Filter each column of a data.frame based on a specific value

问题:

Consider the following data frame:

df 

Using dplyr, how can I filter, on each column (without implicitly naming them), for all values greater than 2.

Something that would mimic an hypothetical filter_each(funs(. >= 2))

Right now I'm doing:

df %>% filter(X1 >= 2, X2 >= 2, X3 >= 2, X4 >= 2, X5 >= 2) 

Which is equivalent to:

df %>% filter(!rowSums(. 

Note: Let's say I wanted to filter only on the first 4 columns, I would do:

df %>% filter(X1 >= 2, X2 >= 2, X3 >= 2, X4 >= 2)  

or

df %>% filter(!rowSums(.[-5] 

Would there be a more efficient alternative ?

Edit: sub question

How to specify a column name and mimic an hypothethical filter_each(funs(. >= 2), -X5) ?

Benchmark sub question

Since I have to run this on a large dataset, I benchmarked the suggestions.

df % filter(!rowSums(.[,!colnames(.) %in% "X5", drop = FALSE] =", as.name(x), y) }, 2)), Docendo = df %>% slice(which(!rowSums(select(., -matches("X5")) 

Here are the results:

#Unit: milliseconds #    expr       min        lq      mean    median       uq      max neval #   Marat 1209.1235 1320.3233 1358.7994 1362.0590 1390.342 1448.458    50 # Richard 1151.7691 1196.3060 1222.9900 1216.3936 1256.191 1266.669    50 # Docendo  874.0247  933.1399  983.5435  985.3697 1026.901 1053.407    50 

回答1:

Here's another option with slice which can be used similarly to filter in this case. Main difference is that you supply an integer vector to slice whereas filter takes a logical vector.

df %>% slice(which(!rowSums(select(., -matches("X5")) 

What I like about this approach is that because we use select inside rowSums you can make use of all the special functions that select supplies, like matches for example.

---

Let's see how it compares to the other answers:

df % filter(!rowSums(.[,!colnames(.) %in% "X5", drop = FALSE] =", as.name(x), y) }, 2)),     dd_slice = df %>% slice(which(!rowSums(select(., -matches("X5")) 

Edit note: updated with more reliable benchmark with 50 repetitions (times = 50L).

---

Following a comment that base R would have the same speed as the slice approach (without specification of what base R approach is meant exactly), I decided to update my answer with a comparison to base R using almost the same approach as in my answer. For base R I used:

base = df[!rowSums(df[-5L] 

Benchmark:

df % filter(!rowSums(.[,!colnames(.) %in% "X5", drop = FALSE] =", as.name(x), y) }, 2)),   dd_slice = df %>% slice(which(!rowSums(select(., -matches("X5")) 

Not really any better or comparable performance with these two base R approaches.

Edit note #2: added benchmark with base R options.

回答2:

Here's an idea that makes it fairly simple to choose the names. You can set up a list of calls to send to the .dots argument of filter_(). First a function that creates an unevaluated call.

Call =") call(fun, as.name(x), value) 

Now we use filter_(), passing a list of calls into the .dots argument using lapply(), choosing any name and value you want.

nm 

You can have a look at the unevaluated calls created by Call(), for example X4 and X5, with

lapply(names(df)[4:5], Call, 2L) # [[1]] # X4 >= 2L # # [[2]] # X5 >= 2L 

So if you adjust the names() in the X argument of lapply(), you should be fine.

回答3:

How to specify a column name and mimic an hypothethical filter_each(funs(. >= 2), -X5) ?

It might be not the most elegant solution, but it gets the job done:

df %>% filter(!rowSums(.[,!colnames(.)%in%'X5',drop=F] 

In case of several excluded columns (e.g. X3,X5), one can use:

df %>% filter(!rowSums(.[,!colnames(.)%in%c('X3','X5'),drop=F] 

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