EJERCICIO PRÁCTICO
# a)
Id <- c("ID_001", "ID_002", "ID_003", "ID_004", "ID_005", "ID_006", "ID_007", "ID_008", "ID_009", "ID_010", "ID_011", "ID_012", "ID_013", "ID_014", "ID_015", "ID_016", "ID_017", "ID_018", "ID_019", "ID_020", "ID_021", "ID_022", "ID_023", "ID_024", "ID_025", "ID_026", "ID_027", "ID_028", "ID_029", "ID_030")
Edad <- c(58, 48, 34, 62, 27, 40, 58, 38, 42, 30, 30, 43, 55, 59, 43, 22, 41, 21, 43, 63, 49, 57, 21, 40, 52, 31, 41, 63, 44, 46)
Gene <- c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)
Trat <- c("B", "A", "C", "C", "C", "A", "A", "B","A", "C", "C", "A", "C", "C", "A", "A", "C", "C", "C", "B", "B", "B", "A", "B", "A", "A", "B", "B", "B", "C")
Peso <- c(50.7, 66.3, 55.7, 42.2, 69.6, 56.1, 55.6, 53.1, 61.2, 64.7, 47.8, 59.5, 56.7, 61.9, 52.6, 87.9, 69.4, 61.8, 86.9, 75.1, 76.6, 68.0, 69.2, 73.1, 83.2, 86.2, 74.8, 83.4, 50.3, 75.9)
Alt <- c(158, 166, 159, 156, 173, 163, 164, 155, 161, 165, 157, 166, 160, 162, 160, 189, 176, 169, 182, 167, 177, 162, 167, 177, 181, 177, 175, 174, 166, 171)
Datos <- data.frame(Id, Edad, Gene, Trat, Peso, Alt)
# b)
summary(Datos)
## Id Edad Gene Trat Peso
## Length :30 Min. :21.00 Min. :1.0 Length :30 Min. :42.20
## N.unique :30 1st Qu.:35.00 1st Qu.:1.0 N.unique : 3 1st Qu.:55.80
## N.blank : 0 Median :43.00 Median :1.5 N.blank : 0 Median :65.50
## Min.nchar: 6 Mean :43.37 Mean :1.5 Min.nchar: 1 Mean :65.85
## Max.nchar: 6 3rd Qu.:54.25 3rd Qu.:2.0 Max.nchar: 1 3rd Qu.:75.03
## Max. :63.00 Max. :2.0 Max. :87.90
## Alt
## Min. :155.0
## 1st Qu.:161.2
## Median :166.0
## Mean :167.8
## 3rd Qu.:174.8
## Max. :189.0
# c)
IMC <- Datos$Peso/(Datos$Alt/100)^2
IMC
## [1] 20.30925 24.06010 22.03236 17.34057 23.25504 21.11483 20.67222 22.10198
## [9] 23.61020 23.76492 19.39227 21.59239 22.14844 23.58634 20.54687 24.60737
## [17] 22.40444 21.63790 26.23475 26.92818 24.45019 25.91068 24.81265 23.33301
## [25] 25.39605 27.51444 24.42449 27.54657 18.25374 25.95670
Datos_IMC <- data.frame(Datos, IMC)
Datos_IMC
## Id Edad Gene Trat Peso Alt IMC
## 1 ID_001 58 1 B 50.7 158 20.30925
## 2 ID_002 48 1 A 66.3 166 24.06010
## 3 ID_003 34 1 C 55.7 159 22.03236
## 4 ID_004 62 1 C 42.2 156 17.34057
## 5 ID_005 27 1 C 69.6 173 23.25504
## 6 ID_006 40 1 A 56.1 163 21.11483
## 7 ID_007 58 1 A 55.6 164 20.67222
## 8 ID_008 38 1 B 53.1 155 22.10198
## 9 ID_009 42 1 A 61.2 161 23.61020
## 10 ID_010 30 1 C 64.7 165 23.76492
## 11 ID_011 30 1 C 47.8 157 19.39227
## 12 ID_012 43 1 A 59.5 166 21.59239
## 13 ID_013 55 1 C 56.7 160 22.14844
## 14 ID_014 59 1 C 61.9 162 23.58634
## 15 ID_015 43 1 A 52.6 160 20.54687
## 16 ID_016 22 2 A 87.9 189 24.60737
## 17 ID_017 41 2 C 69.4 176 22.40444
## 18 ID_018 21 2 C 61.8 169 21.63790
## 19 ID_019 43 2 C 86.9 182 26.23475
## 20 ID_020 63 2 B 75.1 167 26.92818
## 21 ID_021 49 2 B 76.6 177 24.45019
## 22 ID_022 57 2 B 68.0 162 25.91068
## 23 ID_023 21 2 A 69.2 167 24.81265
## 24 ID_024 40 2 B 73.1 177 23.33301
## 25 ID_025 52 2 A 83.2 181 25.39605
## 26 ID_026 31 2 A 86.2 177 27.51444
## 27 ID_027 41 2 B 74.8 175 24.42449
## 28 ID_028 63 2 B 83.4 174 27.54657
## 29 ID_029 44 2 B 50.3 166 18.25374
## 30 ID_030 46 2 C 75.9 171 25.95670
# d)
Df_Hombres <- subset(Datos_IMC, Datos_IMC$Gene == 2)
