set.seed(2016)
e=rlnorm(n=120,meanlog=5,sdlog=0.85)
options(digits=4)
e## [1] 68.20 347.60 141.46 190.98 13.84 116.71 77.56 82.91 202.76
## [10] 173.39 102.81 116.82 463.24 68.73 575.75 197.11 70.42 359.60
## [19] 81.73 81.96 103.93 85.07 112.60 246.18 172.90 63.25 85.64
## [28] 466.56 388.19 59.46 63.79 138.09 422.20 41.41 155.29 468.85
## [37] 72.77 74.83 97.31 41.80 139.03 220.22 114.00 87.24 312.49
## [46] 116.98 50.18 958.23 302.29 34.78 129.69 69.67 17.50 56.95
## [55] 169.50 37.27 595.25 51.61 111.70 105.89 270.38 263.52 93.08
## [64] 91.89 222.64 482.58 118.35 57.50 106.00 300.29 335.77 199.82
## [73] 29.22 144.80 143.79 294.42 179.98 317.87 146.86 108.44 234.35
## [82] 231.80 235.32 73.88 41.88 809.09 92.11 262.39 118.37 176.91
## [91] 466.61 245.16 54.16 278.23 109.60 90.34 170.51 115.71 21.42
## [100] 421.25 253.31 48.54 62.11 546.92 195.69 343.63 1051.11 1395.43
## [109] 176.88 411.21 162.91 1346.12 106.82 185.29 752.57 143.07 231.49
## [118] 743.48 592.20 56.97set.seed(2016)
e=rbinom(n=120,size=20,prob=0.8)
e## [1] 18 18 14 18 16 18 16 14 20 19 17 17 17 14 17 17 15 18 16 18 17 15 17 16 14
## [26] 16 18 16 13 16 15 14 18 14 14 18 17 17 17 15 17 18 17 15 17 17 15 17 16 16
## [51] 18 16 17 17 14 16 14 19 18 17 18 16 16 18 14 14 19 18 16 14 14 13 18 15 17
## [76] 19 17 14 19 18 16 18 15 14 17 18 17 15 14 16 17 15 18 15 12 18 15 16 19 17
## [101] 16 18 18 17 20 16 18 16 16 18 19 15 13 14 18 16 17 17 17 12set.seed(2016)
e=rpois(n=120,lambda=10.5)
e## [1] 7 6 11 13 14 9 19 9 14 7 9 13 14 7 8 6 9 12 11 7 9 10 5 7 7
## [26] 5 14 15 12 5 5 7 9 8 10 12 12 6 4 7 16 6 15 6 8 12 12 8 6 14
## [51] 9 6 10 7 11 4 11 11 12 10 10 9 6 12 12 7 4 8 10 11 8 12 6 6 8
## [76] 9 18 12 11 12 10 10 11 13 8 19 11 14 10 18 9 11 16 10 12 16 15 6 13 10
## [101] 11 7 4 14 14 8 10 9 10 10 12 11 11 5 12 9 11 10 11 6set.seed(2016)
seq(1,300,1)## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
## [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
## [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
## [253] 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270
## [271] 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
## [289] 289 290 291 292 293 294 295 296 297 298 299 300sample.int(seq(300,300),120, replace = TRUE)## [1] 32 152 50 62 267 172 252 104 294 175 170 150 197 229 190 187 54 179
## [19] 242 16 113 107 98 65 26 103 32 68 251 167 21 296 264 61 3 2
## [37] 268 272 214 72 42 207 22 83 247 6 100 119 265 124 299 101 72 234
## [55] 19 122 122 25 131 21 20 163 120 242 264 214 288 154 117 215 228 262
## [73] 295 297 266 57 52 90 52 141 250 176 157 123 189 291 158 92 216 58
## [91] 201 184 225 28 264 209 292 32 244 150 251 41 297 17 85 216 47 155
## [109] 52 133 144 235 38 62 98 36 235 283 133 3library(purrr)
set.seed(2016)
bernoulli=rbernoulli(120,0.75)
ifelse(bernoulli==TRUE,'presente','ausente')## [1] "ausente" "ausente" "presente" "ausente" "presente" "ausente"
## [7] "presente" "presente" "ausente" "ausente" "presente" "presente"
## [13] "ausente" "presente" "ausente" "presente" "presente" "ausente"
## [19] "presente" "ausente" "presente" "presente" "presente" "presente"
## [25] "presente" "presente" "ausente" "presente" "presente" "presente"
## [31] "presente" "presente" "ausente" "presente" "presente" "ausente"
## [37] "ausente" "presente" "ausente" "presente" "presente" "ausente"
## [43] "presente" "presente" "presente" "presente" "presente" "presente"
## [49] "presente" "presente" "ausente" "presente" "presente" "presente"
## [55] "presente" "presente" "presente" "ausente" "ausente" "presente"
## [61] "ausente" "presente" "presente" "ausente" "presente" "presente"
## [67] "ausente" "ausente" "presente" "presente" "presente" "presente"
## [73] "ausente" "presente" "ausente" "ausente" "presente" "presente"
## [79] "ausente" "ausente" "presente" "ausente" "presente" "presente"
## [85] "presente" "ausente" "presente" "presente" "presente" "presente"
## [91] "presente" "presente" "ausente" "presente" "presente" "ausente"
## [97] "presente" "presente" "ausente" "presente" "presente" "ausente"
## [103] "ausente" "presente" "ausente" "presente" "ausente" "presente"
## [109] "presente" "ausente" "ausente" "presente" "presente" "presente"
## [115] "ausente" "presente" "presente" "presente" "presente" "presente"set.seed(2016)
gl(n=3,k=40, labels = c("S", "PA", "MA"))## [1] S S S S S S S S S S S S S S S S S S S S S S S S S
## [26] S S S S S S S S S S S S S S S PA PA PA PA PA PA PA PA PA PA
## [51] PA PA PA PA PA PA PA PA PA PA PA PA PA PA PA PA PA PA PA PA PA PA PA PA PA
## [76] PA PA PA PA PA MA MA MA MA MA MA MA MA MA MA MA MA MA MA MA MA MA MA MA MA
## [101] MA MA MA MA MA MA MA MA MA MA MA MA MA MA MA MA MA MA MA MA
## Levels: S PA MAset.seed(2016)
x<-runif(n=120, min=0, max=1.2)
ifelse(x<0.5, yes="FO",no="FI")## [1] "FO" "FO" "FI" "FO" "FI" "FO" "FI" "FI" "FO" "FO" "FO" "FO" "FO" "FI" "FO"
## [16] "FO" "FI" "FO" "FI" "FO" "FO" "FI" "FO" "FI" "FI" "FI" "FO" "FI" "FI" "FI"
## [31] "FI" "FI" "FO" "FI" "FI" "FO" "FO" "FO" "FO" "FI" "FO" "FO" "FO" "FI" "FO"
## [46] "FO" "FI" "FO" "FI" "FI" "FO" "FO" "FO" "FO" "FI" "FI" "FI" "FO" "FO" "FO"
## [61] "FO" "FI" "FI" "FO" "FI" "FI" "FO" "FO" "FI" "FI" "FI" "FI" "FO" "FI" "FO"
## [76] "FO" "FO" "FI" "FO" "FO" "FI" "FO" "FI" "FI" "FO" "FO" "FO" "FI" "FI" "FI"
## [91] "FO" "FI" "FO" "FI" "FI" "FO" "FI" "FI" "FO" "FO" "FI" "FO" "FO" "FO" "FO"
## [106] "FI" "FO" "FI" "FI" "FO" "FO" "FI" "FI" "FI" "FO" "FI" "FO" "FO" "FO" "FI"set.seed(2016)
biomasa<-rlnorm(n=120,meanlog=5,sdlog=0.85)
options(digits=4)
Biomasa_dt<-data.frame(biomasa)
colnames(Biomasa_dt)=c("Gramos")
Biomasa_dt## Gramos
## 1 68.20
## 2 347.60
## 3 141.46
## 4 190.98
## 5 13.84
## 6 116.71
## 7 77.56
## 8 82.91
## 9 202.76
## 10 173.39
## 11 102.81
## 12 116.82
## 13 463.24
## 14 68.73
## 15 575.75
## 16 197.11
## 17 70.42
## 18 359.60
## 19 81.73
## 20 81.96
## 21 103.93
## 22 85.07
## 23 112.60
## 24 246.18
## 25 172.90
## 26 63.25
## 27 85.64
## 28 466.56
## 29 388.19
## 30 59.46
## 31 63.79
## 32 138.09
## 33 422.20
## 34 41.41
## 35 155.29
## 36 468.85
## 37 72.77
## 38 74.83
## 39 97.31
## 40 41.80
## 41 139.03
## 42 220.22
## 43 114.00
## 44 87.24
## 45 312.49
## 46 116.98
## 47 50.18
## 48 958.23
## 49 302.29
## 50 34.78
## 51 129.69
## 52 69.67
## 53 17.50
## 54 56.95
## 55 169.50
## 56 37.27
## 57 595.25
## 58 51.61
## 59 111.70
## 60 105.89
## 61 270.38
## 62 263.52
## 63 93.08
## 64 91.89
## 65 222.64
## 66 482.58
## 67 118.35
## 68 57.50
## 69 106.00
## 70 300.29
## 71 335.77
## 72 199.82
## 73 29.22
## 74 144.80
## 75 143.79
## 76 294.42
## 77 179.98
## 78 317.87
## 79 146.86
## 80 108.44
## 81 234.35
## 82 231.80
## 83 235.32
## 84 73.88
## 85 41.88
## 86 809.09
## 87 92.11
## 88 262.39
## 89 118.37
## 90 176.91
## 91 466.61
## 92 245.16
## 93 54.16
## 94 278.23
## 95 109.60
## 96 90.34
## 97 170.51
## 98 115.71
## 99 21.42
## 100 421.25
## 101 253.31
## 102 48.54
## 103 62.11
## 104 546.92
## 105 195.69
## 106 343.63
## 107 1051.11
## 108 1395.43
## 109 176.88
## 110 411.21
## 111 162.91
## 112 1346.12
## 113 106.82
## 114 185.29
## 115 752.57
## 116 143.07
## 117 231.49
## 118 743.48
## 119 592.20
## 120 56.97set.seed(2016)
flores.r=rbinom(n=120,size=20,prob=0.8)
