DATA Advertising
library(mice)
## Warning: package 'mice' was built under R version 4.0.4
##
## Attaching package: 'mice'
## The following object is masked from 'package:stats':
##
## filter
## The following objects are masked from 'package:base':
##
## cbind, rbind
# Carga de los datos
Advertising <- read.csv("C:/Users/HUAWEI/Downloads/9_A_PARTAGER-20210327T202526Z-001/9_A_PARTAGER/3_DatosPerdidos/Advertising.csv")
# Vista de los datos
dplyr::glimpse(Advertising)
## Rows: 200
## Columns: 5
## $ X <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1~
## $ TV <dbl> 230.1, 44.5, 17.2, 151.5, 180.8, 8.7, 57.5, 120.2, 8.6, 199.~
## $ Radio <dbl> 37.8, 39.3, 45.9, 41.3, 10.8, 48.9, 32.8, 19.6, 2.1, 2.6, 5.~
## $ Newspaper <dbl> 69.2, 45.1, 69.3, 58.5, 58.4, 75.0, 23.5, 11.6, 1.0, 21.2, 2~
## $ Sales <dbl> 22.1, 10.4, 9.3, 18.5, 12.9, 7.2, 11.8, 13.2, 4.8, 10.6, 8.6~
str(Advertising)
## 'data.frame': 200 obs. of 5 variables:
## $ X : int 1 2 3 4 5 6 7 8 9 10 ...
## $ TV : num 230.1 44.5 17.2 151.5 180.8 ...
## $ Radio : num 37.8 39.3 45.9 41.3 10.8 48.9 32.8 19.6 2.1 2.6 ...
## $ Newspaper: num 69.2 45.1 69.3 58.5 58.4 75 23.5 11.6 1 21.2 ...
## $ Sales : num 22.1 10.4 9.3 18.5 12.9 7.2 11.8 13.2 4.8 10.6 ...
#NA CREAR
publicidad <- Advertising
publicidad$TV[sample(1:200,10,replace=F)] <- NA
publicidad$Radio[sample(1:200,10,replace=F)] <- NA
# NA
publicidad$TV[sample(1:200,3)] <- NA
publicidad$Radio[sample(1:200,3)] <- NA
# Vista de los NA
apply(publicidad, 2, function(x){sum(is.na(x))})
## X TV Radio Newspaper Sales
## 0 13 13 0 0
summary(publicidad)
## X TV Radio Newspaper
## Min. : 1.00 Min. : 0.70 Min. : 0.00 Min. : 0.30
## 1st Qu.: 50.75 1st Qu.: 74.05 1st Qu.: 9.45 1st Qu.: 12.75
## Median :100.50 Median :156.60 Median :21.30 Median : 25.75
## Mean :100.50 Mean :147.91 Mean :22.92 Mean : 30.55
## 3rd Qu.:150.25 3rd Qu.:219.15 3rd Qu.:36.05 3rd Qu.: 45.10
## Max. :200.00 Max. :296.40 Max. :49.60 Max. :114.00
## NA's :13 NA's :13
## Sales
## Min. : 1.60
## 1st Qu.:10.38
## Median :12.90
## Mean :14.02
## 3rd Qu.:17.40
## Max. :27.00
##
# Visualizar los valores Perdidos
mice::md.pattern(publicidad, rotate.names=TRUE)
## X Newspaper Sales TV Radio
## 175 1 1 1 1 1 0
## 12 1 1 1 1 0 1
## 12 1 1 1 0 1 1
## 1 1 1 1 0 0 2
## 0 0 0 13 13 26
mice::md.pairs(publicidad)
## $rr
## X TV Radio Newspaper Sales
## X 200 187 187 200 200
## TV 187 187 175 187 187
## Radio 187 175 187 187 187
## Newspaper 200 187 187 200 200
## Sales 200 187 187 200 200
##
## $rm
## X TV Radio Newspaper Sales
## X 0 13 13 0 0
## TV 0 0 12 0 0
## Radio 0 12 0 0 0
## Newspaper 0 13 13 0 0
## Sales 0 13 13 0 0
##
## $mr
## X TV Radio Newspaper Sales
## X 0 0 0 0 0
## TV 13 0 12 13 13
## Radio 13 12 0 13 13
## Newspaper 0 0 0 0 0
## Sales 0 0 0 0 0
##
## $mm
## X TV Radio Newspaper Sales
## X 0 0 0 0 0
## TV 0 13 1 0 0
## Radio 0 1 13 0 0
## Newspaper 0 0 0 0 0
## Sales 0 0 0 0 0
# Realizando Imputacion - MEDIA
library(Hmisc)
## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 4.0.3
##
## Attaching package: 'Hmisc'
## The following objects are masked from 'package:base':
##
## format.pval, units
publicidad_mean <- publicidad
publicidad_mean$TV_mean <- with(publicidad, impute(TV, mean))
publicidad_mean
## X TV Radio Newspaper Sales TV_mean
## 1 1 230.1 37.8 69.2 22.1 230.1000
## 2 2 44.5 39.3 45.1 10.4 44.5000
## 3 3 17.2 45.9 69.3 9.3 17.2000
## 4 4 151.5 41.3 58.5 18.5 151.5000
## 5 5 180.8 10.8 58.4 12.9 180.8000
## 6 6 8.7 48.9 75.0 7.2 8.7000
## 7 7 57.5 32.8 23.5 11.8 57.5000
## 8 8 120.2 19.6 11.6 13.2 120.2000
## 9 9 8.6 2.1 1.0 4.8 8.6000
## 10 10 199.8 NA 21.2 10.6 199.8000
## 11 11 66.1 5.8 24.2 8.6 66.1000
## 12 12 214.7 24.0 4.0 17.4 214.7000
## 13 13 NA 35.1 65.9 9.2 147.9139
## 14 14 97.5 7.6 7.2 9.7 97.5000
## 15 15 204.1 32.9 46.0 19.0 204.1000
## 16 16 195.4 47.7 52.9 22.4 195.4000
## 17 17 67.8 36.6 114.0 12.5 67.8000
## 18 18 281.4 39.6 55.8 24.4 281.4000
## 19 19 69.2 20.5 18.3 11.3 69.2000
## 20 20 147.3 23.9 19.1 14.6 147.3000
## 21 21 218.4 NA 53.4 18.0 218.4000
## 22 22 237.4 5.1 23.5 12.5 237.4000
## 23 23 13.2 15.9 49.6 5.6 13.2000
## 24 24 228.3 16.9 26.2 15.5 228.3000
## 25 25 NA 12.6 18.3 9.7 147.9139
## 26 26 262.9 3.5 19.5 12.0 262.9000
## 27 27 142.9 NA 12.6 15.0 142.9000
## 28 28 240.1 16.7 22.9 15.9 240.1000
## 29 29 248.8 27.1 22.9 18.9 248.8000
## 30 30 70.6 16.0 40.8 10.5 70.6000
## 31 31 292.9 28.3 43.2 21.4 292.9000
## 32 32 NA 17.4 38.6 11.9 147.9139
## 33 33 97.2 1.5 30.0 9.6 97.2000
## 34 34 265.6 20.0 0.3 17.4 265.6000
## 35 35 95.7 1.4 7.4 9.5 95.7000
## 36 36 290.7 4.1 8.5 12.8 290.7000
## 37 37 NA 43.8 5.0 25.4 147.9139
## 38 38 74.7 49.4 45.7 14.7 74.7000
## 39 39 43.1 26.7 35.1 10.1 43.1000
## 40 40 228.0 37.7 32.0 21.5 228.0000
## 41 41 202.5 NA 31.6 16.6 202.5000
## 42 42 177.0 33.4 38.7 17.1 177.0000
## 43 43 293.6 27.7 1.8 20.7 293.6000
## 44 44 206.9 8.4 26.4 12.9 206.9000
## 45 45 25.1 25.7 43.3 8.5 25.1000
## 46 46 175.1 22.5 31.5 14.9 175.1000
## 47 47 89.7 NA 35.7 10.6 89.7000
## 48 48 239.9 41.5 18.5 23.2 239.9000
## 49 49 227.2 15.8 49.9 14.8 227.2000
## 50 50 66.9 11.7 36.8 9.7 66.9000
## 51 51 199.8 3.1 34.6 11.4 199.8000
## 52 52 NA 9.6 3.6 10.7 147.9139
## 53 53 216.4 41.7 39.6 22.6 216.4000
## 54 54 182.6 46.2 58.7 21.2 182.6000
## 55 55 262.7 28.8 15.9 20.2 262.7000
## 56 56 198.9 49.4 60.0 23.7 198.9000
