Shinypresentation.Rmd

# INTRODUCTION
This R markdown document sets out to download crime information about Liverpool from Jan-Dec 2017. The dataset we use is UK Police Data, extracted using an API. We information includes the lat and long of where the crime was reported. Therefore, any spatial representations of crime will be skewed, since most crimes are reported at a Police station! Nevertheless, let's explore where crimes are more likely to take place in Liverpool. 

* No knowledge of R is required to run this R-markdown and all datasets are downloaded from the web :) - Just click run on each chunk or run all chunks at once.

# LOAD PACKAGES
```{r}
## Load several packages
ipak <- function(pkg){
  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
  if (length(new.pkg)) 
    install.packages(new.pkg, dependencies = TRUE, repos="http://cran.rstudio.com/")
  sapply(pkg, require, character.only = TRUE)
}
packages <- c("jsonlite", "httr", "lubridate", "tidyverse", "RCurl", "GISTools","rgeos", "rjson","rgdal", "leaflet", "leaflet.extras", "shiny", "leaflet.minicharts","plotly", "RColorBrewer", "knitr", "ggplot2", "magrittr", "plotly","mapview")
ipak(packages)
```

# POLICE API FUNCTION
```{r}
# define function for getting lat and long
getLonLat <- function(x) as.numeric(c(x$location$longitude, x$location$latitude))
# define function for obtaining crime category, street name, location type and month
getAttr <- function(x) c( x$category, x$location$street$name, x$location_type, x$month)

####### FUNCTION WRAPPER TO DOWNLOAD ALL CRIME DATA IN LIVERPOOL
# define default lat, long and date 
map.crime <- function(lat=53.401439, lng=-2.988477, date="2017-01") { # lat, lng and date act as dummy criteria)

# getForm() to retrieve crime data + custom polygon extracted from IMD 2015 LSOA data
    df <- getForm( 'http://data.police.uk/api/crimes-street/all-crime?poly=53.4756989923,-3.02206976313:53.475739291,-2.87295531616:53.3804245543,-2.81669712724:53.3244578818,-2.80580844551:53.3245418051,-2.87325777954:53.3820482956,-2.99594066867:53.3834915693,-2.99842835176&date=', # defined bounding box for retreiving crime data within the Liverpool area 
                            lat=lat, # 
                            long=lng,
                            date=date) # defined date range
    
    # convert from json to df
    crimes <- fromJSON(df)
    
    # extract location info - lat&lng
    crimes.loc <- t(sapply(crimes,getLonLat)) 
    # extract defined attributes
    crimes.attr <- as.data.frame(t(sapply(crimes,getAttr))) 
    # change attribute column names
    colnames(crimes.attr) <- c("category", "street", "location_type", "month")
    
    # convert to SpatialPointsDataFrame
    df <- SpatialPointsDataFrame(crimes.loc,crimes.attr) 
    # Specify CRS - WSG84 for web-mapping purposes
    proj4string(df) <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
    # create 2 new columns, lat&lng
    df$lng <- as.numeric(as.character(df$coords.x1))
    df$lat <- as.numeric(as.character(df$coords.x2))
    return(df)
}

# apply police API function to list of df with the defined months crime information is desired
## NOTE - THIS WILL TAKE SOME TIME
ff <- list(c1 <- map.crime(date=("2017-01")),
              c2 <- map.crime(date=("2017-02")),
              c3 <- map.crime(date=("2017-03")),
              c4 <- map.crime(date=("2017-04")),
              c5 <- map.crime(date=("2017-05")),
              c6 <- map.crime(date=("2017-06")),
              c7 <- map.crime(date=("2017-07")),
              c8 <- map.crime(date=("2017-08")),
              c9 <- map.crime(date=("2017-09")),
              c10 <- map.crime(date=("2017-10")),
              c11 <- map.crime(date=("2017-11")),
              c12 <- map.crime(date=("2017-12")) )
#name each df in list
names(ff) <- c("c1","c2", "c3", "c4", "c5","c6", "c7", "c8", "c9", "c10", "c11", "c12")
# export df in list to the global environment
list2env(ff ,.GlobalEnv)
```

# DOWNLOAD LSOAS FOR THE WHOLE UK & OVERLAY WITH CRIME DATA- LARGE FILE
```{r}
# downloaded LSOA shapefile for all England & Wales LSOAs from Arcgis
## don't worry about the time taking to download it - it's quite a large file 229.3 mb to be exact
tmp <- tempfile()
download.file("http://geoportal1-ons.opendata.arcgis.com/datasets/da831f80764346889837c72508f046fa_0.zip", destfile = tmp)
unzip(zipfile = tmp, exdir = ".")
