for的for循环中的if / else语句-希望if语句包含多个可能的值

Question

I'm using R and I'm trying to write a for loop to analyze my data. The problem I'm having is that some of my replicates were sampled 6 times and some were sampled 4 times (I was growing cultures and some died after 4 time points and others lived throughout the experiment and were sampled 6 times). I just want to set up a conditional if/else loop within my for loop to cut off NA values (for points at which that culture wasn't sampled). Here's what I have:

names <- colnames(culture_data)
for(i in 1:72){
    n <- as.factor(names[i])
    chl.sub <- subset(culture_data, select = n)
    if (n == c("X1", "X2", "X3", "X10", "X11", "X12", "X37", "X38", "X39", "X46", "X47", "X48"))
        chl.sub <- as.matrix(chl.sub[1:4,])
        data <- (data.frame(time_newAgNP, chl.sub))
        }
    else {
        data <- (data.frame(time, chl.sub))
        }

So I want the subsetted data for cultures 1-3, 10-12, 37-39, and 46-48 to be cut after row 4 and the subsetted data for all the other cultures to be used completely.

I think my main problem is in the first "if" statement in that I don't know how to word it to tell R to do this conditional if n equals any of those values.

Let me know if something's unclear. Thank you!

Answer 1

Assuming your data looks like this

       t1         t2          t3         t4         t5
1  1.00867689 -1.0286160 -0.13429176 -2.3891856  1.2285634
2 -0.06094606 -0.5265711 -0.52767898         NA         NA
3  1.60603566  0.8295580 -0.44729021 -0.1297540 -1.5007802
4  0.13809702  0.5940972  0.80628674         NA         NA
5  0.45239500  0.6797742 -0.03644485  0.7555041  0.4816549

then

missing = subset(test,is.na(rowSums(test)))
nonmissing = subset(test,!is.na(rowSums(test)))

will work.

Although, as Aaron said, make sure that discarding this data is wise before you get rid of it. a categorical variable identifying the early death populations might work. There are probably better ways to deal with it though.

Cheers, Davy

Answer 2

If you are trying to create 2 data sets, one data set with just columns with no missing observations and one data set with just columns containing missing observations maybe something like this will work:

a <- matrix(c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,NA,NA,19,20,NA,NA,21,22), nrow=6, byrow=T)
a

zz <- which( !(is.na( colSums(a))) )

# columns without missing observations
b<- a[,zz]
b

# columns with missing observations
c<- a[1:4,-zz]
c

Here is a loop that selects rows in a column

for(i in 1:ncol(a)) {    if(is.na(sum(a[,i]) )) b = a[1:4,i] else b= a[1:6,i] ; print(b) }

Neither might work if there are missing observations in rows 1 through 4.

Answer 3

May be just na.omit? It will remove incomplete cases from your dataset.

data <- na.omit(culture_data)