title: "Getting Started" author: "Steven M. Mortimer" date: "2018-05-18" output: rmarkdown::html_vignette: toc: true toc_depth: 4 keep_md: true vignette: > %\VignetteIndexEntry{Getting Started} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}
First, load the squareupr
package and login. There are two ways to authenticate:
1) OAuth 2.0 or a 2) Personal Access Token (PAT). It is recommended to use OAuth 2.0 so that
your PAT does not have to be shared/embedded within scripts. However, note that before
using OAuth 2.0 authentication it is necessary that you set up your own Connected App
in the Square dashboard. An App ID and App Secret will be provided, then you will
be able to plug into your script like so:
options(squareupr.app_id = "sq0-99-thisisatest99connected33app22id")
options(squareupr.app_secret = "sq0-Th1s1sMyAppS3cr3t")
sq_auth()
OAuth 2.0 User credentials will be stored in locally cached file entitled ".httr-oauth-squareupr" in the current working directory.
library(tidyverse)
library(squareupr)
# Using Personal Access Token (PAT)
sq_auth(personal_access_token = "sq-Th1s1sMyPers0nalAcessT0ken")
# Using OAuth 2.0 authentication
sq_auth()
Transactions are organized by location. With the v2 Locations endpoint you can pull
information regarding all locations first to obtain the location IDs. Then with the
sq_list_transactions()
function you can provide the location and timeframe to search.
The function defaults to pulling transactions from the previous day using Sys.Date() - 1
.
Once you obtain the transactions the tenders
field lists all methods of payment
used to pay in the transaction.
# list all locations
our_locations <- sq_list_locations()
our_transactions <- sq_list_transactions(location = our_locations$id[2],
begin_time = as.Date('2018-05-11'),
end_time = as.Date('2018-05-12'))
our_transactions
#> # A tibble: 245 x 6
#> id location_id created_at tenders product client_id
#> <chr> <chr> <chr> <list> <chr> <chr>
#> 1 bUjFGVjBvN… DRDCJ2X8E2P… 2018-05-12T0… <list … REGIST… D5528FBA-E5DE-4…
#> 2 5PZP31N5Zs… DRDCJ2X8E2P… 2018-05-11T2… <list … REGIST… A3A1FF51-325A-4…
#> 3 BTrGydD6he… DRDCJ2X8E2P… 2018-05-11T2… <list … REGIST… 2B3D32EB-8E58-4…
#> 4 XsqOAHl68z… DRDCJ2X8E2P… 2018-05-11T2… <list … REGIST… C50AF3D7-BE32-4…
#> 5 vmLRzrwByS… DRDCJ2X8E2P… 2018-05-11T2… <list … REGIST… 52E40E1B-2333-4…
#> 6 pTbzQApZW7… DRDCJ2X8E2P… 2018-05-11T2… <list … REGIST… 962766FF-1436-4…
#> 7 lnE20zklpP… DRDCJ2X8E2P… 2018-05-11T2… <list … REGIST… A02191CC-9AC9-4…
#> 8 DSumrqQW0L… DRDCJ2X8E2P… 2018-05-11T2… <list … REGIST… 1135FF4F-9B89-4…
#> 9 tPwFXetIwe… DRDCJ2X8E2P… 2018-05-11T2… <list … REGIST… 0D95E79D-B44C-4…
#> 10 bqUuFrzH71… DRDCJ2X8E2P… 2018-05-11T2… <list … REGIST… 48FD6A49-80A9-4…
#> # ... with 235 more rows
Once you pull data about transactions you can take the customer_id from the transaction
tenders
field and match that up with customer details. In Square customers can
be placed into groups that allow for the analysis of transactions at a group-level.
# list customers created in the last 90 days
created_start <- format(Sys.Date()-90, '%Y-%m-%dT00:00:00-00:00')
created_end <- format(Sys.Date(), '%Y-%m-%dT00:00:00-00:00')
our_customers <- sq_search_customers(query = list(filter=
list(created_at=
list(start_at=created_start,
end_at=created_end))))
our_customers$given_name <- "{HIDDEN}"
our_customers$family_name <- "{HIDDEN}"
our_customers %>% select(id, created_at, updated_at,
given_name, family_name, preferences, groups)
#> # A tibble: 8,053 x 7
#> id created_at updated_at given_name family_name preferences groups
#> <chr> <chr> <chr> <chr> <chr> <list> <list>
#> 1 BCKGB… 2018-08-14… 2018-08-3… {HIDDEN} {HIDDEN} <list [1]> <list…
#> 2 YMZQ5… 2018-07-05… 2018-07-0… {HIDDEN} {HIDDEN} <list [1]> <list…
#> 3 KBZVT… 2018-08-31… 2018-08-3… {HIDDEN} {HIDDEN} <list [1]> <list…
#> 4 0M4VW… 2018-07-24… 2018-07-2… {HIDDEN} {HIDDEN} <list [1]> <list…
#> 5 K9JR3… 2018-07-10… 2018-07-1… {HIDDEN} {HIDDEN} <list [1]> <list…
#> 6 E735V… 2018-09-10… 2018-09-1… {HIDDEN} {HIDDEN} <list [1]> <list…
#> 7 VDZZ7… 2018-07-25… 2018-07-2… {HIDDEN} {HIDDEN} <list [1]> <list…
#> 8 RWTS7… 2018-09-03… 2018-09-0… {HIDDEN} {HIDDEN} <list [1]> <list…
#> 9 RFJP4… 2018-08-25… 2018-08-2… {HIDDEN} {HIDDEN} <list [1]> <list…
#> 10 T1HZ3… 2018-08-29… 2018-08-2… {HIDDEN} {HIDDEN} <list [1]> <list…
#> # ... with 8,043 more rows
# show the groups that each customer belongs to
# filter to the groups designated automatically by Square
sq_extract_cust_groups(our_customers) %>%
filter(grepl("^CQ689YH4KCJMY", groups.id))
#> # A tibble: 3,599 x 3
#> id groups.id groups.name
#> <chr> <chr> <chr>
#> 1 BCKGBSEV4555AZ0B09VXG7AFWC CQ689YH4KCJMY.LOYALTY_ALL Loyalty Enrollees
#> 2 YMZQ5X2SX13W8VXHTSWCXP4R2C CQ689YH4KCJMY.REACHABLE Reachable
#> 3 KBZVT7KPFD1TMN0D1NDAF4XRKC CQ689YH4KCJMY.LOYAL Regulars
#> 4 KBZVT7KPFD1TMN0D1NDAF4XRKC CQ689YH4KCJMY.LOYALTY_ALL Loyalty Enrollees
#> 5 0M4VWF9NT9532R8E103Z1FWZGC CQ689YH4KCJMY.REACHABLE Reachable
#> 6 VDZZ7XA41S6MYK1GZT75N1FP8M CQ689YH4KCJMY.REACHABLE Reachable
#> 7 RWTS7E3B0X61WY8AW9K6SW2BFR CQ689YH4KCJMY.LOYAL Regulars
#> 8 RWTS7E3B0X61WY8AW9K6SW2BFR CQ689YH4KCJMY.LOYALTY_ALL Loyalty Enrollees
#> 9 T1HZ36NEAX2YPJGX45BSYM4EKW CQ689YH4KCJMY.LOYALTY_ALL Loyalty Enrollees
#> 10 56811Y4J2N152VH3SCDJ3N0E4G CQ689YH4KCJMY.REACHABLE Reachable
#> # ... with 3,589 more rows
The squareupr package has quite a bit of unit test coverage to track any changes made between newly released versions of the Square APIs. These tests are great source of examples for how to interect with the API. The tests are available here.
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