[英]Select value from previous group based on condition
I have the following df我有以下 df
df<-data.frame(value = c(1,1,1,2,1,1,2,2,1,2),
group = c(5,5,5,6,7,7,8,8,9,10),
no_rows = c(3,3,3,1,2,2,2,2,1,1))
where identical consecutive values form a group, ie, values in rows 1:3 fall under group 5. Column "no_rows" tells us how many rows/entries each group has, ie, group 5 has 3 rows/entries.其中相同的连续值形成一个组,即行 1:3 中的值属于组 5。列“no_rows”告诉我们每个组有多少行/条目,即组 5 有 3 行/条目。
I am trying to substitute all values, where no_rows < 2, with the value from a previous group.我试图用前一组的值替换所有值,其中 no_rows < 2。 I expect my end df to look like this:
我希望我的最终 df 看起来像这样:
df_end<-data.frame(value = c(1,1,1,1,1,1,2,2,2,2),
group = c(5,5,5,6,7,7,8,8,9,10),
no_rows = c(3,3,3,1,2,2,2,2,1,1))
I came up with this combination of if...else in a for loop, which gives me the desired output, however it is very slow and I am looking for a way to optimise it.我在 for 循环中提出了 if...else 的这种组合,它为我提供了所需的输出,但是它非常慢,我正在寻找一种优化它的方法。
for (i in 2:length(df$group)){
if (df$no_rows[i] < 2){
df$value[i] <- df$value[i-1]
}
}
I have also tried with dplyr::mutate and lag() but it does not give me the desired output (it only removes the first value per group instead of taking the value of a previous group).我也尝试过 dplyr::mutate 和 lag() 但它没有给我想要的输出(它只删除每组的第一个值,而不是取前一组的值)。
df<-df%>%
group_by(group) %>%
mutate(value = ifelse(no_rows < 2, lag(value), value))
I looked for a solution now for a few days but I could not find anything that fit my problem completly.我现在已经寻找了几天的解决方案,但我找不到任何完全适合我的问题的东西。 Any ideas?
有任何想法吗?
a data.table approach...数据表方法...
first, get the values of groups with length >=2, then fill in missing values (NA) by last-observation-carried-forward.首先,获取长度>=2的组的值,然后通过last-observation-carried-forward填充缺失值(NA)。
library(data.table)
# make it a data.table
setDT(df, key = "group")
# get values for groups of no_rows >= 2
df[no_rows >= 2, new_value := value][]
# value group no_rows new_value
# 1: 1 5 3 1
# 2: 1 5 3 1
# 3: 1 5 3 1
# 4: 2 6 1 NA
# 5: 1 7 2 1
# 6: 1 7 2 1
# 7: 2 8 2 2
# 8: 2 8 2 2
# 9: 1 9 1 NA
#10: 2 10 1 NA
# fill down missing values in new_value
setnafill(df, "locf", cols = c("new_value"))
# value group no_rows new_value
# 1: 1 5 3 1
# 2: 1 5 3 1
# 3: 1 5 3 1
# 4: 2 6 1 1
# 5: 1 7 2 1
# 6: 1 7 2 1
# 7: 2 8 2 2
# 8: 2 8 2 2
# 9: 1 9 1 2
#10: 2 10 1 2
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