R：如何交叉数据帧列表和特定列

Question

I am trying to find all matching values in a specific column, in a list of data.frames. However, I keep getting a returned value of character(0) .

I have tried the following: Simple subset (very time consuming) -> eg dat[[i]][[i]] lapply w/ Reduce and intersect (as seen here

LocA<-data.frame(obs.date=c("2018-01-10","2018-01-14","2018-01-20),
obs.count=c(2,0,1))
LocB<-data.frame(obs.date=c("2018-01-09","2018-01-14","2018-01-20),
obs.count=c(0,3,5))
LocC<-data.frame(obs.date=c("2018-01-12","2018-01-14","2018-01-19"),
obs.count=c(2,0,1))
LocD<-data.frame(obs.date=c("2018-01-11","2018-01-16","2018-01-21"),
obs.count=c(2,0,1))

dfList<-list(LocA,LocB,LocC,LocD)

##List of all dates 

lapply(dfList,'[[',1)
[1]"2018-01-10" "2018-01-14" "2018-01-20" "2018-01-09"...

Attempts (failure)

>Reduce(intersect,lapply(dfList,'[[',1))
character (0)

I expect the output of this function to be an output identifying the data.frames that share a common date.

*Extra smiles if someone know how to identify shared dates and mutate in to a single data frame where..Col1 = dataframe name, Col2=obs.date,Col3 = obs.count

Answer 1

You can first merge all the data frames so you only have one:

a <- Reduce(function(x, y) merge(x, y, all = TRUE), dfList)

Or you can merge them like this:

a <-rbind(LocA,LocB,LocC,LocD)

Afterwards, you can extract all the duplicates:

b <- a[duplicated(a$obs.date), ]

Or if you want to keep all the unique ones and keep the duplicates:

c <- a[!duplicated(a$obs.date), ]

Answer 2

If by "intersect" you mean doing an "inner join" or "merging" with a specific column as key, then -- you want to use dplyr::inner_join or merge .

First, between two data.frames:

library(dplyr)
inner_join(LocA, LocB, by='obs.date')
# 2 rows
inner_join(LocC, LocD, by='obs.date')
# zero rows

So, not infinite merging.

Stack, then count

We'll combine the data first, then count the occurences. Notice the use of the .id -argument to track where the row originated.

library(dplyr)
bind_rows(dfList, .id = 'id') %>%
  add_count(obs.date) %>% 
  filter(n > 1)
# A tibble: 5 x 4
  id    obs.date   obs.count     n
  <chr> <chr>          <dbl> <int>
1 1     2018-01-14         0     3
2 1     2018-01-20         1     2
3 2     2018-01-14         3     3
4 2     2018-01-20         5     2
5 3     2018-01-14         0     3