Flores.r_dt<-data.frame(flores.r)
colnames(Flores.r_dt)=c("Conteo de flores en tres ramas")
Flores.r_dt## Conteo de flores en tres ramas
## 1 18
## 2 18
## 3 14
## 4 18
## 5 16
## 6 18
## 7 16
## 8 14
## 9 20
## 10 19
## 11 17
## 12 17
## 13 17
## 14 14
## 15 17
## 16 17
## 17 15
## 18 18
## 19 16
## 20 18
## 21 17
## 22 15
## 23 17
## 24 16
## 25 14
## 26 16
## 27 18
## 28 16
## 29 13
## 30 16
## 31 15
## 32 14
## 33 18
## 34 14
## 35 14
## 36 18
## 37 17
## 38 17
## 39 17
## 40 15
## 41 17
## 42 18
## 43 17
## 44 15
## 45 17
## 46 17
## 47 15
## 48 17
## 49 16
## 50 16
## 51 18
## 52 16
## 53 17
## 54 17
## 55 14
## 56 16
## 57 14
## 58 19
## 59 18
## 60 17
## 61 18
## 62 16
## 63 16
## 64 18
## 65 14
## 66 14
## 67 19
## 68 18
## 69 16
## 70 14
## 71 14
## 72 13
## 73 18
## 74 15
## 75 17
## 76 19
## 77 17
## 78 14
## 79 19
## 80 18
## 81 16
## 82 18
## 83 15
## 84 14
## 85 17
## 86 18
## 87 17
## 88 15
## 89 14
## 90 16
## 91 17
## 92 15
## 93 18
## 94 15
## 95 12
## 96 18
## 97 15
## 98 16
## 99 19
## 100 17
## 101 16
## 102 18
## 103 18
## 104 17
## 105 20
## 106 16
## 107 18
## 108 16
## 109 16
## 110 18
## 111 19
## 112 15
## 113 13
## 114 14
## 115 18
## 116 16
## 117 17
## 118 17
## 119 17
## 120 12set.seed(2016)
flores.d=rpois(n=120,lambda=10.5)
Flores.d_dt<-data.frame(flores.d)
colnames(Flores.d_dt)=c("Conteo de flores desprendidas")
Flores.d_dt## Conteo de flores desprendidas
## 1 7
## 2 6
## 3 11
## 4 13
## 5 14
## 6 9
## 7 19
## 8 9
## 9 14
## 10 7
## 11 9
## 12 13
## 13 14
## 14 7
## 15 8
## 16 6
## 17 9
## 18 12
## 19 11
## 20 7
## 21 9
## 22 10
## 23 5
## 24 7
## 25 7
## 26 5
## 27 14
## 28 15
## 29 12
## 30 5
## 31 5
## 32 7
## 33 9
## 34 8
## 35 10
## 36 12
## 37 12
## 38 6
## 39 4
## 40 7
## 41 16
## 42 6
## 43 15
## 44 6
## 45 8
## 46 12
## 47 12
## 48 8
## 49 6
## 50 14
## 51 9
## 52 6
## 53 10
## 54 7
## 55 11
## 56 4
## 57 11
## 58 11
## 59 12
## 60 10
## 61 10
## 62 9
## 63 6
## 64 12
## 65 12
## 66 7
## 67 4
## 68 8
## 69 10
## 70 11
## 71 8
## 72 12
## 73 6
## 74 6
## 75 8
## 76 9
## 77 18
## 78 12
## 79 11
## 80 12
## 81 10
## 82 10
## 83 11
## 84 13
## 85 8
## 86 19
## 87 11
## 88 14
## 89 10
## 90 18
## 91 9
## 92 11
## 93 16
## 94 10
## 95 12
## 96 16
## 97 15
## 98 6
## 99 13
## 100 10
## 101 11
## 102 7
## 103 4
## 104 14
## 105 14
## 106 8
## 107 10
## 108 9
## 109 10
## 110 10
## 111 12
## 112 11
## 113 11
## 114 5
## 115 12
## 116 9
## 117 11
## 118 10
## 119 11
## 120 6set.seed(2016)
seq(1,300,1)## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## [55] 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## [73] 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## [91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## [145] 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## [163] 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180
## [181] 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198
## [199] 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216
## [217] 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
## [235] 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252
## [253] 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270
## [271] 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
## [289] 289 290 291 292 293 294 295 296 297 298 299 300hojas.d=sample.int(seq(300,300),120, replace = TRUE)
Hojas.d_dt<-data.frame(hojas.d)
colnames(Hojas.d_dt)=c("Conteo de hojas desprendidas")
Hojas.d_dt## Conteo de hojas desprendidas
## 1 32
## 2 152
## 3 50
## 4 62
## 5 267
## 6 172
## 7 252
## 8 104
## 9 294
## 10 175
## 11 170
## 12 150
## 13 197
## 14 229
## 15 190
## 16 187
## 17 54
## 18 179
## 19 242
## 20 16
## 21 113
## 22 107
## 23 98
## 24 65
## 25 26
## 26 103
## 27 32
## 28 68
## 29 251
## 30 167
## 31 21
## 32 296
## 33 264
## 34 61
## 35 3
## 36 2
## 37 268
## 38 272
## 39 214
## 40 72
## 41 42
## 42 207
## 43 22
## 44 83
## 45 247
## 46 6
## 47 100
## 48 119
## 49 265
## 50 124
## 51 299
## 52 101
## 53 72
## 54 234
## 55 19
## 56 122
## 57 122
## 58 25
## 59 131
## 60 21
## 61 20
## 62 163
## 63 120
## 64 242
## 65 264
## 66 214
## 67 288
## 68 154
## 69 117
## 70 215
## 71 228
## 72 262
## 73 295
## 74 297
## 75 266
## 76 57
## 77 52
## 78 90
## 79 52
## 80 141
## 81 250
## 82 176
## 83 157
## 84 123
## 85 189
## 86 291
## 87 158
## 88 92
## 89 216
## 90 58
## 91 201
## 92 184
## 93 225
## 94 28
## 95 264
## 96 209
## 97 292
## 98 32
## 99 244
## 100 150
## 101 251
## 102 41
## 103 297
## 104 17
## 105 85
## 106 216
## 107 47
## 108 155
## 109 52
## 110 133
## 111 144
## 112 235
## 113 38
## 114 62
## 115 98
## 116 36
## 117 235
## 118 283
## 119 133
## 120 3library(purrr)
set.seed(2016)
Plaga=bernoulli=rbernoulli(120,0.75)
plaga_dt<-data.frame(Plaga)
ifelse(data.frame(Plaga)==TRUE,'presente','ausente')## Plaga
## [1,] "ausente"
## [2,] "ausente"
## [3,] "presente"
## [4,] "ausente"
## [5,] "presente"
## [6,] "ausente"
## [7,] "presente"
## [8,] "presente"
## [9,] "ausente"
## [10,] "ausente"
## [11,] "presente"
## [12,] "presente"
## [13,] "ausente"
## [14,] "presente"
## [15,] "ausente"
## [16,] "presente"
## [17,] "presente"
## [18,] "ausente"
## [19,] "presente"
## [20,] "ausente"
## [21,] "presente"
## [22,] "presente"
## [23,] "presente"
## [24,] "presente"
## [25,] "presente"
## [26,] "presente"
## [27,] "ausente"
## [28,] "presente"
## [29,] "presente"
## [30,] "presente"
## [31,] "presente"
## [32,] "presente"
## [33,] "ausente"
## [34,] "presente"
## [35,] "presente"
## [36,] "ausente"
## [37,] "ausente"
## [38,] "presente"
## [39,] "ausente"
## [40,] "presente"
## [41,] "presente"
## [42,] "ausente"
## [43,] "presente"
## [44,] "presente"
## [45,] "presente"
## [46,] "presente"
## [47,] "presente"
## [48,] "presente"
## [49,] "presente"
## [50,] "presente"
## [51,] "ausente"
## [52,] "presente"
## [53,] "presente"
## [54,] "presente"
## [55,] "presente"
## [56,] "presente"
## [57,] "presente"
## [58,] "ausente"
## [59,] "ausente"
## [60,] "presente"
## [61,] "ausente"
## [62,] "presente"
## [63,] "presente"
## [64,] "ausente"
## [65,] "presente"
## [66,] "presente"
## [67,] "ausente"
## [68,] "ausente"
## [69,] "presente"
## [70,] "presente"
## [71,] "presente"
## [72,] "presente"
## [73,] "ausente"
## [74,] "presente"
## [75,] "ausente"
## [76,] "ausente"
## [77,] "presente"
## [78,] "presente"
## [79,] "ausente"
## [80,] "ausente"
## [81,] "presente"
## [82,] "ausente"
## [83,] "presente"
## [84,] "presente"
## [85,] "presente"
## [86,] "ausente"
## [87,] "presente"
## [88,] "presente"
## [89,] "presente"
## [90,] "presente"
## [91,] "presente"
## [92,] "presente"
## [93,] "ausente"
## [94,] "presente"
## [95,] "presente"
## [96,] "ausente"
## [97,] "presente"
## [98,] "presente"
## [99,] "ausente"
## [100,] "presente"
## [101,] "presente"
## [102,] "ausente"
## [103,] "ausente"
## [104,] "presente"
## [105,] "ausente"
## [106,] "presente"
## [107,] "ausente"
## [108,] "presente"
## [109,] "presente"
## [110,] "ausente"
## [111,] "ausente"
## [112,] "presente"
## [113,] "presente"
## [114,] "presente"
## [115,] "ausente"
## [116,] "presente"
## [117,] "presente"
## [118,] "presente"
## [119,] "presente"
## [120,] "presente"set.seed(2016)
Status=gl(n=3,k=40, labels = c("S", "PA", "MA"))
