plot 1 code

gistfile1.txt

data_311 <- readRDS("all_data.RDS")
all_complaints <- data_311 %>% select(2, 6, 25) #Select "Complaint Type" and "Borough" columns
all_complaints$`Created Date` <- as.POSIXct(strptime(all_complaints$`Created Date`, "%m/%d/%Y %I:%M:%S %p"))
all_complaints$Year <- format(all_complaints$`Created Date`, "%Y")
all_complaints <- filter(all_complaints, Year != "2016")
all_complaints <- filter(all_complaints, Year != "2015")
all_complaints <- filter(all_complaints, Year != "2014")
all_complaints$`Complaint Type`[grepl("^Noise.*", all_complaints$`Complaint Type`)] <- "Noise" 
all_complaints <- all_complaints %>%
    group_by(Borough, `Complaint Type`) %>% summarise(Count = n())
all_complaints_NY <- all_complaints %>%
    group_by(`Complaint Type`) %>%
    summarise(Count = sum(Count)) %>%
    arrange(desc(Count))
top10_complaints_NY <- top_n(all_complaints_NY, 10, Count)
#### function below to fix all capitalized words
capwords <- function(s, strict = FALSE) {
    cap <- function(s) paste(toupper(substring(s, 1, 1)),
                  {s <- substring(s, 2); if(strict) tolower(s) else s},
                             sep = "", collapse = " " )
    sapply(strsplit(s, split = " "), cap, USE.NAMES = !is.null(names(s)))
}
##### function below to make subplots
figure1 <- function(df){
    df <- df[order(df$Count),]
    order <- df$`Complaint Type`
    order <- sapply(order, capwords, T)
    df$`Complaint Type` <- order
    df$`Complaint Type` <- factor(df$`Complaint Type`, levels = order)
    return(ggplot(df) + geom_bar(aes(x=`Complaint Type` , y = Count
                                     ), fill = "Orange",
             stat = "identity") + theme_minimal() +
    xlab("") + ylab("Number of Complaints") + coord_flip() )
}
# subplot 1
p_top10_complaints_NY  <- figure1(top10_complaints_NY) + ggtitle("New York City \nTop 10 Complaints 2010-2013")

top10_complaints_borough <- top_n(group_by(all_complaints, Borough), 10, Count)
top10_complaints_Man <- filter(top10_complaints_borough, Borough == "MANHATTAN")
# subplot 2
p_top10_complaints_Man  <- figure1(top10_complaints_Man) + ggtitle("Manhattan \nTop 10 Complaints 2010-2013") 

top10_complaints_Qns <- filter(top10_complaints_borough, Borough == "QUEENS")
# subplot 3
p_top10_complaints_Qns  <- figure1(top10_complaints_Qns) + ggtitle("Queens \nTop 10 Complaints 2010-2013")

top10_complaints_Bn <- filter(top10_complaints_borough, Borough == "BROOKLYN")
# subplot 4
p_top10_complaints_Bn  <- figure1(top10_complaints_Bn) + ggtitle("Brooklyn \nTop 10 Complaints 2010-2013")

top10_complaints_Brx <- filter(top10_complaints_borough, Borough == "BRONX")
# subplot 5
p_top10_complaints_Brx  <- figure1(top10_complaints_Brx) + ggtitle("Bronx \nTop 10 Complaints 2010-2013")

top10_complaints_SI <- filter(top10_complaints_borough, Borough == "STATEN ISLAND")
top10_complaints_SI$`Complaint Type`[4] <- "Missed Collection"
# subplot 6
p_top10_complaints_SI  <- figure1(top10_complaints_SI) + ggtitle("Staten Island \nTop 10 Complaints 2010-2013")

#### The following function allos multiple graphs in one plot, the following multiplot 
#### function is from http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/
multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {...}
##################################
################# final plot
multiplot(p_top10_complaints_NY, p_top10_complaints_Man, p_top10_complaints_Bn,
          p_top10_complaints_Qns, p_top10_complaints_Brx, p_top10_complaints_SI,
          cols=2)