The code below perfectly returns what I need: the household median income for each puma using 2019 ACS (1-year). However, what is missing is the States name. I tried the option of state="all" but it did not work. How can I obtain my data of interest by states and puma?
Thanks,
NM
PUMA_level <- get_acs(geography = "puma",
variable = "B19013_001",
survey = "acs1",
# state="all",
year = 2019)
Using the usmap::fips_info function you could get a list of state codes, names and abbreviations which you could then merge to your census data like so:
library(tidycensus)
library(usmap)
PUMA_level <- get_acs(geography = "puma",
variable = "B19013_001",
survey = "acs1",
year = 2019,
keep_geo_vars = TRUE)
#> Getting data from the 2019 1-year ACS
#> The 1-year ACS provides data for geographies with populations of 65,000 and greater.
PUMA_level$fips <- substr(PUMA_level$GEOID, 1, 2)
states <- usmap::fips_info(unique(PUMA_level$fips))
#> Warning in get_fips_info(fips_, sortAndRemoveDuplicates): FIPS code(s) 72 not
#> found
PUMA_level <- merge(PUMA_level, states, by = "fips")
head(PUMA_level)
#> fips GEOID
#> 1 01 0100100
#> 2 01 0100200
#> 3 01 0100302
#> 4 01 0100400
#> 5 01 0100500
#> 6 01 0100301
#> NAME
#> 1 Lauderdale, Colbert, Franklin & Marion (Northeast) Counties PUMA; Alabama
#> 2 Limestone & Madison (Outer) Counties--Huntsville City (Far West & Southwest) PUMA, Alabama
#> 3 Huntsville City (Central & South) PUMA, Alabama
#> 4 DeKalb & Jackson Counties PUMA, Alabama
#> 5 Marshall & Madison (Southeast) Counties--Huntsville City (Far Southeast) PUMA, Alabama
#> 6 Huntsville (North) & Madison (East) Cities PUMA, Alabama
#> variable estimate moe abbr full
#> 1 B19013_001 46449 3081 AL Alabama
#> 2 B19013_001 74518 6371 AL Alabama
#> 3 B19013_001 51884 5513 AL Alabama
#> 4 B19013_001 43406 3557 AL Alabama
#> 5 B19013_001 56276 3216 AL Alabama
#> 6 B19013_001 63997 5816 AL Alabama
I have this dataframe here which I filter down to only include counties in the state of Washington and only include columns that are relevant for the answer I am looking for. What I want to do is filter down the dataframe so that I have 10 rows only, which have the highest Black Prison Population out of all of the counties in Washington State regardless of year. The part that I am struggling with is that there can't be repeated counties, so each row should include the highest Black Prison Populations for the top 10 unique county names in the state of Washington. Some of the counties have Null data for the populations for the black prison populations as well. for You should be able to reproduce this to get the updated dataframe.
library(dplyr)
incarceration <- read.csv("https://raw.githubusercontent.com/vera-institute/incarceration-trends/master/incarceration_trends.csv")
blackPrisPop <- incarceration %>%
select(black_prison_pop, black_pop_15to64, year, fips, county_name, state) %>%
filter(state == "WA")
Sample of what the updated dataframe looks like (should include 1911 rows):
fips county_name state year black_pop_15to64 black_prison_pop
130 53005 Benton County WA 2001 1008 25
131 53005 Benton County WA 2002 1143 20
132 53005 Benton County WA 2003 1208 21
133 53005 Benton County WA 2004 1236 27
134 53005 Benton County WA 2005 1310 32
135 53005 Benton County WA 2006 1333 35
You can group_by the county county_name, and then use slice_max taking the row with maximum value for black_prison_pop. If you set n = 1 option you will get one row for each county. If you set with_ties to FALSE, you also will get one row even in case of ties.
You can arrange in descending order the black_prison_pop value to get the overall top 10 values across all counties.
library(dplyr)
incarceration %>%
select(black_prison_pop, black_pop_15to64, year, fips, county_name, state) %>%
filter(state == "WA") %>%
group_by(county_name) %>%
slice_max(black_prison_pop, n = 1, with_ties = FALSE) %>%
arrange(desc(black_prison_pop)) %>%
head(10)
Output
black_prison_pop black_pop_15to64 year fips county_name state
<dbl> <dbl> <int> <int> <chr> <chr>
1 1845 73480 2002 53033 King County WA
2 975 47309 2013 53053 Pierce County WA
3 224 5890 2005 53063 Spokane County WA
4 172 19630 2015 53061 Snohomish County WA
5 137 8129 2016 53011 Clark County WA
6 129 5146 2003 53035 Kitsap County WA
7 102 5663 2009 53067 Thurston County WA
8 58 706 1991 53021 Franklin County WA
9 50 1091 1991 53077 Yakima County WA
10 46 1748 2008 53073 Whatcom County WA
I have a data frame of zip codes that I'm looking to map to a city & state for each specific zip code. Currently, I have played around with the zipcode package a bit but I'm not sure that can solve this specific issue.
