I am trying to plot simple picture like this, using 3 values - xyz loaded from textfile.
Now I need X-axis to go from biggest numbers to lowest (now are biggest numbers on the right, I need them on the left), so that two zeros meet in the same corner. I am using this simple code:
xyz <- read.table("excel")
scatterplot3d(xyz,xlim = c(0, 100000))
xyz
I have tried "rev" with no success. Picture always looks the same. Help will be greatly appreciated.
Sample data stored in file named "excel":
8884 20964 2
8928 5 1
9033 6 2
9261 61307 1
9435 64914 3
9605 5 2
9626 7 3
9718 5 3
10117 48941 7
10599 399 9
20834 5802 10
21337 3 8
21479 556 8
I want my 0,0,0 point to be in right front down corner.
You can choose an angle between >90 and <270
scatterplot3d(xyz,xlim = c(0, 100000),angle=ang)
for example:
z <- seq(-10, 10, 0.01)
x <- cos(z)+1
y <- sin(z)+1
scatterplot3d(x, y, z, highlight.3d=TRUE, col.axis="blue",angle=120,
col.grid="lightblue", main="scatterplot3d - 1", pch=20)
if you don't mind using cloud function from lattice package, then you can simply put the arguments of xlim in reversed order:
require(lattice)
xyz <- read.table( text =
"0 1 2
1 2 3
2 3 4
3 4 5")
cloud(V3~V1*V2,data = xyz, scales = list(arrows = FALSE), drape = T, xlim = c(3,0))
You can rotate the axes with screen parameter to make it look the way you like.
Related
I am working with R. Suppose I have the following data frame:
my_data <- data.frame(
"col" = c("red","red","red","red","red","blue","blue","blue","blue","blue","green", "green", "green", "green","green"),
"x_cor" = c(1,2,5,6,7,4,9,1,0,1,4,4,7,8,2),
"y_cor" = c(2,3,4,5,9,5,8,1,3,9,11,5,7,9,1),
"frame_number" = c(1,2,3,4,5, 1,2,3,4,5, 1,2,3,4,5)
)
my_data$col = as.factor(my_data$col)
head(my_data)
col x_cor y_cor frame_number
1 red 1 2 1
2 red 2 3 2
3 red 5 4 3
4 red 6 5 4
5 red 7 9 5
6 blue 4 5 1
In R, is it possible to create a (two-dimensional) graph that will "animate" each colored point to a new position based on the "frame number"?
For example:
I started following the instructions from this website here: https://www.datanovia.com/en/blog/gganimate-how-to-create-plots-with-beautiful-animation-in-r/
First, I made a static graph:
library(ggplot2)
library(gganimate)
p <- ggplot(
my_data,
aes(x = x_cor, y=y_cor, colour = col)
Then, I tried to animate it:
p + transition_time(frame_number) +
labs(title = "frame_number: {frame_number}")
Unfortunately, this produced an empty plot and the following warnings:
There were 50 or more warnings (use warnings() to see the first 50)
1: Cannot get dimensions of plot table. Plot region might not be fixed
2: values must be length 1,
but FUN(X[[1]]) result is length 15
Can someone please show me how to fix this problem?
Thanks
Here is an excerpt of the dataset I am working on.
Name Value ID Total
A 10 1 3
A 11 2 3
A 10 3 3
B 10 1 4
B 11 2 4
B 11 3 4
B 11 4 4
What I want to do is plot Name on the x-axis ID on the y-axis for all Values of 11; on top of which I want to overlay Total so that when the graph is interpreted, it is possible to see the count of items per a Name group. This might be achieved using length of a group in the Name variable or using Total. Here is what I did and a sample of the output desired.
mydf <- read.csv("./test1.csv", header = T)
x <- ggplot(mydf, aes(Name, ID))+ geom_point(data = subset(mydf, Value==11), size=3, colour="tomato3")+ scale_y_continuous(name="Class ID", limits=c(1,4),breaks=seq(1,4, by=1))
y <- x+ xlab("Class")+theme_bw()
z <- y+scale_x_discrete(limits = c("A","B", "C"))
The three orange asterisks at (A,3) and (B,4) are manual text annotation that I want to replace with either a short line or a circle to indicate the total number of items.
Thank you for your help.
I have this accelerometer dataset and, let's say that I have some n number of observations for each subject (30 subjects total) for body-acceleration x time.
I want to make a plot so that it plots these body acceleration x time points for each subject in a different color on the y axis and the x axis is just an index. I tried this:
ggplot(data = filtered_data_walk, aes(x = seq_along(filtered_data_walk$'body-acceleration-mean-y-time'), y = filtered_data_walk$'body-acceleration-mean-y-time')) +
geom_line(aes(color = filtered_data_walk$subject))
But, the problem is that it doesn't superimpose the 30 lines, instead, they run along side each other. In other words, I end up with n1 + n2 + n3 + ... + n30 x index points, instead of max{n1, n2, ..., n30}. This is my first time posting, so I hope this makes sense (I know my formatting is bad).
