How to slice a dataframe by column value in R-CodePudding

I wanted to create a function that allowed users to slice a dataframe by a column with their input values. I was thinking of doing something like this

slice_df <- function(df, col, values){
  df_small = subset(df, col %in% values)
  return(df_small)

The problem was the returned df_small was empty because the column col provided was a text value whilst the argument in subset required a similar value but without quotation ''. For example, slice_df(mydata, 'month', c('Jan', 'Dec')) wouldn't work because the subset function required month instead of 'month'. Is there a workaround this? Alternatively, is there a better function than that naive slice_df? Thanks.

CodePudding user response：

The problem in your code is the missing bracket within the subset function. Here is the updated function :

 slice_df <- function(df, col, values){
  df_small = subset(df, df[[col]] %in% values)
  return(df_small)
}

to check if the function is correct, I used mtcars and sliced vs for 0 values:

slice_df(mtcars,'vs', 0)

output:

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8