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county_data.Rmd
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county_data.Rmd
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---
title: ""
author: ""
output:
html_document:
css: style.css
---
```{r setup, include=FALSE}
library(forcats)
library(DT)
library(readxl)
library(tidyverse)
library(highcharter)
library(glue)
library(acs)
knitr::knit_hooks$set(inline = function(x) { if(!is.numeric(x)){ x }else{ prettyNum(round(x,2), big.mark=",", digits=6) } })
hcoptslang <- getOption("highcharter.lang")
hcoptslang$thousandsSep <- ","
options(highcharter.lang = hcoptslang)
## Read in County-Level Data
county_pop<- readxl::read_xlsx("county_test.xlsx", sheet = "test")
county_cvap<- readxl::read_xlsx("county_test.xlsx", sheet = "cvap")
county_pop$pop_id<- as.factor(county_pop$pop_id)
#county_cvap$pop_id<- as.factor(county_cvap$pop_id)
county_pop_long <- county_pop %>% select(-pop_group , -geo2, -geo_id) %>%
gather(estimate_type, estimate, -name, -pop_id) %>% select(-estimate_type)
## Create Version 2 so I can map (can probably fig out a way to get rid of this)
county_pop_long2 <- county_pop %>% select(-pop_group , -geo2, -name) %>%
gather(estimate_type, estimate, -geo_id, -pop_id) %>% select(-estimate_type)
county_pop_long2 <- county_pop_long2 %>% mutate(fips = str_sub(geo_id, start= -5))
data(uscountygeojson)
racial_groups<- c(1,2,4,6,12,32,50,60)
aapi_alone<- c(1,12,50)
aapi_combo<- c(1,31,60)
Asian_orign_alone <- c(13:29)
Asian_origin_combo<- c(32:48)
NHPI_origin_alone <- c(51,55,56)
NHPI_origin_combo<- c(61:66)
county_pop_alone <- county_pop_long %>% filter(pop_id %in% aapi_alone )
county_pop_alone$pop_id <- fct_recode(county_pop_alone$pop_id,
"County Population" = "1",
"Asian" = "12",
"NHPI" = "50")
county_pop_combo <- county_pop_long %>% filter(pop_id %in% aapi_combo )
county_pop_combo$pop_id <- fct_recode(county_pop_combo$pop_id,
"County Population" = "1",
"Asian" = "31",
"NHPI" = "60")
county_pop_combo2 <- county_pop_long2 %>% filter(pop_id %in% aapi_combo )
county_pop_combo2$pop_id <- fct_recode(county_pop_combo2$pop_id,
"County Population" = "1",
"Asian" = "31",
"NHPI" = "60")
county_pop_table2_combo <- county_pop_combo2 %>%
spread(pop_id, estimate,fill = " - ")
ix <- 3:5
county_pop_table2_combo[ix] <- lapply(county_pop_table2_combo[ix], as.numeric)
county_pop_table2_combo <- county_pop_table2_combo %>%
mutate( check = case_when(
is.na(Asian) & is.na(NHPI) ~ T,
is.na(Asian) & !is.na(NHPI) ~ F,
!is.na(Asian) & is.na(NHPI) ~ F,
!is.na(Asian) & !is.na(NHPI)~ F))
county_pop_table2_combo <- county_pop_table2_combo %>% rowwise %>% mutate(
aapi = case_when(
check == F ~ sum(Asian,NHPI,na.rm=T)/`County Population`*100))
### CREATING TABLE
```
<div class="jumbotron">
<h1>County Data</h1>
<p>Use the buttons to select the topic and then the tabs for population groups</p>
</div>
# Population {.tabset .tabset-fade .tabset-pills}
These tables use the 2011-2015 5YR data from the American Community Survey.
