内容简介：Have you ever noticed how things seem to get really expensive at specific times of the year? Like Mother’s Day and Valentine’s Day? Have you ever felt a bit ripped off when buying an over-priced bouquet of flowers or box of chocolates? Have you ever wonder
Have you ever noticed how things seem to get really expensive at specific times of the year? Like Mother’s Day and Valentine’s Day? Have you ever felt a bit ripped off when buying an over-priced bouquet of flowers or box of chocolates? Have you ever wondered just how much those prices have been inflated?
Of course you have!
But it’s always been a niggling suspicion, never a fact. Where’s the evidence?
I set out to gather that evidence using the Retail Pricing Data API .
I could have gathered the required data by hitting the API with a series of curl requests from the command line. Or using the {httr} package from R. However, interacting with the API directly is somewhat laborious. It’d be a lot easier if the API was wrapped up in an R package.
The R Package
So I made a hex logo and after that the R package basically wrote itself.
The repository is at https://github.com/datawookie/retail/ and there’s a (WIP) homepage too.
Install the package from GitHub.
> remotes::install_github("datawookie/retail")
Then load it up.
> library(retail)
List of Retailers
The retailer() function gives access to a table of retailers, their website URLs and operating currencies.
> retailer()
# A tibble: 64 x 4
id name url currency
<int>
<chr>
<chr>
<chr>
1 1 EEM Technologies https://www.eemtechnologies.com/ USD
2 2 Clicks https://clicks.co.za/ ZAR
3 3 Dischem https://www.dischem.co.za/ ZAR
4 4 Game https://www.game.co.za/ ZAR
5 5 Woolworths https://www.woolworths.co.za/ ZAR
6 6 Fortnum & Mason https://www.fortnumandmason.com/ GBP
7 7 John Lewis https://www.johnlewis.com/ GBP
8 8 Marks & Spencer https://www.marksandspencer.com/ GBP
9 9 Pick n Pay https://www.pnp.co.za/ ZAR
10 10 Makro https://www.makro.co.za/ ZAR
# … with 54 more rows
</chr>
</chr>
</chr>
</int>
List of Products
To dig any deeper than that you’ll need an API key (ping me if you want one!). Use set_api_key() to specify the key. You only need to do this once. The key will then be used for all subsequent transactions.
> API_KEY = "5bed3ac9-6dc9-4926-aed8-8c97a7cb8057" > set_api_key(API_KEY)
The retailer_products() function yields a product table for a specific retailer, where each product is assigned a name, brand, model, SKU and barcode (if available).
> retailer_products(5) %>% select(id, name, brand, sku)
# A tibble: 39,913 x 4
id name brand sku
<int>
<chr>
<chr>
<chr>
1 611975 Two Toned Skater Print Trunks 3 Pack NA 6009214350547
2 611980 Sock Knit Bumper Sneakers (Size 4-13) Younger Boy NA 6009214831060
3 611983 Pattern Cotton Boxers 2 Pack (&US) 6009214703176
4 611985 Cargo Shorts NA 6009214449494
5 611990 Nautical Cotton Shirt (&US) 6009214476001
6 611992 Dino Cotton Rich Socks 3 Pack NA 6009214359908
7 611997 COUNTRY ROAD Spliced T-Shirt Country Road 9340243972506
8 612000 Restlessness Flatbill Cap (&US) 6009214695327
9 612024 Cuffed Abrasion Stonewash Jeans NA 6009214054353
10 612032 Striped Cotton Shirt NA 6009214471167
# … with 39,903 more rows
</chr>
</chr>
</chr>
</int>
Product Details
You can get more granular, looking at a specific product using the product() and product_prices() functions.
Product: Wine
Let’s take a look at a bottle of wine.
> nederburg_lyric <- product(531589) > names(nederburg_lyric) [1] "id" "retailer_id" "url" "name" "brand" "sku" [7] "barcodes" > nederburg_lyric$name [1] "Nederburg Lyric 750ml" > nederburg_lyric$sku [1] "000000000000230428_EA" > nederburg_lyric$barcodes [1] "6001452314503"
The price history data includes both regular and promotion prices, as well as availability. Availability data are not currently being gathered for this product.
