Extract House Number from (address) string using r

regex address parser
javascript address parser

I want to parse apart (extract) addresses into HouseNumber and Streetname. I should later be able to write the extracted "values" into new columns (shops$HouseNumber and shops$Streetname).

So lets say I have a data frame called "shops":

> shops
      Name                 city        street
 1    Something            Fakecity    New Street 3
 2    SomethingOther       Fakecity    Some-Complicated-Casestreet 1-3
 3    SomethingDifferent   Fakecity    Fake Street 14a

So is there a way to split the street column into two lists one with the streetnames and one for the house numbers including cases like "1-3","14a", so that in the end, the result could be assigned to the data frame and look like.

 > shops
      Name                 city        Streetname                    HouseNumber
 1    Something            Fakecity    New Street                    3
 2    SomethingOther       Fakecity    Some-Complicated-Casestreet   1-3
 3    SomethingDifferent   Fakecity    Fake Street                   14a 

Example: Easyfakestreet 5 --> Easyfakestreet , 5

It gets slightly complicated by the fact that some of my street strings will have hyphenated street addresses and have non numerical components.

Examples: New Street 3 --> ['New Street', '3 '] Some-Complicated-Casestreet 1-3 --> ['Some-Complicated-Casestreet','1-3'] Fake Street 14a --> ['Fake Street', '14a']

I would appreciate some help!


Here's a possible tidyr solution

library(tidyr)
extract(df, "street", c("Streetname", "HouseNumber"), "(\\D+)(\\d.*)")
#                 Name     city                   Streetname HouseNumber
# 1          Something Fakecity                  New Street            3
# 2     SomethingOther Fakecity Some-Complicated-Casestreet          1-3
# 3 SomethingDifferent Fakecity                 Fake Street          14a

Address Parsing in R • postmastr, Imagine that the house number in the example above was 601-603 instead. To parse our grammar of street addresses, functions can be grouped in two postmastr is designed to operate on unique street address strings  A common task related to street addresses is componentizing—breaking the address into components. Given a line that looks like the following, the goal is to determine what the primary (house) number, street, city, state, and ZIP Code are: 123 Main St. Louisville, OH 43071. Essentially, this string needs to be parsed.


You can try:

shops$Streetname <- gsub("(.+)\\s[^ ]+$","\\1", shops$street)
shops$HousNumber <- gsub(".+\\s([^ ]+)$","\\1", shops$street)

data

shops$street
#[1] "New Street 3"                    "Some-Complicated-Casestreet 1-3" "Fake Street 14a" 

results

shops$Streetname
#[1] "New Street"                  "Some-Complicated-Casestreet" "Fake` Street" 

shops$HousNumber
#[1] "3"   "1-3" "14a"

Separate the house number from the street name, Extracting house number from address including characters - 1A, 1B etc use regex to extract any and all numbers from a string which works Check this one, that also takes 1 a into account https://regex101.com/r/QFo4IV/1 I'm new to Regex and am trying to use it to parse apart addresses into House Number and Street. Example: 123 Main St --> ['123', 'Main St'] It gets slightly complicated by the fact that some of my street strings will have hyphenated street addresses, in which case I want to take the first number before the hyphen.


Create a pattern with back references that match both the street and the number and then using sub replace it by each backreference in turn. No packages are needed:

pat <- "(.*) (\\d.*)"
transform(shops,
   street = sub(pat, "\\1", street), 
   HouseNumber = sub(pat, "\\2", street)
)

giving:

                Name     city                      street  HouseNumber
1          Something Fakecity                  New Street            3
2     SomethingOther Fakecity Some-Complicated-Casestreet          1-3
3 SomethingDifferent Fakecity                 Fake Street          14a

Here is a visualization of pat:

(.*) (\d.*)

Debuggex Demo

Note:

1) We used this for shops:

shops <-
structure(list(Name = c("Something", "SomethingOther", "SomethingDifferent"
), city = c("Fakecity", "Fakecity", "Fakecity"), street = c("New Street 3", 
"Some-Complicated-Casestreet 1-3", "Fake Street 14a")), .Names = c("Name", 
"city", "street"), class = "data.frame", row.names = c(NA, -3L))

2) David Arenburg's pattern could alternately be used here. Just set pat to it. The pattern above has the advantage that it allows street names that have embedded numbers in them but David's has the advantage that the space may be missing before the street number.

Extracting house number from address including characters, 1.2.1 Using format() with numbers; 1.2.2 Controlling other aspects of the string; 1.2.3 formatC to pull strings apart, put them back together and use stringr to detect, For example, a simple pattern to match an email address might be, I have a country house with some land, of course, attached to it,; Jack. There are three formulas you can use to extract street number from address. Please copy and paste one of the below formulas to the Formula Bar, then press the Enter key. Formula 1: =IF (ISERROR (VALUE (LEFT (A2,1))),"",LEFT (A2,FIND (" ",A2)-1)) Formula 2. =IF (ISNUMBER (VALUE (LEFT (A2,1))),VALUE (LEFT (A2,FIND (" ",A2)-1)),"")


You could use the package unglue

library(unglue)
unglue_unnest(shops, street, "{street} {value=\\d.*}")
#>                 Name     city                      street value
#> 1          Something Fakecity                  New Street     3
#> 2     SomethingOther Fakecity Some-Complicated-Casestreet   1-3
#> 3 SomethingDifferent Fakecity                 Fake Street   14a

Created on 2019-10-08 by the reprex package (v0.3.0)

String Manipulation in R with stringr, Let's now examine how to position the map on a street address. For that, you call the geocoder and use the resulting coordinates. The format of the address must  In this data set, the zip code appears at the end of the address string. If we assume that this the case for all addresses in the data, the remedy will be really simple. We can specify "[0-9][0-9][0-9][0-9][0-9]$" which would instruct Stata to find a five-digit number at the end of the string.


Pro Oracle Spatial, doubleValue(); // Extract full street address from result String i<f[0].length-3; i++) geocodeInfo[i] = f [1][i+3]; // Center map on the new address and zoom in mv. The code below will attempt to find one space between the Street Number and the Street Name in the Address filed. If found it will be spilited into 2 fields. There could an Address like PO Box 200 or PO BOX 500 Pinon Ct and this case it will be copied into the AddressName field.


Pro Oracle Spatial for Oracle Database 11g, Should you use regular expressions to parse street addresses? the goal is to determine what the primary (house) number, street, city, state, and ZIP Code are: in text, but they have no way of knowing what each part of a string means. Re: Removing house numbers from addresses If it is always a space between the number and the rest you could use =IF (ISERROR (-- (LEFT (TRIM (A1)))),A1,TRIM (SUBSTITUTE (A1,LEFT (A1,FIND (" ",A1)),"")))


Using Regular Expressions for Street Addresses, Introduction In this post, we will learn to work with string data in R using stringr. extract domain name from random email ids; extract image type from url phone number; address: dummy address with door and street names  The entire address is a string to Excel/Your PC, not a string with a number at the end. The difficult part is that your addresses don't follow some kind of pattern, or you could just use Left(), Mid() or Right() functions to pick it out.