newgetArchiveSectionName <- function(year, month, key) {
  
	  create_url <- paste("http://api.nytimes.com/svc/archive/v1/", year,"/",month,".json?api-key=",key, sep="")
	  
	  g1 <- GET(create_url)
	  
	  g2 <- content(g1, 'parsed')
	  
	  g3 <- g2$response
	  
	  g4 <- g3$docs
	  
	  g5 <- data.frame(do.call(rbind, g4), stringsAsFactors=FALSE)
	  
	  g6 <- g5$section_name
	  
	  g7 <- lapply(g6, function(x) ifelse(x=="NULL", NA, x))
	  g8 <- lapply(g7, function(x) as.character((unlist(x))))
	  g9 <- as.data.frame(do.call(cbind, g8))
	  
	  g9$ID <- seq.int(nrow(g9))
	  
	  g10 <- as.data.frame(t(g9))
	  
	  g11 <- as.data.frame(table(g10$V1, useNA="always"))
	  
	  g12 <- cSplit(g11, 'Var1', ";")
	  
	  names(g12)[2] <- 'Var1'
	  
	  g13 <- g12 %>%
	          select(Var1, Freq) %>%
	          group_by(Var1) %>%
	          summarise_all(funs(sum))
	  
	  return(g13)
	  
	}

The second function: 

newgetMonths <- function(year, key) {
  df_total = NULL
  
  for (i in 1:12) {
    i_string <- toString(i)
    assign(paste('sub',i,sep=''), getArchiveSectionName(year,i_string,key))
    name <- paste('sub', i, sep="")
    
  }
  
  freq_name <- paste('Freq', year, sep="-")
  
  cyear <- rbind(sub1, sub2, sub3, sub4, sub5, sub6, sub7, sub8, sub9, sub10, sub11, sub12) %>%
            group_by(Var1) %>%
            summarise_all(funs(sum)) %>%
            arrange(desc(Freq))
  
  g12 <- cSplit(cyear, 'Var1', ";")
  
  names(g12)[2] <- 'Var1'
  
  g13 <- g12 %>%
    select(Var1, Freq) %>%
    group_by(Var1) %>%
    summarise_all(funs(sum)) %>%
    rename(c("Freq" = freq_name))
  
  return(g13)
  
}

It took me several iterations of the second function, `newgetMonths`, to arrive at the solution that finally gave me reliable results, and for which I didn't have to massage the return value after calling the funciton for each year. 

I could take this a step further and create a third function that then took a range of years and merged them together into one dataframe, which is what I do with the following code. 

c17 <- newgetMonths('2017', api_key)
c16 <- newgetMonths('2016', api_key)
c15 <- newgetMonths('2015', api_key)
c14 <- newgetMonths('2014', api_key)
c13 <- newgetMonths('2013', api_key)
c12 <- newgetMonths('2012', api_key)
c11 <- newgetMonths('2011', api_key)
c10 <- newgetMonths('2010', api_key)
c09 <- newgetMonths('2009', api_key)
c08 <- newgetMonths('2008', api_key)
c07 <- newgetMonths('2007', api_key)
c06 <- newgetMonths('2006', api_key)

c06_16 <- c11 %>%
			left_join(c06, by="Var1_1") %>%					
			left_join(c07, by="Var1_1") %>%	
			left_join(c08, by="Var1_1") %>%
			left_join(c09, by="Var1_1") %>%
            left_join(c10, by="Var1_1") %>%
            left_join(c11, by="Var1_1") %>%
            left_join(c12, by="Var1_1") %>%
            left_join(c13, by="Var1_1") %>%
            left_join(c14, by="Var1_1") %>%
            left_join(c15, by="Var1_1") %>%
            left_join(c16, by="Var1_1") %>%
			arrange(desc(`Freq-2016`))      

c06_16_new <- c06_16[1:10,]

I summed the frequencies for each year to make sure that the results looked consistent with what i was expecting (over 100K results for each month). 

For example: 

sum(c16$`Freq-2016`)
sum(c15$`Freq-2015`)
sum(c14$`Freq-2014`)
sum(c13$`Freq-2013`)
sum(c12$`Freq-2012`)
sum(c11$`Freq-2011`)

Here are the top 10 results for 2016, sorted descended by year 2016. 

             Var1 `Freq-2006` `Freq-2007` `Freq-2008` `Freq-2009` `Freq-2010` `Freq-2011` `Freq-2012` `Freq-2013` `Freq-2014` `Freq-2015` `Freq-2016`
            <chr>       <int>       <int>       <int>       <int>       <int>       <int>       <int>       <int>       <int>       <int>       <int>

Articles appeared most in the U.S. section in 2016, and in the World section the year before. World was the second-highest in 2016, and in 2015, Sports was the second-highest. Sports was the fourth-highest in 2016. Business Day was the most frequent section in 2006, followed by sports. 

This graph shows the top ten sections in 2016, and compares them across the prior 10 years. 

![graph_comparison](https://nadinesk.github.io/images/p.png)