How do I change the geom_text legend key symbol? In the example below, I'd like to change the symbol in the legend key from a lower case "a" to, say, an upper case "N". I've looked at an example for doing something similar here, but couldn't get that example to work.
# Some toy data
df <- expand.grid(x = factor(seq(1:5)), y = factor(seq(1:5)), KEEP.OUT.ATTRS = FALSE)
df$Count = seq(1:25)
# An example plot
library(ggplot2)
ggplot(data = df, aes( x = x, y = y, label = Count, size = Count)) +
geom_text() +
scale_size(range = c(2, 10))
EDIT: updating for ggplot version 0.9.2
The original answer (see below) broke at about version 0.9.0 or 0.9.1. The following works in 0.9.2
# Some toy data
df <- expand.grid(x = factor(seq(1:5)), y = factor(seq(1:5)), KEEP.OUT.ATTRS = FALSE)
df$Count = seq(1:25)
# A plot
library(ggplot2)
p = ggplot(data = df, aes( x = x, y = y, label = Count, size = Count)) +
geom_point(colour = NA) +
geom_text(show.legend = FALSE) +
guides(size = guide_legend(override.aes = list(colour = "black", shape = utf8ToInt("N")))) +
scale_size(range = c(2, 10))
p
Original answer
Answering my own question and using the snippet of code in #kohske's comment above:
# Some toy data
df <- expand.grid(x = factor(seq(1:5)), y = factor(seq(1:5)), KEEP.OUT.ATTRS = FALSE)
df$Count = seq(1:25)
# A plot
library(ggplot2)
p = ggplot(data = df, aes( x = x, y = y, label = Count, size = Count)) +
geom_text() +
scale_size(range = c(2, 10))
p
library(grid)
grid.gedit("^key-[-0-9]+$", label = "N")
With gtable version 0.2.0 (ggplot2 v 2.1.0) installed, Kohske's original solution (see the comments) can be made to work.
# Some toy data
df <- expand.grid(x = factor(seq(1:5)), y = factor(seq(1:5)), KEEP.OUT.ATTRS = FALSE)
df$Count = seq(1:25)
# Load packages
library(ggplot2)
library(grid)
# A plot
p = ggplot(data = df, aes( x = x, y = y, label = Count, size = Count)) +
geom_text() +
scale_size(range = c(2, 10))
p
grid.ls(grid.force())
grid.gedit("key-[-0-9]-1-1", label = "N")
Or, to work on a grob object:
# Get the ggplot grob
gp = ggplotGrob(p)
grid.ls(grid.force(gp))
# Edit the grob
gp = editGrob(grid.force(gp), gPath("key-[1-9]-1-1"), grep = TRUE, global = TRUE,
label = "N")
# Draw it
grid.newpage()
grid.draw(gp)
Another option
Modify the geom
# Some toy data
df <- expand.grid(x = factor(seq(1:5)), y = factor(seq(1:5)), KEEP.OUT.ATTRS = FALSE)
df$Count = seq(1:25)
# Load packages
library(ggplot2)
library(grid)
# A plot
p = ggplot(data = df, aes( x = x, y = y, label = Count, size = Count)) +
geom_text() +
scale_size(range = c(2, 10))
p
GeomText$draw_key <- function (data, params, size) {
pointsGrob(0.5, 0.5, pch = "N",
gp = gpar(col = alpha(data$colour, data$alpha),
fontsize = data$size * .pt)) }
p
Related
I have a dataframe of single column with multiple values. I was using basic rplot function like plot() and points(). I successfully plotted the lineplot but I was unable to write point values from the dataframe onto the plot field. Is there anyway to add data values onto the plot?
Below is the following code for test
> x = data.frame(A = rnorm(10))
> plot(x$A, type = "o", pch = 20)**
Sorry, I made an edit to make my question clearer.
