Scales numeric variables between 0 and 1 and creates dummy coding for character and factor variables.
Details
This function processes a dataframe by scaling numeric variables and creating dummy codes for character and factor variables. The numeric variables are scaled between 0 and 1,while the character and factor variables are converted to dummy variables if they have fewer unique values than the specified `categories` parameter.
The function performs the following steps: 1. Identifies numeric variables in the dataframe and scales them. 2. Identifies character and factor variables and creates dummy variables if they meet the criteria. 3. Combines the scaled numeric variables and dummy variables into a single dataframe.
The output is a dataframe with scaled numeric variables and dummy-coded character / factor variables.
Examples
# Example with the 'infert' dataset
recode_scale_dummy(infert)
#> X6.11yrs X12..yrs X0.5yrs age parity induced case spontaneous stratum pooled.stratum
#> 1 0 0 1 0.21739 1.0 0.5 1 1.0 0.00000 0.03226
#> 2 0 0 1 0.91304 0.0 0.5 1 0.0 0.01220 0.00000
#> 3 0 0 1 0.78261 1.0 1.0 1 0.0 0.02439 0.04839
#> 4 0 0 1 0.56522 0.6 1.0 1 0.0 0.03659 0.01613
#> 5 1 0 0 0.60870 0.4 0.5 1 0.5 0.04878 0.50000
#> 6 1 0 0 0.65217 0.6 1.0 1 0.5 0.06098 0.56452
#> 7 1 0 0 0.08696 0.0 0.0 1 0.0 0.07317 0.08065
#> 8 1 0 0 0.47826 0.2 0.0 1 0.0 0.08537 0.33871
#> 9 1 0 0 0.00000 0.0 0.0 1 0.5 0.09756 0.06452
#> 10 1 0 0 0.30435 0.2 0.0 1 0.0 0.10976 0.29032
#> 11 1 0 0 0.34783 0.2 0.5 1 0.0 0.12195 0.30645
#> 12 1 0 0 0.69565 0.6 1.0 1 0.5 0.13415 0.58065
#> 13 1 0 0 0.43478 0.0 0.5 1 0.0 0.14634 0.12903
#> 14 1 0 0 0.34783 0.4 1.0 1 0.0 0.15854 0.45161
#> 15 1 0 0 0.43478 0.2 0.5 1 0.5 0.17073 0.32258
#> 16 1 0 0 0.26087 0.2 1.0 1 0.0 0.18293 0.27419
#> 17 1 0 0 0.39130 0.8 1.0 1 0.5 0.19512 0.59677
#> 18 1 0 0 0.21739 0.0 0.0 1 0.5 0.20732 0.09677
#> 19 1 0 0 0.17391 0.4 1.0 1 0.5 0.21951 0.43548
#> 20 1 0 0 1.00000 0.0 0.0 1 0.5 0.23171 0.25806
#> 21 1 0 0 0.82609 0.0 0.0 1 0.5 0.24390 0.20968
#> 22 1 0 0 0.60870 0.2 1.0 1 0.0 0.25610 0.37097
#> 23 1 0 0 0.30435 0.2 0.0 1 1.0 0.26829 0.29032
#> 24 1 0 0 0.65217 0.0 0.0 1 0.5 0.28049 0.17742
#> 25 1 0 0 0.26087 0.2 0.5 1 0.5 0.29268 0.27419
#> 26 1 0 0 0.82609 0.2 0.0 1 1.0 0.30488 0.41935
