How to convert adjacency list to adjacency matrix in R? - r
I have adjacency list in the form of:
1. 3,4
2. 4
3. 1,4
4. 1,2,3
and I want to transform into adjacency matrix using R.
I have tried various commands like transformation of adjacency list to igraph object and then retransformation of igraph to adjacency matrix, but the obtained adjacency matrix is S4 class. I want simple commands to transform adjacency list to adjacency matrix in R.
data
list(c(1L, 3L, 4L, 8L, 14L, 31L, 2L, 29L, 33L, 7L, 11L, 17L,
5L, 6L, 34L), c(2L, 3L, 4L, 8L, 9L, 12L, 13L, 14L, 18L, 22L,
1L, 10L, 33L, 34L), c(2L, 3L, 4L, 8L, 9L, 12L, 13L, 14L, 18L,
20L, 22L, 32L, 1L, 31L, 34L, 24L), c(2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 1L, 31L, 10L, 28L,
29L), c(4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L,
22L, 32L, 1L, 17L), c(4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L,
14L, 18L, 20L, 22L, 32L, 1L, 17L), c(4L, 5L, 6L, 7L, 8L, 9L,
11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 1L, 17L), c(2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 1L,
31L, 10L, 28L, 29L), c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L,
13L, 14L, 18L, 20L, 22L, 32L, 10L, 28L, 29L, 33L, 34L, 15L, 16L,
19L, 21L, 23L, 24L, 30L, 31L, 27L), c(2L, 4L, 8L, 9L, 10L, 14L,
28L, 29L, 33L, 15L, 16L, 19L, 20L, 21L, 23L, 24L, 27L, 30L, 31L,
32L), c(4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L,
22L, 32L, 1L, 17L), c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L,
13L, 14L, 18L, 20L, 22L, 32L), c(2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L), c(2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 1L, 31L,
10L, 28L, 29L, 33L, 15L, 16L, 19L, 21L, 23L, 24L, 27L, 30L),
c(9L, 15L, 16L, 19L, 21L, 23L, 24L, 30L, 31L, 32L, 10L, 14L,
20L, 27L, 28L, 29L), c(9L, 15L, 16L, 19L, 21L, 23L, 24L,
30L, 31L, 32L, 10L, 14L, 20L, 27L, 28L, 29L), c(1L, 7L, 11L,
17L, 5L, 6L), c(2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L,
13L, 14L, 18L, 20L, 22L, 32L, 31L), c(9L, 15L, 16L, 19L,
21L, 23L, 24L, 30L, 31L, 32L, 10L, 14L, 20L, 27L, 28L, 29L
), c(3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L,
20L, 22L, 32L, 31L, 10L, 15L, 16L, 19L, 21L, 23L, 24L, 27L,
28L, 29L, 30L), c(9L, 15L, 16L, 19L, 21L, 23L, 24L, 30L,
31L, 32L, 10L, 14L, 20L, 27L, 28L, 29L), c(2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L, 20L, 22L, 32L, 31L
), c(9L, 15L, 16L, 19L, 21L, 23L, 24L, 30L, 31L, 32L, 10L,
14L, 20L, 27L, 28L, 29L), c(24L, 25L, 32L, 3L, 34L, 27L,
33L, 9L, 15L, 16L, 19L, 21L, 23L, 30L, 31L, 10L, 14L, 20L,
28L, 29L), c(24L, 25L, 32L, 34L, 26L, 29L), c(26L, 28L, 30L,
33L, 34L, 32L, 25L, 29L), c(24L, 27L, 33L, 9L, 10L, 14L,
15L, 16L, 19L, 20L, 21L, 23L, 28L, 29L, 30L, 31L, 32L), c(4L,
8L, 9L, 10L, 14L, 28L, 29L, 33L, 26L, 30L, 32L, 15L, 16L,
19L, 20L, 21L, 23L, 24L, 27L, 31L), c(1L, 4L, 8L, 9L, 10L,
14L, 28L, 29L, 33L, 25L, 26L, 15L, 16L, 19L, 20L, 21L, 23L,
24L, 27L, 30L, 31L, 32L), c(26L, 28L, 30L, 33L, 34L, 9L,
15L, 16L, 19L, 21L, 23L, 24L, 31L, 32L, 10L, 14L, 20L, 27L,
29L), c(1L, 3L, 4L, 8L, 14L, 18L, 20L, 22L, 31L, 33L, 34L,
9L, 15L, 16L, 19L, 21L, 23L, 24L, 30L, 32L, 10L, 27L, 28L,
29L), c(3L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 18L,
20L, 22L, 32L, 26L, 28L, 24L, 25L, 15L, 16L, 19L, 21L, 23L,
30L, 31L, 10L, 27L, 29L), c(1L, 2L, 9L, 10L, 14L, 28L, 29L,
33L, 31L, 34L, 26L, 30L, 24L, 27L), c(1L, 3L, 31L, 33L, 34L,
2L, 26L, 30L, 24L, 25L, 9L))
Suppose el is a list of edge list:
el = list(c(3,4),
c(2,4),
c(1,4),
c(1,2,3))
#Get the matrix dimension
dim <- length(el)
m <- sapply(el, function(x) { r<-rep(0,dim); r[unlist(x)]<-1;r})
[,1] [,2] [,3] [,4]
[1,] 0 0 1 1
[2,] 0 1 0 1
[3,] 1 0 0 1
[4,] 1 1 1 0
Related
Convert Factor to Date column [duplicate]
This question already has an answer here:
month language in the as.date function
(1 answer)
Closed 5 years ago.
