！is.na在其他列中创建NDA

Question

在合并多个数据集的过程中，我试图删除某个特定变量缺少值的数据框的所有行（我想让NAs保持在当前的一些其他专栏）。我用下面的一行：

data.frame <- data.frame[!is.na(data.frame$year),] 

这成功地消除了与NAS进行year，（没有人）都行，但其他列，而此前有数据，现在完全是来港定居。换句话说，非缺失值正被转换为NA。关于这里发生了什么的任何想法？我已经试过这些替代方案，得到了相同的结果：我使用is.na不当

data.frame <- subset(data.frame, !is.na(year)) 

data.frame$x <- ifelse(is.na(data.frame$year) == T, 1, 0); 
data.frame <- subset(data.frame, x == 0) 

是谁？在这种情况下是否有任何替代is.na？任何帮助将不胜感激！

编辑下面是代码，应该重现该问题：

#data 
tc <- read.csv("http://dl.dropbox.com/u/4115584/tc2008.csv") 
frame <- read.csv("http://dl.dropbox.com/u/4115584/frame.csv") 

#standardize NA codes 
tc[tc == "."] <- NA 
tc[tc == -9] <- NA 

#standardize spatial units 
colnames(frame)[1] <- "loser" 
colnames(frame)[2] <- "gainer" 
frame$dyad <- paste(frame$loser,frame$gainer,sep="") 
tc$dyad <- paste(tc$loser,tc$gainer,sep="") 
drops <- c("loser","gainer") 
tc <- tc[,!names(tc) %in% drops] 
frame <- frame[,!names(frame) %in% drops] 
rm(drops) 

#merge tc into frame 
data <- merge(tc, frame, by.x = "year", by.y = "dyad", all.x=T, all.y=T) #year column is duplicated in  this process. I haven't had this problem with nearly identical code using other data. 

rm(tc,frame) 

#the first column in the new data frame is the duplicate year, which does not actually contain years. I'll rename it. 
colnames(data)[1] <- "double" 

summary(data$year) #shows 833 NA's 

summary(data$procedur) #note that at this point there are non-NA values 

#later, I want to create 20 year windows following the events in the tc data. For simplicity, I want to remove cases with NA in the year column. 

new.data <- data[!is.na(data$year),] 

#now let's see what the above operation did 
summary(new.data$year) #missing years were successfully removed 
summary(new.data$procedur) #this variable is now entirely NA's 

Answer 1

我认为实际的问题是您的merge。

合并完并有数据data，如果你这样做：

# > table(data$procedur, useNA="always") 

# 1  2  3  4  5  6 <NA> 
# 122 112 356  59  39  19 192258 

你看有这么多（122+112...+19）值data$procedur。但是，所有这些值都对应于data$year = NA。

> all(is.na(data$year[!is.na(data$procedur)])) 
# [1] TRUE # every value of procedur occurs where year = NA 

所以，基本上，的procedur所有值也将被删除，因为你删除了这些行中year为NA检查。

为了解决这个问题，我认为你应该使用merge为：如果该合并给你想要的结果

merge(tc, frame, all=T) # it'll automatically calculate common columns 
# also this will not result in duplicated year column. 

检查。

Answer 2

尝试complete.cases：

data.frame.clean <- data.frame[complete.cases(data.frame$year),] 

...虽然，如上面提到的，你可能想选择一个更具描述性的名字。