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###Ejercicios Rstudio 1.1 a 1.9
library(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, unionset.seed(2099)
x <- rnorm(120, 5, 0.85)
y <- rbinom(120, 20, 0.8)
z <- rpois(120, 10.5)
p <- sample.int(300, 120, replace = TRUE)
library(purrr)
a <- rbernoulli(120, 0.75)
replace(a, c(TRUE, FALSE), c("presente","ausente")) ## [1] "presente" "FALSE" "ausente" "TRUE" "presente" "TRUE"
## [7] "ausente" "FALSE" "presente" "FALSE" "ausente" "FALSE"
## [13] "presente" "TRUE" "ausente" "TRUE" "presente" "TRUE"
## [19] "ausente" "FALSE" "presente" "TRUE" "ausente" "TRUE"
## [25] "presente" "FALSE" "ausente" "TRUE" "presente" "FALSE"
## [31] "ausente" "TRUE" "presente" "FALSE" "ausente" "TRUE"
## [37] "presente" "FALSE" "ausente" "FALSE" "presente" "TRUE"
## [43] "ausente" "TRUE" "presente" "TRUE" "ausente" "TRUE"
## [49] "presente" "FALSE" "ausente" "TRUE" "presente" "FALSE"
## [55] "ausente" "TRUE" "presente" "FALSE" "ausente" "TRUE"
## [61] "presente" "TRUE" "ausente" "FALSE" "presente" "TRUE"
## [67] "ausente" "TRUE" "presente" "TRUE" "ausente" "TRUE"
## [73] "presente" "TRUE" "ausente" "TRUE" "presente" "TRUE"
## [79] "ausente" "FALSE" "presente" "TRUE" "ausente" "TRUE"
## [85] "presente" "TRUE" "ausente" "TRUE" "presente" "TRUE"
## [91] "ausente" "TRUE" "presente" "TRUE" "ausente" "TRUE"
## [97] "presente" "TRUE" "ausente" "TRUE" "presente" "TRUE"
## [103] "ausente" "TRUE" "presente" "FALSE" "ausente" "TRUE"
## [109] "presente" "TRUE" "ausente" "FALSE" "presente" "TRUE"
## [115] "ausente" "TRUE" "presente" "FALSE" "ausente" "FALSE"S <- gl(3, 1, length(40), labels = "S", ordered = FALSE)
PA <- gl(3, 1, length(40), labels = "PA", ordered = FALSE)
MA <- gl(3, 1, length(40), labels = "MA", ordered = FALSE)
b <- c("S", "PA", "MA")
fert <-runif(120,0, 1.2)
gl(2, 1, length(40))## [1] 1
## Levels: 1 2fertil <- if_else(fert < 0.5,true = "FO", false = "FI", missing = NULL)
df1 <- data.frame(x, y, z, p, a, b, fertil)
colnames(df1) <- c("Biomasa", "Floresr", "Floresd", "Hojasd", "Plaga", "Estatus", "Fertilización")
