Hi all,
In the example presented below, I was interested in observing the frequency of Likert scale across the 5 variables, so I generated a table.
However, I wondered if there was a way to remove rows in which the frequency of events is zero?
Any help here would be appreciated.
dat <- data.frame(
matrix(
sample(1:3,5*12, replace=TRUE),12,5,
dimnames=list(1:12,c("X1","X2","X3","X4","X5"))
),
Sex=rep(c("Male", "Female")))
freq <- as.data.frame(table(dat))
There certainly is!
dat <- data.frame(
matrix(
sample(1:3,5*12, replace=TRUE),12,5,
dimnames=list(1:12,c("X1","X2","X3","X4","X5"))
),
Sex=rep(c("Male", "Female")))
(freq <- as.data.frame(table(dat)))
#> X1 X2 X3 X4 X5 Sex Freq
#> 1 1 1 1 1 1 Female 0
#> 2 2 1 1 1 1 Female 0
#> 3 1 2 1 1 1 Female 0
#> 4 2 2 1 1 1 Female 0
#> 5 1 3 1 1 1 Female 0
#> 6 2 3 1 1 1 Female 0
#> 7 1 1 2 1 1 Female 0
#> 8 2 1 2 1 1 Female 0
#> 9 1 2 2 1 1 Female 0
#> 10 2 2 2 1 1 Female 0
#> 11 1 3 2 1 1 Female 0
#> 12 2 3 2 1 1 Female 0
#> 13 1 1 3 1 1 Female 0
#> 14 2 1 3 1 1 Female 0
#> 15 1 2 3 1 1 Female 0
#> 16 2 2 3 1 1 Female 0
#> 17 1 3 3 1 1 Female 0
#> 18 2 3 3 1 1 Female 0
#> 19 1 1 1 2 1 Female 0
#> 20 2 1 1 2 1 Female 0
#> 21 1 2 1 2 1 Female 0
#> 22 2 2 1 2 1 Female 0
#> 23 1 3 1 2 1 Female 0
#> 24 2 3 1 2 1 Female 0
#> 25 1 1 2 2 1 Female 0
#> 26 2 1 2 2 1 Female 0
#> 27 1 2 2 2 1 Female 0
#> 28 2 2 2 2 1 Female 0
#> 29 1 3 2 2 1 Female 0
#> 30 2 3 2 2 1 Female 0
#> 31 1 1 3 2 1 Female 0
#> 32 2 1 3 2 1 Female 0
#> 33 1 2 3 2 1 Female 0
#> 34 2 2 3 2 1 Female 0
#> 35 1 3 3 2 1 Female 0
#> 36 2 3 3 2 1 Female 0
#> 37 1 1 1 3 1 Female 0
#> 38 2 1 1 3 1 Female 0
#> 39 1 2 1 3 1 Female 0
#> 40 2 2 1 3 1 Female 0
#> 41 1 3 1 3 1 Female 0
#> 42 2 3 1 3 1 Female 0
#> 43 1 1 2 3 1 Female 0
#> 44 2 1 2 3 1 Female 0
#> 45 1 2 2 3 1 Female 0
#> 46 2 2 2 3 1 Female 0
#> 47 1 3 2 3 1 Female 0
#> 48 2 3 2 3 1 Female 0
#> 49 1 1 3 3 1 Female 0
#> 50 2 1 3 3 1 Female 0
#> 51 1 2 3 3 1 Female 0
#> 52 2 2 3 3 1 Female 0
#> 53 1 3 3 3 1 Female 0
#> 54 2 3 3 3 1 Female 0
#> 55 1 1 1 1 2 Female 0
#> 56 2 1 1 1 2 Female 0
#> 57 1 2 1 1 2 Female 0
