R/miscellaneous.R
recode_errors.RdRecode a vector of erroneous values for a vector or column(s) in a dataframe
by replacing those values with NA or another common indicator value. Similar
to na_if but is more flexible, e.g. it can operate on
multiple columns/rows (for data frames), or value indices (for vectors).
recode_errors(
data,
errors,
replacement = NA,
rows = NULL,
cols = NULL,
ind = NULL
)A vector, data frame, tibble, or matrix.
A vector of erroneous values to be recoded.
The value you wish to replace all errors with. Default = NA.
A vector specifying the rows of the data object for which to replace erroneous values. Default is all rows in data. Use "ind" instead if data is a vector.
A vector specifying the columns of the data object for which to replace erroneous values. Default is all columns in data. Use "ind" instead if data is a vector.
If data is a vector, this accepts a vector specifying the indices the data object for which to replace erroneous values. Default is all indices in data. Use "rows" &/or "cols" to specify indices to operate upon if data is not a vector.
#if (hypothetically) values of 0 & 8 were actually data entry errors in the
#mtcars dataset these can easily be recoded as NAs
data(mtcars)
recode_errors(mtcars,
cols = c(8:11), #specify a column number range
errors = c(0, 8))
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 1 21.0 6 160.0 110 3.90 2.620 16.46 NA 1 4 4
#> 2 21.0 6 160.0 110 3.90 2.875 17.02 NA 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 NA 3 1
#> 5 18.7 8 360.0 175 3.15 3.440 17.02 NA NA 3 2
#> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 NA 3 1
#> 7 14.3 8 360.0 245 3.21 3.570 15.84 NA NA 3 4
#> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 NA 4 2
#> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 NA 4 2
#> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 NA 4 4
#> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 NA 4 4
#> 12 16.4 8 275.8 180 3.07 4.070 17.40 NA NA 3 3
#> 13 17.3 8 275.8 180 3.07 3.730 17.60 NA NA 3 3
#> 14 15.2 8 275.8 180 3.07 3.780 18.00 NA NA 3 3
#> 15 10.4 8 472.0 205 2.93 5.250 17.98 NA NA 3 4
#> 16 10.4 8 460.0 215 3.00 5.424 17.82 NA NA 3 4
#> 17 14.7 8 440.0 230 3.23 5.345 17.42 NA NA 3 4
#> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 NA 3 1
#> 22 15.5 8 318.0 150 2.76 3.520 16.87 NA NA 3 2
#> 23 15.2 8 304.0 150 3.15 3.435 17.30 NA NA 3 2
#> 24 13.3 8 350.0 245 3.73 3.840 15.41 NA NA 3 4
#> 25 19.2 8 400.0 175 3.08 3.845 17.05 NA NA 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 4.43 2.140 16.70 NA 1 5 2
#> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> 29 15.8 8 351.0 264 4.22 3.170 14.50 NA 1 5 4
#> 30 19.7 6 145.0 175 3.62 2.770 15.50 NA 1 5 6
#> 31 15.0 8 301.0 335 3.54 3.570 14.60 NA 1 5 NA
#> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2