I was wondering if you kind folks could answer a question I have. In the sample data I've provided below, in column 1 I have a categorical variable, and in column 2 p-values.
x <- c(rep("A",0.1*10000),rep("B",0.2*10000),rep("C",0.65*10000),rep("D",0.05*10000))
categorical_data=as.matrix(sample(x,10000))
p_val=as.matrix(runif(10000,0,1))
combi=as.data.frame(cbind(categorical_data,p_val))
head(combi)
V1 V2
1 A 0.484525170875713
2 C 0.48046557046473
3 C 0.228440979029983
4 B 0.216991128632799
5 C 0.521497668232769
6 D 0.358560319757089
I want to now take one of the categorical variables, let's say "C", and create another variable if it is C (print 1 in column 3, or 0 if it isn't).
combi$NEWVAR[combi$V1=="C"] <-1
combi$NEWVAR[combi$V1!="C" <-0
V1 V2 NEWVAR
1 A 0.484525170875713 0
2 C 0.48046557046473 1
3 C 0.228440979029983 1
4 B 0.216991128632799 0
5 C 0.521497668232769 1
6 D 0.358560319757089 0
I'd like to do this for each of the variables in V1, and then loop over using lapply:
variables=unique(combi$V1)
loopeddata=lapply(variables,function(x){
combi$NEWVAR[combi$V1==x] <-1
combi$NEWVAR[combi$V1!=x]<-0
}
)
My output however looks like this:
[[1]]
[1] 0
[[2]]
[1] 0
[[3]]
[1] 0
[[4]]
[1] 0
My desired output would be like the table in the second block of code, but when looping over the third column would be A=1, while B,C,D=0. Then B=1, A,C,D=0 etc.
If anyone could help me out that would be very much appreciated.
How about something like this:
model.matrix(~ -1 + V1, data=combi)
Then you can cbind it to combi if you desire:
combi <- cbind(combi, model.matrix(~ -1 + V1, data=combi))
model.matrix is definitely the way to do this in R. You can, however, also consider using table.
Here's an example using the result I get when using set.seed(1) (always use a seed when sharing example problems with random data).
LoopedData <- table(sequence(nrow(combi)), combi$V1)
head(LoopedData)
#
# A B C D
# 1 0 1 0 0
# 2 0 0 1 0
# 3 0 0 1 0
# 4 0 0 1 0
# 5 0 1 0 0
# 6 0 0 1 0
## If you want to bind it back with the original data
combi <- cbind(combi, as.data.frame.matrix(LoopedData))
head(combi)
# V1 V2 A B C D
# 1 B 0.0647124934475869 0 1 0 0
# 2 C 0.676612401846796 0 0 1 0
# 3 C 0.735371692571789 0 0 1 0
# 4 C 0.111299667274579 0 0 1 0
# 5 B 0.0466546178795397 0 1 0 0
# 6 C 0.130910312291235 0 0 1 0
Related
As I am new to R, this question may seem to you piece of a cake.
I have a data in txt format. The first column has Cluster Number and the second column has names of different organisms.
For example:
0 org4|gene759
1 org1|gene992
2 org1|gene1101
3 org4|gene757
4 org1|gene1702
5 org1|gene989
6 org1|gene990
7 org1|gene1699
9 org1|gene1102
10 org4|gene2439
10 org1|gene1374
I need to re-arrange/reshape the data in following format.
Cluster No. Org 1 Org 2 org3 org4
0 0 0 1
1 0 0 0
I could not figure out how to do it in R.
Thanks
We could use table
out <- cbind(ClusterNo = seq_len(nrow(df1)), as.data.frame.matrix(table(seq_len(nrow(df1)),
factor(sub("\\|.*", "", df1[[2]]), levels = paste0("org", 1:4)))))
head(out, 2)
# ClusterNo org1 org2 org3 org4
#1 1 0 0 0 1
#2 2 1 0 0 0
It is also possible that we need to use the first column to get the frequency
out1 <- as.data.frame.matrix(table(df1[[1]],
factor(sub("\\|.*", "", df1[[2]]), levels = paste0("org", 1:4))))
Reading the table into R can be done with
input <- read.table('filename.txt')
Then we can extract the relevant number from the org4|gene759 string using a regular expression, and set this to a third column of our input:
input[, 3] <- gsub('^org(.+)\\|.*', '\\1', input[, 2])
Our input data now looks like this:
> input
V1 V2 V3
1 0 org4|gene759 4
2 1 org1|gene992 1
3 2 org1|gene1101 1
4 3 org4|gene757 4
5 4 org1|gene1702 1
6 5 org1|gene989 1
7 6 org1|gene990 1
8 7 org1|gene1699 1
9 9 org1|gene1102 1
10 10 org4|gene2439 4
11 10 org1|gene1374 1
Then we need to list the possible values of org:
possibleOrgs <- seq_len(max(input[, 3])) # = c(1, 2, 3, 4)
Now for the tricky part. The following function takes each unique cluster number in turn (I notice that 10 appears twice in your example data), takes all the rows relating to that cluster, and looks at the org value for those rows.
