First, load the {salesforcer} package and log in. There are two ways to authenticate:
NOTE: Beginning February 1st, 2022, Salesforce will
be requiring customers to enable multi-factor authentication, which
means that basic authentication using password and security token will
no longer work. The function sf_auth() will return the
error message:
Also, it is a best practice to use OAuth 2.0 so that passwords do not
have to be embedded within scripts. OAuth 2.0 credentials are stored
locally in a cached file titled .httr-oauth-salesforcer in
the current working directory. The package will automatically refresh
this cached token when it expires so you will not have to call
sf_auth() during every R session if the
.httr-oauth-salesforcer file is present in the working
directory.
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!
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.
Retrieve 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.
Salesforce 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_recordsNOTE: 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”.
You 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.
Finally, 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.
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_recordsThe {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/