---
title: "Influenza report"
author: "Team Exploration_Breakout"
date: '`r format(Sys.Date(), "%B %d, %Y")`'
output: html_document
---
```{r echo=FALSE, warning=FALSE, message=F}

library(ggplot2)
library(dplyr)

rm(list=ls())
data(zip.regions)


multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) {
  library(grid)
  
  # Make a list from the ... arguments and plotlist
  plots <- c(list(...), plotlist)
  
  numPlots = length(plots)
  
  # If layout is NULL, then use 'cols' to determine layout
  if (is.null(layout)) {
    # Make the panel
    # ncol: Number of columns of plots
    # nrow: Number of rows needed, calculated from # of cols
    layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
                     ncol = cols, nrow = ceiling(numPlots/cols))
  }
  
  if (numPlots==1) {
    print(plots[[1]])
    
  } else {
    # Set up the page
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
    
    # Make each plot, in the correct location
    for (i in 1:numPlots) {
      # Get the i,j matrix positions of the regions that contain this subplot
      matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
      
      print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
                                      layout.pos.col = matchidx$col))
    }
  }
}

# Note: Code above will require installing devtools package and installing and loading choroplethrZip package. To do that use the lines # below:
library(devtools)
#install_github('arilamstein/choroplethrZip@v1.3.0')
library(choroplethrZip)

#### houston zips
#FNY.zips <- c(77002:77051, 77053:77096, 77098, 77099, 77201, 77336, 77338, 77339, 77345,
#                 77346,77357,77365,77373,77375,77377,77379,77386, 77388, 77396, 77401, 77406,
#                 77407,77429,77433,77447,77449,77450,77477,77478,77484,77489,77493,77494, 77498,  
#                 77503,77504,77506, 77520,77530,77532,77536,77546,77547,77571,77587,77598)

user.input <- read.csv("C:/Users/user/Dropbox/R code/FNY report/r shiny/test name_texas - harris_request_report_1443557460.csv")

zip.regions <-read.csv("C:/Users/user/Dropbox/R code/FNY report/r shiny/zip_regions.csv")

FNY.zips <- as.integer(user.input$zipcode)
FNY.name <- user.input$name[1]
FNY.ph <- user.input$phone[1]
FNY.email <- user.input$email[1]




FNY <- read.csv("C:/Users/user/Dropbox/EpiHack Analytics FNY Data/FNY_ZIPDATA_from_2012-08-27_to_2015-09-27.csv")


for (i in 1:2){
 FNY$zips <- paste(rep(0,nrow(FNY)),FNY$zip, sep = "")
}


substrRight <- function(x, n){
 substr(x, nchar(x)-n+1, nchar(x))
}
FNY$zips <- substrRight(FNY$zips, 5)
FNY <- subset(FNY, FNY$zips %in% FNY.zips)

FNY$symptomsum <- rowSums(FNY[c('fever', 'cough', 'sore_throat', 'chills', 
                                               'fatigue', 'nausea', 'diarrhea', 'bodyache', 
                                               'headache')])

df.fny <- aggregate(cbind(ili, participants,users, household, 
                          no_symptoms, nausea, symptomsum,
                          diarrhea) ~ week_of, data = FNY, FUN = sum)
df.fny <- df.fny[df.fny$week_of != "",]
df.fny$week_of <- as.Date(df.fny$week_of, "%Y-%m-%d")
df.fny <- df.fny[order(df.fny$week_of),]
df.fny <- df.fny[df.fny$week_of > "2013-07-01",]
rownames(df.fny) <- 1:nrow(df.fny)

df.fny$prop.participants <- df.fny$ili/df.fny$participants
df.fny$prop.users <- df.fny$ili/df.fny$users
df.fny$prop.users[is.nan(df.fny$prop.users) == T] <- 0


n <- nrow(df.fny)

latest.week <- df.fny$week_of[n]
latest.ili  <- df.fny$ili[n]
latest.participant <- df.fny$participants[n]
latest.ili.prop <- round(latest.ili/latest.participant,3)

prev.week <- df.fny$week_of[n-1]
prev.ili <- df.fny$ili[n-1]
prev.participant <- df.fny$participants[n-1]

df.fny$symptomrate <- df.fny$symptomsum/df.fny$participants
rownames(df.fny) <- 1:nrow(df.fny)

df.fny <- df.fny[complete.cases(df.fny),]
df.fny$proportion <- df.fny$ili/df.fny$participants
df.fny$Season <- format(df.fny$week_of, '%Y')

comp.lw <- if (latest.ili > prev.ili) {"an increase over"
    } else if (latest.ili < prev.ili) {"a decrease from"
    } else  "the same as"

max.ili.prop <- max(df.fny$proportion)

county.fips <- zip.regions$county.fips.numeric[zip.regions$region == FNY.zips[1]]

df.fny2 <- aggregate(cbind(participants) ~ zip, data = FNY, FUN = sum)
names(df.fny2)<-c("region","value")
p4<- zip_choropleth(df.fny2,
              county_zoom=c(county.fips), 
              title="Houston City - Count of FNY Participants per ZIP code",
              legend="Range of FNY Participants in ZIP code") # The value in county_zoom is a FIPS code for Harris County