## 57 57 7.3 28.1 41.4 5.5 7.3000
## 58 58 136.2 19.2 16.6 13.2 136.2000
## 59 59 210.8 49.6 37.7 23.8 210.8000
## 60 60 210.7 29.5 9.3 18.4 210.7000
## 61 61 53.5 2.0 21.4 8.1 53.5000
## 62 62 261.3 42.7 54.7 24.2 261.3000
## 63 63 239.3 15.5 27.3 15.7 239.3000
## 64 64 NA NA 8.4 14.0 147.9139
## 65 65 131.1 42.8 28.9 18.0 131.1000
## 66 66 69.0 9.3 0.9 9.3 69.0000
## 67 67 31.5 24.6 2.2 9.5 31.5000
## 68 68 139.3 14.5 10.2 13.4 139.3000
## 69 69 237.4 NA 11.0 18.9 237.4000
## 70 70 216.8 43.9 27.2 22.3 216.8000
## 71 71 199.1 30.6 38.7 18.3 199.1000
## 72 72 109.8 14.3 31.7 12.4 109.8000
## 73 73 26.8 33.0 19.3 8.8 26.8000
## 74 74 129.4 5.7 31.3 11.0 129.4000
## 75 75 213.4 24.6 13.1 17.0 213.4000
## 76 76 16.9 43.7 89.4 8.7 16.9000
## 77 77 27.5 1.6 20.7 6.9 27.5000
## 78 78 120.5 28.5 14.2 14.2 120.5000
## 79 79 5.4 29.9 9.4 5.3 5.4000
## 80 80 116.0 7.7 23.1 11.0 116.0000
## 81 81 76.4 26.7 22.3 11.8 76.4000
## 82 82 239.8 4.1 36.9 12.3 239.8000
## 83 83 NA 20.3 32.5 11.3 147.9139
## 84 84 68.4 44.5 35.6 13.6 68.4000
## 85 85 213.5 43.0 33.8 21.7 213.5000
## 86 86 193.2 18.4 65.7 15.2 193.2000
## 87 87 76.3 27.5 16.0 12.0 76.3000
## 88 88 110.7 40.6 63.2 16.0 110.7000
## 89 89 88.3 25.5 73.4 12.9 88.3000
## 90 90 109.8 47.8 51.4 16.7 109.8000
## 91 91 134.3 4.9 9.3 11.2 134.3000
## 92 92 28.6 1.5 33.0 7.3 28.6000
## 93 93 NA 33.5 59.0 19.4 147.9139
## 94 94 250.9 36.5 72.3 22.2 250.9000
## 95 95 107.4 14.0 10.9 11.5 107.4000
## 96 96 163.3 31.6 52.9 16.9 163.3000
## 97 97 197.6 3.5 5.9 11.7 197.6000
## 98 98 184.9 21.0 22.0 15.5 184.9000
## 99 99 289.7 42.3 51.2 25.4 289.7000
## 100 100 135.2 41.7 45.9 17.2 135.2000
## 101 101 222.4 4.3 49.8 11.7 222.4000
## 102 102 296.4 36.3 100.9 23.8 296.4000
## 103 103 NA 10.1 21.4 14.8 147.9139
## 104 104 187.9 17.2 17.9 14.7 187.9000
## 105 105 238.2 34.3 5.3 20.7 238.2000
## 106 106 137.9 46.4 59.0 19.2 137.9000
## 107 107 25.0 11.0 29.7 7.2 25.0000
## 108 108 90.4 0.3 23.2 8.7 90.4000
## 109 109 13.1 0.4 25.6 5.3 13.1000
## 110 110 255.4 26.9 5.5 19.8 255.4000
## 111 111 225.8 8.2 56.5 13.4 225.8000
## 112 112 241.7 38.0 23.2 21.8 241.7000
## 113 113 175.7 15.4 2.4 14.1 175.7000
## 114 114 209.6 20.6 10.7 15.9 209.6000
## 115 115 78.2 46.8 34.5 14.6 78.2000
## 116 116 75.1 35.0 52.7 12.6 75.1000
## 117 117 NA 14.3 25.6 12.2 147.9139
## 118 118 76.4 0.8 14.8 9.4 76.4000
## 119 119 125.7 36.9 79.2 15.9 125.7000
## 120 120 19.4 16.0 22.3 6.6 19.4000
## 121 121 141.3 26.8 46.2 15.5 141.3000
## 122 122 18.8 21.7 50.4 7.0 18.8000
## 123 123 224.0 2.4 15.6 11.6 224.0000
## 124 124 123.1 34.6 12.4 15.2 123.1000
## 125 125 229.5 32.3 74.2 19.7 229.5000
## 126 126 87.2 11.8 25.9 