I am using R and I have data like
California | Los Angeles
California | San Diego
California | San Francisco
New York | Albany
New York | New York City
which I would like to transform to
California | New York
Los Angeles | Albany
San Diego | New York City
San Francisco | NA
I am trying to use spread() in tidyr but can't quite get it to give me the output the way I need it. The closest I can come is
California | New York
Los Angeles | NA
San Diego | NA
San Francisco | NA
NA | Albany
NA | New York City
Can someone please help me get it in the desired format?
Here's how I do it in base:
df<-data.frame(v1=c(rep("California",3), rep("New York",2)), v2=c("Los Angeles", "San Diego", "San Franciso", "Albany", "New York City"))
cali<-as.character(df[df$v1=="California", 2])
ny<-as.character(df[df$v1=="New York", 2])
new <- data.frame(California=cali, NewYork=c(ny, NA))
new
California NewYork
1 Los Angeles Albany
2 San Diego New York City
3 San Franciso <NA>
Related
I have two dataframes in R: df1 and df2 as follows-
**df1**
Cust_id Cust_name Cust_dob Cust_address
1 Andrew 10/11/1990 New York
2 Dillain 01/02/1970 San Francisco
3 Alma 07/11/1985 Miami
4 Wesney 21/10/1979 New York
5 Kiko 10/12/1994 Miami
**df2**
Cust_address Latitude Longitude
New York 40.7128 74.0060
San Francisco 37.7749 122.4194
Miami 25.7617 80.1918
Texas 31.9686 99.9018
Dallas 32.7767 96.7970
I want to join these datasets together so that I get the following result: The latitude and longitude columns from df2 must match the address column of df1
**df3**
Cust_id Cust_name Cust_dob Cust_address Latitude Longitude
1 Andrew 10/11/1990 New York 40.7128 74.0060
2 Dillain 01/02/1970 San Francisco 37.7749 122.4194
3 Alma 07/11/1985 Miami 25.7617 80.1918
4 Wesney 21/10/1979 New York 40.7128 74.0060
5 Kiko 10/12/1994 Miami 25.7617 80.1918
I have tried using joins but cannot get the result that I want. I would really appreciate if someone could help me please. I am new to R. Thank you very much. I have tried in the following ways:
df3 = merge(x=df1,y=df2,by="Cust_address",all=TRUE)
We could use inner_join()
inner_join(): includes all rows in x and y.
library(dplyr)
df3 <- inner_join(df1, df2, by="Cust_address")
Cust_id Cust_name Cust_dob Cust_address Latitude Longitude
1 1 Andrew 10/11/1990 New York 40.7128 74.0060
2 2 Dillain 01/02/1970 San Francisco 37.7749 122.4194
3 3 Alma 07/11/1985 Miami 25.7617 80.1918
4 4 Wesney 21/10/1979 New York 40.7128 74.0060
5 5 Kiko 10/12/1994 Miami 25.7617 80.1918
I'm trying to extract the city and state from the Address column into 2 separate columns labeled City and State in r. This is what my data looks like:
df <- data.frame(address = c("Los Angeles, CA", "Pittsburgh PA", "Miami FL","Baltimore MD", "Philadelphia, PA", "Trenton, NJ")) %>%
separate(address, c("City", "State"), sep=",")
I tried using the separate function but that only gets the ones with commas. Any ideas on how to do this for both cases?
There is a pattern at the end (space, letter, letter) which I can use to exploit and then remove any commas but not sure how the syntax would work using grep.
Starting from your df
df <- data.frame(address = c("Los Angeles, CA", "Pittsburgh PA", "Miami FL","Baltimore MD", "Philadelphia, PA", "Trenton, NJ"))
> df
address
1 Los Angeles, CA
2 Pittsburgh PA
3 Miami FL
4 Baltimore MD
5 Philadelphia, PA
6 Trenton, NJ
It's possible to use gsub to subset the string like this:
> city=gsub(',','',gsub("(.*).{3}","\\1",df[,1]))
> city
[1] "Los Angeles" "Pittsburgh" "Miami" "Baltimore" "Philadelphia"
[6] "Trenton"
> state=gsub(".*(\\w{2})","\\1",df[,1])
> state
[1] "CA" "PA" "FL" "MD" "PA" "NJ"
df=data.frame(City=city,State=state)
> df
City State
1 Los Angeles CA
2 Pittsburgh PA
3 Miami FL
4 Baltimore MD
5 Philadelphia PA
6 Trenton NJ
This is a little unorthodox but it works well. It assumes that all states are 2 characters long and that there is at least 1 space between the city and state. Comma's are ignored
df <- data.frame(address = c("Los Angeles, CA", "Pittsburgh PA", "Miami FL","Baltimore MD", "Philadelphia, PA", "Trenton, NJ"))
df$city <- substring(sub(",","",df$address),1,nchar(sub(",","",df$address))-3)
df$state <- substring(as.character(df$address),nchar(as.character(df$address))-1,nchar(as.character(df$address)))
df <- within(df,rm(address))
output:
city state
1 Los Angeles CA
2 Pittsburgh PA
3 Miami FL
4 Baltimore MD
5 Philadelphia PA
6 Trenton NJ
I run into problems assigning a county to some city places. When querying via the acs package
> geo.lookup(state = "NY", place = "New York")
state state.name county.name place place.name
1 36 New York <NA> NA <NA>
2 36 New York Bronx County, Kings County, New York County, Queens County, Richmond County 51000 New York city
3 36 New York Oneida County 51011 New York Mills village
, you can see that "New York", for instance, has a bunch of counties. So do Los Angeles, Portland, Oklahoma, Columbus etc. How can such data be assigned to a "county"?
