问题描述
说我有一个由许多行组成的数据框，如下所示：
Say I have a data frame consisting of a number of rows, like this:
df <- data.frame(Group = c(0,0,1,1,1,0),V1=c(0,0,0,4,5,7), V2=c(0,3,0,4,0,1))
Group V1 V2
1 0 0 0
2 0 0 3
3 1 0 0
4 1 4 4
5 1 5 0
6 0 7 1
组为二进制，V1和V2的值为零，通货膨胀（许多观察值== 0）
我想对每列进行子集化（依次）以删除0 obs，然后根据剩余数据计算分位数。至关重要的是，我只想为一个给定的变量删除0，而不是删除整行，因为我想为下一列重新设置并重新设置子集。
Group is binary, V1 and V2 have zero-inflation (many observations == 0)I'd like to subset each column (in turn) to remove 0 obs and then calculate quantiles on remaining data. Crucially, I'd like to remove 0s for a given variable only, and not remove whole rows, as I'd be wanting to reset and subset again for the next column.
下面有我的分位数代码。有什么方法可以潜入子集函数中吗？还是需要其他方法？
I have my code for quantiles below. Is there any way I can sneak in the subset function or do I need an different approach?
#Functions for quantiles
quant25 <- function(x) quantile(x, probs=0.25, na.rm=TRUE)
quant50 <- function(x) quantile(x, probs=0.50, na.rm=TRUE)
quant75 <- function(x) quantile(x, probs=0.75, na.rm=TRUE)
#Grouped calls on these functions
group_by(df, Group) %>%
summarise_each(funs(quant25, quant50, quant75), V1, V2)
推荐答案
我认为出于我的目的已经解决了这一问题： df [，2：3] [df [，2：3] == 0]< ;-NA 声明缺少0个观察值，其余似乎都按预期处理。 （谢谢Jaap）
I think I've figured this one out for my purposes: df[,2:3][df[,2:3]==0] <- NA to declare 0 observations missing and the rest seems to handle as expected. (Thanks, Jaap)
这篇关于dplyr：group_by，子集和摘要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！