set.seed(2023)
options(digits = 3)#install.packages("ggplot2")
library(ggplot2)
#runif()
#rbinom()
x=1
x=c(1,2,3,4)
Frutos=c("manzana","durazno","fresa")
x=rnorm(n = 120,sd = 0.3,mean = 3);x## [1] 2.97 2.71 2.44 2.94 2.81 3.33 2.73 3.30 2.88 2.86 3.10 2.88 3.17 3.20 2.82
## [16] 3.21 3.18 3.14 3.27 3.17 2.88 2.91 3.37 3.07 2.87 2.45 2.81 2.74 3.45 3.82
## [31] 2.92 3.38 2.76 2.99 2.81 2.87 3.15 3.34 3.29 2.96 3.13 3.13 3.13 2.94 3.09
## [46] 3.21 3.71 3.32 3.10 3.23 3.20 2.67 2.87 3.36 3.48 3.68 2.38 2.88 3.09 3.21
## [61] 3.30 2.91 2.42 2.65 3.31 2.96 3.25 2.89 3.31 3.28 2.56 2.72 2.97 2.74 3.20
## [76] 2.98 2.96 2.88 3.01 2.66 3.07 3.52 2.96 3.15 3.04 3.09 3.30 2.84 2.76 2.42
## [91] 2.58 3.13 2.91 3.68 2.65 3.37 2.55 2.93 2.75 2.60 3.02 2.97 3.27 3.29 3.28
## [106] 2.99 2.78 3.09 2.73 3.42 3.20 3.00 3.08 2.38 3.23 3.00 2.70 3.77 3.21 3.56y=rnorm(120,3,0.3);y## [1] 2.86 3.11 3.03 3.10 2.77 3.33 2.79 2.94 2.90 3.38 2.96 3.72 3.11 3.29 2.91
## [16] 3.39 2.45 3.19 2.95 3.11 2.93 2.95 3.21 2.48 3.00 3.17 3.11 3.05 2.97 2.85
## [31] 3.14 3.81 2.65 3.28 2.81 3.28 3.11 3.00 3.23 3.48 3.52 3.10 3.15 3.24 2.72
## [46] 3.10 2.96 2.98 3.08 3.00 3.14 3.09 2.74 2.45 2.67 2.85 3.60 2.61 2.84 3.35
## [61] 3.02 3.00 3.29 3.07 2.69 3.28 2.89 3.23 3.04 2.77 3.08 2.90 2.72 2.78 2.82
## [76] 3.43 3.53 3.15 3.47 3.12 2.42 3.46 3.10 3.71 3.16 3.23 3.16 3.02 3.09 2.81
## [91] 2.51 3.06 2.85 2.71 3.04 2.83 2.73 2.53 2.92 3.51 2.40 2.74 3.00 2.55 3.16
## [106] 2.57 2.76 2.66 2.79 2.71 2.63 3.04 2.87 3.12 2.03 2.64 3.43 3.12 2.79 3.18z=rnorm(n = 120,sd = 0.3,mean = 3);z## [1] 2.90 2.83 3.34 3.06 3.05 2.79 2.63 3.15 2.56 2.61 2.64 2.60 3.13 3.02 2.81
## [16] 3.33 2.89 2.97 2.63 2.79 3.09 3.16 2.31 2.97 3.09 2.79 2.84 2.78 3.09 3.31
## [31] 3.15 3.23 2.83 2.71 3.01 3.03 3.24 3.06 3.23 3.73 2.53 3.05 3.32 3.15 3.06
## [46] 2.75 2.69 3.27 3.15 3.20 3.57 3.36 2.81 3.19 2.92 3.08 2.93 2.78 3.03 2.95
## [61] 3.25 4.37 2.70 2.67 3.13 3.05 2.87 3.00 2.68 3.39 2.54 3.07 2.65 2.93 2.63
## [76] 2.89 3.22 3.43 3.15 2.75 3.37 2.92 3.16 3.31 3.13 2.91 2.90 3.30 2.89 2.98
## [91] 3.28 2.50 3.04 2.87 3.44 2.71 3.00 2.56 2.56 3.17 3.01 3.16 3.40 3.57 2.89
## [106] 2.84 2.91 3.16 2.80 2.93 3.19 2.33 2.75 2.76 2.99 3.12 2.85 3.01 2.84 2.60#x
#cREAR dATA fRAME y llamarla de excel
DF=data.frame(x,y,z)
#install.packages("readxl")
library(readxl)
Palma <- read_excel("G:/Mi unidad/MAESTRÍA CIENCIA Y TECNOLOGÍA ALIMENTOS/Métodos Multivariados/Clases/Clase 6 18.02.2023/Palma.xlsx")
