2.2 Data: Sensory Profiles of Sausages
Overview:
Data: Sensory Profiles of Sausages Rows: 64 obs, 8 types of sausages Columns: 19 attributes + Session (Sausage Type) Panel: 9 Panelists in total but we had to eliminate 3 Panelists (JUAN, Mine, Raul) that did not rate all products (this is to satisfy the balance-between-groups condition in MFA). Note: Ratings on 10-point scale
Load necessary packages:
library(readxl) #to read data the excel files
library(ggplot2) #to plot really pretty graphs
library(corrplot) #to assess correlation and make correlation plots
library(PerformanceAnalytics) #another cool package to look at correlation
library(PTCA4CATA) #amazing package from Dr. Abdi to do support works 4 analysis
library(data4PCCAR) #amazing package no.2 from Dr. Abdi to do PCA & CA
library(ExPosition) #package for PCA calculations
library(InPosition) #another package for PCA calculation that also include inference (BR, CI)
library(graphics) #default on in R. Just making sure. For fine-tunings
library(dplyr) #for data manipulations
library(tidyverse) #collection of vital packages
library(data.table) #for data manipulation
library(knitr) #to make nice Rmarkdown including neat html tables
library(kableExtra) #make scrollable tables
library(gridExtra)
require(ggplotify) #these 2 helps combine plots
#install.packages("devtools")
#devtools::install_github('HerveAbdi/data4PCCAR')
#devtools::install_github('HerveAbdi/PTCA4CATA')Load data:
#use readxl to read from excel files
sausage.raw <- read_excel("Sausage Data.xlsx") #don't hard code location,
#just direct to data file by name as long as it is in the same folder
a <- kable(sausage.raw) #taking a peak at our data
scroll_box(a, width = "100%", height = "300px")| Panelist | Product | Session | Aroma impact | Flavor impact | Salty | Umami | Acidic | Bitter | Alliaceous | Animalic savory | Bloody | Floury | Dark meat | Eggy | Fatty | HVP | Juicy savory | Rubbery | Smokey | Spicy | White meat |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| XEL | Salchicha de Pavo Nutrideli | 1 | 4.0 | 6.0 | 3.0 | 4.0 | 1.0 | 0.0 | 2.5 | 2.0 | 5.0 | 1.5 | 3.0 | 1.0 | 2.1 | 2.0 | 4.0 | 4.0 | 3.0 | 3.0 | 5.0 |
