I'd like to convert a matrix of values into a matrix of 'bits'.
I have been looking for solutions and found this, which seems to be part of a solution.
I'll try to explain what I am looking for.
I have a matrix like
> x<-matrix(1:20,5,4)
> x
[,1] [,2] [,3] [,4]
[1,] 1 6 11 16
[2,] 2 7 12 17
[3,] 3 8 13 18
[4,] 4 9 14 19
[5,] 5 10 15 20
which I would like to convert into
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0
2 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0
3 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0
4 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0
5 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1
so for each value in the row a "1" in the corresponding column.
If I use
> table(sequence(length(x)),t(x))
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
5 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
6 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
9 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
13 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
16 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
17 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
this is close to what I am looking for, but returns a line for each value.
I would only need to consolidate all values from one row into one row.
Because a
> table(x)
x
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
gives alls values of the whole table, so what do I need to do to get the values per row.
Here is another option using table() function:
table(row(x), x)
# x
# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
# 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0
# 2 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0
# 3 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0
# 4 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0
# 5 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1
bit_x = matrix(0, nrow = nrow(x), ncol = max(x))
for (i in 1:nrow(x)) {bit_x[i,x[i,]] = 1}
Let
(x <- matrix(c(1, 3), 2, 2))
[,1] [,2]
[1,] 1 1
[2,] 3 3
One approach would be
M <- matrix(0, nrow(x), max(x))
M[cbind(c(row(x)), c(x))] <- 1
M
# [,1] [,2] [,3]
# [1,] 1 0 0
# [2,] 0 0 1
In one line:
replace(matrix(0, nrow(x), max(x)), cbind(c(row(x)), c(x)), 1).
Following your approach, and similarly to #Psidom's suggestion:
table(rep(1:nrow(x), ncol(x)), x)
# x
# 1 3
# 1 2 0
# 2 0 2
We can use the reshape2 package.
library(reshape2)
# At first we make the matrix you provided
x <- matrix(1:20, 5, 4)
# then melt it based on first column
da <- melt(x, id.var = 1)
# then cast it
dat <- dcast(da, Var1 ~ value, fill = 0, fun.aggregate = length)
which gives us this
Var1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0
2 2 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0
3 3 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0
4 4 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0
5 5 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1
Related
I use library(ergm) and library(igraph) and generate a ERGM network. But I want the adjacency matrix of that network. I am unable to find any function which can produce that.
library(ergm)
library(igraph)
g.use <- network(16,density=0.1,directed=FALSE)
#
# Starting from this network let's draw 3 realizations
# of a edges and 2-star network
#
g.sim <- simulate(~edges+kstar(2), nsim=3, coef=c(-1.8,0.03),
basis=g.use, control=control.simulate(
MCMC.burnin=1000,
MCMC.interval=100))
#g.sim[[3]]
summary(g.sim)
Is it possible to find the adjacency matrix from g.sim? and how?
EGRM package uses the network package and not the igraph package. You should maintain everythig in network and not load igraph as the two have some conflicting functions with same names.