10.6 87.2000
## 127 127 7.8 38.9 50.6 6.6 7.8000
## 128 128 80.2 0.0 9.2 8.8 80.2000
## 129 129 220.3 49.0 3.2 24.7 220.3000
## 130 130 59.6 NA 43.1 9.7 59.6000
## 131 131 0.7 39.6 8.7 1.6 0.7000
## 132 132 265.2 2.9 43.0 12.7 265.2000
## 133 133 8.4 27.2 2.1 5.7 8.4000
## 134 134 219.8 33.5 45.1 19.6 219.8000
## 135 135 36.9 38.6 65.6 10.8 36.9000
## 136 136 48.3 47.0 8.5 11.6 48.3000
## 137 137 25.6 NA 9.3 9.5 25.6000
## 138 138 273.7 28.9 59.7 20.8 273.7000
## 139 139 43.0 25.9 20.5 9.6 43.0000
## 140 140 184.9 43.9 1.7 20.7 184.9000
## 141 141 73.4 17.0 12.9 10.9 73.4000
## 142 142 193.7 35.4 75.6 19.2 193.7000
## 143 143 220.5 33.2 37.9 20.1 220.5000
## 144 144 104.6 5.7 34.4 10.4 104.6000
## 145 145 NA 14.8 38.9 11.4 147.9139
## 146 146 140.3 1.9 9.0 10.3 140.3000
## 147 147 240.1 7.3 8.7 13.2 240.1000
## 148 148 243.2 49.0 44.3 25.4 243.2000
## 149 149 38.0 40.3 11.9 10.9 38.0000
## 150 150 44.7 25.8 20.6 10.1 44.7000
## 151 151 280.7 13.9 37.0 16.1 280.7000
## 152 152 121.0 8.4 48.7 11.6 121.0000
## 153 153 197.6 23.3 14.2 16.6 197.6000
## 154 154 171.3 NA 37.7 19.0 171.3000
## 155 155 187.8 21.1 9.5 15.6 187.8000
## 156 156 4.1 11.6 5.7 3.2 4.1000
## 157 157 93.9 NA 50.5 15.3 93.9000
## 158 158 149.8 1.3 24.3 10.1 149.8000
## 159 159 11.7 36.9 45.2 7.3 11.7000
## 160 160 131.7 18.4 34.6 12.9 131.7000
## 161 161 172.5 18.1 30.7 14.4 172.5000
## 162 162 85.7 35.8 49.3 13.3 85.7000
## 163 163 188.4 18.1 25.6 14.9 188.4000
## 164 164 163.5 36.8 7.4 18.0 163.5000
## 165 165 117.2 14.7 5.4 11.9 117.2000
## 166 166 234.5 3.4 84.8 11.9 234.5000
## 167 167 17.9 37.6 21.6 8.0 17.9000
## 168 168 206.8 5.2 19.4 12.2 206.8000
## 169 169 215.4 23.6 57.6 17.1 215.4000
## 170 170 284.3 10.6 6.4 15.0 284.3000
## 171 171 50.0 11.6 18.4 8.4 50.0000
## 172 172 164.5 20.9 47.4 14.5 164.5000
## 173 173 19.6 20.1 17.0 7.6 19.6000
## 174 174 168.4 7.1 12.8 11.7 168.4000
## 175 175 222.4 3.4 13.1 11.5 222.4000
## 176 176 276.9 48.9 41.8 27.0 276.9000
## 177 177 248.4 30.2 20.3 20.2 248.4000
## 178 178 170.2 7.8 35.2 11.7 170.2000
## 179 179 276.7 2.3 23.7 11.8 276.7000
## 180 180 165.6 10.0 17.6 12.6 165.6000
## 181 181 156.6 2.6 8.3 10.5 156.6000
## 182 182 218.5 5.4 27.4 12.2 218.5000
## 183 183 56.2 5.7 29.7 8.7 56.2000
## 184 184 287.6 43.0 71.8 26.2 287.6000
## 185 185 NA 21.3 30.0 17.6 147.9139
## 186 186 205.0 45.1 19.6 22.6 205.0000
## 187 187 139.5 2.1 26.6 10.3 139.5000
## 188 188 191.1 28.7 18.2 17.3 191.1000
## 189 189 286.0 13.9 3.7 15.9 286.0000
## 190 190 18.7 12.1 23.4 6.7 18.7000
## 191 191 39.5 NA 5.8 10.8 39.5000
## 192 192 75.5 10.8 6.0 9.9 75.5000
## 193 193 NA 4.1 31.6 5.9 147.9139
## 194 194 166.8 NA 3.6 19.6 166.8000
## 195 195 149.7 35.6 6.0 17.3 149.7000
## 196 196 38.2 3.7 13.8 7.6 38.2000
## 197 197 94.2 4.9 8.1 9.7 94.2000
## 198 198 177.0 9.3 6.4 12.8 177.0000
## 199 199 283.6 42.0 66.2 25.5 283.6000
## 200 200 232.1 8.6 8.7 13.4 232.1000
publicidad_mean$Radio_mean <- with(publicidad, impute(Radio, mean))
