You have received the following data from CUNYMart, located at 123 Example Street, Anytown, USA.
This data will be used for inventory analysis at the retailer. You are required to prepare the data for analysis by formatting it in JSON, HTML, XML, and Parquet. Additionally, provide the pros and cons of each format. Your must include R code for generating and importing the data into R.
library(readr)
library(jsonlite)
library(xtable)
library(XML)
library(arrow)##
## Attaching package: 'arrow'## The following object is masked from 'package:utils':
##
## timestampraw_data <- "
Category,Item Name,Item ID,Brand,Price,Variation ID,Variation Details
Electronics,Smartphone,101,TechBrand,699.99,101-A,Color: Black, Storage: 64GB
Electronics,Smartphone,101,TechBrand,699.99,101-B,Color: White, Storage: 128GB
Electronics,Laptop,102,CompuBrand,1099.99,102-A,Color: Silver, Storage: 256GB
Electronics,Laptop,102,CompuBrand,1099.99,102-B,Color: Space Gray, Storage: 512GB
Home Appliances,Refrigerator,201,HomeCool,899.99,201-A,Color: Stainless Steel, Capacity: 20 cu ft
Home Appliances,Refrigerator,201,HomeCool,899.99,201-B,Color: White, Capacity: 18 cu ft
Home Appliances,Washing Machine,202,CleanTech,499.99,202-A,Type: Front Load, Capacity: 4.5 cu ft
Home Appliances,Washing Machine,202,CleanTech,499.99,202-B,Type: Top Load, Capacity: 5.0 cu ft
Clothing,T-Shirt,301,FashionCo,19.99,301-A,Color: Blue, Size: S
Clothing,T-Shirt,301,FashionCo,19.99,301-B,Color: Red, Size: M
Clothing,T-Shirt,301,FashionCo,19.99,301-C,Color: Green, Size: L
Clothing,Jeans,302,DenimWorks,49.99,302-A,Color: Dark Blue, Size: 32
Clothing,Jeans,302,DenimWorks,49.99,302-B,Color: Light Blue, Size: 34
Books,Fiction Novel,401,-,14.99,401-A,Format: Hardcover, Language: English
Books,Fiction Novel,401,-,14.99,401-B,Format: Paperback, Language: Spanish
Books,Non-Fiction Guide,402,-,24.99,402-A,Format: eBook, Language: English
Books,Non-Fiction Guide,402,-,24.99,402-B,Format: Paperback, Language: French
Sports Equipment,Basketball,501,SportsGear,29.99,501-A,Size: Size 7, Color: Orange
Sports Equipment,Tennis Racket,502,RacketPro,89.99,502-A,Material: Graphite, Color: Black
Sports Equipment,Tennis Racket,502,RacketPro,89.99,502-B,Material: Aluminum, Color: Silver
"df <- read_csv(raw_data)## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)## Rows: 20 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Category, Item Name, Brand, Variation ID, Variation Details
## dbl (2): Item ID, Price
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.print(df)## # A tibble: 20 × 7
## Category `Item Name` `Item ID` Brand Price `Variation ID`
## <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 Electronics Smartphone 101 TechBrand 700. 101-A
## 2 Electronics Smartphone 101 TechBrand 700. 101-B
## 3 Electronics Laptop 102 CompuBrand 1100. 102-A
## 4 Electronics Laptop 102 CompuBrand 1100. 102-B
## 5 Home Appliances Refrigerator 201 HomeCool 900. 201-A
## 6 Home Appliances Refrigerator 201 HomeCool 900. 201-B
## 7 Home Appliances Washing Machine 202 CleanTech 500. 202-A
## 8 Home Appliances Washing Machine 202 CleanTech 500. 202-B
## 9 Clothing T-Shirt 301 FashionCo 20.0 301-A
## 10 Clothing T-Shirt 301 FashionCo 20.0 301-B
## 11 Clothing T-Shirt 301 FashionCo 20.0 301-C
## 12 Clothing Jeans 302 DenimWorks 50.0 302-A
## 13 Clothing Jeans 302 DenimWorks 50.0 302-B
## 14 Books Fiction Novel 401 - 15.0 401-A
## 15 Books Fiction Novel 401 - 15.0 401-B
## 16 Books Non-Fiction Guide 402 - 25.0 402-A
## 17 Books Non-Fiction Guide 402 - 25.0 402-B
## 18 Sports Equipment Basketball 501 SportsGear 30.0 501-A
## 19 Sports Equipment Tennis Racket 502 RacketPro 90.0 502-A
## 20 Sports Equipment Tennis Racket 502 RacketPro 90.0 502-B
## # ℹ 1 more variable: `Variation Details` <chr>df_json <- toJSON(df, pretty = TRUE)
cat(df_json)## [