df1## Biomasa Floresr Floresd Hojasd Plaga Estatus Fertilización
## 1 5.191382 16 1 150 TRUE S FO
## 2 4.682817 19 13 197 FALSE PA FI
## 3 5.444438 20 10 279 TRUE MA FI
## 4 3.525376 17 13 118 TRUE S FI
## 5 4.860301 17 8 277 FALSE PA FI
## 6 4.284344 16 19 89 TRUE MA FI
## 7 5.057715 16 8 253 TRUE S FO
## 8 6.372872 16 8 54 FALSE PA FO
## 9 4.814129 17 8 121 TRUE MA FO
## 10 5.875504 17 8 37 FALSE S FO
## 11 5.545751 15 12 2 TRUE PA FI
## 12 4.332472 16 10 57 FALSE MA FO
## 13 4.143265 12 11 203 TRUE S FO
## 14 3.942137 17 8 68 TRUE PA FI
## 15 4.085222 16 13 83 TRUE MA FO
## 16 5.135173 14 10 150 TRUE S FO
## 17 4.198869 17 8 237 TRUE PA FO
## 18 3.931221 13 11 32 TRUE MA FI
## 19 4.645347 15 7 281 TRUE S FI
## 20 4.364195 16 5 130 FALSE PA FI
## 21 4.731182 17 8 84 TRUE MA FO
## 22 5.872350 19 14 262 TRUE S FI
## 23 3.566169 17 14 78 TRUE PA FI
## 24 4.533903 20 9 75 TRUE MA FI
## 25 5.494825 14 13 241 TRUE S FO
## 26 3.792585 17 6 300 FALSE PA FO
## 27 3.479056 15 8 256 TRUE MA FI
## 28 6.000100 16 11 38 TRUE S FO
## 29 5.385418 18 10 164 TRUE PA FI
## 30 4.295210 18 10 199 FALSE MA FO
## 31 4.705739 16 12 235 FALSE S FO
## 32 4.967705 14 9 26 TRUE PA FI
## 33 4.883999 16 11 260 FALSE MA FI
## 34 6.685638 17 7 187 FALSE S FI
## 35 6.782740 18 7 224 TRUE PA FI
## 36 4.895407 17 11 227 TRUE MA FI
## 37 6.089084 14 11 222 TRUE S FI
## 38 4.058907 12 10 59 FALSE PA FI
## 39 5.309868 19 13 186 TRUE MA FI
## 40 4.583636 11 10 9 FALSE S FI
## 41 4.390665 13 6 10 TRUE PA FI
## 42 5.553013 16 11 105 TRUE MA FI
## 43 5.399750 18 7 59 TRUE S FI
## 44 4.387268 14 14 18 TRUE PA FI
## 45 5.033227 16 10 43 TRUE MA FO
## 46 4.550117 17 14 258 TRUE S FO
## 47 5.381977 14 7 158 TRUE PA FI
## 48 4.957284 15 11 208 TRUE MA FI
## 49 4.662462 15 9 119 FALSE S FO
## 50 5.744640 15 13 250 FALSE PA FO
## 51 4.572862 14 6 60 FALSE MA FI
## 52 6.061314 16 12 134 TRUE S FI
## 53 4.077621 14 13 138 TRUE PA FI
## 54 5.227363 16 15 38 FALSE MA FI
## 55 6.347073 17 7 31 TRUE S FI
## 56 4.453775 11 6 111 TRUE PA FI
## 57 4.682281 17 5 3 TRUE MA FO
## 58 5.176889 12 13 37 FALSE S FO
## 59 4.389201 17 12 114 TRUE PA FI
## 60 3.284646 16 5 33 TRUE MA FO
## 61 5.837547 19 9 61 FALSE S FI
## 62 4.968740 17 8 188 TRUE PA FO
## 63 3.903301 17 10 58 TRUE MA FO
## 64 6.132158 15 10 286 FALSE S FI
## 65 5.873990 17 12 151 FALSE PA FI
## 66 5.515326 14 14 191 TRUE MA FO
## 67 4.084581 13 13 265 TRUE S FI
## 68 3.723847 18 10 72 TRUE PA FI
## 69 5.188571 17 11 157 TRUE MA FI
## 70 5.086961 15 8 240 TRUE S FI
## 71 5.833276 16 14 133 TRUE PA FI
## 72 4.376451 17 13 170 TRUE MA FI
## 73 3.695728 17 11 66 FALSE S FO
## 74 4.623261 16 5 220 TRUE PA FI
## 75 5.533799 15 16 218 FALSE MA FI
## 76 3.128952 18 12 120 TRUE S FI
## 77 3.984496 16 14 199 TRUE PA FO
## 78 6.174937 15 14 81 TRUE MA FO
## 79 5.831186 16 11 141 TRUE S FO
## 80 4.584803 14 9 185 FALSE PA FO
## 81 4.444875 17 12 216 FALSE MA FI
## 82 5.384299 14 9 22 TRUE S FO
## 83 5.781127 16 11 169 TRUE PA FI
## 84 4.279655 16 8 246 TRUE MA FI
## 85 4.534812 17 10 24 TRUE S FO
## 86 4.458870 17 7 240 TRUE PA FO
## 87 5.413162 14 10 268 TRUE MA FO
## 88 4.561375 18 11 289 TRUE S FI
## 89 6.076244 14 12 14 TRUE PA FO
## 90 5.140797 16 8 225 TRUE MA FO
## 91 4.427752 16 4 268 FALSE S FO
## 92 5.902777 14 9 279 TRUE PA FO
## 93 5.729409 16 10 222 TRUE MA FO
## 94 4.563922 16 8 215 TRUE S FI
## 95 6.536906 15 13 212 TRUE PA FI
## 96 3.892238 16 13 86 TRUE MA FI
## 97 6.248745 16 19 239 TRUE S FI
## 98 6.049258 18 15 171 TRUE PA FI
## 99 5.151745 14 9 150 FALSE MA FI
## 100 4.328072 15 14 220 TRUE S FI
## 101 5.946870 13 7 242 TRUE PA FI
## 102 4.441015 17 10 129 TRUE MA FI
## 103 4.512727 15 13 25 TRUE S FI
## 104 5.241019 11 12 202 TRUE PA FO
## 105 5.047264 17 12 212 FALSE MA FI
## 106 5.032729 19 11 298 FALSE S FI
## 107 4.800188 15 13 264 TRUE PA FI
## 108 4.754542 14 10 18 TRUE MA FI
## 109 4.139596 17 12 199 TRUE S FO
## 110 5.336830 18 7 10 TRUE PA FO
## 111 4.410783 17 11 283 TRUE MA FI
## 112 5.301664 16 12 285 FALSE S FI
## 113 4.248514 15 4 286 TRUE PA FI
## 114 4.482439 16 7 291 TRUE MA FO
## 115 5.199317 16 10 165 TRUE S FO
## 116 4.960738 15 10 236 TRUE PA FI
## 117 4.508527 15 6 131 TRUE MA FI
## 118 4.821758 18 11 82 FALSE S FO
## 119 6.706502 20 12 129 TRUE PA FI
## 120 4.619753 18 9 190 FALSE MA FOdim(df1)## [1] 120 7str(df1)## 'data.frame': 120 obs. of 7 variables:
## $ Biomasa : num 5.19 4.68 5.44 3.53 4.86 ...
## $ Floresr : int 16 19 20 17 17 16 16 16 17 17 ...
## $ Floresd : int 1 13 10 13 8 19 8 8 8 8 ...
## $ Hojasd : int 150 197 279 118 277 89 253 54 121 37 ...
## $ Plaga : logi TRUE FALSE TRUE TRUE FALSE TRUE ...
## $ Estatus : chr "S" "PA" "MA" "S" ...
## $ Fertilización: chr "FO" "FI" "FI" "FI" ...class(df1)## [1] "data.frame"names(df1)## [1] "Biomasa" "Floresr" "Floresd" "Hojasd"
## [5] "Plaga" "Estatus" "Fertilización"is.na(df1)## Biomasa Floresr Floresd Hojasd Plaga Estatus Fertilización
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tib.c <- df1[c(1:120),c(1:5)]
tib.c## Biomasa Floresr Floresd Hojasd Plaga
## 1 5.191382 16 1 150 TRUE
## 2 4.682817 19 13 197 FALSE
## 3 5.444438 20 10 279 TRUE
## 4 3.525376 17 13 118 TRUE
## 5 4.860301 17 8 277 FALSE
## 6 4.284344 16 19 89 TRUE
## 7 5.057715 16 8 253 TRUE
## 8 6.372872 16 8 54 FALSE
## 9 4.814129 17 8 121 TRUE
## 10 5.875504 17 8 37 FALSE
## 11 5.545751 15 12 2 TRUE
## 12 4.332472 16 10 57 FALSE
## 13 4.143265 12 11 203 TRUE
## 14 3.942137 17 8 68 TRUE
## 15 4.085222 16 13 83 TRUE
## 16 5.135173 14 10 150 TRUE
## 17 4.198869 17 8 237 TRUE
## 18 3.931221 13 11 32 TRUE
## 19 4.645347 15 7 281 TRUE
## 20 4.364195 16 5 130 FALSE
## 21 4.731182 17 8 84 TRUE
## 22 5.872350 19 14 262 TRUE
## 23 3.566169 17 14 78 TRUE
## 24 4.533903 20 9 75 TRUE
## 25 5.494825 14 13 241 TRUE
## 26 3.792585 17 6 300 FALSE
## 27 3.479056 15 8 256 TRUE
## 28 6.000100 16 11 38 TRUE
## 29 5.385418 18 10 164 TRUE
## 30 4.295210 18 10 199 FALSE
## 31 4.705739 16 12 235 FALSE
## 32 4.967705 14 9 26 TRUE
## 33 4.883999 16 11 260 FALSE
## 34 6.685638 17 7 187 FALSE
## 35 6.782740 18 7 224 TRUE
## 36 4.895407 17 11 227 TRUE
## 37 6.089084 14 11 222 TRUE
## 38 4.058907 12 10 59 FALSE
## 39 5.309868 19 13 186 TRUE
## 40 4.583636 11 10 9 FALSE
## 41 4.390665 13 6 10 TRUE
## 42 5.553013 16 11 105 TRUE
## 43 5.399750 18 7 59 TRUE
## 44 4.387268 14 14 18 TRUE
## 45 5.033227 16 10 43 TRUE
## 46 4.550117 17 14 258 TRUE
## 47 5.381977 14 7 158 TRUE
## 48 4.957284 15 11 208 TRUE
## 49 4.662462 15 9 119 FALSE
## 50 5.744640 15 13 250 FALSE
## 51 4.572862 14 6 60 FALSE
## 52 6.061314 16 12 134 TRUE
## 53 4.077621 14 13 138 TRUE
## 54 5.227363 16 15 38 FALSE
## 55 6.347073 17 7 31 TRUE
## 56 4.453775 11 6 111 TRUE
## 57 4.682281 17 5 3 TRUE
## 58 5.176889 12 13 37 FALSE
## 59 4.389201 17 12 114 TRUE
## 60 3.284646 16 5 33 TRUE
## 61 5.837547 19 9 61 FALSE
## 62 4.968740 17 8 188 TRUE
## 63 3.903301 17 10 58 TRUE
## 64 6.132158 15 10 286 FALSE
## 65 5.873990 17 12 151 FALSE
## 66 5.515326 14 14 191 TRUE
## 67 4.084581 13 13 265 TRUE
## 68 3.723847 18 10 72 TRUE
## 69 5.188571 17 11 157 TRUE
## 70 5.086961 15 8 240 TRUE
## 71 5.833276 16 14 133 TRUE
## 72 4.376451 17 13 170 TRUE
## 73 3.695728 17 11 66 FALSE
## 74 4.623261 16 5 220 TRUE
## 75 5.533799 15 16 218 FALSE
## 76 3.128952 18 12 120 TRUE
## 77 3.984496 16 14 199 TRUE
## 78 6.174937 15 14 81 TRUE
## 79 5.831186 16 11 141 TRUE
## 80 4.584803 14 9 185 FALSE
## 81 4.444875 17 12 216 FALSE
## 82 5.384299 14 9 22 TRUE
## 83 5.781127 16 11 169 TRUE
## 84 4.279655 16 8 246 TRUE
## 85 4.534812 17 10 24 TRUE
## 86 4.458870 17 7 240 TRUE
## 87 5.413162 14 10 268 TRUE
## 88 4.561375 18 11 289 TRUE
## 89 6.076244 14 12 14 TRUE
## 90 5.140797 16 8 225 TRUE
## 91 4.427752 16 4 268 FALSE
## 92 5.902777 14 9 279 TRUE
## 93 5.729409 16 10 222 TRUE
## 94 4.563922 16 8 215 TRUE
## 95 6.536906 15 13 212 TRUE
## 96 3.892238 16 13 86 TRUE
## 97 6.248745 16 19 239 TRUE
## 98 6.049258 18 15 171 TRUE
## 99 5.151745 14 9 150 FALSE
## 100 4.328072 15 14 220 TRUE
## 101 5.946870 13 7 242 TRUE
## 102 4.441015 17 10 129 TRUE
## 103 4.512727 15 13 25 TRUE
## 104 5.241019 11 12 202 TRUE
## 105 5.047264 17 12 212 FALSE
## 106 5.032729 19 11 298 FALSE
## 107 4.800188 15 13 264 TRUE
## 108 4.754542 14 10 18 TRUE
## 109 4.139596 17 12 199 TRUE
## 110 5.336830 18 7 10 TRUE
## 111 4.410783 17 11 283 TRUE
## 112 5.301664 16 12 285 FALSE
## 113 4.248514 15 4 286 TRUE
## 114 4.482439 16 7 291 TRUE
## 115 5.199317 16 10 165 TRUE
## 116 4.960738 15 10 236 TRUE
## 117 4.508527 15 6 131 TRUE
## 118 4.821758 18 11 82 FALSE
## 119 6.706502 20 12 129 TRUE
## 120 4.619753 18 9 190 FALSElibrary(naniar)
tib.i <- replace_with_na(tib.c, replace= list("Biomasa"=c(-5.191382), "Floresd"=c(-82)))
