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summary_tables_PA_AP.Rmd
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---
title: "Summary Tables ITHIMR"
author: "Rahul Goel, MRC Epidemiology Unit, University of Cambridge, UK"
date: "25 April 2019"
output:
html_document: default
word_document: default
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(comment=NA, prompt=FALSE, cache=FALSE, echo=F, results='asis')
```
# Introduction
These are the summary tables of the following items
1) Individual-level PM2.5 concentrations for baseline and scenarios
2) Individual-level physical activity for baseline and scenarios
3) Distance mode shares for baseline and scenarios
```{r, echo=FALSE}
#setwd('C:/Users/rg574/Dropbox/spatiotemporal analysis fatalities inida/Rajasthan tourism road deaths') #to create pretty tables
```
```{r, message=FALSE, warning=FALSE}
#library(INLA) #loading the INLA package
library(ggplot2) #loading ggplot package for plotting graphs
library(knitr)
library(tidyr)
library(dplyr)
```
# Boxplots of Individual-level PM2.5 Concentrations
```{r, message=FALSE, warning=FALSE, echo=FALSE}
io <- readRDS("results/multi_city/io.rds")
cities <- c("accra", "sao_paulo", "delhi", "bangalore")
high<- c(70, 25, 150,60)
low<- c(40,15,100,40)
for (x in 1: length(cities))
{
names(io[[cities[x]]]$outcomes$pm_conc_pp)[6:11]<-c("baseline","walk_scen", "bike_scen", "car_scen", "MC_scen", "bus_scen")
data_long <- gather(io[[cities[x]]]$outcomes$pm_conc_pp, scenario, pm_conc, baseline:bus_scen, factor_key=TRUE)
y<-ggplot(data_long, aes(x=scenario, y=pm_conc, fill=scenario)) + geom_boxplot(outlier.shape = NA)+ ggtitle(cities[x])+ labs(y="Daily PM2.5 Concentration", x = "Scenarios")+ylim(low[x],high[x])
print(y)
}
```
# Descriptive tables of Individual-level PM2.5 Concentrations
```{r, message=FALSE, warning=FALSE, echo=FALSE}
for (x in 1: length(cities))
{
names(io[[cities[x]]]$outcomes$pm_conc_pp)[6:11]<-c("baseline","walk_scen", "bike_scen", "car_scen", "MC_scen", "bus_scen")
data_long <- gather(io[[cities[x]]]$outcomes$pm_conc_pp, scenario, pm_conc, baseline:bus_scen, factor_key=TRUE)
summary<- as.data.frame(data_long %>% group_by(scenario) %>% summarise('mean'=mean(pm_conc),'5th'=quantile(pm_conc, 0.05),'50th'=quantile(pm_conc, 0.5),'95th'=quantile(pm_conc, 0.9)))
summary<- cbind(summary$scenario ,round(summary[,2:5], digits=1))
summary$change_PM<- round(io[[cities[x]]]$outcomes$scenario_pm - io[[cities[x]]]$outcomes$scenario_pm[1], digits=2)
names(summary)[1]<-"Scenario"
print(kable(summary, caption= cities[x]))
}
```
<!-- # Descriptive tables of emission inventory -->
<!-- ```{r, message=FALSE, warning=FALSE, echo=FALSE} -->
<!-- trans_share<-c(22.5, 40.0, 22.5, 28.1) -->
<!-- pm_conc<- c(50, 18, 122, 47 ) -->
<!-- city<-cities -->
<!-- shares<- as.data.frame(cbind(city,as.numeric(trans_share), pm_conc)) -->
<!-- names(shares)[2]<- "trans_share" -->
<!-- for (x in 1:length(cities)) -->
<!-- { -->
<!-- modes<-names(unlist(io[[cities[x]]]$emission_inventory)) -->
<!-- emissions<-as.data.frame(unlist(io[[cities[x]]]$emission_inventory)) -->