# read downloaded shapefile
LSOA <- readOGR('./Lower_Layer_Super_Output_Areas_December_2011_Full_Clipped__Boundaries_in_England_and_Wales.shp')
# filter liverpool lsoas only
liv_LSOA <- dplyr::filter(LSOA@data, grepl('Liverpool', lsoa11nm))
# convert to list  
liv_LSOA1 <- as.character(liv_LSOA$lsoa11cd)
# subset LSOA shapefile from created list
LSOA <- LSOA[LSOA$lsoa11cd %in% liv_LSOA1, ]
# transform CRS of data to WSG84
LSOA <- spTransform(LSOA, CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"))
# get only LSOA column
LSOA <- LSOA[,c('lsoa11cd')]

library(rmapshaper)
library(classInt)
# simplify geometry 
LSOA <- ms_simplify(LSOA, keep = 0.05, method = 'dp', weighting = 0.7,
  keep_shapes = FALSE, no_repair = FALSE, snap = TRUE, explode = FALSE,
  force_FC = TRUE, drop_null_geometries = TRUE, snap_interval = NULL,
  sys = FALSE)
# transform to right projection
LSOA <- spTransform(LSOA, CRS("+proj=longlat +datum=WGS84"))

# check it plots ok
plot(LSOA)
```

# DATA WRANGLING 
```{r}
# join all months together
crime2017 <-  rbind(c1,c2,c3,c4,c5,c6,c7,c8,c9,c10,c11,c12)
# ensure right projection
crime2017 <- spTransform(crime2017, CRS("+proj=longlat +datum=WGS84"))

# since the number of crimes are roughly 90,000 spatial points, this will not give a good visualization of where crimes are more likely. ALthough we could use KDE or IDW to visualize this, we chose to use simple choropleth mapping. This allows us to explore how the Geography of crime changes over time using interactive mapping. 

# we are also interested in the top 5 reported crime categories
crime2017 <- crime2017[crime2017$category == "anti-social-behaviour" | crime2017$category =="burglary" | crime2017$category =="violent-crime" | crime2017$category =="shoplifting" | crime2017$category =="criminal-damage-arson", ]
# convert to character for subsetting
crime2017$category <- as.character(crime2017$category)
crime2017$month <- as.character(crime2017$month)
# change row names 
crime2017$category[crime2017$category == "anti-social-behaviour"] <- "anti"
crime2017$category[crime2017$category == "burglary"] <- "burg"
crime2017$category[crime2017$category == "violent-crime"] <- "vio"
crime2017$category[crime2017$category == "shoplifting"] <- "shop"
crime2017$category[crime2017$category == "criminal-damage-arson"] <- "crim"

# create count column 
crime2017$count <- 1
# overlay spatial point data with lsoa information
crime2017$street <- over(crime2017, LSOA[,"lsoa11cd"])
crime2017$lsoa11cd <- crime2017$street$lsoa11cd

# 1.0 aggregate crime by type
# aggregate crime counts by lsoa, category and month
crime2017 <- aggregate(count ~ category+lsoa11cd+month, data=crime2017, FUN=sum) 
# subset by month
jan <- crime2017[crime2017$month == '2017-01', ]
feb <- crime2017[crime2017$month == '2017-02', ]
mar <- crime2017[crime2017$month == '2017-03', ]
apr <- crime2017[crime2017$month == '2017-04', ]
may <- crime2017[crime2017$month == '2017-05', ]
jun <- crime2017[crime2017$month == '2017-06', ]
jul <- crime2017[crime2017$month == '2017-07', ]
aug <- crime2017[crime2017$month == '2017-08', ]
sep <- crime2017[crime2017$month == '2017-09', ]
oct <- crime2017[crime2017$month == '2017-10', ]
nov <- crime2017[crime2017$month == '2017-11', ]
dec <- crime2017[crime2017$month == '2017-12', ]

# load library to reshape a data frame by aggregated form
library(reshape)
# cast with aggregation - this ensures that all MSOAs in each month have a value - those with no observations = 0
jan <- as.data.frame(cast(jan,lsoa11cd~category))