#> 58 2 2 1 1 2 Female 0
#> 59 1 3 1 1 2 Female 0
#> 60 2 3 1 1 2 Female 0
#> 61 1 1 2 1 2 Female 0
#> 62 2 1 2 1 2 Female 0
#> 63 1 2 2 1 2 Female 0
#> 64 2 2 2 1 2 Female 0
#> 65 1 3 2 1 2 Female 0
#> 66 2 3 2 1 2 Female 0
#> 67 1 1 3 1 2 Female 0
#> 68 2 1 3 1 2 Female 0
#> 69 1 2 3 1 2 Female 0
#> 70 2 2 3 1 2 Female 0
#> 71 1 3 3 1 2 Female 0
#> 72 2 3 3 1 2 Female 1
#> 73 1 1 1 2 2 Female 0
#> 74 2 1 1 2 2 Female 1
#> 75 1 2 1 2 2 Female 0
#> 76 2 2 1 2 2 Female 0
#> 77 1 3 1 2 2 Female 1
#> 78 2 3 1 2 2 Female 0
#> 79 1 1 2 2 2 Female 0
#> 80 2 1 2 2 2 Female 0
#> 81 1 2 2 2 2 Female 0
#> 82 2 2 2 2 2 Female 0
#> 83 1 3 2 2 2 Female 0
#> 84 2 3 2 2 2 Female 0
#> 85 1 1 3 2 2 Female 0
#> 86 2 1 3 2 2 Female 0
#> 87 1 2 3 2 2 Female 0
#> 88 2 2 3 2 2 Female 0
#> 89 1 3 3 2 2 Female 0
#> 90 2 3 3 2 2 Female 0
#> 91 1 1 1 3 2 Female 0
#> 92 2 1 1 3 2 Female 0
#> 93 1 2 1 3 2 Female 0
#> 94 2 2 1 3 2 Female 0
#> 95 1 3 1 3 2 Female 0
#> 96 2 3 1 3 2 Female 0
#> 97 1 1 2 3 2 Female 0
#> 98 2 1 2 3 2 Female 0
#> 99 1 2 2 3 2 Female 0
#> 100 2 2 2 3 2 Female 0
#> 101 1 3 2 3 2 Female 0
#> 102 2 3 2 3 2 Female 0
#> 103 1 1 3 3 2 Female 0
#> 104 2 1 3 3 2 Female 1
#> 105 1 2 3 3 2 Female 0
#> 106 2 2 3 3 2 Female 0
#> 107 1 3 3 3 2 Female 0
#> 108 2 3 3 3 2 Female 0
#> 109 1 1 1 1 3 Female 0
#> 110 2 1 1 1 3 Female 0
#> 111 1 2 1 1 3 Female 0
#> 112 2 2 1 1 3 Female 0
#> 113 1 3 1 1 3 Female 0
#> 114 2 3 1 1 3 Female 0
#> 115 1 1 2 1 3 Female 0
#> 116 2 1 2 1 3 Female 0
#> 117 1 2 2 1 3 Female 0
#> 118 2 2 2 1 3 Female 0
#> 119 1 3 2 1 3 Female 0
#> 120 2 3 2 1 3 Female 0
#> 121 1 1 3 1 3 Female 0
#> 122 2 1 3 1 3 Female 0
#> 123 1 2 3 1 3 Female 0
#> 124 2 2 3 1 3 Female 0
#> 125 1 3 3 1 3 Female 0
#> 126 2 3 3 1 3 Female 0
#> 127 1 1 1 2 3 Female 0
#> 128 2 1 1 2 3 Female 0
#> 129 1 2 1 2 3 Female 0
#> 130 2 2 1 2 3 Female 0
#> 131 1 3 1 2 3 Female 0
#> 132 2 3 1 2 3 Female 0
#> 133 1 1 2 2 3 Female 0
#> 134 2 1 2 2 3 Female 0
#> 135 1 2 2 2 3 Female 0
#> 136 2 2 2 2 3 Female 0
#> 137 1 3 2 2 3 Female 0
#> 138 2 3 2 2 3 Female 0
#> 139 1 1 3 2 3 Female 1