| LALO | Salchicha de Pavo Nutrideli | 1 | 5.0 | 8.0 | 2.5 | 4.0 | 2.0 | 0.0 | 3.0 | 1.0 | 2.0 | 3.0 | 2.2 | 3.0 | 4.0 | 2.0 | 4.0 | 4.0 | 4.5 | 2.0 | 5.0 |
| JUAN | Salchicha de Pavo Nutrideli | 1 | 3.0 | 6.0 | 3.5 | 2.0 | 2.0 | 0.0 | 2.5 | 3.0 | 0.0 | 3.0 | 2.5 | 0.5 | 4.0 | 2.0 | 2.0 | 3.0 | 1.0 | 3.0 | 5.0 |
| MARTHA | Salchicha de Pavo Nutrideli | 1 | 5.0 | 5.5 | 4.0 | 5.0 | 2.0 | 0.0 | 4.0 | 2.0 | 3.0 | 2.0 | 5.2 | 2.0 | 5.1 | 2.0 | 6.0 | 5.0 | 4.0 | 3.0 | 3.0 |
| NERI | Salchicha de Pavo Nutrideli | 1 | 6.0 | 6.0 | 2.5 | 2.0 | 1.5 | 0.0 | 2.0 | 1.0 | 1.5 | 3.0 | 4.0 | 0.0 | 4.0 | 4.5 | 3.0 | 5.2 | 2.0 | 2.0 | 5.0 |
| DIANA | Salchicha de Pavo Nutrideli | 1 | 6.0 | 7.0 | 5.5 | 4.5 | 2.5 | 0.0 | 3.5 | 5.0 | 2.0 | 4.0 | 4.0 | 2.0 | 5.1 | 2.0 | 2.5 | 3.0 | 3.5 | 7.0 | 6.0 |
| DULCE | Salchicha de Pavo Nutrideli | 1 | 4.0 | 6.0 | 3.0 | 4.0 | 1.0 | 0.0 | 4.0 | 3.0 | 2.0 | 4.5 | 4.0 | 2.0 | 2.1 | 6.0 | 3.5 | 3.0 | 4.0 | 3.0 | 5.0 |
| RAUL | Salchicha de Pavo Nutrideli | 1 | 3.0 | 9.0 | 5.0 | 1.0 | 2.0 | 0.0 | 3.0 | 4.0 | 3.0 | 1.5 | 3.5 | 3.0 | 2.1 | 3.0 | 4.0 | 2.2 | 2.0 | 3.0 | 6.0 |
| XEL | Salchicha de pavo FUD | 1 | 3.0 | 5.0 | 3.0 | 2.0 | 2.0 | 3.0 | 2.0 | 2.0 | 3.9 | 2.0 | 2.0 | 3.0 | 2.0 | 2.0 | 1.0 | 4.0 | 4.0 | 3.0 | 5.0 |
| LALO | Salchicha de pavo FUD | 1 | 4.5 | 8.0 | 3.0 | 3.0 | 2.0 | 5.0 | 2.0 | 1.5 | 0.9 | 4.0 | 2.0 | 1.0 | 3.0 | 2.5 | 3.0 | 3.5 | 2.0 | 2.0 | 5.0 |
| MINE | Salchicha de pavo FUD | 1 | 5.0 | 6.0 | 2.0 | 2.5 | 1.0 | 2.0 | 3.0 | 2.0 | 2.0 | 3.0 | 2.0 | 2.0 | 4.0 | 3.0 | 2.0 | 2.4 | 3.0 | 3.0 | 4.0 |
| MARTHA | Salchicha de pavo FUD | 1 | 5.0 | 6.0 | 3.0 | 2.0 | 1.0 | 4.0 | 2.0 | 1.0 | 0.9 | 3.5 | 2.0 | 0.0 | 4.0 | 2.0 | 5.0 | 3.0 | 0.0 | 3.0 | 3.0 |
| NERI | Salchicha de pavo FUD | 1 | 6.0 | 6.0 | 3.0 | 4.0 | 2.5 | 3.5 | 4.0 | 2.0 | 1.5 | 4.0 | 4.0 | 1.0 | 2.0 | 4.0 | 3.0 | 4.0 | 2.0 | 2.5 | 6.0 |
| DIANA | Salchicha de pavo FUD | 1 | 4.0 | 5.0 | 3.0 | 2.0 | 4.0 | 3.0 | 3.0 | 4.5 | 3.0 | 3.0 | 2.5 | 3.5 | 5.0 | 2.0 | 3.5 | 2.4 | 2.0 | 3.5 | 5.0 |
| DULCE | Salchicha de pavo FUD | 1 | 3.0 | 6.0 | 5.0 | 5.0 | 2.0 | 2.0 | 3.5 | 1.0 | 3.9 | 4.0 | 2.0 | 4.0 | 3.0 | 3.0 | 2.0 | 4.0 | 2.0 | 3.0 | 5.0 |
| RAUL | Salchicha de pavo FUD | 1 | 3.0 | 5.0 | 2.0 | 2.0 | 4.0 | 4.0 | 2.5 | 3.0 | 2.0 | 2.0 | 6.0 | 2.0 | 5.0 | 0.0 | 1.0 | 5.4 | 3.5 | 5.0 | 3.0 |
