So I'm trying to merge two dataframes. Dataframe x looks something like:
Name ParentID
Steve 1
Kevin 1
Stacy 1
Paula 4
Evan 7
Dataframe y looks like:
ParentID OtherStuff
1 things
2 stuff
3 item
4 ideas
5 short
6 help
7 me
The dataframe I want would look like:
Name ParentID OtherStuff
Steve 1 things
Kevin 1 things
Stacy 1 things
Paula 4 ideas
Evan 7 me
Using a left merge gives me substantially more observations than I want, with many duplicates. Any idea how to merge things, where y is duplicated where appropriate to match x?
I'm working with a databases set up similarly to the example. x has 5013 observations, while y has 6432. Using the merge function as described by Joel and thelatemail gives me 1627727 observations.
We can use match from base R
df1$OtherStuff <- with(df1, df2$OtherStuff[match(ParentID, df2$ParentID)])
df1
# Name ParentID OtherStuff
#1 Steve 1 things
#2 Kevin 1 things
#3 Stacy 1 things
#4 Paula 4 ideas
#5 Evan 7 me
Related
This is my data frame
ID=c(1,2,3,4,5,6,7,8,9,10,11,12)
favFruit=c('apple','lemon','pear',
'apple','apple','pear',
'apple','lemon','pear',
'pear','pear','pear')
surveyDate = ('1/1/2005','1/1/2005','1/1/2005',
'2/1/2005','2/1/2005','2/1/2005',
'3/1/2005','3/1/2005','3/1/2005',
'4/1/2005','4/1/2005','4/1/2005')
df<-data.frame(ID,favFruit, surveyDate)
I need to aggregate it so I can plot a line graph in R for count of favFruit by date split by favFruit but I am unable to create an aggregate table. My data has 45000 rows so a manual solution is not possible.
surveyYear favFruit count
1/1/2005 apple 1
1/1/2005 lemon 1
1/1/2005 pear 1
2/1/2005 apple 2
2/1/2005 lemon 0
2/1/2005 pear 1
... etc
I tried this but R printed an error
df2 <- aggregate(df, favFruit, FUN = sum)
and I tried this, another error
df2 <- aggregate(df, date ~ favFruit, sum)
I checked for solutions online but their data generally included a column of quantities which I dont have and the solutions were overly complex. Is there an easy way to do this? Thanx in advance. Thank you to whoever suggested the link as a possible duplicate but it has has date and number of rows. But my question needs number of rows by date and favFruit (one more column) 1
Update:
Ronak Shah's solution worked. Thanx!
The solution provided by Ronak is very good.
In case you prefer to keep the zero counts in your dataframe.
You could use table function:
data.frame(with(df, table(favFruit, surveyDate)))
Output:
favFruit surveyDate Freq
1 apple 1/1/2005 1
2 lemon 1/1/2005 1
3 pear 1/1/2005 1
4 apple 2/1/2005 2
5 lemon 2/1/2005 0
6 pear 2/1/2005 1
7 apple 3/1/2005 1
8 lemon 3/1/2005 1
9 pear 3/1/2005 1
10 apple 4/1/2005 0
11 lemon 4/1/2005 0
12 pear 4/1/2005 3
I'm trying to use visNetwork to create a node diagram. However, my data is not in the correct format and I haven't been able to find any help on this on the internet.
My current data frame looks similar to this:
name town car color age school
John Bringham Swift Red 22 Brighton
Sarah Bringham Corolla Red 33 Rustal
Beth Burb Swift Blue 43 Brighton
Joe Spring Polo Black 18 Riding
I'm wanting to change use this to create nodes and edges lists that can be used to create a vis network.
I know that the "nodes" list will be made from the unique values in the "name" column but I'm not sure how I would use the rest of the data to create the "edges" list?
I was thinking that it may be possible to group by each column and then read back the matches from this function but I am not sure how to implement this. The idea that I thought of is to weight the edges based on how many matches they detect in the various group by functions. I'm not sure how to actually implement this yet.
For example, Joe will not match with anyone because he shares no common columns with any of the others. John and Sarah will have a weight of 2 because they share two common columns.
Also open to solutions in python!
One option is to compar row by row, in order to calculate the number of commun values.