## {
## "Category": "Electronics",
## "Item Name": "Smartphone",
## "Item ID": 101,
## "Brand": "TechBrand",
## "Price": 699.99,
## "Variation ID": "101-A",
## "Variation Details": "Color: Black, Storage: 64GB"
## },
## {
## "Category": "Electronics",
## "Item Name": "Smartphone",
## "Item ID": 101,
## "Brand": "TechBrand",
## "Price": 699.99,
## "Variation ID": "101-B",
## "Variation Details": "Color: White, Storage: 128GB"
## },
## {
## "Category": "Electronics",
## "Item Name": "Laptop",
## "Item ID": 102,
## "Brand": "CompuBrand",
## "Price": 1099.99,
## "Variation ID": "102-A",
## "Variation Details": "Color: Silver, Storage: 256GB"
## },
## {
## "Category": "Electronics",
## "Item Name": "Laptop",
## "Item ID": 102,
## "Brand": "CompuBrand",
## "Price": 1099.99,
## "Variation ID": "102-B",
## "Variation Details": "Color: Space Gray, Storage: 512GB"
## },
## {
## "Category": "Home Appliances",
## "Item Name": "Refrigerator",
## "Item ID": 201,
## "Brand": "HomeCool",
## "Price": 899.99,
## "Variation ID": "201-A",
## "Variation Details": "Color: Stainless Steel, Capacity: 20 cu ft"
## },
## {
## "Category": "Home Appliances",
## "Item Name": "Refrigerator",
## "Item ID": 201,
## "Brand": "HomeCool",
## "Price": 899.99,
## "Variation ID": "201-B",
## "Variation Details": "Color: White, Capacity: 18 cu ft"
## },
## {
## "Category": "Home Appliances",
## "Item Name": "Washing Machine",
## "Item ID": 202,
## "Brand": "CleanTech",
## "Price": 499.99,
## "Variation ID": "202-A",
## "Variation Details": "Type: Front Load, Capacity: 4.5 cu ft"
## },
## {
## "Category": "Home Appliances",
## "Item Name": "Washing Machine",
## "Item ID": 202,
## "Brand": "CleanTech",
## "Price": 499.99,
## "Variation ID": "202-B",
## "Variation Details": "Type: Top Load, Capacity: 5.0 cu ft"
## },
## {
## "Category": "Clothing",
## "Item Name": "T-Shirt",
## "Item ID": 301,
## "Brand": "FashionCo",
## "Price": 19.99,
## "Variation ID": "301-A",
## "Variation Details": "Color: Blue, Size: S"
## },
## {
## "Category": "Clothing",
## "Item Name": "T-Shirt",
## "Item ID": 301,
## "Brand": "FashionCo",
## "Price": 19.99,
## "Variation ID": "301-B",
## "Variation Details": "Color: Red, Size: M"
## },
## {
## "Category": "Clothing",
## "Item Name": "T-Shirt",
## "Item ID": 301,
## "Brand": "FashionCo",
## "Price": 19.99,
## "Variation ID": "301-C",
## "Variation Details": "Color: Green, Size: L"
## },
## {
## "Category": "Clothing",
## "Item Name": "Jeans",
## "Item ID": 302,
## "Brand": "DenimWorks",
## "Price": 49.99,
## "Variation ID": "302-A",
## "Variation Details": "Color: Dark Blue, Size: 32"
## },
## {
## "Category": "Clothing",
## "Item Name": "Jeans",
## "Item ID": 302,
## "Brand": "DenimWorks",
## "Price": 49.99,
## "Variation ID": "302-B",
## "Variation Details": "Color: Light Blue, Size: 34"
## },
## {
## "Category": "Books",
## "Item Name": "Fiction Novel",
## "Item ID": 401,
## "Brand": "-",
## "Price": 14.99,
## "Variation ID": "401-A",
## "Variation Details": "Format: Hardcover, Language: English"
## },
## {
## "Category": "Books",
## "Item Name": "Fiction Novel",
## "Item ID": 401,
## "Brand": "-",
## "Price": 14.99,
## "Variation ID": "401-B",
## "Variation Details": "Format: Paperback, Language: Spanish"
## },
## {
## "Category": "Books",
## "Item Name": "Non-Fiction Guide",
## "Item ID": 402,
## "Brand": "-",
## "Price": 24.99,