#> 140 2 1 3 2 3 Female 0
#> 141 1 2 3 2 3 Female 0
#> 142 2 2 3 2 3 Female 0
#> 143 1 3 3 2 3 Female 0
#> 144 2 3 3 2 3 Female 0
#> 145 1 1 1 3 3 Female 1
#> 146 2 1 1 3 3 Female 0
#> 147 1 2 1 3 3 Female 0
#> 148 2 2 1 3 3 Female 0
#> 149 1 3 1 3 3 Female 0
#> 150 2 3 1 3 3 Female 0
#> 151 1 1 2 3 3 Female 0
#> 152 2 1 2 3 3 Female 0
#> 153 1 2 2 3 3 Female 0
#> 154 2 2 2 3 3 Female 0
#> 155 1 3 2 3 3 Female 0
#> 156 2 3 2 3 3 Female 0
#> 157 1 1 3 3 3 Female 0
#> 158 2 1 3 3 3 Female 0
#> 159 1 2 3 3 3 Female 0
#> 160 2 2 3 3 3 Female 0
#> 161 1 3 3 3 3 Female 0
#> 162 2 3 3 3 3 Female 0
#> 163 1 1 1 1 1 Male 0
#> 164 2 1 1 1 1 Male 0
#> 165 1 2 1 1 1 Male 0
#> 166 2 2 1 1 1 Male 1
#> 167 1 3 1 1 1 Male 0
#> 168 2 3 1 1 1 Male 0
#> 169 1 1 2 1 1 Male 0
#> 170 2 1 2 1 1 Male 0
#> 171 1 2 2 1 1 Male 0
#> 172 2 2 2 1 1 Male 0
#> 173 1 3 2 1 1 Male 0
#> 174 2 3 2 1 1 Male 0
#> 175 1 1 3 1 1 Male 0
#> 176 2 1 3 1 1 Male 0
#> 177 1 2 3 1 1 Male 0
#> 178 2 2 3 1 1 Male 0
#> 179 1 3 3 1 1 Male 0
#> 180 2 3 3 1 1 Male 0
#> 181 1 1 1 2 1 Male 0
#> 182 2 1 1 2 1 Male 0
#> 183 1 2 1 2 1 Male 0
#> 184 2 2 1 2 1 Male 0
#> 185 1 3 1 2 1 Male 0
#> 186 2 3 1 2 1 Male 0
#> 187 1 1 2 2 1 Male 0
#> 188 2 1 2 2 1 Male 0
#> 189 1 2 2 2 1 Male 0
#> 190 2 2 2 2 1 Male 0
#> 191 1 3 2 2 1 Male 0
#> 192 2 3 2 2 1 Male 0
#> 193 1 1 3 2 1 Male 0
#> 194 2 1 3 2 1 Male 0
#> 195 1 2 3 2 1 Male 0
#> 196 2 2 3 2 1 Male 0
#> 197 1 3 3 2 1 Male 0
#> 198 2 3 3 2 1 Male 0
#> 199 1 1 1 3 1 Male 0
#> 200 2 1 1 3 1 Male 0
#> 201 1 2 1 3 1 Male 0
#> 202 2 2 1 3 1 Male 0
#> 203 1 3 1 3 1 Male 0
#> 204 2 3 1 3 1 Male 0
#> 205 1 1 2 3 1 Male 0
#> 206 2 1 2 3 1 Male 1
#> 207 1 2 2 3 1 Male 0
#> 208 2 2 2 3 1 Male 0
#> 209 1 3 2 3 1 Male 0
#> 210 2 3 2 3 1 Male 0
#> 211 1 1 3 3 1 Male 0
#> 212 2 1 3 3 1 Male 0
#> 213 1 2 3 3 1 Male 0
#> 214 2 2 3 3 1 Male 0
#> 215 1 3 3 3 1 Male 0
#> 216 2 3 3 3 1 Male 0
#> 217 1 1 1 1 2 Male 0
#> 218 2 1 1 1 2 Male 0
#> 219 1 2 1 1 2 Male 0
#> 220 2 2 1 1 2 Male 0
#> 221 1 3 1 1 2 Male 0
#> 222 2 3 1 1 2 Male 0
#> 223 1 1 2 1 2 Male 0
#> 224 2 1 2 1 2 Male 0
#> 225 1 2 2 1 2 Male 0
