Building a Corporate R Package

8 minute read

Analytical Functions

Creating functions is a programming best practice. They are easier to monitor, test, and teach to others. If your team performs routine calculations, establish a standard by adding functions to the shared library and encourage everyone to use those functions. If your company works a lot with customer phone numbers, then write a function to standardize the formatting. See the example below for how to create and document these functions.

#' Format A Column of Phone Numbers
#' This function takes a dataframe and formats a column
#' to match a 10-digit phone number (XXX) XXX-XXXX
#' @usage cleanPhoneNumbers(dat, phoneColumnName, returnExtColumn=FALSE)
#' @keywords cleaning transformation etl salesforce
#' @param dat a data.frame containing a column for formatting
#' @param phoneColumnName a character string defining the target data.frame column to
#' format as (XXX) XXX-XXXX
#' @param returnExtColumn a logical specifying whether to return digits seemingly formatted as
#' an extension to the main phone number
#' @return A \code{data.frame} with a clean phone number column in the format (XXX) XXX-XXXX
#' @examples
#' \dontrun{
#' my_data <- data.frame(Phone=c('2349325339 x7779', '8888-888-888', 
#'                               '2343-234-2341', '(832) 234-3494'))
#' my_data %>% cleanPhoneNumbers('Phone')
#' }
#' @export
cleanPhoneNumbers <- function(dat, phoneColumnName, returnExtColumn=FALSE){
  if(!(phoneColumnName %in% names(dat))){
    message(paste(phoneColumnName, 'was not in the dataset, no cleaning performed.'))
  } else {
    original_values <- dat[,phoneColumnName]
    ext_part <- gsub('(.*)(extension|ext|ex|x)(.*)', '\\3', dat[,phoneColumnName])
    ext_part <- ifelse(ext_part == dat[,phoneColumnName], '', ext_part)
    ext_part <- ifelse((( | 
                          (ext_part == '')), '', 
                       gsub("[^\\d]+", "", ext_part, perl=TRUE))
    dat[,phoneColumnName] <- gsub('(.*)(extension|ext|ex|x)(.*)', '\\1', 
    dat[,phoneColumnName] <- ifelse((([,phoneColumnName])) | 
                                        (dat[,phoneColumnName] == '')), '', 
                                     gsub("[^\\d]+", "", dat[,phoneColumnName], perl=TRUE))
    # convert 11 digit numbers starting with 1 to 10 because we assume US country code
    dat[,phoneColumnName] <- ifelse(((nchar(dat[,phoneColumnName]) == 11) & 
                                       (grepl('^1', dat[,phoneColumnName]))), 
                                     substring(dat[,phoneColumnName], 2), 
    # if not 10 digits, then set to ''
    dat[,phoneColumnName] <- ifelse((nchar(dat[,phoneColumnName]) == 10), 
                                    dat[,phoneColumnName], '')
    # if weird then set to ''
    wierd_vals <- c('0000000000', '1111111111', '1234567789', '1231231234', 
                    '1234567890', '8888888888', '9999999999')
    dat[,phoneColumnName] <- ifelse((dat[,phoneColumnName] %in% wierd_vals), '', 
    dat[,phoneColumnName] <- gsub('([0-9]{3})([0-9]{3})([0-9]{4})', '(\\1) \\2-\\3', 
      dat[,'phone_ext_parsed_out'] <- ext_part
      dat[,'phone_ext_parsed_out'] <- ifelse(dat[,phoneColumnName] == '', '', 
      dat[,'phone_ext_parsed_out'] <- strtrim(dat[,'phone_ext_parsed_out'], 5)
      pos <- which(names(dat) == phoneColumnName)
      pos2 <- which(names(dat) == 'phone_ext_parsed_out')
      rest <- seq(pos+1, ncol(dat))
      rest <- rest[rest!=pos2]
      dat <- dat[,c(1:pos, pos2, rest)]

I strongly encourage using the Google Style Guide or Hadley’s Style Guide. Between the two of these guides I believe the two most important rules are:

  1. Use snake case
  2. Name your functions with verbs and variables with nouns

Company Constants

Again, if you’re looking to enforce standards, it is a great practice to include company-wide constants in your R package. Create an R file called constants.R and define these constants as follows:

#' @export
default_price <- 9.99 # code that defines a default price constant value
lockBinding("default_price", environment())

#' @export
min_price <- 4.50 # also define a minimum price of item
lockBinding("min_price", environment())

#' @export
price_cutoffs <- c(0, 5, 10, 15, Inf) # define a vector to consistently bin data
lockBinding("price_cutoffs", environment())


A little bit of formatting goes a long way if your team routinely presents figures internally and/or externally. Consider adapting a ggplot2 theme to your company’s colors, fonts, and other styling. Here are two links to get you started in making custom theme:

  1. Roger Peng’s Mastering Software Development in R

  2. Joey Stanley’s Blog - Custom Themes in ggplot2

Also, you might find that a majority of stakeholders in your company prefer receiving data in Excel. You can export data from R to Excel with bolded, proper case column names, format columns as percentages and dollar signs so that your stakeholders are happy getting data from you. I highly recommend working with the openxlsx package, which does not require Java. Below is a snippet of code that will transform column names, set column widths based on the data, and format them based on some key words in the column names like formatting columns ending in “pct” as a percentage. All of this before exporting to an Excel file so that your stakeholders are happy.

#' Add Data and Format it in an Excel Workbook
#' This function is a convenience function for standardizing one-off reports
#' in a certain Excel format that is pretty and well-organized for consumers
#' @usage add_data_to_excel(wb, sheet, dat, startRow=1, startCol=1)
#' @concept writes data to Excel workbook in a pretty format
#' @keywords excel microsoft data export formatting
#' @seealso \code{\link[openxlsx]{createWorkbook}}
#' @param wb a Workbook object containing a worksheet. 
#' See \code{\link[openxlsx]{createWorkbook}}.
#' @param sheet the worksheet to write to. Can be the worksheet index or name.
#' @param dat data.frame to be written in the sheet
#' @param startRow a vector specifiying the starting column to write to
#' @param startCol a vector specifiying the starting row to write to
#' @return a logical indicating the success of adding the data to 
#' the workbook and formatting
#' @examples 
#' \dontrun{
#' my_wb <- createWorkbook()
#' addWorksheet(my_wb, "Iris")
#' # returns invisble TRUE
#' add_data_to_excel(my_wb, "Iris", iris)
#' saveWorkbook(my_wb, file='~/iris.xlsx', overwrite = TRUE)
#' }
#' @export
add_data_to_excel <- function(wb, sheet, dat, startCol=1, startRow=1){
  # convert column names to more human readable format (with spaces and proper case)
  names(dat) <- gsub("_", " ", tolower(names(dat)))
  names(dat) <- str_to_title(names(dat))
  writeData(wb=wb, sheet=sheet, x=dat, startCol=startCol, startRow=startRow, 
  for(i in 1:ncol(dat)){
                 cols = i + (startCol - 1), 
                 widths = pmax(12, 
                                                  as.character)), na.rm=TRUE), 
    if(grepl('Percent|Pct|%', names(dat)[i])){
               style = createStyle(numFmt='0.0%'), 
               rows = (startRow + 1):(nrow(dat) + 100), 
               cols = i + (startCol - 1), 
               gridExpand = TRUE)
    } else if(grepl('Dollars', names(dat)[i])){
               style = createStyle(numFmt='$#,##0'),
               rows = (startRow + 1):(nrow(dat) + 100), 
               cols = i + (startCol - 1), 
               gridExpand = TRUE)
    } else {
      # dont add any formatting

Data Source Connections

On the job you’re probably accessing the same data sources over and over. Help out yourself and fellow teammates by standardizing how to connect and pull data. This could be database connections, API connections, or other server connections. Below are two examples, one for connecting to MS SQL Server database and one for connecting to the Salesforce API.