is.na(tib.i)## Biomasa Floresr Floresd Hojasd Plaga
## 1 FALSE FALSE FALSE FALSE FALSE
## 2 FALSE FALSE FALSE FALSE FALSE
## 3 FALSE FALSE FALSE FALSE FALSE
## 4 FALSE FALSE FALSE FALSE FALSE
## 5 FALSE FALSE FALSE FALSE FALSE
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## 120 FALSE FALSE FALSE FALSE FALSEtib.i## Biomasa Floresr Floresd Hojasd Plaga
## 1 5.191382 16 1 150 TRUE
## 2 4.682817 19 13 197 FALSE
## 3 5.444438 20 10 279 TRUE
## 4 3.525376 17 13 118 TRUE
## 5 4.860301 17 8 277 FALSE
## 6 4.284344 16 19 89 TRUE
## 7 5.057715 16 8 253 TRUE
## 8 6.372872 16 8 54 FALSE
## 9 4.814129 17 8 121 TRUE
## 10 5.875504 17 8 37 FALSE
## 11 5.545751 15 12 2 TRUE
## 12 4.332472 16 10 57 FALSE
## 13 4.143265 12 11 203 TRUE
## 14 3.942137 17 8 68 TRUE
## 15 4.085222 16 13 83 TRUE
## 16 5.135173 14 10 150 TRUE
## 17 4.198869 17 8 237 TRUE
## 18 3.931221 13 11 32 TRUE
## 19 4.645347 15 7 281 TRUE
## 20 4.364195 16 5 130 FALSE
## 21 4.731182 17 8 84 TRUE
## 22 5.872350 19 14 262 TRUE
## 23 3.566169 17 14 78 TRUE
## 24 4.533903 20 9 75 TRUE
## 25 5.494825 14 13 241 TRUE
## 26 3.792585 17 6 300 FALSE
## 27 3.479056 15 8 256 TRUE
## 28 6.000100 16 11 38 TRUE
## 29 5.385418 18 10 164 TRUE
## 30 4.295210 18 10 199 FALSE
## 31 4.705739 16 12 235 FALSE
## 32 4.967705 14 9 26 TRUE
## 33 4.883999 16 11 260 FALSE
## 34 6.685638 17 7 187 FALSE
## 35 6.782740 18 7 224 TRUE
## 36 4.895407 17 11 227 TRUE
## 37 6.089084 14 11 222 TRUE
## 38 4.058907 12 10 59 FALSE
## 39 5.309868 19 13 186 TRUE
## 40 4.583636 11 10 9 FALSE
## 41 4.390665 13 6 10 TRUE
## 42 5.553013 16 11 105 TRUE
## 43 5.399750 18 7 59 TRUE
## 44 4.387268 14 14 18 TRUE
## 45 5.033227 16 10 43 TRUE
## 46 4.550117 17 14 258 TRUE
## 47 5.381977 14 7 158 TRUE
## 48 4.957284 15 11 208 TRUE
## 49 4.662462 15 9 119 FALSE
## 50 5.744640 15 13 250 FALSE
## 51 4.572862 14 6 60 FALSE
## 52 6.061314 16 12 134 TRUE
## 53 4.077621 14 13 138 TRUE
## 54 5.227363 16 15 38 FALSE
## 55 6.347073 17 7 31 TRUE
## 56 4.453775 11 6 111 TRUE
## 57 4.682281 17 5 3 TRUE
## 58 5.176889 12 13 37 FALSE
## 59 4.389201 17 12 114 TRUE
## 60 3.284646 16 5 33 TRUE
## 61 5.837547 19 9 61 FALSE
## 62 4.968740 17 8 188 TRUE
## 63 3.903301 17 10 58 TRUE
## 64 6.132158 15 10 286 FALSE
## 65 5.873990 17 12 151 FALSE
## 66 5.515326 14 14 191 TRUE
## 67 4.084581 13 13 265 TRUE
## 68 3.723847 18 10 72 TRUE
## 69 5.188571 17 11 157 TRUE
## 70 5.086961 15 8 240 TRUE
## 71 5.833276 16 14 133 TRUE
## 72 4.376451 17 13 170 TRUE
## 73 3.695728 17 11 66 FALSE
## 74 4.623261 16 5 220 TRUE
## 75 5.533799 15 16 218 FALSE
## 76 3.128952 18 12 120 TRUE
## 77 3.984496 16 14 199 TRUE
## 78 6.174937 15 14 81 TRUE
## 79 5.831186 16 11 141 TRUE
## 80 4.584803 14 9 185 FALSE
## 81 4.444875 17 12 216 FALSE
## 82 5.384299 14 9 22 TRUE
## 83 5.781127 16 11 169 TRUE
## 84 4.279655 16 8 246 TRUE
## 85 4.534812 17 10 24 TRUE
## 86 4.458870 17 7 240 TRUE
## 87 5.413162 14 10 268 TRUE
## 88 4.561375 18 11 289 TRUE
## 89 6.076244 14 12 14 TRUE
## 90 5.140797 16 8 225 TRUE
## 91 4.427752 16 4 268 FALSE
## 92 5.902777 14 9 279 TRUE
## 93 5.729409 16 10 222 TRUE
## 94 4.563922 16 8 215 TRUE
## 95 6.536906 15 13 212 TRUE
## 96 3.892238 16 13 86 TRUE
## 97 6.248745 16 19 239 TRUE
## 98 6.049258 18 15 171 TRUE
## 99 5.151745 14 9 150 FALSE
## 100 4.328072 15 14 220 TRUE
## 101 5.946870 13 7 242 TRUE
## 102 4.441015 17 10 129 TRUE
## 103 4.512727 15 13 25 TRUE
## 104 5.241019 11 12 202 TRUE
## 105 5.047264 17 12 212 FALSE
## 106 5.032729 19 11 298 FALSE
## 107 4.800188 15 13 264 TRUE
## 108 4.754542 14 10 18 TRUE
## 109 4.139596 17 12 199 TRUE
## 110 5.336830 18 7 10 TRUE
## 111 4.410783 17 11 283 TRUE
## 112 5.301664 16 12 285 FALSE
## 113 4.248514 15 4 286 TRUE
## 114 4.482439 16 7 291 TRUE
## 115 5.199317 16 10 165 TRUE
## 116 4.960738 15 10 236 TRUE
## 117 4.508527 15 6 131 TRUE
## 118 4.821758 18 11 82 FALSE
## 119 6.706502 20 12 129 TRUE
## 120 4.619753 18 9 190 FALSEtib.c %>% select(Biomasa)## Biomasa
## 1 5.191382
## 2 4.682817
## 3 5.444438
## 4 3.525376
## 5 4.860301
## 6 4.284344
## 7 5.057715
## 8 6.372872
## 9 4.814129
## 10 5.875504
## 11 5.545751
## 12 4.332472
## 13 4.143265
## 14 3.942137
## 15 4.085222
## 16 5.135173
## 17 4.198869
## 18 3.931221
## 19 4.645347
## 20 4.364195
## 21 4.731182
## 22 5.872350
## 23 3.566169
## 24 4.533903
## 25 5.494825
## 26 3.792585
## 27 3.479056
## 28 6.000100
## 29 5.385418
## 30 4.295210
## 31 4.705739
## 32 4.967705
## 33 4.883999
## 34 6.685638
## 35 6.782740
## 36 4.895407
## 37 6.089084
## 38 4.058907
## 39 5.309868
## 40 4.583636
## 41 4.390665
## 42 5.553013
## 43 5.399750
## 44 4.387268
## 45 5.033227
## 46 4.550117
## 47 5.381977
## 48 4.957284
## 49 4.662462
## 50 5.744640
## 51 4.572862
## 52 6.061314
## 53 4.077621
## 54 5.227363
## 55 6.347073
## 56 4.453775
## 57 4.682281
## 58 5.176889
## 59 4.389201
## 60 3.284646
## 61 5.837547
## 62 4.968740
## 63 3.903301
## 64 6.132158
## 65 5.873990
## 66 5.515326
## 67 4.084581
## 68 3.723847
## 69 5.188571
## 70 5.086961
## 71 5.833276
## 72 4.376451
## 73 3.695728
## 74 4.623261
## 75 5.533799
## 76 3.128952
## 77 3.984496
## 78 6.174937
## 79 5.831186
## 80 4.584803
## 81 4.444875
## 82 5.384299
## 83 5.781127
## 84 4.279655
## 85 4.534812
## 86 4.458870
## 87 5.413162
## 88 4.561375
## 89 6.076244
## 90 5.140797
## 91 4.427752
## 92 5.902777
## 93 5.729409
## 94 4.563922
## 95 6.536906
## 96 3.892238
## 97 6.248745
## 98 6.049258
## 99 5.151745
## 100 4.328072
## 101 5.946870
## 102 4.441015
## 103 4.512727
## 104 5.241019
## 105 5.047264
## 106 5.032729
## 107 4.800188
## 108 4.754542
## 109 4.139596
## 110 5.336830
## 111 4.410783
## 112 5.301664
## 113 4.248514
## 114 4.482439
## 115 5.199317
## 116 4.960738
## 117 4.508527
## 118 4.821758
## 119 6.706502
## 120 4.619753tib.c %>% select(Floresd : Plaga)## Floresd Hojasd Plaga
## 1 1 150 TRUE
## 2 13 197 FALSE
## 3 10 279 TRUE
## 4 13 118 TRUE
## 5 8 277 FALSE
## 6 19 89 TRUE
## 7 8 253 TRUE
## 8 8 54 FALSE
## 9 8 121 TRUE
## 10 8 37 FALSE
## 11 12 2 TRUE
## 12 10 57 FALSE
## 13 11 203 TRUE
## 14 8 68 TRUE
## 15 13 83 TRUE
## 16 10 150 TRUE
## 17 8 237 TRUE
## 18 11 32 TRUE
## 19 7 281 TRUE
## 20 5 130 FALSE
## 21 8 84 TRUE
## 22 14 262 TRUE
## 23 14 78 TRUE
## 24 9 75 TRUE
## 25 13 241 TRUE
## 26 6 300 FALSE
## 27 8 256 TRUE
## 28 11 38 TRUE
## 29 10 164 TRUE
## 30 10 199 FALSE
## 31 12 235 FALSE
## 32 9 26 TRUE
## 33 11 260 FALSE
## 34 7 187 FALSE
## 35 7 224 TRUE
## 36 11 227 TRUE
## 37 11 222 TRUE
## 38 10 59 FALSE
## 39 13 186 TRUE
## 40 10 9 FALSE
## 41 6 10 TRUE
## 42 11 105 TRUE
## 43 7 59 TRUE
## 44 14 18 TRUE
## 45 10 43 TRUE
## 46 14 258 TRUE
## 47 7 158 TRUE
## 48 11 208 TRUE
## 49 9 119 FALSE
## 50 13 250 FALSE
## 51 6 60 FALSE
## 52 12 134 TRUE
## 53 13 138 TRUE
## 54 15 38 FALSE
## 55 7 31 TRUE
## 56 6 111 TRUE
## 57 5 3 TRUE
## 58 13 37 FALSE
## 59 12 114 TRUE
## 60 5 33 TRUE
## 61 9 61 FALSE
## 62 8 188 TRUE
## 63 10 58 TRUE
## 64 10 286 FALSE
## 65 12 151 FALSE
## 66 14 191 TRUE
## 67 13 265 TRUE
## 68 10 72 TRUE
## 69 11 157 TRUE
## 70 8 240 TRUE
## 71 14 133 TRUE
## 72 13 170 TRUE
## 73 11 66 FALSE
## 74 5 220 TRUE
## 75 16 218 FALSE
## 76 12 120 TRUE
## 77 14 199 TRUE
## 78 14 81 TRUE
## 79 11 141 TRUE
## 80 9 185 FALSE
## 81 12 216 FALSE
## 82 9 22 TRUE
## 83 11 169 TRUE
## 84 8 246 TRUE
## 85 10 24 TRUE
## 86 7 240 TRUE
## 87 10 268 TRUE
## 88 11 289 TRUE
## 89 12 14 TRUE
## 90 8 225 TRUE
## 91 4 268 FALSE
## 92 9 279 TRUE
## 93 10 222 TRUE
## 94 8 215 TRUE
## 95 13 212 TRUE
## 96 13 86 TRUE
## 97 19 239 TRUE
## 98 15 171 TRUE
## 99 9 150 FALSE
## 100 14 220 TRUE
## 101 7 242 TRUE
## 102 10 129 TRUE
## 103 13 25 TRUE
## 104 12 202 TRUE
## 105 12 212 FALSE
## 106 11 298 FALSE
## 107 13 264 TRUE
## 108 10 18 TRUE
## 109 12 199 TRUE
## 110 7 10 TRUE
## 111 11 283 TRUE
## 112 12 285 FALSE
## 113 4 286 TRUE
## 114 7 291 TRUE
## 115 10 165 TRUE
## 116 10 236 TRUE
## 117 6 131 TRUE
## 118 11 82 FALSE
## 119 12 129 TRUE
## 120 9 190 FALSEtib.c %>% select(!Floresd : Plaga)## Biomasa Floresr
## 1 5.191382 16
## 2 4.682817 19
## 3 5.444438 20
## 4 3.525376 17
## 5 4.860301 17
## 6 4.284344 16
## 7 5.057715 16
## 8 6.372872 16
## 9 4.814129 17
## 10 5.875504 17
## 11 5.545751 15
## 12 4.332472 16
## 13 4.143265 12
## 14 3.942137 17
## 15 4.085222 16
## 16 5.135173 14
## 17 4.198869 17
## 18 3.931221 13
## 19 4.645347 15
## 20 4.364195 16
## 21 4.731182 17
## 22 5.872350 19
## 23 3.566169 17
## 24 4.533903 20
## 25 5.494825 14
## 26 3.792585 17
## 27 3.479056 15
## 28 6.000100 16
## 29 5.385418 18
## 30 4.295210 