I have been doing some hierarchical clusterings in R. Its worked out fine up til now, producing hclust objects left and center, but suddenly not anymore. Now it will only produce lists when performing:
mydata.clusters <- hclust(dist(mydata[, 1:8]))
mydata.clustercut <- cutree(mydata.clusters, 4)
and when trying to:
table(mydata.clustercut, mydata$customer_lifetime)
it doesnt produce a table, but an endless print of the values (Im guessing from the list).
The cutree function provide the grouping to which each observation belong to. For example:
iris.clust <- hclust(dist(iris[,1:4]))
iris.clustcut <- cutree(iris.clust, 4)
iris.clustcut
# [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
# [52] 2 2 3 2 3 2 3 2 3 3 3 3 2 3 2 3 3 2 3 2 3 2 2 2 2 2 2 2 3 3 3 3 2 3 2 2 2 3 3 3 2 3 3 3 3 3 2 3 3 2 2
# [103] 4 2 2 4 3 4 2 4 2 2 2 2 2 2 2 4 4 2 2 2 4 2 2 4 2 2 2 4 4 4 2 2 2 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Additional comparison can then be done by using this as a grouping variable for the observed data:
new.iris <- data.frame(iris, gp=iris.clustcut)
# example to visualise quickly the Species membership of each group
library(ggplot2)
ggplot(new.iris, aes(gp, fill=Species)) +
geom_bar()
Related
I have conducted a discrete choice experiment using google forms and written up the results in a csv in excel. I am having problems understanding how to take the data from a standard csv format to a format that I can analyse using the gmnl package.
I am using this data below which has been dummy coded
personid choiceid alt payment management assessment crop
1 1 1 3 2 2 3
1 2 2 2 2 1 3
1 3 1 3 2 1 3
1 4 1 2 1 3 1
1 5 1 2 1 3 1
1 6 2 1 1 2 1
1 7 2 3 1 2 3
1 8 2 3 1 2 3
1 9 2 3 1 1 2
1 10 2 3 1 1 2
1 11 2 3 1 2 1
1 12 2 2 1 1 3
1 13 3 1 2 1 1
1 14 2 1 1 2 3
1 15 2 2 1 2 2
1 16 2 1 1 1 3
2 17 3 1 2 1 2
2 18 3 1 3 1 2
2 19 1 3 1 1 3
test <- as.data.frame(testchoices)
choices <- mlogit.data(test, shape = "long", idx = list(c("choiceid", "personid")),
idnames = c("management", "crops", "assessment", "price"))
write_csv(choices, "choicesnext.csv")
It works fine up to write csv where the error is thrown saying 'Error in [.data.frame (x, start:min(NROW(x), start + len)) : undefined columns selected
I would be grateful for any assistance
I am using kmeans() to create groups based on a score. The goal is to assign star ratings, so that the individuals with the highest scores get four stars, and the individuals with the lowest scores get 1 star. I would like to create the star variable based on the kmeans()$cluster value. However, as it stands, kmeans()$cluster indexes the clusters, but the index does not correspond to the relative position of the group.
Is there a way to manually assign the cluster indexes, or to set the index to be assigned in a certain order? I'm hoping to have kmeans()$cluster=1 for the low score group, kmeans()$cluster=2 for second lowest, etc.
id <- 1:500
set.seed(12); score <- runif(500, 0, 1)
dat <- data.frame(id, score)
km = kmeans(dat$score, 4, nstart=10)
plot(dat$score,
col = c(km$cluster),
main="K-Means result with 4 clusters",
pch=20,
cex=0.8)
dat$star <- km$cluster
plot(dat$score,
dat$star,
main="Score v. cluster number")
Any of these will yield a new cluster assignment vector such that 1 refers to the cluster with the smallest center, 2 the next and so on. The first is expressed solely in terms of fitted(km) whereas the second is expressed in terms of km$centers and km$cluster and the last is expressed in terms of fitted(km) and km$center
fit <- fitted(km)
factor(fit, labels = 1:nlevels(factor(fit)))
rank(km$centers)[km$cluster])
match(fitted(km), sort(km$centers))
Yes. You can just use a small table of what you want the values to be and use the original cluster number to look them up. Here is an example.
set.seed(2017)
KM3 = kmeans(iris[,1:4], 3)
KM3$cluster
[1] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[39] 2 2 2 2 2 2 2 2 2 2 2 2 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[77] 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 1 1 1 1 3 1 1 1 1 1 1 3
[115] 3 1 1 1 1 3 1 3 1 3 1 1 3 3 1 1 1 1 1 3 1 1 1 1 3 1 1 1 3 1 1 1 3 1 1 3
The clusters are in an awkward order. I want the low numbered points to be in cluster 1, the middle in cluster 2 and the high numbered points in cluster 3. So I want to change all of the 1's to 3, the 2's to 1 and the 3's to 2.