My data frame is:
x=structure(list(V1 = structure(c(33L, 35L, 36L, 37L, 39L, 4L,
6L, 7L, 8L, 10L, 14L, 16L, 18L, 19L, 21L, 25L, 27L, 28L, 29L,
30L, 1L, 17L, 31L, 32L, 34L, 38L, 40L, 2L, 3L, 5L, 9L, 11L, 12L,
13L, 15L, 20L, 22L, 23L, 24L, 26L), .Label = c("1-Feb-71", "10-Feb-71",
"11-Feb-71", "11-Jan-71", "12-Feb-71", "12-Jan-71", "13-Jan-71",
"14-Jan-71", "15-Feb-71", "15-Jan-71", "16-Feb-71", "17-Feb-71",
"18-Feb-71", "18-Jan-71", "19-Feb-71", "19-Jan-71", "2-Feb-71",
"20-Jan-71", "21-Jan-71", "22-Feb-71", "22-Jan-71", "23-Feb-71",
"24-Feb-71", "25-Feb-71", "25-Jan-71", "26-Feb-71", "26-Jan-71",
"27-Jan-71", "28-Jan-71", "29-Jan-71", "3-Feb-71", "4-Feb-71",
"4-Jan-71", "5-Feb-71", "5-Jan-71", "6-Jan-71", "7-Jan-71", "8-Feb-71",
"8-Jan-71", "9-Feb-71"), class = "factor"), V2 = structure(c(1L,
15L, 2L, 4L, 3L, 5L, 10L, 5L, 7L, 12L, 8L, 16L, 16L, 22L, 16L,
19L, 22L, 12L, 17L, 23L, 24L, 24L, 21L, 17L, 19L, 16L, 6L, 11L,
9L, 25L, 25L, 8L, 5L, 13L, 20L, 18L, 16L, 13L, 12L, 14L), .Label = c("7.1359",
"7.1367", "7.1382", "7.1386", "7.1390", "7.1397", "7.1403", "7.1406",
"7.1410", "7.1411", "7.1412", "7.1414", "7.1418", "7.1420", "7.1422",
"7.1429", "7.1431", "7.1434", "7.1435", "7.1437", "7.1439", "7.1443",
"7.1445", "7.1465", "ND"), class = "factor")), .Names = c("V1",
"V2"), class = "data.frame", row.names = c(NA, -40L))
I am trying to convert column V1 to Date, but it is not working. Ive been looking some topics but it just doesnt work.
This my code:
x$V1 <- as.Date(x$V1, format="%d-%b-%y")
It works for some rows of V1 column but not for others.
Any help?
In my version of R, the conversion in your example only works for January and not for February. I think it is related to the language.
For example, in French, February is coded as fév and so Feb is not recognized.
Once I did:
x$V1=gsub("Feb", "fév", x$V1)
it worked.
It probably depends on which language your version of R uses.
Byte code version mismatch when using subset
I have been working on the same R script now for 5 months, had some minor coding problems, but this morning I got a problem that makes me unable to run the whole script. To clean my imported data I use a lot of subset(), but this morning when running the code I got the Warning:
Error in subset(T23810, date < as.Date("2015-10-22")) : byte code version mismatch
It appears that I only get this warning after trying to run a subset function, but it blocks my whole script at the moment. What could be the cause and solution for this?
