library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.0     v dplyr   1.0.5
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(caret)

## Warning: package 'caret' was built under R version 4.0.5

## Loading required package: lattice

## 
## Attaching package: 'caret'

## The following object is masked from 'package:purrr':
## 
##     lift

library(ggplot2)
library(dplyr)
library(GGally)

## Warning: package 'GGally' was built under R version 4.0.5

## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2

library(corrplot)

## Warning: package 'corrplot' was built under R version 4.0.5

## corrplot 0.88 loaded

library(rpart)
library(gplots)

## Warning: package 'gplots' was built under R version 4.0.5

## 
## Attaching package: 'gplots'

## The following object is masked from 'package:stats':
## 
##     lowess

library(ggpubr)

## Warning: package 'ggpubr' was built under R version 4.0.5

lap.df <- read.csv("LaptopSalesJanuary2008.csv")
store.avg <- aggregate(lap.df$Retail.Price ~ lap.df$Store.Postcode, data = lap.df, mean)
store.avg

##    lap.df$Store.Postcode lap.df$Retail.Price
## 1                CR7 8LE            488.6190
## 2                 E2 0RY            483.1717
## 3                 E7 8NW            494.3814
## 4                KT2 5AU            493.9048
## 5                N17 6QA            494.6341
## 6                 N3 1DH            487.3684
## 7                NW5 2QH            486.5805
## 8                S1P 3AU            486.2500
## 9                SE1 2BN            486.6802
## 10               SE8 3JD            492.1778
## 11              SW12 9HD            485.2957
## 12              SW18 1NN            493.0389
## 13              SW1P 3AU            488.5069
## 14              SW1V 4QQ            489.3450
## 15               W10 6HQ            489.8667
## 16                W4 3PH            481.0063

ggplot(store.avg)+geom_col(aes(x=store.avg$`lap.df$Store.Postcode`,y=store.avg$`lap.df$Retail.Price`))+theme(axis.text.x = element_text(angle = 90)) + coord_cartesian(ylim=c(475, 500))

## Warning: Use of `store.avg$`lap.df$Store.Postcode`` is discouraged. Use
## `lap.df$Store.Postcode` instead.

## Warning: Use of `store.avg$`lap.df$Retail.Price`` is discouraged. Use
## `lap.df$Retail.Price` instead.

ggplot(lap.df) + geom_boxplot(aes(lap.df$Store.Postcode, lap.df$Retail.Price)) + theme(axis.text.x = element_text(angle = 90))

## Warning: Use of `lap.df$Store.Postcode` is discouraged. Use `Store.Postcode`
## instead.

## Warning: Use of `lap.df$Retail.Price` is discouraged. Use `Retail.Price`
## instead.