<!-- city_emissions<-cbind(as.data.frame(modes), as.data.frame(emissions$`unlist(io[[cities[x]]]$emission_inventory)`)) -->
<!-- names(city_emissions)[2]<- "emissions" -->
<!-- select<- c("car", "motorcycle", "bus_driver", "truck", "big_truck") -->
<!-- city_emissions$modes<- as.character(city_emissions$modes) -->
<!-- city_emissions$modes[!(city_emissions$modes %in% select)]<- "other" -->
<!-- summary<-city_emissions %>% group_by(modes) %>% summarise(sum(emissions)) -->
<!-- names(summary)[2]<- "emissions" -->
<!-- summary$emissions <- round(summary$emissions*100/sum(summary$emissions), digits=1) -->
<!-- summary<-as.data.frame(summary) -->
<!-- summary[nrow(summary)+1,1]<-"Transport share" -->
<!-- summary[nrow(summary),2]<-as.character(shares$trans_share[x]) -->
<!-- summary[nrow(summary)+1,1]<-"PM2.5 Conc" -->
<!-- summary[nrow(summary),2]<-as.character(shares$pm_conc[x]) -->
<!-- print(kable(summary, caption= cities[x])) -->
<!-- } -->
<!-- ``` -->
# Descriptive tables of emission inventory
```{r, message=FALSE, warning=FALSE, echo=FALSE}
trans_share<-c(22.5, 40.0, 22.5, 28.1)
pm_conc<- c(50, 18, 122, 47 )
city<-cities
shares<- as.data.frame(cbind(city,as.numeric(trans_share), pm_conc))
names(shares)[2]<- "trans_share"
for (x in 1:length(cities))
{
modes<-names(unlist(io[[cities[x]]]$emission_inventory))
emissions<-as.data.frame(unlist(io[[cities[x]]]$emission_inventory))
city_emissions<-cbind(as.data.frame(modes), as.data.frame(emissions$`unlist(io[[cities[x]]]$emission_inventory)`))
names(city_emissions)[2]<- "emissions"
select<- c("car", "motorcycle", "bus_driver", "truck", "big_truck")
city_emissions$modes<- as.character(city_emissions$modes)
city_emissions$modes[!(city_emissions$modes %in% select)]<- "other"
summary<-city_emissions %>% group_by(modes) %>% summarise(sum(emissions))
names(summary)[2]<- cities[x]
summary[[cities[x]]] <- round(summary[[cities[x]]]*100/sum(summary[[cities[x]]]), digits=1)
summary<-as.data.frame(summary)
summary[nrow(summary)+1,1]<-"Transport share"
summary[nrow(summary),2]<-as.character(shares$trans_share[x])
summary[nrow(summary)+1,1]<-"PM2.5 Conc"
summary[nrow(summary),2]<-as.character(shares$pm_conc[x])
io[[cities[x]]]$summary_emission<- summary
}
summary_all<- cbind(io[[cities[1]]]$summary_emission,io[[cities[2]]]$summary_emission,io[[cities[3]]]$summary_emission,io[[cities[4]]]$summary_emission)
summary_all<- summary_all[-c(3,5,7)]
summary_all$modes[2]<- "bus"
print(kable(summary_all))
```
# Boxplots of Individual-level Physical activity (MMETs)
```{r, message=FALSE, warning=FALSE, echo=FALSE}
limit=100
for (x in 1: length(cities))
{
names(io[[cities[x]]]$outcomes$mmets)[5:10]<-c("baseline","walk_scen", "bike_scen", "car_scen", "MC_scen", "bus_scen")
data_long <- gather(io[[cities[x]]]$outcomes$mmets, scenario, mmet, baseline:bus_scen, factor_key=TRUE)
y<-ggplot(data_long, aes(x=scenario, y=mmet, fill=scenario)) + geom_boxplot(outlier.shape = NA)+ ggtitle(cities[x])+ labs(y="Weekly MMET", x = "Scenarios")+ ylim(0, limit)
print(y)
}
```
