Shorthand to remove elements from a data frame based on filter and save as the same name
Examples
# this function removes rows matching the filter expression
data.01 <- mtcars
data.02 <- airquality
#task: remove all mpg > 20
data.01 #data.01 data before pop
#> 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
data_pop_filter(data.01,mpg > 15) #computes and resaves to variable
#note: this is different from subset(data.01,data.01$mpg > 15)
data.01 #modified data after pop based on filter
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4
#> Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
#> Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4
#> Maserati Bora 15.0 8 301 335 3.54 3.570 14.60 0 1 5 8
#task: remove all multiple. remove all elements where Month == 5 or Solar.R > 50
data.02 #data.02 data before pop
#> Ozone Solar.R Wind Temp Month Day
#> 1 41 190 7.4 67 5 1
#> 2 36 118 8.0 72 5 2
#> 3 12 149 12.6 74 5 3
#> 4 18 313 11.5 62 5 4
#> 5 NA NA 14.3 56 5 5
#> 6 28 NA 14.9 66 5 6
#> 7 23 299 8.6 65 5 7
#> 8 19 99 13.8 59 5 8
#> 9 8 19 20.1 61 5 9
#> 10 NA 194 8.6 69 5 10
#> 11 7 NA 6.9 74 5 11
#> 12 16 256 9.7 69 5 12
#> 13 11 290 9.2 66 5 13
#> 14 14 274 10.9 68 5 14
#> 15 18 65 13.2 58 5 15
#> 16 14 334 11.5 64 5 16
#> 17 34 307 12.0 66 5 17
#> 18 6 78 18.4 57 5 18
#> 19 30 322 11.5 68 5 19
#> 20 11 44 9.7 62 5 20
#> 21 1 8 9.7 59 5 21
#> 22 11 320 16.6 73 5 22
#> 23 4 25 9.7 61 5 23
#> 24 32 92 12.0 61 5 24
#> 25 NA 66 16.6 57 5 25
#> 26 NA 266 14.9 58 5 26
#> 27 NA NA 8.0 57 5 27
#> 28 23 13 12.0 67 5 28
#> 29 45 252 14.9 81 5 29
#> 30 115 223 5.7 79 5 30
#> 31 37 279 7.4 76 5 31
#> 32 NA 286 8.6 78 6 1
#> 33 NA 287 9.7 74 6 2
#> 34 NA 242 16.1 67 6 3
#> 35 NA 186 9.2 84 6 4
#> 36 NA 220 8.6 85 6 5
#> 37 NA 264 14.3 79 6 6
#> 38 29 127 9.7 82 6 7
#> 39 NA 273 6.9 87 6 8
#> 40 71 291 13.8 90 6 9
#> 41 39 323 11.5 87 6 10
#> 42 NA 259 10.9 93 6 11
#> 43 NA 250 9.2 92 6 12
#> 44 23 148 8.0 82 6 13
#> 45 NA 332 13.8 80 6 14
#> 46 NA 322 11.5 79 6 15
#> 47 21 191 14.9 77 6 16
#> 48 37 284 20.7 72 6 17
#> 49 20 37 9.2 65 6 18
#> 50 12 120 11.5 73 6 19
#> 51 13 137 10.3 76 6 20
#> 52 NA 150 6.3 77 6 21
#> 53 NA 59 1.7 76 6 22
#> 54 NA 91 4.6 76 6 23
#> 55 NA 250 6.3 76 6 24
#> 56 NA 135 8.0 75 6 25
#> 57 NA 127 8.0 78 6 26
#> 58 NA 47 10.3 73 6 27
#> 59 NA 98 11.5 80 6 28
#> 60 NA 31 14.9 77 6 29
#> 61 NA 138 8.0 83 6 30
#> 62 135 269 4.1 84 7 1
#> 63 49 248 9.2 85 7 2
#> 64 32 236 9.2 81 7 3
#> 65 NA 101 10.9 84 7 4
#> 66 64 175 4.6 83 7 5
#> 67 40 314 10.9 83 7 6
#> 68 77 276 5.1 88 7 7
#> 69 97 267 6.3 92 7 8
#> 70 97 272 5.7 92 7 9