For instance for John (first row) and Sarah (second row):
sum(df[1,] == df[2,])
# 2
Then you use the function combn() from library utils to know in advance the number of pair-combinaison you have to calculate:
nodes <- matrix(combn(df$name, 2), ncol = 2, byrow = T) %>% as.data.frame()
nodes$V1 <- as.character(nodes$V1)
nodes$V2 <- as.character(nodes$V2)
nodes$weight <- NA
(nodes)
# V1 V2 weight
#1 John Sarah NA
#2 John Beth NA
#3 John Joe NA
#4 Sarah Beth NA
#5 Sarah Joe NA
#6 Beth Joe NA
Finally a loop to calculate weight for each node.
for(n in 1:nrow(nodes)){
name1 <- df[df$name == nodes$V1[n],]
name2 <- df[df$name == nodes$V2[n],]
nodes$weight[n] <- sum(name1 == name2)
}
# V1 V2 weight
#1 John Sarah 2
#2 John Beth 2
#3 John Joe 0
#4 Sarah Beth 0
#5 Sarah Joe 0
#6 Beth Joe 0
I think node will be the kind of dataframe that you can use in the function visNetwork().
We have a daily meeting when participants nominate each other to speak. The first person is chosen randomly.
I have a dataframe that consists of names and the order of speech every day.
I have a day1, a day2 ,a day3 , etc. in the columns.
The data in the rows are numbers, meaning the order of speech on that particular day.
NA means that the person did not participate on that day.
Name day1 day2 day3 day4 ...
Albert 1 3 1 ...
Josh 2 2 NA
Veronica 3 5 3
Tim 4 1 2
Stew 5 4 4
...
I want to create two analysis, first, I want to create a dataframe who has chosen who the most times. (I know that the result depends on if a participant was nominated before and therefore on that day that participant cannot be nominated again, I will handle it later, but for now this is enough)
It should look like this:
Name Favorite
Albert Stew
Josh Veronica
Veronica Tim
Tim Stew
...
My questions (feel free to answer only one if you can):
1. What code shall I use for it without having to manunally put the names in a different dataframe?
2. How shall I handle a tie, for example Josh chose Veronica and Tim first the same number of times? Later I want to visualise it and I have no idea how to handle ties.
I also would like to analyse the results to visualise strong connections.
Like to show that there are people who usually chose each other, etc.
Is there a good package that is specialised for these? Or how should I get to it?
I do not need DNA sequences, only this simple ones, but I have not found a suitable one yet.
Thanks for your help!
If I am not misunderstanding your problem, here is some code to get the number of occurences of who choose who as next speaker. I added a fourth day to have some count that is not 1. There are ties in the result, choosing the first couple of each group by speaker ('who') may be a solution :
df <- read.table(textConnection(
"Name,day1,day2,day3,day4
Albert,1,3,1,3
Josh,2,2,,2
Veronica,3,5,3,1
Tim,4,1,2,4
Stew,5,4,4,5"),header=TRUE,sep=",",stringsAsFactors=FALSE)
purrr::map(colnames(df)[-1],
function (x) {
who <- df$Name[order(df[x],na.last=NA)]
data.frame(who,lead(who),stringsAsFactors=FALSE)
}
) %>%
replyr::replyr_bind_rows() %>%
filter(!is.na(lead.who.)) %>%
group_by(who,lead.who.) %>% summarise(n=n()) %>%
arrange(who,desc(n))
Input:
Name day1 day2 day3 day4
1 Albert 1 3 1 3
2 Josh 2 2 NA 2
3 Veronica 3 5 3 1
4 Tim 4 1 2 4
5 Stew 5 4 4 5
Result:
# A tibble: 12 x 3
# Groups: who [5]
who lead.who. n
<chr> <chr> <int>
1 Albert Tim 2
2 Albert Josh 1
3 Albert Stew 1
4 Josh Albert 2
5 Josh Veronica 1
6 Stew Veronica 1
7 Tim Stew 2
8 Tim Josh 1
9 Tim Veronica 1
10 Veronica Josh 1
11 Veronica Stew 1
12 Veronica Tim 1
I have a data frame named as Records having 2 vectors Rank and Name
Rank Name
1 Ashish
1 Ashish
2 Ashish
3 Mark
4 Mark
1 Mark
3 Spencer
2 Spencer
1 Spencer
2 Mary
4 Joseph
I want that every name should be placed in either 1, 2 ,3 or 4 tag depending on their occurrence and uniqueness:
I want to create a new vector which will be named as Tagging
So The output should be:
Rank 1 has three unique elements Mark Spencer and Ashish so the tag is 1 for all three.