18
## 31 4.705739 16
## 32 4.967705 14
## 33 4.883999 16
## 34 6.685638 17
## 35 6.782740 18
## 36 4.895407 17
## 37 6.089084 14
## 38 4.058907 12
## 39 5.309868 19
## 40 4.583636 11
## 41 4.390665 13
## 42 5.553013 16
## 43 5.399750 18
## 44 4.387268 14
## 45 5.033227 16
## 46 4.550117 17
## 47 5.381977 14
## 48 4.957284 15
## 49 4.662462 15
## 50 5.744640 15
## 51 4.572862 14
## 52 6.061314 16
## 53 4.077621 14
## 54 5.227363 16
## 55 6.347073 17
## 56 4.453775 11
## 57 4.682281 17
## 58 5.176889 12
## 59 4.389201 17
## 60 3.284646 16
## 61 5.837547 19
## 62 4.968740 17
## 63 3.903301 17
## 64 6.132158 15
## 65 5.873990 17
## 66 5.515326 14
## 67 4.084581 13
## 68 3.723847 18
## 69 5.188571 17
## 70 5.086961 15
## 71 5.833276 16
## 72 4.376451 17
## 73 3.695728 17
## 74 4.623261 16
## 75 5.533799 15
## 76 3.128952 18
## 77 3.984496 16
## 78 6.174937 15
## 79 5.831186 16
## 80 4.584803 14
## 81 4.444875 17
## 82 5.384299 14
## 83 5.781127 16
## 84 4.279655 16
## 85 4.534812 17
## 86 4.458870 17
## 87 5.413162 14
## 88 4.561375 18
## 89 6.076244 14
## 90 5.140797 16
## 91 4.427752 16
## 92 5.902777 14
## 93 5.729409 16
## 94 4.563922 16
## 95 6.536906 15
## 96 3.892238 16
## 97 6.248745 16
## 98 6.049258 18
## 99 5.151745 14
## 100 4.328072 15
## 101 5.946870 13
## 102 4.441015 17
## 103 4.512727 15
## 104 5.241019 11
## 105 5.047264 17
## 106 5.032729 19
## 107 4.800188 15
## 108 4.754542 14
## 109 4.139596 17
## 110 5.336830 18
## 111 4.410783 17
## 112 5.301664 16
## 113 4.248514 15
## 114 4.482439 16
## 115 5.199317 16
## 116 4.960738 15
## 117 4.508527 15
## 118 4.821758 18
## 119 6.706502 20
## 120 4.619753 18tib.c %>% select(!ends_with("d"))## Biomasa Floresr Plaga
## 1 5.191382 16 TRUE
## 2 4.682817 19 FALSE
## 3 5.444438 20 TRUE
## 4 3.525376 17 TRUE
## 5 4.860301 17 FALSE
## 6 4.284344 16 TRUE
## 7 5.057715 16 TRUE
## 8 6.372872 16 FALSE
## 9 4.814129 17 TRUE
## 10 5.875504 17 FALSE
## 11 5.545751 15 TRUE
## 12 4.332472 16 FALSE
## 13 4.143265 12 TRUE
## 14 3.942137 17 TRUE
## 15 4.085222 16 TRUE
## 16 5.135173 14 TRUE
## 17 4.198869 17 TRUE
## 18 3.931221 13 TRUE
## 19 4.645347 15 TRUE
## 20 4.364195 16 FALSE
## 21 4.731182 17 TRUE
## 22 5.872350 19 TRUE
## 23 3.566169 17 TRUE
## 24 4.533903 20 TRUE
## 25 5.494825 14 TRUE
## 26 3.792585 17 FALSE
## 27 3.479056 15 TRUE
## 28 6.000100 16 TRUE
## 29 5.385418 18 TRUE
## 30 4.295210 18 FALSE
## 31 4.705739 16 FALSE
## 32 4.967705 14 TRUE
## 33 4.883999 16 FALSE
## 34 6.685638 17 FALSE
## 35 6.782740 18 TRUE
## 36 4.895407 17 TRUE
## 37 6.089084 14 TRUE
## 38 4.058907 12 FALSE
## 39 5.309868 19 TRUE
## 40 4.583636 11 FALSE
## 41 4.390665 13 TRUE
## 42 5.553013 16 TRUE
## 43 5.399750 18 TRUE
## 44 4.387268 14 TRUE
## 45 5.033227 16 TRUE
## 46 4.550117 17 TRUE
## 47 5.381977 14 TRUE
## 48 4.957284 15 TRUE
## 49 4.662462 15 FALSE
## 50 5.744640 15 FALSE
## 51 4.572862 14 FALSE
## 52 6.061314 16 TRUE
## 53 4.077621 14 TRUE
## 54 5.227363 16 FALSE
## 55 6.347073 17 TRUE
## 56 4.453775 11 TRUE
## 57 4.682281 17 TRUE
## 58 5.176889 12 FALSE
## 59 4.389201 17 TRUE
## 60 3.284646 16 TRUE
## 61 5.837547 19 FALSE
## 62 4.968740 17 TRUE
## 63 3.903301 17 TRUE
## 64 6.132158 15 FALSE
## 65 5.873990 17 FALSE
## 66 5.515326 14 TRUE
## 67 4.084581 13 TRUE
## 68 3.723847 18 TRUE
## 69 5.188571 17 TRUE
## 70 5.086961 15 TRUE
## 71 5.833276 16 TRUE
## 72 4.376451 17 TRUE
## 73 3.695728 17 FALSE
## 74 4.623261 16 TRUE
## 75 5.533799 15 FALSE
## 76 3.128952 18 TRUE
## 77 3.984496 16 TRUE
## 78 6.174937 15 TRUE
## 79 5.831186 16 TRUE
## 80 4.584803 14 FALSE
## 81 4.444875 17 FALSE
## 82 5.384299 14 TRUE
## 83 5.781127 16 TRUE
## 84 4.279655 16 TRUE
## 85 4.534812 17 TRUE
## 86 4.458870 17 TRUE
## 87 5.413162 14 TRUE
## 88 4.561375 18 TRUE
## 89 6.076244 14 TRUE
## 90 5.140797 16 TRUE
## 91 4.427752 16 FALSE
## 92 5.902777 14 TRUE
## 93 5.729409 16 TRUE
## 94 4.563922 16 TRUE
## 95 6.536906 15 TRUE
## 96 3.892238 16 TRUE
## 97 6.248745 16 TRUE
## 98 6.049258 18 TRUE
## 99 5.151745 14 FALSE
## 100 4.328072 15 TRUE
## 101 5.946870 13 TRUE
## 102 4.441015 17 TRUE
## 103 4.512727 15 TRUE
## 104 5.241019 11 TRUE
## 105 5.047264 17 FALSE
## 106 5.032729 19 FALSE
## 107 4.800188 15 TRUE
## 108 4.754542 14 TRUE
## 109 4.139596 17 TRUE
## 110 5.336830 18 TRUE
## 111 4.410783 17 TRUE
## 112 5.301664 16 FALSE
## 113 4.248514 15 TRUE
## 114 4.482439 16 TRUE
## 115 5.199317 16 TRUE
## 116 4.960738 15 TRUE
## 117 4.508527 15 TRUE
## 118 4.821758 18 FALSE
## 119 6.706502 20 TRUE
## 120 4.619753 18 FALSEtib.c %>% select(starts_with("Fl"))## Floresr Floresd
## 1 16 1
## 2 19 13
## 3 20 10
## 4 17 13
## 5 17 8
## 6 16 19
## 7 16 8
## 8 16 8
## 9 17 8
## 10 17 8
## 11 15 12
## 12 16 10
## 13 12 11
## 14 17 8
## 15 16 13
## 16 14 10
## 17 17 8
## 18 13 11
## 19 15 7
## 20 16 5
## 21 17 8
## 22 19 14
## 23 17 14
## 24 20 9
## 25 14 13
## 26 17 6
## 27 15 8
## 28 16 11
## 29 18 10
## 30 18 10
## 31 16 12
## 32 14 9
## 33 16 11
## 34 17 7
## 35 18 7
## 36 17 11
## 37 14 11
## 38 12 10
## 39 19 13
## 40 11 10
## 41 13 6
## 42 16 11
## 43 18 7
## 44 14 14
## 45 16 10
## 46 17 14
## 47 14 7
## 48 15 11
## 49 15 9
## 50 15 13
## 51 14 6
## 52 16 12
## 53 14 13
## 54 16 15
## 55 17 7
## 56 11 6
## 57 17 5
## 58 12 13
## 59 17 12
## 60 16 5
## 61 19 9
## 62 17 8
## 63 17 10
## 64 15 10
## 65 17 12
## 66 14 14
## 67 13 13
## 68 18 10
## 69 17 11
## 70 15 8
## 71 16 14
## 72 17 13
## 73 17 11
## 74 16 5
## 75 15 16
## 76 18 12
## 77 16 14
## 78 15 14
## 79 16 11
## 80 14 9
## 81 17 12
## 82 14 9
## 83 16 11
## 84 16 8
## 85 17 10
## 86 17 7
## 87 14 10
## 88 18 11
## 89 14 12
## 90 16 8
## 91 16 4
## 92 14 9
## 93 16 10
## 94 16 8
## 95 15 13
## 96 16 13
## 97 16 19
## 98 18 15
## 99 14 9
## 100 15 14
## 101 13 7
## 102 17 10
## 103 15 13
## 104 11 12
## 105 17 12
## 106 19 11
## 107 15 13
## 108 14 10
## 109 17 12
## 110 18 7
## 111 17 11
## 112 16 12
## 113 15 4
## 114 16 7
## 115 16 10
## 116 15 10
## 117 15 6
## 118 18 11
## 119 20 12
## 120 18 9tib.c %>% select(starts_with("F"), ends_with("d"))## Floresr Floresd Hojasd
## 1 16 1 150
## 2 19 13 197
## 3 20 10 279
## 4 17 13 118
## 5 17 8 277
## 6 16 19 89
## 7 16 8 253
## 8 16 8 54
## 9 17 8 121
## 10 17 8 37
## 11 15 12 2
## 12 16 10 57
## 13 12 11 203
## 14 17 8 68
## 15 16 13 83
## 16 14 10 150
## 17 17 8 237
## 18 13 11 32
## 19 15 7 281
## 20 16 5 130
## 21 17 8 84
## 22 19 14 262
## 23 17 14 78
## 24 20 9 75
## 25 14 13 241
## 26 17 6 300
## 27 15 8 256
## 28 16 11 38
## 29 18 10 164
## 30 18 10 199
## 31 16 12 235
## 32 14 9 26
## 33 16 11 260
## 34 17 7 187
## 35 18 7 224
## 36 17 11 227
## 37 14 11 222
## 38 12 10 59
## 39 19 13 186
## 40 11 10 9
## 41 13 6 10
## 42 16 11 105
## 43 18 7 59
## 44 14 14 18
## 45 16 10 43
## 46 17 14 258
## 47 14 7 158
## 48 15 11 208
## 49 15 9 119
## 50 15 13 250
## 51 14 6 60
## 52 16 12 134
## 53 14 13 138
## 54 16 15 38
## 55 17 7 31
## 56 11 6 111
## 57 17 5 3
## 58 12 13 37
## 59 17 12 114
## 60 16 5 33
## 61 19 9 61
## 62 17 8 188
## 63 17 10 58
## 64 15 10 286
## 65 17 12 151
## 66 14 14 191
## 67 13 13 265
## 68 18 10 72
## 69 17 11 157
## 70 15 8 240
## 71 16 14 133
## 72 17 13 170
## 73 17 11 66
## 74 16 5 220
## 75 15 16 218
## 76 18 12 120
## 77 16 14 199
## 78 15 14 81
## 79 16 11 141
## 80 14 9 185
## 81 17 12 216
## 82 14 9 22
## 83 16 11 169
## 84 16 8 246
## 85 17 10 24
## 86 17 7 240
## 87 14 10 268
## 88 18 11 289
## 89 14 12 14
## 90 16 8 225
## 91 16 4 268
## 92 14 9 279
## 93 16 10 222
## 94 16 8 215
## 95 15 13 212
## 96 16 13 86
## 97 16 19 239
## 98 18 15 171
## 99 14 9 150
## 100 15 14 220
## 101 13 7 242
## 102 17 10 129
## 103 15 13 25
## 104 11 12 202
## 105 17 12 212
## 106 19 11 298
## 107 15 13 264
## 108 14 10 18
## 109 17 12 199
## 110 18 7 10
## 111 17 11 283
## 112 16 12 285
## 113 15 4 286
## 114 16 7 291
## 115 16 10 165
## 116 15 10 236
## 117 15 6 131
## 118 18 11 82
## 119 20 12 129
## 120 18 9 190agrup <- tib.c %>% select(Plaga)
varstatus <- agrup %>% group_by("Estatus")
arrange(varstatus, by_group= FALSE)## # A tibble: 120 x 2
