I'm trying to create a forest plot in R from meta-analysis results. However, I'm having difficulties adjusting the line thickness & the center points as well as getting rid of the automatic legend and creating my own legend.
#d is a data frame with 4 columns
#d$x gives variable names
#d$y gives center point
#d$ylo gives lower limits
#d$yhi gives upper limits
#data
d <- data.frame(x = toupper(letters[1:10]),
y = rnorm(10, 0, 0.1))
d <- transform(d, ylo = y-1/10, yhi=y+1/10)
d$x <- factor(d$x, levels=rev(d$x)) #Reverse ordering in the way that it's is in the
#function
credplot.gg <- function(d){
require(ggplot2)
p <- ggplot(d, aes(x=x, y=y, ymin=ylo, ymax=yhi,group=x,colour=x))+
geom_pointrange()+ theme_bw()+ coord_flip()+
guides(color=guide_legend(title="Cohort"))+
geom_hline(aes(x=0),colour = 'red', lty=1)+
xlab('Cohort') + ylab('Beta') + ggtitle('rs6467890_CACNA2D1')
return(p)
}
credplot.gg(d)
The issues that I'm having are:
when insert "size" into ggplot(d, aes(x=x, y=y, ymin=ylo, ymax=yhi, group=x,colour=x), size=1.5) the line and points are extremely large
How do I get rid of the legend that is automatically generated with the plot and how do I create my own legend?
I'm fairly new to r so and any help is gladly appreciated
Related
I would like to show in the same plot interpolated data and a histogram of the raw data of each predictor. I have seen in other threads like this one, people explain how to do marginal histograms of the same data shown in a scatter plot, in this case, the histogram is however based on other data (the raw data).
Suppose we see how price is related to carat and table in the diamonds dataset:
library(ggplot2)
p = ggplot(diamonds, aes(x = carat, y = table, color = price)) + geom_point()
We can add a marginal frequency plot e.g. with ggMarginal
library(ggExtra)
ggMarginal(p)
How do we add something similar to a tile plot of predicted diamond prices?
library(mgcv)
model = gam(price ~ s(table, carat), data = diamonds)
newdat = expand.grid(seq(55,75, 5), c(1:4))
names(newdat) = c("table", "carat")
newdat$predicted_price = predict(model, newdat)
ggplot(newdat,aes(x = carat, y = table, fill = predicted_price)) +
geom_tile()
Ideally, the histograms go even beyond the margins of the tileplot, as these data points also influence the predictions. I would, however, be already very happy to know how to plot a histogram for the range that is shown in the tileplot. (Maybe the values that are outside the range could just be added to the extreme values in different color.)
PS. I managed to more or less align histograms to the margins of the sides of a tile plot, using the method of the accepted answer in the linked thread, but only if I removed all kind of labels. It would be particularly good to keep the color legend, if possible.
EDIT:
eipi10 provided an excellent solution. I tried to modify it slightly to add the sample size in numbers and to graphically show values outside the plotted range since they also affect the interpolated values.
I intended to include them in a different color in the histograms at the side. I hereby attempted to count them towards the lower and upper end of the plotted range. I also attempted to plot the sample size in numbers somewhere on the plot. However, I failed with both.