feb <- as.data.frame(cast(feb,lsoa11cd~category))
mar <- as.data.frame(cast(mar,lsoa11cd~category))
apr <- as.data.frame(cast(apr,lsoa11cd~category))
may <- as.data.frame(cast(may,lsoa11cd~category))
jun <- as.data.frame(cast(jun,lsoa11cd~category))
jul <- as.data.frame(cast(jul,lsoa11cd~category))
aug <- as.data.frame(cast(aug,lsoa11cd~category))
sep <- as.data.frame(cast(sep,lsoa11cd~category))
oct <- as.data.frame(cast(oct,lsoa11cd~category))
nov <- as.data.frame(cast(nov,lsoa11cd~category))
dec <- as.data.frame(cast(dec,lsoa11cd~category))

# combine all crime counts
c_all <- aggregate(count~lsoa11cd+month,crime2017,FUN=sum)

# subset by month
jan2 <- c_all[c_all$month == '2017-01', ] 
feb2 <- c_all[c_all$month == '2017-02', ]
mar2 <- c_all[c_all$month == '2017-03', ]
apr2 <- c_all[c_all$month == '2017-04', ]
may2 <- c_all[c_all$month == '2017-05', ]
jun2 <- c_all[c_all$month == '2017-06', ]
jul2 <- c_all[c_all$month == '2017-07', ]
aug2 <- c_all[c_all$month == '2017-08', ]
sep2 <- c_all[c_all$month == '2017-09', ]
oct2 <- c_all[c_all$month == '2017-10', ]
nov2 <- c_all[c_all$month == '2017-11', ]
dec2 <- c_all[c_all$month == '2017-12', ]
# merge crime count and type together
jan2 <- merge(jan2,jan,by='lsoa11cd')
feb2 <- merge(feb2,jan,by='lsoa11cd')
mar2 <- merge(mar2,jan,by='lsoa11cd')
apr2 <- merge(apr2,jan,by='lsoa11cd')
may2 <- merge(may2,jan,by='lsoa11cd')
jun2 <- merge(jun2,jan,by='lsoa11cd')
jul2 <- merge(jul2,jan,by='lsoa11cd')
aug2 <- merge(aug2,jan,by='lsoa11cd')
sep2 <- merge(sep2,jan,by='lsoa11cd')
oct2 <- merge(oct2,jan,by='lsoa11cd')
nov2 <- merge(nov2,jan,by='lsoa11cd')
dec2 <- merge(dec2,jan,by='lsoa11cd')


# make each month spatial by joining with the LSOA shapefile 
jan2 <- merge(LSOA@data,jan2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
feb2 <- merge(LSOA@data,feb2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
mar2 <- merge(LSOA@data,mar2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
apr2 <- merge(LSOA@data,apr2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
may2 <- merge(LSOA@data,may2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
jun2 <- merge(LSOA@data,jun2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
jul2 <- merge(LSOA@data,jul2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
aug2 <- merge(LSOA@data,aug2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
sep2 <- merge(LSOA@data,sep2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
oct2 <- merge(LSOA@data,oct2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
nov2 <- merge(LSOA@data,nov2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
dec2 <- merge(LSOA@data,dec2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)

# create simple function
na.function <- function(x){
  x[is.na(x)] = 0
  return(x)
}

# apply na function to all dfs
jan2 <- na.function(jan2)
feb2 <- na.function(feb2)
mar2 <- na.function(mar2)
apr2 <- na.function(apr2)
may2 <- na.function(may2)
jun2 <- na.function(jun2)
jul2 <- na.function(jul2)
aug2 <- na.function(aug2)
sep2 <- na.function(sep2)
oct2 <- na.function(oct2)
nov2 <- na.function(nov2)
dec2 <- na.function(dec2)

# make each month spatial by joining with the LSOA shapefile 
jan2 <- merge(LSOA,jan2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
feb2 <- merge(LSOA,feb2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
mar2 <- merge(LSOA,mar2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