#> 27 1 0 0 0.73913 0.2 0.0 1 1.0 0.31707 0.40323
#> 28 1 0 0 0.56522 0.4 0.0 1 1.0 0.32927 0.48387
#> 29 1 0 0 0.30435 0.6 0.5 1 1.0 0.34146 0.53226
#> 30 1 0 0 0.39130 0.6 1.0 1 0.0 0.35366 0.54839
#> 31 1 0 0 0.47826 0.0 0.0 1 0.5 0.36585 0.14516
#> 32 1 0 0 0.56522 0.2 0.5 1 0.0 0.37805 0.35484
#> 33 1 0 0 0.91304 0.0 0.5 1 0.0 0.39024 0.24194
#> 34 1 0 0 0.47826 0.2 0.0 1 1.0 0.40244 0.33871
#> 35 1 0 0 0.78261 0.0 0.5 1 0.0 0.41463 0.19355
#> 36 1 0 0 0.60870 0.2 0.0 1 1.0 0.42683 0.37097
#> 37 1 0 0 0.65217 0.0 0.0 1 0.5 0.43902 0.17742
#> 38 1 0 0 0.56522 0.4 0.5 1 1.0 0.45122 0.48387
#> 39 1 0 0 0.39130 0.4 0.0 1 0.0 0.46341 0.46774
#> 40 1 0 0 0.30435 0.0 0.0 1 0.5 0.47561 0.11290
#> 41 1 0 0 0.78261 0.4 0.0 1 1.0 0.48780 0.51613
#> 42 1 0 0 0.60870 0.0 0.0 1 0.0 0.50000 0.16129
#> 43 1 0 0 0.86957 0.0 0.0 1 0.0 0.51220 0.22581
#> 44 1 0 0 0.69565 0.2 0.5 1 0.5 0.52439 0.38710
#> 45 0 1 0 0.39130 0.0 0.0 1 0.0 0.53659 0.69355
#> 46 0 1 0 0.69565 0.0 0.5 1 0.0 0.54878 0.75806
#> 47 0 1 0 0.30435 0.2 0.0 1 1.0 0.56098 0.80645
#> 48 0 1 0 0.26087 0.6 1.0 1 0.0 0.57317 0.96774
#> 49 0 1 0 0.21739 0.2 1.0 1 0.0 0.58537 0.77419
#> 50 0 1 0 0.73913 0.4 0.0 1 1.0 0.59756 0.95161
#> 51 0 1 0 0.13043 0.4 0.5 1 1.0 0.60976 0.88710
#> 52 0 1 0 0.65217 0.8 0.5 1 1.0 0.62195 0.98387
#> 53 0 1 0 0.26087 0.4 0.5 1 0.5 0.63415 0.90323
#> 54 0 1 0 0.30435 0.0 0.0 1 0.5 0.64634 0.66129
#> 55 0 1 0 0.34783 0.2 0.0 1 1.0 0.65854 0.82258
#> 56 0 1 0 0.65217 0.2 0.0 1 1.0 0.67073 0.87097
#> 57 0 1 0 0.30435 0.2 0.5 1 0.0 0.68293 0.80645
#> 58 0 1 0 0.30435 0.2 0.0 1 1.0 0.69512 0.80645
#> 59 0 1 0 0.30435 0.0 0.0 1 0.5 0.70732 0.66129
#> 60 0 1 0 0.26087 0.2 0.0 1 1.0 0.71951 0.79032
#> 61 0 1 0 0.60870 0.2 0.0 1 1.0 0.73171 0.85484
#> 62 0 1 0 0.17391 0.0 0.0 1 0.5 0.74390 0.64516
#> 63 0 1 0 0.56522 0.0 0.0 1 0.5 0.75610 0.74194
#> 64 0 1 0 0.43478 0.2 0.0 1 1.0 0.76829 0.83871
#> 65 0 1 0 0.21739 0.2 0.5 1 0.0 0.78049 0.77419
#> 66 0 1 0 0.47826 0.0 0.0 1 0.5 0.79268 0.72581
#> 67 0 1 0 0.00000 0.0 0.0 1 0.5 0.80488 0.61290
#> 68 0 1 0 0.30435 0.4 0.5 1 1.0 0.81707 0.91935
#> 69 0 1 0 0.69565 0.4 0.0 1 1.0 0.82927 0.93548
#> 70 0 1 0 0.17391 0.0 0.5 1 0.0 0.84146 0.64516
#> 71 0 1 0 0.47826 0.0 0.5 1 0.0 0.85366 0.72581
#> 72 0 1 0 0.17391 0.0 0.0 1 0.5 0.86585 0.64516