DB Connection

#' Establish a connection to Our DB and execute a query
#' This function connects to the DBMS using the shared_user, executes query, 
#' closes the connection, and returns the recordset if possible.
#' @usage queryOurDB(query, db_environment=c('prod','dev'), username=NULL, password=NULL)
#' @importFrom RODBC odbcDriverConnect odbcConnect sqlQuery odbcClose
#' @param query a character string defining a query to be executed against the DB
#' @param db_environment a character string either 'prod' or 'dev' upon 
#' which environment to execute against
#' @param username a character string defining your database username credential
#' @param password a character string defining your database password credential
#' @return A \code{data.frame} object of the query's resultset
#' @seealso
#'  \code{\link[RODBC]{odbcConnect}},
#'  \code{\link[DBI]{sqlQuery}}
#' @examples
#' \dontrun{
#' # This query pulls the top 10 records from CustomerTable
#' my_query <- "SELECT TOP 10 * FROM DB.dbo.CustomerTable"
#' query_result <- queryOurDB(my_query)
#' }
#' @export
queryOurDB <- function(query, db_environment=c('prod', 'dev'), 
                      username=NULL, password=NULL){
  # try to prevent anything really bad resulting from dropping tables by accident
  stopifnot(!grepl("drop table", query,
  # set the username and password
  if(is.null(username) | is.null(password)){
    username <- "shared_user"
    password <- "shared_password"
  # determine which database to query
  which_db <- match.arg(db_environment)
    host <- "" #Production DB
  } else {
    host <- "" #Development DB 
  # run query
  query_result <- tryCatch({
    con <- odbcDriverConnect(
      sprintf('driver={SQL Server};server=%s,1433;database=DUMMY;uid=%s;pwd=%s',
              host, username, password)
    message("running query")
    sqlQuery(con, query)
  }, error = function(e){
    message("There was an error with your query. See details below.")
  }, finally = {
        message("closed database connection")

API Connection

#' Establish a session with the Salesforce API
#' This function connects to a Salesforce instance via the API
#' @usage createSFAPISession(environment=c('prod', 'qa'), 
#'                           user=NULL, password=NULL, apiVersion="38.0")
#' @importFrom RForcecom rforcecom.login
#' @param username a character string defining the Salesforce API username credential
#' @param password a character string defining the Salesforce API password credential.
#' Note: that this should be the concatenation of your password and security token. See the
#' instructions in \code{\link[RForcecom]{rforcecom.login}} for an example format.
#' @param apiVersion (optional) Version of the REST API and SOAP API that you want to use. 
#' (e.g. "38.0"). Supported versions from v20.0 and up.
#' @return A session object as returned by \code{\link[RForcecom]{rforcecom.login}}
#' that can be used for subsequent calls to the Salesforce API.
#' @seealso
#'  \code{\link[RForcecom]{rforcecom.login}},
#' @examples
#' \dontrun{
#' # establish a connection
#' sf_session <- createSFAPISession()
#' # logout of that session
#' rforcecom.logout(sf_session)
#' }
#' @export
createSFAPISession <- function(environment=c('prod', 'qa'), 
                              user=NULL, password=NULL, apiVersion="38.0"){
  which_env <- match.arg(environment)
  if(is.null(user) | is.null(password)){
    if(which_env == 'qa'){
      username <- ""
      password <- "shared_password"
      url <- ''
    } else if(which_env == 'prod'){
      username <- ""
      password <- "shared_password"
      url <- ''
    } else {
      stop(paste("Cannot find the specified environment:", which_env))

  sf_session <- rforcecom.login(username=username, password=password, 
                                loginURL=url, apiVersion=apiVersion)

Note: If you do not feel comfortable centralizing credentials for security reasons or your team does not have shared credentials, then have those stored locally in a profile and called by the package in a standardized way.


Finally, but maybe most importantly, R packages help teammembers grow and on-board faster. You can include sample datasets and plenty of examples within the R package so that new members of your team don’t have to ask around how to do certain things. They can read the documentation and try the examples for themselves. Consider the R package a legacy asset of your team that will stand the test of time well after you’ve left the company.