salesforcer

{salesforcer} is an R package that connects to Salesforce Platform APIs using tidy principles. The package implements actions from the REST, SOAP, Bulk 1.0, Bulk 2.0, Reports and Dashboards, and Metadata APIs.

Package features include:

OAuth 2.0 (Single Sign On) and Basic (Username-Password) Authentication methods (sf_auth())
CRUD (Create, Retrieve, Update, Delete) methods for records using the SOAP, REST, and Bulk APIs
Query records via the SOAP, REST, Bulk 1.0, and Bulk 2.0 APIs using sf_query()
Manage and execute reports and dashboards with:
- sf_list_reports(), sf_create_report(), sf_run_report(), and more
Retrieve and modify metadata (Custom Objects, Fields, etc.) using the Metadata API with:
- sf_describe_objects(), sf_create_metadata(), sf_update_metadata(), and more
Utilize backwards compatible functions for the {RForcecom} package, such as:
- rforcecom.login(), rforcecom.getObjectDescription(), rforcecom.query(), rforcecom.create()
Basic utility calls (sf_user_info(), sf_server_timestamp(), sf_list_objects())
Functions to assist with master data management (MDM) or data integrity of records by finding duplicates (sf_find_duplicates(), sf_find_duplicates_by_id()), merging records (sf_merge()), and converting leads (sf_convert_lead())
Recover (sf_undelete()) or delete from the Recycle Bin (sf_empty_recycle_bin()) and list ids of records deleted (sf_get_deleted()) or updated (sf_get_updated()) within a specific timeframe
Passing API call control parameters such as, “All or None”, “Duplicate Rule”, “Assignment Rule” execution and many more!

Installation
Vignettes
Usage
Future
Credits
More Information

Installation

# install the current CRAN version
install.packages("salesforcer")

# or get the development version on GitHub
# install.packages("remotes")
remotes::install_github("StevenMMortimer/salesforcer")

If you encounter an issue while using this package, please file a minimal reproducible example on GitHub.

Vignettes

The README below outlines the basic package functionality. For more information please feel free to browse the {salesforcer} website at https://stevenmmortimer.github.io/salesforcer/ which contains the following vignettes:

Usage

Authenticate

First, load the {salesforcer} package and log in. There are two ways to authenticate:

OAuth 2.0
~~Basic Username-Password~~

NOTE: Beginning February 1, 2022 authentication via a username and password will not work in most Salesforce organizations. On that date Salesforce will begin requiring customers to enable multi-factor authentication (MFA). The function sf_auth() will return the error message:

INVALID_LOGIN: Invalid username, password, security token; or user locked out.

It has always been recommended to use OAuth 2.0 so that passwords do not have to be shared or embedded within scripts. For more information on how OAuth 2.0 works within the {salesforcer} package, please read the Getting Started vignette.

library(dplyr, warn.conflicts = FALSE)
library(salesforcer)

# Using OAuth 2.0 authentication
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"

Create

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, object_name = "Contact")
created_records
#> # A tibble: 2 × 2
#>   id                 success
#>   <chr>              <lgl>  
#> 1 0033s00001BXHqaAAH TRUE   
#> 2 0033s00001BXHqbAAH TRUE

Query

Salesforce has proprietary form of SQL called SOQL (Salesforce Object Query Language). SOQL is a powerful tool that allows you to return the attributes of records on 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 × 3
#>   Id                 FirstName LastName        
#>   <chr>              <chr>     <chr>           
#> 1 0033s00001BXHqaAAH Test      Contact-Create-1
#> 2 0033s00001BXHqbAAH Test      Contact-Create-2

NOTE: 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 <a rel="noopener noreferrer" target="_blank" href="https://github.com/stevenmmortimer/salesforcer/issues/78">#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.

Update

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 × 2
#>   id                 success
#>   <chr>              <lgl>  
#> 1 0033s00001BXHqaAAH TRUE   
#> 2 0033s00001BXHqbAAH TRUE

Bulk Operations

For really large operations (inserts, updates, upserts, deletes, and queries) Salesforce provides the Bulk 1.0 and Bulk 2.0 APIs. In order to use the Bulk APIs in {salesforcer} you can just add api_type = "Bulk 1.0" or api_type = "Bulk 2.0" to your functions and the operation will be executed using the Bulk APIs. It’s that simple.

The benefits of using the Bulk API for larger datasets is that the operation will reduce the number of individual API calls (organization usually have a limit on total calls) and batching the requests in bulk is usually quicker than running thousands of individuals calls when your data is large. Note: the Bulk 2.0 API does NOT guarantee the order of the data submitted is preserved in the output. This means that you must join on other data columns to match up the Ids that are returned in the output with the data you submitted. For this reason, Bulk 2.0 may not be a good solution for creating, updating, or upserting records where you need to keep track of the created Ids. The Bulk 2.0 API would be fine for deleting records where you only need to know which Ids were successfully deleted.