This was my attempt to graphically illustrate the sample size beyond the plotted area:
plot_data = diamonds
plot_data <- transform(plot_data, carat_range = ifelse(carat < 1 | carat > 4, "outside", "within"))
plot_data <- within(plot_data, carat[carat < 1] <- 1)
plot_data <- within(plot_data, carat[carat > 4] <- 4)
plot_data$carat_range = as.factor(plot_data$carat_range)
p2 = ggplot(plot_data, aes(carat, fill = carat_range)) +
geom_histogram() +
thm +
coord_cartesian(xlim=xrng)
I tried to add the sample size in numbers with geom_text. I tried fitting it in the far right panel but it was difficult (/impossible for me) to adjust. I tried to put it on the main graph (which would anyway probably not be the best solution), but it didn’t work either (it removed the histogram and legend, on the right side and it did not plot all geom_texts). I also tried to add a third row of plots and writing it there. My attempt:
n_table_above = nrow(subset(diamonds, table > 75))
n_table_below = nrow(subset(diamonds, table < 55))
n_table_within = nrow(subset(diamonds, table >= 55 & table <= 75))
text_p = ggplot()+
geom_text(aes(x = 0.9, y = 2, label = paste0("N(>75) = ", n_table_above)))+
geom_text(aes(x = 1, y = 2, label = paste0("N = ", n_table_within)))+
geom_text(aes(x = 1.1, y = 2, label = paste0("N(<55) = ", n_table_below)))+
thm
library(egg)
pobj = ggarrange(p2, ggplot(), p1, p3,
ncol=2, widths=c(4,1), heights=c(1,4))
grid.arrange(pobj, leg, text_p, ggplot(), widths=c(6,1), heights =c(6,1))
I would be very happy to receive help on either or both tasks (adding sample size as text & adding values outside plotted range in a different color).
Based on your comment, maybe the best approach is to roll your own layout. Below is an example. We create the marginal plots as separate ggplot objects and lay them out with the main plot. We also extract the legend and put it outside the marginal plots.
Set-up
library(ggplot2)
library(cowplot)
# Function to extract legend
#https://github.com/hadley/ggplot2/wiki/Share-a-legend-between-two-ggplot2-graphs
g_legend<-function(a.gplot){
tmp <- ggplot_gtable(ggplot_build(a.gplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend) }
thm = list(theme_void(),
guides(fill=FALSE),
theme(plot.margin=unit(rep(0,4), "lines")))
xrng = c(0.6,4.4)
yrng = c(53,77)
Plots
p1 = ggplot(newdat, aes(x = carat, y = table, fill = predicted_price)) +
geom_tile() +
theme_classic() +
coord_cartesian(xlim=xrng, ylim=yrng)
leg = g_legend(p1)
p1 = p1 + thm[-1]
p2 = ggplot(diamonds, aes(carat)) +
geom_line(stat="density") +
thm +
coord_cartesian(xlim=xrng)
p3 = ggplot(diamonds, aes(table)) +
geom_line(stat="density") +
thm +
coord_flip(xlim=yrng)
plot_grid(
plot_grid(plotlist=list(p2, ggplot(), p1, p3), ncol=2,
rel_widths=c(4,1), rel_heights=c(1,4), align="hv", scale=1.1),
leg, rel_widths=c(5,1))
UPDATE: Regarding your comment about the space between the plots: This is an Achilles heel of plot_grid and I don't know if there's a way to fix it. Another option is ggarrange from the experimental egg package, which doesn't add so much space between plots. Also, you need to save the output of ggarrange first and then lay out the saved object with the legend. If you run ggarrange inside grid.arrange you get two overlapping copies of the plot:
# devtools::install_github('baptiste/egg')
library(egg)
pobj = ggarrange(p2, ggplot(), p1, p3,
ncol=2, widths=c(4,1), heights=c(1,4))
grid.arrange(pobj, leg, widths=c(6,1))
dt<- fread("Book2.txt")
names(dt)
Book2.txt <- data.frame(table('d$sample','d$expression', 'd$SD'))
names(dt) <- c("sample","expression", "SD")
ticks <- c('wt','clone16','clone14','clone20')
SD <-c(0.001793815,0.000869683,0.000106077,6.04489E-05)
gg <- ggplot(data=dt, aes(x=sample, y=expression)) + geom_bar(stat="identity")
gg <- gg + scale_x_discrete(limits=ticks, labels = ticks) +
geom_errorbar(aes(ymin=expression-SD, ymax=expression+SD), color= "grey", width=.1)
Please, Could anyone help me out in solving this problem.The graph A is obtained using excel from the same set of values, while the graph B is obtained using ggplot2. The SD deviation in the geom_bar graph is not actual and assigned to the graph according to the height of the graph.