| XEL | Salchicha pavo CHERO | 1 | 4.0 | 5.3 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 2.0 | 2.0 | 5.0 | 3.0 | 1.0 | 3.0 | 3.0 | 2.0 | 0.0 | 2.0 | 3.0 | 2.0 |
| LALO | Salchicha pavo CHERO | 1 | 5.0 | 5.5 | 3.0 | 2.0 | 3.0 | 1.5 | 2.0 | 4.0 | 1.5 | 4.5 | 3.0 | 1.5 | 2.0 | 1.5 | 2.0 | 0.0 | 3.0 | 2.0 | 1.2 |
| JUAN | Salchicha pavo CHERO | 1 | 4.0 | 8.3 | 3.0 | 2.0 | 2.0 | 0.0 | 2.0 | 4.0 | 1.0 | 4.0 | 5.0 | 3.0 | 3.0 | 3.0 | 4.0 | 0.0 | 3.0 | 2.0 | 3.0 |
| MARTHA | Salchicha pavo CHERO | 1 | 6.0 | 5.0 | 4.0 | 3.0 | 1.0 | 0.5 | 3.0 | 4.0 | 0.0 | 4.0 | 3.0 | 0.0 | 3.0 | 2.0 | 0.5 | 0.0 | 2.0 | 3.0 | 1.2 |
| NERI | Salchicha pavo CHERO | 1 | 6.0 | 6.0 | 3.0 | 2.0 | 4.0 | 1.0 | 2.5 | 2.5 | 2.0 | 5.0 | 4.0 | 1.5 | 2.0 | 2.0 | 3.0 | 0.0 | 1.5 | 1.0 | 4.2 |
| DIANA | Salchicha pavo CHERO | 1 | 7.5 | 8.0 | 5.0 | 3.0 | 4.0 | 1.5 | 3.0 | 4.5 | 0.0 | 7.0 | 4.0 | 0.0 | 3.0 | 4.0 | 5.0 | 0.0 | 4.5 | 2.0 | 4.2 |
| DULCE | Salchicha pavo CHERO | 1 | 5.0 | 7.0 | 4.0 | 3.0 | 2.0 | 0.0 | 3.0 | 2.0 | 2.0 | 6.0 | 4.0 | 2.0 | 3.0 | 3.0 | 3.0 | 0.0 | 4.0 | 3.0 | 1.2 |
| RAUL | Salchicha pavo CHERO | 1 | 4.0 | 7.0 | 3.0 | 0.0 | 4.0 | 2.0 | 4.0 | 2.0 | 5.0 | 6.0 | 4.0 | 1.0 | 5.0 | 3.0 | 3.0 | 0.0 | 2.0 | 5.0 | 3.5 |
| XEL | SALCHICHA DE PAVO CHIMEX | 1 | 3.0 | 4.9 | 2.0 | 4.0 | 2.0 | 0.0 | 3.0 | 2.0 | 2.0 | 3.0 | 3.0 | 1.0 | 3.0 | 0.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 |
| LALO | SALCHICHA DE PAVO CHIMEX | 1 | 5.0 | 5.0 | 3.5 | 3.0 | 1.0 | 0.0 | 3.0 | 3.5 | 2.5 | 2.0 | 0.0 | 3.0 | 3.0 | 0.0 | 2.5 | 2.0 | 3.5 | 3.5 | 4.5 |
| JUAN | SALCHICHA DE PAVO CHIMEX | 1 | 5.0 | 7.9 | 4.0 | 3.0 | 1.0 | 0.0 | 3.0 | 1.3 | 1.0 | 2.0 | 2.0 | 1.0 | 2.0 | 0.0 | 1.0 | 4.0 | 4.0 | 2.0 | 3.0 |
| MINE | SALCHICHA DE PAVO CHIMEX | 1 | 3.0 | 7.9 | 2.0 | 3.0 | 1.5 | 0.0 | 3.0 | 4.3 | 1.0 | 4.0 | 0.0 | 3.0 | 4.0 | 0.0 | 5.0 | 3.0 | 2.5 | 3.0 | 5.0 |
| MARTHA | SALCHICHA DE PAVO CHIMEX | 1 | 5.0 | 6.0 | 3.0 | 4.0 | 1.0 | 0.0 | 3.0 | 1.3 | 0.0 | 4.0 | 0.0 | 0.0 | 3.0 | 0.0 | 2.0 | 4.0 | 2.0 | 2.0 | 4.0 |
| NERI | SALCHICHA DE PAVO CHIMEX | 1 | 7.0 | 7.0 | 5.0 | 2.0 | 2.5 | 0.0 | 2.0 | 1.5 | 1.0 | 2.0 | 3.0 | 2.0 | 3.0 | 0.0 | 3.5 | 3.0 | 3.0 | 2.0 | 5.0 |
| DIANA | SALCHICHA DE PAVO CHIMEX | 1 | 6.0 | 7.9 | 2.0 | 6.0 | 3.0 | 0.0 | 4.0 | 4.3 | 2.0 | 4.0 | 0.0 | 2.5 | 6.0 | 0.0 | 5.0 | 6.0 | 4.0 | 3.5 | 7.0 |
