I have dataset as follow
EstablishmentName Freq
bahria university 20
bahria university islamabad 12
arid agriculture 3
arid agriculture university 15
arid rawalpindi 9
college of e&me, nust 20
college of e & me (nust) 15
college of eme 30
As you can see above that Bahria University and Bahria University Islamabad are almost same, so goes for other strings. I want to unify them into one such that
Expected Output
EstablishmentName Freq
Bahria University 32
Arid Agriculture 27
College of EME 30
I have tried the following solution but it doesn't seems to work.
library(SnowballC)
library(dplyr)
mutate(df, word = wordStem(EstablishmentName)) %>%
group_by(EstablishmentName) %>%
summarise(total = sum(Freq))
Related
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 dataset with a column, CatSex, that's got data in it in a form similar to "American.Indian.or.Alaska.Native.men"--the characters after the last period, I want to turn into a new pivoted column, so I have two columns, one called Cat with only the demographic info in it, and one called Sex with the sex in it. The characters before the sex designation don't follow any clear pattern. I am not very good at R, but it's better than Tableau Prep with large data sets, it seems. What I ultimately want is to pivot the data so that I have two distinct columns for the different categories here. I used this code to get part of the way there (the original data held like 119 columns with names like "Grand.total.men..C2005_A_RV..First.major..Area..ethnic..cultural..and.gender.studies...Degrees.total"), but I can't figure out how to do this with the pattern I'm now left with in the column CatSex:
pivot_longer(
cols = -c(UnitID, Institution.Name),
names_to = c("CatSex", "Disc"),
names_pattern = "(.*)..C2005_A_RV..First.major..(.*)",
values_to = "Count",
values_drop_na = TRUE
)
Here's a screenshot of the data structure I have now. I'm sorry for not putting in reproducible code--I don't know how to do that in this context!
EDIT: Here's a head(df) of the cleaned data so far:
# A tibble: 6 × 5
UnitID Institution.Name CatSex Disc Count
<int> <fct> <chr> <chr> <int>
1 177834 A T Still University of Health Sciences Grand.total.men Health.professions.and.related.clinical.sciences...Degrees.total. 212
2 177834 A T Still University of Health Sciences Grand.total.women Health.professions.and.related.clinical.sciences...Degrees.total. 359
3 177834 A T Still University of Health Sciences White.non.Hispanic.men Health.professions.and.related.clinical.sciences...Degrees.total. 181
4 177834 A T Still University of Health Sciences White.non.Hispanic.women Health.professions.and.related.clinical.sciences...Degrees.total. 317
5 177834 A T Still University of Health Sciences Black.non.Hispanic.men Health.professions.and.related.clinical.sciences...Degrees.total. 3
6 177834 A T Still University of Health Sciences Black.non.Hispanic.women Health.professions.and.related.clinical.sciences...Degrees.total. 5
Using extract from tidyr package (it is in tidyverse)
Capture 2 groups with ()
Define second group to have one or more characters that are not . up to the end $
library(dplyr)
library(tidyr)
df %>%
extract(CatSex, c("Cat", "Sex"), "(.*)\\.([^.]+)$")
