If I have a vector from 1 to 200, how would I create a variable that creates an ordered cluster of these numbers. Example would be that the first 10 numbers would be assigned a 1, the next 10 would be assigned a 2, etc.
You can use rep with the each argument. Substitute the length of your vector for 200 and the number wanted in each group for 10 respectively, and truncate if you aren't dividing into even groups.
rep(1:(200/10), each = 10)
#> [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3
#> [24] 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5
#> [47] 5 5 5 5 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7
#> [70] 7 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 10 10
#> [93] 10 10 10 10 10 10 10 10 11 11 11 11 11 11 11 11 11 11 12 12 12 12 12
#> [116] 12 12 12 12 12 13 13 13 13 13 13 13 13 13 13 14 14 14 14 14 14 14 14
#> [139] 14 14 15 15 15 15 15 15 15 15 15 15 16 16 16 16 16 16 16 16 16 16 17
#> [162] 17 17 17 17 17 17 17 17 17 18 18 18 18 18 18 18 18 18 18 19 19 19 19
#> [185] 19 19 19 19 19 19 20 20 20 20 20 20 20 20 20 20
Created on 2019-04-26 by the reprex package (v0.2.1)
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I already checked many questions and I don't seem to find the suitable answer.
I have this df
df = data.frame(x = 1:10,y=11:20)
the output
x y
1 1 11
2 2 12
3 3 13
4 4 14
5 5 15
6 6 16
7 7 17
8 8 18
9 9 19
10 10 20
I just wish the output to be:
1 2 3 4 5 6 7 8 9 10
x 1 2 3 4 5 6 7 8 9 10
y 11 12 13 14 15 16 17 18 19 20
thanks
Try t() like below
> data.frame(t(df), check.names = FALSE)
1 2 3 4 5 6 7 8 9 10
x 1 2 3 4 5 6 7 8 9 10
y 11 12 13 14 15 16 17 18 19 20
A transpose should do it
setNames(data.frame(t(df)), df[,"x"])
1 2 3 4 5 6 7 8 9 10
x 1 2 3 4 5 6 7 8 9 10
y 11 12 13 14 15 16 17 18 19 20
I'm trying to do a repeated measures correlation in R using rmcorr, but received the above error, even though I have more than 3 subjects.
> scores$SUBJECT
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
[36] 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
[71] 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5
[106] 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
[141] 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8
[176] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9
[211] 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 11 11 11 11 11
[246] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[281] 12 12 12 12 12 12 12 12 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 14 14 14
[316] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[351] 15 15 15 15 15 15 15 15 15 15 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 17
[386] 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 18 18 18 18 18 18 18 18 18 18 18 18
[421] 18 18 18 18 18 18 18 18 18 18 18 18 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19 19
[456] 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 21 21 21 21 21 21 21 21 21 21
[491] 21 21 21 21 21 21 21 21 21 21 21 21 21 21
# Convert data types
scores$SUBJECT<-factor(scores$SUBJECT)
scores$FACTOR1<-factor(scores$FACTOR1)
scores$FACTOR2<-factor(scores$FACTOR2)
Interestingly, I was able to perform the correlation on some subsets of the data but not others.
# SUBSETS
subset1 <- subset(scores, FACTOR1 == "m1")
subset1a <- subset(subset1, FACTOR2 == "a")
subset1b <- subset(subset1, FACTOR2 == "b")
subset1c <- subset(subset1, FACTOR2 == "c")
subset2 <- subset(scores, FACTOR1 == "mp")
subset2a <- subset(subset2, FACTOR2 == "a")
subset2b <- subset(subset2, FACTOR2 == "b")
subset2c <- subset(subset2, FACTOR2 == "c")
rmcorr(participant = subset1$SUBJECT, measure1 = subset1$SCORE, measure2 = subset2$SCORE, dataset = scores)
rmcorr(participant = subset1a$SUBJECT, measure1 = subset1a$SCORE, measure2 = subset2a$SCORE, dataset = scores)
rmcorr(participant = subset1b$SUBJECT, measure1 = subset1b$SCORE, measure2 = subset2b$SCORE, dataset = scores)
rmcorr(participant = subset1c$SUBJECT, measure1 = subset1c$SCORE, measure2 = subset2c$SCORE, dataset = scores)
Specifically
rmcorr(participant = subset1$SUBJECT, measure1 = subset1$SCORE, measure2 = subset2$SCORE, dataset = scores)
worked, but all of the other calls to rmcorr generated the error. Does anyone know where I went wrong?
could you guys help me?
I have a matrix like this. the first column and row are the IDs.
I need to sort it by column and row ID like this.
Thanks!