Following code is currently used to match "county.name" with the corresponding county FIPS code. Unfortunately, it only works for cases of only one county name output in the query.
Script
dat <- c("New York, NY","Boston, MA","Los Angeles, CA","Dallas, TX","Palo Alto, CA")
dat <- strsplit(dat, ",")
dat
library(tigris)
library(acs)
data(fips_codes) # FIPS codes with state, code, county information
GeoLookup <- lapply(dat,function(x) {
geo.lookup(state = trimws(x[2]), place = trimws(x[1]))[2,]
})
df <- bind_rows(GeoLookup)
#Rename cols to match
colnames(fips_codes) = c("state.abb", "statefips", "state.name", "countyfips", "county.name")
# Here is a problem, because it works with one item in "county.name" but not more than one (see output below).
df <- df %>% left_join(fips_codes, by = c("state.name", "county.name"))
df
Returns:
state state.name county.name place place.name state.abb statefips countyfips
1 36 New York Bronx County, Kings County, New York County, Queens County, Richmond County 51000 New York city <NA> <NA> <NA>
2 25 Massachusetts Suffolk County 7000 Boston city MA 25 025
3 6 California Los Angeles County 20802 East Los Angeles CDP CA 06 037
4 48 Texas Collin County, Dallas County, Denton County, Kaufman County, Rockwall County 19000 Dallas city <NA> <NA> <NA>
5 6 California San Mateo County 20956 East Palo Alto city CA 06 081
In order to retain data, the left_join might better be matched as "look for county.name that contains place.name (without the appending xy city in the name), or choose the first item by default. It would be great to see how this could be done.
In general: I assume, there's no better way than this approach?
Thanks for your help!
What about something like the code below to create a "long" data frame for joining. We use the tidyverse pipe operator to chain operations. strsplit returns a list, which we unnest to stack the list values (the county names that go with each combination of state.name and place.name) into a long data frame where each county.name now gets its own row.
library(tigris)
library(acs)
library(tidyverse)
dat = geo.lookup(state = "NY", place = "New York")
state state.name county.name place place.name
1 36 New York <NA> NA <NA>
2 36 New York Bronx County, Kings County, New York County, Queens County, Richmond County 51000 New York city
3 36 New York Oneida County 51011 New York Mills village
dat = dat %>%
group_by(state.name, place.name) %>%
mutate(county.name = strsplit(county.name, ", ")) %>%
unnest
state state.name place place.name county.name
<chr> <chr> <int> <chr> <chr>
1 36 New York NA <NA> <NA>
2 36 New York 51000 New York city Bronx County
3 36 New York 51000 New York city Kings County
4 36 New York 51000 New York city New York County
5 36 New York 51000 New York city Queens County
6 36 New York 51000 New York city Richmond County
7 36 New York 51011 New York Mills village Oneida County
UPDATE: Regarding the second question in your comment, assuming you have the vector of metro areas already, how about this:
dat <- c("New York, NY","Boston, MA","Los Angeles, CA","Dallas, TX","Palo Alto, CA")
df <- map_df(strsplit(dat, ", "), function(x) {
geo.lookup(state = x[2], place = x[1])[-1, ] %>%
group_by(state.name, place.name) %>%
mutate(county.name = strsplit(county.name, ", ")) %>%
unnest
})
df
state state.name place place.name county.name
1 36 New York 51000 New York city Bronx County
2 36 New York 51000 New York city Kings County
3 36 New York 51000 New York city New York County
4 36 New York 51000 New York city Queens County
5 36 New York 51000 New York city Richmond County
6 36 New York 51011 New York Mills village Oneida County
7 25 Massachusetts 7000 Boston city Suffolk County
8 25 Massachusetts 7000 Boston city Suffolk County
9 6 California 20802 East Los Angeles CDP Los Angeles County
10 6 California 39612 Lake Los Angeles CDP Los Angeles County
11 6 California 44000 Los Angeles city Los Angeles County
12 48 Texas 19000 Dallas city Collin County
13 48 Texas 19000 Dallas city Dallas County
14 48 Texas 19000 Dallas city Denton County
15 48 Texas 19000 Dallas city Kaufman County
16 48 Texas 19000 Dallas city Rockwall County
17 48 Texas 40516 Lake Dallas city Denton County
18 6 California 20956 East Palo Alto city San Mateo County
19 6 California 55282 Palo Alto city Santa Clara County
UPDATE 2: If I understand your comments, for cities (actually place names in the example) with more than one county, we want only the county that includes the same name as the city (for example, New York County in the case of New York city), or the first county in the list otherwise. The following code selects a county with the same name as the city or, if there isn't one, the first county for that city. You might have to tweak it a bit to make it work for the entire U.S. For example, for it to work for Louisiana, you might need gsub(" County| Parish"... instead of gsub(" County"....
