ACS_001

ACS_001

library(acs)

setwd("/Users/subasishdas1/Desktop/TRB2021")
api.key.install(key="12c55b28a8b13fd30e88db9e59bee9edd0fa8ce1")
## Grab the spatial data for all counties

library(tigris)

## To enable 
## caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.

## 
## Attaching package: 'tigris'

## The following object is masked from 'package:graphics':
## 
##     plot

library(leaflet)
library(mapview)
library(stringr)
library(maptools)

## Loading required package: sp

## Checking rgeos availability: TRUE

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:acs':
## 
##     combine

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

tracts <- tracts(state = 'TX', cb=TRUE)

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fetched <- acs.fetch(
  geography = geo.make(state = "TX", county="*", tract = "*"),
  endyear = 2012, span = 5,# Package only goes to 2013, so end=2012
  table.number = "B19001", # Table showing 'Income'
  col.names = "pretty")    # Gives the full column definitions

## Warning in (function (endyear, span = 5, dataset = "acs", keyword, table.name, : temporarily downloading and using archived XML variable lookup files;
##   since this is *much* slower, recommend running
##   acs.tables.install()

names(attributes(fetched))    # see what's available

##  [1] "endyear"        "span"           "acs.units"      "currency.year" 
##  [5] "modified"       "geography"      "acs.colnames"   "estimate"      
##  [9] "standard.error" "class"

attr(fetched, "acs.colnames")

##  [1] "Household Income: Total:"              
##  [2] "Household Income: Less than $10,000"   
##  [3] "Household Income: $10,000 to $14,999"  
##  [4] "Household Income: $15,000 to $19,999"  
##  [5] "Household Income: $20,000 to $24,999"  
##  [6] "Household Income: $25,000 to $29,999"  
##  [7] "Household Income: $30,000 to $34,999"  
##  [8] "Household Income: $35,000 to $39,999"  
##  [9] "Household Income: $40,000 to $44,999"  
## [10] "Household Income: $45,000 to $49,999"  
## [11] "Household Income: $50,000 to $59,999"  
## [12] "Household Income: $60,000 to $74,999"  
## [13] "Household Income: $75,000 to $99,999"  
## [14] "Household Income: $100,000 to $124,999"
## [15] "Household Income: $125,000 to $149,999"
## [16] "Household Income: $150,000 to $199,999"
## [17] "Household Income: $200,000 or more"

acs_df <- data.frame(
  paste0(
    str_pad(fetched@geography$state,  2, "left", pad="0"), 
    str_pad(fetched@geography$county, 3, "left", pad="0"), 
    str_pad(fetched@geography$tract,  6, "left", pad="0")),
  fetched@estimate[,c("Household Income: Total:", "Household Income: $200,000 or more")], 
  stringsAsFactors = FALSE)

acs_df           <- select(acs_df, 1:3) %>% tbl_df()
rownames(acs_df) <- 1:nrow(acs_df)

## Warning: Setting row names on a tibble is deprecated.

names(acs_df)    <- c("GEOID", "total", "over_200")
acs_df$percent   <- 100*(acs_df$over_200/acs_df$total)

df_merged <- geo_join(tracts, acs_df, "GEOID", "GEOID")

# there are some tracts with no land that we should exclude
df_merged <- df_merged[df_merged$ALAND>0,]


popup <- paste0("GEOID: ", df_merged$GEOID, "<br>", "Percent of Households above $200k: ", round(df_merged$percent,2))
pal <- colorNumeric(
  palette = "YlGnBu",
  domain = df_merged$percent
)
leaflet() %>%
  addProviderTiles("CartoDB.Positron") %>%
  addPolygons(data = df_merged, 
              fillColor = ~pal(percent), 
              color = "#b2aeae", # you need to use hex colors
              fillOpacity = 0.7, 
              weight = 1, 
              smoothFactor = 0.2,
              popup = popup) %>%
  addLegend(pal = pal, 
            values = df_merged$percent, 
            position = "bottomright", 
            title = "Percent of Households<br>above $200k",
            labFormat = labelFormat(suffix = "%"))