Rank 2 has one unique records which is Mary as Ashish has already been assigned tag 1 so Mary is tagged as 2.
Rank 3 has no unique records as Spencer and Mark has already been assigned 1 so I cannot tag 3 to anybody.
Rank 4 has one unique record Joseph so he gets tagged as 4.
Let me know which function can help me do this.
I do not want to use looping as this is 1000000 row database
The below solution follows the principle that the highest Rank of a person is going to be that person's tag too.
tbl <- read.table(header=TRUE, text='
Rank Name
1 Ashish
1 Ashish
2 Ashish
3 Mark
4 Mark
1 Mark
3 Spencer
2 Spencer
1 Spencer
2 Mary
4 Joseph
')
Ordering the 'tbl' dataframe by Rank
tbl_ord <- tbl[with(tbl,order(Rank)),]
Removing multiple occurrence of name within same Rank
> name_ord<- tbl_ord[duplicated(tbl_ord$Rank),]
> name_ord
Rank Name
2 1 Ashish
6 1 Mark
9 1 Spencer
8 2 Spencer
10 2 Mary
7 3 Spencer
11 4 Joseph
Displaying unique Names
#name_ord[unique(name_ord$Name),] #this will work too
> name_ord[!duplicated(name_ord$Name),]
Rank Name
2 1 Ashish
6 1 Mark
9 1 Spencer
10 2 Mary
11 4 Joseph
Using the setkey function of data.table package and unique:
library(data.table)
dt<-data.table(Rank=c(1,1,2,3,4,1,3,2,1,2,4), Name=c(rep("Ashish", 3), rep("Mark", 3), rep("Spencer", 3), "Mary", "Joseph"))
setkey(dt, Rank, Name)
dt<-unique(dt)
setkey(dt, Name)
dt<-unique(dt) # works because of the above setkey call which sorted it
setkey(dt, Rank) # if you want to order them by Rank again
I have a data.frame with 1,000 rows and 3 columns. It contains a large number of duplicates and I've used plyr to combine the duplicate rows and add a count for each combination as explained in this thread.
Here's an example of what I have now (I still also have the original data.frame with all of the duplicates if I need to start from there):
name1 name2 name3 total
1 Bob Fred Sam 30
2 Bob Joe Frank 20
3 Frank Sam Tom 25
4 Sam Tom Frank 10
5 Fred Bob Sam 15
However, column order doesn't matter. I just want to know how many rows have the same three entries, in any order. How can I combine the rows that contain the same entries, ignoring order? In this example I would want to combine rows 1 and 5, and rows 3 and 4.
Define another column that's a "sorted paste" of the names, which would have the same value of "Bob~Fred~Sam" for rows 1 and 5. Then aggregate based on that.
Brief code snippet (assumes original data frame is dd): it's all really intuitive. We create a lookup column (take a look and should be self explanatory), get the sums of the total column for each combination, and then filter down to the unique combinations...
dd$lookup=apply(dd[,c("name1","name2","name3")],1,
function(x){paste(sort(x),collapse="~")})
tab1=tapply(dd$total,dd$lookup,sum)
ee=dd[match(unique(dd$lookup),dd$lookup),]
ee$newtotal=as.numeric(tab1)[match(ee$lookup,names(tab1))]
You now have in ee a set of unique rows and their corresponding total counts. Easy - and no external packages needed. And crucially, you can see at every stage of the process what is going on!
(Minor update to help OP:) And if you want a cleaned-up version of the final answer:
outdf = with(ee,data.frame(name1,name2,name3,
total=newtotal,stringsAsFactors=FALSE))
This gives you a neat data frame with the three all-important name columns, and with the aggregated totals in a column called total rather than newtotal.
Sort the index columns, then use ddply to aggregate and sum:
Define the data:
dat <- " name1 name2 name3 total
1 Bob Fred Sam 30
2 Bob Joe Frank 20
3 Frank Sam Tom 25
4 Sam Tom Frank 10
5 Fred Bob Sam 15"
x <- read.table(text=dat, header=TRUE)
Create a copy:
xx <- x
Use apply to sort the columns, then aggregate:
xx[, -4] <- t(apply(xx[, -4], 1, sort))
library(plyr)
ddply(xx, .(name1, name2, name3), numcolwise(sum))
name1 name2 name3 total
1 Bob Frank Joe 20
2 Bob Fred Sam 45
3 Frank Sam Tom 35