Assuming I have a matrix looks like below, the values up or down the diagonal are the same. In other words, [,1] x [2,] and [,2] x [1,] both are 2 in the matrix.
> m = cbind(c(1,2,3),c(2,4,5),c(3,5,6))
> m
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 2 4 5
[3,] 3 5 6
Then I have real name for 1, 2, and 3 as well.
>Real_name
A B C # A represents 1, B represents 2, and C represents 3.
If I would like to convert the matrix to 3 columns containing corresponding real name for each pair, and the pair must be unique, A x B is the same as B x A, so we keep A x B only. How can I achieve it using R?
A A 1
A B 2
A C 3
B B 4
B C 5
C C 6
The following is straightforward:
m <- cbind(c(1,2,3), c(2,4,5), c(3,5,6))
## read `?lower.tri` and try `v <- lower.tri(m, diag = TRUE)` to see what `v` is
## read `?which` and try `which(v, arr.ind = TRUE)` to see what it gives
ij <- which(lower.tri(m, diag = TRUE), arr.ind = TRUE)
Real_name <- LETTERS[1:3]
data.frame(row = Real_name[ij[, 1]], col = Real_name[ij[, 2]], val = c(m[ij]))
# row col val
#1 A A 1
#2 B A 2
#3 C A 3
#4 B B 4
#5 C B 5
#6 C C 6
colnames(m) <- c("A", "B", "C")
rownames(m) <- c("A", "B", "C")
m[lower.tri(m)] = NA # replace lower triangular elements with NA
data.table::melt(m, na.rm = TRUE) # melt and remove NA
# Var1 Var2 value
#1 A A 1
#4 A B 2
#5 B B 4
#7 A C 3
#8 B C 5
#9 C C 6
Or you can do it in a single line: melt(replace(m, lower.tri(m), NA), na.rm = TRUE)
This will also work:
g <- expand.grid(1:ncol(m), 1:ncol(m))
g <- g[g[,2]>=g[,1],]
cbind.data.frame(sapply(g, function(x) Real_name[x]), Val=m[as.matrix(g)])
Var1 Var2 Val
1 A A 1
2 A B 2
3 B B 4
4 A C 3
5 B C 5
6 C C 6
Related
I want to make this dataframe
into this matrix
I have tried:
x <- read.csv("sample1.csv")
ax <- matrix(c(x[1,1],x[2,1],x[1,3],x[1,1],x[3,1],x[1,4],x[1,1],x[4,1],x[1,5],x[1,1],x[5,1],x[1,6],x[1,1],x[6,1],x[1,7],x[2,1],x[1,1],x[2,2],x[2,1],x[3,1],x[2,4],x[2,1],x[4,1],x[2,5],x[2,1],x[5,1],x[2,6],x[3,1],x[6,1],x[2,7],x[3,1],x[1,1],x[3,2],x[3,1],x[2,1],x[3,3],x[3,1],x[4,1],x[3,5],x[3,1],x[5,1],x[3,6],x[3,1],x[6,1],x[3,7],x[4,1],x[1,1],x[4,2],x[4,1],x[2,1],x[4,3],x[4,1],x[3,1],x[4,4],x[4,1],x[5,1],x[4,6],x[4,1],x[6,1],x[4,7],x[5,1],x[1,1],x[2,2],x[5,1],x[2,1],x[2,4],x[5,1],x[3,1],x[2,5],x[5,1],x[4,1],x[2,6],x[5,1],x[6,1],x[2,7],x[6,1],x[1,1],x[2,2],x[6,1],x[2,1],x[2,4],x[6,1],x[3,1],x[2,5],x[6,1],x[4,1],x[2,6],x[6,1],x[5,1],x[2,7]),10,3, byrow=TRUE)
bx <- ax[order(ax[,3], decreasing = TRUE),]
But it's not beautiful at all, and also it's gonna be lots of work if I got different sample data.
So I wish to simplified it if possible, any suggestion?
This can be achieved by using melt() function from reshape2 package:
> a = matrix(c(1:9), nrow = 3, ncol = 3, dimnames = list(LETTERS[1:3], letters[1:3]))
> a
a b c
A 1 4 7
B 2 5 8
C 3 6 9
> library(reshape2)
> melt(a, na.rm = TRUE)
Var1 Var2 value
1 A a 1
2 B a 2
3 C a 3
4 A b 4
5 B b 5
6 C b 6
7 A c 7
8 B c 8
9 C c 9
I wanted to merge different elements of atomic vectors by elements names stored in list. See example:
ls = list(a = c(a = 1, b = 2, d = 2), b = c(b = 2, c = 3), c = c(a = 1, b = 2))
Now, I wanted to get output like this:
a b c
a 1 NA 1
b 2 2 2
c NA 3 NA
d 2 NA NA
I tried Reduce, but it is not working. I do not want to use any external package for this problem.