EDIT: Reproducible example
x = structure(list(names = structure(c(11L, 3L, 5L, 27L, 26L, 15L,
18L, 13L, 8L, 2L, 22L, 12L, 1L, 25L, 29L, 31L, 6L, 23L, 28L,
14L, 19L, 4L, 10L, 16L, 9L, 17L, 21L, 30L, 7L, 6L, 27L, 26L,
12L, 13L, 14L, 4L, 28L, 15L, 31L, 23L, 1L, 22L, 11L, 18L, 3L,
20L, 8L, 5L, 16L, 2L, 25L, 30L, 21L, 4L, 6L, 3L, 5L, 27L, 14L,
11L, 26L, 31L, 13L, 18L, 15L, 1L, 23L, 2L, 8L, 28L, 30L, 20L,
22L, 12L, 10L, 16L, 21L, 25L, 17L, 24L, 32L, 31L, 23L, 26L, 1L,
18L, 11L, 12L, 3L, 15L, 27L, 28L, 5L, 22L, 6L, 17L, 20L, 2L,
8L, 21L, 30L, 13L, 25L, 24L, 7L, 4L, 10L, 16L, 14L), .Label = c("50/50",
"Babylon", "Big Rock Market", "Core Gut", "Customs House", "David's Dropoff",
"David's Dropoff Deep", "Diamond Rock", "Giles Quarter", "Green Island",
"Greer Gut", "Hole in the Corner", "Hot Springs", "Ladder Labyrinth",
"Man O War", "Mount Michel", "Muck Dive", "Outer Limits", "Poriotes Point",
"Porites Point", "Rays & Anchors", "Shark Shoals", "Tedran",
"Tent Boulders", "Tent Deep", "Tent Reef", "Tent Wall", "Third Encounter",
"Torens Point", "Torrens Point", "Twilight Zone", "Wells Bay"
), class = "factor")), .Names = "names", row.names = c(NA, -109L
), class = "data.frame")
Then if I execute the following:
x[x=="Torens Point"] = "Torrens Point"
x[x=="Poriotes Point"] = "Porites Point"
x = droplevels(subset(x, names != "Muck Dive"))
I get the error:
Error in subset(x, names != "Muck Dive") : byte code version mismatch
Okay solved it and in the end it was pretty easy. Since I am working on a server and rely on versions of R that are installed on that server I didn't realize how to update R itself. Now I got it it seems to work. Thank you all for your help! This one is SOLVED!
Show difference between two matrices as lines through a 3d surface
I have a surface that I have represented as a matrix that has observed values. Some of these values are controls, and form a grid throughout my surface. I extracted JUST the controls, and interpolated/extrapolated in between to create a "control surface". Then, I can subtract the control surface from the observed surface to get the normalized values.
(sub)Question 1: The interpolation looks unnecessarily irregular... is my interpolation method correct? If not, is there a better method?
(main)Question 2: How can I visualize the result as lines poking through the control surface with the ids at the top? I would like something like this:
Code follows (sorry for ugly dput):
library(dplyr)
library(tidyr)
library(akima)
b <- structure(list(Entr = 1:931, Row = c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L),
Col = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L,
37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L,
49L, 49L, 48L, 47L, 46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L,
38L, 37L, 36L, 35L, 34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L,
26L, 25L, 24L, 23L, 22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L,
14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L,
1L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L,
26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L,
38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L,
49L, 48L, 47L, 46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L,
37L, 36L, 35L, 34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L,
25L, 24L, 23L, 22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L,
13L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L,
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L,
39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 49L,
48L, 47L, 46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L,
36L, 35L, 34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L,
24L, 23L, 22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L,
