I'm trying to generate a circos plot with a simple genomic notation from BED files. However, when I use circos.genomeRect this results in an error, or in a track that does not plot rectangles, but semicircles as I show below.
Consider the following reproducible example:
library("circlize")
library("tidyverse")
circos.par(start.degree = 90,
cell.padding = c(0, 0, 0, 0),
#points.overflow.warning=FALSE,
track.height = 0.10
)
# Initialize genome (bed file with genome sizes)
genome <- tibble(chr=c("chr1","chr2"), start = c(1,1), end = c(6000000, 3000000))
circos.genomicInitialize(genome, plotType = c("axis"), major.by = 1000000)
# Add track with annotation
feature <- tibble(chr = c("chr1", "chr1"), start = c(2500, 4500000), end = c(4150000, 6350000))
circos.genomicTrack(feature, ylim=c(0,1),
panel.fun = function(region, value, ...) {
circos.genomicRect(region, value, ytop.column = 1, ybottom = 0, col="blue")
})
circos.clear()
This returns an error:
Error in if (sum(l) && circos.par("points.overflow.warning")) { :
missing value where TRUE/FALSE needed
In addition: Warning message:
In is.na(x) | is.na(y) :
Error in if (sum(l) && circos.par("points.overflow.warning")) { :
missing value where TRUE/FALSE needed
At this point, if points.overflow.warning=FALSE is set in circos.par above, the error disappears, but some other error must be occurring, that this does not plot rectangles:
Am I missing something? what is wrong with this simple example? Thank you
EDIT
I just noticed that the feature dataframe I plot has one coordinate wrong, since it extends longer than the actual size of the chromosome. However, if this is fixed, eg: feature <- tibble(chr = c("chr1", "chr1"), start = c(2500, 4500000), end = c(4150000, 5350000)), a new error appears!!
Warning message:
In is.na(x) | is.na(y) :
longer object length is not a multiple of shorter object length
It seems to work with data.frames instead of tibbles:
library("circlize")
circos.par(start.degree = 90,
cell.padding = c(0, 0, 0, 0),
#points.overflow.warning=FALSE,
track.height = 0.10
)
# Initialize genome (bed file with genome sizes)
genome <- data.frame(chr=c("chr1","chr2"), start = c(1,1), end = c(6000000, 3000000))
circos.genomicInitialize(genome, plotType = c("axis"), major.by = 1000000)
# Add track with annotation
feature <- data.frame(chr = c("chr1", "chr1"), start = c(2500, 4500000), end = c(4150000, 5350000))
circos.genomicTrack(feature, ylim=c(0,1),
panel.fun = function(region, value, ...) {
circos.genomicRect(region, value, col="blue")
})
circos.clear()
Created on 2020-08-11 by the reprex package (v0.3.0)
Related
I am generating a landscape pattern that evolves over time. The problem with the code is that I have clearly defined a window for the object bringing up the error but the window is not being recognised. I also do not see how any points are falling outside of the window, or how that would make a difference.
library(spatstat)
library(dplyr)
# Define the window
win <- owin(c(0, 100), c(0, 100))
# Define the point cluster
cluster1 <- rMatClust(kappa = 0.0005, scale = 0.1, mu = 20,
win = win, center = c(5,5))
# define the spread of the points
spread_rate <- 1
new_nests_per_year<-5
years<-10
# Plot the initial cluster
plot(win, main = "Initial cluster")
points(cluster1, pch = 20, col = "red")
newpoints<-list()
# Loop for n years
for (i in 1:years) {
# Generate new points that spread from the cluster
newpoints[[1]] <-rnorm(new_nests_per_year, mean = centroid.owin(cluster1)$y, sd = spread_rate)
newpoints[[2]] <-rnorm(new_nests_per_year, mean = centroid.owin(cluster1)$x, sd = spread_rate)
# Convert the list to a data frame
newpoints_df <- data.frame(newpoints)
# Rename the columns of the data frame
colnames(newpoints_df) <- c("x", "y")
# Combine the new points with the existing points
cluster1_df <- data.frame(cluster1)
newtotaldf<-bind_rows(cluster1_df,newpoints_df)
cluster1<-as.ppp(newtotaldf, x = newtotaldf$x, y = newtotaldf$y,
window = win)
# Plot the updated cluster
plot(win, main = paste("Cluster after year", i))
points(cluster1, pch = 20, col = "red")
}
However, when I run line:
cluster1<-as.ppp(newtotaldf, x = newtotaldf$x, y = newtotaldf$y,
window = win)
I recieve the error:
Error: x,y coords given but no window specified
Why would this be the case?
