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$a[ c(TRUE, rep(FALSE,4)) ], each=5)
df
#--------
a b c d
1 1 2 3 1
2 2 3 4 1
3 3 4 5 1
4 4 5 6 1
5 5 6 7 1
6 6 7 8 6
7 7 8 9 6
8 8 9 10 6
9 9 10 11 6
10 10 11 12 6
11 11 12 13 11
12 12 13 14 11
13 13 14 15 11
14 14 15 16 11
15 15 16 17 11
16 16 17 18 16
17 17 18 19 16
18 18 19 20 16
19 19 20 21 16
20 20 21 22 16
Related
I already checked many questions and I don't seem to find the suitable answer.
I have this df
df = data.frame(x = 1:10,y=11:20)
the output
x y
1 1 11
2 2 12
3 3 13
4 4 14
5 5 15
6 6 16
7 7 17
8 8 18
9 9 19
10 10 20
I just wish the output to be:
1 2 3 4 5 6 7 8 9 10
x 1 2 3 4 5 6 7 8 9 10
y 11 12 13 14 15 16 17 18 19 20
thanks
Try t() like below
> data.frame(t(df), check.names = FALSE)
1 2 3 4 5 6 7 8 9 10
x 1 2 3 4 5 6 7 8 9 10
y 11 12 13 14 15 16 17 18 19 20
A transpose should do it
setNames(data.frame(t(df)), df[,"x"])
1 2 3 4 5 6 7 8 9 10
x 1 2 3 4 5 6 7 8 9 10
y 11 12 13 14 15 16 17 18 19 20
I'm trying to do a repeated measures correlation in R using rmcorr, but received the above error, even though I have more than 3 subjects.
> scores$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?
I have a simple df with one column, and I want to create multiple new columns using a single function (sum_x in this case) with only an argument changing. Is there a way to do this more efficiently than the way I have shown below in dplyr? Ideally I could incorporate sum_vec and do this in a single line to create 100 new columns. This seems to be a very simple problem, but I don't know how to solve this efficiently using dplyr.
df <- data.frame(x = 1:20)
sum_x <- function(x, y){
x + y
}
sum_vec <- 1:100
df %>% mutate(x_1 = sum_x(x, 1)) %>% mutate(x_2 = sum_x(x, 2)) %>% mutate(x_3 = sum_x(x, 3))
try it this way
library(tidyverse)
bind_cols(df, map_dfc(1:3, ~ df %>% transmute(!!paste0("x_", .x) := x + .x)))
x x_1 x_2 x_3
1 1 2 3 4
2 2 3 4 5
3 3 4 5 6
4 4 5 6 7
5 5 6 7 8
6 6 7 8 9
7 7 8 9 10
8 8 9 10 11
9 9 10 11 12
10 10 11 12 13
11 11 12 13 14
12 12 13 14 15
13 13 14 15 16
14 14 15 16 17
15 15 16 17 18
16 16 17 18 19
17 17 18 19 20
18 18 19 20 21
19 19 20 21 22
20 20 21 22 23
If you don't like for loop, I am with you. So I'm not sure if this is good/efficient. Interested in a better solution.
library(dplyr)
for (i in 1:100) {
x_header = paste("x", i, sep = "_")
df = df %>%
mutate(!!x_header := sum_x(x, i))
}
> df[1:11, 1:11]
x x_1 x_2 x_3 x_4 x_5 x_6 x_7 x_8 x_9 x_10
1 1 2 3 4 5 6 7 8 9 10 11
2 2 3 4 5 6 7 8 9 10 11 12
3 3 4 5 6 7 8 9 10 11 12 13
4 4 5 6 7 8 9 10 11 12 13 14
5 5 6 7 8 9 10 11 12 13 14 15
6 6 7 8 9 10 11 12 13 14 15 16
7 7 8 9 10 11 12 13 14 15 16 17
8 8 9 10 11 12 13 14 15 16 17 18
9 9 10 11 12 13 14 15 16 17 18 19
10 10 11 12 13 14 15 16 17 18 19 20
11 11 12 13 14 15 16 17 18 19 20 21
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