I'm a beginner on R so apologies for errors, and thank you for helping.
I have a dataset (liver) where rows are patient ID numbers, and columns include what region the patient resides in (London, Yorkshire etc) and what unit the patient was treated in (hospital name). Some of the units are private units. I've identified 120 patients from London, of whom 100 were treated across three private units. I want to remove the 100 London patients treated in private units but I keep accidentally removing all patients treated in the private units (around 900 patients). I'd be grateful for advice on how to just remove the London patients treated privately.
I've tried various combinations of using subset and filter with different exclamation points and brackets in different places including for example:
liver <- filter(liver, region_name != "London" & unit_name!="Primrose Hospital" & unit_name != "Oak Hospital" & unit_name != "Wilson Hospital")
Thank you very much.
Your unit_name condition is zeroing your results. Try using the match function which is more commonly seen in its infix form %in%:
liver <- filter(liver,
region_name != "London",
! unit_name %in% c("Primrose Hospital",
"Oak Hospital",
"Wilson Hospital"))
Also you can separate logical AND conditions using a comma.
Building on Pariksheet's great start (still drops outside-London private hospital patients). Here we need to use the OR | operator within the filter function. I've made an example dataframe which demonstrates how this works for your case. The example tibble contains your three private London hospitals plus one non-private hospital that we want to keep. Plus, it has Manchester patients who attend both Manch and one of the private hospitals, all of whom we want to keep.
EDITED: Now includes character vectors to allow generalisation of combinations to exclude.
liver <- tibble(region_name = rep(c('London', 'Liverpool', 'Glasgow', 'Manchester'), each = 4),
unit_name = c(rep(c('Primrose Hospital',
'Oak Hospital',
'Wilson Hospital',
'State Hospital'), times = 3),
rep(c('Manch General', 'Primrose Hospital'), each = 2)))
liver
# A tibble: 16 x 2
region_name unit_name
<chr> <chr>
1 London Primrose Hospital
2 London Oak Hospital
3 London Wilson Hospital
4 London State Hospital
5 Liverpool Primrose Hospital
6 Liverpool Oak Hospital
7 Liverpool Wilson Hospital
8 Liverpool State Hospital
9 Glasgow Primrose Hospital
10 Glasgow Oak Hospital
11 Glasgow Wilson Hospital
12 Glasgow State Hospital
13 Manchester Manch General
14 Manchester Manch General
15 Manchester Primrose Hospital
16 Manchester Primrose Hospital
excl.private.regions <- c('London',
'Liverpool',
'Glasgow')
excl.private.hospitals <- c('Primrose Hospital',
'Oak Hospital',
'Wilson Hospital')
liver %>%
filter(! region_name %in% excl.private.regions |
! unit_name %in% excl.private.hospitals)
# A tibble: 7 x 2
region_name unit_name
<chr> <chr>
1 London State Hospital
2 Liverpool State Hospital
3 Glasgow State Hospital
4 Manchester Manch General
5 Manchester Manch General
6 Manchester Primrose Hospital
7 Manchester Primrose Hospital
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 am trying to link two files with left join, in R. I do not get the output I need.
Here is an example of two files:
here is file_1:
And when I do a left_join in R, I do not get what I want. Here is the code:
minap_piv_na_stemi_nstemi <- left_join(file_1, file_2)
As you can see in the last line, Rochdale Infirmary should be populated with the provider_name and trust_code. This is not happening? Can someone help ?
Try this. Use left_join to join by hospital_name. Use e.g. coalesce to fill in the missing information for trust_code and provider_name. Get rid of the .x and .y columns:
library(dplyr)
left_join(file_1, file_2, by = "hospital_name") %>%
mutate(trust_code = coalesce(trust_code.x, trust_code.y),
provider_name = coalesce(provider_name.x, provider_name.y)) %>%
select(-ends_with(".x"), -ends_with(".y"))
#> # A tibble: 182 x 3
#> hospital_name trust_code provider_name
#> <chr> <chr> <chr>
#> 1 Addenbrooke's Hospital RGT Cambridge University Hospitals NHS Founda…
#> 2 Royal Albert Edward In… RRF Wrightington, Wigan and Leigh NHS Foundat…
#> 3 Airedale General Hospi… RCF Airedale NHS Trust
#> 4 Wycombe Hospital RXQ Buckinghamshire Healthcare NHS Trust
#> 5 Barnsley Hospital RFF Barnsley Hospital NHS Foundation Trust
#> 6 Basildon Hospital RDD Basildon and Thurrock University Hospital…
#> 7 Royal United Hospital … RD1 Royal United Hospital Bath NHS Trust
#> 8 Bedford Hospital RC1 Bedford Hospital NHS Trust
#> 9 Broomfield Hospital RQ8 Mid Essex Hospital Services NHS Trust
#> 10 Rochdale Infirmary RW6 Pennine Acute Hospitals NHS Trust
#> # … with 172 more rows
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.