#> 73 0 1 0 0.43478 0.0 0.0 1 0.5 0.87805 0.70968
#> 74 0 1 0 0.73913 1.0 0.0 1 1.0 0.89024 1.00000
#> 75 0 1 0 0.21739 0.2 0.0 1 1.0 0.90244 0.77419
#> 76 0 1 0 0.43478 0.0 0.0 1 0.5 0.91463 0.70968
#> 77 0 1 0 0.43478 0.2 0.0 1 0.5 0.92683 0.83871
#> 78 0 1 0 0.17391 0.0 0.5 1 0.0 0.93902 0.64516
#> 79 0 1 0 0.43478 0.0 0.0 1 0.5 0.95122 0.70968
#> 80 0 1 0 0.56522 0.0 0.0 1 0.5 0.96341 0.74194
#> 81 0 1 0 0.60870 0.2 1.0 1 0.0 0.97561 0.85484
#> 82 0 1 0 0.34783 0.0 0.0 1 0.5 0.98780 0.67742
#> 83 0 1 0 0.08696 0.0 0.0 1 0.5 1.00000 0.62903
#> 84 0 0 1 0.21739 1.0 1.0 0 0.0 0.00000 0.03226
#> 85 0 0 1 0.91304 0.0 0.0 0 0.0 0.01220 0.00000
#> 86 0 0 1 0.78261 1.0 1.0 0 0.0 0.02439 0.04839
#> 87 0 0 1 0.56522 0.6 0.0 0 0.5 0.03659 0.01613
#> 88 1 0 0 0.60870 0.4 1.0 0 0.0 0.04878 0.50000
#> 89 1 0 0 0.65217 0.6 0.5 0 0.5 0.06098 0.56452
#> 90 1 0 0 0.08696 0.0 0.0 0 0.0 0.07317 0.08065
#> 91 1 0 0 0.47826 0.2 1.0 0 0.0 0.08537 0.33871
#> 92 1 0 0 0.00000 0.0 0.0 0 0.5 0.09756 0.06452
#> 93 1 0 0 0.30435 0.2 0.0 0 0.5 0.10976 0.29032
#> 94 1 0 0 0.34783 0.2 0.0 0 0.0 0.12195 0.30645
#> 95 1 0 0 0.69565 0.6 0.5 0 0.5 0.13415 0.58065
#> 96 1 0 0 0.43478 0.0 0.0 0 0.0 0.14634 0.12903
#> 97 1 0 0 0.34783 0.4 0.0 0 0.5 0.15854 0.45161
#> 98 1 0 0 0.43478 0.2 0.5 0 0.0 0.17073 0.32258
#> 99 1 0 0 0.26087 0.2 0.5 0 0.0 0.18293 0.27419
#> 100 1 0 0 0.39130 0.8 0.0 0 1.0 0.19512 0.59677
#> 101 1 0 0 0.21739 0.0 0.0 0 0.0 0.20732 0.09677
#> 102 1 0 0 0.17391 0.4 0.0 0 0.5 0.21951 0.43548
#> 103 1 0 0 1.00000 0.0 0.0 0 0.0 0.23171 0.25806
#> 104 1 0 0 0.82609 0.0 0.0 0 0.0 0.24390 0.20968
#> 105 1 0 0 0.60870 0.2 0.0 0 0.0 0.25610 0.37097
#> 106 1 0 0 0.30435 0.2 0.0 0 0.0 0.26829 0.29032
#> 107 1 0 0 0.65217 0.0 0.0 0 0.0 0.28049 0.17742
#> 108 1 0 0 0.26087 0.2 0.0 0 0.5 0.29268 0.27419
#> 109 1 0 0 0.82609 0.2 0.0 0 0.0 0.30488 0.41935
#> 110 1 0 0 0.73913 0.2 0.0 0 0.0 0.31707 0.40323
#> 111 1 0 0 0.56522 0.4 0.0 0 0.0 0.32927 0.48387
#> 112 1 0 0 0.30435 0.6 0.0 0 1.0 0.34146 0.53226
#> 113 1 0 0 0.39130 0.6 0.5 0 0.5 0.35366 0.54839
#> 114 1 0 0 0.47826 0.0 0.0 0 0.0 0.36585 0.14516
#> 115 1 0 0 0.56522 0.2 0.5 0 0.0 0.37805 0.35484
#> 116 1 0 0 0.91304 0.0 0.5 0 0.0 0.39024 0.24194
#> 117 1 0 0 0.47826 0.2 0.0 0 0.0 0.40244 0.33871