Relabel = c(3,1,2)
KM3$cluster = Relabel[KM3$cluster]
KM3$cluster
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[39] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[77] 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 3 3 3 3 2 3 3 3 3 3 3 2
[115] 2 3 3 3 3 2 3 2 3 2 3 3 2 2 3 3 3 3 3 2 3 3 3 3 2 3 3 3 2 3 3 3 2 3 3 2
Just a little extra detail. It says Relabel = c(3,1,2) because I want 1 to become 3, so the first location has a 3. I want 2 to become 1, so the second location has a 1. And i want 3 to become 2 so the third location has a 2.
I have my data in txt file, contain the following number, how to read into R
I tied fread but did not work
Error in fread("x.txt") :
Expected sep (' ') but new line, EOF (or other non printing character) ends field 0 when detecting types ( first):
Here is the data:
2 3 3 2 1 2 3 2 3 2 1 3 1 2
1 1 3 2 3 1 2 1 2 3 3 2
3 1 1 1 2 1 1 3 1 2 2 2
1 3 1 1 3 2 3 3 1 1 2 2
1 3 2 3 2 1 3 1 1 1 3 1
1 3 1 2 3 3 2 2 2 2 3 3
1 3 2 3 2 3 2 2 2 1 3 1
3 2 1 2 2 3 3 2 3 2 3 3
2 1
Try this.
x <- scan("x.txt")
data <- as.data.frame(x)
The dataframe looks like this :
Customer_id A B C D E F G
10000001 1 1 2 3 1 3 1
10000001 1 2 3 1 2 1 3
10000002 2 2 2 3 1 3 1
10000002 2 2 1 4 2 3 1
10000003 1 5 2 4 7 2 4
10000003 1 5 2 6 3 7 2
10000003 1 1 2 2 1 2 1
10000004 1 2 3 1 2 3 1
10000004 1 3 2 3 1 3 2
10000004 1 3 2 1 3 2 1
10000004 1 4 1 4 1 3 1
10000006 1 2 3 4 5 1 2
10000006 1 3 1 4 1 2 1
10000008 2 3 2 3 2 1 2
10000008 2 3 1 1 2 1 2
10000008 1 3 1 1 2 2 1
There are multiple entries for each customer_id. I need to create another data frame from this existing data frame. The new data frame should contain only the last row for every customer_id. It should look like this
10000001 1 1 2 3 1 3 1
10000002 2 2 1 4 2 3 1
10000003 1 1 2 2 1 2 1
10000004 1 4 1 4 1 3 1
10000006 1 3 1 4 1 2 1
10000008 1 3 1 1 2 2 1
Something like this (hard to code without the data in R format):
dataframe[ rev(!duplicated(rev(dataframe$Customer_id))),]
or better
dataframe[ !duplicated(dataframe$Customer_id,fromLast=TRUE),]
You can also use aggregate
aggregate(. ~ Customer_id, data = DF, FUN = tail, 1)
## Customer_id A B C D E F G
## 1 10000001 1 2 3 1 2 1 3
## 2 10000002 2 2 1 4 2 3 1
## 3 10000003 1 1 2 2 1 2 1
## 4 10000004 1 4 1 4 1 3 1
## 5 10000006 1 3 1 4 1 2 1
## 6 10000008 1 3 1 1 2 2 1
Assume your data is named dat,
Here's one way using by and rbind, although the other two methods (aggregate and duplicated) are much nicer:
> do.call(rbind, by(dat,dat$Customer_id,FUN=tail,1))
## Customer_id A B C D E F G
## 2 10000001 1 2 3 1 2 1 3
## 4 10000002 2 2 1 4 2 3 1
## 7 10000003 1 1 2 2 1 2 1
## 11 10000004 1 4 1 4 1 3 1
## 13 10000006 1 3 1 4 1 2 1
## 16 10000008 1 3 1 1 2 2 1
I'm sure this has been asked before but for the life of me I can't figure out what to search for!
I have the following data:
x y
1 3
1 3
1 3
1 2
1 2
2 2
2 4
3 4
3 4
And I would like to output a running count that resets everytime either x or y changes value.
x y o
1 3 1
1 3 2
1 3 3
1 2 1
1 2 2
2 2 1
2 4 1
3 4 1
3 4 2
Try something like
df<-read.table(header=T,text="x y
1 3
1 3
1 3
1 2
1 2
2 2
2 4
3 4
3 4")
cbind(df,o=sequence(rle(paste(df$x,df$y))$lengths))
> cbind(df,o=sequence(rle(paste(df$x,df$y))$lengths))
x y o
1 1 3 1
2 1 3 2
3 1 3 3
4 1 2 1
5 1 2 2
6 2 2 1
7 2 4 1
8 3 4 1
9 3 4 2
After seeing #ttmaccer's I see my first attempt with ave was wrong and this is perhaps what is needed:
> dat$o <- ave(dat$y, list(dat$y, dat$x), FUN=seq )
# there was a warning but the answer is corect.
> dat
x y o
1 1 3 1
2 1 3 2
3 1 3 3
4 1 2 1
5 1 2 2
6 2 2 1
7 2 4 1
8 3 4 1
9 3 4 2