result <- vapply(unique(input[, 1]), function (x)
possibleOrgs %in% input[input[, 1] == x, 3], logical(4)))
We can then format this result as we like, perhaps using t to transform its orientation, * 1 to convert from TRUEs and FALSEs to 1s and 0s, and colnames to title its columns:
result <- t(result) * 1
colnames (result) <- paste0('org', possibleOrgs)
rownames(result) <- unique(input[, 1])
I hope that this is what you were looking for -- it wasn't quite clear from your question!
Output:
> result
org1 org2 org3 org4
0 0 0 0 1
1 1 0 0 0
2 1 0 0 0
3 0 0 0 1
4 1 0 0 0
5 1 0 0 0
6 1 0 0 0
7 1 0 0 0
9 1 0 0 0
10 1 0 0 1
I have a sequence which looks like this
SEQENCE
1 A
2 B
3 B
4 C
5 A
Now from this sequence, I want to get the matrix like this where i the row and jth column element denotes how many times movement occurred from ith row node to jth column node
A B C
A 0 1 0
B 0 1 1
C 1 0 0
How Can I get this in R
1) Use table like this:
s <- DF[, 1]
table(tail(s, -1), head(s, -1))
giving:
A B C
A 0 0 1
B 1 1 0
C 0 1 0
2) or like this. Since embed does not work with factors we convert the factor to character,
s <- as.character(DF[, 1])
do.call(table, data.frame(embed(s, 2)))
giving:
X2
X1 A B C
A 0 0 1
B 1 1 0
C 0 1 0
3) xtabs also works:
s <- as.character(DF[, 1])
xtabs(data = data.frame(embed(s, 2)))
giving:
X2
X1 A B C
A 0 0 1
B 1 1 0
C 0 1 0
Note: The input DF in reproducible form is:
Lines <- " SEQENCE
1 A
2 B
3 B
4 C
5 A"
DF <- read.table(text = Lines, header = TRUE)
This question already has answers here:
Table by row with R
(4 answers)
Closed 6 years ago.
Imagine a group of three of machines (a,b,c) capture data in a series of tests. I need to count per test how many of each possible outcome has happened.
Using this test data and sample output, how might you solve it (assume that the test results may be numbers or alpha).
tests <- data.table(
a = c(1,2,2,3,0),
b = c(1,2,3,0,3),
c = c(2,2,3,0,2)
)
sumry <- data.table(
V0 = c(0,0,0,2,1),
V1 = c(2,0,0,0,0),
V2 = c(1,3,1,0,1),
V3 = c(0,0,2,1,1),
v4 = c(0,0,0,0,0)
)
tests
sumry
The output from sumry shows a column for each possible outcome/value (prefixed with V as in 'value' measured). Note: the sumry output indicates that there is the potential for a value of 4 but that is not observed in any of the test data here and therefore is always zero.
> tests
a b c
1: 1 1 2
2: 2 2 2
3: 2 3 3
4: 3 0 0
5: 0 3 2
> sumry
V0 V1 V2 V3 v4
1: 0 2 1 0 0
2: 0 0 3 0 0
3: 0 0 1 2 0
4: 2 0 0 1 0
5: 1 0 1 1 0
the V0 column from sumry indicates how many times the value zero is observed from any machine in test #1. For this set of test data zero is only observed in the 4th and 5th tests. The same holds true for V1-V4
I'm sure there's a simple name for this.