#> 118 1 0 0 0.78261 0.0 0.0 0 0.0 0.41463 0.19355
#> 119 1 0 0 0.60870 0.2 0.0 0 0.0 0.42683 0.37097
#> 120 1 0 0 0.65217 0.0 0.0 0 0.0 0.43902 0.17742
#> 121 1 0 0 0.56522 0.4 1.0 0 0.0 0.45122 0.48387
#> 122 1 0 0 0.39130 0.4 0.0 0 1.0 0.46341 0.46774
#> 123 1 0 0 0.30435 0.0 0.5 0 0.0 0.47561 0.11290
#> 124 1 0 0 0.78261 0.4 0.5 0 0.0 0.48780 0.51613
#> 125 1 0 0 0.60870 0.0 0.0 0 0.0 0.50000 0.16129
#> 126 1 0 0 0.86957 0.0 0.0 0 0.0 0.51220 0.22581
#> 127 1 0 0 0.69565 0.2 0.0 0 0.0 0.52439 0.38710
#> 128 0 1 0 0.39130 0.0 0.5 0 0.0 0.53659 0.69355
#> 129 0 1 0 0.69565 0.0 0.0 0 0.0 0.54878 0.75806
#> 130 0 1 0 0.30435 0.2 0.5 0 0.0 0.56098 0.80645
#> 131 0 1 0 0.26087 0.6 1.0 0 0.5 0.57317 0.96774
#> 132 0 1 0 0.21739 0.2 0.5 0 0.0 0.58537 0.77419
#> 133 0 1 0 0.73913 0.4 0.5 0 0.0 0.59756 0.95161
#> 134 0 1 0 0.13043 0.4 1.0 0 0.5 0.60976 0.88710
#> 135 0 1 0 0.65217 0.8 0.5 0 0.5 0.62195 0.98387
#> 136 0 1 0 0.26087 0.4 0.5 0 0.5 0.63415 0.90323
#> 137 0 1 0 0.30435 0.0 0.5 0 0.0 0.64634 0.66129
#> 138 0 1 0 0.34783 0.2 0.5 0 0.0 0.65854 0.82258
#> 139 0 1 0 0.65217 0.2 0.5 0 0.0 0.67073 0.87097
#> 140 0 1 0 0.30435 0.2 0.5 0 0.5 0.68293 0.80645
#> 141 0 1 0 0.30435 0.2 1.0 0 0.0 0.69512 0.80645
#> 142 0 1 0 0.30435 0.0 0.5 0 0.0 0.70732 0.66129
#> 143 0 1 0 0.26087 0.2 0.5 0 0.0 0.71951 0.79032
#> 144 0 1 0 0.60870 0.2 1.0 0 0.0 0.73171 0.85484
#> 145 0 1 0 0.17391 0.0 0.5 0 0.0 0.74390 0.64516
#> 146 0 1 0 0.56522 0.0 0.0 0 0.0 0.75610 0.74194
#> 147 0 1 0 0.43478 0.2 0.0 0 0.0 0.76829 0.83871
#> 148 0 1 0 0.21739 0.2 0.0 0 0.5 0.78049 0.77419
#> 149 0 1 0 0.47826 0.0 0.0 0 0.0 0.79268 0.72581
#> 150 0 1 0 0.00000 0.0 0.0 0 0.5 0.80488 0.61290
#> 151 0 1 0 0.30435 0.4 1.0 0 0.0 0.81707 0.91935
#> 152 0 1 0 0.69565 0.4 0.5 0 0.5 0.82927 0.93548
#> 153 0 1 0 0.17391 0.0 0.0 0 0.0 0.84146 0.64516
#> 154 0 1 0 0.47826 0.0 0.5 0 0.0 0.85366 0.72581
#> 155 0 1 0 0.17391 0.0 0.0 0 0.0 0.86585 0.64516
#> 156 0 1 0 0.43478 0.0 0.0 0 0.5 0.87805 0.70968
#> 157 0 1 0 0.21739 0.2 0.0 0 1.0 0.90244 0.77419
#> 158 0 1 0 0.43478 0.0 0.0 0 0.0 0.91463 0.70968
#> 159 0 1 0 0.43478 0.2 1.0 0 0.0 0.92683 0.83871
#> 160 0 1 0 0.17391 0.0 0.0 0 0.0 0.93902 0.64516
#> 161 0 1 0 0.43478 0.0 0.0 0 0.0 0.95122 0.70968
#> 162 0 1 0 0.56522 0.0 0.0 0 0.0 0.96341 0.74194