| DULCE | SALCHICHA DE PAVO CHIMEX | 1 | 4.0 | 5.0 | 2.0 | 4.0 | 3.0 | 0.0 | 4.0 | 3.0 | 2.0 | 3.0 | 3.0 | 0.0 | 3.0 | 0.0 | 3.0 | 5.0 | 4.0 | 1.0 | 3.0 |
| XEL | Salchicha Viena Nutrideli | 2 | 4.0 | 7.0 | 2.0 | 4.0 | 0.0 | 0.0 | 2.0 | 2.0 | 1.5 | 0.0 | 5.0 | 3.0 | 4.0 | 3.5 | 3.0 | 2.0 | 4.5 | 0.0 | 4.8 |
| LALO | Salchicha Viena Nutrideli | 2 | 5.0 | 4.0 | 3.5 | 3.0 | 0.0 | 0.0 | 4.0 | 1.0 | 3.0 | 0.0 | 2.0 | 2.0 | 4.0 | 4.0 | 3.0 | 4.0 | 4.0 | 0.0 | 2.0 |
| JUAN | Salchicha Viena Nutrideli | 2 | 4.0 | 8.0 | 3.0 | 1.5 | 0.0 | 0.0 | 2.0 | 4.0 | 1.5 | 0.0 | 4.0 | 2.0 | 3.0 | 0.0 | 5.0 | 2.0 | 3.0 | 0.0 | 3.0 |
| MARTHA | Salchicha Viena Nutrideli | 2 | 3.0 | 8.0 | 2.0 | 5.0 | 0.0 | 0.0 | 3.5 | 1.0 | 0.0 | 0.0 | 4.0 | 1.0 | 4.0 | 1.0 | 3.0 | 2.0 | 1.0 | 0.0 | 1.8 |
| NERI | Salchicha Viena Nutrideli | 2 | 3.0 | 5.0 | 3.0 | 4.0 | 0.0 | 0.0 | 2.0 | 2.0 | 2.5 | 0.0 | 4.0 | 3.0 | 2.5 | 2.0 | 3.0 | 2.0 | 3.0 | 0.0 | 4.8 |
| DIANA | Salchicha Viena Nutrideli | 2 | 5.0 | 5.0 | 5.0 | 3.0 | 0.0 | 0.0 | 3.0 | 3.0 | 2.0 | 0.0 | 6.0 | 1.0 | 4.5 | 2.0 | 3.0 | 2.0 | 3.5 | 0.0 | 4.0 |
| DULCE | Salchicha Viena Nutrideli | 2 | 6.0 | 6.0 | 2.0 | 1.0 | 0.0 | 0.0 | 2.0 | 4.5 | 1.0 | 0.0 | 5.5 | 0.5 | 3.0 | 2.0 | 4.0 | 3.0 | 2.0 | 0.0 | 4.0 |
| RAUL | Salchicha Viena Nutrideli | 2 | 4.0 | 6.0 | 3.0 | 3.0 | 0.0 | 0.0 | 3.0 | 0.0 | 3.0 | 0.0 | 4.0 | 1.0 | 5.0 | 3.0 | 4.0 | 5.0 | 3.0 | 0.0 | 3.0 |
| XEL | Salchicha viena FUD | 2 | 4.0 | 5.0 | 3.0 | 4.0 | 1.0 | 0.0 | 4.0 | 2.0 | 2.0 | 0.0 | 5.0 | 2.0 | 3.0 | 0.0 | 3.0 | 0.0 | 3.0 | 0.0 | 0.0 |
| LALO | Salchicha viena FUD | 2 | 4.0 | 6.0 | 4.0 | 3.5 | 2.0 | 0.0 | 3.0 | 2.0 | 2.0 | 0.0 | 5.0 | 3.0 | 3.5 | 0.0 | 3.0 | 0.0 | 3.5 | 0.0 | 3.0 |
| JUAN | Salchicha viena FUD | 2 | 5.0 | 8.0 | 2.0 | 3.0 | 1.5 | 0.0 | 3.0 | 2.5 | 2.0 | 0.0 | 5.0 | 3.5 | 3.0 | 0.0 | 5.0 | 0.0 | 3.0 | 0.0 | 2.0 |
| MARTHA | Salchicha viena FUD | 2 | 4.0 | 6.0 | 2.0 | 5.0 | 1.0 | 0.0 | 3.0 | 4.0 | 1.0 | 0.0 | 3.0 | 0.0 | 3.0 | 0.0 | 4.0 | 0.0 | 3.0 | 0.0 | 2.0 |
| NERI | Salchicha viena FUD | 2 | 7.0 | 8.0 | 2.5 | 2.0 | 2.5 | 0.0 | 2.0 | 1.5 | 3.0 | 0.0 | 6.0 | 0.0 | 4.0 | 0.0 | 1.5 | 0.0 | 3.0 | 0.0 | 3.0 |