# create contacts using the Bulk API
n <- 2
new_contacts <- tibble(FirstName = rep("Test", n),
                       LastName = paste0("Contact-Create-", 1:n))
created_records <- sf_create(new_contacts, "Contact", api_type = "Bulk 1.0")
created_records
#> # A tibble: 2 × 4
#>   Id                 Success Created Error
#>   <chr>              <lgl>   <lgl>   <lgl>
#> 1 0033s00001BXHqfAAH TRUE    TRUE    NA   
#> 2 0033s00001BXHqgAAH TRUE    TRUE    NA

# query large recordsets using the Bulk API
my_soql <- sprintf("SELECT Id,
                           FirstName, 
                           LastName
                    FROM Contact 
                    WHERE Id in ('%s')", 
                   paste0(created_records$Id , collapse = "','"))

queried_records <- sf_query(my_soql, "Contact", api_type = "Bulk 1.0")
queried_records
#> # A tibble: 2 × 3
#>   Id                 FirstName LastName        
#>   <chr>              <chr>     <chr>           
#> 1 0033s00001BXHqfAAH Test      Contact-Create-1
#> 2 0033s00001BXHqgAAH Test      Contact-Create-2

# delete these records using the Bulk 2.0 API
deleted_records <- sf_delete(queried_records$Id, "Contact", api_type = "Bulk 2.0")
deleted_records
#> # A tibble: 2 × 4
#>   Id                 sf__Id             sf__Created sf__Error
#>   <chr>              <chr>              <lgl>       <lgl>    
#> 1 0033s00001BXHqfAAH 0033s00001BXHqfAAH FALSE       NA       
#> 2 0033s00001BXHqgAAH 0033s00001BXHqgAAH FALSE       NA

Using the Metadata API

Salesforce is a very flexible platform in that it provides the Metadata API for users to create, read, update and delete their entire Salesforce environment from objects to page layouts and more. This makes it very easy to programmatically setup and teardown the Salesforce environment. One common use case for the Metadata API is retrieving information about an object (fields, permissions, etc.). You can use the sf_read_metadata() function to return a list of objects and their metadata. In the example below we retrieve the metadata for the Account and Contact objects. Note that the metadata_type argument is “CustomObject”. Standard Objects are an implementation of CustomObjects, so they are returned using that metadata type.

read_obj_result <- sf_read_metadata(metadata_type='CustomObject',
                                    object_names=c('Account', 'Contact'))
read_obj_result[[1]][c('fullName', 'label', 'sharingModel', 'enableHistory')]
#> $fullName
#> [1] "Account"
#> 
#> $label
#> [1] "Account"
#> 
#> $sharingModel
#> [1] "ReadWrite"
#> 
#> $enableHistory
#> [1] "false"
first_two_fields_idx <- head(which(names(read_obj_result[[1]]) == 'fields'), 2)
# show the first two returned fields of the Account object
read_obj_result[[1]][first_two_fields_idx]
#> $fields
#> $fields$fullName
#> [1] "AccountNumber"
#> 
#> $fields$trackFeedHistory
#> [1] "false"
#> 
#> 
#> $fields
#> $fields$fullName
#> [1] "AccountSource"
#> 
#> $fields$trackFeedHistory
#> [1] "false"
#> 
#> $fields$type
#> [1] "Picklist"

The data is returned as a list because object definitions are highly nested representations. You may notice that we are missing some really specific details, such as, the picklist values of a field with type “Picklist”. You can get that information using sf_describe_object_fields(). Here is an example using sf_describe_object_fields() where we get a tbl_df with one row for each field on the Account object:

acct_fields <- sf_describe_object_fields('Account')
acct_fields %>% select(name, label, length, soapType, type)
#> # A tibble: 68 × 5
#>   name           label            length soapType    type     
#>   <chr>          <chr>            <chr>  <chr>       <chr>    
#> 1 Id             Account ID       18     tns:ID      id       
#> 2 IsDeleted      Deleted          0      xsd:boolean boolean  
#> 3 MasterRecordId Master Record ID 18     tns:ID      reference
#> 4 Name           Account Name     255    xsd:string  string   
#> 5 Type           Account Type     255    xsd:string  picklist 
#> # … with 63 more rows

# show the picklist selection options for the Account Type field
acct_fields %>% 
  filter(label == "Account Type") %>% 
  .$picklistValues
#> [[1]]
#> # A tibble: 7 × 4
#>   active defaultValue label                      value                     
#>   <chr>  <chr>        <chr>                      <chr>                     
#> 1 true   false        Prospect                   Prospect                  
#> 2 true   false        Customer - Direct          Customer - Direct         
#> 3 true   false        Customer - Channel         Customer - Channel        
#> 4 true   false        Channel Partner / Reseller Channel Partner / Reseller
#> 5 true   false        Installation Partner       Installation Partner      
#> # … with 2 more rows

Future

Future APIs to support (roughly in priority order):

Credits

This application uses other open source software components. The authentication components are mostly verbatim copies of the routines established in the {googlesheets} package (https://github.com/jennybc/googlesheets). Methods are inspired by the {RForcecom} package (https://github.com/hiratake55/RForcecom). We acknowledge and are grateful to these developers for their contributions to open source.

More Information

Salesforce provides client libraries and examples in many programming languages (Java, Python, Ruby, and PhP) but unfortunately R is not a supported language. However, most all operations supported by the Salesforce APIs are available via this package. This package makes requests best formatted to match what the APIs require as input. This articulation is not perfect and continued progress will be made to add and improve functionality. For details on formatting, attributes, and methods please refer to Salesforce’s documentation as they are explained better there. More information is also available on the {salesforcer} pkgdown website at https://stevenmmortimer.github.io/salesforcer/.

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StevenMMortimer / salesforcer

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