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Reading the data

df =read.csv("http://bit.ly/CarreFourDataset")
head(df)
##    Invoice.ID Branch Customer.type Gender           Product.line Unit.price
## 1 750-67-8428      A        Member Female      Health and beauty      74.69
## 2 226-31-3081      C        Normal Female Electronic accessories      15.28
## 3 631-41-3108      A        Normal   Male     Home and lifestyle      46.33
## 4 123-19-1176      A        Member   Male      Health and beauty      58.22
## 5 373-73-7910      A        Normal   Male      Sports and travel      86.31
## 6 699-14-3026      C        Normal   Male Electronic accessories      85.39
##   Quantity     Tax      Date  Time     Payment   cogs gross.margin.percentage
## 1        7 26.1415  1/5/2019 13:08     Ewallet 522.83                4.761905
## 2        5  3.8200  3/8/2019 10:29        Cash  76.40                4.761905
## 3        7 16.2155  3/3/2019 13:23 Credit card 324.31                4.761905
## 4        8 23.2880 1/27/2019 20:33     Ewallet 465.76                4.761905
## 5        7 30.2085  2/8/2019 10:37     Ewallet 604.17                4.761905
## 6        7 29.8865 3/25/2019 18:30     Ewallet 597.73                4.761905
##   gross.income Rating    Total
## 1      26.1415    9.1 548.9715
## 2       3.8200    9.6  80.2200
## 3      16.2155    7.4 340.5255
## 4      23.2880    8.4 489.0480
## 5      30.2085    5.3 634.3785
## 6      29.8865    4.1 627.6165

##Data Cleaning

# Checking null values
colSums(is.na(df))
##              Invoice.ID                  Branch           Customer.type 
##                       0                       0                       0 
##                  Gender            Product.line              Unit.price 
##                       0                       0                       0 
##                Quantity                     Tax                    Date 
##                       0                       0                       0 
##                    Time                 Payment                    cogs 
##                       0                       0                       0 
## gross.margin.percentage            gross.income                  Rating 
##                       0                       0                       0 
##                   Total 
##                       0
#There is no null values in our dataset
sum(duplicated(df))
## [1] 0
#there are no duplicates in our data

PCA

numcols= df[c(6:8,12,14:16)]
head(numcols)
##   Unit.price Quantity     Tax   cogs gross.income Rating    Total
## 1      74.69        7 26.1415 522.83      26.1415    9.1 548.9715
## 2      15.28        5  3.8200  76.40       3.8200    9.6  80.2200
## 3      46.33        7 16.2155 324.31      16.2155    7.4 340.5255
## 4      58.22        8 23.2880 465.76      23.2880    8.4 489.0480
## 5      86.31        7 30.2085 604.17      30.2085    5.3 634.3785
## 6      85.39        7 29.8865 597.73      29.8865    4.1 627.6165
# Apply PCA using prcomp function
#The prcomp function takes in the data as input, and it is highly recommended to set the argument scale=TRUE. This standardize the input data so that it has zero mean and variance one before doing PCA
# Need to scale / Normalize as
# PCA depends on distance measure
my_pca <- prcomp(numcols, scale = TRUE,
                center = TRUE, retx = T)
names(my_pca)
## [1] "sdev"     "rotation" "center"   "scale"    "x"
#it has 5 variables
pc=princomp(numcols, cor = TRUE)
names(pc)
## [1] "sdev"     "loadings" "center"   "scale"    "n.obs"    "scores"   "call"
# bar plot
plot(pc)

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, union
library(ggplot2)
library(GGally)
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
library(devtools)
## Loading required package: usethis
library(ggbiplot)
## Loading required package: plyr
## ------------------------------------------------------------------------------
## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)
## ------------------------------------------------------------------------------
## 
## Attaching package: 'plyr'
## The following objects are masked from 'package:dplyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## Loading required package: scales
## Loading required package: grid
ggbiplot(my_pca)

#From the graph we will see that the variables unit price, gross income and quantity contribute mostle to pc2 whereas rating contributes to pc 1
#Adding more detail to the plot, we provide arguments rownames as labels.
ggbiplot(my_pca, labels=rownames(numcols), obs.scale = 1, var.scale = 1)

##Conclusion

  1. Pc1 contributes to majority of the variance in our data whereas Pc2 and Pc3 are almost equal.

  2. The variables unit price, gross income and quantity contribute mostle to pc2 whereas rating contributes to pc 1