Here below is the example plot for 10 random numbers
Plot lines, then add text:
#data
set.seed(1); x = data.frame(A = rnorm(10))
#base plot
plot(x$A, type = "o", pch = 20, ylim = range(x$A * 1.3))
text(x = seq_along(x$A), y = x$A + 0.3, labels = round(x$A, 2), srt = 90)
Or using ggplot with ggrepel for pretty labels:
#ggplot
library(ggplot2)
library(ggrepel) # pretty labels, avoid overlap:
ggplot(cbind(x = seq_along(x$A), x),
aes(x = x, y = A, label = round(A, 2))) +
geom_line() +
geom_point() +
geom_label_repel()
#geom_text_repel()
Probably this is more than what you are asking, but you can add labels to the values you have in your line plot using ggplot:
library(ggplot2)
x = data.frame(A = rnorm(10),
pos = runif(10, 0.1, 0.7))
ggplot(x) +
geom_point(aes(x = A),
y = 0) +
geom_line(aes(x = A),
y = 0) +
geom_segment(aes(x = A,
xend = A,
y = 0,
yend = pos),
linetype = 2) +
geom_label(aes(x = A,
y = pos,
label = round(A, 2)),
size = 3) +
scale_y_continuous(name = "",
limits = c(0, 0.8)) +
guides(y = "none") +
theme_bw()
You could make a base R "type b" equivalent.
The OP hasn't specified that every y value should be set to zero.
library(ggh4x)
#> Loading required package: ggplot2
set.seed(1)
x = data.frame(A = rnorm(10))
ggplot(x, aes(1:10, A)) +
geom_pointpath(shape = NA) +
geom_text(aes(label = round(A,2))) +
labs(x= "Index")
Created on 2022-05-27 by the reprex package (v2.0.1)
I am using the ggerrorplot () function of the ggpubr package to create the graph below. My question is whether there is any way to change the colors of the dots without changing the color of the point that represents the mean and standard deviation? Observe the image:
My code:
# loading packages
library(ggpubr)
# Create data frame
GROUP <- c()
TEST <- c()
VALUE <- c()
for (i in 0:100) {
gp <- c('Group1','Group2','Group1 and Group2')
ts <- c('Test1','Test2')
GROUP <- append(GROUP, sample(gp, 1))
TEST <- append(TEST, sample(ts, 1))
VALUE <- append(VALUE, sample(1:200, 1))
}
df <- data.frame(GROUP, TEST, VALUE)
# Seed
set.seed(123)
# Plot
ggerrorplot(df, x = "GROUP", y = "VALUE",
desc_stat = "mean_sd",
add = c("jitter"),
color = "TEST",
palette = "jco",
add.params = list(size = 0.2),
order = c('Group1','Group2','Group1 and Group2')
) +
labs(x = '', y = 'Values\n') +
theme(legend.title = element_blank())
Can you accomplish this by simply passing in color to add.params?
# loading packages
library(ggpubr)
#> Loading required package: ggplot2
# Create data frame
GROUP <- c()
TEST <- c()
VALUE <- c()
for (i in 0:100) {
gp <- c('Group1','Group2','Group1 and Group2')
ts <- c('Test1','Test2')
GROUP <- append(GROUP, sample(gp, 1))
TEST <- append(TEST, sample(ts, 1))
VALUE <- append(VALUE, sample(1:200, 1))
}
df <- data.frame(GROUP, TEST, VALUE)
# Seed
set.seed(123)
# Plot
ggerrorplot(df, x = "GROUP", y = "VALUE",
desc_stat = "mean_sd",
add = c("jitter"),
color = "TEST",
palette = "jco",
add.params = list(size = 0.2, color = "red"),
order = c('Group1','Group2','Group1 and Group2')
) +
labs(x = '', y = 'Values\n') +
theme(legend.title = element_blank())
Created on 2021-03-10 by the reprex package (v0.3.0)
Another potential workaround - replicate the plot using ggplot() and geom_linerange(), e.g.