## "Variation ID": "402-A",
## "Variation Details": "Format: eBook, Language: English"
## },
## {
## "Category": "Books",
## "Item Name": "Non-Fiction Guide",
## "Item ID": 402,
## "Brand": "-",
## "Price": 24.99,
## "Variation ID": "402-B",
## "Variation Details": "Format: Paperback, Language: French"
## },
## {
## "Category": "Sports Equipment",
## "Item Name": "Basketball",
## "Item ID": 501,
## "Brand": "SportsGear",
## "Price": 29.99,
## "Variation ID": "501-A",
## "Variation Details": "Size: Size 7, Color: Orange"
## },
## {
## "Category": "Sports Equipment",
## "Item Name": "Tennis Racket",
## "Item ID": 502,
## "Brand": "RacketPro",
## "Price": 89.99,
## "Variation ID": "502-A",
## "Variation Details": "Material: Graphite, Color: Black"
## },
## {
## "Category": "Sports Equipment",
## "Item Name": "Tennis Racket",
## "Item ID": 502,
## "Brand": "RacketPro",
## "Price": 89.99,
## "Variation ID": "502-B",
## "Variation Details": "Material: Aluminum, Color: Silver"
## }
## ]df_html <- print(xtable(df), type = 'html')## <!-- html table generated in R 4.4.1 by xtable 1.8-4 package -->
## <!-- Mon Oct 28 02:47:49 2024 -->
## <table border=1>
## <tr> <th> </th> <th> Category </th> <th> Item Name </th> <th> Item ID </th> <th> Brand </th> <th> Price </th> <th> Variation ID </th> <th> Variation Details </th> </tr>
## <tr> <td align="right"> 1 </td> <td> Electronics </td> <td> Smartphone </td> <td align="right"> 101.00 </td> <td> TechBrand </td> <td align="right"> 699.99 </td> <td> 101-A </td> <td> Color: Black, Storage: 64GB </td> </tr>
## <tr> <td align="right"> 2 </td> <td> Electronics </td> <td> Smartphone </td> <td align="right"> 101.00 </td> <td> TechBrand </td> <td align="right"> 699.99 </td> <td> 101-B </td> <td> Color: White, Storage: 128GB </td> </tr>
## <tr> <td align="right"> 3 </td> <td> Electronics </td> <td> Laptop </td> <td align="right"> 102.00 </td> <td> CompuBrand </td> <td align="right"> 1099.99 </td> <td> 102-A </td> <td> Color: Silver, Storage: 256GB </td> </tr>
## <tr> <td align="right"> 4 </td> <td> Electronics </td> <td> Laptop </td> <td align="right"> 102.00 </td> <td> CompuBrand </td> <td align="right"> 1099.99 </td> <td> 102-B </td> <td> Color: Space Gray, Storage: 512GB </td> </tr>
## <tr> <td align="right"> 5 </td> <td> Home Appliances </td> <td> Refrigerator </td> <td align="right"> 201.00 </td> <td> HomeCool </td> <td align="right"> 899.99 </td> <td> 201-A </td> <td> Color: Stainless Steel, Capacity: 20 cu ft </td> </tr>
## <tr> <td align="right"> 6 </td> <td> Home Appliances </td> <td> Refrigerator </td> <td align="right"> 201.00 </td> <td> HomeCool </td> <td align="right"> 899.99 </td> <td> 201-B </td> <td> Color: White, Capacity: 18 cu ft </td> </tr>
## <tr> <td align="right"> 7 </td> <td> Home Appliances </td> <td> Washing Machine </td> <td align="right"> 202.00 </td> <td> CleanTech </td> <td align="right"> 499.99 </td> <td> 202-A </td> <td> Type: Front Load, Capacity: 4.5 cu ft </td> </tr>
## <tr> <td align="right"> 8 </td> <td> Home Appliances </td> <td> Washing Machine </td> <td align="right"> 202.00 </td> <td> CleanTech </td> <td align="right"> 499.99 </td> <td> 202-B </td> <td> Type: Top Load, Capacity: 5.0 cu ft </td> </tr>
## <tr> <td align="right"> 9 </td> <td> Clothing </td> <td> T-Shirt </td> <td align="right"> 301.00 </td> <td> FashionCo </td> <td align="right"> 19.99 </td> <td> 301-A </td> <td> Color: Blue, Size: S </td> </tr>
## <tr> <td align="right"> 10 </td> <td> Clothing </td> <td> T-Shirt </td> <td align="right"> 301.00 </td> <td> FashionCo </td> <td align="right"> 19.99 </td> <td> 