Here's sample data of what I have now:
str(all_key$zip)
chr [1:406] "43031" "24517" "43224" "43832" "53022" "60185" "84104" "43081"
"85226" "85193" "54656" "43215" "94533" "95826" "64804" "49548" "54467"
The expected output would be adding a city & state column to each row of the data frame referring to the individual zips:
head(all_key)
zip city state
1 43031 city1 state1
2 24517 city2 state2
3 43224 city3 state3
4 43832 city4 state4
5 53022 city5 state5
6 60185 city6 state6
Thanks in advance for your help.
Another Update - February 2023
Another package (zipcodeR) has been added that makes this easier. See below.
Answer updated - January 2020
The zipcode package seems to have disappeared, so this answer has been updated to show how to add lat-lon from an external file. New answer at bottom.
Original answer
You can get the data from the zipcode package and just do a merge to look things up.
zip = c("43031", "24517", "43224", "43832", "53022",
"60185", "84104", "43081", "85226", "85193", "54656",
"43215", "94533", "95826", "64804", "49548", "54467")
ZC = data.frame(zip)
library(zipcode)
data(zipcode)
merge(ZC, zipcode)
zip city state latitude longitude
1 24517 Altavista VA 37.12754 -79.27409
2 43031 Johnstown OH 40.15198 -82.66944
3 43081 Westerville OH 40.10951 -82.91606
4 43215 Columbus OH 39.96513 -83.00431
5 43224 Columbus OH 40.03991 -82.96772
6 43832 Newcomerstown OH 40.27738 -81.59662
7 49548 Grand Rapids MI 42.86823 -85.66391
8 53022 Germantown WI 43.21916 -88.12043
9 54467 Plover WI 44.45228 -89.54399
10 54656 Sparta WI 43.96977 -90.80796
11 60185 West Chicago IL 41.89198 -88.20502
12 64804 Joplin MO 37.04716 -94.51124
13 84104 Salt Lake City UT 40.75063 -111.94077
14 85193 Casa Grande AZ 32.86000 -111.83000
15 85226 Chandler AZ 33.31221 -111.93177
16 94533 Fairfield CA 38.26958 -122.03701
17 95826 Sacramento CA 38.55010 -121.37492
If you need to keep the rows in the same order, you can just set the rownames on the zipcode data and use that to select the desired rows and columns.
rownames(zipcode) = zipcode$zip
zipcode[zip, 1:3]
zip city state
43031 43031 Johnstown OH
24517 24517 Altavista VA
43224 43224 Columbus OH
43832 43832 Newcomerstown OH
53022 53022 Germantown WI
60185 60185 West Chicago IL
84104 84104 Salt Lake City UT
43081 43081 Westerville OH
85226 85226 Chandler AZ
85193 85193 Casa Grande AZ
54656 54656 Sparta WI
43215 43215 Columbus OH
94533 94533 Fairfield CA
95826 95826 Sacramento CA
64804 64804 Joplin MO
49548 49548 Grand Rapids MI
54467 54467 Plover WI
Updated Answer - January 2020
Since the zipcode package has disappeared, this shows how to add lat-lon information from a downloaded data set. The file that I am using exists today but the method should work for other files. See the GIS StackExchange for some leads on where to download data.
## Original Data to match
zip = c("43031", "24517", "43224", "43832", "53022",
"60185", "84104", "43081", "85226", "85193", "54656",
"43215", "94533", "95826", "64804", "49548", "54467")
ZC = data.frame(zip)
## Download source file, unzip and extract into table
ZipCodeSourceFile = "http://download.geonames.org/export/zip/US.zip"
temp <- tempfile()
download.file(ZipCodeSourceFile , temp)
ZipCodes <- read.table(unz(temp, "US.txt"), sep="\t")
unlink(temp)
names(ZipCodes) = c("CountryCode", "zip", "PlaceName",
"AdminName1", "AdminCode1", "AdminName2", "AdminCode2",
"AdminName3", "AdminCode3", "latitude", "longitude", "accuracy")
## merge extra info onto original data
fZC_Info = merge(ZC, ZipCodes[,c(2:6,10:11)])
head(ZC_Info)
zip PlaceName AdminName1 AdminCode1 AdminName2 latitude longitude
1 24517 Altavista Virginia VA Campbell 37.1222 -79.2911
2 43031 Johnstown Ohio OH Licking 40.1445 -82.6973
3 43081 Westerville Ohio OH Franklin 40.1146 -82.9105
4 43215 Columbus Ohio OH Franklin 39.9671 -83.0044
5 43224 Columbus Ohio OH Franklin 40.0425 -82.9689
6 43832 Newcomerstown Ohio OH Tuscarawas 40.2739 -81.5940
Second Update - February 2023
Another package, zipcodeR, is now available that makes this easier. Here is some simple code to demonstrate it.
