clase componentes
Viviana Bravo
2024-08-27
Tabs
Análisis de componentes principales
paquetes
Dataset
Clusterización k-means
contenido del segundo tab
Análisis de regresión
packages <- c("shiny", "shinydashboard", "readr", "corrplot", "DataExplorer", "data.table", "ggplot2",
"dslabs", "dplyr","viridisLite","RColorBrewer","plotly","DT","corrr", "corrplot", "FactoMineR","factoextra","rworldmap","rnaturalearth","rnaturalearthdata","sf" )
install.load::install_load(packages)##
## Adjuntando el paquete: 'shinydashboard'## The following object is masked from 'package:graphics':
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## box## corrplot 0.92 loaded##
## Adjuntando el paquete: 'dplyr'## The following objects are masked from 'package:data.table':
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## between, first, last## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union##
## Adjuntando el paquete: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layout##
## Adjuntando el paquete: 'DT'## The following objects are masked from 'package:shiny':
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## dataTableOutput, renderDataTable## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa## Cargando paquete requerido: sp## ### Welcome to rworldmap ##### For a short introduction type : vignette('rworldmap')##
## Adjuntando el paquete: 'rnaturalearthdata'## The following object is masked from 'package:rnaturalearth':
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## countries110## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE#Matriz de correlación
library(corrr)
library(corrplot)
#PCA
library(FactoMineR)
library(factoextra)
#Gráficos
library(ggplot2)
library(plotly)
#Manejo datos
library(dplyr)
#Mapas
library(rworldmap)
library(rnaturalearth)
library(rnaturalearthdata)
library(sf)protein_data<-read.csv("protein.csv")colSums(is.na(protein_data))## Country Red_Meat White_Meat
## 0 0 0
## Eggs Milk Fish
## 0 0 0
## Cereals Starchy_Foods Pulses_nuts_oilseeds
## 0 0 0
## Fruits_Vegetables Total
## 0 0numerical_data <- protein_data[,2:10]apply(X=numerical_data, MARGIN=2,FUN=mean)##media## Red_Meat White_Meat Eggs
## 9.80 7.92 3.08
## Milk Fish Cereals
## 17.28 4.28 32.32
## Starchy_Foods Pulses_nuts_oilseeds Fruits_Vegetables
## 4.36 3.08 4.20apply(X=numerical_data, MARGIN=2,FUN=var) ##Varianza## Red_Meat White_Meat Eggs
## 11.583333 13.993333 1.243333
## Milk Fish Cereals
## 50.376667 12.043333 121.226667
## Starchy_Foods Pulses_nuts_oilseeds Fruits_Vegetables
## 2.740000 4.076667 3.666667apply(X=numerical_data, MARGIN=2,FUN=sd)## desviacion estandar## Red_Meat White_Meat Eggs
## 3.403430 3.740766 1.115049
## Milk Fish Cereals
## 7.097652 3.470351 11.010298
## Starchy_Foods Pulses_nuts_oilseeds Fruits_Vegetables
## 1.655295 2.019076 1.914854data_normalized <- scale(numerical_data)mean(data_normalized[,1])## [1] -2.192951e-16apply(X=data_normalized, MARGIN =2,FUN = mean) #Media## Red_Meat White_Meat Eggs
## -2.192951e-16 1.097646e-17 -6.438426e-17
## Milk Fish Cereals
## -1.701417e-16 -6.438426e-17 -1.774622e-17
## Starchy_Foods Pulses_nuts_oilseeds Fruits_Vegetables
## -1.976479e-16 -1.833169e-17 -1.021492e-16data.cpa <- princomp(data_normalized)
data.cpa$loadings##
## Loadings:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8
## Red_Meat 0.311 0.355 0.597 0.397 0.377 0.228
## White_Meat 0.316 0.215 -0.628 -0.311 0.146
## Eggs 0.421 0.255 -0.665 0.467
## Milk 0.379 0.169 0.404 -0.318 -0.718 -0.102
## Fish 0.134 -0.652 0.300 -0.235 -0.304 0.237 0.441
## Cereals -0.430 0.254 0.185 0.194 -0.343 0.721
## Starchy_Foods 0.296 -0.389 -0.281 -0.305 0.673 -0.326
## Pulses_nuts_oilseeds -0.422 -0.129 0.140 0.251 -0.587 -0.218
## Fruits_Vegetables -0.122 -0.504 -0.340 0.604 -0.228 0.158 -0.359
## Comp.9
## Red_Meat 0.251
## White_Meat 0.577
## Eggs -0.275
## Milk 0.190
## Fish 0.260
## Cereals 0.192
## Starchy_Foods 0.150
## Pulses_nuts_oilseeds 0.567
## Fruits_Vegetables -0.211
##
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5 Comp.6 Comp.7 Comp.8 Comp.9
## SS loadings 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## Proportion Var 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111 0.111
## Cumulative Var 0.111 0.222 0.333 0.444 0.556 0.667 0.778 0.889 1.000summary(data.cpa)## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 1.9828553 1.2489623 1.0207403 0.9321032 0.6400533
## Proportion of Variance 0.4550596 0.1805448 0.1205915 0.1005574 0.0474153
## Cumulative Proportion 0.4550596 0.6356044 0.7561959 0.8567534 0.9041687
## Comp.6 Comp.7 Comp.8 Comp.9
## Standard deviation 0.57711577 0.50866787 0.35936288 0.32716279
## Proportion of Variance 0.03854891 0.02994711 0.01494695 0.01238837
## Cumulative Proportion 0.94271757 0.97266468 0.98761163 1.00000000corr_matrix<-cor(data_normalized)
ggcorrplot::ggcorrplot(corr_matrix)
fviz_eig(data.cpa,addlabels = TRUE)
fviz_pca_var(data.cpa)
fviz_cos2(data.cpa,choice = "var",axes=1:2)