One solution I thought of was to create a new variable which gives a value of 1 to n for all the observations of each subject. So, for example, if I had 6 observations for subject1, 4 observations for subject2, and 9 observations for subject3, this new variable would be sequenced like:
1 2 3 4 5 6 1 2 3 4 1 2 3 4 5 6 7 8 9
Is there an easy way to do this? Please help, ty.
Assuming your data is formatted as a data.frame or matrix, for a toy dataset like
x <- data.frame(replicate(5, rnorm(10)))
x
# X1 X2 X3 X4 X5
# 1 -1.36452272 -1.46446475 2.0444381 0.001585876 -1.1085990
# 2 -1.41303046 -0.14690269 1.6179084 -0.310162018 -1.5528733
# 3 -0.15319554 -0.18779791 -0.3005058 0.351619212 1.6282955
# 4 -0.38712167 -0.14867239 -1.0776359 0.106694311 -0.7065382
# 5 -0.50711166 -0.95992916 1.3522922 1.437085757 -0.7921355
# 6 -0.82377208 0.50423328 -0.5366513 -1.315263679 1.0604499
# 7 -0.01462037 -1.15213287 0.9910678 0.372623508 1.9002438
# 8 1.49721113 -0.84914197 0.2422053 0.337141898 1.2405208
# 9 1.95914245 -1.43041783 0.2190829 -1.797396822 0.4970690
# 10 -1.75726827 -0.04123615 -0.1660454 -1.071688768 -0.3331887
...you might be able to get there with something like
plot(x[,1], type='l', xlim=c(1, nrow(x)), ylim=c(min(x), max(x)))
for(i in 2:ncol(x)) lines(x[,i], col=i)
You could play with formatting some more, of course, do things with lty= and lwd= and maybe a color ramp of your own choosing, etc.
If your data is in the format below...
x <- data.frame(id=c("A","A","A","B","B","B","B","C","C"), acc=rnorm(9))
x
# id acc
# 1 A 0.1796964
# 2 A 0.8770237
# 3 A -2.4413527
# 4 B 0.9379746
# 5 B -0.3416141
# 6 B -0.2921062
# 7 B 0.1440221
# 8 C -0.3248310
# 9 C -0.1058267
...you could get there with
maxn <- max(with(x, tapply(acc, id, length)))
ids <- sort(unique(x$id))
plot(x$acc[x$id==ids[1]], type='l', xlim=c(1,maxn), ylim=c(min(x$acc),max(x$acc)))
for(i in 2:length(ids)) lines(x$acc[x$id==ids[i]], col=i)
Hope this helps, and that I interpreted your problem right--
That's pretty quick to do if you are OK with using dplyr. group_by to enforce a separate counter for each subject, mutate to add the actual counter, and your ggplot should work. Example with iris dataset:
group_by(iris, Species) %>%
mutate(index = seq_along(Petal.Length)) %>%
ggplot() + geom_line(aes(x=index, y=Petal.Length, color=Species))
I have a large data set that I am trying to discretise and create a 3d surface plot with:
rowColFoVCell wpbCount Feret
1 001001001001 1 0.58
2 001001001001 1 1.30
3 001001001001 1 0.58
4 001001001001 1 0.23
5 001001001001 2 0.23
6 001001001001 2 0.58
There are currently 695302 rows in this data set. I am trying to discretise the third 'Feret' column based on the second column, so for each 'wpbCount' bin the 'Feret' column.
I think the solution will involve using cut but I am not sure how to go about this. I would like to end up with a data frame something like this:
wpbCount Feret Count
1 1 [0.0,0.2] 3
2 1 [0.2,0.4] 5
3 1 [0.4,0.6] 6
4 1 [0.8,0.8] 9
5 2 [0.0,0.2] 6
6 2 [0.4,0.6] 23
This is to answer the first part:
Create Some data
DF <- data.frame(wpbCount = sample(1:1000, 1000),
Feret = sample(seq(0, 1, 0.001), 1000))
1) Discretize
Use cut with right = FALSE so the intervals are [)
I normally find this more usefull than the default
DF$cut_it <- cut(DF$Feret, right = FALSE,
breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1))
2) Aggregate
TABLE <- data.frame(table(DT$cut_it))
EDIT Another attempt
library(data.table)
DT <- data.table(DF)
DT <- DT[, list(wpbCount = length(wpbCount),
Feret = length(Feret)
), by=cut_it]
Perhaps you are just trying to discretize and not aggregate.
Try this:
DF2 <- data.frame(wpbCount = sample(1:3, 1000, replace=T),
Feret = sample(seq(0, 1, 0.001), 1000))
DF2$Feret2 <- cut(DF$Feret, right = FALSE,
breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1.1))
DF2 <- DF2[, c(1, 3)]
Thanks very much for your help I used the following functions in R:
x$bin <- cut(x$Feret, right = FALSE, breaks = seq(0,max(wpbFeatures$Feret), by=0.1))
y <-aggregate(x$bin, by = x[c('wpbCount', 'bin')], length)
From your suggestions I have been able to get the data frame that I require:
wpbCount | bin | x
1 [0.2,0.3) 72
2 [0.2,0.3) 142
3 [0.2,0.3) 224
4 [0.2,0.3) 299
5 [0.2,0.3) 421
6 [0.2,0.3) 479
Now I need to plot this in 3D and I am not sure how to do so with a non-numerical column i.e. the bin column which is factors.