301-B </td> <td> Color: Red, Size: M </td> </tr>
## <tr> <td align="right"> 11 </td> <td> Clothing </td> <td> T-Shirt </td> <td align="right"> 301.00 </td> <td> FashionCo </td> <td align="right"> 19.99 </td> <td> 301-C </td> <td> Color: Green, Size: L </td> </tr>
## <tr> <td align="right"> 12 </td> <td> Clothing </td> <td> Jeans </td> <td align="right"> 302.00 </td> <td> DenimWorks </td> <td align="right"> 49.99 </td> <td> 302-A </td> <td> Color: Dark Blue, Size: 32 </td> </tr>
## <tr> <td align="right"> 13 </td> <td> Clothing </td> <td> Jeans </td> <td align="right"> 302.00 </td> <td> DenimWorks </td> <td align="right"> 49.99 </td> <td> 302-B </td> <td> Color: Light Blue, Size: 34 </td> </tr>
## <tr> <td align="right"> 14 </td> <td> Books </td> <td> Fiction Novel </td> <td align="right"> 401.00 </td> <td> - </td> <td align="right"> 14.99 </td> <td> 401-A </td> <td> Format: Hardcover, Language: English </td> </tr>
## <tr> <td align="right"> 15 </td> <td> Books </td> <td> Fiction Novel </td> <td align="right"> 401.00 </td> <td> - </td> <td align="right"> 14.99 </td> <td> 401-B </td> <td> Format: Paperback, Language: Spanish </td> </tr>
## <tr> <td align="right"> 16 </td> <td> Books </td> <td> Non-Fiction Guide </td> <td align="right"> 402.00 </td> <td> - </td> <td align="right"> 24.99 </td> <td> 402-A </td> <td> Format: eBook, Language: English </td> </tr>
## <tr> <td align="right"> 17 </td> <td> Books </td> <td> Non-Fiction Guide </td> <td align="right"> 402.00 </td> <td> - </td> <td align="right"> 24.99 </td> <td> 402-B </td> <td> Format: Paperback, Language: French </td> </tr>
## <tr> <td align="right"> 18 </td> <td> Sports Equipment </td> <td> Basketball </td> <td align="right"> 501.00 </td> <td> SportsGear </td> <td align="right"> 29.99 </td> <td> 501-A </td> <td> Size: Size 7, Color: Orange </td> </tr>
## <tr> <td align="right"> 19 </td> <td> Sports Equipment </td> <td> Tennis Racket </td> <td align="right"> 502.00 </td> <td> RacketPro </td> <td align="right"> 89.99 </td> <td> 502-A </td> <td> Material: Graphite, Color: Black </td> </tr>
## <tr> <td align="right"> 20 </td> <td> Sports Equipment </td> <td> Tennis Racket </td> <td align="right"> 502.00 </td> <td> RacketPro </td> <td align="right"> 89.99 </td> <td> 502-B </td> <td> Material: Aluminum, Color: Silver </td> </tr>
## </table>xml_doc <- newXMLDoc()
root <- newXMLNode("inventory", doc = xml_doc)
suppressWarnings(
for (row in 1:nrow(df)) {
raw_node <- newXMLNode("item", parent = root)
for (col in names(df)) {
newXMLNode(col, df[row, col], parent = raw_node)
}
}
)
df_xml <- saveXML(xml_doc)
cat(df_xml)## <?xml version="1.0"?>
## <inventory>
## <item>
## <Category>Electronics</Category>
## <Item Name>Smartphone</Item Name>
## <Item ID>101</Item ID>
## <Brand>TechBrand</Brand>
## <Price>699.99</Price>
## <Variation ID>101-A</Variation ID>
## <Variation Details>Color: Black, Storage: 64GB</Variation Details>
## </item>
## <item>
## <Category>Electronics</Category>
## <Item Name>Smartphone</Item Name>
## <Item ID>101</Item ID>
## <Brand>TechBrand</Brand>
## <Price>699.99</Price>
## <Variation ID>101-B</Variation ID>
## <Variation Details>Color: White, Storage: 128GB</Variation Details>
## </item>
## <item>
## <Category>Electronics</Category>
## <Item Name>Laptop</Item Name>
## <Item ID>102</Item ID>
## <Brand>CompuBrand</Brand>
## <Price>1099.99</Price>
## <Variation ID>102-A</Variation ID>
## <Variation Details>Color: Silver, Storage: 256GB</Variation Details>
## </item>
## <item>
## <Category>Electronics</Category>
## <Item Name>Laptop</Item Name>
## <Item ID>102</Item ID>