12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L,
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L,
28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L,
40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 49L, 48L,
47L, 46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L,
35L, 34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L,
23L, 22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L,
11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L,
41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 49L, 48L, 47L,
46L, 45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L,
34L, 33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L,
22L, 21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L,
10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L,
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 49L, 48L, 47L, 46L,
45L, 44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L, 34L,
33L, 32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L, 22L,
21L, 20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L,
9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L,
31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L,
43L, 44L, 45L, 46L, 47L, 48L, 49L, 49L, 48L, 47L, 46L, 45L,
44L, 43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L, 34L, 33L,
32L, 31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L, 22L, 21L,
20L, 19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L, 9L,
8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L,
32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L,
44L, 45L, 46L, 47L, 48L, 49L, 49L, 48L, 47L, 46L, 45L, 44L,
43L, 42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L, 34L, 33L, 32L,
31L, 30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L, 22L, 21L, 20L,
19L, 18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L,
7L, 6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L,
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L,
33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 49L, 48L, 47L, 46L, 45L, 44L, 43L,
42L, 41L, 40L, 39L, 38L, 37L, 36L, 35L, 34L, 33L, 32L, 31L,
30L, 29L, 28L, 27L, 26L, 25L, 24L, 23L, 22L, 21L, 20L, 19L,
18L, 17L, 16L, 15L, 14L, 13L, 12L, 11L, 10L, 9L, 8L, 7L,
6L, 5L, 4L, 3L, 2L, 1L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L,
22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L,
34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L,
46L, 47L, 48L, 49L), Value = c(3597L, 3519L, 2974L, 3499L,
3437L, 3669L, 2972L, 2953L, 3088L, 3224L, 2739L, 2762L, 3238L,
2838L, 2821L, 2765L, 3487L, 3696L, 3708L, 3369L, 3702L, 3362L,
3275L, 3073L, 3313L, 3316L, 3656L, 3898L, 3999L, 4074L, 3768L,
3846L, 3630L, 4130L, 3787L, 3418L, 3053L, 3764L, 3745L, 3187L,
3628L, 3147L, 3441L, 3465L, 3953L, 3288L, 3122L, 2208L, 3008L,
3487L, 3248L, 3411L, 3402L, 3627L, 3232L, 3713L, 3432L, 3657L,
3282L, 3859L, 3464L, 3161L, 3297L, 3308L, 3392L, 3334L, 3187L,
3396L, 3213L, 4019L, 3516L, 3578L, 3670L, 3484L, 3552L, 3365L,
3441L, 4022L, 3881L, 3343L, 3466L, 3128L, 3477L, 3398L, 3761L,
3540L, 3627L, 2864L, 3630L, 3320L, 3849L, 3939L, 3658L, 3424L,
3524L, 3626L, 3177L, 2923L, 3655L, 3750L, 4447L, 4426L, 4891L,
4705L, 4274L, 4398L, 4448L, 4148L, 4210L, 4141L, 4255L, 4083L,
4295L, 3190L, 3939L, 3258L, 3855L, 4181L, 3930L, 3871L, 3977L,
3594L, 4107L, 3416L, 4057L, 3692L, 3967L, 3943L, 4012L, 3852L,
4065L, 4019L, 3687L, 3663L, 4081L, 4015L, 3847L, 3690L, 3994L,
3447L, 3559L, 3636L, 3409L, 3121L, 2958L, 2899L, 3017L, 3158L,
3053L, 3572L, 2975L, 3431L, 3725L, 3702L, 3363L, 3487L, 3562L,
3190L, 3219L, 3923L, 3585L, 3989L, 4005L, 3658L, 3810L, 3983L,
3555L, 3712L, 3699L, 3774L, 3471L, 3428L, 3552L, 3468L, 3099L,
3069L, 3303L, 3470L, 3637L, 3624L, 3813L, 4344L, 3866L, 4044L,