library(zipcodeR)
zip = c("43031", "24517", "43224", "43832", "53022",
"60185", "84104", "43081", "85226", "85193", "54656",
"43215", "94533", "95826", "64804", "49548", "54467")
reverse_zipcode(zip)[,c(1,3,7)]
# A tibble: 17 × 3
zipcode major_city state
<chr> <chr> <chr>
1 85193 Casa Grande AZ
2 85226 Chandler AZ
3 94533 Fairfield CA
4 95826 Sacramento CA
5 60185 West Chicago IL
6 49548 Grand Rapids MI
7 64804 Joplin MO
8 43031 Johnstown OH
9 43081 Westerville OH
10 43215 Columbus OH
11 43224 Columbus OH
12 43832 Newcomerstown OH
13 84104 Salt Lake City UT
14 24517 Altavista VA
15 53022 Germantown WI
16 54467 Plover WI
17 54656 Sparta WI
You can still use the "zipcode" package by downloading it from the archives
https://cran.r-project.org/src/contrib/Archive/zipcode/
Once you download the tar.gz file to your computer, you can install it from the RStudio GUI Packages pane. After clicking "Install", you can change the option to "Package Archive File" and point to the downloaded tar.gz file.
Install/use the USA package, also described here, which contains a tibble (zips and lats/longs) from the archived zipcode package.
library(usa)
zcs <- usa::zipcodes
head(zcs)
# A tibble: 6 x 5
zip city state lat long
<chr> <chr> <chr> <dbl> <dbl>
1 00210 Portsmouth NH 43.0 -71.0
2 00211 Portsmouth NH 43.0 -71.0
3 00212 Portsmouth NH 43.0 -71.0
4 00213 Portsmouth NH 43.0 -71.0
5 00214 Portsmouth NH 43.0 -71.0
6 00215 Portsmouth NH 43.0 -71.0
You can use the data frame in the R package zipcodeR.
To add the city and state to your data frame, you can select the variables you want from the data frame provided in zipcodeR (called zip_code_db), then join it with your data frame:
library(dplyr)
library(zipcodeR)
zip_code_db_selected =
zip_code_db %>%
select(zipcode, major_city, state)
all_key_with_city_st =
left_join(all_key, zip_code_db_selected, by = c("zip" = "zipcode"))
I'm writing an R script that parses out the a state abbreviation from a column in a data.frame. It then uses the which() function to determine the index of the found state abbreviation in a look up data frame that contains state abbreviations and their corresponding full state names. I then use the found index to access the the full state name and append it to a vector called completeList. I then add the vector completeList which should contain the full state names to my original data frame under a newly created column STATE_NAME.
However, for some reason completeList only contains the indexes that were found earlier and not the full state names that I expected. What did I do wrong?
