I have two dataframes. One is just the names of my facebook friends and another one is the links with a sorce and target columns. I want to turn the names in the links dataframe to numbers based on the row index of that name in the friends dataframe.
friends
name
1 Andrewt Thomas
2 Robbie McCord
3 Mohammad Mojadidi
4 Andrew John
5 Professor Owk
6 Joseph Charles
links
source target
1 Andrewt Thomas Andrew John
2 Andrewt Thomas James Zou
3 Robbie McCord Bz Benz
4 Robbie McCord Yousef AL-alawi
5 Robbie McCord Sherhan Asimov
6 Robbie McCord Aigerim Aig
Seems trivial, but I cannot figure it out. Thanks for help.
Just use a simple match
links$source <- match(links$source, friends$name)
links
# source target
# 1 1 Andrew John
# 2 1 James Zou
# 3 2 Bz Benz
# 4 2 Yousef AL-alawi
# 5 2 Sherhan Asimov
# 6 2 Aigerim Aig
Something like this?
links$source <- vapply(links$source, function(x) which(friends$name == x), integer(1))
Full example
links <- data.frame(source = c("John", "John", "Alice"), target = c("Jimmy", "Al", "Chris"))
links$source <- vapply(links$source, function(x) which(friends$name == x), integer(1))
links$source
[1] 3 3 2
Related
I already asked and got the solution about this topic.
However, additionally, I want to check which data are replaced in the new column. I just tried below,
df$check <- str_match_all(df, "\\d{11}") %% unlist
but, it cannot work. Ultimately, I want to get the below data set.
original edited check
1 010-1234-5678 010-1234-5678
2 John 010-8888-8888 John 010-8888-8888
3 Phone: 010-1111-2222 Phone: 010-1111-2222
4 Peter 018.1111.3333 Peter 018.1111.3333
5 Year(2007,2019,2020) Year(2007,2019,2020)
6 Alice 01077776666 Alice 010-9999-9999 01077776666
Here is my code.
x = c("010-1234-5678",
"John 010-8888-8888",
"Phone: 010-1111-2222",
"Peter 018.1111.3333",
"Year(2007,2019,2020)",
"Alice 01077776666")
df = data.frame(
original = x
)
df$edited <- gsub("\\d{11}", "010-9999-9999", df$original)
df$check <- c("","","","","","01077776666") # I want to know the way here.
Thank you.
In an ifelse using `==` you could test if the columns match, then if not, use gsub to match the first digit and get it and the rest of the string out of "original".
transform(df, check=ifelse(!do.call(`==`, df[c("original", "edited")]),
gsub('(\\D*)(\\d.*)', '\\2', original),
NA))
# original edited check
# 1 010-1234-5678 010-1234-5678 <NA>
# 2 John 010-8888-8888 John 010-8888-8888 <NA>
# 3 Phone: 010-1111-2222 Phone: 010-1111-2222 <NA>
# 4 Peter 018.1111.3333 Peter 018.1111.3333 <NA>
# 5 Year(2007,2019,2020) Year(2007,2019,2020) <NA>
# 6 Alice 01077776666 Alice 010-9999-9999 01077776666
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)
I have a data frame like this:
names <- c('Mike','Mike','Mike','John','John','John','David','David','David','David')
dates <- c('04-26','04-26','04-27','04-28','04-27','04-26','04-01','04-02','04-02','04-03')
values <- c(NA,1,2,4,5,6,1,2,NA,NA)
test <- data.frame(names,dates,values)
Which is:
names dates values
1 Mike 04-26 NA
2 Mike 04-26 1
3 Mike 04-27 2
4 John 04-28 4
5 John 04-27 5
6 John 04-26 6
7 David 04-01 1
8 David 04-02 2
9 David 04-02 NA
10 David 04-03 NA
I'd like to get rid of duplicates with NA values. So, in this case, I have a valid observation from Mike on 04-26 and also have a valid observation from David on 04-02, so rows 1 and 9 should be erased and I will end up with:
names dates values
1 Mike 04-26 1
2 Mike 04-27 2
3 John 04-28 4
4 John 04-27 5
5 John 04-26 6
6 David 04-01 1
7 David 04-02 2
8 David 04-03 NA
I tried to use duplicated function, something like this:
test[!duplicated(test[,c('names','dates')]),]
But that does not work since some NA values come before the valid value. Do you have any suggestions without trying things like merge or making another data frame?
Update: I'd like to keep rows with NA that are not duplicates.
What about this way?
library(dplyr)
test %>% group_by(names, dates) %>% filter((n()>=2 & !is.na(values)) | n()==1)
Source: local data frame [8 x 3]
Groups: names, dates [8]
names dates values
(fctr) (fctr) (dbl)
1 Mike 04-26 1
2 Mike 04-27 2
3 John 04-28 4
4 John 04-27 5
5 John 04-26 6
6 David 04-01 1
7 David 04-02 2
8 David 04-03 NA
Here is an attempt in data.table:
# set up
libary(data.table)
setDT(test)
# construct condition
test[, dupes := max(duplicated(.SD)), .SDcols=c("names", "dates"), by=c("names", "dates")]
# print out result
test[dupes == 0 | !is.na(values),]
Here is a similar method using base R, except that the dupes variable is kept separately from the data.frame:
dupes <- duplicated(test[c("names", "dates")])
# this generates warnings, but works nonetheless
dupes <- ave(dupes, test$names, test$dates, FUN=max)
# print out result
test[dupes == 0 | !is.na(test$values),]
If there are duplicated rows where the values variable is NA, and these duplicates add nothing to the data, then you can drop them prior to running the code above:
testNoNADupes <- test[!(duplicated(test) & is.na(test$values)),]
This should work based on your sample.
