Transitioning from RForcecom
In salesforcer: An Implementation of 'Salesforce' APIs Using Tidy Principles

NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  purl = NOT_CRAN,
  eval = NOT_CRAN
)
options(tibble.print_min = 5L, tibble.print_max = 5L)

While writing the {salesforcer} package we were keenly aware that many folks were already using the {RForcecom} package to connect to Salesforce. In order to foster adoption and switching between the packages {salesforcer} replicates the functionality of many {RForcecom} functions so that you will only need to swap out library(RForcecom) for library(salesforcer) and still have production scripts perform as expected.

RForcecom Removed from CRAN

As of June 9, 2021, the {RForcecom} package was removed from CRAN. You can still use it by installing from the archive, but we strongly recommend using {salesforcer} instead. The existing functionality in {RForcecom} has been further optimized within {salesforcer} and new functionality has been added too.

Salesforce Requires MFA Which Prevents RForcecom Basic Auth Log in

Basic authentication (password and security token) will no longer work since Salesforce announced that all customers will be migrated to MFA beginning February 1st, 2022 (link). As a result, the basic authentication routine used {RForcecom} and the legacy, compatibility method written into {salesforcer} will no longer work. Please migrate to {salesforcer} and use sf_auth() to generate an OAuth 2.0 token. The examples below will no longer work.

Authentication

{salesforcer} supports OAuth 2.0 authentication which is preferred, but for backward compatibility provides the username-password authentication routine implemented by {RForcecom}. Here is an example running the function from each of the packages side-by-side and producing the same result.

First, authenticate and load any required packages for your analysis.

suppressWarnings(suppressMessages(library(dplyr)))
suppressWarnings(suppressMessages(library(here)))
library(salesforcer)
token_path <- Sys.getenv("SALESFORCER_TOKEN_PATH")
sf_auth(token = paste0(token_path, "salesforcer_token.rds"))

library(salesforcer)
sf_auth()

# Beginning February 1, 2022, basic authentication will no longer work. You must
# log in to Salesforce using MFA (generating an OAuth 2.0 token typically from 
# the browser).

# the RForcecom way
# RForcecom::rforcecom.login(username, paste0(password, security_token), 
#                            apiVersion=getOption("salesforcer.api_version"))
# replicated in salesforcer package
session <- salesforcer::rforcecom.login(username, 
                                         paste0(password, security_token), 
                                         apiVersion = getOption("salesforcer.api_version"))
session['sessionID'] <- "{MASKED}"
session

Note that we must set the API version here because calls to session will not create a new sessionId and then we are stuck with version 35.0 (the default from RForcecom::rforcecom.login()). Some functions in {salesforcer} implement API calls that are only available after version 35.0.

CRUD Operations

"CRUD" operations (Create, Retrieve, Update, Delete) in the {RForcecom} package only operate on one record at a time. One benefit to using the {salesforcer} package is that these operations will accept a named vector (one record) or an entire data.frame or tbl_df of records to churn through. However, rest assured that the replicated functions behave exactly the same way if you are hesitant to making the switch.

Here is an example showing the reduction in code of using {salesforcer} if you would like to create multiple records.

n <- 2
new_contacts <- tibble(FirstName = rep("Test", n),
                       LastName = paste0("Contact-Create-", 1:n))

# the RForcecom way (requires a loop)
# rforcecom_results <- NULL
# for(i in 1:nrow(new_contacts)){
#   temp <- RForcecom::rforcecom.create(session, 
#                                       objectName = "Contact", 
#                                       fields = unlist(slice(new_contacts,i)))
#   rforcecom_results <- bind_rows(rforcecom_results, temp)
# }

# the better way in salesforcer to do multiple records
salesforcer_results <- sf_create(new_contacts, object_name="Contact")
salesforcer_results

Query

{salesforcer} also has better printing and type-casting when returning query result thanks to features of the {readr} package.

this_soql <- "SELECT Id, Email FROM Contact LIMIT 5"

# the RForcecom way
# RForcecom::rforcecom.query(session, soqlQuery = this_soql)

# the better way in salesforcer to query
salesforcer_results <- sf_query(this_soql)
salesforcer_results

Describe

The {RForcecom} package has the function rforcecom.getObjectDescription() which returns a data.frame with one row per field on an object. The same function in {salesforcer} is named sf_describe_object_fields() and also has better printing and datatype casting by using tibbles.

# the RForcecom way
# RForcecom::rforcecom.getObjectDescription(session, objectName='Account')

# the better way in salesforcer to get object fields
result2 <- salesforcer::sf_describe_object_fields('Account')
result2