library(ggpubr)
library(ggsci)
library(cowplot)
# Create data frame
GROUP <- c()
TEST <- c()
VALUE <- c()
for (i in 0:100) {
gp <- c('Group1','Group2','Group1 and Group2')
ts <- c('Test1','Test2')
GROUP <- append(GROUP, sample(gp, 1))
TEST <- append(TEST, sample(ts, 1))
VALUE <- append(VALUE, sample(1:200, 1))
}
df <- data.frame(GROUP, TEST, VALUE)
# Seed
set.seed(123)
data_summary <- function(data, varname, groupnames){
require(plyr)
summary_func <- function(x, col){
c(mean = mean(x[[col]], na.rm=TRUE),
sd = sd(x[[col]], na.rm=TRUE))
}
data_sum<-ddply(data, groupnames, .fun=summary_func,
varname)
data_sum <- rename(data_sum, c("mean" = varname))
return(data_sum)
}
df2 <- data_summary(df, varname = "VALUE", groupnames = c("TEST", "GROUP"))
# Plot
p1 <- ggplot(df, aes(x = factor(GROUP, levels = c('Group1','Group2','Group1 and Group2')),
y = VALUE, color = TEST)) +
geom_jitter(shape = 21, fill = "black", stroke = 0,
position = position_jitterdodge(jitter.width = 0.2)) +
geom_linerange(data = df2, aes(ymin=VALUE-sd, ymax=VALUE+sd),
position=position_dodge(width = .75)) +
geom_point(data = df2, aes(y = VALUE), size = 3,
position = position_dodge(width = 0.75)) +
scale_color_jco() +
labs(x = '', y = 'Values\n') +
theme_classic(base_size = 14) +
theme(legend.title = element_blank(),
legend.position = "top")
p2 <- ggerrorplot(df, x = "GROUP", y = "VALUE",
desc_stat = "mean_sd",
add = c("jitter"),
color = "TEST",
palette = "jco",
add.params = list(size = 0.2),
order = c('Group1','Group2','Group1 and Group2')
) +
labs(x = '', y = 'Values\n') +
theme(legend.title = element_blank())
cowplot::plot_grid(p1, p2, nrow = 1, ncol = 2, labels = "AUTO")
When you plot them side-by-side you can see that they aren't exactly the same, but this might work for you nonetheless.
Edit
An advantage of this approach is that you can adjust the 'fill' scale separately if you don't want all the dots to be the same colour, but you do want them to be different to the lines, e.g.
p1 <- ggplot(df, aes(x = factor(GROUP, levels = c('Group1','Group2','Group1 and Group2')),
y = VALUE, color = TEST)) +
geom_jitter(aes(fill = TEST), shape = 21, stroke = 0,
position = position_jitterdodge(jitter.width = 0.2)) +
geom_linerange(data = df2, aes(ymin=VALUE-sd, ymax=VALUE+sd),
position=position_dodge(width = .75)) +
geom_point(data = df2, aes(y = VALUE), size = 3,
position = position_dodge(width = 0.75)) +
scale_color_jco() +
scale_fill_npg() +
labs(x = '', y = 'Values\n') +
theme_classic(base_size = 14) +
theme(legend.title = element_blank(),
legend.position = "top")
p2 <- ggerrorplot(df, x = "GROUP", y = "VALUE",
desc_stat = "mean_sd",
add = c("jitter"),
color = "TEST",
palette = "jco",
add.params = list(size = 0.2),
order = c('Group1','Group2','Group1 and Group2')
) +
labs(x = '', y = 'Values\n') +
theme(legend.title = element_blank())
cowplot::plot_grid(p1, p2, nrow = 1, ncol = 2, labels = "AUTO")
A common layout in many sites is to draw the grid as shaded bars:
I'm doing this with this function:
grid_bars <- function(data, y, n = 5, fill = "gray90") {
breaks <- pretty(data[[y]], n)
len <- length(breaks)-1
all_bars <- data.frame(
b.id = rep(1:len, 4),
b.x = c(rep(-Inf, len), rep(Inf, len*2), rep(-Inf, len)),
b.y = c(rep(breaks[-length(breaks)], 2), rep(breaks[-1], 2))
)
bars <- all_bars[all_bars$b.id %in% (1:len)[c(FALSE, TRUE)], ]
grid <- list(
geom_polygon(data = bars, aes(b.x, b.y, group = b.id),
fill = fill, colour = fill),
scale_y_continuous(breaks = breaks),
theme(panel.grid = element_blank())
)
return(grid)
}
#-------------------------------------------------
dat <- data.frame(year = 1875:1972,
level = as.vector(LakeHuron))
ggplot(dat, aes(year, level)) +
grid_bars(dat, "level", 10) +
geom_line(colour = "steelblue", size = 1.2) +
theme_classic()
But it needs to specify data and y again. How to take those directly from the ggplot?
