, df, vec1))
df2 
#          V1        V2       V3
#1  0.0000000 0.0000000 2.000000
#2  1.0000000 0.0000000 2.000000
#3  0.0000000 0.0000000 1.000000
#4  0.0000000 0.0000000 2.000000
#5  0.0000000 0.0000000 1.000000
#6  0.1256117 0.0000000 1.000000
#7  0.0000000 0.0000000 1.000000
#8  0.0000000 0.0000000 2.000000
#9  0.0000000 0.3037231 2.000000
#10 0.0000000 0.0000000 2.000000
#11 0.1256117 0.0000000 1.000000
#12 0.0000000 0.0000000 5.544408
#13 1.0000000 2.0000000 1.000000
#14 0.0000000 0.0000000 2.000000
#15 0.0000000 0.0000000 0.000000
#16 0.0000000 1.0000000 2.000000
#17 1.0000000 0.0000000 5.544408
#18 1.0000000 1.0000000 1.000000
#19 0.0000000 0.0000000 1.000000
#20 0.0000000 0.0000000 2.000000

identical(df1, df2)
#[1] TRUE

Or an option with data.table 

library(data.table)#v1.9.5+
setDT(df)
for(j in seq_along(df)){
 set(df, i=NULL, j=j, value= as.numeric(df[[j]]))
 set(df, i=which(df[[j]]==10), j=j, value= df[[j]][df[[j]]==10]*vec1[j])
}

df
#          V1        V2       V3
#1: 0.0000000 0.0000000 2.000000
#2: 1.0000000 0.0000000 2.000000
#3: 0.0000000 0.0000000 1.000000
#4: 0.0000000 0.0000000 2.000000
#5: 0.0000000 0.0000000 1.000000
#6: 0.1256117 0.0000000 1.000000
#7: 0.0000000 0.0000000 1.000000
#8: 0.0000000 0.0000000 2.000000
#9: 0.0000000 0.3037231 2.000000
#10:0.0000000 0.0000000 2.000000
#11:0.1256117 0.0000000 1.000000
#12:0.0000000 0.0000000 5.544408
#13:1.0000000 2.0000000 1.000000
#14:0.0000000 0.0000000 2.000000
#15:0.0000000 0.0000000 0.000000
#16:0.0000000 1.0000000 2.000000
#17:1.0000000 0.0000000 5.544408
#18:1.0000000 1.0000000 1.000000
#19:0.0000000 0.0000000 1.000000
#20:0.0000000 0.0000000 2.000000

data 

df <- structure(list(V1 = c(0L, 1L, 0L, 0L, 0L, 10L, 0L, 0L, 0L, 0L, 
10L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L), V2 = c(0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 10L, 0L, 0L, 0L, 2L, 0L, 0L, 1L, 0L, 1L, 
0L, 0L), V3 = c(2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 10L, 
1L, 2L, 0L, 2L, 10L, 1L, 1L, 2L)), .Names = c("V1", "V2", "V3"
), class = "data.frame", row.names = c(NA, -20L))

vec1 <-  c(v1=0.01256117, v2 =0.03037231,v3  =0.55444079)

solution3
 1  2015-07-05 06:52:34

Here's another suggestion: 

arr <- which(df==10, arr.ind=TRUE)
df[arr] <- df[arr] * v[arr[,2]]
#> df
#          V1        V2       V3
#1  0.0000000 0.0000000 2.000000
#2  1.0000000 0.0000000 2.000000
#3  0.0000000 0.0000000 1.000000
#4  0.0000000 0.0000000 2.000000
#5  0.0000000 0.0000000 1.000000
#6  0.1256117 0.0000000 1.000000
#7  0.0000000 0.0000000 1.000000
#8  0.0000000 0.0000000 2.000000
#9  0.0000000 0.3037231 2.000000
#10 0.0000000 0.0000000 2.000000
#11 0.1256117 0.0000000 1.000000
#12 0.0000000 0.0000000 5.544408
#13 1.0000000 2.0000000 1.000000
#14 0.0000000 0.0000000 2.000000
#15 0.0000000 0.0000000 0.000000
#16 0.0000000 1.0000000 2.000000
#17 1.0000000 0.0000000 5.544408
#18 1.0000000 1.0000000 1.000000
#19 0.0000000 0.0000000 1.000000
#20 0.0000000 0.0000000 2.000000

data 

df <- structure(list(V1 = c(0L, 1L, 0L, 0L, 0L, 10L, 0L, 0L, 0L, 0L,10L, 
0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L), V2 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 10L, 0L, 0L, 0L, 2L, 0L, 0L, 1L, 0L, 1L, 0L, 0L), 
V3 = c(2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 10L, 1L, 2L, 0L, 2L,
10L, 1L, 1L, 2L)), .Names = c("V1", "V2", "V3"), class = "data.frame", 
row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", 
"11", "12", "13", "14", "15", "16", "17", "18", "19", "20"))

v <- c(0.01256117, 0.03037231, 0.55444079)

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