## # Groups: "Estatus" [1]
## Plaga `"Estatus"`
## <lgl> <chr>
## 1 TRUE Estatus
## 2 FALSE Estatus
## 3 TRUE Estatus
## 4 TRUE Estatus
## 5 FALSE Estatus
## 6 TRUE Estatus
## 7 TRUE Estatus
## 8 FALSE Estatus
## 9 TRUE Estatus
## 10 FALSE Estatus
## # ... with 110 more rowsfilter(df1, Estatus == "MA", Estatus == "MA")## Biomasa Floresr Floresd Hojasd Plaga Estatus Fertilización
## 1 5.444438 20 10 279 TRUE MA FI
## 2 4.284344 16 19 89 TRUE MA FI
## 3 4.814129 17 8 121 TRUE MA FO
## 4 4.332472 16 10 57 FALSE MA FO
## 5 4.085222 16 13 83 TRUE MA FO
## 6 3.931221 13 11 32 TRUE MA FI
## 7 4.731182 17 8 84 TRUE MA FO
## 8 4.533903 20 9 75 TRUE MA FI
## 9 3.479056 15 8 256 TRUE MA FI
## 10 4.295210 18 10 199 FALSE MA FO
## 11 4.883999 16 11 260 FALSE MA FI
## 12 4.895407 17 11 227 TRUE MA FI
## 13 5.309868 19 13 186 TRUE MA FI
## 14 5.553013 16 11 105 TRUE MA FI
## 15 5.033227 16 10 43 TRUE MA FO
## 16 4.957284 15 11 208 TRUE MA FI
## 17 4.572862 14 6 60 FALSE MA FI
## 18 5.227363 16 15 38 FALSE MA FI
## 19 4.682281 17 5 3 TRUE MA FO
## 20 3.284646 16 5 33 TRUE MA FO
## 21 3.903301 17 10 58 TRUE MA FO
## 22 5.515326 14 14 191 TRUE MA FO
## 23 5.188571 17 11 157 TRUE MA FI
## 24 4.376451 17 13 170 TRUE MA FI
## 25 5.533799 15 16 218 FALSE MA FI
## 26 6.174937 15 14 81 TRUE MA FO
## 27 4.444875 17 12 216 FALSE MA FI
## 28 4.279655 16 8 246 TRUE MA FI
## 29 5.413162 14 10 268 TRUE MA FO
## 30 5.140797 16 8 225 TRUE MA FO
## 31 5.729409 16 10 222 TRUE MA FO
## 32 3.892238 16 13 86 TRUE MA FI
## 33 5.151745 14 9 150 FALSE MA FI
## 34 4.441015 17 10 129 TRUE MA FI
## 35 5.047264 17 12 212 FALSE MA FI
## 36 4.754542 14 10 18 TRUE MA FI
## 37 4.410783 17 11 283 TRUE MA FI
## 38 4.482439 16 7 291 TRUE MA FO
## 39 4.508527 15 6 131 TRUE MA FI
## 40 4.619753 18 9 190 FALSE MA FOfilter(df1, Biomasa > 5)## Biomasa Floresr Floresd Hojasd Plaga Estatus Fertilización
## 1 5.191382 16 1 150 TRUE S FO
## 2 5.444438 20 10 279 TRUE MA FI
## 3 5.057715 16 8 253 TRUE S FO
## 4 6.372872 16 8 54 FALSE PA FO
## 5 5.875504 17 8 37 FALSE S FO
## 6 5.545751 15 12 2 TRUE PA FI
## 7 5.135173 14 10 150 TRUE S FO
## 8 5.872350 19 14 262 TRUE S FI
## 9 5.494825 14 13 241 TRUE S FO
## 10 6.000100 16 11 38 TRUE S FO
## 11 5.385418 18 10 164 TRUE PA FI
## 12 6.685638 17 7 187 FALSE S FI
## 13 6.782740 18 7 224 TRUE PA FI
## 14 6.089084 14 11 222 TRUE S FI
## 15 5.309868 19 13 186 TRUE MA FI
## 16 5.553013 16 11 105 TRUE MA FI
## 17 5.399750 18 7 59 TRUE S FI
## 18 5.033227 16 10 43 TRUE MA FO
## 19 5.381977 14 7 158 TRUE PA FI
## 20 5.744640 15 13 250 FALSE PA FO
## 21 6.061314 16 12 134 TRUE S FI
## 22 5.227363 16 15 38 FALSE MA FI
## 23 6.347073 17 7 31 TRUE S FI
## 24 5.176889 12 13 37 FALSE S FO
## 25 5.837547 19 9 61 FALSE S FI
## 26 6.132158 15 10 286 FALSE S FI
## 27 5.873990 17 12 151 FALSE PA FI
## 28 5.515326 14 14 191 TRUE MA FO
## 29 5.188571 17 11 157 TRUE MA FI
## 30 5.086961 15 8 240 TRUE S FI
## 31 5.833276 16 14 133 TRUE PA FI
## 32 5.533799 15 16 218 FALSE MA FI
## 33 6.174937 15 14 81 TRUE MA FO
## 34 5.831186 16 11 141 TRUE S FO
## 35 5.384299 14 9 22 TRUE S FO
## 36 5.781127 16 11 169 TRUE PA FI
## 37 5.413162 14 10 268 TRUE MA FO
## 38 6.076244 14 12 14 TRUE PA FO
## 39 5.140797 16 8 225 TRUE MA FO
## 40 5.902777 14 9 279 TRUE PA FO
## 41 5.729409 16 10 222 TRUE MA FO
## 42 6.536906 15 13 212 TRUE PA FI
## 43 6.248745 16 19 239 TRUE S FI
## 44 6.049258 18 15 171 TRUE PA FI
## 45 5.151745 14 9 150 FALSE MA FI
## 46 5.946870 13 7 242 TRUE PA FI
## 47 5.241019 11 12 202 TRUE PA FO
## 48 5.047264 17 12 212 FALSE MA FI
## 49 5.032729 19 11 298 FALSE S FI
## 50 5.336830 18 7 10 TRUE PA FO
## 51 5.301664 16 12 285 FALSE S FI
## 52 5.199317 16 10 165 TRUE S FO
## 53 6.706502 20 12 129 TRUE PA FIfilter(df1, Estatus == "PA", Fertilización == "FO")## Biomasa Floresr Floresd Hojasd Plaga Estatus Fertilización
## 1 6.372872 16 8 54 FALSE PA FO
## 2 4.198869 17 8 237 TRUE PA FO
## 3 3.792585 17 6 300 FALSE PA FO
## 4 5.744640 15 13 250 FALSE PA FO
## 5 4.968740 17 8 188 TRUE PA FO
## 6 3.984496 16 14 199 TRUE PA FO
## 7 4.584803 14 9 185 FALSE PA FO
## 8 4.458870 17 7 240 TRUE PA FO
## 9 6.076244 14 12 14 TRUE PA FO
## 10 5.902777 14 9 279 TRUE PA FO
## 11 5.241019 11 12 202 TRUE PA FO
## 12 5.336830 18 7 10 TRUE PA FOfilter(df1, Estatus == "PA", Fertilización == "FI")## Biomasa Floresr Floresd Hojasd Plaga Estatus Fertilización
## 1 4.682817 19 13 197 FALSE PA FI
## 2 4.860301 17 8 277 FALSE PA FI
## 3 5.545751 15 12 2 TRUE PA FI
## 4 3.942137 17 8 68 TRUE PA FI
## 5 4.364195 16 5 130 FALSE PA FI
## 6 3.566169 17 14 78 TRUE PA FI
## 7 5.385418 18 10 164 TRUE PA FI
## 8 4.967705 14 9 26 TRUE PA FI
## 9 6.782740 18 7 224 TRUE PA FI
## 10 4.058907 12 10 59 FALSE PA FI
## 11 4.390665 13 6 10 TRUE PA FI
## 12 4.387268 14 14 18 TRUE PA FI
## 13 5.381977 14 7 158 TRUE PA FI
## 14 4.077621 14 13 138 TRUE PA FI
## 15 4.453775 11 6 111 TRUE PA FI
## 16 4.389201 17 12 114 TRUE PA FI
## 17 5.873990 17 12 151 FALSE PA FI
## 18 3.723847 18 10 72 TRUE PA FI
## 19 5.833276 16 14 133 TRUE PA FI
## 20 4.623261 16 5 220 TRUE PA FI
## 21 5.781127 16 11 169 TRUE PA FI
## 22 6.536906 15 13 212 TRUE PA FI
## 23 6.049258 18 15 171 TRUE PA FI
## 24 5.946870 13 7 242 TRUE PA FI
## 25 4.800188 15 13 264 TRUE PA FI
## 26 4.248514 15 4 286 TRUE PA FI
## 27 4.960738 15 10 236 TRUE PA FI
## 28 6.706502 20 12 129 TRUE PA FImediana <- median(df1$Floresd, na.rm = FALSE)
df1 %>% filter_at(vars(Plaga), all_vars(. < mediana))## Biomasa Floresr Floresd Hojasd Plaga Estatus Fertilización
## 1 5.191382 16 1 150 TRUE S FO
## 2 4.682817 19 13 197 FALSE PA FI
## 3 5.444438 20 10 279 TRUE MA FI
## 4 3.525376 17 13 118 TRUE S FI
## 5 4.860301 17 8 277 FALSE PA FI
## 6 4.284344 16 19 89 TRUE MA FI
## 7 5.057715 16 8 253 TRUE S FO
## 8 6.372872 16 8 54 FALSE PA FO
## 9 4.814129 17 8 121 TRUE MA FO
## 10 5.875504 17 8 37 FALSE S FO
## 11 5.545751 15 12 2 TRUE PA FI
## 12 4.332472 16 10 57 FALSE MA FO
## 13 4.143265 12 11 203 TRUE S FO
## 14 3.942137 17 8 68 TRUE PA FI
## 15 4.085222 16 13 83 TRUE MA FO
## 16 5.135173 14 10 150 TRUE S FO
## 17 4.198869 17 8 237 TRUE PA FO
## 18 3.931221 13 11 32 TRUE MA FI
## 19 4.645347 15 7 281 TRUE S FI
## 20 4.364195 16 5 130 FALSE PA FI
## 21 4.731182 17 8 84 TRUE MA FO
## 22 5.872350 19 14 262 TRUE S FI
## 23 3.566169 17 14 78 TRUE PA FI
## 24 4.533903 20 9 75 TRUE MA FI
## 25 5.494825 14 13 241 TRUE S FO
## 26 3.792585 17 6 300 FALSE PA FO
## 27 3.479056 15 8 256 TRUE MA FI
## 28 6.000100 16 11 38 TRUE S FO
## 29 5.385418 18 10 164 TRUE PA FI
## 30 4.295210 18 10 199 FALSE MA FO
## 31 4.705739 16 12 235 FALSE S FO
## 32 4.967705 14 9 26 TRUE PA FI
## 33 4.883999 16 11 260 FALSE MA FI
## 34 6.685638 17 7 187 FALSE S FI
## 35 6.782740 18 7 224 TRUE PA FI
## 36 4.895407 17 11 227 TRUE MA FI
## 37 6.089084 14 11 222 TRUE S FI
## 38 4.058907 12 10 59 FALSE PA FI
## 39 5.309868 19 13 186 TRUE MA FI
## 40 4.583636 11 10 9 FALSE S FI
## 41 4.390665 13 6 10 TRUE PA FI
## 42 5.553013 16 11 105 TRUE MA FI
## 43 5.399750 18 7 59 TRUE S FI
## 44 4.387268 14 14 18 TRUE PA FI
## 45 5.033227 16 10 43 TRUE MA FO
## 46 4.550117 17 14 258 TRUE S FO
## 47 5.381977 14 7 158 TRUE PA FI
## 48 4.957284 15 11 208 TRUE MA FI
## 49 4.662462 15 9 119 FALSE S FO
## 50 5.744640 15 13 250 FALSE PA FO
## 51 4.572862 14 6 60 FALSE MA FI
## 52 6.061314 16 12 134 TRUE S FI
## 53 4.077621 14 13 138 TRUE PA FI
## 54 5.227363 16 15 38 FALSE MA FI
## 55 6.347073 17 7 31 TRUE S FI
## 56 4.453775 11 6 111 TRUE PA FI
## 57 4.682281 17 5 3 TRUE MA FO
## 58 5.176889 12 13 37 FALSE S FO
## 59 4.389201 17 12 114 TRUE PA FI
## 60 3.284646 16 5 33 TRUE MA FO
## 61 5.837547 19 9 61 FALSE S FI
## 62 4.968740 17 8 188 TRUE PA FO
## 63 3.903301 17 10 58 TRUE MA FO
## 64 6.132158 15 10 286 FALSE S FI
## 65 5.873990 17 12 151 FALSE PA FI
## 66 5.515326 14 14 191 TRUE MA FO
## 67 4.084581 13 13 265 TRUE S FI
## 68 3.723847 18 10 72 TRUE PA FI
## 69 5.188571 17 11 157 TRUE MA FI
## 70 5.086961 15 8 240 TRUE S FI
## 71 5.833276 16 14 133 TRUE PA FI
## 72 4.376451 17 13 170 TRUE MA FI
## 73 3.695728 17 11 66 FALSE S FO
## 74 4.623261 16 5 220 TRUE PA FI
## 75 5.533799 15 16 218 FALSE MA FI
## 76 3.128952 18 12 120 TRUE S FI
## 77 3.984496 16 14 199 TRUE PA FO
## 78 6.174937 15 14 81 TRUE MA FO
## 79 5.831186 16 11 141 TRUE S FO
## 80 4.584803 14 9 185 FALSE PA FO
## 81 4.444875 17 12 216 FALSE MA FI
## 82 5.384299 14 9 22 TRUE S FO
## 83 5.781127 16 11 169 TRUE PA FI
## 84 4.279655 16 8 246 TRUE MA FI
## 85 4.534812 17 10 24 TRUE S FO
## 86 4.458870 17 7 240 TRUE PA FO
## 87 5.413162 14 10 268 TRUE MA FO
## 88 4.561375 18 11 289 TRUE S FI
## 89 6.076244 14 12 14 TRUE PA FO
## 90 5.140797 16 8 225 TRUE MA FO
## 91 4.427752 16 4 268 FALSE S FO
## 92 5.902777 14 9 279 TRUE PA FO
## 93 5.729409 16 10 222 TRUE MA FO
## 94 4.563922 16 8 215 TRUE S FI
## 95 6.536906 15 13 212 TRUE PA FI
## 96 3.892238 16 13 86 TRUE MA FI
## 97 6.248745 16 19 239 TRUE S FI
## 98 6.049258 18 15 171 TRUE PA FI
## 99 5.151745 14 9 150 FALSE MA FI
## 100 4.328072 15 14 220 TRUE S FI
## 101 5.946870 13 7 242 TRUE PA FI
## 102 4.441015 17 10 129 TRUE MA FI
## 103 4.512727 15 13 25 TRUE S FI
## 104 5.241019 11 12 202 TRUE PA FO
## 105 5.047264 17 12 212 