3490L, 3809L, 3428L, 3839L, 2540L, 4349L, 3584L, 3627L, 3799L,
3800L, 2887L, 3523L, 3389L, 3411L, 3193L, 3111L, 3112L, 3222L,
3363L, 3551L, 3430L, 3483L, 3049L, 3340L, 4034L, 3447L, 3865L,
3626L, 3699L, 3758L, 4002L, 3500L, 3650L, 3354L, 3321L, 4088L,
3259L, 3520L, 3444L, 3191L, 3578L, 3369L, 2479L, 4070L, 4171L,
4093L, 4184L, 4295L, 2681L, 3597L, 3901L, 2720L, 2700L, 2717L,
3483L, 3311L, 3223L, 3046L, 3310L, 2531L, 3317L, 3233L, 2134L,
3020L, 3360L, 3679L, 2773L, 3665L, 3124L, 4042L, 3713L, 3862L,
3961L, 4109L, 3794L, 4062L, 4078L, 4181L, 3940L, 4602L, 4149L,
3849L, 3582L, 4035L, 3431L, 3954L, 4244L, 3353L, 3519L, 3496L,
3408L, 2988L, 3327L, 3086L, 3180L, 4583L, 3742L, 4580L, 4707L,
4247L, 4422L, 4426L, 4100L, 4042L, 4096L, 3703L, 4001L, 4002L,
4265L, 3249L, 4765L, 4280L, 4628L, 4905L, 4611L, 4010L, 4125L,
4452L, 5044L, 4932L, 4613L, 4768L, 5033L, 4199L, 3944L, 3951L,
4179L, 4192L, 4195L, 3889L, 3928L, 3301L, 3764L, 3537L, 3843L,
4342L, 3792L, 3973L, 4251L, 4169L, 4374L, 4172L, 4028L, 3050L,
4488L, 4068L, 4697L, 4824L, 4184L, 3930L, 4012L, 3219L, 3519L,
3663L, 3493L, 2939L, 3363L, 3383L, 3464L, 2789L, 2927L, 3059L,
2884L, 2782L, 3090L, 3158L, 3132L, 3644L, 3803L, 3895L, 3885L,
3265L, 3682L, 3464L, 3171L, 3539L, 3474L, 3265L, 3666L, 3549L,
3591L, 3249L, 3173L, 3088L, 2563L, 3530L, 3234L, 3453L, 3200L,
3405L, 3471L, 3750L, 2906L, 3241L, 3186L, 3789L, 3174L, 2977L,
3281L, 3479L, 3241L, 3783L, 3339L, 3503L, 3591L, 3379L, 3392L,
3399L, 3675L, 3624L, 3772L, 3873L, 3477L, 3950L, 3538L, 4347L,
3818L, 4332L, 3727L, 4028L, 3679L, 3737L, 3444L, 3258L, 3535L,
3555L, 3474L, 3447L, 3748L, 3423L, 3577L, 3725L, 3227L, 2903L,
3526L, 3670L, 3256L, 3282L, 3396L, 3719L, 3598L, 3608L, 3259L,
3610L, 3373L, 3432L, 3393L, 3001L, 2867L, 2982L, 3345L, 3311L,
2727L, 3106L, 3108L, 2950L, 2714L, 3520L, 3016L, 2939L, 3435L,
3020L, 3175L, 3805L, 2779L, 3895L, 3308L, 2995L, 3083L, 3080L,
3432L, 3318L, 4486L, 3876L, 3588L, 3742L, 3986L, 3765L, 3758L,
3523L, 3696L, 3040L, 3448L, 2687L, 3282L, 4166L, 4169L, 3742L,
4032L, 3986L, 4306L, 4371L, 4231L, 4260L, 3585L, 4342L, 4188L,
3220L, 3464L, 3536L, 3595L, 4045L, 3937L, 3886L, 4774L, 3696L,
4214L, 4250L, 4543L, 4550L, 4517L, 4691L, 5042L, 3956L, 3953L,
3986L, 4032L, 3643L, 3562L, 3833L, 3803L, 3634L, 3895L, 4299L,
3862L, 3403L, 3272L, 3406L, 3253L, 3233L, 3344L, 3481L, 3363L,
2646L, 3631L, 3869L, 3246L, 3357L, 3696L, 3859L, 4296L, 3438L,
4000L, 3703L, 3960L, 3477L, 4247L, 3791L, 3853L, 3696L, 3835L,
3742L, 3588L, 3276L, 3093L, 3360L, 3207L, 3576L, 3025L, 3305L,
3295L, 3788L, 3963L, 3999L, 3294L, 3931L, 3448L, 2959L, 3304L,
3131L, 2685L, 3314L, 2887L, 3653L, 3141L, 3425L, 3542L, 3282L,
3478L, 3191L, 2639L, 3027L, 3504L, 3578L, 2806L, 4163L, 3735L,
3203L, 3419L, 3588L, 3545L, 3535L, 3333L, 3392L, 3806L, 3587L,
3134L, 2971L, 3069L, 3316L, 4520L, 3562L, 3665L, 3744L, 3207L,
3409L, 3744L, 3181L, 3096L, 3576L, 3572L, 3363L, 3386L, 3318L,
3791L, 3582L, 4032L, 4394L, 4087L, 4248L, 4342L, 4189L, 4656L,
3781L, 4335L, 3504L, 4267L, 4032L, 3824L, 3609L, 4012L, 3992L,
4417L, 4003L, 3846L, 4140L, 3683L, 3302L, 3859L, 4100L, 3847L,
3621L, 4176L, 4359L, 4081L, 3654L, 4062L, 3442L, 3560L, 3780L,
3295L, 3487L, 3409L, 3439L, 2362L, 3364L, 3412L, 3266L, 4051L,
3990L, 4068L, 3971L, 3197L, 3677L, 3765L, 3638L, 3439L, 4004L,
3648L, 3628L, 3475L, 3945L, 4167L, 3942L, 3929L, 4013L, 3906L,
3168L, 3606L, 4012L, 4317L, 4000L, 3781L, 4199L, 3997L, 4576L,
3997L, 4273L, 3891L, 3543L, 3294L, 3911L, 3715L, 4276L, 3660L,
4090L, 3921L, 3595L, 3513L, 3301L, 3470L, 3363L, 3989L, 3307L,
3565L, 3301L, 3738L, 3907L, 3653L, 3819L, 3232L, 3695L, 3435L,
2906L, 3620L, 3686L, 3284L, 4237L, 4100L, 4420L, 3654L, 2503L,
1680L, 3614L, 3314L, 4302L, 3114L, 2840L, 3036L, 1144L, 4153L,