After having a look at the options for extending ggplot2 in Hadley Wickham's book on ggplot2 you probably have to set up your own Geom or Stat layer to achieve the desired result. This way you can access the data and aesthetics specified in ggplot() or even pass different data and aesthetics to your fun. Still a newbie in writing extensions for ggplot2 but a first approach may look like so:
library(ggplot2)
# Make bars dataframe
make_bars_df <- function(y, n) {
breaks <- pretty(y, n)
len <- length(breaks) - 1
all_bars <- data.frame(
group = rep(1:len, 4),
x = c(rep(-Inf, len), rep(Inf, len * 2), rep(-Inf, len)),
y = c(rep(breaks[-length(breaks)], 2), rep(breaks[-1], 2))
)
all_bars[all_bars$group %in% (1:len)[c(FALSE, TRUE)], ]
}
# Setup Geom
geom_grid_bars_y <- function(mapping = NULL, data = NULL, stat = "identity",
position = "identity", na.rm = FALSE, show.legend = NA,
inherit.aes = TRUE, n = 5, ...) {
layer(
geom = GeomGridBarsY, mapping = mapping, data = data, stat = stat,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(n = n, ...)
)
}
GeomGridBarsY <- ggproto("GeomGridBarsY", Geom,
required_aes = c("y"),
default_aes = aes(alpha = NA, colour = NA, fill = "gray90", group = NA,
linetype = "solid", size = 0.5, subgroup = NA),
non_missing_aes = aes("n"),
setup_data = function(data, params) {
transform(data)
},
draw_group = function(data, panel_scales, coord, n = n) {
bars <- make_bars_df(data[["y"]], n)
# setup data for GeomPolygon
## If you want this to work with facets you have to take care of the PANEL
bars$PANEL <- factor(1)
# Drop x, y, group from data
d <- data[ , setdiff(names(data), c("x", "y", "group"))]
d <- d[!duplicated(d), ]
# Merge information in data to bars
bars <- merge(bars, d, by = "PANEL")
# Set color = fill
bars[["colour"]] <- bars[["fill"]]
# Draw
grid::gList(
ggplot2::GeomPolygon$draw_panel(bars, panel_scales, coord)
)
},
draw_key = draw_key_rect
)
grid_bars <- function(n = 5, fill = "gray90") {
list(
geom_grid_bars_y(n = n, fill = fill),
scale_y_continuous(breaks = scales::pretty_breaks(n = n)),
theme(panel.grid = element_blank())
)
}
dat <- data.frame(year = 1875:1972,
level = as.vector(LakeHuron))
ggplot(dat, aes(year, level)) +
grid_bars(n = 10, fill = "gray95") +
geom_line(colour = "steelblue", size = 1.2) +
theme_classic()
Just for reference:
A first and simple approach to get grid bars one could simply adjust the size of the grid lines via theme() like so:
# Simple approach via theme
ggplot(dat, aes(year, level)) +
geom_line(colour = "steelblue", size = 1.2) +
scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
theme_classic() +
theme(panel.grid.major.y = element_line(size = 8))
Created on 2020-06-14 by the reprex package (v0.3.0)
I need to produce plots for statistical analyses and I am stumped by a difference in behaviour between stats and ggplot. Who can help out?
I am trying to produce a pdf with histograms, including normal curves, side-by-side with qqplots, with the next plot continuing on the same page. Preferably using ggplot (because prettier plots). I have a large number of variables in my real dataset, so I am using a 'for' loop.
library(ggplot2)
library(stats)
library(datasets)
This piece of ggplot code does what I want it to do.
ggplot(airquality, aes(Wind)) +
geom_histogram(aes(y = ..density..),colour = "black", fill = "white") +
stat_function(fun = dnorm, args = list(mean = mean(airquality$Wind), sd = sd(airquality$Wind)), colour = "red", size = 1) +
xlab("Wind")
qplot(sample = airquality$Wind, stat = "qq")
I am fine with the binwidth warning, I want that picked automatically, and I will build in a suppression for that message later on. I am not sure wat to do though with: '"stat" is deprecated' Anyone?
