Loading the dataset

I am importing the libraries needed to run these notes.

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(readr)
my_data_2 <- read_delim("C:/Users/Surya CST/Documents/CSV_files/Bundy_Shoe_Shop.csv",delim=",",show_col_types = FALSE)
## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
## • `` -> `...14`
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
##   dat <- vroom(...)
##   problems(dat)
# Print the modified data frame
head(my_data_2)
## # A tibble: 6 × 14
##   Inferential statistics…¹ ...2  ...3   ...4 ...5  ...6   ...7 ...8   ...9 ...10
##   <chr>                    <chr> <chr> <dbl> <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 Al Bundy's shoe shop     <NA>  <NA>     NA <NA>  <NA>   NA   <NA>   NA    <NA>
## 2 <NA>                     <NA>  <NA>     NA <NA>  <NA>   NA   <NA>   NA    <NA>
## 3 InvoiceNo                Date  Coun…    NA Shop  Gend…  NA   Size…  NA   "Uni…
## 4 52389                    1/1/… Unit…  2152 UK2   Male   11   44     10.5 "$15…
## 5 52390                    1/1/… Unit…  2230 US15  Male   11.5 44-45  11   "$19…
## 6 52391                    1/1/… Cana…  2160 CAN7  Male    9.5 42-43   9   "$14…
## # ℹ abbreviated name: ¹​`Inferential statistics. Confidence intervals`
## # ℹ 4 more variables: ...11 <chr>, ...12 <dbl>, ...13 <dbl>, ...14 <chr>

Cleaning my dataset

I am renaming my titles from{X1,X2} to {Invoice,Date},,etc to make the date more simple and clear.

new_names <- c("InvoiceNo",'Date', "Country", "ProductID",'Shop','Gender','Size(US)','Size (Europe)',   'Size (UK)','UnitPrice','Discount', 'Year','Month','SalePrice')

# Assign the new column names to the data frame
colnames(my_data_2) <- new_names

# Verify that the column names have been changed
colnames(my_data_2)
##  [1] "InvoiceNo"     "Date"          "Country"       "ProductID"    
##  [5] "Shop"          "Gender"        "Size(US)"      "Size (Europe)"
##  [9] "Size (UK)"     "UnitPrice"     "Discount"      "Year"         
## [13] "Month"         "SalePrice"

I am removing the first 3 rows to remove null values and un-necessary titles for my data set

my_data_2 <- my_data_2[-c(1:3), ]


# Print the modified data frame
print(my_data_2)
## # A tibble: 14,967 × 14
##    InvoiceNo Date     Country  ProductID Shop  Gender `Size(US)` `Size (Europe)`
##    <chr>     <chr>    <chr>        <dbl> <chr> <chr>       <dbl> <chr>          
##  1 52389     1/1/2014 United …      2152 UK2   Male         11   44             
##  2 52390     1/1/2014 United …      2230 US15  Male         11.5 44-45          
##  3 52391     1/1/2014 Canada        2160 CAN7  Male          9.5 42-43          
##  4 52392     1/1/2014 United …      2234 US6   Female        9.5 40             
##  5 52393     1/1/2014 United …      2222 UK4   Female        9   39-40          
##  6 52394     1/1/2014 United …      2173 US15  Male         10.5 43-44          
##  7 52395     1/2/2014 Germany       2200 GER2  Female        9   39-40          
##  8 52396     1/2/2014 Canada        2238 CAN5  Male         10   43             
##  9 52397     1/2/2014 United …      2191 US13  Male         10.5 43-44          
## 10 52398     1/2/2014 United …      2237 UK1   Female        9   39-40          
## # ℹ 14,957 more rows
## # ℹ 6 more variables: `Size (UK)` <dbl>, UnitPrice <chr>, Discount <chr>,
## #   Year <dbl>, Month <dbl>, SalePrice <chr>
my_data_2$SalePrice <- gsub("\\$", "", my_data_2$SalePrice)
my_data_2$SalePrice <- as.numeric(my_data_2$SalePrice)

class(my_data_2$SalePrice)
## [1] "numeric"
# removing $ for Unit Price

my_data_2$UnitPrice <- gsub("\\$", "", my_data_2$UnitPrice)

my_data_2$UnitPrice <- as.numeric(my_data_2$UnitPrice)

class(my_data_2$UnitPrice)
## [1] "numeric"

1) About my dataset.

I collected this data-set from an online educator course. There are 2 columns named ProductID and Shop, which I did not understand before reading the documentation. They could have encoded to disclose the information. I would not have understand the details about the ID and shop if I did not read the documentation

2) Unclear part

There is a column named Size(Europe) in my dataset. Some of the values in dataset like 38-39,40-41 or 42-43 etc. But these are the standard measurement in the Europe. So,they may be data of 42-43 for a shoe size of 9.5 in the USA

3) Visualization for Size(Europe) vs Saleprice Column( mentioned in 2nd answer) in my dataset.

ggplot(my_data_2, aes(x = `Size (Europe)`, y = SalePrice)) +
  geom_point() +
  labs(title = "Scatter Plot of SalePrice vs. Size(Europe)", x = "Size(Europe)", y = "SalePrice")

X-axis represents the size of shoes in the Europe. We can understand the pattern of size of shoes in europe by looking the plot.