Status_dt=data.frame(Status)
Status_dt## Status
## 1 S
## 2 S
## 3 S
## 4 S
## 5 S
## 6 S
## 7 S
## 8 S
## 9 S
## 10 S
## 11 S
## 12 S
## 13 S
## 14 S
## 15 S
## 16 S
## 17 S
## 18 S
## 19 S
## 20 S
## 21 S
## 22 S
## 23 S
## 24 S
## 25 S
## 26 S
## 27 S
## 28 S
## 29 S
## 30 S
## 31 S
## 32 S
## 33 S
## 34 S
## 35 S
## 36 S
## 37 S
## 38 S
## 39 S
## 40 S
## 41 PA
## 42 PA
## 43 PA
## 44 PA
## 45 PA
## 46 PA
## 47 PA
## 48 PA
## 49 PA
## 50 PA
## 51 PA
## 52 PA
## 53 PA
## 54 PA
## 55 PA
## 56 PA
## 57 PA
## 58 PA
## 59 PA
## 60 PA
## 61 PA
## 62 PA
## 63 PA
## 64 PA
## 65 PA
## 66 PA
## 67 PA
## 68 PA
## 69 PA
## 70 PA
## 71 PA
## 72 PA
## 73 PA
## 74 PA
## 75 PA
## 76 PA
## 77 PA
## 78 PA
## 79 PA
## 80 PA
## 81 MA
## 82 MA
## 83 MA
## 84 MA
## 85 MA
## 86 MA
## 87 MA
## 88 MA
## 89 MA
## 90 MA
## 91 MA
## 92 MA
## 93 MA
## 94 MA
## 95 MA
## 96 MA
## 97 MA
## 98 MA
## 99 MA
## 100 MA
## 101 MA
## 102 MA
## 103 MA
## 104 MA
## 105 MA
## 106 MA
## 107 MA
## 108 MA
## 109 MA
## 110 MA
## 111 MA
## 112 MA
## 113 MA
## 114 MA
## 115 MA
## 116 MA
## 117 MA
## 118 MA
## 119 MA
## 120 MAset.seed(2016)
Fertilizacion=x<-runif(n=120, min=0, max=1.2)
x## [1] 0.216196 0.171532 1.009976 0.160290 0.573003 0.145510 0.739958 1.068657
## [9] 0.003148 0.064151 0.466425 0.327544 0.267096 1.051889 0.296003 0.471945
## [17] 0.771862 0.158156 0.687120 0.106719 0.399510 0.822657 0.466944 0.672675
## [25] 1.091679 0.718872 0.219092 0.734665 1.133562 0.514379 0.756898 0.985346
## [33] 0.228262 1.054420 1.021319 0.177297 0.289657 0.337147 0.290904 0.808313
## [41] 0.405067 0.118848 0.307576 0.803764 0.447160 0.329903 0.869037 0.374689
## [49] 0.685562 0.581719 0.189378 0.497445 0.310611 0.448811 1.093313 0.597337
## [57] 1.045212 0.068677 0.169117 0.455406 0.192317 0.601115 0.559443 0.206288
## [65] 1.068777 1.028963 0.079890 0.111915 0.625508 1.054966 1.094420 1.132734
## [73] 0.241041 0.824203 0.252290 0.078908 0.371706 1.049651 0.081605 0.198177
## [81] 0.563256 0.193644 0.814518 0.990864 0.453781 0.240157 0.319149 0.755946
## [89] 0.971367 0.639018 0.467689 0.867008 0.121250 0.834471 1.183068 0.118648
## [97] 0.958426 0.633751 0.052709 0.429349 0.524363 0.211101 0.224180 0.377260
## [105] 0.007136 0.652337 0.155860 0.700789 0.674517 0.114343 0.062406 0.807558
## [113] 1.138656 1.047973 0.128397 0.628470 0.442854 0.382527 0.414732 1.162946Fertilizacion_dt<-data.frame(Fertilizacion)
ifelse(Fertilizacion_dt<0.5, yes = "FO", no = "FI")## Fertilizacion
## [1,] "FO"
## [2,] "FO"
## [3,] "FI"
## [4,] "FO"
## [5,] "FI"
## [6,] "FO"
## [7,] "FI"
## [8,] "FI"
## [9,] "FO"
## [10,] "FO"
## [11,] "FO"
## [12,] "FO"
## [13,] "FO"
## [14,] "FI"
## [15,] "FO"
## [16,] "FO"
## [17,] "FI"
## [18,] "FO"
## [19,] "FI"
## [20,] "FO"
## [21,] "FO"
## [22,] "FI"
## [23,] "FO"
## [24,] "FI"
## [25,] "FI"
## [26,] "FI"
## [27,] "FO"
## [28,] "FI"
## [29,] "FI"
## [30,] "FI"
## [31,] "FI"
## [32,] "FI"
## [33,] "FO"
## [34,] "FI"
## [35,] "FI"
## [36,] "FO"
## [37,] "FO"
## [38,] "FO"
## [39,] "FO"
## [40,] "FI"
## [41,] "FO"
## [42,] "FO"
## [43,] "FO"
## [44,] "FI"
## [45,] "FO"
## [46,] "FO"
## [47,] "FI"
## [48,] "FO"
## [49,] "FI"
## [50,] "FI"
## [51,] "FO"
## [52,] "FO"
## [53,] "FO"
## [54,] "FO"
## [55,] "FI"
## [56,] "FI"
## [57,] "FI"
## [58,] "FO"
## [59,] "FO"
## [60,] "FO"
## [61,] "FO"
## [62,] "FI"
## [63,] "FI"
## [64,] "FO"
## [65,] "FI"
## [66,] "FI"
## [67,] "FO"
## [68,] "FO"
## [69,] "FI"
## [70,] "FI"
## [71,] "FI"
## [72,] "FI"
## [73,] "FO"
## [74,] "FI"
## [75,] "FO"
## [76,] "FO"
## [77,] "FO"
## [78,] "FI"
## [79,] "FO"
## [80,] "FO"
## [81,] "FI"
## [82,] "FO"
## [83,] "FI"
## [84,] "FI"
## [85,] "FO"
## [86,] "FO"
## [87,] "FO"
## [88,] "FI"
## [89,] "FI"
## [90,] "FI"
## [91,] "FO"
## [92,] "FI"
## [93,] "FO"
## [94,] "FI"
## [95,] "FI"
## [96,] "FO"
## [97,] "FI"
## [98,] "FI"
## [99,] "FO"
## [100,] "FO"
## [101,] "FI"
## [102,] "FO"
## [103,] "FO"
## [104,] "FO"
## [105,] "FO"
## [106,] "FI"
## [107,] "FO"
## [108,] "FI"
## [109,] "FI"
## [110,] "FO"
## [111,] "FO"
## [112,] "FI"
## [113,] "FI"
## [114,] "FI"
## [115,] "FO"
## [116,] "FI"
## [117,] "FO"
## [118,] "FO"
## [119,] "FO"
## [120,] "FI"gramos=rlnorm(n=120,meanlog=5,sdlog=0.85)
options(digits=4)
dim.data.frame(Biomasa_dt)## [1] 120 1str(gramos)## num [1:120] 270.4 263.5 93.1 91.9 222.6 ...class(gramos)## [1] "numeric"names(Biomasa_dt)## [1] "Gramos"is.na(Biomasa_dt)## Gramos
## [1,] FALSE
## [2,] FALSE
## [3,] FALSE
## [4,] FALSE
## [5,] FALSE
## [6,] FALSE
## [7,] FALSE
## [8,] FALSE
## [9,] FALSE
## [10,] FALSE
## [11,] FALSE
## [12,] FALSE
## [13,] FALSE
## [14,] FALSE
## [15,] FALSE
## [16,] FALSE
## [17,] FALSE
## [18,] FALSE
## [19,] FALSE
## [20,] FALSE
## [21,] FALSE
## [22,] FALSE
## [23,] FALSE
## [24,] FALSE
## [25,] FALSE
## [26,] FALSE
## [27,] FALSE
## [28,] FALSE
## [29,] FALSE
## [30,] FALSE
## [31,] FALSE
## [32,] FALSE
## [33,] FALSE
## [34,] FALSE
## [35,] FALSE
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## [37,] FALSE
## [38,] FALSE
## [39,] FALSE
## [40,] FALSE
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## [44,] FALSE
## [45,] FALSE
## [46,] FALSE
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## [48,] FALSE
## [49,] FALSE
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## [60,] FALSE
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## [64,] FALSE
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## [72,] FALSE
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## [86,] FALSE
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## [89,] FALSE
## [90,] FALSE
## [91,] FALSE
## [92,] FALSE
## [93,] FALSE
## [94,] FALSE
## [95,] FALSE
## [96,] FALSE
## [97,] FALSE
## [98,] FALSE
## [99,] FALSE
## [100,] FALSE
## [101,] FALSE
## [102,] FALSE
## [103,] FALSE
## [104,] FALSE
## [105,] FALSE
## [106,] FALSE
## [107,] FALSE
## [108,] FALSE
## [109,] FALSE
## [110,] FALSE
## [111,] FALSE
## [112,] FALSE
## [113,] FALSE
## [114,] FALSE
## [115,] FALSE
## [116,] FALSE
## [117,] FALSE
## [118,] FALSE
## [119,] FALSE
## [120,] FALSEconteodefloresentresramas=rbinom(n=120,size=20,prob=0.8)
dim.data.frame(Flores.r_dt)## [1] 120 1str(conteodefloresentresramas)## int [1:120] 14 17 17 14 16 14 14 19 19 17 ...class(conteodefloresentresramas)## [1] "integer"names(Flores.r_dt)## [1] "Conteo de flores en tres ramas"is.na(Flores.r_dt)## Conteo de flores en tres ramas