#View(Palma)
#head(Palma)
Palma## # A tibble: 20 × 7
## trt AD10 AD20 AD30 AD40 AD50 AD60
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 ctrl 1.09 1.20 2.53 4.27 5.47 10.7
## 2 ctrl 0.956 1.05 2.22 3.75 4.80 8.66
## 3 ctrl 0.727 0.800 1.69 2.85 3.65 5.70
## 4 ctrl 1.12 1.23 2.60 4.39 5.62 11.1
## 5 ctrl 0.901 0.992 2.09 3.53 4.53 7.91
## 6 ctrl 0.765 0.842 1.78 3.00 3.84 6.15
## 7 ctrl 0.875 0.963 2.03 3.43 4.39 7.55
## 8 ctrl 1.14 1.25 2.64 4.46 5.71 11.4
## 9 ctrl 0.721 0.794 1.67 2.83 3.62 5.63
## 10 ctrl 1.02 1.12 2.37 4.00 5.13 9.62
## 11 quim 0.151 0.159 0.311 2.07 2.23 2.16
## 12 quim 0.196 0.206 0.405 1.94 2.14 2.10
## 13 quim 0.660 0.693 1.36 1.69 2.38 3.54
## 14 quim 0.502 0.527 1.04 2.09 2.62 3.17
## 15 quim 0.437 0.459 0.901 1.88 2.34 2.69
## 16 quim 0.767 0.805 1.58 1.73 2.54 4.23
## 17 quim 0.297 0.311 0.611 1.85 2.16 2.23
## 18 quim 0.301 0.316 0.621 2.11 2.43 2.50
## 19 quim 0.555 0.583 1.14 1.68 2.26 2.99
## 20 quim 0.614 0.644 1.26 2.00 2.65 3.60DF2=data.frame(Palma);DF2## trt AD10 AD20 AD30 AD40 AD50 AD60
## 1 ctrl 1.089 1.198 2.527 4.27 5.47 10.66
## 2 ctrl 0.956 1.051 2.218 3.75 4.80 8.66
## 3 ctrl 0.727 0.800 1.687 2.85 3.65 5.70
## 4 ctrl 1.119 1.231 2.596 4.39 5.62 11.13
## 5 ctrl 0.901 0.992 2.091 3.53 4.53 7.91
## 6 ctrl 0.765 0.842 1.776 3.00 3.84 6.15
## 7 ctrl 0.875 0.963 2.030 3.43 4.39 7.55
## 8 ctrl 1.137 1.250 2.637 4.46 5.71 11.41
## 9 ctrl 0.721 0.794 1.674 2.83 3.62 5.63
## 10 ctrl 1.021 1.123 2.369 4.00 5.13 9.62
## 11 quim 0.151 0.159 0.311 2.07 2.23 2.16
## 12 quim 0.196 0.206 0.405 1.94 2.14 2.10
## 13 quim 0.660 0.693 1.360 1.69 2.38 3.54
## 14 quim 0.502 0.527 1.035 2.09 2.62 3.17
## 15 quim 0.437 0.459 0.901 1.88 2.34 2.69
## 16 quim 0.767 0.805 1.580 1.73 2.54 4.23
## 17 quim 0.297 0.311 0.611 1.85 2.16 2.23
## 18 quim 0.301 0.316 0.621 2.11 2.43 2.50
## 19 quim 0.555 0.583 1.144 1.68 2.26 2.99
## 20 quim 0.614 0.644 1.265 2.00 2.65 3.60dim(DF2)## [1] 20 7colnames(DF2)## [1] "trt" "AD10" "AD20" "AD30" "AD40" "AD50" "AD60"DF2$AD10## [1] 1.089 0.956 0.727 1.119 0.901 0.765 0.875 1.137 0.721 1.021 0.151 0.196
## [13] 0.660 0.502 0.437 0.767 0.297 0.301 0.555 0.614#cREAR GRAFICOS
#library(graphics)
par(mfrow=c(2,2))
plot(DF2$AD10,DF2$AD20,type = "p",main = "Area de Daño" ,xlab ="Area Daño 10" ,ylab ="Area Daño 20" )
smoothScatter(DF2$AD10,DF2$AD20, xlab = "Area de daño 20", ylab = "Area de daño 10", main="Scatter Plot")
