Kaizen_project

library(corrplot)

TRUE corrplot 0.95 loaded

#import the required libraries 
library(pacman)
library(readxl)
library(knitr)
library(kableExtra)
# library(tidyverse)

getwd()

TRUE [1] "/cloud/project"

setwd("/cloud/project")
list.files()

TRUE [1] "markdown report.Rmd"        "markdown-report_files"     
TRUE [3] "markdown-report.html"       "markdown-report.pdf"       
TRUE [5] "markdown-report.Rmd"        "project.Rproj"             
TRUE [7] "python_Kaizen.py"           "R-code-Kaizen.R"           
TRUE [9] "SPP-Mozzarella Kaizen.xlsx"

#read the excel file 
df = read_excel("SPP-Mozzarella Kaizen.xlsx",sheet ="Transpose")
print(dim(df)) # print the columns and rows 

TRUE [1] 34 13

head(df,5)  

TRUE # A tibble: 5 × 13
TRUE   `Stretch Water Temp` `Curd Salting Level (Kgs/L)` Curd Quantity in the machi…¹
TRUE                  <dbl>                        <dbl>                        <dbl>
TRUE 1                   85                         0.03                           27
TRUE 2                   85                         0.03                           27
TRUE 3                   85                         0.03                           27
TRUE 4                   85                         0.03                           27
TRUE 5                   85                         0.03                           27
TRUE # ℹ abbreviated name: ¹​`Curd Quantity in the machine (Kgs)`
TRUE # ℹ 10 more variables: `Fat content` <dbl>, Protein <dbl>, Lactose <dbl>,
TRUE #   `Moisture content` <chr>, Yield <dbl>, `Molding staff no.` <dbl>,
TRUE #   Browning <dbl>, Stretching <dbl>, Melting <dbl>, Salt <dbl>

#columns
colnames(df)

TRUE  [1] "Stretch Water Temp"                 "Curd Salting Level (Kgs/L)"        
TRUE  [3] "Curd Quantity in the machine (Kgs)" "Fat content"                       
TRUE  [5] "Protein"                            "Lactose"                           
TRUE  [7] "Moisture content"                   "Yield"                             
TRUE  [9] "Molding staff no."                  "Browning"                          
TRUE [11] "Stretching"                         "Melting"                           
TRUE [13] "Salt"

# Select relevant columns for correlation analysis
correlation_data <- df[, c("Fat content", 
                           "Protein", 
                           "Lactose", 
                           "Moisture content", 
                           "Yield", 
                           "Browning", 
                           "Stretching", 
                           "Melting", 
                           "Salt")]

# Ensure the selected columns are numeric
correlation_data <- as.data.frame(lapply(correlation_data, as.numeric))

# Perform the correlation analysis
correlation_matrix <- cor(correlation_data, use = "complete.obs")

# Display the correlation matrix as a table
# kable(correlation_matrix, caption = "Correlation Matrix")
kable(correlation_matrix, caption = "Correlation Matrix") %>%
  kable_styling(full_width = FALSE, position = "center") %>%
  add_header_above(c(" " = 1, "Correlation Matrix" = ncol(correlation_matrix))) %>%
  column_spec(1, bold = TRUE, border_right = TRUE) %>%
  row_spec(0, bold = TRUE) %>%
  kable_classic(full_width = F, html_font = "Arial")

Correlation Matrix

	Correlation Matrix
	Fat.content	Protein	Lactose	Moisture.content	Yield	Browning	Stretching	Melting	Salt
Fat.content	1.0000000	0.9999976	0.9999977	0.9999988	0.9999941	0.1294462	0.0883494	-0.0284967	0.0446232
Protein	0.9999976	1.0000000	0.9999999	0.9999980	0.9999938	0.1300714	0.0886632	-0.0290382	0.0439371
Lactose	0.9999977	0.9999999	1.0000000	0.9999981	0.9999940	0.1301277	0.0887624	-0.0289026	0.0439506
Moisture.content	0.9999988	0.9999980	0.9999981	1.0000000	0.9999949	0.1296485	0.0881584	-0.0283691	0.0447856
Yield	0.9999941	0.9999938	0.9999940	0.9999949	1.0000000	0.1300172	0.0890561	-0.0279532	0.0446349
Browning	0.1294462	0.1300714	0.1301277	0.1296485	0.1300172	1.0000000	0.1770463	0.1407619	0.1628959
Stretching	0.0883494	0.0886632	0.0887624	0.0881584	0.0890561	0.1770463	1.0000000	0.5256011	0.1437617
Melting	-0.0284967	-0.0290382	-0.0289026	-0.0283691	-0.0279532	0.1407619	0.5256011	1.0000000	0.2931643
Salt	0.0446232	0.0439371	0.0439506	0.0447856	0.0446349	0.1628959	0.1437617	0.2931643	1.0000000

# Plot the correlation matrix
corrplot(correlation_matrix, method = "color", type = "upper", 
         tl.col = "black", tl.srt = 45, 
         title = "Correlation Matrix", mar = c(0, 0, 2, 0))

Strong Positive Correlations:

“Fat content” has strong positive correlations with “Protein,” “Lactose,” “Moisture content,” and “Yield.”

Weak or No Correlation:

There are lighter colors indicating weak correlations for certain variable pairs, such as “Stretching” and “Browning,” which show little association.

Negative Correlations:

“Salt” shows some negative correlation with other variables, as indicated by the reddish color, which could suggest an inverse relationship.