#read in csv weather data file
file <- read.csv(header = TRUE, file = "C:\\Users\\michael.guarino1\\Desktop\\Work\\weather\\nov_2_1976\\734677_cleaned.csv")
#read in csv state Abbreviation file
abbreviationsFile<-read.csv(header=TRUE, file="C:\\Users\\michael.guarino1\\Desktop\\Work\\weather\\stateAbbreviationMatches.csv")
#iterate through STATION_NAME and store abreviations
completeList<-c()
for(stateAbvr in file$STATION_NAME){
addTo<-(substring(stateAbvr,(nchar(stateAbvr)-4),(nchar(stateAbvr)-3)))
index<-which(abbreviationsFile$Abbreviation==addTo)
addCompleteStateName<-(abbreviationsFile[index,1])
completeList<-append(completeList, addCompleteStateName)
}
file["STATE_NAME"]<-completeList
>completeList
[1] 27 17 17 29 42 50 20 53 45 19 22 52 9 29 26 37 8 58 35
Here is the csv file where the abbreviation of the station is found
STATION STATION_NAME ELEVATION
GHCND:USC00202381 EAST JORDAN MI US 180.1
GHCND:USC00111290 CARLYLE RESERVOIR IL US 153
GHCND:USC00116661 PAW PAW 2 S IL US 274.9
GHCND:USC00228556 SUMRALL MS US 88.1
GHCND:USC00340292 ARDMORE OK US 267.9
GHCND:USC00408522 SPARTA WASTEWATER PLANT TN US 289.9
GHCND:USC00148341 VALLEY FALLS KS US 283.5
GHCND:USW00014742 BURLINGTON INTERNATIONAL AIRPORT VT US 101.2
GHCND:USC00367782 SALINA 3 W PA US 338
GHCND:USC00134142 IOWA FALLS IA US 356.9
GHCND:USC00161565 CARVILLE 2 SW LA US 9.1
GHCND:USC00421446 CITY CRK WATER PLANT UT US 1628.9
GHCND:USW00013781 WILMINGTON NEW CASTLE CO AIRPORT DE US 22.6
GHCND:USC00229400 WATER VALLEY MS US 116.1
GHCND:USC00190562 BELCHERTOWN MA US 171
GHCND:USW00094728 NEW YORK CENTRAL PARK OBS BELVEDERE TOWER NY US 40.2
GHCND:USC00060973 BURLINGTON CT US 155.4
GHCND:USC00475516 MINOCQUA WI US 484.9
GHCND:USC00286055 NEW BRUNSWICK 3 SE NJ US 38.1
Here is the csv file where we look up abbreviations and find the corresponding full state name
State/Possession Abbreviation
Alabama AL
Alaska AK
American Samoa AS
Arizona AZ
Arkansas AR
California CA
Colorado CO
Connecticut CT
Delaware DE
District of Columbia DC
Federated States of Micronesia FM
Florida FL
Georgia GA
Guam GU
Hawaii HI
Idaho ID
Illinois IL
Indiana IN
Iowa IA
Kansas KS
Kentucky KY
Louisiana LA
Maine ME
Marshall Islands MH
Maryland MD
Massachusetts MA
Michigan MI
Minnesota MN
Mississippi MS
Missouri MO
Montana MT
Nebraska NE
Nevada NV
New Hampshire NH
New Jersey NJ
New Mexico NM
New York NY
North Carolina NC
North Dakota ND
Northern Mariana Islands MP
Ohio OH
Oklahoma OK
Oregon OR
Palau PW
Pennsylvania PA
Puerto Rico PR
Rhode Island RI
South Carolina SC
South Dakota SD
Tennessee TN
Texas TX
Utah UT
Vermont VT
Virgin Islands VI
Virginia VA
Washington WA
West Virginia WV
Wisconsin WI
Wyoming WY
Why am I not getting the full state name?
figured it out 😎
#read in csv weather data file
file <- read.csv(header = TRUE, file = "C:\\Users\\michael.guarino1\\Desktop\\Work\\weather\\nov_2_1976\\734677_cleaned.csv")
#read in csv state Abbreviation file
abbreviationsFile<-read.csv(header=TRUE, file="C:\\Users\\michael.guarino1\\Desktop\\Work\\weather\\stateAbbreviationMatches.csv")
#iterate through STATION_NAME and store abreviations
completeList<-c()
for(stateAbvr in file$STATION_NAME){
addTo<-(substring(stateAbvr,(nchar(stateAbvr)-4),(nchar(stateAbvr)-3)))
index<-which(abbreviationsFile$Abbreviation==addTo)
addCompleteStateName<-(abbreviationsFile[index,1])
completeList<-append(completeList, toString(addCompleteStateName))
}
file["STATE_NAME"]<-completeList
the type was being forced to an integer
The variable addCompleteStateName is a factor. You can convert it to a character to append the labels.
#iterate through STATION_NAME and store abreviations
completeList<-c()
for(stateAbvr in file$STATION_NAME){
addTo<-(substring(stateAbvr,(nchar(stateAbvr)-4),(nchar(stateAbvr)-3)))
index<-which(abbreviationsFile$Abbreviation==addTo)
addCompleteStateName<-(abbreviationsFile[index,1])
# modified to convert addCompleteStateName to character
completeList<-append(completeList, as.character(addCompleteStateName))
}
file["STATE_NAME"]<-completeList
I have a dataset looking like this, with 600 columns:
COUNTY_NAME STATE_NAME STATE_FIPS CNTY_FIPS FIPS Year
Boone Illinois 17 007 17007 2010
Bureau Illinois 17 011 17011 2008
Champaign Illinois 17 019 17019 2010
Cook Illinois 17 031 17031 2006
I need to get the centroids of smallest possible unit/area (counties?) for further analysis.
Is it possible to get this information in latitude longitude in R?