| DIANA | Salchicha viena FUD | 2 | 7.0 | 6.0 | 4.0 | 3.5 | 2.0 | 0.0 | 2.5 | 4.0 | 2.0 | 0.0 | 5.0 | 0.0 | 3.5 | 0.0 | 4.0 | 0.0 | 4.0 | 0.0 | 3.0 |
| DULCE | Salchicha viena FUD | 2 | 4.5 | 7.0 | 3.0 | 4.0 | 2.0 | 0.0 | 4.0 | 2.0 | 3.0 | 0.0 | 6.0 | 3.0 | 2.0 | 0.0 | 4.0 | 0.0 | 4.0 | 0.0 | 1.0 |
| RAUL | Salchicha viena FUD | 2 | 5.0 | 6.0 | 5.0 | 3.5 | 1.0 | 0.0 | 2.0 | 0.0 | 4.0 | 0.0 | 6.0 | 0.0 | 5.0 | 0.0 | 3.0 | 0.0 | 4.0 | 0.0 | 1.0 |
| XEL | Salchicha VIENA VIVA | 2 | 4.0 | 3.0 | 2.0 | 4.0 | 1.0 | 0.0 | 3.0 | 3.0 | 2.0 | 2.0 | 4.0 | 2.0 | 3.0 | 3.0 | 3.0 | 4.0 | 3.0 | 3.0 | 3.0 |
| LALO | Salchicha VIENA VIVA | 2 | 4.0 | 5.0 | 3.0 | 2.5 | 0.5 | 0.0 | 2.0 | 0.7 | 2.5 | 4.0 | 5.0 | 3.5 | 3.0 | 4.0 | 2.0 | 3.5 | 3.0 | 3.0 | 2.5 |
| JUAN | Salchicha VIENA VIVA | 2 | 3.0 | 8.0 | 3.0 | 2.0 | 1.0 | 0.0 | 3.5 | 3.7 | 2.0 | 3.0 | 5.0 | 4.0 | 3.0 | 4.0 | 5.0 | 3.0 | 2.0 | 3.5 | 3.5 |
| MARTHA | Salchicha VIENA VIVA | 2 | 6.3 | 6.0 | 2.0 | 3.0 | 0.5 | 0.0 | 5.5 | 3.7 | 0.0 | 3.5 | 4.0 | 0.5 | 4.0 | 3.0 | 3.0 | 4.0 | 3.0 | 3.0 | 2.0 |
| NERI | Salchicha VIENA VIVA | 2 | 6.0 | 5.0 | 2.5 | 2.5 | 1.5 | 0.0 | 2.0 | 1.5 | 3.0 | 4.0 | 5.0 | 0.0 | 4.0 | 0.0 | 4.0 | 3.5 | 2.0 | 3.0 | 4.0 |
| DIANA | Salchicha VIENA VIVA | 2 | 6.3 | 7.0 | 6.0 | 4.0 | 2.0 | 0.0 | 3.5 | 3.0 | 1.5 | 4.0 | 3.5 | 1.0 | 4.5 | 5.0 | 4.0 | 5.5 | 3.0 | 1.5 | 6.0 |
| DULCE | Salchicha VIENA VIVA | 2 | 4.0 | 6.0 | 2.0 | 3.0 | 1.0 | 0.0 | 4.0 | 3.0 | 1.0 | 5.0 | 5.0 | 2.0 | 1.0 | 4.0 | 5.0 | 6.0 | 4.0 | 3.0 | 2.0 |
| RAUL | Salchicha VIENA VIVA | 2 | 3.0 | 6.0 | 4.0 | 2.0 | 1.0 | 0.0 | 3.0 | 0.7 | 4.0 | 5.0 | 5.0 | 0.0 | 4.0 | 3.0 | 2.0 | 6.0 | 2.0 | 3.0 | 3.0 |
| XEL | SALCHICHA VIENA CHIMEX | 2 | 5.0 | 5.0 | 3.0 | 4.0 | 1.0 | 0.0 | 3.0 | 2.0 | 3.0 | 3.0 | 0.0 | 2.0 | 2.0 | 3.0 | 3.0 | 2.0 | 3.0 | 3.0 | 5.0 |
| LALO | SALCHICHA VIENA CHIMEX | 2 | 4.5 | 6.0 | 3.5 | 4.0 | 2.0 | 0.0 | 2.5 | 2.0 | 2.0 | 3.0 | 0.0 | 2.0 | 3.0 | 2.5 | 3.0 | 3.5 | 3.0 | 3.5 | 5.0 |
| JUAN | SALCHICHA VIENA CHIMEX | 2 | 5.5 | 8.0 | 4.0 | 3.0 | 1.0 | 0.0 | 3.0 | 0.5 | 1.0 | 2.0 | 0.0 | 2.0 | 2.0 | 1.0 | 2.0 | 3.0 | 4.0 | 1.0 | 3.0 |
| MINE | SALCHICHA VIENA CHIMEX | 2 | 5.0 | 9.0 | 2.0 | 3.0 | 1.5 | 0.0 | 3.0 | 3.5 | 0.0 | 3.0 | 0.0 | 3.0 | 2.0 | 2.0 | 4.0 | 2.0 | 2.0 | 4.0 | 5.0 |