publicidad_mean
## X TV Radio Newspaper Sales TV_mean Radio_mean
## 1 1 230.1 37.8 69.2 22.1 230.1000 37.80000
## 2 2 44.5 39.3 45.1 10.4 44.5000 39.30000
## 3 3 17.2 45.9 69.3 9.3 17.2000 45.90000
## 4 4 151.5 41.3 58.5 18.5 151.5000 41.30000
## 5 5 180.8 10.8 58.4 12.9 180.8000 10.80000
## 6 6 8.7 48.9 75.0 7.2 8.7000 48.90000
## 7 7 57.5 32.8 23.5 11.8 57.5000 32.80000
## 8 8 120.2 19.6 11.6 13.2 120.2000 19.60000
## 9 9 8.6 2.1 1.0 4.8 8.6000 2.10000
## 10 10 199.8 NA 21.2 10.6 199.8000 22.92299
## 11 11 66.1 5.8 24.2 8.6 66.1000 5.80000
## 12 12 214.7 24.0 4.0 17.4 214.7000 24.00000
## 13 13 NA 35.1 65.9 9.2 147.9139 35.10000
## 14 14 97.5 7.6 7.2 9.7 97.5000 7.60000
## 15 15 204.1 32.9 46.0 19.0 204.1000 32.90000
## 16 16 195.4 47.7 52.9 22.4 195.4000 47.70000
## 17 17 67.8 36.6 114.0 12.5 67.8000 36.60000
## 18 18 281.4 39.6 55.8 24.4 281.4000 39.60000
## 19 19 69.2 20.5 18.3 11.3 69.2000 20.50000
## 20 20 147.3 23.9 19.1 14.6 147.3000 23.90000
## 21 21 218.4 NA 53.4 18.0 218.4000 22.92299
## 22 22 237.4 5.1 23.5 12.5 237.4000 5.10000
## 23 23 13.2 15.9 49.6 5.6 13.2000 15.90000
## 24 24 228.3 16.9 26.2 15.5 228.3000 16.90000
## 25 25 NA 12.6 18.3 9.7 147.9139 12.60000
## 26 26 262.9 3.5 19.5 12.0 262.9000 3.50000
## 27 27 142.9 NA 12.6 15.0 142.9000 22.92299
## 28 28 240.1 16.7 22.9 15.9 240.1000 16.70000
## 29 29 248.8 27.1 22.9 18.9 248.8000 27.10000
## 30 30 70.6 16.0 40.8 10.5 70.6000 16.00000
## 31 31 292.9 28.3 43.2 21.4 292.9000 28.30000
## 32 32 NA 17.4 38.6 11.9 147.9139 17.40000
## 33 33 97.2 1.5 30.0 9.6 97.2000 1.50000
## 34 34 265.6 20.0 0.3 17.4 265.6000 20.00000
## 35 35 95.7 1.4 7.4 9.5 95.7000 1.40000
## 36 36 290.7 4.1 8.5 12.8 290.7000 4.10000
## 37 37 NA 43.8 5.0 25.4 147.9139 43.80000
## 38 38 74.7 49.4 45.7 14.7 74.7000 49.40000
## 39 39 43.1 26.7 35.1 10.1 43.1000 26.70000
## 40 40 228.0 37.7 32.0 21.5 228.0000 37.70000
## 41 41 202.5 NA 31.6 16.6 202.5000 22.92299
## 42 42 177.0 33.4 38.7 17.1 177.0000 33.40000
## 43 43 293.6 27.7 1.8 20.7 293.6000 27.70000
## 44 44 206.9 8.4 26.4 12.9 206.9000 8.40000
## 45 45 25.1 25.7 43.3 8.5 25.1000 25.70000
## 46 46 175.1 22.5 31.5 14.9 175.1000 22.50000
## 47 47 89.7 NA 35.7 10.6 89.7000 22.92299
## 48 48 239.9 41.5 18.5 23.2 239.9000 41.50000
## 49 49 227.2 15.8 49.9 14.8 227.2000 15.80000
## 50 50 66.9 11.7 36.8 9.7 66.9000 11.70000
## 51 51 199.8 