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"))
Related
I'm new to R and I'm not sure how to rephrase the question, but basically, I have this dataset coming from the following code:
data_url <- 'https://prod-scores-api.ausopen.com/year/2021/stats'
dat <- jsonlite::fromJSON(data_url)
men_aces <- bind_rows(dat$statistics$rankings[[1]]$players[1])
men_aces_table <- dat$players %>%
inner_join(men_aces, by = c('uuid' = 'player_id')) %>% select(full_name, nationality)
Which resulted in this data frame:
full_name nationality.uuid nationality.name nationality.code
1 Novak Djokovic 99da9b29-eade-4ac3-a7b0-b0b8c2192df7 Serbia SRB
2 Alexander Zverev 99d83e85-3173-4ccc-9d91-8368720f4a47 Germany GER
3 Milos Raonic 07779acb-6740-4b26-a664-f01c0b54b390 Canada CAN
4 Daniil Medvedev fa925d2d-337f-4074-a0bd-afddb38d66e1 Russia RUS
5 Nick Kyrgios 9b11f78c-47c1-43c4-97d0-ba3381eb9f07 Australia AUS
nationality is the nested object inside the player object if you check the JSON url, it contains the above properties (uuid, name, code), if I select the full_name property I would get the value (which is of type character) right back.
I'm not sure how to select the name and from that data frame (nationality) and rename it to country.
My expected outcome is:
full_name country
1 Novak Djokovic Serbia
2 Alexander Zverev Germany
3 Milos Raonic Canada
4 Daniil Medvedev Russia
5 Nick Kyrgios Australia
I would appreciate some help. Sorry I was unclear.
Use purrr::pmap_chr
library(tidyverse)
dat$players %>%
inner_join(men_aces, by = c('uuid' = 'player_id')) %>%
select(full_name, nationality) %>%
mutate(nationality = pmap_chr(nationality, ~ ..2))
full_name nationality
1 Novak Djokovic Serbia
2 Alexander Zverev Germany
3 Milos Raonic Canada
4 Daniil Medvedev Russia
5 Nick Kyrgios Australia
6 Alexander Bublik Kazakhstan
7 Reilly Opelka United States of America
8 Jiri Vesely Czech Republic
9 Andrey Rublev Russia
10 Lloyd Harris South Africa
11 Aslan Karatsev Russia
12 Taylor Fritz United States of America
13 Matteo Berrettini Italy
14 Grigor Dimitrov Bulgaria
15 Feliciano Lopez Spain
16 Stefanos Tsitsipas Greece
17 Felix Auger-Aliassime Canada
18 Thanasi Kokkinakis Australia
19 Ugo Humbert France
20 Borna Coric Croatia
You could do:
bind_cols(full_name = dat$players$full_name, country = dat$players$nationality$name)
# A tibble: 169 x 2
full_name country
<chr> <chr>
1 Novak Djokovic Serbia
2 Alexander Zverev Germany
3 Milos Raonic Canada
4 Daniil Medvedev Russia
5 Nick Kyrgios Australia
6 Alexander Bublik Kazakhstan
7 Reilly Opelka United States of America
8 Jiri Vesely Czech Republic
9 Andrey Rublev Russia
10 Lloyd Harris South Africa
just add this line at the end
newdf <- data.frame(full_name = men_aces_table$full_name, country = men_aces_table$nationality$name)
So I'm trying to make a bar chart that displays the most popular airports that flew to Chicago. For some reason, I'm finding it to be extremely difficult to have my bars be labeled by the airport names specifically.
I have a data frame called ty
> ty
Name
1 Atlanta, GA: Hartsfield-Jackson Atlanta International
2 New York, NY: LaGuardia
3 Minneapolis, MN: Minneapolis-St Paul International
4 Los Angeles, CA: Los Angeles International
5 Denver, CO: Denver International
6 Washington, DC: Ronald Reagan Washington National
7 Orlando, FL: Orlando International
8 Phoenix, AZ: Phoenix Sky Harbor International
9 Detroit, MI: Detroit Metro Wayne County
10 Las Vegas, NV: McCarran International
11 San Francisco, CA: San Francisco International
12 Dallas/Fort Worth, TX: Dallas/Fort Worth International
13 Boston, MA: Logan International
14 Philadelphia, PA: Philadelphia International
15 Newark, NJ: Newark Liberty International
I also have a data frame called df
id numArrivals
1 10397 964
2 12953 962
3 13487 883
4 12892 823
5 11292 776
6 11278 771
7 13204 725
8 14107 700
9 11433 672
10 12889 647
11 14771 611
12 11298 580
13 10721 569
14 14100 567
15 11618 488
The id corresponds to the airport name 10397 is Atlanta, GA: Hartsfield-Jackson Atlanta International and they continue in that order.
However, when I run:
plotly::plot_ly(df,x=ty["Name"],y=df$numArrivals,type="bar",color=I("rgba(0,92,124,1)"))
I am given this chart.
