# Working with Reports

#### Steven M. Mortimer

#### 2020-07-19

## Overview

The following vignette outlines how to execute and manage reports in your Org. Note: These features are still experimental and are likely to change and/or have bugs. Please take this into account, and if you run into any issues please consider submitting an issue HERE in the GitHub repository so that we can help troubleshoot and fix, if needed. Thank you!

In order to pull down the results of a report created within Salesforce all you need to know and have is the report’s Salesforce Id. If you want to fully leverage the features of the Reports and Dashboards REST API I recommend reading the following so that you are better equipped to understand and use the functions within {salesforcer}.

- Running a Report Synchronously or Asynchronously
- Understanding a Report Fact Map
- Filter Reports on Demand
- Reports and Dashboards REST API Documentation Home Page

## Authenticate

First, load the {salesforcer} package and login either by OAuth 2.0 (SSO) or using your username, password, and security key.

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

## Running a Report in your Org

In Salesforce there is a dedicated page to displaying the list of reports in your Org. It typically follows the pattern: `https://na1.salesforce.com/00O/o` (replace `na1` with your server instance). When you click on a report in the GUI you should see the results. Below is a screenshot of how a report may look in your Org. Note the report Id in the URL bar.

Alt

The report Id above (`"00O3s000006tE7zEAE"`) is the only information needed to pull those same results from an R session, like so:

    my_report_id <- "00O3s000006tE7zEAE"
    results <- sf_run_report(my_report_id)
    results
    #> # A tibble: 14 × 8
    #>   `Contact ID`    `First Name` `test number` `Contact Owner` `Account ID`
    #>   <chr>           <chr>                <dbl> <chr>           <chr>
    #> 1 0036A000002C6MW Rose                    NA Steven Mortimer 0016A0000035mJ4
    #> 2 0036A000002C6MX Sean                    NA Steven Mortimer 0016A0000035mJ4
    #> 3 0036A000002C6MY Jack                    99 Steven Mortimer 0016A0000035mJ5
    #> 4 0036A000002C6Mb Tim                     NA Steven Mortimer 0016A0000035mJ8
    #> 5 0036A000002C6Mc John                    23 Steven Mortimer 0016A0000035mJ8
    #> # ℹ 9 more rows
    #> # ℹ 3 more variables: `Account Name` <chr>, `Billing City` <chr>,
    #> #   `Account Owner` <chr>

Note that the `sf_run_report()` function will, by default, run the report asynchronously. This means that a report instance will be requested and then the function will wait to retrieve the results. The advantage to using an asynchronous report is that the results of such a report are stored for 24 hours and can be retrieved again saving CPU resources, if needed. A more in-depth discussion on synchronous vs. asynchronous reports is available here https://developer.salesforce.com/docs/atlas.en-us.api_analytics.meta/api_analytics/sforce_analytics_rest_api_get_reportdata.htm. However, for reports with a relatively small number of records a synchronous report may be faster without having to do the round trip of creating then querying a report instance.

## Filtering a Report on the Fly

The neat thing about using the API is that you can retrieve the results of a report with different filters applied. This allows you to obtain exactly the results needed without having to create separate copies of the same report. It takes some basic understanding of how report operators need to be supplied to the API, but it is not too difficult. Below is an example that only includes contact records created prior to this month and belong to an Account with a non-NULL Billing City.

    # filter records that was created before this month
    filter1 <- list(column = "CREATED_DATE",
                            operator = "lessThan",
                            value = "THIS_MONTH")

    # filter records where the account billing address city is not empty
    filter2 <-  list(column = "ACCOUNT.ADDRESS1_CITY",
                            operator = "notEqual",
                            value = "")

    # combine filter1 and filter2 using 'AND' which means that records must meet both filters
    results_using_AND <- sf_run_report(my_report_id,
                                       report_boolean_logic = "1 AND 2",
                                       report_filters = list(filter1, filter2))
    results_using_AND
    #> # A tibble: 14 × 8
    #>   `Contact ID`    `First Name` `test number` `Contact Owner` `Account ID`
    #>   <chr>           <chr>                <dbl> <chr>           <chr>
    #> 1 0036A000002C6MW Rose                    NA Steven Mortimer 0016A0000035mJ4
    #> 2 0036A000002C6MX Sean                    NA Steven Mortimer 0016A0000035mJ4
    #> 3 0036A000002C6MY Jack                    99 Steven Mortimer 0016A0000035mJ5
    #> 4 0036A000002C6Mb Tim                     NA Steven Mortimer 0016A0000035mJ8
    #> 5 0036A000002C6Mc John                    23 Steven Mortimer 0016A0000035mJ8
    #> # ℹ 9 more rows
    #> # ℹ 3 more variables: `Account Name` <chr>, `Billing City` <chr>,
    #> #   `Account Owner` <chr>

This second example shows how to return only the Top N number of records and combine the filter using the logical “OR” instead of “AND”.