3.1 34.6 11.4 199.8000 3.10000
## 52 52 NA 9.6 3.6 10.7 147.9139 9.60000
## 53 53 216.4 41.7 39.6 22.6 216.4000 41.70000
## 54 54 182.6 46.2 58.7 21.2 182.6000 46.20000
## 55 55 262.7 28.8 15.9 20.2 262.7000 28.80000
## 56 56 198.9 49.4 60.0 23.7 198.9000 49.40000
## 57 57 7.3 28.1 41.4 5.5 7.3000 28.10000
## 58 58 136.2 19.2 16.6 13.2 136.2000 19.20000
## 59 59 210.8 49.6 37.7 23.8 210.8000 49.60000
## 60 60 210.7 29.5 9.3 18.4 210.7000 29.50000
## 61 61 53.5 2.0 21.4 8.1 53.5000 2.00000
## 62 62 261.3 42.7 54.7 24.2 261.3000 42.70000
## 63 63 239.3 15.5 27.3 15.7 239.3000 15.50000
## 64 64 NA NA 8.4 14.0 147.9139 22.92299
## 65 65 131.1 42.8 28.9 18.0 131.1000 42.80000
## 66 66 69.0 9.3 0.9 9.3 69.0000 9.30000
## 67 67 31.5 24.6 2.2 9.5 31.5000 24.60000
## 68 68 139.3 14.5 10.2 13.4 139.3000 14.50000
## 69 69 237.4 NA 11.0 18.9 237.4000 22.92299
## 70 70 216.8 43.9 27.2 22.3 216.8000 43.90000
## 71 71 199.1 30.6 38.7 18.3 199.1000 30.60000
## 72 72 109.8 14.3 31.7 12.4 109.8000 14.30000
## 73 73 26.8 33.0 19.3 8.8 26.8000 33.00000
## 74 74 129.4 5.7 31.3 11.0 129.4000 5.70000
## 75 75 213.4 24.6 13.1 17.0 213.4000 24.60000
## 76 76 16.9 43.7 89.4 8.7 16.9000 43.70000
## 77 77 27.5 1.6 20.7 6.9 27.5000 1.60000
## 78 78 120.5 28.5 14.2 14.2 120.5000 28.50000
## 79 79 5.4 29.9 9.4 5.3 5.4000 29.90000
## 80 80 116.0 7.7 23.1 11.0 116.0000 7.70000
## 81 81 76.4 26.7 22.3 11.8 76.4000 26.70000
## 82 82 239.8 4.1 36.9 12.3 239.8000 4.10000
## 83 83 NA 20.3 32.5 11.3 147.9139 20.30000
## 84 84 68.4 44.5 35.6 13.6 68.4000 44.50000
## 85 85 213.5 43.0 33.8 21.7 213.5000 43.00000
## 86 86 193.2 18.4 65.7 15.2 193.2000 18.40000
## 87 87 76.3 27.5 16.0 12.0 76.3000 27.50000
## 88 88 110.7 40.6 63.2 16.0 110.7000 40.60000
## 89 89 88.3 25.5 73.4 12.9 88.3000 25.50000
## 90 90 109.8 47.8 51.4 16.7 109.8000 47.80000
## 91 91 134.3 4.9 9.3 11.2 134.3000 4.90000
## 92 92 28.6 1.5 33.0 7.3 28.6000 1.50000
## 93 93 NA 33.5 59.0 19.4 147.9139 33.50000
## 94 94 250.9 36.5 72.3 22.2 250.9000 36.50000
## 95 95 107.4 14.0 10.9 11.5 107.4000 14.00000
## 96 96 163.3 31.6 52.9 16.9 163.3000 31.60000
## 97 97 197.6 3.5 5.9 11.7 197.6000 3.50000
## 98 98 184.9 21.0 22.0 15.5 184.9000 21.00000
## 99 99 289.7 42.3 51.2 25.4 289.7000 42.30000
## 100 100 135.2 41.7 45.9 17.2 135.2000 41.70000
## 101 101 222.4 4.3 49.8 11.7 222.4000 4.30000
## 102 102 296.4 36.3 100.9 23.8 296.4000 36.30000
## 103 103 NA 10.1 21.4 14.8 147.9139 10.10000
## 104 104 187.9 17.2 17.9 14.7 187.9000 17.20000
## 105 105 238.2 34.3 5.3 20.7 238.2000 34.30000
## 106 106 137.9 46.4 59.0 19.2 137.9000 46.40000
## 107 107 25.0 11.0 29.7 7.2 25.0000 11.00000
## 108 108 90.4 0.3 23.2 