UnitID Institution.Name Cat Sex
1 222178 Abilene Christian University Hispanic men
2 222178 Abilene Christian University Hispanic women
3 222178 Abilene Christian University American.Indian.or.Alaska.Native men
4 222178 Abilene Christian University American.Indian.or.Alaska.Native women
5 222178 Abilene Christian University Asian.or.Pacific.Islander women
6 222178 Abilene Christian University Asian.or.Pacific.Islander men
7 222178 Abilene Christian University Grand.total men
8 222178 Abilene Christian University Grand.total women
9 222178 Abilene Christian University White.non.Hispanic men
10 222178 Abilene Christian University White.non.Hispanic women
11 222178 Abilene Christian University lack.non.Hispanic men
12 222178 Abilene Christian University Black.non.Hispanic women
13 222178 Abilene Christian University Hispanic men
14 222178 Abilene Christian University Hispanic women
15 222178 Abilene Christian University American.Indian.or.Alaska.Native men
Disc
1 Communication journalism..and.related.programs
2 Communication journalism and.related.programs
3 Communication journalism..and.related.programs
4 Communication..journalism..and.related.programs
5 Communication..journalism..and.related.programs
6 Communication .journalism..and.related.program
7 Computer.and.information.sciences.and.support.serv
8 computer.and.information.sciences.and.support.servi
9 Computer.and.information.sciences.and.support.servi
10 Computer.and.information.sciences.and.support.servi
11 Computer.and.information.sciences.and.support.servi
12 Computer.and.information.sciences.and.support.servi.
13 Computer.and.information.sciences.and.support.serv
14 Computer.and.information.sciences.and.support.servi.
15 Computer.and.information.sciences.and.support.servi
pivot_longer is not the right function in this context.
Here are few options -
Using tidyr::separate
tidyr::separate(df, 'CatSex', c('Cat', 'Sex'), sep = '(\\.)(?!.*\\.)')
#. Cat Sex
#1 Grand.total men
#2 Grand.total women
#3 White.non.Hispanic men
#4 White.non.Hispanic women
#5 Black.non.Hispanic men
#6 Black.non.Hispanic women
Using stringr functions
library(dplyr)
library(stringr)
df %>%
mutate(Sex = str_extract(CatSex, 'men|women'),
Cat = str_remove(CatSex, '\\.(men|women)'))
In base R
transform(df, Sex = sub('.*\\.(men|women)', '\\1', CatSex),
Cat = sub('\\.(men|women)', '', CatSex))
data
It is easier to help if you provide data in a reproducible format
df <- data.frame(CatSex = c("Grand.total.men", "Grand.total.women",
"White.non.Hispanic.men", "White.non.Hispanic.women",
"Black.non.Hispanic.men", "Black.non.Hispanic.women"))
This question already has an answer here:
Using Reshape from wide to long in R [closed]
(1 answer)
Closed 2 years ago.
I'm trying to calculate the total number of matches played by each team in the year 2019 and put them in a table along with the corresponding team names
teams<-c("Sunrisers Hyderabad", "Mumbai Indians", "Gujarat Lions", "Rising Pune Supergiants",
"Royal Challengers Bangalore","Kolkata Knight Riders","Delhi Daredevils",
"Kings XI Punjab", "Deccan Chargers","Rajasthan Royals", "Chennai Super Kings",
"Kochi Tuskers Kerala", "Pune Warriors", "Delhi Capitals", " Gujarat Lions")
for (j in teams) {
print(j)
ipl_table %>%
filter(season==2019 & (team1==j | team2 ==j)) %>%
summarise(match_count=n())->kl
print(kl)
match_played<-data.frame(Teams=teams,Match_count=kl)
}
The match played by last team (i.e Gujarat Lions is 0 and its filling 0's for all other teams as well.
The output match_played can be found on the link given below.
I'd be really glad if someone could help me regarding this error as I'm very new to R.
filter for the particular season, get data in long format and then count number of matches.
library(dplyr)
matches %>%
filter(season == 2019) %>%
tidyr::pivot_longer(cols = c(team1, team2), values_to = 'team_name') %>%
count(team_name) -> result
result
# team_name n
# <chr> <int>
#1 Chennai Super Kings 17
#2 Delhi Capitals 16
#3 Kings XI Punjab 14
#4 Kolkata Knight Riders 14
#5 Mumbai Indians 16
#6 Rajasthan Royals 14
#7 Royal Challengers Bangalore 14
#8 Sunrisers Hyderabad 15
Here is an example
library(tidyr)
df_2019 <- matches[matches$season == 2019, ] # get the season you need
df_long <- gather(df_2019, Team_id, Team_Name, team1:team2) # Make it long format
final_count <- data.frame(t(table(df_long$Team_Name)))[-1] # count the number of matches
names(final_count) <- c("Team", "Matches")
Team Matches
1 Chennai Super Kings 17
2 Delhi Capitals 16
3 Kings XI Punjab 14
4 Kolkata Knight Riders 14
5 Mumbai Indians 16
6 Rajasthan Royals 14
7 Royal Challengers Bangalore 14
8 Sunrisers Hyderabad 15
Or by using base R
final_count <- data.frame(t(table(c(df_2019$team1, df_2019$team2))))[-1]
names(final_count) <- c("Team", "Matches")
final_count
my data looks like
> company
name category_list
11 1-4 All Entertainment|Games|Software
12 1.618 Technology Networking|Real Estate|Web Hosting
13 1-800-DENTIST Health and Wellness
14 1-800-DOCTORS Health and Wellness
15 1-800-PublicRelations, Inc. Internet Marketing|Media|Public Relations
i will have to split the category_list column based the values. when the values are pipe separated, the row should be split.
