R/Leadership.getPositions.R

In umatter/pvsR: Interact with the Project Vote Smart API for Scientific Research

##' Get leadership positions by state and office
##' 
##' This function is a wrapper for the Leadership.getPositions() method of the PVS API Leadership class which returns leadership positions by state and office. The function sends a request with this method to the PVS API for all state and office IDs given as a function input, extracts the XML values from the returned XML file(s) and returns them arranged in one data frame.
##' @usage Leadership.getPositions(stateId="NA", officeId=NULL)
##' @param stateId (optional) a character string or list of character strings with the state ID(s) (default: "NA", for national) (see references for details)
##' @param officeId (optional) a character string or list of character strings with the office ID(s) (default: All) (see references for details)
##' @return A data frame with a row for each leadership position and columns with the following variables describing the position:\cr leadership.position*.leadershipId,\cr leadership.position*.name,\cr leadership.position*.officeId,\cr leadership.position*.officeName.
##' @references http://api.votesmart.org/docs/Leadership.html\cr
##' Use State.getStateIDs() to get a list of state IDs.\cr
##' See http://api.votesmart.org/docs/semi-static.html for a list of office IDs or use Office.getOfficesByType(), Office.getOfficesByLevel(), Office.getOfficesByTypeLevel() or Office.getOfficesByBranchLevel() to get a list of office ID(s).\cr
##' See also: Matter U, Stutzer A (2015) pvsR: An Open Source Interface to Big Data on the American Political Sphere. PLoS ONE 10(7): e0130501. doi: 10.1371/journal.pone.0130501
##' @author Ulrich Matter <ulrich.matter-at-unibas.ch>
##' @examples
##' # First, make sure your personal PVS API key is saved as an option
##' # (options("pvs.key" = "yourkey")) or in the pvs.key variable:
##' \dontrun{pvs.key <- "yourkey"}
##' # get leadership positions by state ID and office ID
##' \dontrun{positions <- Leadership.getPositions(list("AL","FL"),8)}
##' \dontrun{positions}
##' @export



Leadership.getPositions <-
	function (stateId="NA", officeId=NULL) {
		
		if (length(officeId)==0) {
			
			# internal function
			Leadership.getPositions.basic1 <- 
				function (.stateId) {
					
					request <-  "Leadership.getPositions?"
					inputs  <-  paste("&stateId=",.stateId,sep="")
					output  <-  pvsRequest4(request,inputs)
					output$stateId <- .stateId
					
					return(output)
			}
			
			
			# Main function  
			output.list <- lapply(stateId, FUN= function (s) {
				Leadership.getPositions.basic1(.stateId=s)
			}
			)
			
			output.list <- redlist(output.list)
			output <- bind_rows(output.list)

		} else {
			
			# internal function
			Leadership.getPositions.basic2 <- 
				function (.stateId, .officeId) {
					
					request <-  "Leadership.getPositions?"
					inputs  <-  paste("&stateId=",.stateId, "&officeId=", .officeId, sep="")
					output  <-  pvsRequest4(request,inputs)
					output$stateId <- .stateId
					output$officeId.input <- .officeId
					return(output)
				}
			
			# Main function  
			output.list <- lapply(stateId, FUN= function (s) {
				lapply(officeId, FUN= function (c) {
					Leadership.getPositions.basic2( .stateId=s, .officeId=c)
				}
				)
			}
			)
			
			output.list <- redlist(output.list)
			output <- bind_rows(output.list)
			
			# Avoids that output is missleading, because officeId is already given in request-output, but also a
			# additionally generated (as officeId.input). Problem exists because some request-outputs might be empty
			# and therefore only contain one "officeId" whereas the non-empty ones contain two. (see basic function)
			output$officeId[c(as.vector(is.na(output$officeId)))] <- output$officeId.input[as.vector(is.na(output$officeId))]
			output$officeId.input <- NULL
		}
		return(output)
	}