8.7 90.4000 0.30000
## 109 109 13.1 0.4 25.6 5.3 13.1000 0.40000
## 110 110 255.4 26.9 5.5 19.8 255.4000 26.90000
## 111 111 225.8 8.2 56.5 13.4 225.8000 8.20000
## 112 112 241.7 38.0 23.2 21.8 241.7000 38.00000
## 113 113 175.7 15.4 2.4 14.1 175.7000 15.40000
## 114 114 209.6 20.6 10.7 15.9 209.6000 20.60000
## 115 115 78.2 46.8 34.5 14.6 78.2000 46.80000
## 116 116 75.1 35.0 52.7 12.6 75.1000 35.00000
## 117 117 NA 14.3 25.6 12.2 147.9139 14.30000
## 118 118 76.4 0.8 14.8 9.4 76.4000 0.80000
## 119 119 125.7 36.9 79.2 15.9 125.7000 36.90000
## 120 120 19.4 16.0 22.3 6.6 19.4000 16.00000
## 121 121 141.3 26.8 46.2 15.5 141.3000 26.80000
## 122 122 18.8 21.7 50.4 7.0 18.8000 21.70000
## 123 123 224.0 2.4 15.6 11.6 224.0000 2.40000
## 124 124 123.1 34.6 12.4 15.2 123.1000 34.60000
## 125 125 229.5 32.3 74.2 19.7 229.5000 32.30000
## 126 126 87.2 11.8 25.9 10.6 87.2000 11.80000
## 127 127 7.8 38.9 50.6 6.6 7.8000 38.90000
## 128 128 80.2 0.0 9.2 8.8 80.2000 0.00000
## 129 129 220.3 49.0 3.2 24.7 220.3000 49.00000
## 130 130 59.6 NA 43.1 9.7 59.6000 22.92299
## 131 131 0.7 39.6 8.7 1.6 0.7000 39.60000
## 132 132 265.2 2.9 43.0 12.7 265.2000 2.90000
## 133 133 8.4 27.2 2.1 5.7 8.4000 27.20000
## 134 134 219.8 33.5 45.1 19.6 219.8000 33.50000
## 135 135 36.9 38.6 65.6 10.8 36.9000 38.60000
## 136 136 48.3 47.0 8.5 11.6 48.3000 47.00000
## 137 137 25.6 NA 9.3 9.5 25.6000 22.92299
## 138 138 273.7 28.9 59.7 20.8 273.7000 28.90000
## 139 139 43.0 25.9 20.5 9.6 43.0000 25.90000
## 140 140 184.9 43.9 1.7 20.7 184.9000 43.90000
## 141 141 73.4 17.0 12.9 10.9 73.4000 17.00000
## 142 142 193.7 35.4 75.6 19.2 193.7000 35.40000
## 143 143 220.5 33.2 37.9 20.1 220.5000 33.20000
## 144 144 104.6 5.7 34.4 10.4 104.6000 5.70000
## 145 145 NA 14.8 38.9 11.4 147.9139 14.80000
## 146 146 140.3 1.9 9.0 10.3 140.3000 1.90000
## 147 147 240.1 7.3 8.7 13.2 240.1000 7.30000
## 148 148 243.2 49.0 44.3 25.4 243.2000 49.00000
## 149 149 38.0 40.3 11.9 10.9 38.0000 40.30000
## 150 150 44.7 25.8 20.6 10.1 44.7000 25.80000
## 151 151 280.7 13.9 37.0 16.1 280.7000 13.90000
## 152 152 121.0 8.4 48.7 11.6 121.0000 8.40000
## 153 153 197.6 23.3 14.2 16.6 197.6000 23.30000
## 154 154 171.3 NA 37.7 19.0 171.3000 22.92299
## 155 155 187.8 21.1 9.5 15.6 187.8000 21.10000
## 156 156 4.1 11.6 5.7 3.2 4.1000 11.60000
## 157 157 93.9 NA 50.5 15.3 93.9000 22.92299
## 158 158 149.8 1.3 24.3 10.1 149.8000 1.30000
## 159 159 11.7 36.9 45.2 7.3 11.7000 36.90000
## 160 160 131.7 18.4 34.6 12.9 131.7000 18.40000
## 161 161 172.5 18.1 30.7 14.4 172.5000 18.10000
## 162 162 85.7 35.8 49.3 13.3 85.7000 35.80000
## 163 163 188.4 18.1 25.6 14.9 188.4000 18.10000
## 164 164 