FALSE MA FI
## 106 5.032729 19 11 298 FALSE S FI
## 107 4.800188 15 13 264 TRUE PA FI
## 108 4.754542 14 10 18 TRUE MA FI
## 109 4.139596 17 12 199 TRUE S FO
## 110 5.336830 18 7 10 TRUE PA FO
## 111 4.410783 17 11 283 TRUE MA FI
## 112 5.301664 16 12 285 FALSE S FI
## 113 4.248514 15 4 286 TRUE PA FI
## 114 4.482439 16 7 291 TRUE MA FO
## 115 5.199317 16 10 165 TRUE S FO
## 116 4.960738 15 10 236 TRUE PA FI
## 117 4.508527 15 6 131 TRUE MA FI
## 118 4.821758 18 11 82 FALSE S FO
## 119 6.706502 20 12 129 TRUE PA FI
## 120 4.619753 18 9 190 FALSE MA FOdf1 %>% filter_at(vars(Plaga, Biomasa), all_vars(. < mediana))## Biomasa Floresr Floresd Hojasd Plaga Estatus Fertilización
## 1 5.191382 16 1 150 TRUE S FO
## 2 4.682817 19 13 197 FALSE PA FI
## 3 5.444438 20 10 279 TRUE MA FI
## 4 3.525376 17 13 118 TRUE S FI
## 5 4.860301 17 8 277 FALSE PA FI
## 6 4.284344 16 19 89 TRUE MA FI
## 7 5.057715 16 8 253 TRUE S FO
## 8 6.372872 16 8 54 FALSE PA FO
## 9 4.814129 17 8 121 TRUE MA FO
## 10 5.875504 17 8 37 FALSE S FO
## 11 5.545751 15 12 2 TRUE PA FI
## 12 4.332472 16 10 57 FALSE MA FO
## 13 4.143265 12 11 203 TRUE S FO
## 14 3.942137 17 8 68 TRUE PA FI
## 15 4.085222 16 13 83 TRUE MA FO
## 16 5.135173 14 10 150 TRUE S FO
## 17 4.198869 17 8 237 TRUE PA FO
## 18 3.931221 13 11 32 TRUE MA FI
## 19 4.645347 15 7 281 TRUE S FI
## 20 4.364195 16 5 130 FALSE PA FI
## 21 4.731182 17 8 84 TRUE MA FO
## 22 5.872350 19 14 262 TRUE S FI
## 23 3.566169 17 14 78 TRUE PA FI
## 24 4.533903 20 9 75 TRUE MA FI
## 25 5.494825 14 13 241 TRUE S FO
## 26 3.792585 17 6 300 FALSE PA FO
## 27 3.479056 15 8 256 TRUE MA FI
## 28 6.000100 16 11 38 TRUE S FO
## 29 5.385418 18 10 164 TRUE PA FI
## 30 4.295210 18 10 199 FALSE MA FO
## 31 4.705739 16 12 235 FALSE S FO
## 32 4.967705 14 9 26 TRUE PA FI
## 33 4.883999 16 11 260 FALSE MA FI
## 34 6.685638 17 7 187 FALSE S FI
## 35 6.782740 18 7 224 TRUE PA FI
## 36 4.895407 17 11 227 TRUE MA FI
## 37 6.089084 14 11 222 TRUE S FI
## 38 4.058907 12 10 59 FALSE PA FI
## 39 5.309868 19 13 186 TRUE MA FI
## 40 4.583636 11 10 9 FALSE S FI
## 41 4.390665 13 6 10 TRUE PA FI
## 42 5.553013 16 11 105 TRUE MA FI
## 43 5.399750 18 7 59 TRUE S FI
## 44 4.387268 14 14 18 TRUE PA FI
## 45 5.033227 16 10 43 TRUE MA FO
## 46 4.550117 17 14 258 TRUE S FO
## 47 5.381977 14 7 158 TRUE PA FI
## 48 4.957284 15 11 208 TRUE MA FI
## 49 4.662462 15 9 119 FALSE S FO
## 50 5.744640 15 13 250 FALSE PA FO
## 51 4.572862 14 6 60 FALSE MA FI
## 52 6.061314 16 12 134 TRUE S FI
## 53 4.077621 14 13 138 TRUE PA FI
## 54 5.227363 16 15 38 FALSE MA FI
## 55 6.347073 17 7 31 TRUE S FI
## 56 4.453775 11 6 111 TRUE PA FI
## 57 4.682281 17 5 3 TRUE MA FO
## 58 5.176889 12 13 37 FALSE S FO
## 59 4.389201 17 12 114 TRUE PA FI
## 60 3.284646 16 5 33 TRUE MA FO
## 61 5.837547 19 9 61 FALSE S FI
## 62 4.968740 17 8 188 TRUE PA FO
## 63 3.903301 17 10 58 TRUE MA FO
## 64 6.132158 15 10 286 FALSE S FI
## 65 5.873990 17 12 151 FALSE PA FI
## 66 5.515326 14 14 191 TRUE MA FO
## 67 4.084581 13 13 265 TRUE S FI
## 68 3.723847 18 10 72 TRUE PA FI
## 69 5.188571 17 11 157 TRUE MA FI
## 70 5.086961 15 8 240 TRUE S FI
## 71 5.833276 16 14 133 TRUE PA FI
## 72 4.376451 17 13 170 TRUE MA FI
## 73 3.695728 17 11 66 FALSE S FO
## 74 4.623261 16 5 220 TRUE PA FI
## 75 5.533799 15 16 218 FALSE MA FI
## 76 3.128952 18 12 120 TRUE S FI
## 77 3.984496 16 14 199 TRUE PA FO
## 78 6.174937 15 14 81 TRUE MA FO
## 79 5.831186 16 11 141 TRUE S FO
## 80 4.584803 14 9 185 FALSE PA FO
## 81 4.444875 17 12 216 FALSE MA FI
## 82 5.384299 14 9 22 TRUE S FO
## 83 5.781127 16 11 169 TRUE PA FI
## 84 4.279655 16 8 246 TRUE MA FI
## 85 4.534812 17 10 24 TRUE S FO
## 86 4.458870 17 7 240 TRUE PA FO
## 87 5.413162 14 10 268 TRUE MA FO
## 88 4.561375 18 11 289 TRUE S FI
## 89 6.076244 14 12 14 TRUE PA FO
## 90 5.140797 16 8 225 TRUE MA FO
## 91 4.427752 16 4 268 FALSE S FO
## 92 5.902777 14 9 279 TRUE PA FO
## 93 5.729409 16 10 222 TRUE MA FO
## 94 4.563922 16 8 215 TRUE S FI
## 95 6.536906 15 13 212 TRUE PA FI
## 96 3.892238 16 13 86 TRUE MA FI
## 97 6.248745 16 19 239 TRUE S FI
## 98 6.049258 18 15 171 TRUE PA FI
## 99 5.151745 14 9 150 FALSE MA FI
## 100 4.328072 15 14 220 TRUE S FI
## 101 5.946870 13 7 242 TRUE PA FI
## 102 4.441015 17 10 129 TRUE MA FI
## 103 4.512727 15 13 25 TRUE S FI
## 104 5.241019 11 12 202 TRUE PA FO
## 105 5.047264 17 12 212 FALSE MA FI
## 106 5.032729 19 11 298 FALSE S FI
## 107 4.800188 15 13 264 TRUE PA FI
## 108 4.754542 14 10 18 TRUE MA FI
## 109 4.139596 17 12 199 TRUE S FO
## 110 5.336830 18 7 10 TRUE PA FO
## 111 4.410783 17 11 283 TRUE MA FI
## 112 5.301664 16 12 285 FALSE S FI
## 113 4.248514 15 4 286 TRUE PA FI
## 114 4.482439 16 7 291 TRUE MA FO
## 115 5.199317 16 10 165 TRUE S FO
## 116 4.960738 15 10 236 TRUE PA FI
## 117 4.508527 15 6 131 TRUE MA FI
## 118 4.821758 18 11 82 FALSE S FO
## 119 6.706502 20 12 129 TRUE PA FI
## 120 4.619753 18 9 190 FALSE MA FOv1 <- c("Floresr", "Floresd")
v1 <- c("20", "8")
##df1 %>% filter(df1[v1[1]] > v2[[1]], df1[v1[2]] > v2[[2]])
medbiom <- mean(df1$Biomasa)
desvbiom <- sd(df1$Biomasa)
zbiom <- (x - mean(x))/sd(x)
medfld <- mean(df1$Floresd)
desfld <- sd(df1$Floresd)
zfld <- (y - mean(y))/sd(y)
medflr <- mean(df1$Floresr)
desflr <- sd(df1$Floresr)
zflr <- (z - mean(z))/sd(z)
medho <- mean(df1$Hojasd)
desho <- sd(df1$Hojasd)
zho <- (p - mean(p))/sd(p)
zscores <- c("zbiom", "zfld", "zflr", "zho")
tib.e <- df1 %>%
select(Biomasa, Floresd, Floresr, Hojasd, Plaga) %>%
mutate(Biomasa = zbiom,
Floresd = zfld,
Floresr = zflr,
Hojasd = zho,
Plaga = a
)
View(tib.e)
cociente <- y/z
df2 <- cbind(cociente, tib.e)
View(df2)
nueva1 <- df2 %>% select(cociente)
nueva2 <- tib.e %>% group_by(Plaga)
min1 <- min(nueva1)
min2 <- min(nueva2)
rangomin <- c("min1","min2")
rename(df2, Frutosd = Floresd , Frutosr = Floresr)## cociente Biomasa Frutosd Frutosr Hojasd Plaga
## 1 16.0000000 0.32256828 0.04975065 -3.07600613 -0.06819586 TRUE
## 2 1.4615385 -0.32105542 1.67795358 0.92892048 0.45796645 FALSE
## 3 2.0000000 0.64282878 2.22068789 -0.07231117 1.37595177 TRUE
## 4 1.3076923 -1.78587855 0.59248496 0.92892048 -0.42643403 TRUE
## 5 2.1250000 -0.09643693 0.59248496 -0.73979894 1.35356188 FALSE
## 6 0.8421053 -0.82535081 0.04975065 2.93138378 -0.75108738 TRUE
## 7 2.0000000 0.15340339 0.04975065 -0.73979894 1.08488326 TRUE
## 8 2.0000000 1.81782722 0.04975065 -0.73979894 -1.14291038 FALSE
## 9 2.1250000 -0.15487179 0.59248496 -0.73979894 -0.39284920 TRUE
## 10 2.1250000 1.18837345 0.59248496 -0.73979894 -1.33322440 FALSE
## 11 1.2500000 0.77104769 -0.49298367 0.59517659 -1.72504740 TRUE
## 12 1.6000000 -0.76444236 0.04975065 -0.07231117 -1.10932555 FALSE
## 13 1.0909091 -1.00389634 -2.12118660 0.26143271 0.52513611 TRUE
## 14 2.1250000 -1.25843803 0.59248496 -0.73979894 -0.98618118 TRUE
## 15 1.2307692 -1.07735441 0.04975065 0.92892048 -0.81825703 TRUE
## 16 1.4000000 0.25143209 -1.03571798 -0.07231117 -0.06819586 TRUE
## 17 2.1250000 -0.93352575 0.59248496 -0.73979894 0.90576417 TRUE
## 18 1.1818182 -1.27225371 -1.57845229 0.26143271 -1.38919912 TRUE
## 19 2.1428571 -0.36847687 -0.49298367 -1.07354283 1.39834166 TRUE
## 20 3.2000000 -0.72429479 0.04975065 -1.74103060 -0.29209472 FALSE
## 21 2.1250000 -0.25984598 0.59248496 -0.73979894 -0.80706209 TRUE
## 22 1.3571429 1.18438226 1.67795358 1.26266436 1.18563774 TRUE
## 23 1.2142857 -1.73425175 0.59248496 1.26266436 -0.87423175 TRUE
## 24 2.2222222 -0.50951637 2.22068789 -0.40605506 -0.90781658 TRUE
## 25 1.0769231 0.70659682 -1.03571798 0.92892048 0.95054394 TRUE
## 26 2.8333333 -1.44770686 0.59248496 -1.40728671 1.61104557 FALSE
## 27 1.8750000 -1.84449917 -0.49298367 -0.73979894 1.11846808 TRUE
## 28 1.4545455 1.34605887 0.04975065 0.26143271 -1.32202946 TRUE
## 29 1.8000000 0.56813472 1.13521927 -0.07231117 0.08853334 TRUE
## 30 1.8000000 -0.81160003 1.13521927 -0.07231117 0.48035634 FALSE
## 31 1.3333333 -0.29204630 0.04975065 0.59517659 0.88337428 FALSE
## 32 1.5555556 0.03949019 -1.03571798 -0.40605506 -1.45636878 TRUE
## 33 1.4545455 -0.06644559 0.04975065 0.26143271 1.16324786 FALSE
## 34 2.4285714 2.21365470 0.59248496 -1.07354283 0.34601703 FALSE
## 35 2.5714286 2.33654413 1.13521927 -1.07354283 0.76022991 TRUE
## 36 1.5454545 -0.05200864 0.59248496 0.26143271 0.79381474 TRUE
## 37 1.2727273 1.45867401 -1.03571798 0.26143271 0.73784003 TRUE
## 38 1.2000000 -1.11065822 -2.12118660 -0.07231117 -1.08693566 FALSE
## 39 1.4615385 0.47252080 1.67795358 0.92892048 0.33482208 TRUE
## 40 1.1000000 -0.44657575 -2.66392091 -0.07231117 -1.64668280 FALSE
## 41 2.1666667 -0.69079403 -1.57845229 -1.40728671 -1.63548786 TRUE