#> 163 0 1 0 0.60870 0.2 0.0 0 0.0 0.97561 0.85484
#> 164 0 1 0 0.34783 0.0 0.0 0 0.5 0.98780 0.67742
#> 165 0 1 0 0.08696 0.0 0.0 0 0.5 1.00000 0.62903
#> 166 0 0 1 0.21739 1.0 1.0 0 0.0 0.00000 0.03226
#> 167 0 0 1 0.91304 0.0 0.0 0 0.0 0.01220 0.00000
#> 168 0 0 1 0.78261 1.0 1.0 0 0.0 0.02439 0.04839
#> 169 0 0 1 0.56522 0.6 0.0 0 1.0 0.03659 0.01613
#> 170 1 0 0 0.60870 0.4 0.0 0 0.0 0.04878 0.50000
#> 171 1 0 0 0.65217 0.6 0.0 0 1.0 0.06098 0.56452
#> 172 1 0 0 0.08696 0.0 0.0 0 0.0 0.07317 0.08065
#> 173 1 0 0 0.47826 0.2 0.0 0 0.5 0.08537 0.33871
#> 174 1 0 0 0.00000 0.0 0.5 0 0.0 0.09756 0.06452
#> 175 1 0 0 0.30435 0.2 0.0 0 0.5 0.10976 0.29032
#> 176 1 0 0 0.34783 0.2 0.0 0 0.5 0.12195 0.30645
#> 177 1 0 0 0.69565 0.6 0.0 0 0.5 0.13415 0.58065
#> 178 1 0 0 0.43478 0.0 0.0 0 0.0 0.14634 0.12903
#> 179 1 0 0 0.34783 0.4 0.0 0 1.0 0.15854 0.45161
#> 180 1 0 0 0.43478 0.2 0.5 0 0.0 0.17073 0.32258
#> 181 1 0 0 0.26087 0.2 0.0 0 0.0 0.18293 0.27419
#> 182 1 0 0 0.39130 0.8 0.5 0 1.0 0.19512 0.59677
#> 183 1 0 0 0.21739 0.0 0.5 0 0.0 0.20732 0.09677
#> 184 1 0 0 0.17391 0.4 0.5 0 0.5 0.21951 0.43548
#> 185 1 0 0 1.00000 0.0 0.5 0 0.0 0.23171 0.25806
#> 186 1 0 0 0.82609 0.0 0.0 0 0.0 0.24390 0.20968
#> 187 1 0 0 0.60870 0.2 0.0 0 0.0 0.25610 0.37097
#> 188 1 0 0 0.30435 0.2 1.0 0 0.0 0.26829 0.29032
#> 189 1 0 0 0.65217 0.0 0.0 0 0.5 0.28049 0.17742
#> 190 1 0 0 0.26087 0.2 0.0 0 1.0 0.29268 0.27419
#> 191 1 0 0 0.82609 0.2 0.0 0 0.0 0.30488 0.41935
#> 192 1 0 0 0.73913 0.2 0.0 0 0.0 0.31707 0.40323
#> 193 1 0 0 0.56522 0.4 0.0 0 0.0 0.32927 0.48387
#> 194 1 0 0 0.30435 0.6 1.0 0 0.5 0.34146 0.53226
#> 195 1 0 0 0.39130 0.6 0.5 0 0.5 0.35366 0.54839
#> 196 1 0 0 0.47826 0.0 0.0 0 0.0 0.36585 0.14516
#> 197 1 0 0 0.56522 0.2 0.0 0 0.0 0.37805 0.35484
#> 198 1 0 0 0.91304 0.0 0.0 0 0.0 0.39024 0.24194
#> 199 1 0 0 0.47826 0.2 1.0 0 0.0 0.40244 0.33871
#> 200 1 0 0 0.78261 0.0 0.0 0 0.0 0.41463 0.19355
#> 201 1 0 0 0.60870 0.2 0.0 0 0.0 0.42683 0.37097
#> 202 1 0 0 0.65217 0.0 0.0 0 0.0 0.43902 0.17742
#> 203 1 0 0 0.56522 0.4 1.0 0 0.0 0.45122 0.48387
#> 204 1 0 0 0.39130 0.4 0.0 0 0.5 0.46341 0.46774
#> 205 1 0 0 0.30435 0.0 0.0 0 0.0 0.47561 0.11290
#> 206 1 0 0 0.78261 0.4 0.0 0 0.0 0.48780 0.51613
#> 207 1 0 0 0.60870 0.0 0.0 0 0.0 0.50000 0.16129