163.5 36.8 7.4 18.0 163.5000 36.80000
## 165 165 117.2 14.7 5.4 11.9 117.2000 14.70000
## 166 166 234.5 3.4 84.8 11.9 234.5000 3.40000
## 167 167 17.9 37.6 21.6 8.0 17.9000 37.60000
## 168 168 206.8 5.2 19.4 12.2 206.8000 5.20000
## 169 169 215.4 23.6 57.6 17.1 215.4000 23.60000
## 170 170 284.3 10.6 6.4 15.0 284.3000 10.60000
## 171 171 50.0 11.6 18.4 8.4 50.0000 11.60000
## 172 172 164.5 20.9 47.4 14.5 164.5000 20.90000
## 173 173 19.6 20.1 17.0 7.6 19.6000 20.10000
## 174 174 168.4 7.1 12.8 11.7 168.4000 7.10000
## 175 175 222.4 3.4 13.1 11.5 222.4000 3.40000
## 176 176 276.9 48.9 41.8 27.0 276.9000 48.90000
## 177 177 248.4 30.2 20.3 20.2 248.4000 30.20000
## 178 178 170.2 7.8 35.2 11.7 170.2000 7.80000
## 179 179 276.7 2.3 23.7 11.8 276.7000 2.30000
## 180 180 165.6 10.0 17.6 12.6 165.6000 10.00000
## 181 181 156.6 2.6 8.3 10.5 156.6000 2.60000
## 182 182 218.5 5.4 27.4 12.2 218.5000 5.40000
## 183 183 56.2 5.7 29.7 8.7 56.2000 5.70000
## 184 184 287.6 43.0 71.8 26.2 287.6000 43.00000
## 185 185 NA 21.3 30.0 17.6 147.9139 21.30000
## 186 186 205.0 45.1 19.6 22.6 205.0000 45.10000
## 187 187 139.5 2.1 26.6 10.3 139.5000 2.10000
## 188 188 191.1 28.7 18.2 17.3 191.1000 28.70000
## 189 189 286.0 13.9 3.7 15.9 286.0000 13.90000
## 190 190 18.7 12.1 23.4 6.7 18.7000 12.10000
## 191 191 39.5 NA 5.8 10.8 39.5000 22.92299
## 192 192 75.5 10.8 6.0 9.9 75.5000 10.80000
## 193 193 NA 4.1 31.6 5.9 147.9139 4.10000
## 194 194 166.8 NA 3.6 19.6 166.8000 22.92299
## 195 195 149.7 35.6 6.0 17.3 149.7000 35.60000
## 196 196 38.2 3.7 13.8 7.6 38.2000 3.70000
## 197 197 94.2 4.9 8.1 9.7 94.2000 4.90000
## 198 198 177.0 9.3 6.4 12.8 177.0000 9.30000
## 199 199 283.6 42.0 66.2 25.5 283.6000 42.00000
## 200 200 232.1 8.6 8.7 13.4 232.1000 8.60000
# Realizando Imputacion - KNN
library(DMwR2)
## Warning: package 'DMwR2' was built under R version 4.0.4
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
publicidad_imp <- DMwR::knnImputation(publicidad)
#View(sleep_imp)
summary(publicidad_imp)
## X TV Radio Newspaper
## Min. : 1.00 Min. : 0.7 Min. : 0.00 Min. : 0.30
## 1st Qu.: 50.75 1st Qu.: 75.4 1st Qu.:10.47 1st Qu.: 12.75
## Median :100.50 Median :150.8 Median :21.86 Median : 25.75
## Mean :100.50 Mean :147.7 Mean :22.94 Mean : 30.55
## 3rd Qu.:150.25 3rd Qu.:217.2 3rd Qu.:35.17 3rd Qu.: 45.10
## Max. :200.00 Max. :296.4 Max. :49.60 Max. :114.00
## Sales
## Min. : 1.60
## 1st Qu.:10.38
## Median :12.90
## Mean :14.02
## 3rd Qu.:17.40
## Max. :27.00
# Comparando los datos
par(mfrow=c(1,2))
hist(publicidad_mean$TV_mean)
hist(publicidad_imp$TV)
par(mfrow=c(1,2))
hist(publicidad_mean$Radio_mean)
hist(publicidad_imp$Radio)
par(mfrow=c(1,1))