## 42 1.4545455 0.78023818 0.04975065 0.26143271 -0.57196829 TRUE
## 43 2.5714286 0.58627361 1.13521927 -1.07354283 -1.08693566 TRUE
## 44 1.0000000 -0.69509353 -1.03571798 1.26266436 -1.54592832 TRUE
## 45 1.6000000 0.12241306 0.04975065 -0.07231117 -1.26605475 TRUE
## 46 1.2142857 -0.48899677 0.59248496 1.26266436 1.14085797 TRUE
## 47 2.0000000 0.56377988 -1.03571798 -1.07354283 0.02136368 TRUE
## 48 1.3636364 0.02630149 -0.49298367 0.26143271 0.58111083 TRUE
## 49 1.6666667 -0.34681684 -0.49298367 -0.40605506 -0.41523909 FALSE
## 50 1.1538462 1.02275545 -0.49298367 0.92892048 1.05129843 FALSE
## 51 2.3333333 -0.46021191 -1.03571798 -1.40728671 -1.07574072 FALSE
## 52 1.3333333 1.42352952 0.04975065 0.59517659 -0.24731495 TRUE
## 53 1.0769231 -1.08697317 -1.03571798 0.92892048 -0.20253517 TRUE
## 54 1.0666667 0.36810524 0.04975065 1.59640825 -1.32202946 FALSE
## 55 2.4285714 1.78517702 0.59248496 -1.07354283 -1.40039406 TRUE
## 56 1.8333333 -0.61092407 -2.66392091 -1.40728671 -0.50479863 TRUE
## 57 3.4000000 -0.32173428 0.59248496 -1.74103060 -1.71385246 TRUE
## 58 0.9230769 0.30422749 -2.12118660 0.92892048 -1.33322440 FALSE
## 59 1.4166667 -0.69264701 0.59248496 0.59517659 -0.47121380 TRUE
## 60 3.2000000 -2.09053892 0.04975065 -1.74103060 -1.37800418 TRUE
## 61 2.1111111 1.14033630 1.67795358 -0.40605506 -1.06454578 FALSE
## 62 2.1250000 0.04079962 0.59248496 -0.73979894 0.35721197 TRUE
## 63 1.7000000 -1.30758817 0.59248496 -0.07231117 -1.09813060 TRUE
## 64 1.5000000 1.51318757 -0.49298367 -0.07231117 1.45431637 FALSE
## 65 1.4166667 1.18645697 0.59248496 0.59517659 -0.05700092 FALSE
## 66 1.0000000 0.73254234 -1.03571798 1.26266436 0.39079680 TRUE
## 67 1.0000000 -1.07816572 -1.57845229 0.92892048 1.21922257 TRUE
## 68 1.8000000 -1.53469908 1.13521927 -0.07231117 -0.94140140 TRUE
## 69 1.5454545 0.31901096 0.59248496 0.26143271 0.01016874 TRUE
## 70 1.8750000 0.19041647 -0.49298367 -0.73979894 0.93934900 TRUE
## 71 1.1428571 1.13493130 0.04975065 1.26266436 -0.25850989 TRUE
## 72 1.3076923 -0.70878406 0.59248496 0.92892048 0.15570300 TRUE
## 73 1.5454545 -1.57028584 0.59248496 0.26143271 -1.00857106 FALSE
## 74 3.2000000 -0.39642840 0.04975065 -1.74103060 0.71545014 TRUE
## 75 0.9375000 0.75592187 -0.49298367 1.93015213 0.69306025 FALSE
## 76 1.5000000 -2.28758147 1.13521927 0.59517659 -0.40404415 TRUE
## 77 1.1428571 -1.20483062 0.04975065 1.26266436 0.48035634 TRUE
## 78 1.0714286 1.56732639 -0.49298367 1.26266436 -0.84064692 TRUE
## 79 1.4545455 1.13228578 0.04975065 0.26143271 -0.16895035 TRUE
## 80 1.5555556 -0.44509883 -1.03571798 -0.40605506 0.32362714 FALSE
## 81 1.4166667 -0.62218761 0.59248496 0.59517659 0.67067037 FALSE
## 82 1.5555556 0.56671929 -1.03571798 -0.40605506 -1.50114855 TRUE
## 83 1.4545455 1.06893279 0.04975065 0.26143271 0.14450805 TRUE
## 84 2.0000000 -0.83128609 0.04975065 -0.73979894 1.00651866 TRUE
## 85 1.7000000 -0.50836651 0.59248496 -0.07231117 -1.47875866 TRUE
## 86 2.4285714 -0.60447683 0.59248496 -1.07354283 0.93934900 TRUE
## 87 1.4000000 0.60324740 -1.03571798 -0.07231117 1.25280740 TRUE
## 88 1.6363636 -0.47474949 1.13521927 0.26143271 1.48790120 TRUE
## 89 1.1666667 1.44242444 -1.03571798 0.59517659 -1.59070809 TRUE
## 90 2.0000000 0.25854963 0.04975065 -0.73979894 0.77142485 TRUE
## 91 4.0000000 -0.64385782 0.04975065 -2.07477448 1.25280740 FALSE
## 92 1.5555556 1.22288870 -1.03571798 -0.40605506 1.37595177 TRUE
## 93 1.6000000 1.00347946 0.04975065 -0.07231117 0.73784003 TRUE
## 94 2.0000000 -0.47152551 0.04975065 -0.73979894 0.65947543 TRUE
## 95 1.1538462 2.02542354 -0.49298367 0.92892048 0.62589060 TRUE
## 96 1.2307692 -1.32158885 0.04975065 0.92892048 -0.78467220 TRUE
## 97 0.8421053 1.66073563 0.04975065 2.93138378 0.92815405 TRUE
## 98 1.2000000 1.40827116 1.13521927 1.59640825 0.16689794 TRUE
## 99 1.5555556 0.27240553 -1.03571798 -0.40605506 -0.06819586 FALSE
## 100 1.0714286 -0.77001103 -0.49298367 1.26266436 0.71545014 TRUE
## 101 1.8571429 1.27869202 -1.57845229 -1.07354283 0.96173888 TRUE
## 102 1.7000000 -0.62707287 0.59248496 -0.07231117 -0.30328966 TRUE
## 103 1.1538462 -0.53631614 -0.49298367 0.92892048 -1.46756372 TRUE
## 104 0.9166667 0.38538859 -2.66392091 0.59517659 0.51394117 TRUE
## 105 1.4166667 0.14017781 0.59248496 0.59517659 0.62589060 FALSE
## 106 1.7272727 0.12178195 1.67795358 0.26143271 1.58865568 FALSE
## 107 1.1538462 -0.17251493 -0.49298367 0.92892048 1.20802763 TRUE
## 108 1.4000000 -0.23028283 -1.03571798 -0.07231117 -1.54592832 TRUE
## 109 1.4166667 -1.00854026 0.59248496 0.59517659 0.48035634 TRUE
## 110 2.5714286 0.50664358 1.13521927 -1.07354283 -1.63548786 TRUE
## 111 1.5454545 -0.66533452 0.59248496 0.26143271 1.42073154 TRUE
## 112 1.3333333 0.46213819 0.04975065 0.59517659 1.44312143 FALSE
## 113 3.7500000 -0.87069699 -0.49298367 -2.07477448 1.45431637 TRUE
## 114 2.2857143 -0.57464870 0.04975065 -1.07354283 1.51029108 TRUE
## 115 1.6000000 0.33261121 0.04975065 -0.07231117 0.09972828 TRUE
## 116 1.5000000 0.03067253 -0.49298367 -0.07231117 0.89456923 TRUE
## 117 2.5000000 -0.54163218 -0.49298367 -1.40728671 -0.28089977 TRUE
## 118 1.6363636 -0.14521670 1.13521927 0.26143271 -0.82945198 FALSE
## 119 1.6666667 2.24005907 2.22068789 0.59517659 -0.30328966 TRUE
## 120 2.0000000 -0.40086739 1.13521927 -0.40605506 0.37960185 FALSEmayúsculas <- rename_with(df2,toupper)
View(mayúsculas)
select(tib.i, Biomasa)## Biomasa
## 1 5.191382
## 2 4.682817
## 3 5.444438
## 4 3.525376
## 5 4.860301
## 6 4.284344
## 7 5.057715
## 8 6.372872
## 9 4.814129
## 10 5.875504
## 11 5.545751
## 12 4.332472
## 13 4.143265
## 14 3.942137
## 15 4.085222
## 16 5.135173
## 17 4.198869
## 18 3.931221
## 19 4.645347
## 20 4.364195
## 21 4.731182
## 22 5.872350
## 23 3.566169
## 24 4.533903
## 25 5.494825
## 26 3.792585
## 27 3.479056
## 28 6.000100
## 29 5.385418
## 30 4.295210
## 31 4.705739
## 32 4.967705
## 33 4.883999
## 34 6.685638
## 35 6.782740
## 36 4.895407
## 37 6.089084
## 38 4.058907
## 39 5.309868
## 40 4.583636
## 41 4.390665
## 42 5.553013
## 43 5.399750
## 44 4.387268
## 45 5.033227
## 46 4.550117
## 47 5.381977
## 48 4.957284
## 49 4.662462
## 50 5.744640
## 51 4.572862
## 52 6.061314
## 53 4.077621
## 54 5.227363
## 55 6.347073
## 56 4.453775
## 57 4.682281
## 58 5.176889
## 59 4.389201
## 60 3.284646
## 61 5.837547
## 62 4.968740
## 63 3.903301
## 64 6.132158
## 65 5.873990
## 66 5.515326
## 67 4.084581
## 68 3.723847
## 69 5.188571
## 70 5.086961
## 71 5.833276
## 72 4.376451
## 73 3.695728
## 74 4.623261
## 75 5.533799
## 76 3.128952
## 77 3.984496
## 78 6.174937
## 79 5.831186
## 80 4.584803
## 81 4.444875
## 82 5.384299
## 83 5.781127
## 84 4.279655
## 85 4.534812
## 86 4.458870
## 87 5.413162
## 88 4.561375
## 89 6.076244
## 90 5.140797
## 91 4.427752
## 92 5.902777
## 93 5.729409
## 94 4.563922
## 95 6.536906
## 96 3.892238
## 97 6.248745
## 98 6.049258
## 99 5.151745
## 100 4.328072
## 101 5.946870
## 102 4.441015
## 103 4.512727
## 104 5.241019
## 105 5.047264
## 106 5.032729
## 107 4.800188
## 108 4.754542
## 109 4.139596
## 110 5.336830
## 111 4.410783
## 112 5.301664
## 113 4.248514
## 114 4.482439
## 115 5.199317
## 116 4.960738
## 117 4.508527
## 118 4.821758
## 119 6.706502
## 120 4.619753tib.i %>% summarise(mean(Biomasa)) ## mean(Biomasa)
## 1 4.936502agrup <- df1 %>% select(Biomasa, Fertilización)
agrup %>% group_by(Fertilización)## # A tibble: 120 x 2
## # Groups: Fertilización [2]
## Biomasa Fertilización
## <dbl> <chr>
## 1 5.19 FO
## 2 4.68 FI
## 3 5.44 FI
## 4 3.53 FI
## 5 4.86 FI
## 6 4.28 FI
## 7 5.06 FO
## 8 6.37 FO
## 9 4.81 FO
## 10 5.88 FO
## # ... with 110 more rowsagrup %>% summarise(qs = quantile(c(0.10,0.10,0.30,0.40,0.50))) ## qs
## 1 0.1
## 2 0.1
## 3 0.3
## 4 0.4
## 5 0.5tib.i %>% summarise(mean(Biomasa, trim=0.5), median(Biomasa, na.rm = FALSE), sd(Biomasa, na.rm = FALSE), min(Biomasa, na.rm = FALSE), max(Biomasa, na.rm = FALSE),var(Biomasa, y=NULL, na.rm=FALSE)) ## mean(Biomasa, trim = 0.5) median(Biomasa, na.rm = FALSE)
## 1 4.841029 4.841029
## sd(Biomasa, na.rm = FALSE) min(Biomasa, na.rm = FALSE)
## 1 0.7901577 3.128952
## max(Biomasa, na.rm = FALSE) var(Biomasa, y = NULL, na.rm = FALSE)
## 1 6.78274 0.6243492filtro <- filter(df1, Estatus %in% c("s"))
filtro %>% summarise(mean(Estatus, trim=0.5), median(Estatus, na.rm = FALSE), sd(Estatus, na.rm = FALSE), min(Estatus, na.rm = FALSE), max(Estatus, na.rm = FALSE),var(Estatus, y=NULL, na.rm=FALSE)) ## Warning in mean.default(Estatus, trim = 0.5): argument is not numeric or
