update 2019 ACS 1 year

DESCRIPTION

@@ -1,10 +1,10 @@
 Package: totalcensus
 Type: Package
 Title: Extract Decennial Census and American Community Survey Data
-Version: 0.6.3
+Version: 0.6.4
 Author: Guanglai Li
 Maintainer: Guanglai Li <[email protected]>
-Date: 2019-12-20
+Date: 2020-09-26
 Description: Download summary files from Census Bureau <https://www2.census.gov/> 
     and extract data, in particular high resolution data at 
     block, block group, and tract level, from decennial census and 
@@ -15,7 +15,7 @@ License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
 Depends: 
-    R (>= 3.2.1)
+    R (>= 3.5.0)
 Imports:
     stringr (>= 1.2.0), 
     data.table (>= 1.10.1),
@@ -27,4 +27,4 @@ Suggests:
     rmarkdown,
     ggmap,
     ggplot2
-RoxygenNote: 7.0.2
+RoxygenNote: 7.1.1

R/data_acs.R

@@ -269,6 +269,37 @@
 "lookup_acs5year_2009"
 
 
+
+# lookup_acs1year_2019 =========================================================
+#' ACS 1-year 2019 file segment and table lookup data
+#'
+#' There is slightly difference in the lookup tables of each year.
+#'
+#' @docType data
+#'
+#' @format A data.table with 35527 rows and 7 variables
+#' \describe{
+#'   \item{file_segment}{sequence number of segment data files, from "0001" to "0166"}
+#'   \item{table_content}{description of columns in a table}
+#'   \item{reference}{reference of the table content, such as "B01001_002". The reference
+#'         is used to extract data of table content.}
+#'   \item{restriction}{restrictions applied the the table_content}
+#'   \item{table_number}{table number such as "B01001"}
+#'   \item{table_name}{description of table. A table has multiple columns (table_content)}
+#'   \item{universe}{the universe of the data}
+#' }
+#'
+#' @keywords datasets
+#'
+#' @source Check for each year of ACS 1-year and 5-year
+#' \href{https://www.census.gov/programs-surveys/acs/technical-documentation/summary-file-documentation.2015.html}{Sequence Number/Table Number Lookup File}.
+#'
+
+"lookup_acs1year_2019"
+
+
+
+
 # lookup_acs1year_2018 =========================================================
 #' ACS 1-year 2018 file segment and table lookup data
 #'
@@ -945,7 +976,8 @@
 #' \describe{
 #'   \item{table_number}{table number such as "C27013"}
 #'   \item{table_name}{description of the table}
-#'   \item{acs1_2017}{whether the table is available in 2018}
+#'   \item{acs1_2019}{whether the table is available in 2019}
+#'   \item{acs1_2018}{whether the table is available in 2018}
 #'   \item{acs1_2017}{whether the table is available in 2017}
 #'   \item{acs1_2016}{whether the table is available in 2016}
 #'   \item{acs1_2015}{whether the table is available in 2015}

README.Rmd

@@ -25,10 +25,10 @@ Download summary files from [Census Bureau](https://www2.census.gov/) and extrac
 
 
 ## Update
-**1/8/2020**: Version 0.6.3 is on CRAN. The 2018 ACS 5 year data was added to the package. The package now includes all latest data since 2000:
+**9/26/2020**: Version 0.6.4 is on CRAN. The 2019 ACS 1 year data was added to the package. The package now includes all latest data since 2000:
 
 - Decennial census 2000 and 2010
-- ACS 1 year: 2005 - 2018
+- ACS 1 year: 2005 - 2019
 - ACS 5 year: 2009 - 2018
 
 

application/E4_medium_home_values_all_block_groups.R

@@ -3,53 +3,53 @@ library(data.table)
 library(magrittr)
 library(ggplot2)
 library(ggmap)
-goog_api <- Sys.getenv("GOOG_API_KEY")
+goog_api <- Sys.getenv("GGMAP_GOOGLE_API_KEY")
 register_google(goog_api)
 
 # # national home value ==========================================================
-# home_national <- read_acs5year(
-#     year = 2016,
-#     states = states_DC,
-#     # all 50 states plus DC
-#     table_contents = "home_value = B25077_001",
-#     summary_level = "block group"
-# ) %>%
-#     .[!is.na(home_value)] %>%
-#     .[order(home_value)]
-#
-#
-# us_map <- get_map("united states", zoom = 4, color = "bw")
-#
-# ggmap(us_map) +
-#     geom_point(
-#         data = home_national,
-#         # displace higher values
-#         aes(lon, lat, size = population, color = home_value),
-#         alpha = 1
-#     ) +
-#     ylim(25, 50) +
-#     scale_size_area(max_size = 0.05) +
-#     scale_color_continuous(
-#         low = "green",
-#         high = "red",
-#         breaks = c(100000, 500000, 1000000, 1500000, 2000000),
-#         labels = scales::unit_format("K", 1e-3)
-#     ) +
-#     guides(size = FALSE) +
-#     labs(color = "value ($)",
-#          caption = "Source: 2012-2016 ACS 5-year estimate",
-#          title = "Median Home Values in Each Block Group") +
-#     theme(
-#         panel.background = element_blank(),
-#         axis.title = element_blank(),
-#         axis.text = element_blank(),
-#         axis.ticks = element_blank(),
-#         legend.position = c(0.928, 0.197)
-#     )
-#
-# ggsave(filename = "application/national_home_value.png",
-#        width = 9, dpi = 600)
-#
+home_national <- read_acs5year(
+    year = 2018,
+    states = states_DC,
+    # all 50 states plus DC
+    table_contents = "home_value = B25077_001",
+    summary_level = "block group"
+) %>%
+    .[!is.na(home_value)] %>%
+    .[order(home_value)]
+
+
+us_map <- get_map("united states", zoom = 4, color = "bw")
+
+ggmap(us_map) +
+    geom_point(
+        data = home_national,
+        # displace higher values
+        aes(lon, lat, size = population, color = home_value),
+        alpha = 1
+    ) +
+    ylim(25, 49) +
+    scale_size_area(max_size = 0.05) +
+    scale_color_continuous(
+        low = "#00CCFF",
+        high = "red",
+        breaks = c(100000, 500000, 1000000, 1500000, 2000000),
+        labels = scales::unit_format(unit = "K", scale = 1e-3)
+    ) +
+    guides(size = FALSE) +
+    labs(color = "value ($)",
+         caption = "Source: 2014-2018 ACS 5-year estimate",
+         title = "Median Home Values in Each Block Group") +
+    theme(
+        panel.background = element_blank(),
+        axis.title = element_blank(),
+        axis.text = element_blank(),
+        axis.ticks = element_blank(),
+        legend.position = c(0.928, 0.197)
+    )
+
+ggsave(filename = "application/national_home_value.png",
+       width = 9, dpi = 600)
+
 
 # New York home value ==========================================================
 home_ny <- read_acs5year(
@@ -87,10 +87,9 @@ ggmap(ny_map) +
     scale_size_area(max_size = 0.5) +
     scale_color_continuous(
         low = "green",
-        high = "red"
-        # ,
-        # breaks = c(100000, 500000, 1000000, 1500000, 2000000),
-        # labels = scales::unit_format("K", 1e-3)
+        high = "red",
+        breaks = c(100000, 500000, 1000000, 1500000, 2000000),
+        labels = scales::unit_format(unit = "K", scale = 1e-3)
     ) +
     guides(size = FALSE) +
     labs(color = "value ($)",