Estudiante: Justo Fuentes (6820995)
URL en RPubs: https://rpubs.com/justofuentes/Actividad_del_1er_50
acero <- read.csv("acero.csv",dec = ",")
acero
# Muestra la estructura del data frame
str(acero)
## 'data.frame': 117 obs. of 20 variables:
## $ consumo : num 135.3 84.1 131.6 90.5 120 ...
## $ pr.tbc : int 6840 443 7270 5031 9365 9281 3223 10490 7394 8654 ...
## $ pr.cc : int 830 903 572 694 1054 1003 1118 1077 1204 851 ...
## $ pr.ca : int 0 58 36 122 157 172 0 179 167 0 ...
## $ pr.galv1 : int 579 611 982 896 403 605 643 737 580 828 ...
## $ pr.galv2 : int 1401 1636 1963 1568 1480 1525 1424 1333 934 1326 ...
## $ pr.pint : int 0 717 243 0 0 473 732 93 247 607 ...
## $ linea : chr "A" "A" "A" "A" ...
## $ hora : chr "1º" "2º" "3º" "4º" ...
## $ temperatura: chr "Alta" "Alta" "Baja" "Baja" ...
## $ averias : chr "Si" "No" "No" "No" ...
## $ naverias : int 1 0 0 0 0 1 0 0 0 3 ...
## $ sistema : chr "OFF" "OFF" "OFF" "ON" ...
## $ ProdTotal : int 11266 7251 11066 8311 12459 13059 8555 18253 11697 13194 ...
## $ NOx : num 0.49 0.0725 1.49 1.715 0.465 ...
## $ CO : num 3.54 2.9 5.01 2.16 4.84 ...
## $ COV : num 0.545 0.425 0.69 0.36 0.662 ...
## $ SO2 : num 0.038 0.047 0.062 0.066 0.086 0.056 0.07 0.103 0.058 0.066 ...
## $ CO2 : num 101.5 63.6 98.8 70.2 88.5 ...
## $ N2O : num 6.35 2.23 5.99 3.66 6.06 6.15 7.75 9.09 8.69 5 ...
# Mostrando la variable consumo y la variable de producción total
acero$consumo
## [1] 135.31 84.08 131.62 90.46 120.04 153.68 99.09 226.38 140.07 161.22
## [11] 135.92 83.84 57.84 102.66 66.18 61.16 99.37 82.80 92.29 95.57
## [21] 149.24 131.95 87.34 123.58 143.07 104.00 130.08 141.55 33.18 83.63
## [31] 147.88 87.29 83.18 76.41 66.71 197.65 124.09 99.36 54.03 90.13
## [41] 160.49 128.62 154.28 125.91 115.92 120.04 66.39 104.76 193.44 191.32
## [51] 63.81 135.10 167.76 49.77 19.07 38.39 163.58 197.77 141.82 39.72
## [61] 17.50 39.38 201.88 224.51 186.18 163.53 182.48 175.06 161.52 127.07
## [71] 245.74 193.15 137.47 165.17 131.50 220.96 179.38 143.82 85.79 165.36
## [81] 194.92 191.02 156.03 234.39 221.26 68.30 173.10 290.72 250.73 190.64
## [91] 180.44 200.75 126.06 165.56 97.94 156.41 136.05 146.18 163.15 185.71
## [101] 126.67 110.45 131.95 149.88 187.36 246.54 222.88 191.01 195.61 160.87
## [111] 183.26 184.70 113.75 105.06 168.88 195.61 213.23
acero$ProdTotal
## [1] 11266 7251 11066 8311 12459 13059 8555 18253 11697 13194 11279 7661
## [13] 9540 11425 6647 5574 9247 9023 11525 12434 12361 10964 7867 10326
## [25] 11860 9103 10785 11749 3384 7277 12195 8186 8362 7413 6033 16048
## [37] 10355 8943 4994 8424 13198 10729 12723 10591 9927 10081 6455 8882
## [49] 15746 15562 5785 11282 13718 5667 2300 3870 13421 16098 11734 3850
## [61] 2187 3879 16387 18117 15159 13466 14931 14311 13225 10656 19783 15738
## [73] 11525 13528 11968 17776 14648 12017 7482 13531 15817 15574 12852 18892
## [85] 17803 6075 14146 23202 20111 15540 14747 16279 10501 13594 8349 12889
## [97] 11324 12073 13588 15137 10615 10166 11026 12407 15188 19798 17984 15511
## [109] 15916 13176 14908 14998 11322 8939 13845 15906 17247
# install.packages("carData") # una sola vez
library(carData)
data(Soils)
str(Soils)
## 'data.frame': 48 obs. of 14 variables:
## $ Group : Factor w/ 12 levels "1","2","3","4",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ Contour: Factor w/ 3 levels "Depression","Slope",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ Depth : Factor w/ 4 levels "0-10","10-30",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ Gp : Factor w/ 12 levels "D0","D1","D3",..: 9 9 9 9 10 10 10 10 11 11 ...
## $ Block : Factor w/ 4 levels "1","2","3","4": 1 2 3 4 1 2 3 4 1 2 ...
## $ pH : num 5.4 5.65 5.14 5.14 5.14 5.1 4.7 4.46 4.37 4.39 ...
## $ N : num 0.188 0.165 0.26 0.169 0.164 0.094 0.1 0.112 0.112 0.058 ...
## $ Dens : num 0.92 1.04 0.95 1.1 1.12 1.22 1.52 1.47 1.07 1.54 ...
## $ P : int 215 208 300 248 174 129 117 170 121 115 ...
## $ Ca : num 16.4 12.2 13 11.9 14.2 ...
## $ Mg : num 7.65 5.15 5.68 7.88 8.12 ...
## $ K : num 0.72 0.71 0.68 1.09 0.7 0.81 0.39 0.7 0.74 0.77 ...
## $ Na : num 1.14 0.94 0.6 1.01 2.17 2.67 3.32 3.76 5.74 5.85 ...
## $ Conduc : num 1.09 1.35 1.41 1.64 1.85 3.18 4.16 5.14 5.73 6.45 ...