This question already has answers here:
How to join (merge) data frames (inner, outer, left, right)
(13 answers)
Find complement of a data frame (anti - join)
(7 answers)
Closed 1 year ago.
I am trying to use the %in% function to match the observations of one of my datasets to the observations of another dataset. Essentially, I would like to make two new datasets, one that contains the observations of the second dataset, and another which contains all other observations. Here is an example dataset:
Df
Last.Name First.Name Group
Williams Bob A
Williams Dan C
Miller Bob A
Smith Dan C
Williams Rick A
Smart Jeff C
Miller Bob A
Smith Dan C
Jones Bob A
Williams Buddy C
Miller Bob A
Hends Dan C
Williams Rick A
Smart Jeff C
Millers Bob A
Smith Danny C
Here is a dataset that I am trying to match the observations:
dfMatch
LastName FirstName
Williams Bob
Williams Buddy
Miller Bob
Smith Dan
Williams Rick
Smart Jeff
Miller Bob
Smith Dan
I tried various versions of the following code:
newdf<-Df[ Df$Last.Name %in% DfMatch$LastName & Df$First.Name %in% DfMatch$FirstName,]
and
newdf<-Df[ which(Df$Last.Name %in% DfMatch$LastName & Df$First.Name %in% DfMatch$FirstName),]
To get this new dataset:
newDF
Last.Name First.Name Group
Williams Bob A
Miller Bob A
Smith Dan C
Williams Rick A
Smart Jeff C
Miller Bob A
Smith Dan C
Williams Buddy C
However, this does not work.
I would also like to use similar code to build a dataset which includes all observations not listed in the dfMatch set, such as:
DfNoMatch
Last.Name First.Name Group
Williams Dan C
Jones Bob A
Miller Bob A
Hends Dan C
Williams Rick A
Smart Jeff C
Millers Bob A
Smith Danny C
By using code similar to:
DfNoMatch<-Df[ !Df$Last.Name %in% DfMatch$LastName & Df$First.Name %in% DfMatch$FirstName,]
and
DfNoMatch<-Df[! which(Df$Last.Name %in% DfMatch$LastName & Df$First.Name %in% DfMatch$FirstName),]
Thank you in advance and any help is greatly appreciated!
To really match the observations use the match-function. The %in%-function only tells you that there is a match, but it doesn't tell you what is matched where.
Related
This question already has answers here:
How can I match fuzzy match strings from two datasets?
(7 answers)
Closed 3 years ago.
I have 2 dataframes and I want to join by name, but names are not written exactly the same:
Df1:
ID Name Age
1 Jose 13
2 M. Jose 12
3 Laura 8
4 Karol P 32
Df2:
Name Surname
José Hall
María José Perez
Laura Alza
Karol Smith
I need to join and get this:
ID Name Age Surname
1 Jose 13 Hall
2 M. Jose 12 Perez
3 Laura 8 Alza
4 Karol P 32 Smith
How to consider that the names are not exactly the same before to join?
You can get close to your result using stringdist_left_join from fuzzyjoin
library(fuzzyjoin)
stringdist_left_join(df1, df2, by = "Name")
# ID Name.x Age Name.y Surname
#1 1 Jose 13 José Hall
#2 2 M. Jose 12 <NA> <NA>
#3 3 Laura 8 Laura Alza
#4 4 Karol P 32 Karol Smith
For the example shared it does not work for 1 entry since it is difficult to match Maria with M.. You can get the result for it by adjusting the max_dist argument to a higher value (default is 2) however, this will screw up other results and would give unwanted matches. If you have minimal NA entries (like the example shared) after this join you could just match them by "hand".
I would clean the database before (for example deleting those ´, in excel is easy doing those replace) and then use
new_df <- merge(df1, df2, by="name")
or you could try to assign an ID for df2 that coincide with df2 if it is possible.
I have data like this:
ID 1a 2a 3a 1b 2b 3b Name Team
cb128c James John Bill Jeremy Ed Simon Simon Wolves
cb128c John James Randy Simon David Ben John Tigers
ko351u Adam Alex Jacob Bob Oscar David Oscar Sparrows
ko351u Adam Matt Sam Fred Frank Harry Adam Wildcats
And I want to add columns indicating teams A and B by matching the row ID of that row in the ID column, and by matching one of the names in one of the "a" columns of that row in the "Name" column (for Team A), and doing the same for Team B using one of the names in one of the "b" columns of that row:
ID 1a 2a 3a 1b 2b 3b Name Team Team A Team B
cb128c James John Bill Jeremy Ed Simon Simon Wolves Tigers Wolves
cb128c John James Randy Simon David Ben John Tigers Tigers Wolves
ko351u Adam Alex Jacob Bob Oscar David Oscar Sparrows Wildcats Sparrows
ko351u Adam Matt Sam Fred Frank Harry Adam Wildcats Wildcats Sparrows
In row 1, we know Team A is Tigers because we match the ID of row 1, cb128c, in the ID column, and one of the "a" names of row 1 (either James, John or Bill) in the Name column. In this case, Row 2 has that ID, cb128c, and has "John" in the Name column. The Team in row 2 is "Tigers." Therefore, Row 1's Team A is Tigers. Team B is the Wolves because we match row 1's ID, still cb128c, and one of the "b" names in row 1 (either Jeremy, Ed or Simon) in the Name column. In this case, row 1 itself has the data we're looking for since one of the "b" names appears in the "Name" column of that row (Simon). The "Team" listed in each row will always either be the Team A or the Team B for that row.