#> 71 85 175 7.4 89 7 10
#> 72 NA 139 8.6 82 7 11
#> 73 10 264 14.3 73 7 12
#> 74 27 175 14.9 81 7 13
#> 75 NA 291 14.9 91 7 14
#> 76 7 48 14.3 80 7 15
#> 77 48 260 6.9 81 7 16
#> 78 35 274 10.3 82 7 17
#> 79 61 285 6.3 84 7 18
#> 80 79 187 5.1 87 7 19
#> 81 63 220 11.5 85 7 20
#> 82 16 7 6.9 74 7 21
#> 83 NA 258 9.7 81 7 22
#> 84 NA 295 11.5 82 7 23
#> 85 80 294 8.6 86 7 24
#> 86 108 223 8.0 85 7 25
#> 87 20 81 8.6 82 7 26
#> 88 52 82 12.0 86 7 27
#> 89 82 213 7.4 88 7 28
#> 90 50 275 7.4 86 7 29
#> 91 64 253 7.4 83 7 30
#> 92 59 254 9.2 81 7 31
#> 93 39 83 6.9 81 8 1
#> 94 9 24 13.8 81 8 2
#> 95 16 77 7.4 82 8 3
#> 96 78 NA 6.9 86 8 4
#> 97 35 NA 7.4 85 8 5
#> 98 66 NA 4.6 87 8 6
#> 99 122 255 4.0 89 8 7
#> 100 89 229 10.3 90 8 8
#> 101 110 207 8.0 90 8 9
#> 102 NA 222 8.6 92 8 10
#> 103 NA 137 11.5 86 8 11
#> 104 44 192 11.5 86 8 12
#> 105 28 273 11.5 82 8 13
#> 106 65 157 9.7 80 8 14
#> 107 NA 64 11.5 79 8 15
#> 108 22 71 10.3 77 8 16
#> 109 59 51 6.3 79 8 17
#> 110 23 115 7.4 76 8 18
#> 111 31 244 10.9 78 8 19
#> 112 44 190 10.3 78 8 20
#> 113 21 259 15.5 77 8 21
#> 114 9 36 14.3 72 8 22
#> 115 NA 255 12.6 75 8 23
#> 116 45 212 9.7 79 8 24
#> 117 168 238 3.4 81 8 25
#> 118 73 215 8.0 86 8 26
#> 119 NA 153 5.7 88 8 27
#> 120 76 203 9.7 97 8 28
#> 121 118 225 2.3 94 8 29
#> 122 84 237 6.3 96 8 30
#> 123 85 188 6.3 94 8 31
#> 124 96 167 6.9 91 9 1
#> 125 78 197 5.1 92 9 2
#> 126 73 183 2.8 93 9 3
#> 127 91 189 4.6 93 9 4
#> 128 47 95 7.4 87 9 5
#> 129 32 92 15.5 84 9 6
#> 130 20 252 10.9 80 9 7
#> 131 23 220 10.3 78 9 8
#> 132 21 230 10.9 75 9 9
#> 133 24 259 9.7 73 9 10
#> 134 44 236 14.9 81 9 11
#> 135 21 259 15.5 76 9 12
#> 136 28 238 6.3 77 9 13
#> 137 9 24 10.9 71 9 14
#> 138 13 112 11.5 71 9 15
#> 139 46 237 6.9 78 9 16
#> 140 18 224 13.8 67 9 17
#> 141 13 27 10.3 76 9 18
#> 142 24 238 10.3 68 9 19
#> 143 16 201 8.0 82 9 20
#> 144 13 238 12.6 64 9 21
#> 145 23 14 9.2 71 9 22
#> 146 36 139 10.3 81 9 23
#> 147 7 49 10.3 69 9 24
#> 148 14 20 16.6 63 9 25
#> 149 30 193 6.9 70 9 26
#> 150 NA 145 13.2 77 9 27
#> 151 14 191 14.3 75 9 28
#> 152 18 131 8.0 76 9 29
#> 153 20 223 11.5 68 9 30
data_pop_filter(data.02,Month == 5 | Solar.R > 50) #computes and resaves to variable
data.02 #modified data after pop based on filter
#> Ozone Solar.R Wind Temp Month Day
#> 49 20 37 9.2 65 6 18
#> 58 NA 47 10.3 73 6 27
#> 60 NA 31 14.9 77 6 29
#> 76 7 48 14.3 80 7 15
#> 82 16 7 6.9 74 7 21
#> 94 9 24 13.8 81 8 2
#> 114 9 36 14.3 72 8 22
#> 137 9 24 10.9 71 9 14
#> 141 13 27 10.3 76 9 18
#> 145 23 14 9.2 71 9 22
#> 147 7 49 10.3 69 9 24
#> 148 14 20 16.6 63 9 25