How can I make the labels of my bars into the names of the airport rather than just numbers?
Feel free to use ggplotly() to create your plot. I used the code below to create a small example.
example <- data.frame(airport = c("Atlanta, GA: Hartsfield-Jackson Atlanta International","New York, NY: LaGuardia","Minneapolis, MN: Minneapolis-St Paul International"),
id = c(10397,12953,13487),
numArrivals = c(964,962,883),stringsAsFactors = F)
library(ggplot2)
library(plotly)
a <- ggplot(example,aes(x=airport,y=numArrivals,fill=id)) + geom_bar(stat = "identity") + coord_flip()
ggplotly(a)
The final result looks like this.
I'm working on a web scraping / mapping project where I've scraped address data from a restaurant website and I've stored the results as a list - in this example, called loc_list.
Question is, how best to convert these list items into a single data.frame / tibble (currently using bind_rows( )) but ALSO, in the new data.frame, have a column titled metro which corresponds to each list item name. In my example, the output would have 3 alpharettas, followed by 3 atlanta, then 1 buford.
loc_list
$alpharetta
# A tibble: 3 x 2
names address
<chr> <chr>
1 East Roswell US 2630 Holcomb Bridge Rd Alpharetta, GA 30022
2 Old Milton US 4305 Old Milton Parkway Ste 101 Alpharetta, GA 30022
3 Windward US 875 N Main Street Ste 306 Alpharetta, GA 30009
$atlanta
# A tibble: 3 x 2
names address
<chr> <chr>
1 Philips Arena US 100 Techwood Drive Atlanta, GA 30303
2 Virginia Highlands US 1006 N Highland Ave Atlanta, GA 30306
3 Perimeter US 1211 Ashford Crossing Atlanta, GA 30346
$buford
# A tibble: 1 x 2
names address
<chr> <chr>
1 Woodward US 3250 Woodward Crossing Blvd Buford, GA 30519
Targeted output:
names address metro
East Ros... US 2630... alpharetta
As alistaire pointed out bind_rows is enough with .id. Here is example data:
alpharetta <- tibble(names=c("East Roswell", "Old Milton"),
address = c("US 2630 Holcomb Bridge Rd Alpharetta, GA 30022", "4305 Old Milton Parkway Ste 101 Alpharetta, GA 30022"))
atlanta <- tibble(names=c("Philips Arena", "Virginia Highlands"),
address = c("US 100 Techwood Drive Atlanta, GA 30303", "US 1006 N Highland Ave Atlanta, GA 30306"))
loc_list <- list(alpharetta = alpharetta, atlanta = atlanta)
bind_rows(loc_list, .id="metro")
# A tibble: 4 x 3
metro names address
<chr> <chr> <chr>
1 alpharetta East Roswell US 2630 Holcomb Bridge Rd Alpharetta, GA 30022
2 alpharetta Old Milton 4305 Old Milton Parkway Ste 101 Alpharetta, GA 30022
3 atlanta Philips Arena US 100 Techwood Drive Atlanta, GA 30303
4 atlanta Virginia Highlands US 1006 N Highland Ave Atlanta, GA 30306
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 some data for sites across a bunch of cities that looks about like this:
CITY STATE LAT LON SCORE
Jacksonville FL 30.328539 -81.65101 5
Jacksonville FL 30.392888 -81.67933 6
Jacksonville FL 30.268572 -81.73987 4
Jacksonville FL 30.348585 -81.49965 3
Lake Worth FL 26.579714 -80.07437 6
Lake Worth FL 26.609226 -80.12874 3
Miami FL 25.813808 -80.2058 3
Miami FL 25.753927 -80.27034 2
Miami FL 25.786326 -80.2029 6
Miami FL 25.817325 -80.19046 8
Miami FL 25.812625 -80.2369 9
Miami FL 25.885739 -80.23264 4
Miami FL 25.962069 -80.14465 5
I want to count the records for each city and average the score. I know I could do that with ddply if the cities were unique, but they aren't. There's a "Miami, KS" or something in there. So I need to do ddply on the combined city and state. Something like:
ddply(sometable, .(CITY, STATE), summarise,
mean.score=mean(SCORE),
record.count=length(SCORE)
)
Is there a way to do that? I also need to grab one of the lat/lon pairs for each city. Doesn't matter which one.
library(plyr)
ddply(data,c(.(CITY),.(STATE)),summarise,count=length(SCORE),mean=mean(SCORE))
or you can use:
library(data.table)
data <- data.table(data)
data[, list(count=length(SCORE), mean=mean(SCORE)), by=c("CITY", "STATE")]
or this:
aggregate(SCORE~CITY+STATE,data,function(x) cbind(length(x),mean(x)))
CITY STATE count mean
1 Jacksonville FL 4 4.500000
2 Lake Worth FL 2 4.500000
3 Miami FL 7 5.285714