    # combine filter1 and filter2 using 'OR' which means that records must meet one
    # of the filters but also throw in a row limit based on a specific sort order
    results_using_OR <- sf_run_report(my_report_id,
                                      report_boolean_logic = "1 OR 2",
                                      report_filters = list(filter1, filter2),
                                      sort_by = "Contact.test_number__c",
                                      decreasing = TRUE,
                                      top_n = 5)
    results_using_OR
    #> # A tibble: 5 × 8
    #>   `Contact ID`    `First Name` `test number` `Contact Owner` `Account ID`
    #>   <chr>           <chr>                <dbl> <chr>           <chr>
    #> 1 0033s00001CWqsU Test                  1000 Steven Mortimer 0013s00001Aizxg
    #> 2 0033s00001CWqsV Test                  1000 Steven Mortimer 0013s00001Aizxg
    #> 3 0033s00001CWqt7 Test                  1000 Steven Mortimer 0013s00001Aizxg
    #> 4 0033s00001CWqt6 Test                  1000 Steven Mortimer 0013s00001Aizxg
    #> 5 0033s00001CWqsW Test                  1000 Steven Mortimer 0013s00001Aizxg
    #> # ℹ 3 more variables: `Account Name` <chr>, `Billing City` <chr>,
    #> #   `Account Owner` <chr>

I was able to determine some of the potential ways to filter by first reviewing the `reportFilters` element in the existing report metadata and also reviewing the list of report filter operators.

First, you can always take the report filter specification from the report metadata and tailor it to your needs. Below is an example showing how to get that metadata for our report. You can select specific elements to better understand the structure of the report.

    report_details <- sf_describe_report(my_report_id)
    report_details$reportMetadata$reportType$type
    #> [1] "ContactList"
    report_details$reportMetadata$reportFilters
    #> [[1]]
    #> [[1]]$column
    #> [1] "CREATED_DATE"
    #>
    #> [[1]]$filterType
    #> [1] "fieldValue"
    #>
    #> [[1]]$isRunPageEditable
    #> [1] TRUE
    #>
    #> [[1]]$operator
    #> [1] "lessThan"
    #>
    #> [[1]]$value
    #> [1] "2019-07-19T04:00:00Z"
    #>
    #>
    #> [[2]]
    #> [[2]]$column
    #> [1] "ACCOUNT.ADDRESS1_CITY"
    #>
    #> [[2]]$filterType
    #> [1] "fieldValue"
    #>
    #> [[2]]$isRunPageEditable
    #> [1] TRUE
    #>
    #> [[2]]$operator
    #> [1] "notEqual"
    #>
    #> [[2]]$value
    #> [1] ""

Second, Salesforce has a few API endpoints that tell you the fields on the report or the report type, more generally, and all the ways you can declare a filter on a particular field type and. The `reportTypeMetadata` element returned on the report description also has detailed information on how to filter the report. For example, it already contains the start and end dates that would be applied when using the “LAST_MONTH” filter value on a date field.

    report_details$reportTypeMetadata$standardDateFilterDurationGroups[[6]]$standardDateFilterDurations[[1]]
    #> $endDate
    #> [1] "2024-10-31"
    #>
    #> $label
    #> [1] "Last Month"
    #>
    #> $startDate
    #> [1] "2024-10-01"
    #>
    #> $value
    #> [1] "LAST_MONTH"

Digging into the metadata of the report will allow you to better understand what filters you can set when filtering on the fly. In the example below you should notice that the field names on the report do not match the names of the typical API field names for the object, so please review carefully the fields on the report. For example, the `CREATED_DATE` report field is based on the `CreatedDate` object field.