> product_prices(531589) product_id time price price_promotion available 1 531589 2020-03-07T01:04:08+00:00 55.00 45 NA 2 531589 2020-02-22T01:00:32+00:00 55.00 NA NA 3 531589 2020-02-15T01:00:02+00:00 55.00 45 NA 4 531589 2020-02-08T00:43:46+00:00 51.99 45 NA 5 531589 2020-02-01T00:57:02+00:00 51.99 45 NA
Product: Clothing
Let’s take a look at an item of clothing from another retailer. In this case we are gathering availability data.
> product_prices(788165) product_id time price price_promotion available 1 788165 2020-03-12T01:24:47+00:00 79.99 NA FALSE 2 788165 2020-03-11T01:14:35+00:00 79.99 NA FALSE 3 788165 2020-03-10T01:16:10+00:00 79.99 NA FALSE 4 788165 2020-03-09T01:20:16+00:00 79.99 NA FALSE 5 788165 2020-03-08T01:13:43+00:00 79.99 NA FALSE 6 788165 2020-03-07T01:18:16+00:00 79.99 NA FALSE 7 788165 2020-03-06T01:07:52+00:00 79.99 NA FALSE 8 788165 2020-03-05T01:18:41+00:00 79.99 NA FALSE 9 788165 2020-03-04T01:10:30+00:00 79.99 NA FALSE 10 788165 2020-03-03T01:12:49+00:00 79.99 NA FALSE 11 788165 2020-03-02T01:17:34+00:00 79.99 NA FALSE 12 788165 2020-03-01T04:51:23+00:00 79.99 NA FALSE 13 788165 2020-02-27T01:18:01+00:00 79.99 NA TRUE 14 788165 2020-02-26T01:23:26+00:00 79.99 NA TRUE 15 788165 2020-02-25T01:10:53+00:00 79.99 NA TRUE 16 788165 2020-02-24T01:21:07+00:00 79.99 NA TRUE 17 788165 2020-02-23T01:21:20+00:00 79.99 NA TRUE 18 788165 2020-02-22T01:22:24+00:00 79.99 NA TRUE 19 788165 2020-02-21T01:24:15+00:00 79.99 NA TRUE 20 788165 2020-02-20T01:23:19+00:00 79.99 NA TRUE 21 788165 2020-02-19T01:34:00+00:00 99.99 NA TRUE 22 788165 2020-02-18T01:13:51+00:00 99.99 NA TRUE 23 788165 2020-02-17T01:40:17+00:00 99.99 NA TRUE 24 788165 2020-02-16T01:30:09+00:00 99.99 NA TRUE 25 788165 2020-02-15T01:37:56+00:00 99.99 NA TRUE 26 788165 2020-02-14T01:21:11+00:00 119.99 NA TRUE 27 788165 2020-02-13T01:30:18+00:00 119.99 NA TRUE
This product was initially selling at R 119.99. The price dropped to R 99.99 on 15 February 2020 and then R 79.99 on 20 February 2020. On 1 March 2020 it sold out (no longer available).
Let’s take a look at the product in question.
Yeah, I can see why that sold out!
Valentine’s Day
Let’s return to our original question: to what degree are prices inflated around “special” days like Valentine’s Day?
Getting Screwed
If you want to get screwed on Valentines day, buy roses.
From the middle of January the price of this bunch of roses increased repeatedly, so that by Valentine’s Day, you were paying at least 50% more per bunch. That’s really getting screwed. And not in a good way.
Here’s another example where the price of a bunch of 100 roses varied wildly before Valentine’s Day, the final price being roughly 60% higher than it was a month earlier.
The price of a small vase of pink roses would set you back by less than R300 at the beginning of January, but this shot up to just less than R500 in the latter part of January and remained around that level until after Valentine’s Day.
Clearly flowers in general (and roses in particular!) are not an economical gift on Valentine’s Day.
Not Getting Screwed
What if you don’t want to get screwed? Well, then the obvious solution is… buy beer.
Why? Because the price of beer, from the same retailer, remains completely unchanged.
The price of a bunch of roses is included in the above plot (grey dashed line) for reference.
Conclusion
This is just a fun illustration of what’s possible with retail price historical data. There are many serious and useful things that you can do with it too! If you’re interested in these data, then check out the project page and get in touch for an API key.
Finally, a note on sampling frequency: we are currently sampling most retailers only once a week. If there is interest in these data then we’ll be able to scale out our infrastructure and start to sample more retailers daily. Ultimate goal is to sample all retailers and products once per day.
以上所述就是小编给大家介绍的《Retail Data: R Package》，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持！
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