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I have a text file containing information on book title, author name, and country of birth which appear in seperate lines as shown below:
Oscar Wilde
De Profundis
Ireland
Nathaniel Hawthorn
Birthmark
USA
James Joyce
Ulysses
Ireland
Walt Whitman
Leaves of Grass
USA
Is there any way to convert the text to a dataframe with these three items appearing as different columns:
ID Author Book Country
1 "Oscar Wilde" "De Profundis" "Ireland"
2 "Nathaniel Hawthorn" "Birthmark" "USA"
There are built-in functions for dealing with this kind of data:
data.frame(scan(text=xx, multi.line=TRUE,
what=list(Author="", Book="", Country=""), sep="\n"))
# Author Book Country
#1 Oscar Wilde De Profundis Ireland
#2 Nathaniel Hawthorn Birthmark USA
#3 James Joyce Ulysses Ireland
#4 Walt Whitman Leaves of Grass USA
You can create a 3-column matrix from one column of data.
dat <- read.table('data.txt', sep = ',')
result <- matrix(dat$V1, ncol = 3, byrow = TRUE) |>
data.frame() |>
setNames(c('Author', 'Book', 'Country'))
result <- cbind(ID = 1:nrow(result), result)
result
# ID Author Book Country
#1 1 Oscar Wilde De Profundis Ireland
#2 2 Nathaniel Hawthorn Birthmark USA
#3 3 James Joyce Ulysses Ireland
#4 4 Walt Whitman Leaves of Grass USA
There aren't any built in functions that handle data like this. But you can reshape your data after importing.
#Test data
xx <- "Oscar Wilde
De Profundis
Ireland
Nathaniel Hawthorn
Birthmark
USA
James Joyce
Ulysses
Ireland
Walt Whitman
Leaves of Grass
USA"
writeLines(xx, "test.txt")
And then the code
library(dplyr)
library(tidyr)
lines <- read.csv("test.txt", header=FALSE)
lines %>%
mutate(
rid = ((row_number()-1) %% 3)+1,
pid = (row_number()-1) %/%3 + 1) %>%
mutate(col=case_when(rid==1~"Author",rid==2~"Book", rid==3~"Country")) %>%
select(-rid) %>%
pivot_wider(names_from=col, values_from=V1)
Which returns
# A tibble: 4 x 4
pid Author Book Country
<dbl> <chr> <chr> <chr>
1 1 Oscar Wilde De Profundis Ireland
2 2 Nathaniel Hawthorn Birthmark USA
3 3 James Joyce Ulysses Ireland
4 4 Walt Whitman Leaves of Grass USA
I have a dataframe ‘dfa’ of affiliations that contains city names, for which the country is sometimes missing, e.g. like rows 4 (BAGHDAD) and 7 (BERLIN):
dfa <- data.frame(affiliation=c("DEPARTMENT OF PHARMACY, AMSTERDAM UNIVERSITY, AMSTERDAM, THE NETHERLANDS",
"DEPARTMENT OF BIOCHEMISTRY, LADY HARDINGE MEDICAL COLLEGE, NEW DELHI, INDIA.",
"DEPARTMENT OF PATHOLOGY, CHILDREN'S HOSPITAL, LOS ANGELES, UNITED STATES",
"COLLEGE OF EDUCATION FOR PURE SCIENCE, UNIVERSITY OF BAGHDAD.",
"DEPARTMENT OF CLINICAL LABORATORY, BEIJING GENERAL HOSPITAL, BEIJING, CHINA.",
"LABORATORY OF MOLECULAR BIOLOGY, ISTITUTO ORTOPEDICO, MILAN, ITALY.",
"DEPARTMENT OF AGRICULTURE, BERLIN INSTITUTE OF HEALTH, BERLIN",
"INSTITUTE OF LABORATORY MEDICINE, UNIVERSITY HOSPITAL, MUNICH, GERMANY.",
"DEPARTMENT OF CLINICAL PATHOLOGY, MAHIDOL UNIVERSITY, BANGKOK, THAILAND.",
"DEPARTMENT OF BIOLOGY, WASEDA UNIVERSITY, TOKYO, JAPAN",
"DEPARTMENT OF MOLECULAR BIOLOGY, MINISTRY OF HEALTH, TEHRAN, IRAN.",
"LABORATORY OF CARDIOVASCULAR DISEASE, FUWAI HOSPITAL, BEIJING, CHINA."))
I have now a second dataframe ‘dfb’ that contains a list of cities and corresponding country, some of which are present in 'dfa':
dfb <- data.frame(city=c("AGRI","AMSTERDAM","ATHENS","AUCKLAND","BUENOS AIRES","BEIJING","BAGHDAD","BANGKOK","BERLIN","BUDAPEST"),
country=c("TURKEY","NETHERLANDS","GREECE","NEW ZEALAND","ARGENTINA","CHINA","IRAQ","THAILAND","GERMANY","HUNGARY"))
How can I add cities and corresponding countries in two new columns only for cities that are present in both ‘dfa’ and ‘dfb’ (and even when the country is missing, as for BAGHDAD and BERLIN)?