3416L, 4484L, 3159L, 3839L, 3961L, 3373L, 3722L, 3605L, 3116L,
3818L, 3977L, 3527L, 3562L, 3794L, 4162L, 3800L, 3680L, 3578L,
3924L, 3484L, 3204L, 3200L, 3223L, 3536L, 3187L, 3171L, 3057L,
3268L, 3099L, 3517L, 3477L, 3751L, 3174L, 3569L, 3295L, 3229L,
3451L, 3200L, 3530L, 3798L, 3562L, 3484L, 2718L, 3980L, 3746L,
3576L, 3464L, 3302L, 4107L, 3452L, 3315L, 3680L, 3383L, 3462L,
3478L, 3888L, 3634L, 3445L, 3092L, 3445L, 2923L, 3040L, 2623L,
2874L, 3552L, 2336L, 3011L, 2671L, 2029L, 4002L, 3379L, 3779L,
3763L, 3496L, 3454L, 3613L, 3901L, 3727L, 3365L, 3836L, 2750L,
3763L, 3389L, 3542L, 3699L, 3904L, 3836L, 3399L, 3634L, 4162L,
3545L, 4182L, 3506L, 3849L, 3755L, 3770L, 2936L, 3670L, 3758L,
3487L, 3807L, 2868L, 3523L, 3148L, 3774L, 2851L, 2903L, 3181L,
3067L, 2695L, 3389L, 3670L, 2554L, 3494L, 4162L, 3533L, 2780L,
2822L, 2946L, 3324L, 1791L, 3530L, 3872L, 3676L, 3252L, 3395L,
3370L, 2662L, 2567L, 2786L, 2714L, 2479L, 1465L, 2000L, 3663L,
4375L, 3758L, 3742L, 3259L, 2985L, 3784L, 3373L, 2978L, 3487L,
3379L, 2953L, 3478L, 2890L, 2597L, 3001L, 2861L, 3988L, 3455L,
2950L, 3771L, 3550L, 2998L, 2991L, 3219L, 3073L, 3458L, 3585L,
3546L, 3637L, 4198L, 2903L, 3144L, 2825L, 2806L, 3409L, 1846L,
2564L, 3005L, 2675L, 2936L, 2124L, 2900L, 2388L, 2531L, 2916L,
778L, 2812L, 2577L, 2401L, 2868L, 3041L, 2039L, 2408L, 2104L,
3142L, 2610L, 3748L, 3370L, 2754L, 3546L, 2962L, 2453L, 3014L,
2626L, 2864L, 3399L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L)), class = "data.frame", row.names = c(NA,
-931L), .Names = c("Entr", "Row", "Col", "Value"))
## Control every 10
b$Control[b$Entr%%10==0] <- 1
b$Control[is.na(b$Control)] <- 0
## Isolate controls
b$Yield2[b$Control==1] <- b$Value[b$Control==1]
b$Yield2[is.na(b$Yield2)] <- 0
## -------------------- GENERATE MATRIX FOR SURFACE CALCULATION----------------
## ALL DATA
MaxCol <- max(b$Col)
MaxRow <- max(b$Row)
RealSurf <- matrix(b$Value,MaxRow,MaxCol)
## Matrix of just controls, first emptyish
ControlSurf <- matrix(b$Yield2,MaxRow,MaxCol)
## Interpolate empty data...
idx <- which(ControlSurf > 0, arr.ind=TRUE)
RealSurf.nz <- ControlSurf[idx]
## ... into a fullish matrix
InterpolatedControls <- interp.new(idx[,1], idx[,2], RealSurf.nz, xo=1:MaxRow, yo=1:MaxCol, extrap=TRUE)
ControlSurf <- matrix(InterpolatedControls$z,MaxRow,MaxCol)
##################### ???????????? IS THIS EVEN CORRECT??????? ##################
## Plot surfaces
par(mfrow=c(1,2))
persp( z=ControlSurf, theta = 50, phi = 30, expand = 0.1, col = "lightblue")
persp( z=RealSurf, theta = 50, phi = 30, expand = 0.1, col = "red")
## How to make the real surface "poke" through the Control Surface?
Create and output multiple plots from list
I am attempting to create and output as pdfs a list of 64 items. My data takes the form: QQJAN List of 64 file1: List of 2 ..$x: num [1:161] 96.7 96.8 97.5 ... ..$y: num [1:161] 9.3 10.3 17.3 ... .................................................................. file64: List of 2 ..$x: num [1:161] 42.6 59.9 70.4 ... ..$y: num [1:161] 9.3 10.3 17.3 ... I can do this for any single item in the list using: plot(QQJAN$file1) and can then output these files to my working directory as pdfs, but how can all 64 files in the list be plotted and outputted with their names, i.e. file1.pdf, file 2.pdf etc. Can the lapply function be used here? A reproducible example: QQJAN$file1$x=c(1,2,3,4) QQJAN$file1$y=c(2,4,5,6) QQJAN$file2$x=c(2,2,3,5) QQJAN$file2$y=c(2,4,5,6)
Not tested due to lack of a reproducible example:
for (i in seq_along(QQJAN)) {
pdf(sprintf("plot%i.pdf", i)) #or pdf(paste0(names(QQJAN)[i], ".pdf"))
plot(QQJAN[[i]])
dev.off()
}
If you are only interested in side effects, such as plotting, a for loop is usually appropriate. You should use lapply if you need a return value.
