connecting lines between means of factors in ggplot2 - r
I was trying to create a simple line graph of means and interactions. I have a DV (reading times) on the y-axis, one factor (Length) on the x-axis, and another as a grouping variable (position).
The syntax I used is below. The data plotted as single points on a line for each of the two Length conditions, but did not connect with lines between the two Length conditions. What am I missing in terms of syntax?
I am using R i386 2.15.2, and updated ggplot2 last week.
Here is a reproducible example
SubjectID <- c(101,101,101,101,101,101,101,101,102,102,102,102,102,102,102,102,
201,201,201,201,201,201,201,201,202,202,202,202,202,202,202,202)
Group <- c("PWA","PWA","PWA","PWA","PWA","PWA","PWA","PWA","PWA","PWA","PWA",
"PWA","PWA","PWA","PWA","PWA","Control","Control","Control",
"Control","Control","Control","Control","Control","Control",
"Control","Control","Control","Control","Control","Control",
"Control")
Length <- c(1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2)
Pos <- c(1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2,1,1,2,2)
ReadT <- c(6.7,7.6,6.4,7.9,5.4,6.4,6.3,7.4,6.9,7.2,6.7,7.4,5.7,6.1,6.5,7.8,
6.1,5.7,4.9,6.1,4.7,6.5,6.1,6.2,6.9,5.9,4.8,6.5,4.6,6.3,6.7,6.6)
data <- data.frame (SubjectID, Group,Length,Pos,ReadT)
data$Length <- factor(data$Length, order = TRUE,
levels = c(1,2),
labels = c("Length 1", "Length 2"))
data$Pos <- factor(data$Pos, order = TRUE,
levels = c(1,2),
labels = c("Position 1", "Position 2"))
qplot(Length, data=data, ReadT, geom=c("point", "line"),
stat="summary", fun.y=mean, group=Pos, colour=Pos,
facets = ~Group)
I don't think you have reproduced any inconsistency, but your issues in part are clouded by trying condense everything into single qplot call.
Your x variable Length is a factor, therefore ggplot is sensibly considering Length 1 and Length 2 to be independent, and won't connect the lines.
Secondly, you won't be able to use stat_summary to summarize by your x values, without forcing these to be a factor (and hence independant).
I find it easiest to presummarize the data and not rely on ggplot.
eg
library(plyr)
data.means <- ddply(data, .(Group, Pos, Length), summarize, ReadT = mean(ReadT))
Then construct the plot using ggplot not qplot, to give you the flexibility (and transparency) required.
The trick to get the lines connected is to consider x numeric within the call to geom_line see here for example
ggplot(data.means, aes(x= Length, y= ReadT, colour = Pos)) +
geom_point() +
geom_line(aes(x=as.numeric(Length))) +
facet_grid(~Group)
If you insisted on using the raw data, and stat_xxxx functions, you could also replicate this using stat_smooth to estimate the means (which would keep x classified as numeric)
ggplot(data, aes(x = Length, y= ReadT, colour = Pos)) +
stat_summary(fun.y = 'mean', geom = 'point')+
stat_smooth(method = 'lm', aes(x=as.numeric(Length)), se = FALSE) +
facet_grid(~Group)
Related
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Consider reshaping the data into long format than plotting with facets. Here both x and y scales are free since plot differ in magnitude across the columns. rdf <- reshape(mtcars, varying = names(mtcars), v.names = "value", times = names(mtcars), timevar = "variable", new.row.names = 1:1000, direction = "long") ggplot(rdf, aes(x = value)) + geom_density() + geom_vline(aes(xintercept = mean(value)), linetype = "dashed", size = 0.6) + facet_grid(~variable, scales="free")
Contour plot or heatmap from three continuous variables
I have a model which has told me there is an interaction between two variables: a and b, which is significantly influencing my response variable: c. All three are continuous numeric variables. For detail c is the rate in change my response variable, b is the rate of change in my predictor and a is mean annual rainfall. The unit of analysis is pixels in a raster. So my model is telling me mean annual rainfall modifies how my predictor affects my response.