In your code, if you use the command W = win it should solve the issue. I also believe you can simplify the command without specifying x and y:
## ...[previous code]...
cluster1 <- as.ppp(newtotaldf, W = win)
plot(win)
points(cluster1, pch = 20, col = "red")
I am doing Wavelet Analysis in R using Biwavelet. However, I receive the error message:
Error in check.datum(y) :
The step size must be constant (see approx function to interpolate)
When I run the following code:
wtc.AB = wtc(t1, t2, nrands = nrands)
Please share your help here. Complete Code is:
# Import your data
Data <- read.csv("https://dl.dropboxusercontent.com/u/18255955/Tutorials/Commodities.csv")
# Attach your data so that you can access variables directly using their
# names
attach(Data)
# Define two sets of variables with time stamps
t1 = cbind(DATE, ISLX)
t2 = cbind(DATE, GOLD)
# Specify the number of iterations. The more, the better (>1000). For the
# purpose of this tutorial, we just set it = 10
nrands = 10
wtc.AB = wtc(t1, t2, nrands = nrands)
# Plotting a graph
par(oma = c(0, 0, 0, 1), mar = c(5, 4, 5, 5) + 0.1)
plot(wtc.AB, plot.phase = TRUE, lty.coi = 1, col.coi = "grey", lwd.coi = 2,
lwd.sig = 2, arrow.lwd = 0.03, arrow.len = 0.12, ylab = "Scale", xlab = "Period",
plot.cb = TRUE, main = "Wavelet Coherence: A vs B")```
> dput(head(inputData))
structure(list(Date = c("2018:07:00", "2018:06:00", "2018:05:00",
"2018:04:00", "2018:03:00", "2018:02:00"), IIP = c(125.8, 127.5,
129.7, 122.6, 140.3, 127.4), CPI = c(139.8, 138.5, 137.8, 137.1,
136.5, 136.4), `Term Spread` = c(1.580025, 1.89438, 2.020112,
1.899074, 1.470544, 1.776862), RealMoney = c(142713.9916, 140728.6495,
140032.2762, 139845.5215, 139816.4682, 139625.865), NSE50 = c(10991.15682,
10742.97381, 10664.44773, 10472.93333, 10232.61842, 10533.10526
), CallMoneyRate = c(6.161175, 6.10112, 5.912088, 5.902226, 5.949956,
5.925538), STCreditSpread = c(-0.4977, -0.3619, 0.4923, 0.1592,
0.3819, -0.1363)), row.names = c(NA, -6L), class = c("tbl_df",
"tbl", "data.frame"))
I want to make my autoregressive plot like this plot:
#------> importing all libraries
library(readr)
install.packages("lubridtae")
library("lubridate")
install.packages("forecast")
library('ggplot2')
library('fpp')
library('forecast')
library('tseries')
#--------->reading data
inputData <- read_csv("C:/Users/sanat/Downloads/exercise_1.csv")
#--------->calculating the lag=1 for NSE50
diff_NSE50<-(diff(inputData$NSE50, lag = 1, differences = 1)/lag(inputData$NSE50))
diff_RealM2<-(diff(inputData$RealMoney, lag = 1, differences = 1)/lag(inputData$RealMoney))
plot.ts(diff_NSE50)
#--------->
lm_fit = dynlm(IIP ~ CallMoneyRate + STCreditSpread + diff_NSE50 + diff_RealM2, data = inputData)
summary(lm_fit)
#--------->
inputData_ts = ts(inputData, frequency = 12, start = 2012)
#--------->area of my doubt is here
VAR_data <- window(ts.union(ts(inputData$IIP), ts(inputData$CallMoneyRate)))
VAR_est <- VAR(y = VAR_data, p = 12)
plot(VAR_est)
I want to my plots to get plotted together in same plot. How do I serparate the var() plots to two separate ones.
Current plot:
My dataset :
dataset
Okay, so this still needs some work, but it should set the right framework for you. I would look more into working with the ggplot2 for future.
Few extra packages needed, namely library(vars) and library(dynlm).
Starting from,
VAR_est <- VAR(y = VAR_data, p = 12)
Now we extract the values we want from the VAR_est object.
y <- as.numeric(VAR_est$y[,1])
z <- as.numeric(VAR_est$y[,2])
x <- 1:length(y)
## second data set on a very different scale
par(mar = c(5, 4, 4, 4) + 0.3) # Leave space for z axis
plot(x, y, type = "l") # first plot
par(new = TRUE)
plot(x, z, type = "l", axes = FALSE, bty = "n", xlab = "", ylab = "")
axis(side=4, at = pretty(range(z)))
mtext("z", side=4, line=3)
I will leave you to add the dotted lines on etc...