## <Brand>CompuBrand</Brand>
## <Price>1099.99</Price>
## <Variation ID>102-B</Variation ID>
## <Variation Details>Color: Space Gray, Storage: 512GB</Variation Details>
## </item>
## <item>
## <Category>Home Appliances</Category>
## <Item Name>Refrigerator</Item Name>
## <Item ID>201</Item ID>
## <Brand>HomeCool</Brand>
## <Price>899.99</Price>
## <Variation ID>201-A</Variation ID>
## <Variation Details>Color: Stainless Steel, Capacity: 20 cu ft</Variation Details>
## </item>
## <item>
## <Category>Home Appliances</Category>
## <Item Name>Refrigerator</Item Name>
## <Item ID>201</Item ID>
## <Brand>HomeCool</Brand>
## <Price>899.99</Price>
## <Variation ID>201-B</Variation ID>
## <Variation Details>Color: White, Capacity: 18 cu ft</Variation Details>
## </item>
## <item>
## <Category>Home Appliances</Category>
## <Item Name>Washing Machine</Item Name>
## <Item ID>202</Item ID>
## <Brand>CleanTech</Brand>
## <Price>499.99</Price>
## <Variation ID>202-A</Variation ID>
## <Variation Details>Type: Front Load, Capacity: 4.5 cu ft</Variation Details>
## </item>
## <item>
## <Category>Home Appliances</Category>
## <Item Name>Washing Machine</Item Name>
## <Item ID>202</Item ID>
## <Brand>CleanTech</Brand>
## <Price>499.99</Price>
## <Variation ID>202-B</Variation ID>
## <Variation Details>Type: Top Load, Capacity: 5.0 cu ft</Variation Details>
## </item>
## <item>
## <Category>Clothing</Category>
## <Item Name>T-Shirt</Item Name>
## <Item ID>301</Item ID>
## <Brand>FashionCo</Brand>
## <Price>19.99</Price>
## <Variation ID>301-A</Variation ID>
## <Variation Details>Color: Blue, Size: S</Variation Details>
## </item>
## <item>
## <Category>Clothing</Category>
## <Item Name>T-Shirt</Item Name>
## <Item ID>301</Item ID>
## <Brand>FashionCo</Brand>
## <Price>19.99</Price>
## <Variation ID>301-B</Variation ID>
## <Variation Details>Color: Red, Size: M</Variation Details>
## </item>
## <item>
## <Category>Clothing</Category>
## <Item Name>T-Shirt</Item Name>
## <Item ID>301</Item ID>
## <Brand>FashionCo</Brand>
## <Price>19.99</Price>
## <Variation ID>301-C</Variation ID>
## <Variation Details>Color: Green, Size: L</Variation Details>
## </item>
## <item>
## <Category>Clothing</Category>
## <Item Name>Jeans</Item Name>
## <Item ID>302</Item ID>
## <Brand>DenimWorks</Brand>
## <Price>49.99</Price>
## <Variation ID>302-A</Variation ID>
## <Variation Details>Color: Dark Blue, Size: 32</Variation Details>
## </item>
## <item>
## <Category>Clothing</Category>
## <Item Name>Jeans</Item Name>
## <Item ID>302</Item ID>
## <Brand>DenimWorks</Brand>
## <Price>49.99</Price>
## <Variation ID>302-B</Variation ID>
## <Variation Details>Color: Light Blue, Size: 34</Variation Details>
## </item>
## <item>
## <Category>Books</Category>
## <Item Name>Fiction Novel</Item Name>
## <Item ID>401</Item ID>
## <Brand>-</Brand>
## <Price>14.99</Price>
## <Variation ID>401-A</Variation ID>
## <Variation Details>Format: Hardcover, Language: English</Variation Details>
## </item>
## <item>
## <Category>Books</Category>
## <Item Name>Fiction Novel</Item Name>
## <Item ID>401</Item ID>
## <Brand>-</Brand>
## <Price>14.99</Price>
## <Variation ID>401-B</Variation ID>
## <Variation Details>Format: Paperback, Language: Spanish</Variation Details>
## </item>
## <item>
## <Category>Books</Category>
## <Item Name>Non-Fiction Guide</Item Name>
## <Item ID>402</Item ID>
## <Brand>-</Brand>
## <Price>24.99</Price>
## <Variation ID>402-A</Variation ID>
## <Variation Details>Format: eBook, Language: English</Variation Details>
## </item>
## <item>
## <Category>Books</Category>
## <Item Name>Non-Fiction