apr2 <- merge(LSOA,apr2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
may2 <- merge(LSOA,may2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
jun2 <- merge(LSOA,jun2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
jul2 <- merge(LSOA,jul2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
aug2 <- merge(LSOA,aug2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
sep2 <- merge(LSOA,sep2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
oct2 <- merge(LSOA,oct2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
nov2 <- merge(LSOA,nov2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
dec2 <- merge(LSOA,dec2,by='lsoa11cd',all=TRUE,duplicateGeoms = TRUE)
# convert all 0 values to correct month names 
jan2$month[jan2$month==0] <- '2017-01'
feb2$month[feb2$month==0] <- '2017-02'
mar2$month[mar2$month==0] <- '2017-03'
apr2$month[apr2$month==0] <- '2017-04'
may2$month[may2$month==0] <- '2017-05'
jun2$month[jun2$month==0] <- '2017-06'
jul2$month[jul2$month==0] <- '2017-07'
aug2$month[aug2$month==0] <- '2017-08'
sep2$month[sep2$month==0] <- '2017-09'
oct2$month[oct2$month==0] <- '2017-10'
nov2$month[nov2$month==0] <- '2017-11'
dec2$month[dec2$month==0] <- '2017-12'
```

# LEAFLET - VISUALIZE DATA
* NOTE MAPBOX BASE MAPS WILL NOT LOAD IN R - NOT AN ERROR - WILL LOAD IN KNITTED R-MARKDOWNS AND SHINY APPS 

```{r}
# define colour scheme
pal <- colorQuantile("Reds", domain = jan2$count, n=6)
pal1 <- colorQuantile("Reds", domain = feb2$count, n=6)
pal2 <- colorQuantile("Reds", domain = mar2$count, n=6)
pal3 <- colorQuantile("Reds", domain = apr2$count, n=6)
pal4 <- colorQuantile("Reds", domain = may2$count, n=6)
pal5 <- colorQuantile("Reds", domain = jun2$count, n=6)
pal6 <- colorQuantile("Reds", domain = jul2$count, n=6)
pal7 <- colorQuantile("Reds", domain = aug2$count, n=6)
pal8 <- colorQuantile("Reds", domain = sep2$count, n=6)
pal9 <- colorQuantile("Reds", domain = oct2$count, n=6)
pal10 <- colorQuantile("Reds", domain = nov2$count, n=6)
pal11 <- colorQuantile("Reds", domain = dec2$count, n=6)

#label function
#label function
labs <- function(df4){
 sprintf("<span style='color: #858585; font: Arial;font-size: 12pt'><strong>Crime Type</strong><br/>
           <span style='color: #000000; font: Arial;font-size: 8pt'><strong>Anti Social Behaviour: %g</strong><br/> 
           <span style='color: #000000; font: Arial; font-size: 8pt'><strong>Burglary: %g</strong><br/>
           <span style='color: #000000; font: Arial; font-size: 8pt'><strong>Violent Crime: %g</strong><br/>
           <span style='color: #000000; font: Arial; font-size: 8pt'><strong>Shoplifting: %g</strong><br/>
           <span style='color: #000000; font: Arial; font-size: 8pt'><strong>Criminal Damage: %g</strong>",
    df4$anti, df4$burg, df4$vio, df4$shop, df4$crim) %>% 
    lapply(htmltools::HTML)
}

#apply label function
janL <- labs(jan2)
febL <- labs(feb2)
marL <- labs(mar2)
aprL <- labs(apr2)
mayL <- labs(may2)
junL <- labs(jun2)
julL <- labs(jul2)
augL <- labs(aug2)
sepL <- labs(sep2)
octL <- labs(oct2)
novL <- labs(nov2)
decL <- labs(dec2)

  
# vector tileset from mapbox
MBaccessToken <- "pk.eyJ1IjoiaWJyZWNraGUiLCJhIjoidVNHX1VpRSJ9.9fPQ1A3rdxyCAzPkeYSYEQ"
MBurlTemplate <- "https://api.mapbox.com/styles/v1/mapbox/dark-v9/tiles/{z}/{x}/{y}?access_token="
MBTemplate <- paste(MBurlTemplate,MBaccessToken,sep="")

#Define leaflet 
liverpoolCrime2017 <- leaflet() %>% 