density(DF2$AD10)##
## Call:
## density.default(x = DF2$AD10)
##
## Data: DF2$AD10 (20 obs.); Bandwidth 'bw' = 0.1513
##
## x y
## Min. :-0.303 Min. :0.002
## 1st Qu.: 0.170 1st Qu.:0.109
## Median : 0.644 Median :0.566
## Mean : 0.644 Mean :0.527
## 3rd Qu.: 1.117 3rd Qu.:0.890
## Max. : 1.591 Max. :1.107hist(DF2$AD40, xlab = "Area de daño 40", ylab = "Frecuencia", main = "Histograma Area de daño",col = "red")
boxplot(DF2$AD50, col = "blue", horizontal = F, main="Boxplot AD 50", xlab="Area de daño", ylab="Número de datos")
#Graficos con GGPlot
#ggplot(DF2)+
#geom_point(aes(x = AD20,y = AD10)
#ggplot(DF2)+
#geom_density(aes(x = AD20),fill="blue")
ggplot(DF2)+
geom_boxplot(aes(AD20),fill=c("blue","red"))+
facet_wrap(~trt)+
coord_flip()+
xlab("x")+
ylab("Area de daño")
#Usar Pipes y Dplyr
#PIPES (ctrl+shift+m)
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, unionDF2 |>
ggplot()+
geom_histogram(aes(AD20,fill=trt))+
facet_wrap(~trt)+
xlab("Area de daño")+
ylab("Frecuencia")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#Arboles colapsables
#install.packages("collapsibleTree")
library(collapsibleTree)
lotes=gl(2,9,18,labels = c("l1","l2"))
gen=gl(3,3,9,labels = c("g1","g2","g3"))
caudal=gl(3,1,3,labels = c("0","10","20"))
arbol=data.frame(lotes,gen,caudal);arbol## lotes gen caudal
## 1 l1 g1 0
## 2 l1 g1 10
## 3 l1 g1 20
## 4 l1 g2 0
## 5 l1 g2 10
## 6 l1 g2 20
## 7 l1 g3 0
## 8 l1 g3 10
## 9 l1 g3 20
## 10 l2 g1 0
## 11 l2 g1 10
## 12 l2 g1 20
## 13 l2 g2 0
## 14 l2 g2 10
## 15 l2 g2 20
## 16 l2 g3 0
## 17 l2 g3 10
## 18 l2 g3 20arbol |>
collapsibleTree(hierarchy = c("lotes","gen","caudal"))#Pipes
DF2 |>
select(trt,AD20)## trt AD20
## 1 ctrl 1.198
## 2 ctrl 1.051
## 3 ctrl 0.800
## 4 ctrl 1.231
## 5 ctrl 0.992
## 6 ctrl 0.842
## 7 ctrl 0.963
## 8 ctrl 1.250
## 9 ctrl 0.794
## 10 ctrl 1.123
## 11 quim 0.159
## 12 quim 0.206
## 13 quim 0.693
## 14 quim 0.527
## 15 quim 0.459
## 16 quim 0.805
## 17 quim 0.311
## 18 quim 0.316
## 19 quim 0.583
## 20 quim 0.644DF2 |>
filter(AD20>=1) |>
select(AD40,AD60) |>
summary()## AD40 AD60
## Min. :3.75 Min. : 8.66
## 1st Qu.:4.00 1st Qu.: 9.62
## Median :4.27 Median :10.66
## Mean :4.17 Mean :10.30
## 3rd Qu.:4.39 3rd Qu.:11.13
## Max. :4.46 Max. :11.41Tabla2 = DF2 |>
filter(AD20>=1) |>
select(AD20,AD40,AD60) #|>
#summary()
Tabla2## AD20 AD40 AD60
## 1 1.20 4.27 10.66
## 2 1.05 3.75 8.66
## 3 1.23 4.39 11.13
## 4 1.25 4.46 11.41
## 5 1.12 4.00 9.62#eSCRITURA lATEX#eS