Further down, we know Team A for row 3 is Wildcats because we match row 3's ID, ko351u and one of row 3's "a" names (either Adam, Alex or Jacob) in the "Name" column. Row 4 has that ID and "Adam" in the Name column. So the Team in Row 4 is Team A for Row 3.
Also notice that David switched teams in Row 3. In Row 2, David was on Simon's team, which we know is the Wolves (as explained above), but when we match Row 3's ID and one of Row 3's "b" names (Bob, Oscar or David), we get the Sparrows (like Row 1, one of the "b" names appears in the name column of that same row, so the Team B is the Team listed in that row).
How can I get this done in R?
df = read.table(text = "ID 1a 2a 3a 1b 2b 3b Name Team
cb128c James John Bill Jeremy Ed Simon Simon Wolves
cb128c John James Randy Simon David Ben John Tigers
ko351u Adam Alex Jacob Bob Oscar David Oscar Sparrows
ko351u Adam Matt Sam Fred Frank Harry Adam Wildcats", header = T)
# convert to character
df[] = lapply(df, as.character)
library(tidyr)
library(dplyr)
The following code 1. gathers to long format, 2. creates "Team_A" and "Team_B" out of the a or b suffix, 3. matches names to fill in the A/B Team Name, 4. removes missing values (no match), 5. gets rid of unnecessary columns, 6. converts back to wide format, 7. joins the A and B teams to the original data.
I'd encourage you to step through the code line by line to understand what's going on. I'll leave reordering the columns to you.
result = gather(df, key = "key", value = "value", starts_with("X")) %>%
mutate(ab = paste0("Team_", toupper(substr(key, start = nchar(key), stop = nchar(key)))),
team = ifelse(Name == value, Team, NA)) %>%
filter(!is.na(team)) %>%
select(ID, ab, team) %>%
spread(key = ab, value = team) %>%
right_join(df)
result
# ID Team_A Team_B X1a X2a X3a X1b X2b X3b Name Team
# 1 cb128c Tigers Wolves James John Bill Jeremy Ed Simon Simon Wolves
# 2 cb128c Tigers Wolves John James Randy Simon David Ben John Tigers
# 3 ko351u Wildcats Sparrows Adam Alex Jacob Bob Oscar David Oscar Sparrows
# 4 ko351u Wildcats Sparrows Adam Matt Sam Fred Frank Harry Adam Wildcats
This question already has answers here:
reshape wide to long with character suffixes instead of numeric suffixes
(3 answers)
Closed 5 years ago.
I have data in this format:
boss employee1 employee2
1 wil james andy
2 james dean bert
3 billy herb collin
4 tony mike david
and I would like it in this format:
boss employee
1 wil james
2 wil andy
3 james dean
4 james bert
5 billy herb
6 billy collin
7 tony mike
8 tony david
I have searched the forums, but I have not yet found anything that helps. I have tried using dplyr and some others, but I am still pretty new to R.
If this question has been answered and you could give me a link that would be greatly appreciated.
Thanks,
Wil
Here is a solution that uses tidyr. Specifically, the gather function is used to combine the two employee columns. This also generates a column bsaed on the column headers (employee1 and employee2) which is called key. We remove that with select from dplyr.
library(tidyr)
library(dplyr)
df <- read.table(
text = "boss employee1 employee2
1 wil james andy
2 james dean bert
3 billy herb collin
4 tony mike david",
header = TRUE,
stringsAsFactors = FALSE
)
df2 <- df %>%
gather(key, employee, -boss) %>%
select(-key)
> df2
boss employee
1 wil james
2 james dean
3 billy herb
4 tony mike
5 wil andy
6 james bert
7 billy collin
8 tony david
I would be shocked if there isn't a slicker, base solution but this should work for you.
Using base R:
df1 <- df[, 1:2]
df2 <- df[, c(1, 3)]
names(df1)[2] <- names(df2)[2] <- "employee"
rbind(df1, df2)
# boss employee
# 1 wil james
# 2 james dean
# 3 billy herb
# 4 tony mike
# 11 wil andy
# 21 james bert
# 31 billy collin
# 41 tony david
Using dplyr:
df %>%
select(boss, employee1) %>%
rename(employee = employee1) %>%
bind_rows(df %>%
select(boss, employee2) %>%
rename(employee = employee2))
# boss employee
# 1 wil james
# 2 james dean
# 3 billy herb
# 4 tony mike
# 5 wil andy
# 6 james bert
# 7 billy collin
# 8 tony david
Data:
df <- read.table(text = "
boss employee1 employee2
1 wil james andy
2 james dean bert
3 billy herb collin
4 tony mike david
", header = TRUE, stringsAsFactors = FALSE)
Sample data frame:
name <- c("Smith John Michael","Smith, John Michael","Smith John, Michael","Smith-John Michael","Smith-John, Michael")
df <- data.frame(name)
df
name
1 Smith John Michael
2 Smith, John Michael
3 Smith John, Michael
4 Smith-John Michael
5 Smith-John, Michael
I need to achieve the following desired output:
name first.name last.name
1 Smith John Michael John Smith
2 Smith, John Michael John Smith
3 Smith John, Michael Michael Smith John
4 Smith-John Michael Michael Smith-John
5 Smith-John, Michael Michael Smith-John
The rules are: if there is a comma in the string, then anything before is the last name. the first word following the comma is first name. If no comma in string, first word is last name, second word is last name. hyphenated words are one word. I would rather acheive this with dplyr and regex but I'll take any solution. Thanks for the help
You can achieve your desired result using strsplit switching between splitting by "," or " " based on whether there is a comma or not in name. Here, we define two functions to make the presentation clearer. You can just as well inline the code within the functions.