Does anyone know how I can plot these three columns against each other?
Check out this link.
There are some 3d plots. However, 3d plots aren't the greatest tool to analize data.
If you insist with the 3d approach, try stat_contout()
from the ggplot2 package.
However, a probably better apprach is to do a few plots in 2d, or use facet_grid().
Take a look at ggplot2 current documentation also.
Try this based on your last answer (not tested):
ggplot(DF, aes(wpbCount , x)) +
geon_point() +
facet_grid(. ~ bin)
The idea is to use the factor variable (in this case, bin) to facet the plot.
How to create a categorical bubble plot, using GNU R, similar to that used in systematic mapping studies (see below)?
EDIT: ok, here's what I've tried so far. First, my dataset (Var1 goes to the x-axis, Var2 goes to the y-axis):
> grid
Var1 Var2 count
1 Does.Not.apply Does.Not.apply 53
2 Not.specified Does.Not.apply 15
3 Active.Learning..general. Does.Not.apply 1
4 Problem.based.Learning Does.Not.apply 2
5 Project.Method Does.Not.apply 4
6 Case.based.Learning Does.Not.apply 22
7 Peer.Learning Does.Not.apply 6
10 Other Does.Not.apply 1
11 Does.Not.apply Not.specified 15
12 Not.specified Not.specified 15
21 Does.Not.apply Active.Learning..general. 1
23 Active.Learning..general. Active.Learning..general. 1
31 Does.Not.apply Problem.based.Learning 2
34 Problem.based.Learning Problem.based.Learning 2
41 Does.Not.apply Project.Method 4
45 Project.Method Project.Method 4
51 Does.Not.apply Case.based.Learning 22
56 Case.based.Learning Case.based.Learning 22
61 Does.Not.apply Peer.Learning 6
67 Peer.Learning Peer.Learning 6
91 Does.Not.apply Other 1
100 Other Other 1
Then, trying to plot the data:
# Based on http://flowingdata.com/2010/11/23/how-to-make-bubble-charts/
grid <- subset(grid, count > 0)
radius <- sqrt( grid$count / pi )
symbols(grid$Var1, grid$Var2, radius, inches=0.30, xlab="Research type", ylab="Research area")
text(grid$Var1, grid$Var2, grid$count, cex=0.5)
Here's the result:
Problems: axis labels are wrong, the dashed grid lines are missing.
Here is ggplot2 solution. First, added radius as new variable to your data frame.
grid$radius <- sqrt( grid$count / pi )
You should play around with size of the points and text labels inside the plot to perfect fit.
library(ggplot2)
ggplot(grid,aes(Var1,Var2))+
geom_point(aes(size=radius*7.5),shape=21,fill="white")+
geom_text(aes(label=count),size=4)+
scale_size_identity()+
theme(panel.grid.major=element_line(linetype=2,color="black"),
axis.text.x=element_text(angle=90,hjust=1,vjust=0))
This will get you started by adding the tick marks to your xaxis.
To add the lines, just add a line at each level
ggs <- subset(gg, count > 0)
radius <- sqrt( ggs$count / pi )
# ggs$Var1 <- as.character(ggs$Var1)
# set up your tick marks
# (this can all be put into a single line in `axis`, but it's placed separate here to be more readable)
#--------------
# at which values to place the x tick marks
x_at <- seq_along(levels(gg$Var1))
# the string to place at each tick mark
x_labels <- levels(gg$Var1)
# use xaxt="n" to supress the standard axis ticks
symbols(ggs$Var1, ggs$Var2, radius, inches=0.30, xlab="Research type", ylab="Research area", xaxt="n")
axis(side=1, at=x_at, labels=x_labels)
text(ggs$Var1, ggs$Var2, ggs$count, cex=0.5)
also, notice that instead of calling the object grid I called it gg, and then ggs for the subset. grid is a function in R. While it is "allowed" to overwrite the function with an object, it is not recommended and can lead to annoying bugs down the line.
Here a version using levelplot from latticeExtra.
library(latticeExtra)
levelplot(count~Var1*Var2,data=dat,
panel=function(x,y,z,...)
{
panel.abline(h=x,v=y,lty=2)
cex <- scale(z)*3
panel.levelplot.points(x,y,z,...,cex=5)
panel.text(x,y,label=z,cex=0.8)
},scales=(x=list(abbreviate=TRUE))) ## to get short labels
To get the size of bubble proprtional to the count , you can do this
library(latticeExtra)
levelplot(count~Var1*Var2,data=dat,
panel=function(x,y,z,...)
{
panel.abline(h=x,v=y,lty=2)
cex <- scale(z)*3
panel.levelplot.points(x,y,z,...,cex=5)
panel.text(x,y,label=z,cex=0.8)
})
I don't display it since the render is not clear as in the fix size case.