If I try to work this into a 'for' loop, I cannot get it to work. It keeps putting every plot on a new page and it leaves out the normal curves:
Variablesairquality<-c("Wind", "Temp", "Month", "Day")
pdf(file = "Normality.pdf", 4, 5)
par(mfrow = c(2,2))
for(i in Variablesairquality){
plot(ggplot(airquality, aes(airquality[,i])) +
geom_histogram(aes(y = ..density..),colour = "black", fill = "white") +
stat_function(fun = dnorm, args = list(mean = mean(airquality[,i]), sd = sd(airquality[,i])), colour = "red", size = 1) +
xlab(i)
)
plot(qplot(sample = airquality[,i], stat = "qq" )
)
}
dev.off()
Which I don’t get, because if I try it using stats, it does exactly what I want:
pdf(file = "Normality2.pdf", 4, 5)
par(mfrow = c(2,2))
for(i in Variablesairquality){
h <- hist(airquality[,i], col = "white", cex.axis=0.50, xlab = i, cex.lab=0.75, main = paste("Distribution"), cex.main= 0.75)
xfit<-seq(min(airquality[,i]),max(airquality[,i]),length=length(airquality[,i]))
yfit<-dnorm(xfit,mean=mean(airquality[,i]),sd=sd(airquality[,i]))
yfit <- yfit*diff(h$mids[1:2])*length(airquality[,i])
lines(xfit, yfit, col="red", lwd=1)
qqnorm(airquality[,i], cex = 0.5, cex.axis=0.50, cex.lab=0.75, main = expression("Q-Q plot for"~paste(i)), cex.main= 0.75)
qqline(airquality[,i], col = "red")
}
dev.off()
(Accept for the thing with the main label which I still need to figure out. Anyone any tips?)
I would be most grateful if someone could point out the mistake in my ggplot code or otherwise explain this behaviour. Thanks!
I use R-programming V3.2.3 and R-studio v0.99.891. (And yes, I read every similar item here, scowered the internet and I read the help files; that did not get me where I need to go.)
On `stat` is deprecated, see Deprecated features in the ggplot2 2.0.0 release notes. Use instead:
ggplot(airquality, aes(sample = Wind)) +
stat_qq()
If you don't wish to use gridExtra::grid.arrange, here's an approach that uses facets. Begin by wrangling the data into a new dataframe with the values we want for x, y, plot type, and geom variables:
d <- as.data.frame(qqnorm(airquality$Wind, plot.it = F))
d$plot <- "QQ plot"
d$geom <- "point"
d <- rbind(d, data.frame(x = airquality$Wind, y = NA,
plot = "Histogram", geom = "bar"))
d <- rbind(d, with(airquality, data.frame(
x = seq(min(Wind), max(Wind), l = 100),
y = dnorm(seq(min(Wind), max(Wind), l = 100),
mean = mean(Wind), sd = sd(Wind)),
plot = "Histogram", geom = "line")))
Then call ggplot, subsetting the data as appropriate for each geom:
ggplot(d, aes(x = x, y = y)) + facet_wrap(~plot, scales = "free") +
geom_histogram(data = subset(d, plot == "Histogram" & geom == "bar"),
aes(y = ..density..),
colour = "black", fill = "white") +
geom_line(data = subset(d, plot == "Histogram" & geom == "line"),
colour = "red", size = 1) +
geom_point(data = subset(d, plot == "QQ plot")) +
labs(x = "Wind")
Output:
To do multiple plots, you can wrap the code above into a for loop, making sure to wrap ggplot inside print:
pdf("path/to/pdf/out.pdf")
Variablesairquality <- c("Wind", "Temp", "Month", "Day")
for (i in rev(Variablesairquality)) {
x <- airquality[[i]]
d <- as.data.frame(qqnorm(x, plot.it = F))
d$plot <- "QQ plot"
d$geom <- "point"
d <- rbind(d, data.frame(x = x, y = NA, plot = "Histogram", geom = "bar"))
d <- rbind(d, data.frame(x = seq(min(x), max(x), l = 100),
y = dnorm(seq(min(x), max(x), l = 100),
mean = mean(x), sd = sd(x)),
plot = "Histogram", geom = "line"))