Df_Hombres
## Id Edad Gene Trat Peso Alt IMC
## 16 ID_016 22 2 A 87.9 189 24.60737
## 17 ID_017 41 2 C 69.4 176 22.40444
## 18 ID_018 21 2 C 61.8 169 21.63790
## 19 ID_019 43 2 C 86.9 182 26.23475
## 20 ID_020 63 2 B 75.1 167 26.92818
## 21 ID_021 49 2 B 76.6 177 24.45019
## 22 ID_022 57 2 B 68.0 162 25.91068
## 23 ID_023 21 2 A 69.2 167 24.81265
## 24 ID_024 40 2 B 73.1 177 23.33301
## 25 ID_025 52 2 A 83.2 181 25.39605
## 26 ID_026 31 2 A 86.2 177 27.51444
## 27 ID_027 41 2 B 74.8 175 24.42449
## 28 ID_028 63 2 B 83.4 174 27.54657
## 29 ID_029 44 2 B 50.3 166 18.25374
## 30 ID_030 46 2 C 75.9 171 25.95670
Df_Mujeres <- subset(Datos_IMC, Datos_IMC$Gene == 1)
Df_Mujeres
## Id Edad Gene Trat Peso Alt IMC
## 1 ID_001 58 1 B 50.7 158 20.30925
## 2 ID_002 48 1 A 66.3 166 24.06010
## 3 ID_003 34 1 C 55.7 159 22.03236
## 4 ID_004 62 1 C 42.2 156 17.34057
## 5 ID_005 27 1 C 69.6 173 23.25504
## 6 ID_006 40 1 A 56.1 163 21.11483
## 7 ID_007 58 1 A 55.6 164 20.67222
## 8 ID_008 38 1 B 53.1 155 22.10198
## 9 ID_009 42 1 A 61.2 161 23.61020
## 10 ID_010 30 1 C 64.7 165 23.76492
## 11 ID_011 30 1 C 47.8 157 19.39227
## 12 ID_012 43 1 A 59.5 166 21.59239
## 13 ID_013 55 1 C 56.7 160 22.14844
## 14 ID_014 59 1 C 61.9 162 23.58634
## 15 ID_015 43 1 A 52.6 160 20.54687
# e)
Datos_juntos <- rbind( Df_Mujeres, Df_Hombres)
Datos_juntos
## Id Edad Gene Trat Peso Alt IMC
## 1 ID_001 58 1 B 50.7 158 20.30925
## 2 ID_002 48 1 A 66.3 166 24.06010
## 3 ID_003 34 1 C 55.7 159 22.03236
## 4 ID_004 62 1 C 42.2 156 17.34057
## 5 ID_005 27 1 C 69.6 173 23.25504
## 6 ID_006 40 1 A 56.1 163 21.11483
## 7 ID_007 58 1 A 55.6 164 20.67222
## 8 ID_008 38 1 B 53.1 155 22.10198
## 9 ID_009 42 1 A 61.2 161 23.61020
## 10 ID_010 30 1 C 64.7 165 23.76492
## 11 ID_011 30 1 C 47.8 157 19.39227
## 12 ID_012 43 1 A 59.5 166 21.59239
## 13 ID_013 55 1 C 56.7 160 22.14844
## 14 ID_014 59 1 C 61.9 162 23.58634
## 15 ID_015 43 1 A 52.6 160 20.54687
## 16 ID_016 22 2 A 87.9 189 24.60737
## 17 ID_017 41 2 C 69.4 176 22.40444
## 18 ID_018 21 2 C 61.8 169 21.63790
## 19 ID_019 43 2 C 86.9 182 26.23475
## 20 ID_020 63 2 B 75.1 167 26.92818
## 21 ID_021 49 2 B 76.6 177 24.45019
## 22 ID_022 57 2 B 68.0 162 25.91068
## 23 ID_023 21 2 A 69.2 167 24.81265
## 24 ID_024 40 2 B 73.1 177 23.33301
## 25 ID_025 52 2 A 83.2 181 25.39605
## 26 ID_026 31 2 A 86.2 177 27.51444
## 27 ID_027 41 2 B 74.8 175 24.42449
## 28 ID_028 63 2 B 83.4 174 27.54657
## 29 ID_029 44 2 B 50.3 166 18.25374
## 30 ID_030 46 2 C 75.9 171 25.95670