I have been struggling in creating a decent looking scatterplot in R. I wouldn't think it was so difficult.
After some research, it seemed to me that ggplot would have been a choice allowing plenty of formatting. However, I'm struggling in understanding how it works.
I'd like to create a scatterplot of two data series, displaying the points with two different colours, and perhaps different shapes, and a legend with series names.
Here is my attempt, based on this:
year1 <- mpg[which(mpg$year==1999),]
year2 <- mpg[which(mpg$year==2008),]
ggplot() +
geom_point(data = year1, aes(x=cty,y=hwy,color="yellow")) +
geom_point(data = year2, aes(x=cty,y=hwy,color="green")) +
xlab('cty') +
ylab('hwy')
Now, this looks almost OK, but with non-matching colors (unless I suddenly became color-blind). Why is that?
Also, how can I add series names and change symbol shapes?
Don't build 2 different dataframes:
df <- mpg[which(mpg$year%in%c(1999,2008)),]
df$year<-as.factor(df$year)
ggplot() +
geom_point(data = df, aes(x=cty,y=hwy,color=year,shape=year)) +
xlab('cty') +
ylab('hwy')+
scale_color_manual(values=c("green","yellow"))+
scale_shape_manual(values=c(2,8))+
guides(colour = guide_legend("Year"),
shape = guide_legend("Year"))
This will work with the way you currently have it set-up:
ggplot() +
geom_point(data = year1, aes(x=cty,y=hwy), col = "yellow", shape=1) +
geom_point(data = year2, aes(x=cty,y=hwy), col="green", shape=2) +
xlab('cty') +
ylab('hwy')
You want:
library(ggplot2)
ggplot(mpg, aes(cty, hwy, color=as.factor(year)))+geom_point()
Related
I want to create a graph that looks like:
Now I found the cowplot package which gave me a quite similar result.
library(ggplot2)
library(cowplot)
library(data.table)
library(ggridges)
d = data.table(iris)
a = ggplot(data = d, aes(x=Sepal.Length, y=..count..)) +
geom_density_line() +
geom_density_line(data = d[Species == "virginica"], aes(), fill="lightblue", color="darkblue") +
theme_bw()
b = ggplot(data = d, aes(x=Sepal.Length, y=..count..)) +
geom_density_line() +
geom_density_line(data = d[Species == "versicolor"], aes(), fill="lightgreen", color="darkgreen") +
theme_bw()
cowplot::plot_grid(a, b, labels=NULL)
The result looks like:
But, there are two points that bother me:
It has a y-axix in both plots
With my real data where I have up to 10 grids, the code becomes very long
I think it must be possible to use facet_grid(), facet_wrap() or something similar to achieve this. But I don't know how I can use a column from the dataframe of the second geometry to create these subsets without changing/losing the greyish background plot.
We can feed one layer a version of the data without Species, so it calculates the whole thing as our background context, and another layer that includes Species to map that to fill and to the appropriate facet.
library(ggplot2); library(ggridges)
ggplot(data = iris, aes(Sepal.Length, ..count..)) +
geom_density_line(data = subset(iris, select = -Species)) +
geom_density_line(aes(fill = Species)) +
facet_wrap(~Species)
I've been trying to plot two histograms by using the fill aesthetic and a specific column with two levels. However, instead of displaying both desired histograms, my code displays one histogram with the whole data and another only for the second classification. I don't know if there is a problem in my syntax neither if this is some kind of tricky issue.
library(tidyverse)
db1 <- data.frame(type=rep("A",100),val=rnorm(n=100,mean=50,sd=10))
db2 <- data.frame(type=rep("B",150),val=rnorm(n=150,mean=50,sd=10))
dbf <- bind_rows(db1,db2)
P1 <- ggplot(db1, aes(x=val)) + geom_histogram()
P2 <- ggplot(db2, aes(x=val)) + geom_histogram()
PF <- ggplot(dbf, aes(x=val)) + geom_histogram()
I want to get this, P1 and P2
ggplot(db1, aes(x=val)) + geom_histogram(fill="red", alpha=0.5) + geom_histogram(data=db2, aes(x=val),fill="green", alpha=0.5)
What I want
But the code I think should work, P1 and P2 with the fill aesthetic for column val
ggplot(dbf, aes(x=val)) + geom_histogram(aes(fill=type), alpha=0.5)
My code
Produces the combination of PF and P2
ggplot(dbf, aes(x=val)) + geom_histogram(fill="red", alpha=0.5) + geom_histogram(data=db2, aes(x=val),fill="green", alpha=0.5)
What I get
Any help or idea will be highly appreciated!
