First, load the {salesforcer} package and login. There are two ways to authenticate:
It is recommended to use OAuth 2.0 so that passwords do not have to be shared/embedded within scripts. User credentials will be stored in locally cached file entitled .httr-oauth-salesforcer in the current working directory.
library(dplyr, warn.conflicts = FALSE)
library(salesforcer)
sf_auth()Setting up your own Connected App for OAuth
Just a note, that it’s not necessary to setup your own Connected App in Salesforce to use OAuth 2.0 authentication. The only difference is that the authentication will be run through the client created and associated with the {salesforcer} package. By using the package client, you will NOT be giving access to Salesforce to anyone, the package is just the medium for you to connect to your own data. If you wanted more control you would specify those options like so:
options(salesforcer.consumer_key = "012345678901-99thisisatest99connected33app22key")
options(salesforcer.consumer_secret = "Th1s1sMyConsumerS3cr3t")
sf_auth()Using a proxy connection
If you are required to connect to Salesforce via proxy you are able to specify all of those parameters as options, as well. For each call via httr these proxy settings will be passed along with the Salesforce authentication.
options(salesforcer.proxy_url = "64.251.21.73") # IP or a named domain
options(salesforcer.proxy_port = 8080)
options(salesforcer.proxy_username = "user")
options(salesforcer.proxy_password = "pass")
options(salesforcer.proxy_auth = "ntlm")
sf_auth()After logging in with sf_auth(), you can check your connectivity by looking at the information returned about the current user. It should be information about you!
# pull down information of person logged in
# it's a simple easy call to get started
# and confirm a connection to the APIs
user_info <- sf_user_info()
sprintf("Organization Id: %s", user_info$organizationId)
#> [1] "Organization Id: 00D6A0000003dN3UAI"
sprintf("User Id: %s", user_info$userId)
#> [1] "User Id: 0056A000000MPRjQAO"Salesforce has objects and those objects contain records. One default object is the “Contact” object. This example shows how to create two records in the Contact object.
n <- 2
new_contacts <- tibble(FirstName = rep("Test", n),
LastName = paste0("Contact-Create-", 1:n))
created_records <- sf_create(new_contacts, "Contact")
created_records
#> # A tibble: 2 x 2
#> id success
#> <chr> <lgl>
#> 1 0033s000014AlA4AAK TRUE
#> 2 0033s000014AlA5AAK TRUERetrieve pulls down a specific set of records and fields. It’s very similar to running a query, but doesn’t use SOQL. Here is an example where we retrieve the data we just created.
retrieved_records <- sf_retrieve(ids=created_records$id,
fields=c("FirstName", "LastName"),
object_name="Contact")
retrieved_records
#> # A tibble: 2 x 4
#> sObject Id FirstName LastName
#> <chr> <chr> <chr> <chr>
#> 1 Contact 0033s000014AlA4AAK Test Contact-Create-1
#> 2 Contact 0033s000014AlA5AAK Test Contact-Create-2Salesforce has proprietary form of SQL called SOQL (Salesforce Object Query Language). SOQL is a powerful tool that allows you to return the fields of records in almost any object in Salesforce including Accounts, Contacts, Tasks, Opportunities, even Attachments! Below is an example where we grab the data we just created including Account object information for which the Contact record is associated with.
my_soql <- sprintf("SELECT Id,
Account.Name,
FirstName,
LastName
FROM Contact
WHERE Id in ('%s')",
paste0(created_records$id , collapse="','"))
queried_records <- sf_query(my_soql)
queried_records
#> # A tibble: 2 x 3
#> Id FirstName LastName
#> <chr> <chr> <chr>
#> 1 0033s000014AlA4AAK Test Contact-Create-1
#> 2 0033s000014AlA5AAK Test Contact-Create-2NOTE: In the example above, you’ll notice that the "Account.Name" column does not appear in the results. This is because the SOAP and REST APIs only return an empty Account object for the record if there is no relationship to an account ( see #78). There is no reliable way to extract and rebuild the empty columns based on the query string. If there were Account information, an additional column titled "Account.Name" would appear in the results. Note, that the Bulk 1.0 and Bulk 2.0 APIs will return "Account.Name" as a column of all NA values for this query because they return results differently.
After creating records you can update them using sf_update(). Updating a record requires you to pass the Salesforce Id of the record. Salesforce creates a unique 18-character identifier on each record and uses that to know which record to attach the update information you provide. Simply include a field or column in your update dataset called “Id” and the information will be matched. Here is an example where we update each of the records we created earlier with a new first name called “TestTest”.
# Update some of those records
queried_records <- queried_records %>%
mutate(FirstName = "TestTest")
updated_records <- sf_update(queried_records, object_name="Contact")
updated_records
#> # A tibble: 2 x 2
#> id success
#> <chr> <lgl>
#> 1 0033s000014AlA4AAK TRUE
#> 2 0033s000014AlA5AAK TRUEYou can also delete records in Salesforce. The method implements a “soft” delete meaning that the deleted records go to the Recycle Bin which can be emptied or queried against later in the event that the record needed.
deleted_records <- sf_delete(updated_records$id)
deleted_records
#> # A tibble: 2 x 2
#> id success
#> <chr> <lgl>
#> 1 0033s000014AlA4AAK TRUE
#> 2 0033s000014AlA5AAK TRUEFinally, Salesforce has a unique method called “upsert” that allows you to create and/or update records at the same time. More specifically, if the record is not found based an an “External Id” field, then Salesforce will create the record instead of updating one. Below is an example where we create 2 records, then upsert 3, where 2 are matched and updated and one is created. NOTE: You will need to create a custom field on the target object and ensure it is labeled as an “External Id” field. Read more at: https://blog.jeffdouglas.com/2010/05/07/using-exernal-id-fields-in-salesforce/.
n <- 2
new_contacts <- tibble(FirstName = rep("Test", n),
LastName = paste0("Contact-Create-", 1:n),
My_External_Id__c=letters[1:n])
created_records <- sf_create(new_contacts, "Contact")
upserted_contacts <- tibble(FirstName = rep("Test", n),
LastName = paste0("Contact-Upsert-", 1:n),
My_External_Id__c=letters[1:n])
new_record <- tibble(FirstName = "Test",
LastName = paste0("Contact-Upsert-", n+1),
My_External_Id__c=letters[n+1])
upserted_contacts <- bind_rows(upserted_contacts, new_record)
upserted_records <- sf_upsert(input_data=upserted_contacts,
object_name="Contact",
external_id_fieldname="My_External_Id__c")
upserted_records
#> # A tibble: 3 x 3
#> id success created
#> <chr> <lgl> <lgl>
#> 1 0033s000014Al9vAAC TRUE FALSE
#> 2 0033s000014Al9wAAC TRUE FALSE
#> 3 0033s000014Al1IAAS TRUE TRUEThe {salesforcer} package has quite a bit of unit test coverage to track any changes made between newly released versions of the Salesforce API (typically 4 each year). These tests are an excellent source of examples because they cover most all cases of utilizing the package functions. You can access them here: https://github.com/StevenMMortimer/salesforcer/tree/main/tests/testthat/