我有一个很大的数据集,其中包含很多 NA 和一些非Na值。 目前,我为每个列计算非 NA 值,如下所示:
I have a big dataset that contains a lot of NAs and some non-Na values. At the moment I count my non-NA values for each column like this:
attach(df) 1000 - (sum(is.na(X1))) 1000 - (sum(is.na(X2))) 1000 - (sum(is.na(X3))) 1000 - (sum(is.na(X4))) 1000 - (sum(is.na(X5))) ... detach(df)所以我的观测总长度-总和我的 NA 值。
So my overall length of my observations - the sum of my NA values.
有没有一种更快的方法,它使用更少的代码行和打字工作,并且给我带来快非 NA 值的所有列和数量的总览?
Is there a faster way which uses less code lines and typing effort and gives me fast overview of all the columns and numbers of non-NA values?
像for循环之类的东西吗?
Like a for loop or something?
我正在寻找这样的东西:
I am looking for something like this:
X1 Amount of Non-Na-Values X2 ... X3 ... X4 X5 X6谢谢:)
推荐答案您也可以致电 is.na (隐式强制为al逻辑矩阵),然后对倒置响应调用 colSums :
You can also call is.na on the entire data frame (implicitly coercing to a logical matrix) and call colSums on the inverted response:
# make sample data set.seed(47) df <- as.data.frame(matrix(sample(c(0:1, NA), 100*5, TRUE), 100)) str(df) #> 'data.frame': 100 obs. of 5 variables: #> $ V1: int NA 1 NA NA 1 NA 1 1 1 NA ... #> $ V2: int NA NA NA 1 NA 1 0 1 0 NA ... #> $ V3: int 1 1 0 1 1 NA NA 1 NA NA ... #> $ V4: int NA 0 NA 0 0 NA 1 1 NA NA ... #> $ V5: int NA NA NA 0 0 0 0 0 NA NA ... colSums(!is.na(df)) #> V1 V2 V3 V4 V5 #> 69 55 62 60 70更多推荐
计算数据框中每一列的非NA值的数量
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