To visualise this interaction I would like to use a contour plot/heat map/level plot with a and b on the x and y axes and c providing the colour to show me how my response variable changes within the space described by a and b. I can do this with a scatter plot but its not very pretty or easy to interpret:
qplot(b, a, colour = c) +
scale_colour_gradient(low="green", high="red") +
When I try to plot a contour plot/heat map/level plot though all I get is errors, blank plots or ugly plots.
geom_contour gives me an error:
ggplot(data = Mod, aes(x = Rain, y = Bomas, z = Fire)) +
geom_contour()
Warning message:
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geom_raster initially gives me Error: cannot allocate vector of size 81567.2 Gb but when I round my data it produces:
ggplot(data = df, aes(x = a, y = b, z = c)) +
geom_raster(aes(fill = c))
Adding interpolate = TRUE to the geom_raster code just makes the lines a little blurry.
geom_tile produces a blank graph but with a scale bar for c:
ggplot(data = df, aes(x = a, y = b, z = c)) +
geom_tile(aes(color = c))
I've also tried using stat_density2d and setting the fill and/or the colour to c, but just got an error, and I've tried using levelplot in the lattice package as well but that produces this:
levelplot(c ~ a * b, data = df,
aspect = "asp", contour = TRUE,
xlab = "a",
ylab = "b")
I suspect the problems I'm encountering are because the functions are not set up to deal with continuous x and y variables, all the examples seem to use factors. I would have thought I could compensate for that by changing bin widths but that doesn't seem to work either. Is there a function that allows you to make a heat map with 3 continuous variables? Or do I need to treat my a and b variables as factors and manually make a dataframe with bins appropriate for my data?
If you want to experiment for yourself then you get similar problems to what I'm having with:
df<- as.data.frame(rnorm(1:1068))
df[,2] <- rnorm(1:1068)
df[,3] <- rnorm(1:1068)
names(df) <- c("a", "b", "c")
You can get automatic bins, and for example calculate the means by using stat_summary_2d: ggplot(df, aes(a, b, z = c)) + stat_summary_2d() + geom_point(shape = 1, col = 'white') + viridis::scale_fill_viridis() Another good option is to slice your data by the third variable, and plot small multiples. This doesn't really show very well for random data though: library(ggplot2) ggplot(df, aes(a, b)) + geom_point() + facet_wrap(~cut_number(c, 4))
how to combine in ggplot line / points with special values?
I'm quite new to ggplot but I like the systematic way how you build your plots. Still, I'm struggeling to achieve desired results. I can replicate plots where you have categorical data. However, for my use I often need to fit a model to certain observations and then highlight them in a combined plot. With the usual plot function I would do:
library(splines)
set.seed(10)
x <- seq(-1,1,0.01)
y <- x^2
s <- interpSpline(x,y)
y <- y+rnorm(length(y),mean=0,sd=0.1)
plot(x,predict(s,x)$y,type="l",col="black",xlab="x",ylab="y")
points(x,y,col="red",pch=4)
points(0,0,col="blue",pch=1)
legend("top",legend=c("True Values","Model values","Special Value"),text.col=c("red","black","blue"),lty=c(NA,1,NA),pch=c(4,NA,1),col=c("red","black","blue"),cex = 0.7)
My biggest problem is how to build the data frame for ggplot which automatically then draws the legend? In this example, how would I translate this into ggplot to get a similar plot? Or is ggplot not made for this kind of plots?
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df <- data.frame(x = x, y = y, fit = predict(s, x)$y)
ggplot(df, aes(x, y)) +
geom_line(aes(y = fit, col = 'Model values')) +
geom_point(aes(col = 'True values')) +
geom_point(aes(col = 'Special value'), x = 0, y = 0) +
scale_color_manual(values = c('True values' = "red",
'Special value' = "blue",
'Model values' = "black"))
How to specify ggplot2 boxplot fill colour for continuous data?