  addTiles(MBTemplate, options=tileOptions(minZoom=11, maxZoom=18)) %>% setView(-2.959761, 53.404049, 11)

liverpoolCrime2017 %>%
   #Jan 2017
   addPolygons(data=jan2, 
   fillColor = ~pal(jan2$count),
   weight=0,
   fillOpacity = 0.6, 
   color = "white",
   dashArray = "0.1",
     highlight = highlightOptions(
    weight = 5,
    color = "#666",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label= janL,
  group = "<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>January</strong></span>") %>%
  
    
     #Feb 2017
   addPolygons(data=feb2, 
   fillColor = ~pal1(feb2$count),
   weight = 0.2,
   color = "white",
   dashArray = "0.1",
   fillOpacity = 0.6, 
     highlight = highlightOptions(
    weight = 5,
    color = "#666",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label= febL,
  group= "<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>February</strong></span>") %>%
  
       #Mar 2017
   addPolygons(data=mar2, 
   fillColor = ~pal2(mar2$count),
   weight = 0.2,
   fillOpacity = 0.6, 
   color = "white",
   dashArray = "0.1",
     highlight = highlightOptions(
    weight = 5,
    color = "#666",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label= marL,
  group= "<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>March</strong></span>") %>%
  
         #Apr 2017
   addPolygons(data=apr2, 
   fillColor = ~pal3(apr2$count),
   weight = 0.2,
   fillOpacity = 0.6,
   color = "white",
   dashArray = "0.1",
     highlight = highlightOptions(
    weight = 5,
    color = "#666",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label= aprL,
  group= "<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>April</strong></span>") %>%
  
         #May 2017
   addPolygons(data=may2, 
   fillColor = ~pal4(may2$count),
   weight = 0.2,
   fillOpacity = 0.6, 
   color = "white",
   dashArray = "0.1",
     highlight = highlightOptions(
    weight = 5,
    color = "#666",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label= mayL,
  group= "<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>May</strong></span>") %>%
  
           #Jun 2017
   addPolygons(data=jun2, 
   fillColor = ~pal5(jun2$count),
   weight = 0.2,
   fillOpacity = 0.6,
   color = "white",
   dashArray = "0.1",
     highlight = highlightOptions(
    weight = 5,
    color = "#666",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label= junL,
  group= "<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>June</strong></span>") %>%
  
             #Jul 2017
   addPolygons(data=jul2, 
   fillColor = ~pal6(jul2$count),
   weight = 0.2,
   fillOpacity = 0.6, 
   color = "white",
   dashArray = "0.1",
     highlight = highlightOptions(
    weight = 5,
    color = "#666",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label= julL,
  group= "<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>July</strong></span>") %>%
  
             #Aug 2017
   addPolygons(data=aug2, 
   fillColor = ~pal7(aug2$count),
   weight = 0.2,
   fillOpacity = 0.6, 
   color = "white",
   dashArray = "0.1",
     highlight = highlightOptions(
    weight = 5,
    color = "#666",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label= augL,
  group= "<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>August</strong></span>") %>%
  
             #Sep 2017
   addPolygons(data=sep2, 
   fillColor = ~pal8(sep2$count),
   weight = 0.2,
   fillOpacity = 0.6,
   color = "white",
   dashArray = "0.1",
     highlight = highlightOptions(