# Descriptive tables of Individual-level Physical activity (MMETs)
```{r, message=FALSE, warning=FALSE, echo=FALSE}
for (x in 1: length(cities))
{
names(io[[cities[x]]]$outcomes$mmets)[5:10]<-c("baseline","walk_scen", "bike_scen", "car_scen", "MC_scen", "bus_scen")
data_long <- gather(io[[cities[x]]]$outcomes$mmets, scenario, mmet, baseline:bus_scen, factor_key=TRUE)
summary<- as.data.frame(data_long %>% group_by(scenario) %>% summarise('mean'=mean(mmet),'5th'=quantile(mmet, 0.05),'50th'=quantile(mmet, 0.5),'95th'=quantile(mmet, 0.9)))
summary<- cbind(summary$scenario ,round(summary[,2:5], digits=1))
names(summary)[1]<-"Scenario"
print(kable(summary, caption= cities[x]))
}
```
# Descriptive tables of injury outcomes
```{r, message=FALSE, warning=FALSE, echo=FALSE}
for (x in 1: length(cities))
{
summary<- as.data.frame(io[[cities[x]]]$outcomes$injuries %>% group_by(scenario) %>% summarise('Ped'=sum(pedestrian,na.rm=T),'Bike'= sum(bicycle), 'Car'=sum(car), "Motorcycle"=sum(motorcycle,na.rm=T), "All modes"=sum(Deaths,na.rm=T)))
summary$scenario<- c("baseline","walk_scen", "bike_scen", "car_scen", "MC_scen", "bus_scen")
summary<- cbind(summary$scenario ,round(summary[,2:6], digits=0))
names(summary)[1]<-"Scenario"
print(kable(summary, caption= cities[x]))
}
```
# Striking vehicles in scenarios
```{r, message=FALSE, warning=FALSE, echo=FALSE}
for (x in 1: length(cities))
{
scenarios<- c('Baseline', 'Scenario 1', 'Scenario 2', 'Scenario 3', 'Scenario 4', 'Scenario 5')
for (i in 1: length(scenarios))
{
whw<-as.data.frame(rowSums(io[[cities[x]]]$outcomes$whw[[scenarios[i]]]$whw))
noov<-as.data.frame(sum(io[[cities[x]]]$outcomes$whw[[scenarios[i]]]$nov))
names(whw)[1]<- as.character(scenarios[i])
names(noov)[1]<-as.character(scenarios[i])
if (i ==1 )
{
summary<-round(rbind(whw,nov=noov), digits = 1)
}
else
{
summary<- cbind(summary, round(rbind(whw, nov=noov), digits=1) )
}
}
names(summary)<-c("base", "walk", "bike", "car", "MC", "bus")
print(kable(summary, caption= cities[x]))
}
```
# Distance share by scenarios in percentages (excludes city-specific minority modes-- auto rickshaws, subway, etc.)
```{r, message=FALSE, warning=FALSE, echo=FALSE}
for (x in 1: length(cities))
{
select<- c("car", "motorcycle", "walking", "bicycle", "bus")
io[[cities[x]]]$trip_scen_sets$trip_mode[!io[[cities[x]]]$trip_scen_sets$trip_mode %in% select]<- "other"
dist_scen<- io[[cities[x]]]$trip_scen_sets %>% group_by(trip_mode, scenario) %>% summarise(sum(trip_distance))
dist_scen<- as.data.frame (dist_scen)
names(dist_scen)[3]<-"distance"
dist_scen$distance<- round(dist_scen$distance, digits=0)
dist_scen<-spread(dist_scen, trip_mode, distance)
dist_scen$sum<-rowSums (dist_scen[2:(ncol(dist_scen))], na.rm = FALSE, dims = 1)
dist_scen_prop<- round(dist_scen[,2:(ncol(dist_scen))]*100/dist_scen$sum, digits=1)
dist_scen_prop<- cbind(dist_scen[,1], dist_scen_prop)
names(dist_scen_prop)[1]<-"Scenario"
dist_scen_prop$Scenario<- c("baseline","walk_scen", "bike_scen", "car_scen", "MC_scen", "bus_scen")
print(kable(dist_scen_prop, caption= cities[x]))
print(kable(dist_scen, caption= cities[x]))
}
```