My plots:
I'm trying to generate a heatmap plot using ggplot's geom_tile. My data have far more rows than columns.
set.seed(1)
df <- data.frame(val=rnorm(100),gene=rep(letters[1:20],5),cell=c(sapply(LETTERS[1:5],function(l) rep(l,20))))
Running:
library(ggplot2)
ggplot(df,aes(y=gene,x=cell,fill=val))+geom_tile(color="white")
produces:
How do I get the heatmap cells to be of symmetric dimensions - squares instead of rectangles (height=width)? without distorting the dimensions of the figure.
An option is to add coord_equal.
The default, ratio = 1, ensures that one unit on the x-axis is the
same length as one unit on the y-axis
ggplot(df, aes(y = gene, x = cell, fill = val)) +
geom_tile(color = "white") +
coord_equal()
Tweak the ratio as follows
set.seed(1)
df <- data.frame(val=rnorm(100),gene=rep(letters[1:20],5),
cell=c(sapply(LETTERS[1:5],function(l) rep(l,20))))
library(ggplot2)
p <- ggplot(df,aes(y=gene,x=cell,fill=val))+geom_tile(color="white")
p <- p + coord_fixed(ratio = 0.7)
p
I'm trying to plot a histogram using ggplot which has some space between the bars.
This is no problem with discrete data:
b= data.frame(x=sample(LETTERS[1:3],size=50, replace=T))
ggplot(b, aes(x=x)) + geom_bar(width=.3)
However, using continuous data, width seems to have no effect.
a= data.frame(x=rnorm(100))
ggplot(a, aes(x=x, width=.5)) +
geom_bar(width=.3, binwidth=1)
How can a histogram with spaced bars be archived for continuous data?
I think doing this is a really bad idea (and ggplot2 doesn't support it).
Here is one possibility:
breaks <- pretty(range(a$x), n = 6, min.n = 1)
mids <- 0.5 * (breaks[-1L] + breaks[-length(breaks)])
ggplot(a, aes(x = cut(x, breaks = breaks, labels = mids))) +
geom_bar(width=.3)
I am plotting a forest plot in ggplot2 and am having issues with the ordering of the labels in the legend matching the order of the labels in the data set. Here is my code below.
data code
d<-data.frame(x=c("Co-K(W) N=720", "IH-K(W) N=67", "IF-K(W) N=198", "CO-K(B)N=78", "IH-K(B) N=13", "CO=A(W) N=874","D-Sco Ad(W) N=346","DR-Ad (W) N=892","CE_A(W) N=274","CO-Ad(B) N=66","D-So Ad(B) N=215","DR-Ad(B) N=123","CE-Ad(B) N=79"),
y = rnorm(13, 0, 0.1))
d <- transform(d, ylo = y-1/13, yhi=y+1/13)
d$x <- factor(d$x, levels=rev(d$x)) # reverse ordering
forest plot code
credplot.gg <- function(d){
# d is a data frame with 4 columns
# d$x gives variable names
# d$y gives center point
# d$ylo gives lower limits
# d$yhi gives upper limits
require(ggplot2)
p <- ggplot(d, aes(x=x, y=y, ymin=ylo, ymax=yhi,group=x,colour=x,)) +
geom_pointrange(size=1) +
theme_bw() +
scale_color_discrete(name="Sample") +
coord_flip() +
theme(legend.key=element_rect(fill='cornsilk2')) +
guides(colour = guide_legend(override.aes = list(size=0.5))) +
geom_hline(aes(x=0), colour = 'red', lty=2) +
xlab('Cohort') + ylab('CI') + ggtitle('Forest Plot')
return(p)
}
credplot.gg(d)
This is what I get. As you can see the labels on the y axis matches the labels in the order that it is in the data. However, it is not the same order in the legend. I'm not sure how to correct this. This is my first time creating a plot in ggplot2. Any feedback is well appreciated.Thanks in advanced
Nice plot, especially for a first ggplot! I've not tested, but I think all you need is to add reverse=TRUE inside your colour's guide_legend(found this in the Cookbook for R).
If I were to make one more comment, I'd say that ordering your vertical factor by numeric value often makes comparisons easier when alphabetical order isn't particularly meaningful. (Though maybe your alpha order is meaningful.)