map_df(strsplit(dat, ", "), function(x) {
geo.lookup(state = x[2], place = x[1])[-1, ] %>%
group_by(state.name, place.name) %>%
mutate(county.name = strsplit(county.name, ", ")) %>%
unnest %>%
slice(max(1, which(grepl(sub(" [A-Za-z]*$","", place.name), gsub(" County", "", county.name))), na.rm=TRUE))
})
state state.name place place.name county.name
<chr> <chr> <int> <chr> <chr>
1 36 New York 51000 New York city New York County
2 36 New York 51011 New York Mills village Oneida County
3 25 Massachusetts 7000 Boston city Suffolk County
4 6 California 20802 East Los Angeles CDP Los Angeles County
5 6 California 39612 Lake Los Angeles CDP Los Angeles County
6 6 California 44000 Los Angeles city Los Angeles County
7 48 Texas 19000 Dallas city Dallas County
8 48 Texas 40516 Lake Dallas city Denton County
9 6 California 20956 East Palo Alto city San Mateo County
10 6 California 55282 Palo Alto city Santa Clara County
Could you prep the data by using something like the below code?
new_york_data <- geo.lookup(state = "NY", place = "New York")
prep_data <- function(full_data){
output <- data.frame()
for(row in 1:nrow(full_data)){
new_rows <- replicateCounty(full_data[row, ])
output <- plyr::rbind.fill(output, new_rows)
}
return(output)
}
replicateCounty <- function(row){
counties <- str_trim(unlist(str_split(row$county.name, ",")))
output <- data.frame(state = row$state,
state.name = row$state.name,
county.name = counties,
place = row$place,
place.name = row$place.name)
return(output)
}
prep_data(new_york_data)
It's a little messy and you'll need the plyr and stringr packages. Once you prep the data, you should be able to join on it
I am relatively new in R.
I have a dataframe locs that has 1 variable V1 and looks like:
V1
edmonton general hospital
cardiovascular institute, hospital san carlos, madrid spain
hospital of santa maria, lisbon, portugal
and another dataframe cities that has two variables that look like this:
city country
edmonton canada
san carlos spain
los angeles united states
santa maria united states
tokyo japan
madrid spain
santa maria portugal
lisbon portugal
I want to create two new variables in locs that relates any string match of V1 within city so that locs looks like this:
V1 city country
edmonton general hospital edmonton canada
hospital san carlos, madrid spain san carlos, madrid spain
hospital of santa maria, lisbon, portugal santa maria, lisbon portugal, united states
A few things to note: V1 may have multiple country names. Also, if there is a repeat country (for instance, both san carlos and madrid are in spain), then I only want one instance of the country.
Please advise.
Thanks.
A solution using tidyverse and stringr. locs2 is the final output.
library(tidyverse)
library(stringr)
locs2 <- locs %>%
rowwise() %>%
mutate(city = list(str_match(V1, cities$city))) %>%
unnest() %>%
drop_na(city) %>%
left_join(cities, by = "city") %>%
group_by(V1) %>%
summarise_all(funs(toString(sort(unique(.)))))
Result
locs2 %>% as.data.frame()
V1 city country
1 cardiovascular institute, hospital san carlos, madrid spain madrid, san carlos spain
2 edmonton general hospital edmonton canada
3 hospital of santa maria, lisbon, portugal lisbon, santa maria portugal, united states
DATA
library(tidyverse)
locs <- data_frame(V1 = c("edmonton general hospital",
"cardiovascular institute, hospital san carlos, madrid spain",
"hospital of santa maria, lisbon, portugal"))
cities <- read.table(text = "city country
edmonton canada
'san carlos' spain
'los angeles' 'united states'
'santa maria' 'united states'
tokyo japan
madrid spain
'santa maria' portugal
lisbon portugal",
header = TRUE, stringsAsFactors = FALSE)
I have two files and they have the same number of lines.
File A:
USA
UK
MEXICO
CHINA
RUSSIA
File B:
Washington DC
London
MEXICO CITY
BEIJING
MOSCOW
How can I merge these two files together using unix commands to make a file like this:
Result File:
USA Washington DC
UK London
MEXICO MEXICO CITY
CHINA BEIJING
RUSSIA MOSCOW
These two columns could be separated by tab or comma or any other thing?
Thank you for any suggestions?
You can try paste
$ paste file1 file2
USA Washington DC
UK London
MEXICO MEXICO CITY
CHINA BEIJING
RUSSIA MOSCOW
This is a job for paste, but this awk will do to:
awk 'FNR==NR{a[NR]=$0;next} {print a[FNR],$0}' fileA fileB
USA Washington DC
UK London
MEXICO MEXICO CITY
CHINA BEIJING
RUSSIA MOSCOW