在R中按顺序合并数据帧对
我有一个数据框架,其中包含来自多个采样间隔的多个站点的标记个体。见下例:在R中按顺序合并数据帧对,r,R,我有一个数据框架,其中包含来自多个采样间隔的多个站点的标记个体。见下例: > df Tag Site Interval Ind_ID 1 507 Golden 7 1 2 507 Golden 8 1 3 552 Golden 2 1 4 552 Golden 1 1 5 847 Golden 4 1 6 847 Golden 6
> df
Tag Site Interval Ind_ID
1 507 Golden 7 1
2 507 Golden 8 1
3 552 Golden 2 1
4 552 Golden 1 1
5 847 Golden 4 1
6 847 Golden 6 1
8 847 Golden 5 1
9 847 Golden 3 1
31 541 Golden 1 1
33 541 Golden 3 1
34 541 Golden 4 1
35 541 Golden 7 1
36 541 Golden 6 1
37 541 Golden 5 1
39 810 Golden 7 1
40 810 Golden 8 1
41 840 Golden 7 1
42 840 Golden 8 1
43 840 Golden 3 1
44 840 Golden 2 1
我想做的是按时间间隔分离标记的个体,我已经用这个for循环完成了:
for (i in 1:nlevels(factor(df$Interval))){
I<-subset(df,Interval==levels(factor(df$Interval))[i])
assign(paste("Interval_", i, sep = ""), I)}
for(i/1:nlevels(因子(df$区间))){
我可能是这样的:
dfs <- split(df,df$Interval)
n <- nlevels(factor(df$Interval))-1
results <- setNames(vector("list",length = n),paste0("IPl",2:(n+1)))
for (i in seq_len(n)){
results[[i]] <- merge(dfs[[i]],dfs[[i+1]],by = c('Tag','Site','Ind_ID'))
}
> head(results)
$IPl2
Tag Site Ind_ID Interval.x Interval.y
1 552 Golden 1 1 2
$IPl3
Tag Site Ind_ID Interval.x Interval.y
1 840 Golden 1 2 3
$IPl4
Tag Site Ind_ID Interval.x Interval.y
1 541 Golden 1 3 4
2 847 Golden 1 3 4
$IPl5
Tag Site Ind_ID Interval.x Interval.y
1 541 Golden 1 4 5
2 847 Golden 1 4 5
$IPl6
Tag Site Ind_ID Interval.x Interval.y
1 541 Golden 1 5 6
2 847 Golden 1 5 6
$IPl7
Tag Site Ind_ID Interval.x Interval.y
1 541 Golden 1 6 7
dfs下面是一个dplyr
解决方案,它将数据帧与其自身连接起来,并将结果放入数据帧中
library(dplyr)
## Join the 'df' to itself based on the intervals to compare; this is done by
## creating a key to indicate which intervals to join on.
resultdf <-
## Create match_interval to next sequential value
df %>% mutate(match_interval = paste0('IPl', as.numeric(Interval)+1)) %>% arrange(Interval, Site) %>%
## Join to self by match_interval and other columns.
inner_join(df %>% mutate(match_interval = paste0('IPl', as.numeric(Interval))),
by = c('Tag', 'Site', 'Ind_ID', 'match_interval')) %>%
## Order columns
select(match_interval, Tag, Site, Ind_ID, Interval.x, Interval.y)
resultsdf
## match_interval Tag Site Ind_ID Interval.x Interval.y
## 1 IPl2 552 Golden 1 1 2
## 2 IPl3 840 Golden 1 2 3
## 3 IPl4 847 Golden 1 3 4
## 4 IPl4 541 Golden 1 3 4
## 5 IPl5 847 Golden 1 4 5
## 6 IPl5 541 Golden 1 4 5
## 7 IPl6 847 Golden 1 5 6
## 8 IPl6 541 Golden 1 5 6
## 9 IPl7 541 Golden 1 6 7
## 10 IPl8 507 Golden 1 7 8
## 11 IPl8 810 Golden 1 7 8
## 12 IPl8 840 Golden 1 7 8
库(dplyr)
##根据要比较的间隔将“df”加入到自身中;这是由
##创建一个键以指示要连接的间隔。
resultdf%变异(match_interval=paste0('IPl',as.numeric(interval)+1))%%>%arrange(interval,Site)%%>%
##通过match_interval和其他列连接到self。
内部连接(df%>%mutate(match_interval=paste0('IPl',as.numeric(interval)),
by=c('Tag'、'Site'、'Ind\u ID'、'match\u interval'))%>%
##订单列
选择(匹配间隔、标记、站点、索引ID、间隔.x、间隔.y)
结果DF
##匹配间隔标记站点标识间隔.x间隔.y
##1 IPl2 552金色1 1 2
##2 IPl3 840金色1 2 3
##3 IPl4 847金色1 3 4
##4 IPl4 541金色1 3 4
##5 IPl5 847金色14 5
##6 IPL5541金色1 4 5
##7 IPl6 847金色1 5 6
##8 IPl6 541黄金1 5 6
##9 IPl7 541黄金16 7
##10 IPl8 507黄金17 8
##11 IPl8 810金色17 8
##12 IPl8 840金色1 7 8
您可能需要查看split()。
library(dplyr)
## Join the 'df' to itself based on the intervals to compare; this is done by
## creating a key to indicate which intervals to join on.
resultdf <-
## Create match_interval to next sequential value
df %>% mutate(match_interval = paste0('IPl', as.numeric(Interval)+1)) %>% arrange(Interval, Site) %>%
## Join to self by match_interval and other columns.
inner_join(df %>% mutate(match_interval = paste0('IPl', as.numeric(Interval))),
by = c('Tag', 'Site', 'Ind_ID', 'match_interval')) %>%
## Order columns
select(match_interval, Tag, Site, Ind_ID, Interval.x, Interval.y)
resultsdf
## match_interval Tag Site Ind_ID Interval.x Interval.y
## 1 IPl2 552 Golden 1 1 2
## 2 IPl3 840 Golden 1 2 3
## 3 IPl4 847 Golden 1 3 4
## 4 IPl4 541 Golden 1 3 4
## 5 IPl5 847 Golden 1 4 5
## 6 IPl5 541 Golden 1 4 5
## 7 IPl6 847 Golden 1 5 6
## 8 IPl6 541 Golden 1 5 6
## 9 IPl7 541 Golden 1 6 7
## 10 IPl8 507 Golden 1 7 8
## 11 IPl8 810 Golden 1 7 8
## 12 IPl8 840 Golden 1 7 8