In your case, you simulate 3 graphs thus you should have 3 adjacency matrices. The code is as below:
library(ergm)
g.use <- network(16,density=0.1,directed=FALSE)
g.sim <- simulate(~edges+kstar(2), nsim=3, coef=c(-1.8,0.03),
basis=g.use, control=control.simulate(
MCMC.burnin=1000,
MCMC.interval=100))
The code you want:
lapply(g.sim, as.matrix)
[[1]]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0
3 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 1
4 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0
5 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0
6 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0
7 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1
8 0 1 0 0 0 0 0 0 0 1 1 1 1 0 1 0
9 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1
10 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0
11 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0
12 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
13 0 0 1 0 1 0 0 1 0 1 1 0 0 0 0 1
14 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
16 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0
[[2]]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0
2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0
3 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0
4 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
5 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1
6 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 1
7 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0
8 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0
9 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0
10 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0
11 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0
12 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1
13 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0
14 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
15 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0
16 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0
[[3]]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1
2 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0
3 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0
4 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
5 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
6 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 0
7 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0
8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
9 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
10 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1
11 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1
12 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0
13 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0
14 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0
15 0 0 1 0 0 1 1 0 0 1 0 1 0 0 0 1
16 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0
I am trying to create rarefaction curves for my data but my raremax and Srare are equal to zero and rarecurve does not create any curve. I have 36 obs and 52 variables, I am trying to assess any potential underestimation of species richness due to low sample size. I think that since the raremax is zero it will not create any curve. Thanks
This is my code:
library(vegan)
Moo<-read.csv("data.csv", strip.white=T)
#total number of species at each site (row of data)
S <- specnumber(Moo)
S
[1] 6 9 3 6 12 3 10 5 3 6 6 8 4 12 3 2 5 0 3 4 4 5 3 10 7 1 3 11 11
[30] 4 4 3 5 4 2 5
# Number of INDIVIDUALS per site
raremax <- min(rowSums(Moo))
raremax
[1] 0
Srare <- rarefy(Moo, raremax)
Srare
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0
attr(,"Subsample")
[1] 0
#Plot rarefaction results
par(mfrow = c(1,2))
plot(S, Srare, xlab = "Observed No. of Species",
ylab = "Rarefied No. of Species",
main = " plot(rarefy(Moo, raremax))")
abline(0, 1)
rarecurve(Moo, step = 20,
sample = raremax,
col = "blue",
cex = 0.6,
main = "rarecurve()")
'> head(Moo)
Trapezia.aereolata Trapezia.globosa Trapezia.formosa Trapezia.lutea Trapezia.punctimanus
1 0 0 0 5 0
2 0 0 0 5 0
3 0 0 0 4 0
4 0 0 0 8 0
5 0 0 0 6 0
6 0 0 0 2 0
Trapezia.septata Trapezia.serenei Trapezia.tigrina Alpheus.lottini Alpheus.sp..White
1 0 0 0 4 0
2 0 0 0 4 0
3 0 0 0 0 0
4 0 0 0 2 0
5 0 0 0 4 0
6 0 0 0 1 0
Acanthanas.sp. Ophiuroids Ophiocoma.erinaceous Ophiocoma.sp Ophiactis.sp.
1 0 3 0 0 0
2 0 1 0 0 0
3 0 0 0 0 0
4 0 1 0 0 0
5 0 0 0 0 0
6 0 0 0 0 0
Echinometra.sp. Chlamys.sp. Conus.sp. Psaumis.cavipes Tiarina.sp. Calcinus.sp.
1 0 0 0 1 1 0
2 0 0 0 0 0 2
3 0 0 0 0 0 0
4 0 0 0 0 0 0
5 0 0 0 0 0 0
6 0 0 0 0 0 0
Harpiliopsis.sp. Jocaste.sp. Pilodius.pugil Pilodius.sp. Coralliocaris Chrysopetalum.Sp.
1 0 0 0 0 0 0
2 0 0 0 1 2 2
3 0 0 0 1 0 0
4 0 0 0 0 2 0
5 0 0 0 0 2 1
6 0 0 0 0 0 0
Menathius.sp. Chlorodiella.nigra Chlorodiella.sp. Synalpheus.sp. Labrid..wrasse.
1 0 0 0 0 0
2 1 1 0 0 0
3 0 0 0 2 0
4 0 0 0 0 1
5 0 0 0 0 0
6 0 0 0 0 0
Liomera.cinctimana Domecia.hispida Domecia Eviota.Sp. Paguris.sp. Palaemonidae Majiidae
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 1 0 0 0 0 0 0
5 0 0 1 1 1 1 0
6 0 0 0 0 0 0 0
Cerithium.litteratum Cerithium.sp. Granulina..margaritula. Coralliophila.monodonta
1 0 0 0 0
2 0 0 0 0
3 0 0 0 0
4 0 0 0 0
5 1 3 0 0
6 0 0 0 0
Muricidae Tanaeid Unknown.shell. Amphipoda Polychaeta Strombus..marginatus.. Buccinidae
1 0 0 0 1 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 1 1 0 0 0 0
6 0 0 0 1 0 0 0
gastropod..glasslike.shell Drupella
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0'
I have an empty data frame I am trying to populate.