i tried the same using separate function but the column is not populated with any values
c1 <- company %>% separate(category_list,into=c("primary_Sector"), sep="|")
Actual output:
name primary_Sector
11 1-4 All
12 1.618 Technology
13 1-800-DENTIST
14 1-800-DOCTORS
15 1-800-PublicRelations, Inc.
Expected output
name category_list
11 1-4 All Entertainment
12 1-4 All Games
13 1-4 All Software
can someone tell me what is wrong?
tidyr::separate() does the column-wise separation, tidyr::separate_rows() does the row-wise separation:
library(tidyr)
read.table(
text="name;category_list
1-4 All;Entertainment|Games|Software
1.618 Technology;Networking|Real Estate|Web Hosting
1-800-DENTIST;Health and Wellness
1-800-DOCTORS;Health and Wellness
1-800-PublicRelations, Inc.;Internet Marketing|Media|Public Relations",
sep=";", header = TRUE, stringsAsFactors = FALSE
) %>%
separate_rows(category_list, sep = "\\|")
## name category_list
## 1 1-4 All Entertainment
## 2 1-4 All Games
## 3 1-4 All Software
## 4 1.618 Technology Networking
## 5 1.618 Technology Real Estate
## 6 1.618 Technology Web Hosting
## 7 1-800-DENTIST Health and Wellness
## 8 1-800-DOCTORS Health and Wellness
## 9 1-800-PublicRelations, Inc. Internet Marketing
## 10 1-800-PublicRelations, Inc. Media
## 11 1-800-PublicRelations, Inc. Public Relations
Yes, I know there are similar questions, I've read the answers and tried those which I could implement. So, sorry in advance in case the question is stupid :)
I'm scraping the age of company board members from Reuters for a list of companies.
Here's the link: http://www.reuters.com/finance/stocks/companyOfficers?symbol=MSFT
I'm using rvest library and selectorgadget to find proper CSS selector.
Here's the code:
library(rvest)
d = read_html("http://www.reuters.com/finance/stocks/companyOfficers?symbol=GAZP.RTS")
d %>% html_nodes("#companyNews:nth-child(1) td:nth-child(2)") %>% html_text()
The result is
character(0)
I think I have the wrong CSS selector. Can you please tell me how to select the table?
You need to use html_session to get the data loaded properly:
library(rvest)
url <- 'http://www.reuters.com/finance/stocks/companyOfficers?symbol=MSFT.O'
site <- html_session(url) %>% read_html()
site %>% html_node('#companyNews:first-child table') %>% html_table()
## Name Age Since Current Position
## 1 John Thompson 66 2014 Independent Chairman of the Board
## 2 Bradford Smith 57 2015 President, Chief Legal Officer
## 3 Satya Nadella 48 2014 Chief Executive Officer, Director
## 4 William Gates 60 2014 Founder and Technology Advisor, Director
## 5 Amy Hood 43 2013 Chief Financial Officer, Executive Vice President
## 6 Christopher Capossela 45 2014 Executive Vice President, Chief Marketing Officer
## 7 Kathleen Hogan 49 2014 Executive Vice President - Human Resources
## 8 Margaret Johnson 54 2014 Executive Vice President - Business Development
## 9 Ifeanyi Amah NA 2016 Chief Technology Officer
## 10 Keith Lorizio NA 2016 Vice President - North America Sales
## 11 Teri List-Stoll 53 2014 Independent Director
## 12 G. Mason Morfit 40 2014 Independent Director
## 13 Charles Noski 63 2003 Independent Director
## 14 Helmut Panke 69 2003 Independent Director
## 15 Charles Scharf 50 2014 Independent Director
## 16 John Stanton 60 2014 Independent Director
## 17 Chris Suh NA NA General Manager - Investor Relations