I want to plot a ggplot2 boxplot using all columns of a data.frame, and I want to reorder the columns by the median for each column, rotate the x-axis labels, and fill each box with the colour corresponding to the same median. I can't figure out how to do the last part. There are plenty of examples where the fill colour corresponds to a factor variable, but I haven't seen a clear example of using a continuous variable to control fill colour. (The reason I'm trying to do this is that the resultant plot will provide context for a force-directed network graph with nodes that will be colour-coded in the same way as the boxplot -- the colour will then provide a mapping between the two plots.) It would be nice if I could re-use the value-to-colour mapping for later plots so that colours are consistent between plots. So, for example, the box corresponding to the column variable with a high median value will have a colour that denotes this mapping and matches perfectly the colour for the same column variable in other plots (such as the corresponding node in a force-directed network graph). So far, I have something like this: # Melt the data.frame: DT.m <- melt(results, id.vars = NULL) # using reshape2 # I can now make a boxplot for every column in the data.frame: g <- ggplot(DT.m, aes(x = reorder(variable, value, FUN=median), y = value)) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + stat_summary(fun.y=mean, colour="darkred", geom="point") + geom_boxplot(???, alpha=0.5) The colour fill information is what I'm stuck on. "value" is a continuous variable in the range [0,1] and there are 55 columns in my data.frame. Various approaches I've tried seem to result in the boxes being split vertically down the middle, and I haven't got any further. Any ideas?
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I built on eipi10's solution and obtained the following code which does what I want:
# "results" is a 55-column data.frame containing
# bootstrapped estimates of the Gini impurity for each column variable
# (But can synthesize fake data for testing with a bunch of rnorms)
DT.m <- melt(results, id.vars = NULL) # using reshape2
g <- ggplot(DT.m %>% group_by(variable) %>%
mutate(median.gini = median(value)),
aes(x = reorder(variable, value, FUN=median), y = value)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
geom_boxplot(aes(fill=median.gini)) +
stat_summary(fun.y=mean, colour="darkred", geom="point") +
scale_fill_gradientn(colours = heat.colors(9)) +
ylab("Gini impurity") +
xlab("Feature") +
guides(fill=guide_colourbar(title="Median\nGini\nimpurity"))
plot(g)
Later, for the second plot:
medians <- lapply(results, median)
color <- colorRampPalette(colors =
heat.colors(9))(1000)[cut(unlist(medians),1000,labels = F)]
color is then a character vector containing the colours of the nodes in my subsequent network graph, and these colours match those in the boxplot. Job done!
ggplot not showing data
I am trying to make a nice plot with ggplot. However, I do not know why it is not showing data.
Here is some minimum code
dummylabels <- c("A","B","C")
dummynumbers <- c(1,2,3)
dummy_frame <- data.frame(dummylabels,dummynumbers)
p= ggplot(data=dummy_frame, aes(x =dummylabels , y = dummynumbers)) + geom_bar(fill = "blue")
p + coord_flip() + labs(title = "Title")
I get the following error message, which I cannot make sense of
Error : Mapping a variable to y and also using stat="bin".
With stat="bin", it will attempt to set the y value to the count of cases in each group.
This can result in unexpected behavior and will not be allowed in a future version of ggplot2.
If you want y to represent counts of cases, use stat="bin" and don't map a variable to y.
If you want y to represent values in the data, use stat="identity".
See ?geom_bar for examples. (Defunct; last used in version 0.9.2)
Why do I get this error?
From the error message you got: If you want y to represent values in the data, use stat="identity". geom_bar expects to be used as a histogram, where it bins the data itself and calculates heights based on frequency. This is the stat="bin" behaviour, and is the default. It throws an error, as you gave it a y value too. To fix it, you want stat="identity": p <- ggplot(data = dummy_frame, aes(x = dummylabels, y = dummynumbers)) + geom_bar(fill = "blue", stat = "identity") + coord_flip() + labs(title = "Title") p