Df1 looks like this:
col1 col2 col3 col4 important_col
1 82 193 104 86 120
2 85 68 116 63 100
3 78 145 10 132 28
4 121 158 103 15 109
5 48 175 168 190 151
6 91 136 156 180 155
Df2 looks like this:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
A data frame full of 0's.
I combine the data frames to make df_fin.
What I am trying to do now is something similar to a dummy variable approach… I have the column in important_col. What I am trying to do is spread this column out, so if important_col = 28 then put a 1 in column 28.
How can I go about creating this?
EDIT: I added a comment to illustrate what I am trying to achieve. I paste it here also.
Say that the important_col is countries, then the column names would
be all the countries in the world. That is in this example all of the
241 countries in the world. However the data I might have already
collected might only contain 200 of these countires. So
one_hot_encoding here would give me 200 columns but I am missing
potentially 41 countries. So if a new user from a country (not
currently in the data) comes to the data and inputs their country,
then it wouldn´t be recognised
Smaller example:
col1 col2 col3 col4 important_col 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 11 14 3 11 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 1 1 19 15 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 3 17 10 10 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 13 10 8 17 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
5 18 5 3 18 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
6 11 10 9 5 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7 5 11 18 16 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
8 5 8 13 8 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
9 10 1 7 16 12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
10 4 17 17 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Expected output:
col1 col2 col3 col4 important_col 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 11 14 3 11 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 1 1 19 15 4 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 3 17 10 10 6 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 13 10 8 17 10 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
5 18 5 3 18 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
6 11 10 9 5 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
7 5 11 18 16 17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
8 5 8 13 8 6 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
9 10 1 7 16 12 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
10 4 17 17 3 4 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
The number of columns is greater than the number of potential entries into important_col. Using the countries example the columns would be all countries in the world and the important_col would consist of a subset of these countries.
Code to generate the above:
df1 <- data.frame(replicate(5, sample(1:20, 10, rep=TRUE)))
colnames(df1) <- c("col1", "col2", "col3", "col4", "important_col")
df2 <- data.frame(replicate(20, sample(0:0, nrow(df1), rep=TRUE)))
colnames(df2) <- gsub("X", "", colnames(df2))
df_fin <- cbind(df1, df2)
df_fin
Does this solve the problem:
Data:
set.seed(123)
df1 <- data.frame(replicate(5, sample(1:20, 10, rep=TRUE)))
colnames(df1) <- c("col1", "col2", "col3", "col4", "important_col")
df2 <- data.frame(replicate(20, sample(0:0, nrow(df1), rep=TRUE)))
colnames(df2) <- gsub("X", "", colnames(df2))
df_fin <- cbind(df1, df2)
Result:
vecp <- colnames(df2)
imp_col <- df1$important_col
m <- matrix(vecp, byrow = TRUE, nrow = length(imp_col), ncol = length(vecp))
d <- ifelse(m == imp_col, 1, 0)
df_fin <- cbind(df1, d)
Output:
col1 col2 col3 col4 important_col 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 6 20 18 20 3 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 16 10 14 19 9 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
3 9 14 13 14 9 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
4 18 12 20 16 8 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
5 19 3 14 1 4 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
6 1 18 15 10 3 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
7 11 5 11 16 5 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
8 18 1 12 5 10 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
9 12 7 6 7 6 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
10 10 20 3 5 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0
What you are trying to do is one hot encoding which you can easily achieve using model.matrix
Below example should take you to the right direction:
df <- data.frame(important_col = as.factor(c(1:3)))
df
important_col
1 1
2 2
3 3
as.data.frame(model.matrix(~.-1, df))
important_col1 important_col2 important_col3
1 1 0 0
2 0 1 0
3 0 0 1
Like Sonny mentioned, model.matrix() should do the job. One potential problem is that you have to add back columns that did not show up in your important_col like the following case:
df <- data.frame(important_col = as.factor(c(1:3, 5)))
df
important_col
1 1
2 2
3 3
4 5
as.data.frame(model.matrix(~.-1, df))
important_col1 important_col2 important_col3 important_col5
1 1 0 0 0
2 0 1 0 0
3 0 0 1 0
4 0 0 0 1
Col4 is missing in the second df, because the important_col does not include value 4. You have to add back the col 4 if you need it for analysis.
