Adidas Vs Nike
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.1 v dplyr 1.0.6
## 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(dplyr)
AN <- read_csv("Adidas Vs Nike.csv")##
## -- Column specification --------------------------------------------------------
## cols(
## `Product Name` = col_character(),
## `Product ID` = col_character(),
## `Listing Price` = col_double(),
## `Sale Price` = col_double(),
## Discount = col_double(),
## Brand = col_character(),
## Description = col_character(),
## Rating = col_double(),
## Reviews = col_double(),
## `Last Visited` = col_datetime(format = "")
## )AN <- read_csv(file.choose())##
## -- Column specification --------------------------------------------------------
## cols(
## `Product Name` = col_character(),
## `Product ID` = col_character(),
## `Listing Price` = col_double(),
## `Sale Price` = col_double(),
## Discount = col_double(),
## Brand = col_character(),
## Description = col_character(),
## Rating = col_double(),
## Reviews = col_double(),
## `Last Visited` = col_datetime(format = "")
## )head(AN)## # A tibble: 6 x 10
## `Product Name` `Product ID` `Listing Price` `Sale Price` Discount Brand
## <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 Women's adidas Ori~ AH2430 14999 7499 50 Adidas~
## 2 Women's adidas Ori~ G27341 7599 3799 50 Adidas~
## 3 Women's adidas Swi~ CM0081 999 599 40 Adidas~
## 4 Women's adidas Spo~ B44832 6999 3499 50 Adidas~
## 5 Women's adidas Ori~ D98205 7999 3999 50 Adidas~
## 6 Women's adidas Spo~ B75586 4799 1920 60 Adidas~
## # ... with 4 more variables: Description <chr>, Rating <dbl>, Reviews <dbl>,
## # Last Visited <dttm>names(AN)## [1] "Product Name" "Product ID" "Listing Price" "Sale Price"
## [5] "Discount" "Brand" "Description" "Rating"
## [9] "Reviews" "Last Visited"Sale and discount
AN %>%
group_by(Brand) %>%
arrange(desc(Discount))## # A tibble: 3,268 x 10
## # Groups: Brand [5]
## `Product Name` `Product ID` `Listing Price` `Sale Price` Discount Brand
## <chr> <chr> <dbl> <dbl> <dbl> <chr>
## 1 Women's adidas Spo~ B75586 4799 1920 60 Adida~
## 2 Men's adidas Runni~ CI9914 4999 2000 60 Adida~
## 3 Women's adidas ORI~ S82260 11999 4800 60 Adida~
## 4 Women's adidas ORI~ BB2344 9999 4000 60 Adida~
## 5 WOMEN'S ADIDAS SPO~ B96563 6599 2640 60 Adida~
## 6 WoMen's adidas TRA~ CP9514 5999 2400 60 Adida~
## 7 Men's adidas RUNNI~ CI1741 4999 2000 60 Adida~
## 8 MEN'S ADIDAS ORIGI~ G28940 18999 7600 60 Adida~
## 9 Women's adidas TRA~ BB3293 4799 1920 60 Adida~
## 10 Women's ADIDAS ORI~ BY2976 10999 4400 60 Adida~
## # ... with 3,258 more rows, and 4 more variables: Description <chr>,
## # Rating <dbl>, Reviews <dbl>, Last Visited <dttm>Total discount
AN %>%
group_by(Brand) %>%
summarise(Total_discount = sum(Discount))## # A tibble: 5 x 2
## Brand Total_discount
## <chr> <dbl>
## 1 Adidas Adidas ORIGINALS 50
## 2 Adidas CORE / NEO 40330
## 3 Adidas ORIGINALS 28220
## 4 Adidas SPORT PERFORMANCE 19230
## 5 Nike 0Group & Count
AN %>%
group_by(Brand) %>%
count(Rating >= 4)## # A tibble: 9 x 3
## # Groups: Brand [5]
## Brand `Rating >= 4` n
## <chr> <lgl> <int>
## 1 Adidas Adidas ORIGINALS TRUE 1
## 2 Adidas CORE / NEO FALSE 690
## 3 Adidas CORE / NEO TRUE 421
## 4 Adidas ORIGINALS FALSE 591
## 5 Adidas ORIGINALS TRUE 316
## 6 Adidas SPORT PERFORMANCE FALSE 395
## 7 Adidas SPORT PERFORMANCE TRUE 211
## 8 Nike FALSE 335
## 9 Nike TRUE 308Pivot
AN %>%
pivot_wider(
names_from = Brand,
values_from = Discount
)## # A tibble: 3,268 x 13
## `Product Name` `Product ID` `Listing Price` `Sale Price` Description Rating
## <chr> <chr> <dbl> <dbl> <chr> <dbl>
## 1 Women's adidas~ AH2430 14999 7499 Channeling ~ 4.8
## 2 Women's adidas~ G27341 7599 3799 A modern ta~ 3.3
## 3 Women's adidas~ CM0081 999 599 These adida~ 2.6
## 4 Women's adidas~ B44832 6999 3499 Inspired by~ 4.1
## 5 Women's adidas~ D98205 7999 3999 This design~ 3.5
## 6 Women's adidas~ B75586 4799 1920 Refine your~ 1
## 7 Women's adidas~ CG4051 4799 2399 Refine your~ 4.4
## 8 Women's adidas~ CM0080 999 599 These adida~ 2.8
## 9 WOMEN'S ADIDAS~ B75990 5599 2799 These women~ 4.5
## 10 Men's adidas O~ EE5761 6599 3959 The Forest ~ 4
## # ... with 3,258 more rows, and 7 more variables: Reviews <dbl>,
## # Last Visited <dttm>, Adidas Adidas ORIGINALS <dbl>, Adidas ORIGINALS <dbl>,
## # Adidas CORE / NEO <dbl>, Adidas SPORT PERFORMANCE <dbl>, Nike <dbl>This data frame has 6 rows: Product Name, Product ID, Listing Price, Sale Price, Discount, Brand, Description, Rating, reviews, and Last Visited. Total number of rating greater than 4 for Adidas is more than Nike based on the reviewers ratings.