```

Dear citizens of **`r FNY.name`**, for the week of **`r latest.week`** we had **`r latest.ili`** out of **`r latest.participant`** report being ill with influenza like illness (ILI). This is **`r comp.lw`** last week. We are grateful for each person who is contributing to our efforts to understand how influenza affects our community. If you wish to participate as a citizen scientist please sign up at FluNearYou.com or contact **`r FNY.name`** health department at **`r FNY.ph`**. 


**Figure 1** shows the proportion of FNY users within **`r FNY.name`** who reported having ILI symptoms. Currently we see **`r latest.ili.prop*100`%** of reporters with ILI-symptoms.

```{r echo = F}

p1 <- ggplot(df.fny, aes(x=week_of, y=proportion, color=Season)) + 
        geom_line() + 
        theme_minimal() +
        xlab('') + 
        ylim(0, max.ili.prop*1.5) +
        ggtitle('Proportion of participants reporting ILI') + ylab('Proportion')

print(p1)
```


**Figure 2** shows the average number of symptoms reported per participant. This past week saw an average of **`r round(df.fny$symptomrate[n],2)`**. This value may suggest the severity of the current status of influenza.

```{r echo = F}
p2 <-  ggplot(df.fny, aes(x=week_of, y=symptomrate, color=Season)) + 
        geom_line() + 
        theme_minimal() +
        xlab('') + 
        ylab('Symptoms rate') + 
        ggtitle('Symptoms reported per participant')

print(p2)
```

This past week we had **`r latest.participant`** reporters. Our goal is to reach a weekly average of 300. 

```{r echo=F}
p3 <- ggplot(df.fny, aes(x = week_of, y = participants, color = Season)) +
        geom_line() +
        theme_minimal() + 
        xlab("Week") +
        ylab("Weekly participation") +
        ylim(0, max(df.fny$participants*1.1)) + 
        ggtitle("Weekly participants who reported to FNY")

print(p3)

```

**Figure 4** is a histogram of symptom rates.

```{r echo = F, warning = FALSE, message = FALSE}
 p4 <- ggplot(df.fny, aes(symptomrate)) + 
  geom_histogram(colour = "lightblue", fill = "lightblue") + theme_minimal() + 
  ylab('Count') + xlab('Symptoms') + ggtitle('Histogram of symptoms per participant')

print(p4)

```

**Figure 5** is a map of participation by ZIP code.

```{r echo = F, warning = FALSE, message = FALSE}
p5<- zip_choropleth(df.fny2,
              county_zoom=c(county.fips), 
              title="Houston City - Count of FNY Participants per ZIP code",
              legend="Range of FNY Participants in ZIP code") # The value in county_zoom is a FIPS code for Harris County

print(p5)

```