| MARTHA | SALCHICHA VIENA CHIMEX | 2 | 7.0 | 6.0 | 4.0 | 4.0 | 2.0 | 0.0 | 4.0 | 2.0 | 0.0 | 3.0 | 0.0 | 0.0 | 2.0 | 1.0 | 3.0 | 4.0 | 3.0 | 2.0 | 4.0 |
| NERI | SALCHICHA VIENA CHIMEX | 2 | 6.0 | 7.0 | 3.0 | 4.0 | 2.5 | 0.0 | 3.0 | 2.0 | 1.0 | 2.0 | 0.0 | 0.0 | 3.0 | 1.5 | 3.5 | 4.0 | 2.5 | 2.0 | 3.0 |
| DIANA | SALCHICHA VIENA CHIMEX | 2 | 7.0 | 7.0 | 5.0 | 4.0 | 3.0 | 0.0 | 3.5 | 4.0 | 0.0 | 6.0 | 0.0 | 2.0 | 3.5 | 3.0 | 4.0 | 4.0 | 2.5 | 2.0 | 7.0 |
| DULCE | SALCHICHA VIENA CHIMEX | 2 | 7.0 | 6.0 | 2.0 | 2.0 | 1.0 | 0.0 | 4.0 | 3.0 | 3.0 | 4.0 | 0.0 | 1.0 | 3.0 | 0.0 | 3.0 | 3.0 | 3.5 | 2.0 | 4.0 |
Convert to data.frame and show snippet of data
Data processing:
Goal is to make as many variations of data as needed. I prefer to work with data frame as it is easy to manipulate.Here I made 2 assumptions: 1. panelist ratings are independent of each others 2. session 1 and 2 are independent and has similar testing conditions.
#take out panelist names and product names
wk1 <- wk0[, -1]
#take out product names
wk2 <- wk1[, -1]
#take out sessions
wk3 <- wk2[,-1]
#make sure data is all numeric now
#summary(wk3)
#take out panelist, products and sessions
wk4 <- wk1[,-2]Find means by product names and finalize:
#covert wk4 back to data tables to find means with dyplyr
wk4.as.dt <- as.data.table(wk4)
wk5 <- wk4 %>%
group_by(Product) %>%
summarise_all("mean")
#Finalize and add product names as row names
wk6 <- as.data.frame(wk5)
sausage.processed.mean <- as.data.frame(wk6[,-1])
rownames(sausage.processed.mean) <- wk6$Product
#just numeric values (we will use this)
sausage.processed <- wk0[, -c(1:3) ]
#just taking a quick look - should be ready to go
#sausage.processedCorrelation Plot:
#correlation plot 3: mixed circle visualization (top) and numbers (bottom)
cor.temp <- cor(sausage.processed)
corrplot3 <- corrplot.mixed(cor(sausage.processed), upper = 'circle',
lower = "number",
tl.pos = "lt",
tl.col = "black",
tl.cex = 0.8,
addCoefasPercent = TRUE,
number.cex=0.8)
Note:
From correlation plot, we can see that the variables have both positive and negative relationships. While most of these relationships make sense in the real-world, we always have to keep in mind that all of our assumptions to only hold true in the context of the dataset.