    weight = 5,
    color = "#666",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label= sepL,
  group= "<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>September</strong></span>") %>%
  
             #Oct 2017
   addPolygons(data=oct2, 
   fillColor = ~pal9(oct2$count),
   weight = 0.2,
   fillOpacity = 0.6,
   color = "white",
   dashArray = "0.1",
     highlight = highlightOptions(
    weight = 5,
    color = "#666",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label= octL,
  group= "<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>October</strong></span>") %>%
  
             #Nov 2017
   addPolygons(data=nov2, 
   fillColor = ~pal10(nov2$count),
   weight = 0.2,
   color = "white",
   dashArray = "0.1",
   fillOpacity = 0.6,
     highlight = highlightOptions(
    weight = 5,
    color = "#666",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label= novL,
  group= "<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>November</strong></span>") %>%
  
             #Dec 2017
   addPolygons(data=dec2, 
   fillColor = ~pal11(dec2$count),
   weight = 0.2,
   fillOpacity = 0.6,
   color = "white",
   dashArray = "0.1",
     highlight = highlightOptions(
    weight = 5,
    color = "#666",
    dashArray = "",
    fillOpacity = 0.7,
    bringToFront = TRUE),
  label= decL,
  group= "<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>December</strong></span>") %>%
  
#add group layers with CSS
  addLayersControl(baseGroups = c(
      "<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>January</strong></span>",
"<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>February</strong></span>",
"<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>March</strong></span>",
"<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>April</strong></span>",
"<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>May</strong></span>",
"<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>June</strong></span>",
"<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>July</strong></span>",
"<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>August</strong></span>",
"<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>September</strong></span>",
"<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>October</strong></span>",
"<span style='color:#858585;background:white:font:Arial;font-size:10pt'><strong>November</strong></span>",
"<span style='color:#858585;background:white;font:Arial;font-size:10pt'><strong>December</strong></span>"), options = layersControlOptions(collapsed = FALSE)) %>%
  
  #add scale bar
  addScaleBar(position = c("bottomleft"), options = scaleBarOptions(metric = TRUE)) 
```

# EXPORT SHAPEFILES
```{r}
writeOGR(jan2,'jan',dsn='./jan.shp',driver = 'ESRI Shapefile')
writeOGR(feb2,'feb',dsn='./feb.shp',driver = 'ESRI Shapefile')
writeOGR(mar2,'mar',dsn='./mar.shp',driver = 'ESRI Shapefile')
writeOGR(apr2,'apr',dsn='./apr.shp',driver = 'ESRI Shapefile')
writeOGR(may2,'may',dsn='./may.shp',driver = 'ESRI Shapefile')
writeOGR(jun2,'jun',dsn='./jun.shp',driver = 'ESRI Shapefile')
writeOGR(jul2,'jul',dsn='./jul.shp',driver = 'ESRI Shapefile')
writeOGR(aug2,'aug',dsn='./aug.shp',driver = 'ESRI Shapefile')
writeOGR(sep2,'sep',dsn='./sep.shp',driver = 'ESRI Shapefile')
writeOGR(oct2,'oct',dsn='./oct.shp',driver = 'ESRI Shapefile')
writeOGR(nov2,'nov',dsn='./nov.shp',driver = 'ESRI Shapefile')
writeOGR(dec,'dec',dsn='./dec.shp',driver = 'ESRI Shapefile')

```