I am trying to create a graph where because there are so many points on the graph, at the edges of the green it starts to fade to black while the center stays green. The code I am currently using to create this graph is:
plot(snb$px,snb$pz,col=snb$event_type,xlim=c(-2,2),ylim=c(1,6))
I looked into contour plotting but that did not work for this. The coloring variable is a factor variable.
Thanks!
This is a great problem for ggplot2.
First, read the data in:
snb <- read.csv('MLB.csv')
With your data frame you could try plotting points that are partly transparent, and setting them to be colored according to the factor event_type:
require(ggplot2)
p1 <- ggplot(data = snb, aes(x = px, y = py, color = event_type)) +
geom_point(alpha = 0.5)
print(p1)
and then you get this:
Or, you might want to think about plotting this as a heatmap using geom_bin2d(), and plotting facets (subplots) for each different event_type, like this:
p2 <- ggplot(data = snb, aes(x = px, y = py)) +
geom_bin2d(binwidth = c(0.25, 0.25)) +
facet_wrap(~ event_type)
print(p2)
which makes a plot for each level of the factor, where the color will be the number of data points in each bins that are 0.25 on each side. But, if you have more than about 5 or 6 levels, this might look pretty bad. From the small data sample you supplied, I got this
If the levels of the factors don't matter, there are some nice examples here of plots with too many points. You could also try looking at some of the examples on the ggplot website or the R cookbook.
Transparency could help, which is easily achieved, as #BenBolker points out, with adjustcolor:
colvect = adjustcolor(c("black", "green"), alpha = 0.2)
plot(snb$px, snb$pz,
col = colvec[snb$event_type],
xlim = c(-2,2),
ylim = c(1,6))
It's built in to ggplot:
require(ggplot2)
p <- ggplot(data = snb, aes(x = px, y = pz, color = event_type)) +
geom_point(alpha = 0.2)
print(p)
Related
Hi I am trying to code for a scatter plot for three variables in R:
Race= [0,1]
YOI= [90,92,94]
ASB_mean = [1.56, 1.59, 1.74]
Antisocial <- read.csv(file = 'Antisocial.csv')
Table_1 <- ddply(Antisocial, "YOI", summarise, ASB_mean = mean(ASB))
Table_1
Race <- unique(Antisocial$Race)
Race
ggplot(data = Table_1, aes(x = YOI, y = ASB_mean, group_by(Race))) +
geom_point(colour = "Black", size = 2) + geom_line(data = Table_1, aes(YOI,
ASB_mean), colour = "orange", size = 1)
Image of plot: https://drive.google.com/file/d/1E-ePt9DZJaEr49m8fguHVS0thlVIodu9/view?usp=sharing
Data file: https://drive.google.com/file/d/1UeVTJ1M_eKQDNtvyUHRB77VDpSF1ASli/view?usp=sharing
Can someone help me understand where I am making mistake? I want to plot mean ASB vs YOI grouped by Race. Thanks.
I am not sure what is your desidered output. Maybe, if I well understood your question I Think that you want somthing like this.
g_Antisocial <- Antisocial %>%
group_by(Race) %>%
summarise(ASB = mean(ASB),
YOI = mean(YOI))
Antisocial %>%
ggplot(aes(x = YOI, y = ASB, color = as_factor(Race), shape = as_factor(Race))) +
geom_point(alpha = .4) +
geom_point(data = g_Antisocial, size = 4) +
theme_bw() +
guides(color = guide_legend("Race"), shape = guide_legend("Race"))
and this is the output:
#Maninder: there are a few things you need to look at.
First of all: The grammar of graphics of ggplot() works with layers. You can add layers with different data (frames) for the different geoms you want to plot.
The reason why your code is not working is that you mix the layer call and or do not really specify (and even mix) what is the scatter and line visualisation you want.
(I) Use ggplot() + geom_point() for a scatter plot
The ultimate first layer is: ggplot(). Think of this as your drawing canvas.
You then speak about adding a scatter plot layer, but you actually do not do it.
For example:
# plotting antisocal data set
ggplot() +
geom_point(data = Antisocial, aes(x = YOI, y = ASB, colour = as.factor(Race)))
will plot your Antiscoial data set using the scatter, i.e. geom_point() layer.
Note that I put Race as a factor to have a categorical colour scheme otherwise you might end up with a continous palette.
(II) line plot
In analogy to above, you would get for the line plot the following:
# plotting Table_1
ggplot() +
geom_line(data = Table_1, aes(x = YOI, y = ASB_mean))
I save showing the plot of the line.