All you need is to pass position = "identity" to your geom_histogram function.
library(tidyverse)
library(ggplot2)
db1 <- data.frame(type=rep("A",100),val=rnorm(n=100,mean=50,sd=10))
db2 <- data.frame(type=rep("B",150),val=rnorm(n=150,mean=50,sd=10))
dbf <- bind_rows(db1,db2)
ggplot(dbf, aes(x=val, fill = type)) + geom_histogram(alpha=0.5, position = "identity")
Is your goal to show the overlap via the color combination? I'm not sure how to force geom_histogram to show the overlap, but geom_density does do what you want. You can play with the bandwidth (bw) to show more or less detail.
dbf %>% ggplot() +
aes(x = val, fill = type) +
geom_density(alpha = .5, bw = .5) +
scale_fill_manual(values = c("red","green"))
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)
status = sample(c(0, 1), 500, replace = TRUE)
value = rnorm(500)
plot(value)
smoothScatter(value)
I'm trying to make a scatterplot of value, but if I were to just plot it, the data is all clumped together and it's not very presentable. I've tried smoothScatter(), which makes the plot look a bit nicer, but I am wondering if there's a way to color code the values based on the corresponding status?
I am trying to see if there's a relationship between status and value. What's another way to present the data nicely? I've tried boxplot, but I'm wondering how I can make the smoothScatter() plot better or if there are other ways to visualize it.
I'm assuming you meant to write plot(status, value) in your example? Regardless, there's not going to be much difference using this data, but you should get the idea of things to maybe look at with the following examples...
Have you looked into jitter?
Some basics:
plot(jitter(status), value)
or perhaps plot(jitter(status, 0.5), value)
Fancier with package ggplot2 you could do:
library(ggplot2)
df <- data.frame(value, status)
ggplot(data=df, aes(jitter(status, 0.10), value)) +
geom_point(alpha = 0.5)
or this...
ggplot(data=df, aes(factor(status), value)) +
geom_violin()
or...
ggplot(data=df, aes(x=status, y=value)) +
geom_density2d() +
scale_x_continuous(limits=c(-1,2))
or...
ggplot(data=df, aes(x=status, y=value)) +
geom_density2d() +
stat_density2d(geom="tile", aes(fill = ..density..), contour=FALSE) +
scale_x_continuous(limits=c(-1,2))
or even this..
ggplot(data=df, aes(fill=factor(status), value)) +
geom_density(alpha=0.2)
The x-axis is time broken up into time intervals. There is an interval column in the data frame that specifies the time for each row. The column is a factor, where each interval is a different factor level.
Plotting a histogram or line using geom_histogram and geom_freqpoly works great, but I'd like to have a line, like that provided by geom_freqpoly, with the area filled.
Currently I'm using geom_freqpoly like this:
ggplot(quake.data, aes(interval, fill=tweet.type)) + geom_freqpoly(aes(group = tweet.type, colour = tweet.type)) + opts(axis.text.x=theme_text(angle=-60, hjust=0, size = 6))
I would prefer to have a filled area, such as provided by geom_density, but without smoothing the line:
The geom_area has been suggested, is there any way to use a ggplot2-generated statistic, such as ..count.., for the geom_area's y-values? Or, does the count aggregation need to occur prior to using ggplot2?
As stated in the answer, geom_area(..., stat = "bin") is the solution:
ggplot(quake.data, aes(interval)) + geom_area(aes(y = ..count.., fill = tweet.type, group = tweet.type), stat = "bin") + opts(axis.text.x=theme_text(angle=-60, hjust=0, size = 6))
produces:
Perhaps you want:
geom_area(aes(y = ..count..), stat = "bin")
geom_ribbon can be used to produce a filled area between two lines without needing to explicitly construct a polygon. There is good documentation here.
ggplot(quake.data, aes(interval, fill=tweet.type, group = 1)) + geom_density()
But I don't think this is a meaningful graphic.
I'm not entirely sure what you're aiming for. Do you want a line or bars. You should check out geom_bar for filled bars. Something like:
p <- ggplot(data, aes(x = time, y = count))
p + geom_bar(stat = "identity")
If you want a line filled in underneath then you should look at geom_area which I haven't personally used but it appears the construct will be almost the same.
p <- ggplot(data, aes(x = time, y = count))
p + geom_area()
Hope that helps. Give some more info and we can probably be more helpful.
Actually i would throw on an index, just the row of the data and use that as x, and then use
p <- ggplot(data, aes(x = index, y = count))
p + geom_bar(stat = "identity") + scale_x_continuous("Intervals",
breaks = index, labels = intervals)