Here's one solution built around tabulate():
res <- suppressWarnings(do.call(rbind,apply(tests+1L,1L,tabulate)));
colnames(res) <- paste0('V',seq(0L,len=ncol(res)));
res;
## V0 V1 V2 V3
## [1,] 0 2 1 0
## [2,] 0 0 3 0
## [3,] 0 0 1 2
## [4,] 2 0 0 1
## [5,] 1 0 1 1
I'm using the table function in R to create a table of two of my variables in R.
I have a data.frame like this
mytable
V1 V2 V3
1 a c
2 c d
3 b b
4 d a
5 d c
when I use the table function table(mytable$V2, mytable$V3) I get the following
a b c d
a 0 0 1 0
b 0 1 0 0
c 0 0 0 1
d 1 0 1 0
Now I actually want to treat situations 'a-b' the same as 'b-a'. Or 'b-c' the same as 'c-b'. Therefore I want to have a table where everything above the diagonal is empty. He needs to add the values from above the diagonal to the values below the diagonal. How can I do this in R?
And furthermore I want this table to be represented as a heatmap, which I normally do with ggplot2. But I don't know if this also works for a table that will have empty values above the diagonal.
Here is the manual way of fixing it:
tab <- table(DF[,2:3])
tab[lower.tri(tab)] <- tab[lower.tri(tab)] + tab[upper.tri(tab)]
tab[upper.tri(tab)] <- NA
# V3
#V2 a b c d
# a 0
# b 0 1
# c 1 0 0
# d 1 0 2 0
I have a dataframe that looks like this:
step var1 score1 score2
1 a 0 0
2 b 1 1
3 d 1 1
4 e 0 0
5 g 0 0
1 b 1 1
2 a 1 0
3 d 1 0
4 e 0 1
5 f 1 1
1 g 0 1
2 d 1 1
etc.
Because I need to correlate variabeles a-g (their scores are in score1) with score2 in only step 5 I think i need to schange my dataset into this first:
a b c d e f g score2_step5
0 1 1 0 0 0
1 1 1 0 1 1
1 0
etc.
I am pretty sure that the Reshape package should be able to help me to do the job, but I haven't been able to make it work yet.
Can anyone help me? Many thanks in advance!
Here's another version. In case there is no step = 5, the value for score2_step = 0. Assuming your data.frame is df:
require(reshape2)
out <- do.call(rbind, lapply(seq(1, nrow(df), by=5), function(ix) {
iy <- min(ix+4, nrow(df))
df.b <- df[ix:iy, ]
tt <- dcast(df.b, 1 ~ var1, fill = 0, value.var = "score1", drop=F)
tt$score2_step5 <- 0
if (any(df.b$step == 5)) {
tt$score2_step5 <- df.b$score2[df.b$step == 5]
}
tt[,-1]
}))
> out
a b d e f g score2_step5
2 0 1 1 0 0 0 0
21 1 1 1 0 1 0 1
22 0 0 1 0 0 0 0
It looks like you want 7 correlations between the variables a-g and score2_step5--is that correct? First, you're going to need another variable. I'm assuming that step repeats continuously from 1 to 5; if not, this is going to be more complicated. I'm assuming your data is called df. I also prefer the newer reshape2 package, so I'm using that.
df$block <- rep(1:(nrow(df)/5),each=5)
df.molten <- melt(df,id.vars=c("var1", "step", "block"),measure.vars=c("score1"))
df2 <- dcast(df.molten, block ~ var1)
score2_step5 <- df$score2[df$step==5]
and then finally
cor(df2, score2_step5, use='pairwise')
There's an extra column (block) in df2 that you can get rid of or just ignore.
I added another row to your example data because my code doesn't work unless there is a step-5 observation in every block.
dat <- read.table(textConnection("
step var1 score1 score2
1 a 0 0
2 b 1 1
3 d 1 1
4 e 0 0
5 g 0 0
1 b 1 1
2 a 1 0
3 d 1 0
4 e 0 1
5 f 1 1
1 g 0 1
2 d 1 1
5 a 1 0"),header=TRUE)
Like #JonathanChristensen, I made another variable (I called it rep instead of block), and I made var1 into a factor (since there are no c values in the example data set given and I wanted a placeholder).
dat <- transform(dat,var1=factor(var1,levels=letters[1:7]),
rep=cumsum(step==1))
tapply makes the table of score1 values:
tab <- with(dat,tapply(score1,list(rep,var1),identity))
add the score2, step-5 values:
data.frame(tab,subset(dat,step==5,select=score2))