---
title: "ETHPOP"
author: "Nathan Green, Imperial College London"
output:
flexdashboard::flex_dashboard:
orientation: columns
vertical_layout: fill
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(dplyr)
library(ggplot2)
library(reshape2)
library(tibble)
library(plotly)
# library(ggthemes)
# library(gridExtra)
library(shiny)
library(knitr)
data_dir <- "C:/Users/ngreen1/Documents/R/cleanETHPOP/output_data"
```
```{r}
# readin population data
clean_births <- read.csv(paste0(data_dir, "/clean_births.csv"))
clean_deaths <- read.csv(paste0(data_dir, "/clean_deaths.csv"))
clean_inmigrants <- read.csv(paste0(data_dir, "/clean_inmigrants.csv"))
clean_outmigrants <- read.csv(paste0(data_dir, "/clean_outmigrants.csv"))
clean_pop <- read.csv(paste0(data_dir, "/clean_pop.csv"))
```
```{r}
# raw_data_dir <- "C:/Users/ngreen1/Documents/data"
#
# # join ethnic group descriptions
# ethnic_grps <- read.csv(paste0(raw_data_dir, "/ETHPOP_ethnicgrps_Chapter18_PopulationProjections_Reese.csv"))
#
# clean_births <- merge(clean_births, ethnic_grps,
# by.x = "ETH.group",
# by.y = "abbreviation")
# clean_deaths <- merge(clean_deaths, ethnic_grps,
# by.x = "ETH.group",
# by.y = "abbreviation")
```
Column {data-width=500}
-----------------------------------------------------------------------
### Births by ethnic group over time
```{r}
# p <-
# clean_births %>%
# select(-X) %>%
# melt(id.vars = c("description", "year")) %>%
# as_tibble() %>%
# filter(variable == "tot_births") %>%
# mutate(description = as.factor(description),
# value = as.numeric(value)) %>%
# ggplot(mapping = aes(x = year, y = value, col = description)) +
# geom_line() +
# xlab("Year") + ylab("Population") +
# theme_bw()
#
# ggplotly(p)
```
Column {data-width=500}
-----------------------------------------------------------------------
```{r}
# selectInput("year",
# label = "Year: ",
# choices = 2011:2060,
# selected = 2011)
```
### Deaths by ethnic group, age groups and year
```{r}
# renderPlotly({
# p <-
# clean_deaths %>%
# select(-X) %>%
# mutate(agegrp = factor(agegrp, levels = c("[0,5)", "[5,10)", "[10,15)", "[15,20)", "[20,25)", "[25,30)",
# "[30,35)", "[35,40)", "[40,45)", "[45,50)", "[50,55)", "[55,60)",
# "[60,65)", "[65,70)", "[70,75)", "[75,80)", "[80,85)", "[85,90)",
# "[90,95)", "[95,100)", "[100,105)"))) %>%
# as_tibble() %>%
# filter(sex == "F",
# year == 2011) %>%
# # year == as.numeric(input$year)) %>%
# mutate(description = as.factor(description),
# deaths = as.numeric(deaths)) %>%
# ggplot(mapping = aes(x = agegrp, y = deaths, col = description, group = description)) +
# geom_line() +
# geom_point() +
# xlab("Age") + ylab("Population") +
# theme_gdocs() +
# theme(axis.text = element_text(size = 7),
# axis.text.x = element_text(angle = 60, hjust = 1))
#
# # ggplotly(p)
# })
```
### Chart C
```{r}
```