#> 226 2 2 2 1 2 Male 0
#> 227 1 3 2 1 2 Male 0
#> 228 2 3 2 1 2 Male 0
#> 229 1 1 3 1 2 Male 0
#> 230 2 1 3 1 2 Male 0
#> 231 1 2 3 1 2 Male 0
#> 232 2 2 3 1 2 Male 0
#> 233 1 3 3 1 2 Male 0
#> 234 2 3 3 1 2 Male 1
#> 235 1 1 1 2 2 Male 0
#> 236 2 1 1 2 2 Male 0
#> 237 1 2 1 2 2 Male 0
#> 238 2 2 1 2 2 Male 0
#> 239 1 3 1 2 2 Male 0
#> 240 2 3 1 2 2 Male 0
#> 241 1 1 2 2 2 Male 0
#> 242 2 1 2 2 2 Male 0
#> 243 1 2 2 2 2 Male 0
#> 244 2 2 2 2 2 Male 0
#> 245 1 3 2 2 2 Male 0
#> 246 2 3 2 2 2 Male 0
#> 247 1 1 3 2 2 Male 0
#> 248 2 1 3 2 2 Male 0
#> 249 1 2 3 2 2 Male 0
#> 250 2 2 3 2 2 Male 0
#> 251 1 3 3 2 2 Male 0
#> 252 2 3 3 2 2 Male 0
#> 253 1 1 1 3 2 Male 0
#> 254 2 1 1 3 2 Male 0
#> 255 1 2 1 3 2 Male 0
#> 256 2 2 1 3 2 Male 0
#> 257 1 3 1 3 2 Male 0
#> 258 2 3 1 3 2 Male 0
#> 259 1 1 2 3 2 Male 0
#> 260 2 1 2 3 2 Male 0
#> 261 1 2 2 3 2 Male 0
#> 262 2 2 2 3 2 Male 0
#> 263 1 3 2 3 2 Male 0
#> 264 2 3 2 3 2 Male 0
#> 265 1 1 3 3 2 Male 0
#> 266 2 1 3 3 2 Male 0
#> 267 1 2 3 3 2 Male 0
#> 268 2 2 3 3 2 Male 0
#> 269 1 3 3 3 2 Male 0
#> 270 2 3 3 3 2 Male 0
#> 271 1 1 1 1 3 Male 0
#> 272 2 1 1 1 3 Male 0
#> 273 1 2 1 1 3 Male 0
#> 274 2 2 1 1 3 Male 0
#> 275 1 3 1 1 3 Male 0
#> 276 2 3 1 1 3 Male 0
#> 277 1 1 2 1 3 Male 0
#> 278 2 1 2 1 3 Male 0
#> 279 1 2 2 1 3 Male 0
#> 280 2 2 2 1 3 Male 0
#> 281 1 3 2 1 3 Male 1
#> 282 2 3 2 1 3 Male 0
#> 283 1 1 3 1 3 Male 0
#> 284 2 1 3 1 3 Male 0
#> 285 1 2 3 1 3 Male 0
#> 286 2 2 3 1 3 Male 0
#> 287 1 3 3 1 3 Male 0
#> 288 2 3 3 1 3 Male 0
#> 289 1 1 1 2 3 Male 0
#> 290 2 1 1 2 3 Male 0
#> 291 1 2 1 2 3 Male 0
#> 292 2 2 1 2 3 Male 0
#> 293 1 3 1 2 3 Male 0
#> 294 2 3 1 2 3 Male 1
#> 295 1 1 2 2 3 Male 0
#> 296 2 1 2 2 3 Male 0
#> 297 1 2 2 2 3 Male 1
#> 298 2 2 2 2 3 Male 0
#> 299 1 3 2 2 3 Male 0
#> 300 2 3 2 2 3 Male 0
#> 301 1 1 3 2 3 Male 0
#> 302 2 1 3 2 3 Male 0
#> 303 1 2 3 2 3 Male 0
#> 304 2 2 3 2 3 Male 0
#> 305 1 3 3 2 3 Male 0
#> 306 2 3 3 2 3 Male 0
#> 307 1 1 1 3 3 Male 0
#> 308 2 1 1 3 3 Male 0
#> 309 1 2 1 3 3 Male 0
#> 310 2 2 1 3 3 Male 0
#> 311 1 3 1 3 3 Male 0
#> 312 2 3 1 3 3 Male 0