Guide</Item Name>
## <Item ID>402</Item ID>
## <Brand>-</Brand>
## <Price>24.99</Price>
## <Variation ID>402-B</Variation ID>
## <Variation Details>Format: Paperback, Language: French</Variation Details>
## </item>
## <item>
## <Category>Sports Equipment</Category>
## <Item Name>Basketball</Item Name>
## <Item ID>501</Item ID>
## <Brand>SportsGear</Brand>
## <Price>29.99</Price>
## <Variation ID>501-A</Variation ID>
## <Variation Details>Size: Size 7, Color: Orange</Variation Details>
## </item>
## <item>
## <Category>Sports Equipment</Category>
## <Item Name>Tennis Racket</Item Name>
## <Item ID>502</Item ID>
## <Brand>RacketPro</Brand>
## <Price>89.99</Price>
## <Variation ID>502-A</Variation ID>
## <Variation Details>Material: Graphite, Color: Black</Variation Details>
## </item>
## <item>
## <Category>Sports Equipment</Category>
## <Item Name>Tennis Racket</Item Name>
## <Item ID>502</Item ID>
## <Brand>RacketPro</Brand>
## <Price>89.99</Price>
## <Variation ID>502-B</Variation ID>
## <Variation Details>Material: Aluminum, Color: Silver</Variation Details>
## </item>
## </inventory>write_parquet(df, "df.parquet")
df_parquet <- read_parquet("df.parquet")
print(df_parquet)## # A tibble: 20 × 7
## Category `Item Name` `Item ID` Brand Price `Variation ID`
## * <chr> <chr> <dbl> <chr> <dbl> <chr>
## 1 Electronics Smartphone 101 TechBrand 700. 101-A
## 2 Electronics Smartphone 101 TechBrand 700. 101-B
## 3 Electronics Laptop 102 CompuBrand 1100. 102-A
## 4 Electronics Laptop 102 CompuBrand 1100. 102-B
## 5 Home Appliances Refrigerator 201 HomeCool 900. 201-A
## 6 Home Appliances Refrigerator 201 HomeCool 900. 201-B
## 7 Home Appliances Washing Machine 202 CleanTech 500. 202-A
## 8 Home Appliances Washing Machine 202 CleanTech 500. 202-B
## 9 Clothing T-Shirt 301 FashionCo 20.0 301-A
## 10 Clothing T-Shirt 301 FashionCo 20.0 301-B
## 11 Clothing T-Shirt 301 FashionCo 20.0 301-C
## 12 Clothing Jeans 302 DenimWorks 50.0 302-A
## 13 Clothing Jeans 302 DenimWorks 50.0 302-B
## 14 Books Fiction Novel 401 - 15.0 401-A
## 15 Books Fiction Novel 401 - 15.0 401-B
## 16 Books Non-Fiction Guide 402 - 25.0 402-A
## 17 Books Non-Fiction Guide 402 - 25.0 402-B
## 18 Sports Equipment Basketball 501 SportsGear 30.0 501-A
## 19 Sports Equipment Tennis Racket 502 RacketPro 90.0 502-A
## 20 Sports Equipment Tennis Racket 502 RacketPro 90.0 502-B
## # ℹ 1 more variable: `Variation Details` <chr>Pros of JSON Easy to read and write Widely supported by programming languages and databases Small file sizes
Cons of JSON Can be slow to parse large datasets Limited data typing capabilities Lack of standard schema definition
Pros of HTML Human-readable format Supports rich media content Widely supported by browsers and tools
Cons of HTML Not designed for structured data analysis Difficult to parse programmatically Large file sizes for tabular data
Pros of XML Self-describing format with standardized schema Supports complex data structures Widely supported by databases and tools
Cons of XML lengthy syntax leads to large file sizes Parsing can be slow for large datasets Less human-readable than simpler formats
Pros of Parquet Fast read/write speeds Supports complex data types and nested structures Optimized for big data analytics
Cons of Parquet Requires specialized libraries/tools to read/write Less human-readable than simpler formats Steeper learning curve for beginners
Key Points JSON is good for flexible, semi-structured data HTML is good for web-facing data presentation XML is good for standardized, self-describing data Parquet is good for large-scale analytics