test <- test[order(test$values),]
test <- test[!(duplicated(test$names) & duplicated(test$dates) & is.na(test$values)),]
I am working with data in the following form:
Country Player Goals
"USA" "Tim" 0
"USA" "Tim" 0
"USA" "Dempsey" 3
"USA" "Dempsey" 5
"Brasil" "Neymar" 6
"Brasil" "Neymar" 2
"Brasil" "Hulk" 5
"Brasil" "Luiz" 2
"England" "Rooney" 4
"England" "Stewart" 2
Each row represents the number of goals that a player scored per game, and also contains that player's country. I would like to have the data in the form such that I can run pairwise correlations to see whether being from the same country has some association with the number of goals that a player scores. The data would look like this:
Player_1 Player_2
0 8 # Tim Dempsey
8 5 # Neymar Hulk
8 2 # Neymar Luiz
5 2 # Hulk Luiz
4 2 # Rooney Stewart
(You can ignore the comments, they are there simply to clarify what each row contains).
How would I do this?
table(df$player)
gets me the number of goals per player, but then how to I generate these pairwise combinations?
This is a pretty classic self-join problem. I'm gonna start by summarizing your data to get the total goals for each player. I like dplyr for this, but aggregate or data.table work just fine too.
library(dplyr)
df <- df %>% group_by(Player, Country) %>% dplyr::summarize(Goals = sum(Goals))
> df
Source: local data frame [7 x 3]
Groups: Player
Player Country Goals
1 Dempsey USA 8
2 Hulk Brasil 5
3 Luiz Brasil 2
4 Neymar Brasil 8
5 Rooney England 4
6 Stewart England 2
7 Tim USA 0
Then, using good old merge, we join it to itself based on country, and then so we don't get each row twice (Dempsey, Tim and Tim, Dempsey---not to mention Dempsey, Dempsey), we'll subset it so that Player.x is alphabetically before Player.y. Since I already loaded dplyr I'll use filter, but subset would do the same thing.
df2 <- merge(df, df, by.x = "Country", by.y = "Country")
df2 <- filter(df2, as.character(Player.x) < as.character(Player.y))
> df2
Country Player.x Goals.x Player.y Goals.y
2 Brasil Hulk 5 Luiz 2
3 Brasil Hulk 5 Neymar 8
6 Brasil Luiz 2 Neymar 8
11 England Rooney 4 Stewart 2
15 USA Dempsey 8 Tim 0
The self-join could be done in dplyr if we made a little copy of the data and renamed the Player and Goals columns so they wouldn't be joined on. Since merge is pretty smart about the renaming, it's easier in this case.
There is probably a smarter way to get from the aggregated data to the pairs, but assuming your data is not too big (national soccer data), you can always do something like:
A<-aggregate(df$Goals~df$Player+df$Country,data=df,sum)
players_in_c<-table(A[,2])
dat<-NULL
for(i in levels(df$Country)) {
count<-players_in_c[i]
pair<-combn(count,m=2)
B<-A[A[,2]==i,]
dat<-rbind(dat, cbind(B[pair[1,],],B[pair[2,],]) )
}
dat
> dat
df$Player df$Country df$Goals df$Player df$Country df$Goals
1 Hulk Brasil 5 Luiz Brasil 2
1.1 Hulk Brasil 5 Neymar Brasil 8
2 Luiz Brasil 2 Neymar Brasil 8
4 Rooney England 4 Stewart England 2
6 Dempsey USA 8 Tim USA 0
I keep reading about the importance of vectorized functionality so hopefully someone can help me out here.
Say I have a data frame with two columns: name and ID. Now I also have another data frame with name and birthplace, but this data frame is much larger than the first, and contains some but not all of the names from the first data frame. How can I add a third column to the the first table that is populated with birthplaces looked up using the second table.
What I have is now is:
corresponding.birthplaces <- sapply(table1$Name,
function(name){return(table2$Birthplace[table2$Name==name])})
This seems inefficient. Thoughts? Does anyone know of a good book/resource for using R 'properly'..I get the feeling that I generally do think in the least computationally effective manner conceivable.
Thanks :)
See ?merge which will perform a database link merge or join.
Here is an example:
set.seed(2)
d1 <- data.frame(ID = 1:5, Name = c("Bill","Bob","Jessica","Jennifer","Robyn"))
d2 <- data.frame(Name = c("Bill", "Gavin", "Bob", "Joris", "Jessica", "Andrie",
"Jennifer","Joshua","Robyn","Iterator"),
Birthplace = sample(c("London","New York",
"San Francisco", "Berlin",
"Tokyo", "Paris"), 10, rep = TRUE))
which gives:
> d1
ID Name
1 1 Bill
2 2 Bob
3 3 Jessica
4 4 Jennifer
5 5 Robyn
> d2
Name Birthplace
1 Bill New York
2 Gavin Tokyo
3 Bob Berlin
4 Joris New York
5 Jessica Paris
6 Andrie Paris
7 Jennifer London
8 Joshua Paris
9 Robyn San Francisco
10 Iterator Berlin
Then we use merge() to do the join:
> merge(d1, d2)
Name ID Birthplace
1 Bill 1 New York
2 Bob 2 Berlin
3 Jennifer 4 London
4 Jessica 3 Paris
5 Robyn 5 San Francisco