## [1,] FALSE
## [2,] FALSE
## [3,] FALSE
## [4,] FALSE
## [5,] FALSE
## [6,] FALSE
## [7,] FALSE
## [8,] FALSE
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## [13,] FALSE
## [14,] FALSE
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## [20,] FALSE
## [21,] FALSE
## [22,] FALSE
## [23,] FALSE
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## [28,] FALSE
## [29,] FALSE
## [30,] FALSE
## [31,] FALSE
## [32,] FALSE
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## [35,] FALSE
## [36,] FALSE
## [37,] FALSE
## [38,] FALSE
## [39,] FALSE
## [40,] FALSE
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## [44,] FALSE
## [45,] FALSE
## [46,] FALSE
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## [48,] FALSE
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## [50,] FALSE
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## [60,] FALSE
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## [64,] FALSE
## [65,] FALSE
## [66,] FALSE
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## [68,] FALSE
## [69,] FALSE
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## [71,] FALSE
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## [73,] FALSE
## [74,] FALSE
## [75,] FALSE
## [76,] FALSE
## [77,] FALSE
## [78,] FALSE
## [79,] FALSE
## [80,] FALSE
## [81,] FALSE
## [82,] FALSE
## [83,] FALSE
## [84,] FALSE
## [85,] FALSE
## [86,] FALSE
## [87,] FALSE
## [88,] FALSE
## [89,] FALSE
## [90,] FALSE
## [91,] FALSE
## [92,] FALSE
## [93,] FALSE
## [94,] FALSE
## [95,] FALSE
## [96,] FALSE
## [97,] FALSE
## [98,] FALSE
## [99,] FALSE
## [100,] FALSE
## [101,] FALSE
## [102,] FALSE
## [103,] FALSE
## [104,] FALSE
## [105,] FALSE
## [106,] FALSE
## [107,] FALSE
## [108,] FALSE
## [109,] FALSE
## [110,] FALSE
## [111,] FALSE
## [112,] FALSE
## [113,] FALSE
## [114,] FALSE
## [115,] FALSE
## [116,] FALSE
## [117,] FALSE
## [118,] FALSE
## [119,] FALSE
## [120,] FALSEconteodefloresdesprendidas=rpois(n=120,lambda=10.5)
dim.data.frame(Flores.d_dt)## [1] 120 1str(conteodefloresdesprendidas)## int [1:120] 9 12 12 8 8 14 14 4 13 8 ...class(conteodefloresdesprendidas)## [1] "integer"names(Flores.d_dt)## [1] "Conteo de flores desprendidas"is.na(Flores.d_dt)## Conteo de flores desprendidas
## [1,] FALSE
## [2,] FALSE
## [3,] FALSE
## [4,] FALSE
## [5,] FALSE
## [6,] FALSE
## [7,] FALSE
## [8,] FALSE
## [9,] FALSE
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## [20,] FALSE
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## [38,] FALSE
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## [109,] FALSE
## [110,] FALSE
## [111,] FALSE
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## [113,] FALSE
## [114,] FALSE
## [115,] FALSE
## [116,] FALSE
## [117,] FALSE
## [118,] FALSE
## [119,] FALSE
## [120,] FALSEconteodehojasdesprendidas=sample.int(seq(300,300),120, replace = TRUE)
dim.data.frame(Hojas.d_dt)## [1] 120 1str(conteodehojasdesprendidas)## int [1:120] 15 180 170 36 130 253 209 207 34 35 ...class(conteodehojasdesprendidas)## [1] "integer"names(Hojas.d_dt)## [1] "Conteo de hojas desprendidas"is.na(Hojas.d_dt)## Conteo de hojas desprendidas
## [1,] FALSE
## [2,] FALSE
## [3,] FALSE
## [4,] FALSE
## [5,] FALSE
## [6,] FALSE
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## [113,] FALSE
## [114,] FALSE
## [115,] FALSE
## [116,] FALSE
## [117,] FALSE
## [118,] FALSE
## [119,] FALSE
## [120,] FALSEPlagas=bernoulli=rbernoulli(120,0.75)
dim.data.frame(plaga_dt)## [1] 120 1str(ifelse(data.frame(Plagas)==TRUE,'presente','ausente'))## chr [1:120, 1] "presente" "presente" "presente" "presente" "ausente" ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr "Plagas"class(Plagas)## [1] "logical"names(plaga_dt)## [1] "Plaga"is.na(plaga_dt)## Plaga
## [1,] FALSE
## [2,] FALSE
## [3,] FALSE
## [4,] FALSE
## [5,] FALSE
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## [114,] FALSE
## [115,] FALSE
## [116,] FALSE
## [117,] FALSE
## [118,] FALSE
## [119,] FALSE
## [120,] FALSEstatus=gl(n=3,k=40, labels = c("S", "PA", "MA"))
dim.data.frame(Status_dt)## [1] 120 1str(status)## Factor w/ 3 levels "S","PA","MA": 1 1 1 1 1 1 1 1 1 1 ...class(status)## [1] "factor"names(Status_dt)## [1] "Status"is.na(Status_dt)## Status
## [1,] FALSE
## [2,] FALSE
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## [120,] FALSEFertilizacion=x<-runif(n=120, min=0, max=1.2)
dim.data.frame(Fertilizacion_dt)## [1] 120 1str(ifelse(Fertilizacion_dt<0.5, yes = "FO", no = "FI"))## chr [1:120, 1] "FO" "FO" "FI" "FO" "FI" "FO" "FI" "FI" "FO" "FO" "FO" "FO" ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr "Fertilizacion"class(Fertilizacion)## [1] "numeric"names(Fertilizacion_dt)## [1] "Fertilizacion"is.na(Fertilizacion_dt)## Fertilizacion
## [1,] FALSE
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## [120,] FALSEtib.c <-data.frame(Biomasa_dt, Flores.r_dt, Flores.d_dt, Hojas.d_dt, plaga_dt, Status_dt, Fertilizacion_dt)
head(tib.c)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 68.20 18 7
## 2 347.60 18 6
## 3 141.46 14 11
## 4 190.98 18 13
## 5 13.84 16 14
## 6 116.71 18 9
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion
## 1 32 FALSE S 0.2162
## 2 152 FALSE S 0.1715
## 3 50 TRUE S 1.0100
## 4 62 FALSE S 0.1603
## 5 267 TRUE S 0.5730
## 6 172 FALSE S 0.1455library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniontib.i <- sample_n(tib.c, 120*0.75)
dim(tib.i)## [1] 90 7tib.i[20,5]<-NA
tib.i[10,2]<-NA
head(tib.i)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 222.64 14 12
## 2 13.84 16 14
## 3 69.67 16 6
## 4 173.39 19 7
## 5 411.21 18 10
## 6 82.91 14 9
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion
## 1 264 TRUE PA 1.06878
## 2 267 TRUE S 0.57300
## 3 101 TRUE PA 0.49745
## 4 175 FALSE S 0.06415
## 5 133 FALSE MA 0.11434
## 6 104 TRUE S 1.06866tib.c_flor<-tib.c %>%
select(Conteo.de.flores.en.tres.ramas)
head(tib.c_flor)## Conteo.de.flores.en.tres.ramas
## 1 18
## 2 18
## 3 14
## 4 18
## 5 16
## 6 18tib.c_3_6<-tib.c %>%
select(3:6)
head(tib.c_3_6)## Conteo.de.flores.desprendidas Conteo.de.hojas.desprendidas Plaga Status
## 1 7 32 FALSE S
## 2 6 152 FALSE S
## 3 11 50 TRUE S
## 4 13 62 FALSE S
## 5 14 267 TRUE S
## 6 9 172 FALSE Stib.c_sin_3_6<-select(tib.c,!(3:6))
head(tib.c_sin_3_6)## Gramos Conteo.de.flores.en.tres.ramas Fertilizacion
