leaflet.Rmd

---
title: "playingWithLeaflet"
author: "Kristin Robinson"
date: "April 30, 2017"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
options(stringsAsFactors = FALSE)

library(devtools)
library(leaflet)
library(dplyr)
library(ggplot2)
library(rjson)
library(jsonlite)
library(rgdal)
library(tidytext)
library(stringr)
```


```{r}
# load data - longitude and latitude already transformed
twitter_data <- read.csv("data/twitter_31days.csv")
twitter_data$Timestamp<-as.POSIXct(twitter_data$Timestamp)
## manipulate long & lat into usable form ##
# split coordinates into two columns and remove special characters
twitter_data$lon <- sapply(strsplit(as.character(twitter_data$Coordinates),','), "[", 1) 
twitter_data$lon <- (sub('^\\[', '', twitter_data$lon)) %>% as.numeric(twitter_data$lon)
twitter_data$lat <- sapply(strsplit(as.character(twitter_data$Coordinates),','), "[", 2)
twitter_data$lat <- (sub(']$', '', twitter_data$lat))

# cast strings as num
twitter_data$lon <-as.numeric(twitter_data$lon)
twitter_data$lat <-as.numeric(twitter_data$lat)

twitter_data <- subset(twitter_data, 
                             twitter_data$Timestamp >= ('2014-08-24 04:20:44') &
                            twitter_data$Timestamp <= ('2014-09-24 04:20:43') )

# set eruption date and time
day1 <- as.Date("2014-08-24")
hour1 <- as.POSIXct("2014-08-24 04:20:44")

# add columns for elapsed days
twitter_data$days <- as.numeric(difftime (as.Date(twitter_data$Timestamp), day1, units = "days"))
twitter_data$days2 <- as.numeric(difftime(twitter_data$Timestamp, hour1, units = "days"))

# add columns for elapsed minutes
twitter_data$minutes <- as.numeric(difftime(twitter_data$Timestamp, hour1, units = "mins"))
twitter_data$seconds <- as.numeric(difftime(twitter_data$Timestamp, hour1, units = "secs"))

# Subset twitter data to 10 minutes post earthquake
twitter_data_10min <- subset(twitter_data, 
                             twitter_data$Timestamp >= ('2014-08-24 04:20:44') &
                            twitter_data$Timestamp <= ('2014-08-24 04:30:43') )
# clean up text and break down tweets by word


replace_reg <- "https://t.co/[A-Za-z\\d]+|http://[A-Za-z\\d]+|&amp;|&lt;|&gt;|RT|https"
unnest_reg <- "([^A-Za-z_\\d#@']|'(?![A-Za-z_\\d#@]))"

tidy_tweets <- twitter_data_10min %>%
  mutate(Text = str_replace_all(Text, replace_reg, "")) %>%
  unnest_tokens(word, Text, token = "regex", pattern = unnest_reg) %>%
  filter(!word %in% stop_words$word,
         str_detect(word, "[a-z]"))
```



```{r cars}
leaflet(tidy_tweets) %>%
  addTiles() %>%
  addCircleMarkers(lng=~lon, lat=~lat,
                   popup=~paste0(word))
```


```{r pressure, echo=FALSE}
plot(pressure)
```

```{r}
CalCen <- readOGR("data/CaliCensus/Cal_Cnty_RacePop.shp")

leaflet(CalCen) %>% addProviderTiles(providers$OpenMapSurfer.Roads) %>% addAwesomeMarkers( clusterOptions = markerClusterOptions()
,twitter_data_10min, lat=twitter_data_10min$lat, lng=twitter_data_10min$lon, popup=~twitter_data_10min$Text) %>% addPolygons(color = "#444444", weight = 1, smoothFactor = 0.5,opacity = 1.0, fillOpacity = 0.5, fillColor = ~colorQuantile("YlOrRd", CalCen$Total)(CalCen$Total), popup=paste0("<b>Total County Population: </b>", CalCen$Total)
,highlightOptions = highlightOptions(color = "white", weight = 2, bringToFront = TRUE))
```


Note that the `echo = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.