Hint: Decompose the VAR_est object, for example, VAR_est$datamat, then see which bit of data corresponds to the part of the plot you want.
Used some of this
When using chartSeries, by default it also shows on the top left of the plot the last value. Is there any way to prevent it from doing it?
When adding a new TA with addTA, you can avoid the last value on the plot by setting the argument legend = "", but only if you're making a new plot for the TA. If the TA is on a previously plotted graphic, it'll show the last value regardless of what you put in the legend argument.
getSymbols ("AAPL", src = "google")
chartSeries(AAPL)
What can I use here to prevent it from printing the last value on the plot?
addTA(EMA(Cl(AAPL)), on = 1, legend = "")
This still prints the last value on the top left of the plot. The weird part is that it doesn't do it if you're plotting on a new plot like this:
addTA(EMA(Cl(AAPL)), legend = "")
Is it like this by default, or is there something I can do to get around it?
The last value is shown by default (yes, annoyingly). You'll likely have to modify the source code to remove the last number showing in addTA.
I don't use addTA, but rather add_TA and chart_Series, because I think they look much better (second generation charts for quantmod). Here is a solution that removes the last number from showing for the add_TA version. But you must be willing to modify the source code.
In add_TA, you'll need to modify approximately lines 56-60 of the source:
Replace the text.exp, which is this:
# this is inside add_TA:
if (is.na(on)) {
plot_object$add_frame(ylim = c(0, 1), asp = 0.15)
plot_object$next_frame()
text.exp <- expression(text(x = c(1, 1 + strwidth(name)),
y = 0.3, labels = c(name, round(last(xdata[xsubset]),
5)), col = c(1, col), adj = c(0, 0), cex = 0.9,
offset = 0, pos = 4))
plot_object$add(text.exp, env = c(lenv, plot_object$Env),
with these modifications:
if (is.na(on)) {
plot_object$add_frame(ylim = c(0, 1), asp = 0.15)
plot_object$next_frame()
text.exp <- expression(text(x = c(strwidth(name)), # <- affects label on the subchart
y = 0.3, labels = name, col = c(col), adj = c(0), cex = 0.9,
offset = 1, pos = 4))
plot_object$add(text.exp, env = c(lenv, plot_object$Env),
expr = TRUE)
...
and assign this modified code to a new variable, called say add_TA.mine:
add_TA.mine <- function (x, order = NULL, on = NA, legend = "auto", yaxis = list(NULL,
NULL), col = 1, taType = NULL, ...)
{
lenv <- new.env()
lenv$name <- deparse(substitute(x))
lenv$plot_ta <- function(x, ta, on, taType, col = col, ...) {
xdata <- x$Env$xdata
....
[all the code for the rest of the function with modifications]....
}
}
plot_object
}
Now, just run the code with the modified function
library(quantmod)
getSymbols("AAPL")
environment(add_TA.mine) <- environment(get("add_TA", envir = asNamespace("quantmod")))
assignInNamespace(x = "add_TA", value = add_TA.mine, ns = "quantmod")
chart_Series(AAPL, subset = "2017")
add_TA(RSI(Cl(AAPL)))
quantmod:::add_TA(RSI(Cl(AAPL)))
You can see the last value is no longer printed:
(You could make the same kinds of changes in the old addTA code (perhaps via chartSeries if you really want to stick to the old plots)
If you're happy with the changes, and want to make them permament in add_TA, you can recompile the quantmod source code yourself with your modifications (i.e. you need to download the quantmod source code and recompile the package) . If you make a mess of things you can always redownload the original quandmod source code again.
I am plotting a time series with the timePlot function of the open air package of R. The graph has grey grid lines in the background that I would like to turn off but I do not find a way to do it. I would expect something simple such as grid = FALSE, but that is not the case. It appears to be rather complex, requiring the use of extra arguments which are passed to xyplot of the library lattice. I believe the answer lies some where in the par.settings function but all attempts have failed. Does anyone have any suggestions to this issue?
Here is by script:
timeozone <- import(i, date="date", date.format = "%m/%d/%Y", header=TRUE, na.strings="")
ROMO = timePlot(timeozone, pollutant = c("C7", "C9", "C10"), group = TRUE, stack = FALSE,y.relation = "same", date.breaks = 9, lty = c(1,2,3), lwd = c(2, 3, 3), fontsize = 15, cols = c("black", "black"), ylab = "Ozone (ppbv)")
panel = function(x, y) {
panel.grid(h = 0, v = 0)
panel.xyplot(x,y)
}