We can use lapply to loop over the names of the list elements, create the pdf file by pasteing the individual names with .pdf, subset the list (QQJAN[[x]]), plot.
invisible(lapply(names(QQJAN), function(x) {
pdf(paste0(x, '.pdf'))
plot(QQJAN[[x]])
dev.off()}))
data
QQJAN <- structure(list(file1 = structure(list(x = c(6L, 5L, 15L, 11L,
14L, 19L, 6L, 16L, 17L, 6L, 13L, 8L, 14L, 14L, 7L, 19L, 4L, 1L,
11L, 3L, 2L, 12L, 15L, 3L, 5L, 14L, 2L, 12L, 13L, 1L, 7L, 5L,
8L, 3L, 19L, 5L, 15L, 13L, 14L, 20L), y = c(29L, 23L, 17L, 14L,
3L, 5L, 24L, 22L, 16L, 21L, 28L, 52L, 28L, 43L, 33L, 60L, 28L,
18L, 11L, 9L, 30L, 15L, 17L, 8L, 44L, 19L, 57L, 59L, 45L, 30L,
9L, 13L, 1L, 60L, 39L, 21L, 35L, 50L, 3L, 44L)), .Names = c("x",
"y")), file2 = structure(list(x = c(11L, 3L, 11L, 5L, 8L, 7L,
6L, 18L, 8L, 17L, 7L, 15L, 19L, 3L, 10L, 12L, 13L, 2L, 9L, 10L,
15L, 13L, 3L, 6L, 16L, 1L, 20L, 5L, 9L, 4L, 12L, 1L, 6L, 13L,
18L, 7L, 18L, 19L, 15L, 13L), y = c(56L, 31L, 40L, 43L, 20L,
45L, 55L, 8L, 43L, 26L, 7L, 52L, 7L, 31L, 11L, 14L, 55L, 26L,
4L, 42L, 34L, 44L, 12L, 4L, 30L, 60L, 23L, 44L, 29L, 55L, 6L,
37L, 11L, 14L, 36L, 52L, 28L, 22L, 31L, 33L)), .Names = c("x",
"y"))), .Names = c("file1", "file2"))
How can I sort one column based on another?
I have the following data:
points <- structure(list(A = structure(c(1L, 1L, 2L, 2L, 3L, 4L,
4L, 5L, 5L, 6L, 6L, 7L, 8L, 9L, 9L, 10L, 11L, 12L, 12L, 13L,
14L, 15L, 16L, 16L, 17L, 17L, 18L, 18L, 19L, 19L, 20L, 21L, 21L,
22L, 23L, 24L, 24L, 25L, 26L, 26L, 27L, 28L, 29L, 30L, 30L, 31L,
32L, 32L, 33L, 34L, 34L, 35L, 35L, 36L, 36L, 37L, 37L, 38L, 38L,
39L, 39L, 40L, 41L), .Label = c("00017dd3-f55e-e011-854c-00237de2db9e",
"0005f624-565a-e011-854c-00237de2db9e", "0007b82f-bfe0-4b55-963e-be5a2a1e7f7b",
"00095b52-fd0a-e011-9264-00237de2db9e", "00098835-9554-4898-8d4b-82d42b8b4464",
"000a727f-8334-e011-854c-00237de2db9e", "000c0a31-f459-4365-aa3a-1978deb89f67",
"000e36a4-6e56-4851-8d36-2caf0bdd63ec", "000f05a6-cf94-4518-8de7-1773cbea8198",
"00105574-a775-43e8-8472-c8b294e46786", "00112a96-3c47-409c-83bd-6f30d8d77100",
"0012f133-f68e-e011-986b-78e7d1fa76f8", "0012f899-1c45-4917-90b7-11bea31e467e",
"0014606b-17b7-46d6-957f-e23b43fcc773", "001478e2-3e50-486c-ae3b-d1ceb36f0fd0",
"00159bab-ce82-454a-9343-f7d8f1500a68", "0015b84e-a48d-443e-936e-cabdb80604dc",
"0018f8ba-c289-4483-bf74-5cd0e6c6ae9e", "0019487f-f31e-4e3e-b499-fd48077f71f9",
"00199523-c42f-47fd-a44a-066fb726f6dd", "0019dace-41e1-439f-8b73-328d02537fe7",
"001a346e-2a15-45d4-9fb1-6b4e2448d362", "001b0c90-5c86-4290-bad3-0d6794a6bfe8",
"001c0d0d-3059-e011-854c-00237de2db9e", "001c9cbb-8c79-4cbf-bc50-219a70ab20b8",
"001dcf83-7492-e011-986b-78e7d1fa76f8", "001dd5cf-3e3b-4ceb-823c-346c15f88878",
"001e0ef7-b977-436a-ab20-8c4af4f5b230", "001fc407-da48-4c42-9325-7756b160cbbd",
"001fdaa1-9471-e011-81d2-78e7d1fa76f8", "0020029f-2667-4c03-b99f-d803eccd27d4",
"00218e00-896e-e011-81d2-78e7d1fa76f8", "002196af-60c7-4baf-abdb-589b3a481686",
"0021a908-7ff6-df11-9264-00237de2db9e", "0021bced-909a-e011-986b-78e7d1fa76f8",