## Asian & NHPI Alone
```{r Table1, echo=FALSE, message=FALSE, warning=FALSE}
#
# county_pop_table <- county_pop_alone %>%
# spread(pop_id, estimate,fill = " - ") %>%
# rename(County= name)
#
# ## Case When
# county_pop_table <- county_pop_table %>%
# mutate( check = case_when(
# is.na(Asian) & is.na(NHPI) ~ T,
# is.na(Asian) & !is.na(NHPI) ~ F,
# !is.na(Asian) & is.na(NHPI) ~ F,
# !is.na(Asian) & !is.na(NHPI)~ F))
#
# county_pop_table <- county_pop_table %>% rowwise %>% mutate(
# aapi = case_when(
# check == F ~ sum(Asian,NHPI,na.rm=T)/`County Population`*100))
#
#
# #Reordering columns
# ix <- 2:4
# county_pop_table[ix] <- lapply(county_pop_table[ix], as.numeric)
#
#
#
# county_pop_table <- county_pop_table [c(1,5,2,3,4)]
#
#
# county_pop_table %>%
# datatable(extensions =
# list("Buttons" = NULL, 'FixedColumns'= T, "Scroller"=T),
# rownames= FALSE,
# options = list(
# dom = 'Brtp',
# buttons = c('copy', 'csv', 'excel'),
# scrollX = TRUE,
# fixedColumns = list(leftColumns = 1),
# columnDefs = list(list(className = 'dt-head-left')),
# lengthMenu = c(25, 50, 100), pageLength = 100)) %>% formatPercentage(columns=c(2),2) %>% formatCurrency(columns = c(3,4,5),currency = "", interval = 3, mark = ",", digits=0)
```
## Asian & NHPI Combo
```{r Table2, echo=FALSE, message=FALSE, warning=FALSE}
#
# county_pop_table_combo <- county_pop_combo %>%
# spread(pop_id, estimate,fill = " - ") %>%
# rename(County= name)
#
#
# ## Case When
# county_pop_table_combo <- county_pop_combo %>%
# mutate( check = case_when(
# is.na(Asian) & is.na(NHPI) ~ T,
# is.na(Asian) & !is.na(NHPI) ~ F,
# !is.na(Asian) & is.na(NHPI) ~ F,
# !is.na(Asian) & !is.na(NHPI)~ F))
#
# county_pop_table_combo <- county_pop_table_combo %>% rowwise %>% mutate(
# aapi = case_when(
# check == F ~ sum(Asian,NHPI,na.rm=T)/`County Population`*100))
#
#
#
#
# #Reordering columns
#
#
# ix <- 2:4
# county_pop_table_combo[ix] <- lapply(county_pop_table_combo[ix], as.numeric)
#
# county_pop_table_combo <- county_pop_table_combo %>%
# mutate(pct_aapi = sum(Asian,NHPI,na.rm = TRUE)/`County Population`)%>% arrange(desc(pct_aapi)) %>% rename(`Percent AAPI` = pct_aapi)
#
# county_pop_table_combo <- county_pop_table_combo [c(1,5,2,3,4)]
#
#
# county_pop_table_combo %>%
# datatable(extensions =
# list("Buttons" = NULL, 'FixedColumns'= T, "Scroller"=T),
# rownames= FALSE,
# options = list(
# dom = 'Brt',
# buttons = c('copy', 'csv', 'excel'),
# scrollX = TRUE,
# fixedColumns = list(leftColumns = 1),
# columnDefs = list(list(className = 'dt-head-left')),
# lengthMenu = c(25, 50, 100), pageLength = 50)) %>% formatPercentage(columns=c(2),2) %>% formatCurrency(columns = c(3,4,5),currency = "", interval = 3, mark = ",", digits=0)
#
```
## Visualize Data
```{r map, echo=FALSE, message=FALSE, warning=FALSE}
#
# x <- c("{point.name}:", "AAPI")
# title<-"AAPI Population"
# y <- c(" {point.County Population:,.0f}", " {point.aapi:.2f}%")
#
# library(stringr)
# county_pop_table2_combo_CA <- county_pop_table2_combo %>% filter(startsWith(fips,"06"))
#
# highchart() %>%
# hc_title(text = "AAPI County Level Population", align = "center") %>%
# hc_subtitle(text = "Source: 2011-2015 ACS using Asian/NHPI Combo", align = "center") %>%
# hc_chart(backgroundColor = "#ffffff") %>%
# hc_add_series_map(uscountygeojson, county_pop_table2_combo,
# value = "aapi", joinBy = "fips")%>%
# # borderColor= "null",
# # boarderWidth = 0) %>%
# hc_tooltip(pointFormat = tooltip_table(x,y,title), useHTML=T, headerFormat= "")%>%
# hc_add_theme(hc_theme_538()) %>%
# hc_colorAxis(min = 0, max=100,stops = color_stops(n=5, RColorBrewer::brewer.pal(5,"Spectral")),labels= list(format = "{value}%"),showInLegend=T) %>%
# hc_mapNavigation(enabled = TRUE)
#
# cities <- geojsonio::us_cities
#
# highchart() %>%
# hc_title(text = "AAPI County Level Population", align = "center") %>%
# hc_subtitle(text = "Source: 2011-2015 ACS using Asian/NHPI Combo", align = "center") %>%
# hc_chart(backgroundColor = "#ffffff") %>%
# hc_add_series_map(uscountygeojson, county_pop_table2_combo_CA,
# value = "aapi", joinBy = "fips")%>%
# # borderColor= "null",
# # boarderWidth = 0) %>%
# hc_tooltip(pointFormat = tooltip_table(x,y,title), useHTML=T, headerFormat= "")%>%
# hc_add_theme(hc_theme_538()) %>%
# hc_colorAxis(min = 0, max=100,stops = color_stops(n=5, RColorBrewer::brewer.pal(5,"Spectral")),labels= list(format = "{value}%"),showInLegend=T) %>%
# hc_mapNavigation(enabled = TRUE)
#
# ```