## 1 68.20 18 0.2162
## 2 347.60 18 0.1715
## 3 141.46 14 1.0100
## 4 190.98 18 0.1603
## 5 13.84 16 0.5730
## 6 116.71 18 0.1455sin_d<-select(tib.c,!ends_with("d"))
head(sin_d)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 68.20 18 7
## 2 347.60 18 6
## 3 141.46 14 11
## 4 190.98 18 13
## 5 13.84 16 14
## 6 116.71 18 9
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion
## 1 32 FALSE S 0.2162
## 2 152 FALSE S 0.1715
## 3 50 TRUE S 1.0100
## 4 62 FALSE S 0.1603
## 5 267 TRUE S 0.5730
## 6 172 FALSE S 0.1455with_fl<-select(tib.c, starts_with("conteo.de.flores"))
head(with_fl)## Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 18 7
## 2 18 6
## 3 14 11
## 4 18 13
## 5 16 14
## 6 18 9start.f_and_end_d<-select(tib.c, starts_with("conteo.de.flores")& ends_with("desprendidas"))
head(start.f_and_end_d)## Conteo.de.flores.desprendidas
## 1 7
## 2 6
## 3 11
## 4 13
## 5 14
## 6 9library(dplyr)
order<-tib.c %>%
group_by(Status) %>%
select(Fertilizacion)## Adding missing grouping variables: `Status`head(order)## # A tibble: 6 × 2
## # Groups: Status [1]
## Status Fertilizacion
## <fct> <dbl>
## 1 S 0.216
## 2 S 0.172
## 3 S 1.01
## 4 S 0.160
## 5 S 0.573
## 6 S 0.146var.estatus <- tib.c %>%
arrange(desc(Fertilizacion), by_group = TRUE)
head(var.estatus)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 109.60 12 12
## 2 56.97 12 6
## 3 106.82 13 11
## 4 388.19 13 12
## 5 199.82 13 12
## 6 335.77 14 8
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion
## 1 264 TRUE MA 1.183
## 2 3 TRUE MA 1.163
## 3 38 TRUE MA 1.139
## 4 251 TRUE S 1.134
## 5 262 TRUE PA 1.133
## 6 228 TRUE PA 1.094Fertilizacion_MA<-tib.c %>%
select(starts_with("Fertilizacion")) %>%
filter(status == "MA")
head(Fertilizacion_MA)## Fertilizacion
## 1 0.5633
## 2 0.1936
## 3 0.8145
## 4 0.9909
## 5 0.4538
## 6 0.2402Bio<-tib.c %>%
filter(Biomasa_dt > 5)
head(Bio)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 68.20 18 7
## 2 347.60 18 6
## 3 141.46 14 11
## 4 190.98 18 13
## 5 13.84 16 14
## 6 116.71 18 9
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion
## 1 32 FALSE S 0.2162
## 2 152 FALSE S 0.1715
## 3 50 TRUE S 1.0100
## 4 62 FALSE S 0.1603
## 5 267 TRUE S 0.5730
## 6 172 FALSE S 0.1455Plantas<-tib.c %>% filter(Status_dt=="PA"|Fertilizacion_dt=="FO")
head(Plantas)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 139.03 17 16
## 2 220.22 18 6
## 3 114.00 17 15
## 4 87.24 15 6
## 5 312.49 17 8
## 6 116.98 17 12
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion
## 1 42 TRUE PA 0.4051
## 2 207 FALSE PA 0.1188
## 3 22 TRUE PA 0.3076
## 4 83 TRUE PA 0.8038
## 5 247 TRUE PA 0.4472
## 6 6 TRUE PA 0.3299plants_PA_FI<-tib.c %>%
filter(Status == "PA"|Fertilizacion == "FI")
head(plants_PA_FI)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 139.03 17 16
## 2 220.22 18 6
## 3 114.00 17 15
## 4 87.24 15 6
## 5 312.49 17 8
## 6 116.98 17 12
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion
## 1 42 TRUE PA 0.4051
## 2 207 FALSE PA 0.1188
## 3 22 TRUE PA 0.3076
## 4 83 TRUE PA 0.8038
## 5 247 TRUE PA 0.4472
## 6 6 TRUE PA 0.3299m.c <- median(tib.c$Conteo.de.flores.en.tres.ramas)
m.i <- median(tib.i$Conteo.de.flores.en.tres.ramas, na.rm = TRUE)
data.frame(m.c,m.i)## m.c m.i
## 1 17 17v1 <- c(tib.c$Conteo.de.hojas.desprendidas, tib.c$Conteo.de.flores.desprendidas)
c1 <- quantile(tib.c$Conteo.de.hojas.desprendidas, 0.75)
c2 <- quantile(tib.c$Conteo.de.flores.desprendidas, 0.25)
v2 <- c(c1,c2)
tib.c %>%
filter((v1[[1]]>v2[[1]]),v1[[2]]>v2[[2]])## [1] Gramos Conteo.de.flores.en.tres.ramas
## [3] Conteo.de.flores.desprendidas Conteo.de.hojas.desprendidas
## [5] Plaga Status
## [7] Fertilizacion
## <0 rows> (or 0-length row.names)mean(tib.c$Gramos)## [1] 229.5sd(tib.c$Gramos)## [1] 244.5tib.c_zcore<-tib.c %>%
mutate(zscore = (Gramos - mean(Gramos))/sd(Gramos))
head(tib.c_zcore)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 68.20 18 7
## 2 347.60 18 6
## 3 141.46 14 11
## 4 190.98 18 13
## 5 13.84 16 14
## 6 116.71 18 9
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion zscore
## 1 32 FALSE S 0.2162 -0.6597
## 2 152 FALSE S 0.1715 0.4832
## 3 50 TRUE S 1.0100 -0.3600
## 4 62 FALSE S 0.1603 -0.1575
## 5 267 TRUE S 0.5730 -0.8821
## 6 172 FALSE S 0.1455 -0.4613min_max_norm <- function (x) {
(x - min (x)) / (max (x) - min (x))
}
tib.c_norm<- (minmax= as.data.frame (lapply (tib.c [2:4], min_max_norm)))
head(tib.c_norm)## Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 0.75 0.2000
## 2 0.75 0.1333
## 3 0.25 0.4667
## 4 0.75 0.6000
## 5 0.50 0.6667
## 6 0.75 0.3333
## Conteo.de.hojas.desprendidas
## 1 0.1010
## 2 0.5051
## 3 0.1616
## 4 0.2020
## 5 0.8923
## 6 0.5724tib.e <-data.frame(tib.c_zcore,tib.c_norm)
head(tib.e)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 68.20 18 7
## 2 347.60 18 6
## 3 141.46 14 11
## 4 190.98 18 13
## 5 13.84 16 14
## 6 116.71 18 9
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion zscore
## 1 32 FALSE S 0.2162 -0.6597
## 2 152 FALSE S 0.1715 0.4832
## 3 50 TRUE S 1.0100 -0.3600
## 4 62 FALSE S 0.1603 -0.1575
## 5 267 TRUE S 0.5730 -0.8821
## 6 172 FALSE S 0.1455 -0.4613
## Conteo.de.flores.en.tres.ramas.1 Conteo.de.flores.desprendidas.1
## 1 0.75 0.2000
## 2 0.75 0.1333
## 3 0.25 0.4667
## 4 0.75 0.6000
## 5 0.50 0.6667
## 6 0.75 0.3333
## Conteo.de.hojas.desprendidas.1
## 1 0.1010
## 2 0.5051
## 3 0.1616
## 4 0.2020
## 5 0.8923
## 6 0.5724tib.c_div<-tib.c %>%
mutate(div_conteo = conteodefloresentresramas/conteodefloresdesprendidas)
head(tib.c_div)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 68.20 18 7
## 2 347.60 18 6
## 3 141.46 14 11
## 4 190.98 18 13
## 5 13.84 16 14
## 6 116.71 18 9
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion div_conteo
## 1 32 FALSE S 0.2162 1.556
## 2 152 FALSE S 0.1715 1.417
## 3 50 TRUE S 1.0100 1.417
## 4 62 FALSE S 0.1603 1.750
## 5 267 TRUE S 0.5730 2.000
## 6 172 FALSE S 0.1455 1.000tib.c_divrang<-tib.c_div %>%
group_by(Plaga)%>%
mutate(rangogim=min_rank(div_conteo))
head(tib.c_divrang)## # A tibble: 6 × 9