(III) combining different layers
# putting both together
ggplot() +
geom_point(data = Antisocial, aes(x = YOI, y = ASB, colour = as.factor(Race))) +
geom_line(data = Table_1, aes(x = YOI, y = ASB_mean)) +
## this is to set the legend title and have a nice(r) name in your colour legend
labs(colour = "Race")
This yields:
That should explain how ggplot-layering works. Keep an eye on the datasets and geoms that you want to use. Before working with inheritance in aes, I recommend to keep the data= and aes() call in the geom_xxxx. This avoids confustion.
You may want to explore with geom_jitter() instead of geom_point() to get a bit of a better presentation of your dataset. The "few" points plotted are the result of many datapoints in the same position (and overplotted).
Moving away from plotting to your question "I want to plot mean ASB vs YOI grouped by Race."
I know too little about your research to fully comprehend what you mean with that.
I take it that the mean ASB you calculated over the whole population is your reference (aka your Table_1), and you would like to see how the Race groups feature vs this population mean.
One option is to group your race data points and show them as boxplots for each YOI.
This might be what you want. The boxplot gives you the median and quartiles, and you can compare this per group against the calculated ASB mean.
For presentation purposes, I highlighted the line by increasing its size and linetype. You can play around with the colours, etc. to give you the aesthetics you aim for.
Please note, that for the grouped boxplot, you also have to treat your integer variable YOI, I coerced into a categorical factor. Boxplot works with fill for the body (colour sets only the outer line). In this setup, you also need to supply a group value to geom_line() (I just assigned it to 1, but that is arbitrary - in other contexts you can assign another variable here).
ggplot() +
geom_boxplot(data = Antisocial, aes(x = as.factor(YOI), y = ASB, fill = as.factor(Race))) +
geom_line(data = Table_1, aes(x = as.factor(YOI), y = ASB_mean, group = 1)
, size = 2, linetype = "dashed") +
labs(x = "YOI", fill = "Race")
Hope this gets you going!
I am trying to create a custom color scale for several graphs. I would like it to be a standard color scheme so that the two graphs can be compared. The data for the first graph has a much smaller range (its maximum is just a bit above 3) while the other one goes to 9. Therefore, I need colors to match numbers 4-9 but do not want them to appear in the first graph. However, they always do and I do not understand why.
Here is the data for the first graph:
df <- data.frame(
x = runif(100),
y = runif(100),
z1 = rnorm(100),
z2 = abs(rnorm(100))
)
And here is the graph, with the custom color scale. However, as you can see all the colors appear in the graph even though only the first 5 colors should show up.
ggplot(df, aes(x, y)) +
geom_point(aes(colour = z2))+scale_colour_gradientn(colours = c('springgreen1', 'springgreen4', 'yellowgreen','yellow2','lightsalmon','orange','orange3','orange4','navajowhite3','white'),breaks=c(0,1,2,3,4,5,6,7,8,9))
The limits term of scale_colour_gradientn can help here:
ggplot(df, aes(x, y)) +
geom_point(aes(colour = z2))+
scale_colour_gradientn(colours = c('springgreen1', 'springgreen4', 'yellowgreen','yellow2',
'lightsalmon','orange','orange3','orange4','navajowhite3','white'),
breaks=c(0,1,2,3,4,5,6,7,8,9),
limits = c(0,9)) +
theme(legend.key.height = unit(1.5, "cm"))
I have data from 2 populations.
I'd like to get the histogram and density plot of both on the same graphic.
With one color for one population and another color for the other one.
I've tried this (example):
library(ggplot2)
AA <- rnorm(100000, 70,20)
BB <- rnorm(100000,120,20)
valores <- c(AA,BB)
grupo <- c(rep("AA", 100000),c(rep("BB", 100000)))
todo <- data.frame(valores, grupo)
ggplot(todo, aes(x=valores, fill=grupo, color=grupo)) +
geom_histogram(aes(y=..density..), binwidth=3)+ geom_density(aes(color=grupo))
But I'm just getting a graphic with a single line and a single color.
I would like to have different colors for the the two density lines. And if possible the histograms as well.
I've done it with ggplot2 but base R would also be OK.
or I don't know what I've changed and now I get this:
ggplot(todo, aes(x=valores, fill=grupo, color=grupo)) +
geom_histogram( position="identity", binwidth=3, alpha=0.5)+
geom_density(aes(color=grupo))
but the density lines were not plotted.
or even strange things like
I suggest this ggplot2 solution:
ggplot(todo, aes(valores, color=grupo)) +
geom_histogram(position="identity", binwidth=3, aes(y=..density.., fill=grupo), alpha=0.5) +
geom_density()
#skan: Your attempt was close but you plotted the frequencies instead of density values in the histogram.
