coffeData = read.table("coffeData.csv", header = TRUE, sep = ";", dec = ".", stringsAsFactors = TRUE)
summary(coffeData)
##        X           Species                  Country.of.Origin Fragrance...Aroma
##  Min.   :   1   Arabica:1309   Mexico                :237     Min.   :5.080    
##  1st Qu.: 335   Robusta:  28   Colombia              :183     1st Qu.:7.420    
##  Median : 669                  Guatemala             :180     Median :7.580    
##  Mean   : 669                  Brazil                :132     Mean   :7.572    
##  3rd Qu.:1003                  Taiwan                : 75     3rd Qu.:7.750    
##  Max.   :1337                  United States (Hawaii): 73     Max.   :8.750    
##                                (Other)               :457                      
##      Flavor        Aftertaste     Salt...Acid      Mouthfeel    
##  Min.   :6.080   Min.   :6.170   Min.   :5.250   Min.   :5.080  
##  1st Qu.:7.330   1st Qu.:7.250   1st Qu.:7.330   1st Qu.:7.330  
##  Median :7.580   Median :7.420   Median :7.580   Median :7.500  
##  Mean   :7.527   Mean   :7.407   Mean   :7.541   Mean   :7.524  
##  3rd Qu.:7.750   3rd Qu.:7.580   3rd Qu.:7.750   3rd Qu.:7.750  
##  Max.   :8.830   Max.   :8.670   Max.   :8.750   Max.   :8.750  
##                                                                 
##     Balance       Bitter...Sweet   Uniform.Cup       Clean.Cup     
##  Min.   : 5.250   Min.   :5.250   Min.   : 6.000   Min.   : 0.000  
##  1st Qu.:10.000   1st Qu.:7.330   1st Qu.:10.000   1st Qu.:10.000  
##  Median :10.000   Median :7.500   Median :10.000   Median :10.000  
##  Mean   : 9.868   Mean   :7.527   Mean   : 9.844   Mean   : 9.849  
##  3rd Qu.:10.000   3rd Qu.:7.670   3rd Qu.:10.000   3rd Qu.:10.000  
##  Max.   :10.000   Max.   :8.580   Max.   :10.000   Max.   :10.000  
##                                                                    
##  Cupper.Points   quality_score  
##  Min.   : 5.17   Min.   :63.08  
##  1st Qu.: 7.25   1st Qu.:81.17  
##  Median : 7.50   Median :82.50  
##  Mean   : 7.51   Mean   :82.17  
##  3rd Qu.: 7.75   3rd Qu.:83.67  
##  Max.   :10.00   Max.   :90.58  
##

Histogramas

library(ggplot2)
g1= ggplot(coffeData, aes(x=Flavor)) + geom_histogram(fill = "blue")
g2= ggplot(coffeData, aes(x=quality_score))+ geom_histogram(fill = "red")

##Diagrama de Cajas

g3= ggplot(coffeData, aes(x = quality_score)) + geom_boxplot(fill = "green")
g4= ggplot(coffeData, aes(x = Flavor)) + geom_boxplot(fill = "yellow")

##Panel Grafico

library(gridExtra)
grid.arrange(g1,g2,g3,g4)

##Analisis con 2 variables numericas

ggplot(coffeData, aes(x= Flavor, y = quality_score)) + geom_jitter() + geom_smooth(method = "lm", colour = "red" )

cor(coffeData$Flavor, coffeData$quality_score)
## [1] 0.8348271

Interpretación

Se tiene que el coeficiente de correlación es de \(r= 0.83\), lo cuál indica una relación alta entre las variables, a mayor calificación del sabor mayor calidad del café.

##Análisis con una numerica y cualitativa

library(dplyr)
## 
## Attaching package: 'dplyr'
## The following object is masked from 'package:gridExtra':
## 
##     combine
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
nuevosDatos = filter(coffeData, Country.of.Origin %in% c("Colombia", "Brazil", "Mexico"))

ggplot(nuevosDatos, aes(x= Country.of.Origin, y = quality_score, fill= Country.of.Origin)) + geom_boxplot() + 
  labs(title= "Diagrama de caja Calidad vs Pais", x= "Pais", y = "Calidad del Café")

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