    # report fields
    report_fields <- sf_list_report_fields(my_report_id)
    head(names(report_fields$equivalentFieldIndices))
    #> [1] "CONTACT_CREATED_ALIAS"     "Contact.My_External_Id__c"
    #> [3] "IS_EMAIL_BOUNCED"          "REPORTS_TO"
    #> [5] "ADDRESS2_ZIP"              "LAST_ACTIVITY"

    report_filters <- sf_list_report_filter_operators()
    unique_supported_fields <- report_filters %>%
      distinct(supported_field_type) %>%
      unlist()
    unique_supported_fields
    #>        supported_field_type1        supported_field_type2
    #>                       "date"                    "address"
    #>        supported_field_type3        supported_field_type4
    #>                     "string"                     "double"
    #>        supported_field_type5        supported_field_type6
    #>                   "picklist"                   "textarea"
    #>        supported_field_type7        supported_field_type8
    #>            "encryptedstring"                    "percent"
    #>        supported_field_type9       supported_field_type10
    #>                        "int"                        "url"
    #>       supported_field_type11       supported_field_type12
    #>                  "reference"                   "datetime"
    #>       supported_field_type13       supported_field_type14
    #>                    "boolean"                      "phone"
    #>       supported_field_type15       supported_field_type16
    #> "datacategorygroupreference"                   "currency"
    #>       supported_field_type17       supported_field_type18
    #>                   "location"                       "html"
    #>       supported_field_type19       supported_field_type20
    #>                         "id"                       "time"
    #>       supported_field_type21       supported_field_type22
    #>                      "email"              "multipicklist"

    # operators to filter a picklist field
    picklist_field_operators <- report_filters %>%
      filter(supported_field_type == "picklist")
    picklist_field_operators
    #> # A tibble: 9 × 3
    #>   supported_field_type label         name
    #>   <chr>                <chr>         <chr>
    #> 1 picklist             equals        equals
    #> 2 picklist             not equal to  notEqual
    #> 3 picklist             less than     lessThan
    #> 4 picklist             greater than  greaterThan
    #> 5 picklist             less or equal lessOrEqual
    #> # ℹ 4 more rows

## Managing your Reports

The API also allows you to perform many admin functions like creating, copying, updating, or deleting reports and report instances. Take advantage of these functions as needed to keep your Org’s report list well-maintained. Below is a simple flow of creating, updating, and deleting a single report, but the amount you’re able to customize is completely up to you. The {salesforcer} package should support any operation that the Reports and Dashboards REST API supports.

    # first, grab all possible reports in your Org
    all_reports <- sf_query("SELECT Id, Name FROM Report")

    # second, get the id of the report to update
    this_report_id <- all_reports$Id[1]

    new_report <- sf_copy_report(this_report_id)
    #> Naming the new report: 'ReportName - Copy'

    # third, update the report (2 ways shown)
    my_updated_report <- sf_update_report(new_report$reportMetadata$id,
                                          report_metadata =
                                            list(reportMetadata =
                                              list(name = "Updated Name!")))
    my_updated_report$reportMetadata$name
    #> [1] "Updated Name!"

    # alternatively, pull down its metadata and update the name
    report_details <- sf_describe_report(new_report$reportMetadata$id)
    report_details$reportMetadata$name <- paste0(report_details$reportMetadata$name,
                                                 " - UPDATED AGAIN!")

    # update the report by passing the metadata
    my_updated_report <- sf_update_report(new_report$reportMetadata$id,
                                          report_metadata = report_details)
    my_updated_report$reportMetadata$name
    #> [1] "Updated Name! - UPDATED AGAIN!"

    # fourth, delete that report using its Id
    success <- sf_delete_report(new_report$reportMetadata$id)
    success
    #> [1] TRUE

## Troubleshooting

If you are having an issue with a report please submit in the {salesforcer} GitHub repository at: https://github.com/StevenMMortimer/salesforcer/issues. As a maintainer, reports can be are tough to debug because every Salesforce Org is unique. When filing your issue please make an attempt to understand the query and debug a little bit on your own. Here are a few suggestions:

1. Slightly modify your function call to `sf_run_report()` to observe the results. Here are a few prompting questions that may assist you:

   - What do you see when you set `verbose=TRUE` argument?

   - What happens if you run sync. vs. async. (e.g. async=TRUE vs. FALSE)?

   - What happens if you try running a different type of report?

2. Double check Salesforce’s Reports and Dashboards REST API Developer Guide to see whether if your report type would be supported or limited in some way.

3. Review report unit tests at: https://github.com/StevenMMortimer/salesforcer/blob/main/tests/testthat/test-report.R. These unit tests were written to cover a variety of use cases and 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 that may be helpful in troubleshooting your own case.

4. Roll up your sleeves and dive into the source code for the {salesforcer} package. The main scripts to review are:

   - https://github.com/StevenMMortimer/salesforcer/blob/main/R/analytics-report.R

   - https://github.com/StevenMMortimer/salesforcer/blob/main/R/utils-report.R