I am working with the R programming language. I have this data:
letters = replicate(52, paste(sample(LETTERS, 10, replace=TRUE), collapse=""))
values = rnorm(52, 100, 100)
my_data = data.frame(letters, values)
I am trying to plot this data:
library(ggplot2)
library(waffle)
waffle(my_data, size = 0.6, rows = 10)
But this gives me the error:
! Insufficient values in manual scale. 51 needed but only 8 provided.
Run `rlang::last_error()` to see where the error occurred.
Normally, I would have manually provided the colors - but 51 colors are a lot to insert manually. Is there some automatic way that can recognize how many colors are required and then fill them all in?
Thanks!
You can use a vector of 53 colors using a palette function such as scales::hue_pal()(53) (note I have had to alter the way the input data is used, since your unmodified example data and code simply returns an error)
waffle(setNames(abs(round(my_data$values/10)),
my_data$letters), size = 0.6, rows = 10,
colors = scales::hue_pal()(53)) +
theme(legend.position = "bottom")
The obvious caveat is that 53 discrete colors is far too many to have in a waffle plot. It is simply unintelligible from a data visualisation point of view. Whatever you are trying to demonstrate, there will certainly be a better way to do it than a waffle chart with 53 discrete colors.
Related
I have applied DBSCAN algorithm on built-in dataset iris in R. But I am getting error when tried to visualise the output using the plot( ).
Following is my code.
library(fpc)
library(dbscan)
data("iris")
head(iris,2)
data1 <- iris[,1:4]
head(data1,2)
set.seed(220)
db <- dbscan(data1,eps = 0.45,minPts = 5)
table(db$cluster,iris$Species)
plot(db,data1,main = 'DBSCAN')
Error: Error in axis(side = side, at = at, labels = labels, ...) :
invalid value specified for graphical parameter "pch"
How to rectify this error?
I have a suggestion below, but first I see two issues:
You're loading two packages, fpc and dbscan, both of which have different functions named dbscan(). This could create tricky bugs later (e.g. if you change the order in which you load the packages, different functions will be run).
It's not clear what you're trying to plot, either what the x- or y-axes should be or the type of plot. The function plot() generally takes a vector of values for the x-axis and another for the y-axis (although not always, consult ?plot), but here you're passing it a data.frame and a dbscan object, and it doesn't know how to handle it.
Here's one way of approaching it, using ggplot() to make a scatterplot, and dplyr for some convenience functions:
# load our packages
# note: only loading dbscacn, not loading fpc since we're not using it
library(dbscan)
library(ggplot2)
library(dplyr)
# run dbscan::dbscan() on the first four columns of iris
db <- dbscan::dbscan(iris[,1:4],eps = 0.45,minPts = 5)
# create a new data frame by binding the derived clusters to the original data
# this keeps our input and output in the same dataframe for ease of reference
data2 <- bind_cols(iris, cluster = factor(db$cluster))
# make a table to confirm it gives the same results as the original code
table(data2$cluster, data2$Species)
# using ggplot, make a point plot with "jitter" so each point is visible
# x-axis is species, y-axis is cluster, also coloured according to cluster
ggplot(data2) +
geom_point(mapping = aes(x=Species, y = cluster, colour = cluster),
position = "jitter") +
labs(title = "DBSCAN")
Here's the image it generates:
If you're looking for something else, please be more specific about what the final plot should look like.
I'm brand new to R, and have a data frame with 8 columns that has daily changes in interest rates. I can plot QQ plots for data each of the 8 columns using the following code:
par(mfrow = c(2,4))
for(i in 1:length(column_names)){
qqnorm(deltaIR.df[,i],main = column_names[i], pch = 16, cex = .5)
qqline(deltaIR.df[,i],cex = .5)
}
I'd like now to use the stat_qq function in the ggplot2 package to do this more elegantly, but just can't get my arms around the syntax - I keep getting it wrong. Would someone kindly help me translate the above code to use ggplot and allow me to view my 8 QQ plots on one page with an appropriate header? Trying the obvious
ggplot(deltaIR.df) + stat_qq(sample = columns[i])
gets me only an error message
Warning: Ignoring unknown parameters: sample
Error: stat_qq requires the following missing aesthetics: sample
and adding in the aesthetics
ggplot(deltaIR.df, aes(column_names)) + stat_qq()
is no better. The error message just changes to
Error: Aesthetics must be either length 1 or the same as the data (5271)
In short, nothing I have done so far (even with Google's assistance) has got me closer to a solution. May I ask for guidance?
I'm new to R and want to plot specific points over an existing plot. I'm using the swiss data frame, which I visualize through the plot(swiss) function.
After this, want to add outliers given by the Mahalanobis distance:
mu_hat <- apply(swiss, 2, mean); sigma_hat <- cov(swiss)
mahalanobis_distance <- mahalanobis(swiss, mu_hat, sigma_hat)
outliers <- swiss[names(mahalanobis_distance[mahalanobis_distance > 10]),]
points(outliers, pch = 'x', col = 'red')
but this last line has no effect, as the outlier points aren't added to the previous plot. I see that if repeat this procedure on a pair of variables, say
plot(swiss[2:3])
points(outliers[2:3], pch = 'x', col = 'red')
the red points are added to the plot.
Ask: is there any restriction to how the points() function can be used for a multivariate data frame?
Here's a solution using GGally::ggpairs. It's a little ugly as we need to modify the ggally_points function to specify the desired color scheme.
