Supermarket Sales
Solmaz
2025-04-01
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Supermarket Sales
In this workshop we will analyze a supermarket dataset to uncover
insights about sales performance; customer behaviour, trends Note that
the echo = FALSE parameter was added to the code chunk to
prevent printing of the R code that generated the plot.
Data Loading
#install.packages('readr')
library(readr)## Warning: package 'readr' was built under R version 4.4.3sales_date <- read.csv("C:/Users/solmaz.ashirova/Downloads/supermarket_sales - Sheet1 (1).csv")
head(sales_date)## Invoice.ID Branch City Customer.type Gender Product.line
## 1 750-67-8428 A Yangon Member Female Health and beauty
## 2 226-31-3081 C Naypyitaw Normal Female Electronic accessories
## 3 631-41-3108 A Yangon Normal Male Home and lifestyle
## 4 123-19-1176 A Yangon Member Male Health and beauty
## 5 373-73-7910 A Yangon Normal Male Sports and travel
## 6 699-14-3026 C Naypyitaw Normal Male Electronic accessories
## Unit.price Quantity Tax.5. Total Date Time Payment cogs
## 1 74.69 7 26.1415 548.9715 1/5/2019 13:08 Ewallet 522.83
## 2 15.28 5 3.8200 80.2200 3/8/2019 10:29 Cash 76.40
## 3 46.33 7 16.2155 340.5255 3/3/2019 13:23 Credit card 324.31
## 4 58.22 8 23.2880 489.0480 1/27/2019 20:33 Ewallet 465.76
## 5 86.31 7 30.2085 634.3785 2/8/2019 10:37 Ewallet 604.17
## 6 85.39 7 29.8865 627.6165 3/25/2019 18:30 Ewallet 597.73
## gross.margin.percentage gross.income Rating
## 1 4.761905 26.1415 9.1
## 2 4.761905 3.8200 9.6
## 3 4.761905 16.2155 7.4
## 4 4.761905 23.2880 8.4
## 5 4.761905 30.2085 5.3
## 6 4.761905 29.8865 4.1#summary(sales_date)
str(sales_date)## 'data.frame': 1000 obs. of 17 variables:
## $ Invoice.ID : chr "750-67-8428" "226-31-3081" "631-41-3108" "123-19-1176" ...
## $ Branch : chr "A" "C" "A" "A" ...
## $ City : chr "Yangon" "Naypyitaw" "Yangon" "Yangon" ...
## $ Customer.type : chr "Member" "Normal" "Normal" "Member" ...
## $ Gender : chr "Female" "Female" "Male" "Male" ...
## $ Product.line : chr "Health and beauty" "Electronic accessories" "Home and lifestyle" "Health and beauty" ...
## $ Unit.price : num 74.7 15.3 46.3 58.2 86.3 ...
## $ Quantity : int 7 5 7 8 7 7 6 10 2 3 ...
## $ Tax.5. : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Total : num 549 80.2 340.5 489 634.4 ...
## $ Date : chr "1/5/2019" "3/8/2019" "3/3/2019" "1/27/2019" ...
## $ Time : chr "13:08" "10:29" "13:23" "20:33" ...
## $ Payment : chr "Ewallet" "Cash" "Credit card" "Ewallet" ...
## $ cogs : num 522.8 76.4 324.3 465.8 604.2 ...
## $ gross.margin.percentage: num 4.76 4.76 4.76 4.76 4.76 ...
## $ gross.income : num 26.14 3.82 16.22 23.29 30.21 ...
## $ Rating : num 9.1 9.6 7.4 8.4 5.3 4.1 5.8 8 7.2 5.9 ...colSums(is.na(sales_date))## Invoice.ID Branch City
## 0 0 0
## Customer.type Gender Product.line
## 0 0 0
## Unit.price Quantity Tax.5.
## 0 0 0
## Total Date Time
## 0 0 0
## Payment cogs gross.margin.percentage
## 0 0 0
## gross.income Rating
## 0 0head(sales_date)## Invoice.ID Branch City Customer.type Gender Product.line
## 1 750-67-8428 A Yangon Member Female Health and beauty
## 2 226-31-3081 C Naypyitaw Normal Female Electronic accessories
## 3 631-41-3108 A Yangon Normal Male Home and lifestyle
## 4 123-19-1176 A Yangon Member Male Health and beauty
## 5 373-73-7910 A Yangon Normal Male Sports and travel
## 6 699-14-3026 C Naypyitaw Normal Male Electronic accessories
## Unit.price Quantity Tax.5. Total Date Time Payment cogs
## 1 74.69 7 26.1415 548.9715 1/5/2019 13:08 Ewallet 522.83
## 2 15.28 5 3.8200 80.2200 3/8/2019 10:29 Cash 76.40
## 3 46.33 7 16.2155 340.5255 3/3/2019 13:23 Credit card 324.31
## 4 58.22 8 23.2880 489.0480 1/27/2019 20:33 Ewallet 465.76
## 5 86.31 7 30.2085 634.3785 2/8/2019 10:37 Ewallet 604.17
## 6 85.39 7 29.8865 627.6165 3/25/2019 18:30 Ewallet 597.73
## gross.margin.percentage gross.income Rating
## 1 4.761905 26.1415 9.1
## 2 4.761905 3.8200 9.6
## 3 4.761905 16.2155 7.4
## 4 4.761905 23.2880 8.4
## 5 4.761905 30.2085 5.3
## 6 4.761905 29.8865 4.1#Total sales by city
#tibble learn
#install.packages("dplyr")
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, unionsales_city <- sales_date %>%
group_by(City) %>%
summarise(sum(Total))
sales_city## # A tibble: 3 × 2
## City `sum(Total)`
## <chr> <dbl>
## 1 Mandalay 106198.
## 2 Naypyitaw 110569.
## 3 Yangon 106200.library(dplyr)
sales_city <- sales_date %>%
mutate(City = recode(City, "Mel" = "Mandalay"))options(repos = c(CRAN = "https://cloud.r-project.org"))
install.packages("ggplot2")## package 'ggplot2' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\solmaz.ashirova\AppData\Local\Temp\RtmpSEyWxF\downloaded_packageslibrary(ggplot2)## Warning: package 'ggplot2' was built under R version 4.4.3ggplot(sales_date, aes(x = City)) +
geom_bar(fill = "steelblue") +
labs(title = "Sales Count by City", x = "City", y = "Count") +
theme_minimal()