print(
ggplot(d, aes(x = x, y = y)) + facet_wrap(~plot, scales = "free") +
geom_histogram(data = subset(d, plot == "Histogram" & geom == "bar"),
aes(y = ..density..),
colour = "black", fill = "white") +
geom_line(data = subset(d, plot == "Histogram" & geom == "line"),
colour = "red", size = 1) +
geom_point(data = subset(d, plot == "QQ plot")) +
labs(x = i)
)
}
dev.off()
I have two populations A and B distributed spatially with one character Z, I want to be able to make an hexbin substracting the proportion of the character in each hexbin. Here I have the code for two theoretical populations A and B
library(hexbin)
library(ggplot2)
set.seed(2)
xA <- rnorm(1000)
set.seed(3)
yA <- rnorm(1000)
set.seed(4)
zA <- sample(c(1, 0), 20, replace = TRUE, prob = c(0.2, 0.8))
hbinA <- hexbin(xA, yA, xbins = 40, IDs = TRUE)
A <- data.frame(x = xA, y = yA, z = zA)
set.seed(5)
xB <- rnorm(1000)
set.seed(6)
yB <- rnorm(1000)
set.seed(7)
zB <- sample(c(1, 0), 20, replace = TRUE, prob = c(0.4, 0.6))
hbinB <- hexbin(xB, yB, xbins = 40, IDs = TRUE)
B <- data.frame(x = xB, y = yB, z = zB)
ggplot(A, aes(x, y, z = z)) + stat_summary_hex(fun = function(z) sum(z)/length(z), alpha = 0.8) +
scale_fill_gradientn(colours = c("blue","red")) +
guides(alpha = FALSE, size = FALSE)
ggplot(B, aes(x, y, z = z)) + stat_summary_hex(fun = function(z) sum(z)/length(z), alpha = 0.8) +
scale_fill_gradientn (colours = c("blue","red")) +
guides(alpha = FALSE, size = FALSE)
here is the two resulting graphs
My goal is to make a third graph with hexbins with the values of the difference between hexbins at the same coordinates but I don't even know how to start to do it, I have done something similar in the raster Package, but I need it as hexbins
Thanks a lot
You need to make sure that both plots use the exact same binning. In order to achieve this, I think it is best to do the binning beforehand and then plot the results with stat_identity / geom_hex. With the variables from your code sample you ca do:
## find the bounds for the complete data
xbnds <- range(c(A$x, B$x))
ybnds <- range(c(A$y, B$y))
nbins <- 30
# function to make a data.frame for geom_hex that can be used with stat_identity
makeHexData <- function(df) {
h <- hexbin(df$x, df$y, nbins, xbnds = xbnds, ybnds = ybnds, IDs = TRUE)
data.frame(hcell2xy(h),
z = tapply(df$z, h#cID, FUN = function(z) sum(z)/length(z)),
cid = h#cell)
}
Ahex <- makeHexData(A)
Bhex <- makeHexData(B)
## not all cells are present in each binning, we need to merge by cellID
byCell <- merge(Ahex, Bhex, by = "cid", all = T)
## when calculating the difference empty cells should count as 0
byCell$z.x[is.na(byCell$z.x)] <- 0
byCell$z.y[is.na(byCell$z.y)] <- 0
## make a "difference" data.frame
Diff <- data.frame(x = ifelse(is.na(byCell$x.x), byCell$x.y, byCell$x.x),
y = ifelse(is.na(byCell$y.x), byCell$y.y, byCell$y.x),
z = byCell$z.x - byCell$z.y)
## plot the results
ggplot(Ahex) +
geom_hex(aes(x = x, y = y, fill = z),
stat = "identity", alpha = 0.8) +
scale_fill_gradientn (colours = c("blue","red")) +
guides(alpha = FALSE, size = FALSE)
ggplot(Bhex) +
geom_hex(aes(x = x, y = y, fill = z),
stat = "identity", alpha = 0.8) +
scale_fill_gradientn (colours = c("blue","red")) +
guides(alpha = FALSE, size = FALSE)
ggplot(Diff) +
geom_hex(aes(x = x, y = y, fill = z),
stat = "identity", alpha = 0.8) +
scale_fill_gradientn (colours = c("blue","red")) +
guides(alpha = FALSE, size = FALSE)