I've assumed that mu_hat = colMeans(swiss) and sigma_hat = cov(swiss).
library(dplyr)
library(GGally)
swiss %>%
bind_cols(distance = mahalanobis(swiss, colMeans(swiss), cov(swiss))) %>%
mutate(is_outlier = ifelse(distance > 10, "yes", "no")) %>%
ggpairs(columns = 1:6,
mapping = aes(color = is_outlier),
upper = list(continuous = function(data, mapping, ...) {
ggally_points(data = data, mapping = mapping) +
scale_colour_manual(values = c("black", "red"))
}),
lower = list(continuous = function(data, mapping, ...) {
ggally_points(data = data, mapping = mapping) +
scale_colour_manual(values = c("black", "red"))
}),
axisLabels = "internal")
Unfortunately this isn't possible the way you're currently doing things. When plotting a data frame R produces many plots and aligns them. What you're actually seeing there is 6 by 6 = 36 individual plots which have all been aligned to look nice.
When you use the dots command, it tells it to place the dots on the current plot. Which doesn't really make sense when you have 36 plots, at least not the way you want it to.
ggplot is a really powerful tool in R, it provides far greater combustibility. For example you could set up the dataframe to include your outliers, but have them labelled as "outlier" and place it in each plot that you have set up as facets. The more you explore it you might find there are better plots which suit your needs as well.
Plotting a dataframe in base R is a good exploratory tool. You could set up those outliers as a separate dataframe and plot it, so you can see each of the 6 by 6 plots side by side and compare. It all depends on your goal. If you're goal is to produce exactly as you've described, the ggplot2 package will help you create something more professional. As #Gregor suggested in the comments, looking up the function ggpairs from the GGally package would be a good place to start.
A quick google image search shows some funky plots akin to what you're after and then some!
Find it here
Does anyone have an idea how is this kind of chart plotted? It seems like heat map. However, instead of using color, size of each cell is used to indicate the magnitude. I want to plot a figure like this but I don't know how to realize it. Can this be done in R or Matlab?
Try scatter:
scatter(x,y,sz,c,'s','filled');
where x and y are the positions of each square, sz is the size (must be a vector of the same length as x and y), and c is a 3xlength(x) matrix with the color value for each entry. The labels for the plot can be input with set(gcf,properties) or xticklabels:
X=30;
Y=10;
[x,y]=meshgrid(1:X,1:Y);
x=reshape(x,[size(x,1)*size(x,2) 1]);
y=reshape(y,[size(y,1)*size(y,2) 1]);
sz=50;
sz=sz*(1+rand(size(x)));
c=[1*ones(length(x),1) repmat(rand(size(x)),[1 2])];
scatter(x,y,sz,c,'s','filled');
xlab={'ACC';'BLCA';etc}
xticks(1:X)
xticklabels(xlab)
set(get(gca,'XLabel'),'Rotation',90);
ylab={'RAPGEB6';etc}
yticks(1:Y)
yticklabels(ylab)
EDIT: yticks & co are only available for >R2016b, if you don't have a newer version you should use set instead:
set(gca,'XTick',1:X,'XTickLabel',xlab,'XTickLabelRotation',90) %rotation only available for >R2014b
set(gca,'YTick',1:Y,'YTickLabel',ylab)
in R, you should use ggplot2 that allows you to map your values (gene expression in your case?) onto the size variable. Here, I did a simulation that resembles your data structure:
my_data <- matrix(rnorm(8*26,mean=0,sd=1), nrow=8, ncol=26,
dimnames = list(paste0("gene",1:8), LETTERS))
Then, you can process the data frame to be ready for ggplot2 data visualization:
library(reshape)
dat_m <- melt(my_data, varnames = c("gene", "cancer"))
Now, use ggplot2::geom_tile() to map the values onto the size variable. You may update additional features of the plot.
library(ggplot2)
ggplot(data=dat_m, aes(cancer, gene)) +
geom_tile(aes(size=value, fill="red"), color="white") +
scale_fill_discrete(guide=FALSE) + ##hide scale
scale_size_continuous(guide=FALSE) ##hide another scale
In R, corrplotpackage can be used. Specifically, you have to use method = 'square' when creating the plot.
Try this as an example:
library(corrplot)
corrplot(cor(mtcars), method = 'square', col = 'red')
I'm trying to play around with pheatmap and getting stuck at the very beginning.
Creating a toy example:
library(pheatmap)
set.seed(1)
my.mat <- matrix(rnorm(90), nrow = 30, ncol = 30)
rownames(my.mat) <- 1:30
colnames(my.mat) <- 1:30
col.scale = colorRampPalette(c("red", "blue"), space = "rgb")(10)
breaks.size = 11
pheatmap(my.mat, color = col.scale, breaks = breaks.size, border_color = NA, cellwidth = 10, cellheight = 10)
Throws this error message:
Error in unit(y, default.units) : 'x' and 'units' must have length > 0
And the plot it produces doesn't seem right:
For example, I can't understand why the top right cells are white. i also thought the setting cellwidth = 10 and cellheight = 10 means getting square cells and not rectangular. And finally, if anyone knows if it's possible to have the row names and col names apear on the same side of the heat map as the dendograms (i.e., at the tips of the dendogram), that'll be great.
Well, the reason you are getting that error is that you are using the breaks= parameter incorrectly. From the ?pheatmap help page
breaks: a sequence of numbers that covers the range of values in mat and is one element longer than color vector. Used for mapping values to colors. Useful, if needed to map certain values to certain colors, to certain values. If value is NA then the breaks are calculated automatically.
You can't just pass a single value like you might with other functions.
Also i'm not sure what you are saying about the cells not being square. You are plotting a 30x30 square shape (at least it is for me). Because you are clustering, you're only getting one color per cluster.
I'm guessing part of the problem may be you're only generating 90 random variables for a 900 cell matrix so those values are repeating (your data is very structured). Perhaps you meant
my.mat <- matrix(rnorm(900), nrow = 30, ncol = 30)
doing so gives you the following plot