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Simple question here: I'm just trying to replace a certain value within a specific column of a data frame with another value. I tried the advice given in: Replacing values from a column using a condition in R and Replace a single column values but it didn't work for me.

> x <- mtcars
> x
                     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
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    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
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
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
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  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
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
> x.new <- x[x$mpg == "21.0"] <- "9000"
> x.new
[1] "9000"
> x.new <- x$mpg[x$mpg == "21.0"] <- "9000"
> x.new
[1] "9000"
> x.new <- x[x$mpg == 21.0] <- 9000
> x.new
[1] 9000

Clearly, x.new does not return the whole data frame.

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warship
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  • The questions you linked explained it pretty clearly. I don't know why you chose to use `<-` twice in one line. – Frank May 02 '16 at 21:12
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    .. er, it's complicated. whenever you assign something `(<-)`, the assignment value is returned invisibly, run `(x[x$mpg == "21.0"] <- "9000")` with the parentheses! and you will see the return value. after that happens, you are assigning the return value of `"9000"` to `x.new` so x.new is 9000 not `x` – rawr May 02 '16 at 21:27

2 Answers2

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mtcars$mpg <- ifelse(mtcars$mpg==21, 9000, mtcars$mpg)

or

mtcars[mtcars$mpg==21,]$mpg <- 9000
Jonas Tundo
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  • I tested your second solution `mtcars[mtcars$mpg==21,]$mpg <- 9000` and it runs quicker on big datasets than the `ifelse` loop. Is there anything even faster that you can propose? – warship May 02 '16 at 21:25
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Try this:

# load library and data
  library(data.table)
  data("mtcars")


# convert your dataset to data.table
  setDT(mtcars)


# Solution
  mtcars[mpg == 21.0, mpg := 9000] 


# here is a slower solution using `ifelse`
  mtcars[, mpg := ifelse(mpg==21.0 , 9000, mpg)]
rafa.pereira
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  • @RafaelPereira Cool solution. It's practically just as fast as `mtcars[mtcars$mpg==21,]$mpg <- 9000`. I'm looking for ways to maximize performance to the hilt. – warship May 02 '16 at 21:34
  • In this case, you're probably looking for a solution based on `data.table`. I've added another solution using the `ifelse` statement, which might get improve performance a bit further. Anyway, the speed difference only gets more apparent for larger data sets. – rafa.pereira May 02 '16 at 21:40
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    Actually I just tested it out... the `ifelse` loop is actually slower. – warship May 02 '16 at 21:42