将一个数据框中的值替换为另一个

Question

I've got two data.frames :

First:

   > dput(head(tbl_mz))
    structure(list(m.z = c(258.1686969, 258.168752, 587.8313625, 
    587.8425292, 523.2863282, 523.2859396), Measured.mass = c(514.3228408, 
    514.3229511, 1173.648172, 802.4706732, 1272.645144, 1044.557326
    )), .Names = c("m.z", "Measured.mass"), row.names = c(NA, 6L), class = "data.frame")

Second:

> dput(head(tbl_exl))
structure(list(V1 = c(802.4706732, 1272.649209, 1272.646875, 
1272.646599, 1272.646521, 1272.645144), V2 = c(NA, NA, NA, NA, 
NA, NA), V3 = c(NA, NA, NA, NA, NA, NA), V4 = c(NA, NA, NA, NA, 
NA, NA), V5 = c(NA, NA, NA, NA, NA, NA), V6 = structure(c(2L, 
2L, 2L, 2L, 2L, 2L), .Label = c("", "Positive"), class = "factor"), 
    V7 = c(28.7, 29.4, 29.4, 23.8, 28.6, 23.3), V8 = c(30.7, 
    31.4, 31.4, 25.8, 30.6, 25.3), X = c(NA, NA, NA, NA, NA, 
    NA), X.1 = c(NA, NA, NA, NA, NA, NA), X.2 = c(NA, NA, NA, 
    NA, NA, NA)), .Names = c("V1", "V2", "V3", "V4", "V5", "V6", 
"V7", "V8", "X", "X.1", "X.2"), row.names = c(NA, 6L), class = "data.frame")

I would like to replace some values from tbl_exl , column V1 by values from the the other table tbl_mz . The values from column V1 (tbl_exl) can be found in the column Measured.mass (tbl_mz) and they should be replaced by the values from the next column mz in tbl_mz data frame.

In another words the values in the V1 should be replaced by the mz values.

The problem is that not all values from V1 can't be find in the other data frame. Those which can be find can be deleted or just left like they are.

The output, which I want to get:

 > dput(head(tbl_exl_modified))
    structure(list(V1 = c(587.8425292, 1272.649209, 1272.646875, 
    1272.646599, 1272.646521, 523.2863282), V2 = c(NA, NA, NA, NA, 
    NA, NA), V3 = c(NA, NA, NA, NA, NA, NA), V4 = c(NA, NA, NA, NA, 
    NA, NA), V5 = c(NA, NA, NA, NA, NA, NA), V6 = structure(c(2L, 
    2L, 2L, 2L, 2L, 2L), .Label = c("", "Positive"), class = "factor"), 
        V7 = c(28.7, 29.4, 29.4, 23.8, 28.6, 23.3), V8 = c(30.7, 
        31.4, 31.4, 25.8, 30.6, 25.3), X = c(NA, NA, NA, NA, NA, 
        NA), X.1 = c(NA, NA, NA, NA, NA, NA), X.2 = c(NA, NA, NA, 
        NA, NA, NA)), .Names = c("V1", "V2", "V3", "V4", "V5", "V6", 
    "V7", "V8", "X", "X.1", "X.2"), row.names = c(NA, 6L), class = "data.frame")

Answer 1

You could try match . Create numeric indexes based on the match between the columns ("Measured.mass", "V1") of the two datasets. Remove the NA values ("indx1", "indxN1") and replace the "V1" values to "mz" based on these index.

indx <- match(tbl_mz$Measured.mass, tbl_exl$V1)
indx1 <- indx[!is.na(indx)]
indxN <-  match(tbl_exl$V1, tbl_mz$Measured.mass)
indxN1 <- indxN[!is.na(indxN)]
tbl_exl$V1[indx1] <- tbl_mz$m.z[indxN1]

identical(tbl_exl, tbl_exl_modified)
#[1] TRUE

Or use left_join from dplyr

library(dplyr)
tbl_exl1 <- left_join(tbl_exl, tbl_mz, by=c('V1'='Measured.mass')) %>%
                mutate(V1= pmax((NA^!is.na(m.z))*V1, m.z,
                                                 na.rm=TRUE)) %>% 
                select(-m.z)

 tbl_exl1
 #        V1 V2 V3 V4 V5       V6   V7   V8  X X.1 X.2
 #1  587.8425 NA NA NA NA Positive 28.7 30.7 NA  NA  NA
 #2 1272.6492 NA NA NA NA Positive 29.4 31.4 NA  NA  NA
 #3 1272.6469 NA NA NA NA Positive 29.4 31.4 NA  NA  NA
 #4 1272.6466 NA NA NA NA Positive 23.8 25.8 NA  NA  NA
 #5 1272.6465 NA NA NA NA Positive 28.6 30.6 NA  NA  NA
 #6  523.2863 NA NA NA NA Positive 23.3 25.3 NA  NA  NA

Answer 2

Here's a solution using data.table s binary join

library(data.table)
setnames(setDT(tbl_exl), 1, "Measured.mass") # Changing the first column name for the join to work
setkey(tbl_exl, Measured.mass) # Keying tbl_exl by `Measured.mass`
setkey(setDT(tbl_mz), Measured.mass) # Keying tbl_exl by `Measured.mass`
tbl_exl[tbl_mz, Measured.mass := i.m.z][] # Joining and retrieving only matched values from `i.m.z`
#    Measured.mass V2 V3 V4 V5       V6   V7   V8  X X.1 X.2
# 1:      587.8425 NA NA NA NA Positive 28.7 30.7 NA  NA  NA
# 2:      523.2863 NA NA NA NA Positive 23.3 25.3 NA  NA  NA
# 3:     1272.6465 NA NA NA NA Positive 28.6 30.6 NA  NA  NA
# 4:     1272.6466 NA NA NA NA Positive 23.8 25.8 NA  NA  NA
# 5:     1272.6469 NA NA NA NA Positive 29.4 31.4 NA  NA  NA
# 6:     1272.6492 NA NA NA NA Positive 29.4 31.4 NA  NA  NA