## OFFICIAL NAMES -------------------------------------------------------------------------------------------------

cleanup_mechs <- function(df_to_clean, report_folder_path, report_start_year = 2014) {

    # Author  : A.Chafetz, USAID
    # Purpose : replace mechanism/partner names with what is current/official in FACTSInfo
    # Date    : Oct 13, 2017
    # Updated : Oct 20, 2017

  #dependencies
    pacman::p_load(readxl, tidyverse)

  #import official mech and partner names; source: FACTS Info
    df_names <- read_excel(Sys.glob(file.path(report_folder_path,"*Standard COP Matrix Report*.xls")), skip = 1)

  #rename variable stubs
    names(df_names) <- gsub("Prime Partner", "primepartner", names(df_names))
    names(df_names) <- gsub("Mechanism Name", "implementingmechanismname", names(df_names))

  #figure out latest name for IM and partner (should both be from the same year)
  	df_names <- df_names %>%

  	  #rename variables that don't fit pattern
  	    rename(operatingunit =  `Operating Unit`, mechanismid = `Mechanism Identifier`,
  	           primepartner__0 = primepartner, implementingmechanismname__0 = implementingmechanismname) %>%
  	  #reshape long
    	  gather(type, name, -operatingunit, -mechanismid) %>%

  	  #split out type and year (eg type = primeparnter__1 --> type = primepartner,  year = 1)
  	    separate(type, c("type", "year"), sep="__") %>%

  	  #add year (assumes first year if report is 2014)
  	    mutate(year = as.numeric(year) + report_start_year) %>%

  	  #drop lines/years with missing names
  	    filter(!is.na(name)) %>%

  	  #group to figure out latest year with names and keep only latest year's names (one obs per mech)
    	  group_by(operatingunit, mechanismid, type) %>%
    	  filter(year==max(year)) %>%
    	  ungroup() %>%

  	  #reshape wide so primepartner and implementingmechanismname are two seperate columsn to match fact view dataset
  	    spread(type, name) %>%

  	  #convert mechanism id to string for merging back onto main df
  	    mutate(mechanismid = ifelse(!is.character(mechanismid), as.character(mechanismid), mechanismid)) %>%

  	  #keep only names with mechid and renaming with _F to identify as from FACTS
    	  select(mechanismid, implementingmechanismname, primepartner) %>%
    	  rename(implementingmechanismname_F = implementingmechanismname, primepartner_F = primepartner)

    #match mechanism id type for compatible merge
	    df_to_clean <- mutate(df_to_clean, mechanismid = ifelse(!is.character(mechanismid), as.character(mechanismid), mechanismid))

    #merge in official names
    	df_to_clean <- left_join(df_to_clean, df_names, by="mechanismid")

    #replace prime partner and mech names with official names
    	df_to_clean <- df_to_clean %>%
    	   mutate(implementingmechanismname = ifelse(is.na(implementingmechanismname_F), implementingmechanismname, implementingmechanismname_F),
    	                  primepartner = ifelse(is.na(primepartner_F), primepartner, primepartner_F)) %>%
    	   select(-ends_with("_F"))
}