get.last.name <- function(name) {
lapply(ifelse(grepl(",",name),strsplit(name,","),strsplit(name," ")),`[[`,1)
}
The result of strsplit is a list. The lapply(...,'[[',1) loops through this list and extracts the first element from each list element, which is the last name.
get.first.name <- function(name) {
d <- lapply(ifelse(grepl(",",name),strsplit(name,","),strsplit(name," ")),`[[`,2)
lapply(strsplit(gsub("^ ","",d), " "),`[[`,1)
}
This function is similar except we extract the second element from each list element returned by strsplit, which contains the first name. We then remove any starting spaces using gsub, and we split again with " " to extract the first element from each list element returned by that strsplit as the first name.
Putting it all together with dplyr:
library(dplyr)
res <- df %>% mutate(first.name=get.first.name(name),
last.name=get.last.name(name))
The result is as expected:
print(res)
## name first.name last.name
## 1 Smith John Michael John Smith
## 2 Smith, John Michael John Smith
## 3 Smith John, Michael Michael Smith John
## 4 Smith-John Michael Michael Smith-John
## 5 Smith-John, Michael Michael Smith-John
Data:
df <- structure(list(name = c("Smith John Michael", "Smith, John Michael",
"Smith John, Michael", "Smith-John Michael", "Smith-John, Michael"
)), .Names = "name", row.names = c(NA, -5L), class = "data.frame")
## name
##1 Smith John Michael
##2 Smith, John Michael
##3 Smith John, Michael
##4 Smith-John Michael
##5 Smith-John, Michael
I am not sure if this is any better than aichao's answer but I gave it a shot anyway. I gives the right output.
df1 <- df %>%
filter(grepl(",",name)) %>%
separate(name, c("last.name","first.middle.name"), sep = "\\,", remove=F) %>%
mutate(first.middle.name = trimws(first.middle.name)) %>%
separate(first.middle.name, c("first.name","middle.name"), sep="\\ ",remove=T) %>%
select(-middle.name)
df2 <- df %>%
filter(!grepl(",",name)) %>%
separate(name, c("last.name","first.name"), sep = "\\ ", remove=F)
df<-rbind(df1,df2)
I can't seem to remember how to code this properly in R -
if I want to remove duplicates within a csv file based on multiple entries - first name and last name that are stored in separate columns
Then I can code: file[(duplicated(file$First.Name),]
but that only looks at the first name, I want it to look at the last same simultaneously.
If this is my starting file:
Steve Jones
Eric Brown
Sally Edwards
Steve Jones
Eric Davis
I want the output to be
Steve Jones
Eric Brown
Sally Edwards
Eric Davis
Only removing names of first and last name matching.
You can use
file[!duplicated(file[c("First.Name", "Last.Name")]), ]
Here is the solution for better performance (using data.table assuming First Name and Last Name are stored in separate columns):
> df <- read.table(text = 'Steve Jones
+ Eric Brown
+ Sally Edwards
+ Steve Jones
+ Eric Davis')
> colnames(df) <- c("First.Name","Last.Name")
> df
First.Name Last.Name
1 Steve Jones
2 Eric Brown
3 Sally Edwards
4 Steve Jones
5 Eric Davis
Here is where data.table specific code begins
> dt <- setDT(df)
> unique(dt,by=c('First.Name','Last.Name'))
First.Name Last.Name
1: Steve Jones
2: Eric Brown
3: Sally Edwards
4: Eric Davis
If there is a single column, use sub to remove the substring (i.e. first name) followed by space, get the logical vector (!duplicated(..) based on that to subset the rows of the dataset.
df1[!duplicated(sub("\\w+\\s+", "", df1$Col1)),,drop=FALSE]
# Col1
#1 Steve Jones
#2 Eric Brown
#3 Sally Edwards
#5 Eric Davis
If it is based on two columns and the dataset have two columns, just do duplicated directly on the dataset to get the logical vector, negate it and subset the rows.
df1[!duplicated(df1), , drop=FALSE]
# first.name second.name
#1 Steve Jones
#2 Eric Brown
#3 Sally Edwards
#5 Eric Davis
try:
!duplicated(paste(File$First.Name,File$Last.Name))