#> 208 1 0 0 0.86957 0.0 0.0 0 0.0 0.51220 0.22581
#> 209 1 0 0 0.69565 0.2 0.0 0 0.0 0.52439 0.38710
#> 210 0 1 0 0.39130 0.0 0.0 0 0.0 0.53659 0.69355
#> 211 0 1 0 0.69565 0.0 0.0 0 0.5 0.54878 0.75806
#> 212 0 1 0 0.30435 0.2 0.5 0 0.0 0.56098 0.80645
#> 213 0 1 0 0.26087 0.6 1.0 0 0.0 0.57317 0.96774
#> 214 0 1 0 0.21739 0.2 0.5 0 0.0 0.58537 0.77419
#> 215 0 1 0 0.73913 0.4 0.5 0 0.0 0.59756 0.95161
#> 216 0 1 0 0.13043 0.4 1.0 0 0.0 0.60976 0.88710
#> 217 0 1 0 0.65217 0.8 1.0 0 0.5 0.62195 0.98387
#> 218 0 1 0 0.26087 0.4 1.0 0 0.0 0.63415 0.90323
#> 219 0 1 0 0.30435 0.0 0.0 0 0.5 0.64634 0.66129
#> 220 0 1 0 0.34783 0.2 0.5 0 0.5 0.65854 0.82258
#> 221 0 1 0 0.65217 0.2 0.0 0 0.5 0.67073 0.87097
#> 222 0 1 0 0.30435 0.2 1.0 0 0.0 0.68293 0.80645
#> 223 0 1 0 0.30435 0.2 0.5 0 0.0 0.69512 0.80645
#> 224 0 1 0 0.30435 0.0 0.0 0 0.0 0.70732 0.66129
#> 225 0 1 0 0.26087 0.2 0.5 0 0.0 0.71951 0.79032
#> 226 0 1 0 0.60870 0.2 0.5 0 0.0 0.73171 0.85484
#> 227 0 1 0 0.17391 0.0 0.5 0 0.0 0.74390 0.64516
#> 228 0 1 0 0.56522 0.0 0.0 0 0.0 0.75610 0.74194
#> 229 0 1 0 0.43478 0.2 0.5 0 0.0 0.76829 0.83871
#> 230 0 1 0 0.21739 0.2 0.0 0 1.0 0.78049 0.77419
#> 231 0 1 0 0.47826 0.0 0.5 0 0.0 0.79268 0.72581
#> 232 0 1 0 0.00000 0.0 0.0 0 0.0 0.80488 0.61290
#> 233 0 1 0 0.30435 0.4 1.0 0 0.0 0.81707 0.91935
#> 234 0 1 0 0.69565 0.4 0.0 0 1.0 0.82927 0.93548
#> 235 0 1 0 0.17391 0.0 0.5 0 0.0 0.84146 0.64516
#> 236 0 1 0 0.47826 0.0 0.0 0 0.0 0.85366 0.72581
#> 237 0 1 0 0.17391 0.0 0.5 0 0.0 0.86585 0.64516
#> 238 0 1 0 0.43478 0.0 0.0 0 0.0 0.87805 0.70968
#> 239 0 1 0 0.73913 1.0 0.0 0 1.0 0.89024 1.00000
#> 240 0 1 0 0.21739 0.2 0.5 0 0.5 0.90244 0.77419
#> 241 0 1 0 0.43478 0.0 0.5 0 0.0 0.91463 0.70968
#> 242 0 1 0 0.43478 0.2 0.0 0 0.5 0.92683 0.83871
#> 243 0 1 0 0.17391 0.0 0.0 0 0.5 0.93902 0.64516
#> 244 0 1 0 0.43478 0.0 0.0 0 0.5 0.95122 0.70968
#> 245 0 1 0 0.56522 0.0 0.0 0 0.0 0.96341 0.74194
#> 246 0 1 0 0.60870 0.2 1.0 0 0.0 0.97561 0.85484
#> 247 0 1 0 0.34783 0.0 0.0 0 0.5 0.98780 0.67742
#> 248 0 1 0 0.08696 0.0 0.0 0 0.5 1.00000 0.62903
# Example with a custom dataframe
df<-data.frame(numeric_var=c(1,2,3,4,5),
factor_var=factor(c('A','B','A','B','C')))
recode_scale_dummy(df)
#> A B C
#> 1 1 0 0
#> 2 0 1 0
#> 3 1 0 0
#> 4 0 1 0
#> 5 0 0 1