How to extract certain columns from a list of listed data frames-CodePudding

I have data as follows:

l = list(list(mtcars), list(mtcars))

Please note the difference with the data below

I would like to apply this solution:

# list of data frames:
l = list(mtcars, mtcars)

# vector of column names I would like to extract
my_names = c("mpg", "wt", "am")
# these columns might be at different positions in the data frames
result = lapply(l, "[", , my_names)

But the data frames in my example are a level deeper. I am having some trouble adapting the above solution to my data.

How could I adapt the syntax to get the columns from a list of listed data frames

CodePudding user response：

I have two solutions for you that could retain your nested list structure, but not sure about the efficiency.

Method 1

unlist one layer of your list in lapply, then index your list with my_names and making it a list again, so that your nested list structure stays the same.

lapply(unlist(l, recursive = F), \(x) list(x[my_names]))

Method 2

Use another lapply within lapply to iterate the inner list.

lapply(l, \(x) lapply(x, "[", my_names))

The above two methods give the same result.

identical(lapply(unlist(l, recursive = F), \(x) list(x[my_names])), 
          lapply(l, \(x) lapply(x, "[", my_names)))
[1] TRUE

Output

[[1]]
[[1]][[1]]
                     mpg    wt am
Mazda RX4           21.0 2.620  1
Mazda RX4 Wag       21.0 2.875  1
Datsun 710          22.8 2.320  1
Hornet 4 Drive      21.4 3.215  0
Hornet Sportabout   18.7 3.440  0
Valiant             18.1 3.460  0
Duster 360          14.3 3.570  0
Merc 240D           24.4 3.190  0
Merc 230            22.8 3.150  0
Merc 280            19.2 3.440  0
Merc 280C           17.8 3.440  0
Merc 450SE          16.4 4.070  0
Merc 450SL          17.3 3.730  0
Merc 450SLC         15.2 3.780  0
Cadillac Fleetwood  10.4 5.250  0
Lincoln Continental 10.4 5.424  0
Chrysler Imperial   14.7 5.345  0
Fiat 128            32.4 2.200  1
Honda Civic         30.4 1.615  1
Toyota Corolla      33.9 1.835  1
Toyota Corona       21.5 2.465  0
Dodge Challenger    15.5 3.520  0
AMC Javelin         15.2 3.435  0
Camaro Z28          13.3 3.840  0
Pontiac Firebird    19.2 3.845  0
Fiat X1-9           27.3 1.935  1
Porsche 914-2       26.0 2.140  1
Lotus Europa        30.4 1.513  1
Ford Pantera L      15.8 3.170  1
Ferrari Dino        19.7 2.770  1
Maserati Bora       15.0 3.570  1
Volvo 142E          21.4 2.780  1


[[2]]
[[2]][[1]]
                     mpg    wt am
Mazda RX4           21.0 2.620  1
Mazda RX4 Wag       21.0 2.875  1
Datsun 710          22.8 2.320  1
Hornet 4 Drive      21.4 3.215  0
Hornet Sportabout   18.7 3.440  0
Valiant             18.1 3.460  0
Duster 360          14.3 3.570  0
Merc 240D           24.4 3.190  0
Merc 230            22.8 3.150  0
Merc 280            19.2 3.440  0
Merc 280C           17.8 3.440  0
Merc 450SE          16.4 4.070  0
Merc 450SL          17.3 3.730  0
Merc 450SLC         15.2 3.780  0
Cadillac Fleetwood  10.4 5.250  0
Lincoln Continental 10.4 5.424  0
Chrysler Imperial   14.7 5.345  0
Fiat 128            32.4 2.200  1
Honda Civic         30.4 1.615  1
Toyota Corolla      33.9 1.835  1
Toyota Corona       21.5 2.465  0
Dodge Challenger    15.5 3.520  0
AMC Javelin         15.2 3.435  0
Camaro Z28          13.3 3.840  0
Pontiac Firebird    19.2 3.845  0
Fiat X1-9           27.3 1.935  1
Porsche 914-2       26.0 2.140  1
Lotus Europa        30.4 1.513  1
Ford Pantera L      15.8 3.170  1
Ferrari Dino        19.7 2.770  1
Maserati Bora       15.0 3.570  1
Volvo 142E          21.4 2.780  1

CodePudding user response：

One possible way to solve your problem:

result = lapply(l, function(x) x[[1L]][my_names])

[[1]]
                     mpg    wt am
Mazda RX4           21.0 2.620  1
Mazda RX4 Wag       21.0 2.875  1
Datsun 710          22.8 2.320  1
Hornet 4 Drive      21.4 3.215  0
Hornet Sportabout   18.7 3.440  0
Valiant             18.1 3.460  0
...

[[2]]
                     mpg    wt am
Mazda RX4           21.0 2.620  1
Mazda RX4 Wag       21.0 2.875  1
Datsun 710          22.8 2.320  1
Hornet 4 Drive      21.4 3.215  0
Hornet Sportabout   18.7 3.440  0
Valiant             18.1 3.460  0
...