Thanks
You can use [ in sapply after you have extracted all elements names.
i <- sort(unique(unlist(lapply(ls, names))))
x <- sapply(ls, "[", i)
rownames(x) <- i
x
# a b c
#a 1 NA 1
#b 2 2 2
#c NA 3 NA
#d 2 NA NA
We could also use bind_rows here
library(dplyr)
library(tibble)
bind_rows(ls, .id = 'x') %>%
column_to_rownames('x') %>%
t
a b c
a 1 NA 1
b 2 2 2
d 2 NA NA
c NA 3 NA
Or using base R
xtabs(values ~ ind + x, do.call(rbind, Map(cbind, x = names(ls), lapply(ls, stack))))
x
ind a b c
a 1 0 1
b 2 2 2
d 2 0 0
c 0 3 0
A data.table option using rbindlist
> t(rbindlist(Map(function(x) data.table(t(x)), lst), fill = TRUE))
[,1] [,2] [,3]
a 1 NA 1
b 2 2 2
d 2 NA NA
c NA 3 NA
I am studying social network analysis and will be using Ucinet to draw network graphs. For this, I have to convert the csv file to an edge list format. Converting the adjacency matrix to the edge list was successful. However, it is difficult to convert an incidence matrix to the edge list format.
The csv file('some.csv') I have, with a incidence matrix like this:
A B C D
a 1 0 3 1
b 0 0 0 2
c 3 2 0 1
The code that converted the adjacency matrix to the edge list was as follows:
x<-read.csv("C:/.../something.csv", header=T, row.names=1)
net<-as.network(x, matrix.type='adjacency', ignore.eval=FALSE, names.eval='dd', loops=FALSE)
el<-edgelist(net, attrname='dd')
write.csv(el, file='C:/.../result.csv')
Now It only succeedded in loading the file. I tried to follow the above method, but I get an error.
y<-read.csv("C:/.../some.csv", header=T, row.names=1)
net2<-network(y, matrix.type='incidence', ignore.eval=FALSE, names.eval='co', loops=FALSE)
Error in network.incidence(x, g, ignore.eval, names.eval, na.rm, edge.check) :
Supplied incidence matrix has empty head/tail lists. (Did you get the directedness right?)
I want to see the result in this way:
a A 1
a C 3
a D 1
b D 2
c A 3
c B 2
c D 1
I tried to put the values as the error said, but I could not get the result i wanted.
Thank you for any assistance with this.
Here's your data:
inc_mat <- matrix(
c(1, 0, 3, 1,
0, 0, 0, 2,
3, 2, 0, 1),
nrow = 3, ncol = 4, byrow = TRUE
)
rownames(inc_mat) <- letters[1:3]
colnames(inc_mat) <- LETTERS[1:4]
inc_mat
#> A B C D
#> a 1 0 3 1
#> b 0 0 0 2
#> c 3 2 0 1
Here's a generalized function that does the trick:
as_edgelist.weighted_incidence_matrix <- function(x, drop_rownames = TRUE) {
melted <- do.call(cbind, lapply(list(row(x), col(x), x), as.vector)) # 3 col matrix of row index, col index, and `x`'s values
filtered <- melted[melted[, 3] != 0, ] # drop rows where column 3 is 0
# data frame where first 2 columns are...
df <- data.frame(mode1 = rownames(x)[filtered[, 1]], # `x`'s rownames, indexed by first column in `filtered``
mode2 = colnames(x)[filtered[, 2]], # `x`'s colnames, indexed by the second column in `filtered`
weight = filtered[, 3], # the third column in `filtered`
stringsAsFactors = FALSE)
out <- df[order(df$mode1), ] # sort by first column
if (!drop_rownames) {
return(out)
}
`rownames<-`(out, NULL)
}
Take it for a spin:
el <- as_edgelist.weighted_incidence_matrix(inc_mat)
el
#> mode1 mode2 weight
#> 1 a A 1
#> 2 a C 3
#> 3 a D 1
#> 4 b D 2
#> 5 c A 3
#> 6 c B 2
#> 7 c D 1
Here are the results you wanted:
control_df <- data.frame(
mode1 = c("a", "a", "a", "b", "c", "c", "c"),
mode2 = c("A", "C", "D", "D", "A", "B", "D"),
weight = c(1, 3, 1, 2, 3, 2, 1),
stringsAsFactors = FALSE
)
control_df
#> mode1 mode2 weight
#> 1 a A 1
#> 2 a C 3
#> 3 a D 1
#> 4 b D 2
#> 5 c A 3
#> 6 c B 2
#> 7 c D 1
Do they match?