## # Groups: Plaga [2]
## Gramos Conteo.de.flores.e… Conteo.de.flores.d… Conteo.de.hojas.d… Plaga Status
## <dbl> <int> <int> <int> <lgl> <fct>
## 1 68.2 18 7 32 FALSE S
## 2 348. 18 6 152 FALSE S
## 3 141. 14 11 50 TRUE S
## 4 191. 18 13 62 FALSE S
## 5 13.8 16 14 267 TRUE S
## 6 117. 18 9 172 FALSE S
## # … with 3 more variables: Fertilizacion <dbl>, div_conteo <dbl>,
## # rangogim <int>names(tib.e)## [1] "Gramos" "Conteo.de.flores.en.tres.ramas"
## [3] "Conteo.de.flores.desprendidas" "Conteo.de.hojas.desprendidas"
## [5] "Plaga" "Status"
## [7] "Fertilizacion" "zscore"
## [9] "Conteo.de.flores.en.tres.ramas.1" "Conteo.de.flores.desprendidas.1"
## [11] "Conteo.de.hojas.desprendidas.1"tib.e## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 68.20 18 7
## 2 347.60 18 6
## 3 141.46 14 11
## 4 190.98 18 13
## 5 13.84 16 14
## 6 116.71 18 9
## 7 77.56 16 19
## 8 82.91 14 9
## 9 202.76 20 14
## 10 173.39 19 7
## 11 102.81 17 9
## 12 116.82 17 13
## 13 463.24 17 14
## 14 68.73 14 7
## 15 575.75 17 8
## 16 197.11 17 6
## 17 70.42 15 9
## 18 359.60 18 12
## 19 81.73 16 11
## 20 81.96 18 7
## 21 103.93 17 9
## 22 85.07 15 10
## 23 112.60 17 5
## 24 246.18 16 7
## 25 172.90 14 7
## 26 63.25 16 5
## 27 85.64 18 14
## 28 466.56 16 15
## 29 388.19 13 12
## 30 59.46 16 5
## 31 63.79 15 5
## 32 138.09 14 7
## 33 422.20 18 9
## 34 41.41 14 8
## 35 155.29 14 10
## 36 468.85 18 12
## 37 72.77 17 12
## 38 74.83 17 6
## 39 97.31 17 4
## 40 41.80 15 7
## 41 139.03 17 16
## 42 220.22 18 6
## 43 114.00 17 15
## 44 87.24 15 6
## 45 312.49 17 8
## 46 116.98 17 12
## 47 50.18 15 12
## 48 958.23 17 8
## 49 302.29 16 6
## 50 34.78 16 14
## 51 129.69 18 9
## 52 69.67 16 6
## 53 17.50 17 10
## 54 56.95 17 7
## 55 169.50 14 11
## 56 37.27 16 4
## 57 595.25 14 11
## 58 51.61 19 11
## 59 111.70 18 12
## 60 105.89 17 10
## 61 270.38 18 10
## 62 263.52 16 9
## 63 93.08 16 6
## 64 91.89 18 12
## 65 222.64 14 12
## 66 482.58 14 7
## 67 118.35 19 4
## 68 57.50 18 8
## 69 106.00 16 10
## 70 300.29 14 11
## 71 335.77 14 8
## 72 199.82 13 12
## 73 29.22 18 6
## 74 144.80 15 6
## 75 143.79 17 8
## 76 294.42 19 9
## 77 179.98 17 18
## 78 317.87 14 12
## 79 146.86 19 11
## 80 108.44 18 12
## 81 234.35 16 10
## 82 231.80 18 10
## 83 235.32 15 11
## 84 73.88 14 13
## 85 41.88 17 8
## 86 809.09 18 19
## 87 92.11 17 11
## 88 262.39 15 14
## 89 118.37 14 10
## 90 176.91 16 18
## 91 466.61 17 9
## 92 245.16 15 11
## 93 54.16 18 16
## 94 278.23 15 10
## 95 109.60 12 12
## 96 90.34 18 16
## 97 170.51 15 15
## 98 115.71 16 6
## 99 21.42 19 13
## 100 421.25 17 10
## 101 253.31 16 11
## 102 48.54 18 7
## 103 62.11 18 4
## 104 546.92 17 14
## 105 195.69 20 14
## 106 343.63 16 8
## 107 1051.11 18 10
## 108 1395.43 16 9
## 109 176.88 16 10
## 110 411.21 18 10
## 111 162.91 19 12
## 112 1346.12 15 11
## 113 106.82 13 11
## 114 185.29 14 5
## 115 752.57 18 12
## 116 143.07 16 9
## 117 231.49 17 11
## 118 743.48 17 10
## 119 592.20 17 11
## 120 56.97 12 6
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion zscore
## 1 32 FALSE S 0.216196 -0.659672
## 2 152 FALSE S 0.171532 0.483237
## 3 50 TRUE S 1.009976 -0.359990
## 4 62 FALSE S 0.160290 -0.157455
## 5 267 TRUE S 0.573003 -0.882059
## 6 172 FALSE S 0.145510 -0.461255
## 7 252 TRUE S 0.739958 -0.621402
## 8 104 TRUE S 1.068657 -0.599511
## 9 294 FALSE S 0.003148 -0.109260
## 10 175 FALSE S 0.064151 -0.229389
## 11 170 TRUE S 0.466425 -0.518085
## 12 150 TRUE S 0.327544 -0.460798
## 13 197 FALSE S 0.267096 0.956254
## 14 229 TRUE S 1.051889 -0.657493
## 15 190 FALSE S 0.296003 1.416507
## 16 187 TRUE S 0.471945 -0.132365
## 17 54 TRUE S 0.771862 -0.650599
## 18 179 FALSE S 0.158156 0.532303
## 19 242 TRUE S 0.687120 -0.604336
## 20 16 FALSE S 0.106719 -0.603389
## 21 113 TRUE S 0.399510 -0.513518
## 22 107 TRUE S 0.822657 -0.590676
## 23 98 TRUE S 0.466944 -0.478056
## 24 65 TRUE S 0.672675 0.068350
## 25 26 TRUE S 1.091679 -0.231380
## 26 103 TRUE S 0.718872 -0.679941
## 27 32 FALSE S 0.219092 -0.588351
## 28 68 TRUE S 0.734665 0.969854
## 29 251 TRUE S 1.133562 0.649274
## 30 167 TRUE S 0.514379 -0.695437
## 31 21 TRUE S 0.756898 -0.677709
## 32 296 TRUE S 0.985346 -0.373790
## 33 264 FALSE S 0.228262 0.788385
## 34 61 TRUE S 1.054420 -0.769273
## 35 3 TRUE S 1.021319 -0.303420
## 36 2 FALSE S 0.177297 0.979212
## 37 268 FALSE S 0.289657 -0.641000
## 38 272 TRUE S 0.337147 -0.632547
## 39 214 FALSE S 0.290904 -0.540588
## 40 72 TRUE S 0.808313 -0.767685
## 41 42 TRUE PA 0.405067 -0.369941
## 42 207 FALSE PA 0.118848 -0.037846
## 43 22 TRUE PA 0.307576 -0.472325
## 44 83 TRUE PA 0.803764 -0.581789
## 45 247 TRUE PA 0.447160 0.339590
## 46 6 TRUE PA 0.329903 -0.460140
## 47 100 TRUE PA 0.869037 -0.733372
## 48 119 TRUE PA 0.374689 2.981045
## 49 265 TRUE PA 0.685562 0.297902
## 50 124 TRUE PA 0.581719 -0.796367
## 51 299 FALSE PA 0.189378 -0.408153
## 52 101 TRUE PA 0.497445 -0.653666
## 53 72 TRUE PA 0.310611 -0.867086
## 54 234 TRUE PA 0.448811 -0.705715
## 55 19 TRUE PA 1.093313 -0.245309
## 56 122 TRUE PA 0.597337 -0.786207
## 57 122 TRUE PA 1.045212 1.496240
## 58 25 FALSE PA 0.068677 -0.727535
## 59 131 FALSE PA 0.169117 -0.481759
## 60 21 TRUE PA 0.455406 -0.505514
## 61 20 FALSE PA 0.192317 0.167343
## 62 163 TRUE PA 0.601115 0.139283
## 63 120 TRUE PA 0.559443 -0.557909
## 64 242 FALSE PA 0.206288 -0.562770
## 65 264 TRUE PA 1.068777 -0.027926
## 66 214 TRUE PA 1.028963 1.035369
## 67 288 FALSE PA 0.079890 -0.454529
## 68 154 FALSE PA 0.111915 -0.703454
## 69 117 TRUE PA 0.625508 -0.505038
## 70 215 TRUE PA 1.054966 0.289704
## 71 228 TRUE PA 1.094420 0.434846
## 72 262 TRUE PA 1.132734 -0.121272
## 73 295 FALSE PA 0.241041 -0.819135
## 74 297 TRUE PA 0.824203 -0.346351
## 75 266 FALSE PA 0.252290 -0.350469
## 76 57 FALSE PA 0.078908 0.265687
## 77 52 TRUE PA 0.371706 -0.202435
## 78 90 TRUE PA 1.049651 0.361613
## 79 52 FALSE PA 0.081605 -0.337910
## 80 141 FALSE PA 0.198177 -0.495071
## 81 250 TRUE MA 0.563256 0.019976
## 82 176 FALSE MA 0.193644 0.009547
## 83 157 TRUE MA 0.814518 0.023936
## 84 123 TRUE MA 0.990864 -0.636444
## 85 189 TRUE MA 0.453781 -0.767329
## 86 291 FALSE MA 0.240157 2.370974
## 87 158 TRUE MA 0.319149 -0.561859
## 88 92 TRUE MA 0.755946 0.134670
## 89 216 TRUE MA 0.971367 -0.454469
## 90 58 TRUE MA 0.639018 -0.215007
## 91 201 TRUE MA 0.467689 0.970023
## 92 184 TRUE MA 0.867008 0.064189
## 93 225 FALSE MA 0.121250 -0.717121
## 94 28 TRUE MA 0.834471 0.199454
## 95 264 TRUE MA 1.183068 -0.490339
## 96 209 FALSE MA 0.118648 -0.569130
## 97 292 TRUE MA 0.958426 -0.241177
## 98 32 TRUE MA 0.633751 -0.465321
## 99 244 FALSE MA 0.052709 -0.851035
## 100 150 TRUE MA 0.429349 0.784510
## 101 251 TRUE MA 0.524363 0.097540
## 102 41 FALSE MA 0.211101 -0.740095
## 103 297 FALSE MA 0.224180 -0.684572
## 104 17 TRUE MA 0.377260 1.298569
## 105 85 FALSE MA 0.007136 -0.138167
## 106 216 TRUE MA 0.652337 0.467006
## 107 47 FALSE MA 0.155860 3.360972
## 108 155 TRUE MA 0.700789 4.769424
## 109 52 TRUE MA 0.674517 -0.215125
## 110 133 FALSE MA 0.114343 0.743419
## 111 144 FALSE MA 0.062406 -0.272254
## 112 235 TRUE MA 0.807558 4.567718
## 113 38 TRUE MA 1.138656 -0.501693
## 114 62 TRUE MA 1.047973 -0.180727
## 115 98 FALSE MA 0.128397 2.139792
## 116 36 TRUE MA 0.628470 -0.353436
## 117 235 TRUE MA 0.442854 0.008259
## 118 283 TRUE MA 0.382527 2.102604
## 119 133 TRUE MA 0.414732 1.483768
## 120 3 TRUE MA 1.162946 -0.705630
## Conteo.de.flores.en.tres.ramas.1 Conteo.de.flores.desprendidas.1
## 1 0.750 0.20000
## 2 0.750 0.13333
## 3 0.250 0.46667
## 4 0.750 0.60000
## 5 0.500 0.66667
## 6 0.750 0.33333
## 7 0.500 1.00000
## 8 0.250 0.33333
## 9 1.000 0.66667
## 10 0.875 0.20000
## 11 0.625 0.33333
## 12 0.625 0.60000
## 13 0.625 0.66667
