[英]Count the number of non-NA numeric values of each row in dplyr
I create a dataframe df.我创建了一个 dataframe df。
df <- data.frame (id = 1:10,
var1 = 10:19,
var2 = sample(c(1:2,NA), 10, replace=T),
var3 = sample(c(3:5, NA), 10, replace=T))
What I need is a new column var4, which count the number of non-NA values of each row (excluding the id column).我需要的是一个新列 var4,它计算每行的非 NA 值的数量(不包括 id 列)。 So for example, if a row is like var1=19, var2=1, var3=NA, then var4=2.
因此,例如,如果一行类似于 var1=19、var2=1、var3=NA,则 var4=2。 I could not find a good way to do this in dplyr. something like:
我在 dplyr 中找不到执行此操作的好方法。类似于:
df %in% mutate(var4= ... )
I appreciate if anyone can help me with that.如果有人可以帮助我,我将不胜感激。
Use select
+ is.na
+ rowSums
, select(., -id)
returns the original data frame ( .
) with id
excluded, and then count number of non-NA values with rowSums(!is.na(...))
: 使用
select
+ is.na
+ rowSums
, select(., -id)
id
select(., -id)
返回包含id
的原始数据帧( .
),然后使用rowSums(!is.na(...))
计算非NA值的rowSums(!is.na(...))
:
df %>% mutate(var4 = rowSums(!is.na(select(., -id))))
# id var1 var2 var3 var4
#1 1 10 NA 4 2
#2 2 11 1 NA 2
#3 3 12 2 5 3
#4 4 13 2 NA 2
#5 5 14 1 NA 2
#6 6 15 1 NA 2
#7 7 16 1 5 3
#8 8 17 NA 4 2
#9 9 18 NA 4 2
#10 10 19 NA NA 1
I know the OP asked for a dplyr
solution, but base R is straightforward here:我知道 OP 要求
dplyr
解决方案,但 base R 在这里很简单:
df$var4 <- rowSums(.is,na(df[:2:4]))
rowSums
calculates the number of values that are not NA ( .is.na
) in columns 2 - 4. rowSums
计算第 2 - 4 列中非 NA ( .is.na
) 的值的数量。
Note, this is summing the logical vector generated by is.na
, which is distinct from:请注意,这是对
is.na
生成的逻辑向量求和,它不同于:
rowSums(df[,2:4], na.rm = TRUE)
Which drops the NA
s and then sums the remaining values.它会丢弃
NA
,然后对剩余值求和。
Another solution using only base-r另一种仅使用 base-r 的解决方案
data.frame(df, var4 = apply(df[,-1], 1, function(x) sum(!is.na(x))))
id var1 var2 var3 var4
1 1 10 1 5 3
2 2 11 2 5 3
3 3 12 2 5 3
4 4 13 NA 3 2
5 5 14 NA 5 2
6 6 15 1 5 3
7 7 16 NA 3 2
8 8 17 NA 4 2
9 9 18 NA 3 2
10 10 19 1 4 3
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