问题描述
我经常遇到看起来像这样的数据:
I often encounter data that looks like this:
#create dummy data frame
data <- as.data.frame(diag(4))
data[data==0] <- NA
data[2,2] <- NA
data
#V1 V2 V3 V4
#1 1 NA NA NA
#2 NA NA NA NA
#3 NA NA 1 NA
#4 NA NA NA 1
行代表参与者，列V1至V4代表参与者所处的条件(例如，V1下的1表示该参与者处于条件1，V4下的1表示该参与者处于条件4).旁注:数据不对称，因此在这四个条件下分布的参与者更多.
Rows represent participants and columns V1 through V4 represent the condition that the participant is in (e.g., a 1 under V1 means this participant is in condition 1, a 1 under V4 means this participant is in condition 4). Sidenote: The data are not symmetric, so there are a lot more participants spread over the 4 conditions.
我想要的是一个向量，每个参与者的条件:
What I want is a vector with the condition for each participant:
1 NA 3 4
我写了以下内容，但想知道是否有更有效的方法(即使用更少的代码行)?
I wrote the following bit, but was wondering if there was a more efficient way (i.e., using fewer lines of code)?
#replace entries with condition numbers
cond <- data + matrix(rep(0:3, 4), 4, byrow=TRUE) #add 0 to 1 for condition 1...
#get all unique elements (ignore NAs)
cond <- apply(cond, 1, function(x)unique(x[!is.na(x)]))
#because I ignored NAs just now, cond[2,2] is numeric(0)
#assign NA to all values that are numeric(0)
cond[sapply(cond, function(x) length(x)==0)] <- NA
cond <- unlist(cond)
cond
#[1] 1 NA 3 4
推荐答案
我们可以在数据中非NA元素的逻辑矩阵上使用max.col和ties.method='first'.为了使仅具有NA元素的行成为NA，我们将max.col索引乘以逻辑矩阵的rowSums，并将0个非NA行转换为NA(NA^).
We can use max.col with ties.method='first' on the logical matrix of non-NA elements in 'data'. To make the rows that have only NA elements as NA, we multiply the max.col index with rowSums of logical matrix with 0 non-NA rows converted to NA (NA^).
max.col(!is.na(data), 'first')* NA^!rowSums(!is.na(data))
#[1] 1 NA 3 4
或另一个选项是pmax.我们将列索引与数据相乘，以便将非NA元素替换为索引.然后，将pmax与na.rm=TRUE结合使用，并获得每行的最大值.
Or another option is pmax. We multiply the column index with the data so that the non-NA elements get replaced by the index. Then, use pmax with na.rm=TRUE and get the max value per each row.
do.call(pmax, c(col(data)*data, na.rm=TRUE))
#[1] 1 NA 3 4
这篇关于行的唯一值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！