A base R solution could be:
hist(AA, probability = T, col = rgb(1,0,0,0.5), border = rgb(1,0,0,1),
xlim=range(AA,BB), breaks= 50, ylim=c(0,0.025), main="AA and BB", xlab = "")
hist(BB, probability = T, col = rgb(0,0,1,0.5), border = rgb(0,0,1,1), add=T)
lines(density(AA))
lines(density(BB), lty=2)
For alpha I used rgb. But there are more ways to get it in. See alpha() in the scales package for instance. I added also the breaks parameter for the plot of the AAs to increase the binwidth compared to the BB group.
I'm currently working on a very simple data.frame, containing three columns:
x contains x-coordinates of a set of points,
y contains y-coordinates of the set of points, and
weight contains a value associated to each point;
Now, working in ggplot2 I seem to be able to plot contour levels for these data, but i can't manage to find a way to fill the plot according to the variable weight. Here's the code that I used:
ggplot(df, aes(x,y, fill=weight)) +
geom_density_2d() +
coord_fixed(ratio = 1)
You can see that there's no filling whatsoever, sadly.
I've been trying for three days now, and I'm starting to get depressed.
Specifying fill=weight and/or color = weight in the general ggplot call, resulted in nothing. I've tried to use different geoms (tile, raster, polygon...), still nothing. Tried to specify the aes directly into the geom layer, also didn't work.
Tried to convert the object as a ppp but ggplot can't handle them, and also using base-R plotting didn't work. I have honestly no idea of what's wrong!
I'm attaching the first 10 points' data, which is spaced on an irregular grid:
x = c(-0.13397460,-0.31698730,-0.13397460,0.13397460,-0.28867513,-0.13397460,-0.31698730,-0.13397460,-0.28867513,-0.26794919)
y = c(-0.5000000,-0.6830127,-0.5000000,-0.2320508,-0.6547005,-0.5000000,-0.6830127,-0.5000000,-0.6547005,0.0000000)
weight = c(4.799250e-01,5.500250e-01,4.799250e-01,-2.130287e+12,5.798250e-01,4.799250e-01,5.500250e-01,4.799250e-01,5.798250e-01,6.618956e-01)
any advise? The desired output would be something along these lines:
click
Thank you in advance.
From your description geom_density doesn't sound right.
You could try geom_raster:
ggplot(df, aes(x,y, fill = weight)) +
geom_raster() +
coord_fixed(ratio = 1) +
scale_fill_gradientn(colours = rev(rainbow(7)) # colourmap
Here is a second-best using fill=..level... There is a good explanation on ..level.. here.
# load libraries
library(ggplot2)
library(RColorBrewer)
library(ggthemes)
# build your data.frame
df <- data.frame(x=x, y=y, weight=weight)
# build color Palette
myPalette <- colorRampPalette(rev(brewer.pal(11, "Spectral")), space="Lab")
# Plot
ggplot(df, aes(x,y, fill=..level..) ) +
stat_density_2d( bins=11, geom = "polygon") +
scale_fill_gradientn(colours = myPalette(11)) +
theme_minimal() +
coord_fixed(ratio = 1)
I have trolled ggplot2 documentation, Stack and the ggplot2 Google groups email list - but to no avail.
Please can someone tell me how to merge the legends for alpha (opacity) and size? They are titled "(1-val2)" and "val2", respectively.
Normally mapping alpha and size to val2 would automatically merge the axes. However because I'm using "val2" and "1-val2", this does not happen. I have played around with scale_size_continuous and scale_alpha_continuous, but didn't manage to come right.
Here is a MWE:
require(ggplot2)
dummy <- data.frame(x=c(runif(12,5,10)),
y=c(runif(12,5,10)),
val1=c("a","b","c","a","b","c","a","b","c","a","b","c"),
val2=c(0.4,0.6,0.7,0.2,0.8,0.6,0.7,0.2,0.5,0.8,0.4,0.7))
p <- ggplot() +
geom_point(data=dummy, aes(x=x, y=y,color=val1, size=val2, alpha=(1-val2)))
Use the range argument of scale_alpha_continuous to invert the scale:
ggplot() +
geom_point(data=dummy, aes(x=x, y=y,color=val1, size=val2, alpha=val2)) +
scale_alpha_continuous(range = c(1, 0.1))
The trans argument may also be useful here:
ggplot() +
geom_point(data=dummy, aes(x = x, y = y, color = val1, size = val2, alpha = val2)) +
scale_alpha_continuous(trans = "reverse")
The description of the trans argument in ?scale_alpha_continuous and ?continuous_scale is pretty thin. However, you can find some examples here.