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Data Set Table

## Rows: 550 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Name, Author, Genre
## dbl (4): User_Rating, Reviews, Price, Year
## 
## 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.
## # A tibble: 6 × 7
##   Name                              Author User_Rating Reviews Price  Year Genre
##   <chr>                             <chr>        <dbl>   <dbl> <dbl> <dbl> <chr>
## 1 10-Day Green Smoothie Cleanse     JJ Sm…         4.7   17350     8  2016 Non …
## 2 11/22/63: A Novel                 Steph…         4.6    2052    22  2011 Fict…
## 3 12 Rules for Life: An Antidote t… Jorda…         4.7   18979    15  2018 Non …
## 4 1984 (Signet Classics)            Georg…         4.7   21424     6  2017 Fict…
## 5 5,000 Awesome Facts (About Every… Natio…         4.8    7665    12  2019 Non …
## 6 A Dance with Dragons (A Song of … Georg…         4.4   12643    11  2011 Fict…

Data Analysis

Conclusions

Based on the data, genre does not impact user ratings or price. Also, the number of reviews is generally smaller for non-fiction books. Book prices are trending lower by year, probably because of online book sales rising, although more data are needed to confirm this.

Citation

@misc{sooter_saalu_2020, title={Amazon Top 50 Bestselling Books 2009 - 2019}, url={https://www.kaggle.com/dsv/1556647}, DOI={10.34740/KAGGLE/DSV/1556647}, publisher={Kaggle}, author={Sooter Saalu}, year={2020} }