## 14 0.250 0.20000
## 15 0.625 0.26667
## 16 0.625 0.13333
## 17 0.375 0.33333
## 18 0.750 0.53333
## 19 0.500 0.46667
## 20 0.750 0.20000
## 21 0.625 0.33333
## 22 0.375 0.40000
## 23 0.625 0.06667
## 24 0.500 0.20000
## 25 0.250 0.20000
## 26 0.500 0.06667
## 27 0.750 0.66667
## 28 0.500 0.73333
## 29 0.125 0.53333
## 30 0.500 0.06667
## 31 0.375 0.06667
## 32 0.250 0.20000
## 33 0.750 0.33333
## 34 0.250 0.26667
## 35 0.250 0.40000
## 36 0.750 0.53333
## 37 0.625 0.53333
## 38 0.625 0.13333
## 39 0.625 0.00000
## 40 0.375 0.20000
## 41 0.625 0.80000
## 42 0.750 0.13333
## 43 0.625 0.73333
## 44 0.375 0.13333
## 45 0.625 0.26667
## 46 0.625 0.53333
## 47 0.375 0.53333
## 48 0.625 0.26667
## 49 0.500 0.13333
## 50 0.500 0.66667
## 51 0.750 0.33333
## 52 0.500 0.13333
## 53 0.625 0.40000
## 54 0.625 0.20000
## 55 0.250 0.46667
## 56 0.500 0.00000
## 57 0.250 0.46667
## 58 0.875 0.46667
## 59 0.750 0.53333
## 60 0.625 0.40000
## 61 0.750 0.40000
## 62 0.500 0.33333
## 63 0.500 0.13333
## 64 0.750 0.53333
## 65 0.250 0.53333
## 66 0.250 0.20000
## 67 0.875 0.00000
## 68 0.750 0.26667
## 69 0.500 0.40000
## 70 0.250 0.46667
## 71 0.250 0.26667
## 72 0.125 0.53333
## 73 0.750 0.13333
## 74 0.375 0.13333
## 75 0.625 0.26667
## 76 0.875 0.33333
## 77 0.625 0.93333
## 78 0.250 0.53333
## 79 0.875 0.46667
## 80 0.750 0.53333
## 81 0.500 0.40000
## 82 0.750 0.40000
## 83 0.375 0.46667
## 84 0.250 0.60000
## 85 0.625 0.26667
## 86 0.750 1.00000
## 87 0.625 0.46667
## 88 0.375 0.66667
## 89 0.250 0.40000
## 90 0.500 0.93333
## 91 0.625 0.33333
## 92 0.375 0.46667
## 93 0.750 0.80000
## 94 0.375 0.40000
## 95 0.000 0.53333
## 96 0.750 0.80000
## 97 0.375 0.73333
## 98 0.500 0.13333
## 99 0.875 0.60000
## 100 0.625 0.40000
## 101 0.500 0.46667
## 102 0.750 0.20000
## 103 0.750 0.00000
## 104 0.625 0.66667
## 105 1.000 0.66667
## 106 0.500 0.26667
## 107 0.750 0.40000
## 108 0.500 0.33333
## 109 0.500 0.40000
## 110 0.750 0.40000
## 111 0.875 0.53333
## 112 0.375 0.46667
## 113 0.125 0.46667
## 114 0.250 0.06667
## 115 0.750 0.53333
## 116 0.500 0.33333
## 117 0.625 0.46667
## 118 0.625 0.40000
## 119 0.625 0.46667
## 120 0.000 0.13333
## Conteo.de.hojas.desprendidas.1
## 1 0.101010
## 2 0.505051
## 3 0.161616
## 4 0.202020
## 5 0.892256
## 6 0.572391
## 7 0.841751
## 8 0.343434
## 9 0.983165
## 10 0.582492
## 11 0.565657
## 12 0.498316
## 13 0.656566
## 14 0.764310
## 15 0.632997
## 16 0.622896
## 17 0.175084
## 18 0.595960
## 19 0.808081
## 20 0.047138
## 21 0.373737
## 22 0.353535
## 23 0.323232
## 24 0.212121
## 25 0.080808
## 26 0.340067
## 27 0.101010
## 28 0.222222
## 29 0.838384
## 30 0.555556
## 31 0.063973
## 32 0.989899
## 33 0.882155
## 34 0.198653
## 35 0.003367
## 36 0.000000
## 37 0.895623
## 38 0.909091
## 39 0.713805
## 40 0.235690
## 41 0.134680
## 42 0.690236
## 43 0.067340
## 44 0.272727
## 45 0.824916
## 46 0.013468
## 47 0.329966
## 48 0.393939
## 49 0.885522
## 50 0.410774
## 51 1.000000
## 52 0.333333
## 53 0.235690
## 54 0.781145
## 55 0.057239
## 56 0.404040
## 57 0.404040
## 58 0.077441
## 59 0.434343
## 60 0.063973
## 61 0.060606
## 62 0.542088
## 63 0.397306
## 64 0.808081
## 65 0.882155
## 66 0.713805
## 67 0.962963
## 68 0.511785
## 69 0.387205
## 70 0.717172
## 71 0.760943
## 72 0.875421
## 73 0.986532
## 74 0.993266
## 75 0.888889
## 76 0.185185
## 77 0.168350
## 78 0.296296
## 79 0.168350
## 80 0.468013
## 81 0.835017
## 82 0.585859
## 83 0.521886
## 84 0.407407
## 85 0.629630
## 86 0.973064
## 87 0.525253
## 88 0.303030
## 89 0.720539
## 90 0.188552
## 91 0.670034
## 92 0.612795
## 93 0.750842
## 94 0.087542
## 95 0.882155
## 96 0.696970
## 97 0.976431
## 98 0.101010
## 99 0.814815
## 100 0.498316
## 101 0.838384
## 102 0.131313
## 103 0.993266
## 104 0.050505
## 105 0.279461
## 106 0.720539
## 107 0.151515
## 108 0.515152
## 109 0.168350
## 110 0.441077
## 111 0.478114
## 112 0.784512
## 113 0.121212
## 114 0.202020
## 115 0.323232
## 116 0.114478
## 117 0.784512
## 118 0.946128
## 119 0.441077
## 120 0.003367renamed.tib.e<-rename(tib.e,Rosas=Conteo.de.flores.en.tres.ramas,
Violetas=Conteo.de.hojas.desprendidas,
Margaritas=Conteo.de.flores.en.tres.ramas.1,
Girasoles=Conteo.de.flores.desprendidas.1)
names(renamed.tib.e)## [1] "Gramos" "Rosas"
## [3] "Conteo.de.flores.desprendidas" "Violetas"
## [5] "Plaga" "Status"
## [7] "Fertilizacion" "zscore"
## [9] "Margaritas" "Girasoles"
## [11] "Conteo.de.hojas.desprendidas.1"tib.e_MAYUS<-(rename_with(tib.e,toupper))
head(tib.e_MAYUS)## GRAMOS CONTEO.DE.FLORES.EN.TRES.RAMAS CONTEO.DE.FLORES.DESPRENDIDAS
## 1 68.20 18 7
## 2 347.60 18 6
## 3 141.46 14 11
## 4 190.98 18 13
## 5 13.84 16 14
## 6 116.71 18 9
## CONTEO.DE.HOJAS.DESPRENDIDAS PLAGA STATUS FERTILIZACION ZSCORE
## 1 32 FALSE S 0.2162 -0.6597
## 2 152 FALSE S 0.1715 0.4832
## 3 50 TRUE S 1.0100 -0.3600
## 4 62 FALSE S 0.1603 -0.1575
## 5 267 TRUE S 0.5730 -0.8821
## 6 172 FALSE S 0.1455 -0.4613
## CONTEO.DE.FLORES.EN.TRES.RAMAS.1 CONTEO.DE.FLORES.DESPRENDIDAS.1
## 1 0.75 0.2000
## 2 0.75 0.1333
## 3 0.25 0.4667
## 4 0.75 0.6000
## 5 0.50 0.6667
## 6 0.75 0.3333
## CONTEO.DE.HOJAS.DESPRENDIDAS.1
## 1 0.1010
## 2 0.5051
## 3 0.1616
## 4 0.2020
## 5 0.8923
## 6 0.5724tib.e_raname_with<-rename_with(tib.e, ~ tolower(gsub(".", "_", .x, fixed = TRUE)))
head(tib.e_raname_with)## gramos conteo_de_flores_en_tres_ramas conteo_de_flores_desprendidas
## 1 68.20 18 7
## 2 347.60 18 6
## 3 141.46 14 11
## 4 190.98 18 13
## 5 13.84 16 14
## 6 116.71 18 9
## conteo_de_hojas_desprendidas plaga status fertilizacion zscore
## 1 32 FALSE S 0.2162 -0.6597
## 2 152 FALSE S 0.1715 0.4832
## 3 50 TRUE S 1.0100 -0.3600
## 4 62 FALSE S 0.1603 -0.1575
## 5 267 TRUE S 0.5730 -0.8821
## 6 172 FALSE S 0.1455 -0.4613
## conteo_de_flores_en_tres_ramas_1 conteo_de_flores_desprendidas_1
## 1 0.75 0.2000
## 2 0.75 0.1333
## 3 0.25 0.4667
## 4 0.75 0.6000
## 5 0.50 0.6667
## 6 0.75 0.3333
## conteo_de_hojas_desprendidas_1
## 1 0.1010
## 2 0.5051
## 3 0.1616
## 4 0.2020
## 5 0.8923
## 6 0.5724tib.i%>%
summarise(mean(Gramos),N_datos=n())## mean(Gramos) N_datos
## 1 216.7 90tib.i%>%
group_by(Fertilizacion) %>%
summarise(mean(Gramos),N_datos=n())## # A tibble: 90 × 3
## Fertilizacion `mean(Gramos)` N_datos
## <dbl> <dbl> <int>
## 1 0.00315 203. 1
## 2 0.00714 196. 1
## 3 0.0527 21.4 1
## 4 0.0624 163. 1
## 5 0.0642 173. 1
## 6 0.0687 51.6 1
## 7 0.0789 294. 1
## 8 0.0816 147. 1
## 9 0.107 82.0 1
## 10 0.112 57.5 1
## # … with 80 more rowstib.i%>%
group_by(Fertilizacion) %>%
summarise(Q.10=quantile(tib.i$Gramos,0.10),
Q.20=quantile(tib.i$Gramos,0.20),
Q.30=quantile(tib.i$Gramos,0.30),
Q.40=quantile(tib.i$Gramos,0.40),
Q.50=quantile(tib.i$Gramos,0.50))## # A tibble: 90 × 6
## Fertilizacion Q.10 Q.20 Q.30 Q.40 Q.50
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.00315 56.4 74.4 91.4 112. 141.
