A function to replace exceptional values with NA. This can be used to quickly remove impossible values, for example, when participants entered their age as 344.
removeExceptionalValues(dat, items = NULL, exception = 0.005, silent = FALSE, stringsAsFactors = FALSE)
| dat | The dataframe containing the items to inspect. |
|---|---|
| items | The items to inspect. |
| exception | How rare a value must be to be considered exceptional (and replaced by NA). |
| silent | Can be used to suppress messages. |
| stringsAsFactors | Whether to convert strings to factors when creating a dataframe from lapply output. |
Note that exceptional values may be errors (e.g. participants accidently
pressed a key twice, or during data entry, something went wrong), but they may
also be indicative of participants who did not seriously participate in the
study. Therefore, it is advised to first use exceptionalScores to
look for patterns where participants enter many exceptional scores.
The dataframe, with exceptional values replaced by NA.
removeExceptionalValues(mtcars, exception=.1);#> No items specified: extracting all variable names in dataframe.#> mpg cyl disp hp drat wt qsec vs am gear carb #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 6 18.1 6 225.0 105 NA 3.460 NA 1 0 3 1 #> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 8 24.4 4 146.7 NA 3.69 3.190 20.00 1 0 4 2 #> 9 22.8 4 140.8 95 3.92 3.150 NA 1 0 4 2 #> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> 15 NA 8 NA 205 NA NA 17.98 0 0 3 4 #> 16 NA 8 NA 215 3.00 NA 17.82 0 0 3 4 #> 17 14.7 8 NA 230 3.23 NA 17.42 0 0 3 4 #> 18 NA 4 NA 66 4.08 2.200 19.47 1 1 4 1 #> 19 30.4 4 NA NA NA NA 18.52 1 1 4 2 #> 20 NA 4 NA NA 4.22 NA 19.90 1 1 4 1 #> 21 21.5 4 120.1 97 3.70 2.465 NA 1 0 3 1 #> 22 15.5 8 318.0 150 NA 3.520 16.87 0 0 3 2 #> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> 24 NA 8 350.0 245 3.73 3.840 NA 0 0 3 4 #> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 27 26.0 4 120.3 91 NA 2.140 16.70 0 1 5 2 #> 28 30.4 4 95.1 113 3.77 NA 16.90 1 1 5 2 #> 29 15.8 8 351.0 NA 4.22 3.170 NA 0 1 5 4 #> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 NA #> 31 15.0 8 301.0 NA 3.54 3.570 NA 0 1 5 NA #> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2