identical(control_df, el)
#> [1] TRUE
This might not be the most efficient way, but it produces expected result:
y <- matrix( c(1,0,3,0,0,2,3,0,0,1,2,1), nrow=3)
colnames(y) <- c("e.A","e.B","e.C","e.D")
dt <- data.frame(rnames=c("a","b","c"))
dt <- cbind(dt, y)
# rnames e.A e.B e.C e.D
#1 a 1 0 3 1
#2 b 0 0 0 2
#3 c 3 2 0 1
# use reshape () function to convert dataframe into the long format
M <- reshape(dt, direction="long", idvar = "rnames", varying = c("e.A","e.B","e.C","e.D"))
M <- M[M$e >0,]
M
# rnames time e
# a.A a A 1
# c.A c A 3
# c.B c B 2
# a.C a C 3
# a.D a D 1
# b.D b D 2
# c.D c D 1
# If M needs to be sorted by the column rnames:
M[order(M$rnames), ]
# rnames time e
# a.A a A 1
# a.C a C 3
# a.D a D 1
# b.D b D 2
# c.A c A 3
# c.B c B 2
# c.D c D 1
I have below line of code for cbind, but I am getting a warning message everytime.
Though the code still functions as it should be, is there any way to resolve the warning?
dateset = subset(all_data[,c("VAR1","VAR2","VAR3","VAR4","VAR5","RATE1","RATE2","RATE3")])
dateset = cbind(dateset[c(1,2,3,4,5)],stack(dateset[,-c(1,2,3,4,5)]))
Warnings :
Warning message:
In data.frame(..., check.names = FALSE) :
row names were found from a short variable and have been discarded
Thanks in advance!
I'm guessing your data.frame has row.names:
A <- data.frame(a = c("A", "B", "C"),
b = c(1, 2, 3),
c = c(4, 5, 6),
row.names=c("A", "B", "C"))
cbind(A[1], stack(A[-1]))
# a values ind
# 1 A 1 b
# 2 B 2 b
# 3 C 3 b
# 4 A 4 c
# 5 B 5 c
# 6 C 6 c
# Warning message:
# In data.frame(..., check.names = FALSE) :
# row names were found from a short variable and have been discarded
What's happening here is that since you can't by default have duplicated row.names in a data.frame and since you don't tell R at any point to duplicate the row.names when recycling the first column to the same number of rows of the stacked column, R just discards the row.names.
Compare with a similar data.frame, but one without row.names:
B <- data.frame(a = c("A", "B", "C"),
b = c(1, 2, 3),
c = c(4, 5, 6))
cbind(B[1], stack(B[-1]))
# a values ind
# 1 A 1 b
# 2 B 2 b
# 3 C 3 b
# 4 A 4 c
# 5 B 5 c
# 6 C 6 c
Alternatively, you can set row.names = NULL in your cbind statement:
cbind(A[1], stack(A[-1]), row.names = NULL)
# a values ind
# 1 A 1 b
# 2 B 2 b
# 3 C 3 b
# 4 A 4 c
# 5 B 5 c
# 6 C 6 c
If your original row.names are important, you can also add them back in with:
cbind(rn = rownames(A), A[1], stack(A[-1]), row.names = NULL)
# rn a values ind
# 1 A A 1 b
# 2 B B 2 b
# 3 C C 3 b
# 4 A A 4 c
# 5 B B 5 c
# 6 C C 6 c
I have a data.frame
orig.DF<-data.frame(V1=c("A", "B", "C"), V2=c(3,2,4))
and I have to expand it so that it takes the following form
A 1
A 2
A 3
B 1
B 2
C 1
C 2
C 3
C 4
I tried taaply and ave but I can't get it to count to 1:x and repeat the V1 accordingly
df <- data.frame(V1 = c("A", "B", "C"), V2 = c(3, 2, 4))
data.frame(x = rep(df$V1, df$V2), y = sequence(df$V2))
x y
1 A 1
2 A 2
3 A 3
4 B 1
5 B 2
6 C 1
7 C 2
8 C 3
9 C 4
Here is one approach:
do.call(
rbind,
apply(orig.DF, 1, function(row) expand.grid(row["V1"], 1:row["V2"]))
)