NB: the goal is to add full city names but not part of them. Below in row 7, an example of what is not wanted: the AGRI city of TURKEY is inappropriately associated with BERLIN because this row includes the 'AGRICULTURE' word.
Is there a simple way to do that, ideally using dplyr?
affiliation city country
1 DEPARTMENT OF PHARMACY, AMSTERDAM UNIVERSITY, AMSTERDAM, THE NETHERLANDS AMSTERDAM NETHERLANDS
2 DEPARTMENT OF BIOCHEMISTRY, LADY HARDINGE MEDICAL COLLEGE, NEW DELHI, INDIA. <NA> <NA>
3 DEPARTMENT OF PATHOLOGY, CHILDREN'S HOSPITAL, LOS ANGELES, UNITED STATES <NA> <NA>
4 COLLEGE OF EDUCATION FOR PURE SCIENCE, UNIVERSITY OF BAGHDAD. BAGHDAD IRAQ
5 DEPARTMENT OF CLINICAL LABORATORY, BEIJING GENERAL HOSPITAL, BEIJING, CHINA. BEIJING CHINA
6 LABORATORY OF MOLECULAR BIOLOGY, ISTITUTO ORTOPEDICO, MILAN, ITALY. <NA> <NA>
7 DEPARTMENT OF AGRICULTURE, BERLIN INSTITUTE OF HEALTH, BERLIN AGRI TURKEY
8 INSTITUTE OF LABORATORY MEDICINE, UNIVERSITY HOSPITAL, MUNICH, GERMANY. <NA> <NA>
9 DEPARTMENT OF CLINICAL PATHOLOGY, MAHIDOL UNIVERSITY, BANGKOK, THAILAND. BANGKOK THAILAND
10 DEPARTMENT OF BIOLOGY, WASEDA UNIVERSITY, TOKYO, JAPAN <NA> <NA>
11 DEPARTMENT OF MOLECULAR BIOLOGY, MINISTRY OF HEALTH, TEHRAN, IRAN. <NA> <NA>
12 LABORATORY OF CARDIOVASCULAR DISEASE, FUWAI HOSPITAL, BEIJING, CHINA. BEIJING CHINA
A combination of str_extract and either a join or another str_extract is one option to get you there.
str_extract will get the first value it encounters, with a paste0 to collapse the cities into a long or string to check against.
library(dplyr)
library(stringr)
dfa %>%
mutate(city = str_extract(dfa$affiliation, paste0("\\b", dfb$city, collapse = "\\b|"))) %>%
left_join(dfb, by = "city")
Edit: added word bounderies in the paste0 so that only whole city names are matched and partial matching is avoided.
affiliation city country
1 DEPARTMENT OF PHARMACY, AMSTERDAM UNIVERSITY, AMSTERDAM, THE NETHERLANDS AMSTERDAM NETHERLANDS
2 DEPARTMENT OF BIOCHEMISTRY, LADY HARDINGE MEDICAL COLLEGE, NEW DELHI, INDIA. <NA> <NA>
3 DEPARTMENT OF PATHOLOGY, CHILDREN'S HOSPITAL, LOS ANGELES, UNITED STATES <NA> <NA>
4 COLLEGE OF EDUCATION FOR PURE SCIENCE, UNIVERSITY OF BAGHDAD. BAGHDAD IRAQ
5 DEPARTMENT OF CLINICAL LABORATORY, BEIJING GENERAL HOSPITAL, BEIJING, CHINA. BEIJING CHINA
6 LABORATORY OF MOLECULAR BIOLOGY, ISTITUTO ORTOPEDICO, MILAN, ITALY. <NA> <NA>
7 DEPARTMENT OF AGRICULTURE, BERLIN INSTITUTE OF HEALTH, BERLIN BERLIN GERMANY
8 INSTITUTE OF LABORATORY MEDICINE, UNIVERSITY HOSPITAL, MUNICH, GERMANY. <NA> <NA>
9 DEPARTMENT OF CLINICAL PATHOLOGY, MAHIDOL UNIVERSITY, BANGKOK, THAILAND. BANGKOK THAILAND
10 DEPARTMENT OF BIOLOGY, WASEDA UNIVERSITY, TOKYO, JAPAN <NA> <NA>
11 DEPARTMENT OF MOLECULAR BIOLOGY, MINISTRY OF HEALTH, TEHRAN, IRAN. <NA> <NA>
12 LABORATORY OF CARDIOVASCULAR DISEASE, FUWAI HOSPITAL, BEIJING, CHINA. BEIJING CHINA
This approach accounts for the possibility that an affiliation could match more than one city name.
