[英]Special use of colSums(), na.rm = TRUE only if 1 or fewer are missing
I need to sum some columns in a data.frame with a rule that says, a column is to be summed to NA
if more than one observation is missing NA
if only 1 or less missing it is to be summed regardless. 我需要总结的一些列的data.frame与说的规则,一列是求和以
NA
如果超过一个观察缺少NA
如果只有1或更低失踪了,无论是要进行求和。
Say I have some data like this, 说我有一些这样的数据,
dfn <- data.frame(
a = c(3, 3, 0, 3),
b = c(1, NA, 0, NA),
c = c(0, 3, NA, 1))
dfn
a b c
1 3 1 0
2 3 NA 3
3 0 0 NA
4 3 NA 1
and I apply my rule, and sum the columns with less then 2 missing NA
. 然后我应用我的规则,并对缺失的
NA
少于2的列求和。 So I get something like this. 所以我得到这样的东西。
a b c
1 3 1 0
2 3 NA 3
3 0 0 NA
4 3 NA 1
5 9 NA 4
I've played around with colSums(dfn, na.rm = FALSE)
and colSums(dfn, na.rm = TRUE)
. 我玩过
colSums(dfn, na.rm = FALSE)
和colSums(dfn, na.rm = TRUE)
。 In my real data there is more then three columns and also more then 4 rows. 在我的真实数据中,多于三列,多于4行。 I imagine I can count the missing some way and use that as a rule?
我想我可以以某种方式计算失踪人数并将其用作规则?
I don't think you can do this with colSums
alone, but you can add to its result using ifelse
: 我不认为您可以单独使用
colSums
来做到这colSums
,但是可以使用ifelse
来添加其结果:
colSums(dfn,na.rm=TRUE) + ifelse(colSums(is.na(dfn)) > 1, NA, 0)
a b c
9 NA 4
Nothing wrong with @James' Answer, but here's a slightly cleaner way: @James'Answer没问题,但是这是一种更简洁的方法:
colSums(apply(dfn, 2, function(col) replace(col, match(NA, col), 0)))
# a b c
# 9 NA 4
match(NA, col)
returns the index of the first NA
in col, replace
replaces it with 0
and returns the new column, and apply
returns a matrix
with all of the new columns. match(NA, col)
返回match(NA, col)
中第一个NA
的索引, replace
将其替换为0
并返回新列, apply
返回包含所有新列的matrix
。
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