## logical: returning NA## Warning in min(Estatus, na.rm = FALSE): no non-missing arguments, returning NA## Warning in max(Estatus, na.rm = FALSE): no non-missing arguments, returning NA## mean(Estatus, trim = 0.5) median(Estatus, na.rm = FALSE)
## 1 NA <NA>
## sd(Estatus, na.rm = FALSE) min(Estatus, na.rm = FALSE)
## 1 NA <NA>
## max(Estatus, na.rm = FALSE) var(Estatus, y = NULL, na.rm = FALSE)
## 1 <NA> NAlibrary(tidyr)
subdatos <- drop_na(tib.i)
filter(df1, Estatus %in% c("PA", "MA"))## Biomasa Floresr Floresd Hojasd Plaga Estatus Fertilización
## 1 4.682817 19 13 197 FALSE PA FI
## 2 5.444438 20 10 279 TRUE MA FI
## 3 4.860301 17 8 277 FALSE PA FI
## 4 4.284344 16 19 89 TRUE MA FI
## 5 6.372872 16 8 54 FALSE PA FO
## 6 4.814129 17 8 121 TRUE MA FO
## 7 5.545751 15 12 2 TRUE PA FI
## 8 4.332472 16 10 57 FALSE MA FO
## 9 3.942137 17 8 68 TRUE PA FI
## 10 4.085222 16 13 83 TRUE MA FO
## 11 4.198869 17 8 237 TRUE PA FO
## 12 3.931221 13 11 32 TRUE MA FI
## 13 4.364195 16 5 130 FALSE PA FI
## 14 4.731182 17 8 84 TRUE MA FO
## 15 3.566169 17 14 78 TRUE PA FI
## 16 4.533903 20 9 75 TRUE MA FI
## 17 3.792585 17 6 300 FALSE PA FO
## 18 3.479056 15 8 256 TRUE MA FI
## 19 5.385418 18 10 164 TRUE PA FI
## 20 4.295210 18 10 199 FALSE MA FO
## 21 4.967705 14 9 26 TRUE PA FI
## 22 4.883999 16 11 260 FALSE MA FI
## 23 6.782740 18 7 224 TRUE PA FI
## 24 4.895407 17 11 227 TRUE MA FI
## 25 4.058907 12 10 59 FALSE PA FI
## 26 5.309868 19 13 186 TRUE MA FI
## 27 4.390665 13 6 10 TRUE PA FI
## 28 5.553013 16 11 105 TRUE MA FI
## 29 4.387268 14 14 18 TRUE PA FI
## 30 5.033227 16 10 43 TRUE MA FO
## 31 5.381977 14 7 158 TRUE PA FI
## 32 4.957284 15 11 208 TRUE MA FI
## 33 5.744640 15 13 250 FALSE PA FO
## 34 4.572862 14 6 60 FALSE MA FI
## 35 4.077621 14 13 138 TRUE PA FI
## 36 5.227363 16 15 38 FALSE MA FI
## 37 4.453775 11 6 111 TRUE PA FI
## 38 4.682281 17 5 3 TRUE MA FO
## 39 4.389201 17 12 114 TRUE PA FI
## 40 3.284646 16 5 33 TRUE MA FO
## 41 4.968740 17 8 188 TRUE PA FO
## 42 3.903301 17 10 58 TRUE MA FO
## 43 5.873990 17 12 151 FALSE PA FI
## 44 5.515326 14 14 191 TRUE MA FO
## 45 3.723847 18 10 72 TRUE PA FI
## 46 5.188571 17 11 157 TRUE MA FI
## 47 5.833276 16 14 133 TRUE PA FI
## 48 4.376451 17 13 170 TRUE MA FI
## 49 4.623261 16 5 220 TRUE PA FI
## 50 5.533799 15 16 218 FALSE MA FI
## 51 3.984496 16 14 199 TRUE PA FO
## 52 6.174937 15 14 81 TRUE MA FO
## 53 4.584803 14 9 185 FALSE PA FO
## 54 4.444875 17 12 216 FALSE MA FI
## 55 5.781127 16 11 169 TRUE PA FI
## 56 4.279655 16 8 246 TRUE MA FI
## 57 4.458870 17 7 240 TRUE PA FO
## 58 5.413162 14 10 268 TRUE MA FO
## 59 6.076244 14 12 14 TRUE PA FO
## 60 5.140797 16 8 225 TRUE MA FO
## 61 5.902777 14 9 279 TRUE PA FO
## 62 5.729409 16 10 222 TRUE MA FO
## 63 6.536906 15 13 212 TRUE PA FI
## 64 3.892238 16 13 86 TRUE MA FI
## 65 6.049258 18 15 171 TRUE PA FI
## 66 5.151745 14 9 150 FALSE MA FI
## 67 5.946870 13 7 242 TRUE PA FI
## 68 4.441015 17 10 129 TRUE MA FI
## 69 5.241019 11 12 202 TRUE PA FO
## 70 5.047264 17 12 212 FALSE MA FI
## 71 4.800188 15 13 264 TRUE PA FI
## 72 4.754542 14 10 18 TRUE MA FI
## 73 5.336830 18 7 10 TRUE PA FO
## 74 4.410783 17 11 283 TRUE MA FI
## 75 4.248514 15 4 286 TRUE PA FI
## 76 4.482439 16 7 291 TRUE MA FO
## 77 4.960738 15 10 236 TRUE PA FI
## 78 4.508527 15 6 131 TRUE MA FI
## 79 6.706502 20 12 129 TRUE PA FI
## 80 4.619753 18 9 190 FALSE MA FOcomplete.cases(tib.i)## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUEdf2$Floresd <- NULL
df2$Floresr <- NULL
View(df2)
select(tib.i, contains("d"))## Floresd Hojasd
## 1 1 150
## 2 13 197
## 3 10 279
## 4 13 118
## 5 8 277
## 6 19 89
## 7 8 253
## 8 8 54
## 9 8 121
## 10 8 37
## 11 12 2
## 12 10 57
## 13 11 203
## 14 8 68
## 15 13 83
## 16 10 150
## 17 8 237
## 18 11 32
## 19 7 281
## 20 5 130
## 21 8 84
## 22 14 262
## 23 14 78
## 24 9 75
## 25 13 241
## 26 6 300
## 27 8 256
## 28 11 38
## 29 10 164
## 30 10 199
## 31 12 235
## 32 9 26
## 33 11 260
## 34 7 187
## 35 7 224
## 36 11 227
## 37 11 222
## 38 10 59
## 39 13 186
## 40 10 9
## 41 6 10
## 42 11 105
## 43 7 59
## 44 14 18
## 45 10 43
## 46 14 258
## 47 7 158
## 48 11 208
## 49 9 119
## 50 13 250
## 51 6 60
## 52 12 134
## 53 13 138
## 54 15 38
## 55 7 31
## 56 6 111
## 57 5 3
## 58 13 37
## 59 12 114
## 60 5 33
## 61 9 61
## 62 8 188
## 63 10 58
## 64 10 286
## 65 12 151
## 66 14 191
## 67 13 265
## 68 10 72
## 69 11 157
## 70 8 240
## 71 14 133
## 72 13 170
## 73 11 66
## 74 5 220
## 75 16 218
## 76 12 120
## 77 14 199
## 78 14 81
## 79 11 141
## 80 9 185
## 81 12 216
## 82 9 22
## 83 11 169
## 84 8 246
## 85 10 24
## 86 7 240
## 87 10 268
## 88 11 289
## 89 12 14
## 90 8 225
## 91 4 268
## 92 9 279
## 93 10 222
## 94 8 215
## 95 13 212
## 96 13 86
## 97 19 239
## 98 15 171
## 99 9 150
## 100 14 220
## 101 7 242
## 102 10 129
## 103 13 25
## 104 12 202
## 105 12 212
## 106 11 298
## 107 13 264
## 108 10 18
## 109 12 199
## 110 7 10
## 111 11 283
## 112 12 285
## 113 4 286
## 114 7 291
## 115 10 165
## 116 10 236
## 117 6 131
## 118 11 82
## 119 12 129
## 120 9 190select(tib.i, everything()) %>%
relocate(c("Floresd", "Floresr"), .before = Biomasa)## Floresd Floresr Biomasa Hojasd Plaga
## 1 1 16 5.191382 150 TRUE
## 2 13 19 4.682817 197 FALSE
## 3 10 20 5.444438 279 TRUE
## 4 13 17 3.525376 118 TRUE
## 5 8 17 4.860301 277 FALSE
## 6 19 16 4.284344 89 TRUE
## 7 8 16 5.057715 253 TRUE
## 8 8 16 6.372872 54 FALSE
## 9 8 17 4.814129 121 TRUE
## 10 8 17 5.875504 37 FALSE
## 11 12 15 5.545751 2 TRUE
## 12 10 16 4.332472 57 FALSE
## 13 11 12 4.143265 203 TRUE
## 14 8 17 3.942137 68 TRUE
## 15 13 16 4.085222 83 TRUE
## 16 10 14 5.135173 150 TRUE
## 17 8 17 4.198869 237 TRUE
## 18 11 13 3.931221 32 TRUE
## 19 7 15 4.645347 281 TRUE
## 20 5 16 4.364195 130 FALSE
## 21 8 17 4.731182 84 TRUE
## 22 14 19 5.872350 262 TRUE
## 23 14 17 3.566169 78 TRUE
## 24 9 20 4.533903 75 TRUE
## 25 13 14 5.494825 241 TRUE
## 26 6 17 3.792585 300 FALSE
## 27 8 15 3.479056 256 TRUE
## 28 11 16 6.000100 38 TRUE
## 29 10 18 5.385418 164 TRUE
## 30 10 18 4.295210 199 FALSE
## 31 12 16 4.705739 235 FALSE
## 32 9 14 4.967705 26 TRUE
## 33 11 16 4.883999 260 FALSE
## 34 7 17 6.685638 187 FALSE
## 35 7 18 6.782740 224 TRUE
## 36 11 17 4.895407 227 TRUE
## 37 11 14 6.089084 222 TRUE
## 38 10 12 4.058907 59 FALSE
## 39 13 19 5.309868 186 TRUE
## 40 10 11 4.583636 9 FALSE
## 41 6 13 4.390665 10 TRUE
## 42 11 16 5.553013 105 TRUE
## 43 7 18 5.399750 59 TRUE
## 44 14 14 4.387268 18 TRUE
## 45 10 16 5.033227 43 TRUE
## 46 14 17 4.550117 258 TRUE
## 47 7 14 5.381977 158 TRUE
## 48 11 15 4.957284 208 TRUE
## 49 9 15 4.662462 119 FALSE
## 50 13 15 5.744640 250 FALSE
## 51 6 14 4.572862 60 FALSE
## 52 12 16 6.061314 134 TRUE
## 53 13 14 4.077621 138 TRUE
## 54 15 16 5.227363 38 FALSE
## 55 7 17 6.347073 31 TRUE
## 56 6 11 4.453775 111 TRUE
## 57 5 17 4.682281 3 TRUE
## 58 13 12 5.176889 37 FALSE
## 59 12 17 4.389201 114 TRUE
## 60 5 16 3.284646 33 TRUE
## 61 9 19 5.837547 61 FALSE
## 62 8 17 4.968740 188 TRUE
## 63 10 17 3.903301 58 TRUE
## 64 10 15 6.132158 286 FALSE
## 65 12 17 5.873990 151 FALSE
## 66 14 14 5.515326 191 TRUE
## 67 13 13 4.084581 265 TRUE
## 68 10 18 3.723847 72 TRUE
## 69 11 17 5.188571 157 TRUE
## 70 8 15 5.086961 240 TRUE
## 71 14 16 5.833276 133 TRUE
## 72 13 17 4.376451 170 TRUE
## 73 11 17 3.695728 66 FALSE
## 74 5 16 4.623261 220 TRUE
## 75 16 15 5.533799 218 FALSE
## 76 12 18 3.128952 120 TRUE
## 77 14 16 3.984496 199 TRUE
## 78 14 15 6.174937 81 TRUE
## 79 11 16 5.831186 141 TRUE
## 80 9 14 4.584803 185 FALSE
## 81 12 17 4.444875 216 FALSE
## 82 9 14 5.384299 22 TRUE
## 83 11 16 5.781127 169 TRUE
## 84 8 16 4.279655 246 TRUE
## 85 10 17 4.534812 24 TRUE
## 86 7 17 4.458870 240 TRUE
## 87 10 14 5.413162 268 TRUE
## 88 11 18 4.561375 289 TRUE
## 89 12 14 6.076244 14 TRUE
## 90 8 16 5.140797 225 TRUE
## 91 4 16 4.427752 268 FALSE
## 92 9 14 5.902777 279 TRUE
## 93 10 16 5.729409 222 TRUE
## 94 8 16 4.563922 215 TRUE
## 95 13 15 6.536906 212 TRUE
## 96 13 16 3.892238 86 TRUE
## 97 19 16 6.248745 239 TRUE
## 98 15 18 6.049258 171 TRUE
## 99 9 14 5.151745 150 FALSE
## 100 14 15 4.328072 220 TRUE
## 101 7 13 5.946870 242 TRUE
## 102 10 17 4.441015 129 TRUE
## 103 13 15 4.512727 25 TRUE
## 104 12 11 5.241019 202 TRUE
## 105 12 17 5.047264 212 FALSE
## 106 11 19 5.032729 298 FALSE
## 107 13 15 4.800188 264 TRUE
## 108 10 14 4.754542 18 TRUE
## 109 12 17 4.139596 199 TRUE
## 110 7 18 5.336830 10 TRUE
## 111 11 17 4.410783 283 TRUE
## 112 12 16 5.301664 285 FALSE
## 113 4 15 4.248514 286 TRUE
## 114 7 16 4.482439 291 TRUE
## 115 10 16 5.199317 165 TRUE
## 116 10 15 4.960738 236 TRUE
## 117 6 15 4.508527 131 TRUE
## 118 11 18 4.821758 82 FALSE
## 119 12 20 6.706502 129 TRUE
## 120 9 18 4.619753 190 FALSE