library(tidyverse)
dfa %>%
mutate(city = map(affiliation, ~ str_extract(.x, dfb$city))) %>%
unnest(cols = c(city)) %>%
group_by(affiliation) %>%
mutate(nmatches = sum(!is.na(city))) %>%
filter((nmatches > 0 & !is.na(city)) | (nmatches == 0 & row_number() == 1)) %>%
ungroup() %>%
left_join(dfb, by = "city") %>%
mutate(country_match = str_detect(affiliation, country))
# A tibble: 12 x 5
affiliation city nmatches country country_match
<chr> <chr> <int> <chr> <lgl>
1 DEPARTMENT OF PHARMACY,… AMSTE… 1 NETHER… TRUE
2 DEPARTMENT OF BIOCHEMIS… NA 0 NA NA
3 DEPARTMENT OF PATHOLOGY… NA 0 NA NA
4 COLLEGE OF EDUCATION FO… BAGHD… 1 IRAQ FALSE
5 DEPARTMENT OF CLINICAL … BEIJI… 1 CHINA TRUE
6 LABORATORY OF MOLECULAR… NA 0 NA NA
7 BERLIN INSTITUTE OF HEA… BERLIN 1 GERMANY FALSE
8 INSTITUTE OF LABORATORY… NA 0 NA NA
9 DEPARTMENT OF CLINICAL … BANGK… 1 THAILA… TRUE
10 DEPARTMENT OF BIOLOGY, … NA 0 NA NA
11 DEPARTMENT OF MOLECULAR… NA 0 NA NA
12 LABORATORY OF CARDIOVAS… BEIJI… 1 CHINA TRUE
You could then double-check cases with 1 nmatches but country_match == F, and when there are 2 or more nmatches you can keep the one with country_match == T.
I have a huge database (more than 65M of rows) and I noticed that some cells are misplaced. As an example, let's say I have this:
library("tidyverse")
DATA <- tribble(
~SURNAME,~NAME,~STATE,~COUNTRY,
'Smith','Emma','California','USA',
'Johnson','Oliia','Texas','USA',
'Williams','James','USA','California',
'Jones','Noah','Pennsylvania','USA',
'Williams','Liam','Illinois','USA',
'Brown','Sophia','USA','Louisiana',
'Daves','Evelyn','USA','Oregon',
'Miller','Jacob','New Mexico','USA',
'Williams','Lucas','Connecticut','USA',
'Daves','John','California','USA',
'Jones','Carl','USA','Illinois'
)
=====
> DATA
# A tibble: 11 x 4
SURNAME NAME STATE COUNTRY
<chr> <chr> <chr> <chr>
1 Smith Emma California USA
2 Johnson Oliia Texas USA
3 Williams James USA California
4 Jones Noah Pennsylvania USA
5 Williams Liam Illinois USA
6 Brown Sophia USA Louisiana
7 Daves Evelyn USA Oregon
8 Miller Jacob New Mexico USA
9 Williams Lucas Connecticut USA
10 Daves John California USA
11 Jones Carl USA Illinois
As you can see, the Country and State are misplaced in some rows, how can I efficiently swap those ones?
Kind Regards,
Luiz.
Using data.table and the in-built state.name vector:
setDT(DATA)
DATA[COUNTRY %in% state.name, `:=`(COUNTRY = STATE, STATE = COUNTRY)]
DATA
# SURNAME NAME STATE COUNTRY
# 1: Smith Emma California USA
# 2: Johnson Oliia Texas USA
# 3: Williams James California USA
# 4: Jones Noah Pennsylvania USA
# 5: Williams Liam Illinois USA
# 6: Brown Sophia Louisiana USA
# 7: Daves Evelyn Oregon USA
# 8: Miller Jacob New Mexico USA
# 9: Williams Lucas Connecticut USA
# 10: Daves John California USA
# 11: Jones Carl Illinois USA
Check this solution (it assumes that COUNTRY column is in ISO3 format e.g. MEX, CAN):
DATA %>%
mutate(
COUNTRY_TMP = if_else(str_detect(COUNTRY, '[A-Z]{3}'), COUNTRY, STATE),
STATE = if_else(str_detect(COUNTRY, '[A-Z]{3}'), STATE, COUNTRY),
COUNTRY = COUNTRY_TMP
) %>%
select(-COUNTRY_TMP)
Assuming all country names are followed ISO3 format, we can first install the countrycode package. In this package, there is a data frame called codelist with a column iso3c with the ISO3 country names. We can use that as follows to swap the country name.
library(tidyverse)
library(countrycode)
DATA2 <- DATA %>%
mutate(STATE2 = ifelse(STATE %in% codelist$iso3c &
!COUNTRY %in% codelist$iso3c, COUNTRY, STATE),
COUNTRY2 = ifelse(!STATE %in% codelist$iso3c &
COUNTRY %in% codelist$iso3c, COUNTRY, STATE)) %>%
select(-STATE, -COUNTRY) %>%
rename(STATE = STATE2, COUNTRY = COUNTRY2)
DATA2
# # A tibble: 11 x 4
# SURNAME NAME STATE COUNTRY
# <chr> <chr> <chr> <chr>
# 1 Smith Emma California USA
# 2 Johnson Oliia Texas USA
# 3 Williams James California USA
# 4 Jones Noah Pennsylvania USA
# 5 Williams Liam Illinois USA
# 6 Brown Sophia Louisiana USA
# 7 Daves Evelyn Oregon USA
# 8 Miller Jacob New Mexico USA
# 9 Williams Lucas Connecticut USA
# 10 Daves John California USA
# 11 Jones Carl Illinois USA
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)