#> 313 1 1 2 3 3 Male 0
#> 314 2 1 2 3 3 Male 0
#> 315 1 2 2 3 3 Male 0
#> 316 2 2 2 3 3 Male 0
#> 317 1 3 2 3 3 Male 0
#> 318 2 3 2 3 3 Male 0
#> 319 1 1 3 3 3 Male 0
#> 320 2 1 3 3 3 Male 0
#> 321 1 2 3 3 3 Male 0
#> 322 2 2 3 3 3 Male 0
#> 323 1 3 3 3 3 Male 0
#> 324 2 3 3 3 3 Male 0
freq[freq$Freq > 0,]
#> X1 X2 X3 X4 X5 Sex Freq
#> 72 2 3 3 1 2 Female 1
#> 74 2 1 1 2 2 Female 1
#> 77 1 3 1 2 2 Female 1
#> 104 2 1 3 3 2 Female 1
#> 139 1 1 3 2 3 Female 1
#> 145 1 1 1 3 3 Female 1
#> 166 2 2 1 1 1 Male 1
#> 206 2 1 2 3 1 Male 1
#> 234 2 3 3 1 2 Male 1
#> 281 1 3 2 1 3 Male 1
#> 294 2 3 1 2 3 Male 1
#> 297 1 2 2 2 3 Male 1
# alternatively...
dplyr::count(dat, X1, X2, X3, X4, X5, Sex)
#> X1 X2 X3 X4 X5 Sex n
#> 1 1 1 1 3 3 Female 1
#> 2 1 1 3 2 3 Female 1
#> 3 1 2 2 2 3 Male 1
#> 4 1 3 1 2 2 Female 1
#> 5 1 3 2 1 3 Male 1
#> 6 2 1 1 2 2 Female 1
#> 7 2 1 2 3 1 Male 1
#> 8 2 1 3 3 2 Female 1
#> 9 2 2 1 1 1 Male 1
#> 10 2 3 1 2 3 Male 1
#> 11 2 3 3 1 2 Female 1
#> 12 2 3 3 1 2 Male 1
Created on 2022-08-29 by the reprex package (v2.0.1)
Hi @JackDavison. My apologies. But the zero frequencies still appear on the RHS? I would like to display only non-zero frequencies if possible?
Hi @AC3112, did you scroll further down?
freq[freq$Freq > 0,]
#> X1 X2 X3 X4 X5 Sex Freq
#> 72 2 3 3 1 2 Female 1
#> 74 2 1 1 2 2 Female 1
#> 77 1 3 1 2 2 Female 1
#> 104 2 1 3 3 2 Female 1
#> 139 1 1 3 2 3 Female 1
#> 145 1 1 1 3 3 Female 1
#> 166 2 2 1 1 1 Male 1
#> 206 2 1 2 3 1 Male 1
#> 234 2 3 3 1 2 Male 1
#> 281 1 3 2 1 3 Male 1
#> 294 2 3 1 2 3 Male 1
#> 297 1 2 2 2 3 Male 1
# alternatively...
dplyr::count(dat, X1, X2, X3, X4, X5, Sex)
#> X1 X2 X3 X4 X5 Sex n
#> 1 1 1 1 3 3 Female 1
#> 2 1 1 3 2 3 Female 1
#> 3 1 2 2 2 3 Male 1
#> 4 1 3 1 2 2 Female 1
#> 5 1 3 2 1 3 Male 1
#> 6 2 1 1 2 2 Female 1
#> 7 2 1 2 3 1 Male 1
#> 8 2 1 3 3 2 Female 1
#> 9 2 2 1 1 1 Male 1
#> 10 2 3 1 2 3 Male 1
#> 11 2 3 3 1 2 Female 1
#> 12 2 3 3 1 2 Male 1Hi @JackDavison.
My apologies. Silly oversight. Really appreciate your help and kind response.
Thanks
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