## 2 0.00714 56.4 74.4 91.4 112. 141.
## 3 0.0527 56.4 74.4 91.4 112. 141.
## 4 0.0624 56.4 74.4 91.4 112. 141.
## 5 0.0642 56.4 74.4 91.4 112. 141.
## 6 0.0687 56.4 74.4 91.4 112. 141.
## 7 0.0789 56.4 74.4 91.4 112. 141.
## 8 0.0816 56.4 74.4 91.4 112. 141.
## 9 0.107 56.4 74.4 91.4 112. 141.
## 10 0.112 56.4 74.4 91.4 112. 141.
## # … with 80 more rowstib.i%>%
group_by(Fertilizacion,Plaga) %>%
summarise(meanbio=mean(Gramos),
medbio=median(Gramos),
maxbio=max(Gramos),
minbio=min(Gramos),
destipica=sd(Gramos),
desmedia=mean(abs(Gramos-mean(Gramos))),
medtru5=mean(Gramos,trim=5/100),
medtru10=mean(Gramos,trim=10/100),
varbio=var(Gramos))## `summarise()` has grouped output by 'Fertilizacion'. You can override using the `.groups` argument.## # A tibble: 90 × 11
## # Groups: Fertilizacion [90]
## Fertilizacion Plaga meanbio medbio maxbio minbio destipica desmedia medtru5
## <dbl> <lgl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.00315 FALSE 203. 203. 203. 203. NA 0 203.
## 2 0.00714 FALSE 196. 196. 196. 196. NA 0 196.
## 3 0.0527 FALSE 21.4 21.4 21.4 21.4 NA 0 21.4
## 4 0.0624 FALSE 163. 163. 163. 163. NA 0 163.
## 5 0.0642 FALSE 173. 173. 173. 173. NA 0 173.
## 6 0.0687 FALSE 51.6 51.6 51.6 51.6 NA 0 51.6
## 7 0.0789 FALSE 294. 294. 294. 294. NA 0 294.
## 8 0.0816 FALSE 147. 147. 147. 147. NA 0 147.
## 9 0.107 FALSE 82.0 82.0 82.0 82.0 NA 0 82.0
## 10 0.112 FALSE 57.5 57.5 57.5 57.5 NA 0 57.5
## # … with 80 more rows, and 2 more variables: medtru10 <dbl>, varbio <dbl>tib.i_planS<-tib.i%>%
group_by(Status) %>%
filter(Status=="S")%>%
select(Fertilizacion,Plaga)## Adding missing grouping variables: `Status`head(tib.i_planS)## # A tibble: 6 × 3
## # Groups: Status [1]
## Status Fertilizacion Plaga
## <fct> <dbl> <lgl>
## 1 S 0.573 TRUE
## 2 S 0.0642 FALSE
## 3 S 1.07 TRUE
## 4 S 0.740 TRUE
## 5 S 0.00315 FALSE
## 6 S 0.296 NAtib.i_planS%>%
group_by(Fertilizacion,Plaga) %>%
summarise(Ndatos=n(),
meanbio=mean(gramos),
medbio=median(gramos),
maxbio=max(gramos),
minbio=min(gramos),
destipica=sd(gramos),
desmedia=mean(abs(gramos-mean(gramos))),
medtru5=mean(gramos,trim=5/100),
medtru10=mean(gramos,trim=10/100),
varbio=var(gramos))## `summarise()` has grouped output by 'Fertilizacion'. You can override using the `.groups` argument.## # A tibble: 31 × 12
## # Groups: Fertilizacion [31]
## Fertilizacion Plaga Ndatos meanbio medbio maxbio minbio destipica desmedia
## <dbl> <lgl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.00315 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 2 0.0642 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 3 0.107 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 4 0.146 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 5 0.160 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 6 0.172 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 7 0.216 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 8 0.219 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 9 0.228 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 10 0.290 FALSE 1 253. 177. 1779. 21.4 282. 173.
## # … with 21 more rows, and 3 more variables: medtru5 <dbl>, medtru10 <dbl>,
## # varbio <dbl>library(tidyr)
tib.i_sinNA <-drop_na(tib.i)
head(tib.i_sinNA)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 222.64 14 12
## 2 13.84 16 14
## 3 69.67 16 6
## 4 173.39 19 7
## 5 411.21 18 10
## 6 82.91 14 9
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion
## 1 264 TRUE PA 1.06878
## 2 267 TRUE S 0.57300
## 3 101 TRUE PA 0.49745
## 4 175 FALSE S 0.06415
## 5 133 FALSE MA 0.11434
## 6 104 TRUE S 1.06866tib.i_planSsinNA<-tib.i_sinNA%>%
group_by(Status) %>%
filter(Status=="S")%>%
select(Fertilizacion,Plaga)## Adding missing grouping variables: `Status`head(tib.i_planS)## # A tibble: 6 × 3
## # Groups: Status [1]
## Status Fertilizacion Plaga
## <fct> <dbl> <lgl>
## 1 S 0.573 TRUE
## 2 S 0.0642 FALSE
## 3 S 1.07 TRUE
## 4 S 0.740 TRUE
## 5 S 0.00315 FALSE
## 6 S 0.296 NAtib.i_planSsinNA%>%
group_by(Fertilizacion,Plaga) %>%
summarise(Ndatos=n(),
meanbio=mean(gramos),
medbio=median(gramos),
maxbio=max(gramos),
minbio=min(gramos),
destipica=sd(gramos),
desmedia=mean(abs(gramos-mean(gramos))),
medtru5=mean(gramos,trim=5/100),
medtru10=mean(gramos,trim=10/100),
varbio=var(gramos))## `summarise()` has grouped output by 'Fertilizacion'. You can override using the `.groups` argument.## # A tibble: 30 × 12
## # Groups: Fertilizacion [30]
## Fertilizacion Plaga Ndatos meanbio medbio maxbio minbio destipica desmedia
## <dbl> <lgl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.00315 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 2 0.0642 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 3 0.107 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 4 0.146 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 5 0.160 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 6 0.172 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 7 0.216 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 8 0.219 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 9 0.228 FALSE 1 253. 177. 1779. 21.4 282. 173.
## 10 0.290 FALSE 1 253. 177. 1779. 21.4 282. 173.
## # … with 20 more rows, and 3 more variables: medtru5 <dbl>, medtru10 <dbl>,
## # varbio <dbl>plantas_PA_MA <- filter(tib.i,Status %in% c("PA", "MA"))
head(plantas_PA_MA)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 222.64 14 12
## 2 69.67 16 6
## 3 411.21 18 10
## 4 92.11 17 11
## 5 546.92 17 14
## 6 17.50 NA 10
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion
## 1 264 TRUE PA 1.0688
## 2 101 TRUE PA 0.4974
## 3 133 FALSE MA 0.1143
## 4 158 TRUE MA 0.3191
## 5 17 TRUE MA 0.3773
## 6 72 TRUE PA 0.3106SinNA_complet.cases<-filter(tib.i[1:90,],complete.cases(tib.i[1:90,]))
head(SinNA_complet.cases)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 222.64 14 12
## 2 13.84 16 14
## 3 69.67 16 6
## 4 173.39 19 7
## 5 411.21 18 10
## 6 82.91 14 9
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion
## 1 264 TRUE PA 1.06878
## 2 267 TRUE S 0.57300
## 3 101 TRUE PA 0.49745
## 4 175 FALSE S 0.06415
## 5 133 FALSE MA 0.11434
## 6 104 TRUE S 1.06866remove_tib.i<-mutate(tib.i,flores.r=NULL,flores.d=NULL)
head(remove_tib.i)## Gramos Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 222.64 14 12
## 2 13.84 16 14
## 3 69.67 16 6
## 4 173.39 19 7
## 5 411.21 18 10
## 6 82.91 14 9
## Conteo.de.hojas.desprendidas Plaga Status Fertilizacion
## 1 264 TRUE PA 1.06878
## 2 267 TRUE S 0.57300
## 3 101 TRUE PA 0.49745
## 4 175 FALSE S 0.06415
## 5 133 FALSE MA 0.11434
## 6 104 TRUE S 1.06866d_contain<-tib.i%>%
select(contains("d"))
head(d_contain)## Conteo.de.flores.en.tres.ramas Conteo.de.flores.desprendidas
## 1 14 12
## 2 16 14
## 3 16 6
## 4 19 7
## 5 18 10
## 6 14 9
## Conteo.de.hojas.desprendidas
## 1 264
## 2 267
## 3 101
## 4 175
## 5 133
## 6 104