Two thoughts:
mat <- matrix(1:25, nr=5, dimnames=list(c('4',3,5,2,1), c('4',3,5,2,1)))
mat
# 4 3 5 2 1
# 4 1 6 11 16 21
# 3 2 7 12 17 22
# 5 3 8 13 18 23
# 2 4 9 14 19 24
# 1 5 10 15 20 25
If you want a strictly alphabetic ordering, then this will work:
mat[order(rownames(mat)),order(colnames(mat))]
# 1 2 3 4 5
# 1 25 20 10 5 15
# 2 24 19 9 4 14
# 3 22 17 7 2 12
# 4 21 16 6 1 11
# 5 23 18 8 3 13
This will not work well if the names are intended to be ordered numerically:
mat <- matrix(1:30, nr=3, dimnames=list(c('2',1,3), c('4',3,5,2,1,6,7,8,9,10)))
mat
# 4 3 5 2 1 6 7 8 9 10
# 2 1 4 7 10 13 16 19 22 25 28
# 1 2 5 8 11 14 17 20 23 26 29
# 3 3 6 9 12 15 18 21 24 27 30
mat[order(rownames(mat)),order(colnames(mat))]
# 1 10 2 3 4 5 6 7 8 9
# 1 14 29 11 5 2 8 17 20 23 26
# 2 13 28 10 4 1 7 16 19 22 25
# 3 15 30 12 6 3 9 18 21 24 27
(1, 10, 2, ...) For that, you need a slight modification:
mat[order(as.numeric(rownames(mat))),order(as.numeric(colnames(mat)))]
# 1 2 3 4 5 6 7 8 9 10
# 1 14 11 5 2 8 17 20 23 26 29
# 2 13 10 4 1 7 16 19 22 25 28
# 3 15 12 6 3 9 18 21 24 27 30
How do I generate a vector in the form
1 2 ... 19 20 19 ... 2 1
Is it possible using the c() function?
You can use seq as well as rev function for the desired purpose.
seq
> c(1:20, seq(19,1,-1))
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
As suggested by #jimbou,
> c(1:20, 19:1)
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
> c(1:20, rev(1:19))
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
I have a dataframe that looks like this:
df$a <- 1:20
df$b <- 2:21
df$c <- 3:22
df <- as.data.frame(df)
> df
a b c
1 1 2 3
2 2 3 4
3 3 4 5
4 4 5 6
5 5 6 7
6 6 7 8
7 7 8 9
8 8 9 10
9 9 10 11
10 10 11 12
11 11 12 13
12 12 13 14
13 13 14 15
14 14 15 16
15 15 16 17
16 16 17 18
17 17 18 19
18 18 19 20
19 19 20 21
20 20 21 22
I would like to add another column to the data frame (df$d) so that every 5 rows (df$d[seq(1, nrow(df), 4)]) would take the value of the start of the respective row in the first column: df$a.
I have tried the manual way, but was wondering if there is a for loop or shorter way that can do this easily. I'm new to R, so I apologize if this seems trivial to some people.
"Manual" way:
df$d[1:5] <- df$a[1]
df$d[6:10] <- df$a[6]
df$d[11:15] <- df$a[11]
df$d[16:20] <- df$a[16]
>df
a b c d
1 1 2 3 1
2 2 3 4 1
3 3 4 5 1
4 4 5 6 1
5 5 6 7 1
6 6 7 8 6
7 7 8 9 6
8 8 9 10 6
9 9 10 11 6
10 10 11 12 6
11 11 12 13 11
12 12 13 14 11
13 13 14 15 11
14 14 15 16 11
15 15 16 17 11
16 16 17 18 16
17 17 18 19 16
18 18 19 20 16
19 19 20 21 16
20 20 21 22 16
I have tried
for (i in 1:nrow(df))
{df$d[i:(i+4)] <- df$a[seq(1, nrow(df), 4)]}
But this is not going the way I want it to. What am I doing wrong?
This should work:
df$d <- rep(df$a[seq(1,nrow(df),5)],each=5)
And here's a data.table solution:
library(data.table)
dt = data.table(df)
dt[, d := a[1], by = (seq_len(nrow(dt))-1) %/% 5]
I'd use logical indexing after initializing to NA
df$d <- NA
df$d <- rep(df$a[ c(TRUE, rep(FALSE,4)) ], each=5)
df
#--------
a b c d
1 1 2 3 1
2 2 3 4 1
3 3 4 5 1
4 4 5 6 1
5 5 6 7 1
6 6 7 8 6
7 7 8 9 6
8 8 9 10 6
9 9 10 11 6
10 10 11 12 6
11 11 12 13 11
12 12 13 14 11
13 13 14 15 11
14 14 15 16 11
15 15 16 17 11
16 16 17 18 16
17 17 18 19 16
18 18 19 20 16
19 19 20 21 16
20 20 21 22 16