I do have the following data structure...
ID value
1 1 1
2 1 63
3 1 2
4 1 58
5 2 3
6 2 4
7 3 34
8 3 25
Now I want to turn it into a kind of dyadic data structure. Every ID with the same value should have a relationship.
I tried several option and:
df_wide <- dcast(df, ID ~ value)
... have brought me a long way down the road...
ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 39 40
1 1001 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 1006 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 1007 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 2 0 0
4 1011 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
5 1018 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
6 1020 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
7 1030 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0
8 1036 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Now is my main problem to turn it into a proper matrix to get a igraph object out of it.
df_wide_matrix <- data.matrix(df_wide)
df_aus_wide_g <- graph.edgelist(df_wide_matrix ,directed = TRUE)
don't get me there...
I also tried to transform it into a adjacency matrix...
df_wide_matrix <- get.adjacency(graph.edgelist(as.matrix(df_wide), directed=FALSE))
... but it didn't work either
If you want to create an edge between all IDs with the same value, try something like this instead. First merge the data frame onto itself by the value. Then, remove the value column, and remove all (undirected) edges that are duplicate or just points. Finally, convert to a two-column matrix and create the edges.
res <- merge(df, df, by='value', all=FALSE)[,c('ID.x','ID.y')]
res <- res[res$ID.x<res$ID.y,]
resg <- graph.edgelist(as.matrix(res))
I have data set
> head(pain_subset2, n= 50)
PatientID RSE SE SECODE
1 1001-01 0 0 0
2 1001-01 0 0 0
3 1001-02 0 0 0
4 1001-02 0 0 0
5 1002-01 0 0 0
6 1002-01 1 2a 1
7 1002-02 0 0 0
8 1002-02 0 0 0
9 1002-02 0 0 0
10 1002-03 0 0 0
11 1002-03 0 0 0
12 1002-03 1 1 1
> dim(pain_subset2)
[1] 817 4
> table(pain_subset2$RSE)
0 1
788 29
> table(pain_subset2$SE)
0 1 2a 2b 3 4 5
788 7 5 1 6 4 6
> table(pain_subset2$SECODE)
0 1
788 29
I want to create matrix with n * 6 (n :# of PatientID, column :6 levels of SE)
I use reshape, I lost many observations
> dim(p)
[1] 246 9
My code:
p <- reshape(pain_subset2, timevar = "SE", idvar = c("PatientID","RSE"),v.names = "SECODE", direction = "wide")
p[is.na(p)] <- 0
> table(p$RSE)
0 1
226 20
Compare with table of RSE, I lost 9 patients having 1.
This is out put I have
PatientID RSE SECODE.0 SECODE.2a SECODE.1 SECODE.5 SECODE.3 SECODE.2b SECODE.4
1 1001-01 0 0 0 0 0 0 0 0
3 1001-02 0 0 0 0 0 0 0 0
5 1002-01 0 0 0 0 0 0 0 0
6 1002-01 1 0 1 0 0 0 0 0
7 1002-02 0 0 0 0 0 0 0 0
10 1002-03 0 0 0 0 0 0 0 0
12 1002-03 1 0 0 1 0 0 0 0
13 1002-04 0 0 0 0 0 0 0 0
15 1003-01 0 0 0 0 0 0 0 0
18 1003-02 0 0 0 0 0 0 0 0
21 1003-03 0 0 0 0 0 0 0 0
24 1003-04 0 0 0 0 0 0 0 0
27 1003-05 0 0 0 0 0 0 0 0
30 1003-06 0 0 0 0 0 0 0 0
32 1003-07 0 0 0 0 0 0 0 0
35 1004-01 0 0 0 0 0 0 0 0
36 1004-01 1 0 0 0 1 0 0 0
40 1004-02a 0 0 0 0 0 0 0 0
Anyone knows what happens, I really appreciate.
Thanks for your help, best.
Try:
library(dplyr)
library(tidyr)
pain_subset2 %>%
spread(SE, SECODE)