我如何在没有循环的情况下通过数据帧中该级别中另一个因子的子集来操作因子级别内的数据

Question

I have a data frame made up of absorption spectra from multiple sample runs (sample a, b, c, d), where Ydata is wavelength and Xdata is absorption. I am calculating a baseline corrected absorption by subtracting the average absorption over a quiet wavelength range away from peaks of interest.

simplified dataframe:

DF <- data.frame(
  group = rep(c("a", "b", "c", "d"),each=10),
  Ydata = rep(1:10, times = 4),
  Xdata = c(seq(1,10,1),seq(5,50,5),seq(20,11,-1),seq(0.3,3,0.3)),
  abscorr = NA
)

I need to correct each sample run by subtracting the mean of a subsetted wavelength range within the run. I've been doing it this way:

for (i in 1:length(levels(DF$group))){
  sub1 <- subset(DF, group == levels(DF$group)[i], select = c(group, Ydata, 
  Xdata));
  sub2 <- subset(sub1, Ydata > 4 & Ydata < 8, select = c(group, Ydata, 
  Xdata));
  sub1$abscorr <- sub1$Xdata - mean(sub2$Xdata);
  DF <- rbind(sub1, DF);
}

and then tidy up all the 'NA's

DF <- na.omit(DF)

The way done above is obviously clunky with use of loops. Is there a better way to go about this task for a large dataset? perhaps dplyr?

Answer 1

Try dplyr :

DF %>%
    group_by(group) %>%
    mutate(abscorr = Xdata - mean(Xdata[Ydata < 8 & Ydata > 4]))

Answer 2

I believe this will do it.

fun <- function(x){
    x$Xdata - mean(x[which(x$Ydata > 4 & x$Ydata < 8), "Xdata"])
}
DF$abscorr <- do.call(c, lapply(split(DF, DF$group), fun))

Note that when I tested it, all.equal gave me a series of differences, namely the attributes of the two results are different. So I ran the following.

fun <- function(x){
    x$Xdata - mean(x[which(x$Ydata > 4 & x$Ydata < 8), "Xdata"])
}
DF2 <- DF
DF2$abscorr <- do.call(c, lapply(split(DF2, DF2$group), fun))

all.equal(DF[order(DF$group, DF$Ydata), ], DF2)
# [1] "Attributes: < Names: 1 string mismatch >"                                         
# [2] "Attributes: < Length mismatch: comparison on first 2 components >"                
# [3] "Attributes: < Component 2: names for target but not for current >"                
# [4] "Attributes: < Component 2: Attributes: < Modes: list, NULL > >"                   
# [5] "Attributes: < Component 2: Attributes: < Lengths: 1, 0 > >"                       
# [6] "Attributes: < Component 2: Attributes: < names for target but not for current > >"
# [7] "Attributes: < Component 2: Attributes: < current is not list-like > >"            
# [8] "Attributes: < Component 2: target is omit, current is numeric >"                  
# [9] "Component “abscorr”: Modes: numeric, logical"                                     
#[10] "Component “abscorr”: target is numeric, current is logical"

As you can see there is no difference in the calculated values of abscorr , only in the attributes. Among those, there are differences in the na.omit attribute or the rownames . I wouldn't worry if I were you, since the values of abscorr are equal.

EDIT.
Note that if I sort DF and then set the problem attributes to NULL both all.equal and the much more strict identical return TRUE .

DF1 <- DF[order(DF$group, DF$Ydata), ]  # Modify a copy, keep the original
row.names(DF1) <- NULL
attr(DF1, "na.action") <- NULL

all.equal(DF1, DF2)
#[1] TRUE
identical(DF1, DF2)
#[1] TRUE