"0021f0fb-cb9f-e011-986b-78e7d1fa76f8", "00228254-9b20-4d40-a4a5-a7c608f81dfa",
"002357ba-5656-4308-bb92-6cc97f50d7aa", "0025eafd-a64f-e011-854c-00237de2db9e",
"0026b36c-ebc2-43f0-a0f7-72f43b70530b", "00277e09-543e-449a-8571-38f71a21cee2"
), class = "factor"), B = structure(c(10L, 10L, 27L,
27L, 28L, 23L, 23L, 38L, 38L, 24L, 24L, 19L, 35L, 26L, 26L, 28L,
5L, 36L, 36L, 21L, 11L, 1L, 14L, 14L, 4L, 4L, 9L, 9L, 16L, 16L,
3L, 7L, 7L, 13L, 37L, 17L, 17L, 29L, 15L, 15L, 12L, 31L, 32L,
8L, 8L, 2L, 30L, 30L, 39L, 6L, 6L, 22L, 22L, 20L, 20L, 34L, 34L,
18L, 18L, 33L, 33L, 25L, 29L), .Label = c("Aashu", "Actonica Studio",
"appyminds", "blackink", "BroeckiE", "Challenge Solutions LLC",
"CPP_MSP", "Datentechnik Innovation GmbH", "DerekM", "Dimension Srl",
"Dmitry Kazarin", "edg3", "fruitymo", "Geckosan", "Genera Interactive SL",
"HandyWare", "Infinite Square", "JTO.C Sq.", "JuJuZ", "Kitten Flavour",
"Krofita", "Mark Agholor", "MCTronix.com", "Michael Snow", "michaloxo",
"mobilewares.net", "NotoMedia LLC", "OKR", "P.F. CHAUVET", "Panoylhs",
"Pratik Gandhi", "raavr", "ReadBooks", "RGP", "Seesmic", "The KeitaCorp",
"viileetek", "Violineage", "Yalla Apps"), class = "factor"),
Date = structure(c(1302926400, 1302926400, 1302408000,
1302408000, 1327467600, 1292994000, 1292994000, 1322370000,
1322370000, 1297486800, 1297486800, 1326949200, 1321333200,
1314763200, 1314763200, 1328418000, 1327381200, 1307505600,
1307505600, 1325221200, 1324530000, 1327381200, 1326862800,
1326862800, 1326171600, 1326171600, 1325566800, 1325566800,
1327122000, 1327122000, 1320379200, 1324702800, 1324702800,
1327726800, 1327986000, 1301544000, 1301544000, 1332302400,
1308369600, 1308369600, 1325912400, 1331611200, 1325912400,
1304481600, 1304481600, 1325653200, 1304395200, 1304395200,
1322542800, 1294117200, 1294117200, 1309147200, 1309147200,
1309320000, 1309320000, 1313208000, 1313208000, 1325739600,
1325739600, 1300334400, 1300334400, 1325826000, 1321938000
), class = c("POSIXct", "POSIXt"), tzone = "")), .Names = c("A",
"B", "Date"), row.names = c(NA, -63L), class = "data.frame")
I am trying to draw a scatter plot with the Y-axis sorted based on the date field. However, I am only able to do the following where I treat A as a factor. Any suggestions on how to achieve this?
p = ggplot(points, aes(Date, factor(A))) +
geom_point(aes(colour=factor(A)), size=1.8) +
scale_shape(solid=FALSE) +
scale_y_discrete("", breaks=NA)
The error comes when you ask ggplot to consider a date value as a discrete variable after just swapping the x and y positions. It goes away when you remove that:
p = ggplot(points, aes(x=A, y=Date)) +
geom_point(aes(colour=factor(A)), size=1.8) +
scale_shape(solid=FALSE)
p
Or you can get rid of the lines by applying the discrete axis call to the x-axis:
p = ggplot(points, aes(x=A, y=Date)) +
geom_point(aes(colour=factor(A)), size=1.8) +
scale_shape(solid=FALSE) +
scale_x_discrete("", breaks=NULL)
P
Unfortunately the title of the question doesn't seem to have a very clear connection to the text of the question, so I cannot tell if this was what you were asking for.