I'm trying to use ggplot2 to create a timeline with annotated events. This is my data:
cambodia = data.frame(Period = c("Funan", "Chenla/Zhenla","Khmer Empire","Dark Ages of Cambodia"),StartDate = c(-500,550,802,1431), EndDate = c(550,802,1431,1863))
cambodia.events = data.frame(Event = c("Migration of peoples from southeastern China\ninto Cambodia"), Date=c(50), disloc = c(1))
This is the code that I'm using:
library(ggplot2)
library(viridis)
library(ggthemes)
ggplot(data=cambodia) +
geom_segment(aes(x=StartDate, xend=EndDate, y=0., yend=0., color=Period) , linetype=1, size=4) +
scale_color_viridis(discrete = TRUE)+
scale_y_continuous(limits=c(0,0.5))+
scale_x_continuous(limits=c(-500,1863), breaks= c(seq(0,1863,by=1863), cambodia$StartDate, cambodia$EndDate))+
xlab("Time")+
ylab("Periods of History")+
theme_minimal() + theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(), axis.title.y=element_blank(),axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
theme(aspect.ratio = .2)+
theme(legend.position="none") +
geom_text(aes(x=StartDate-100 + (EndDate- StartDate)/2,y=0.05,label=Period,angle=25,hjust=0))
What is produced currently looks fine but it doesn't have any annotated events, as found in this Stack Overflow post. I have tried to add this code from that post:
geom_segment(aes(x = Event,y = disloc,xend = Event),data=cambodia.events,yend = 0) +
geom_segment(aes(x = 900,y = 0,xend = 2050,yend = 0),data=cambodia.events,arrow = arrow(length = unit(x = 0.2,units = 'cm'),type = 'closed')) +
geom_text(aes(x = Event,y = disloc,label = Date),data=cambodia.events,hjust = 1.0,vjust = 1.0,parse = FALSE)
but unsurprisingly, it isn't working (I assume because the arguments are conflicting, but I'm not sure how to resolve them).
As a note: The error it throws up when I try to use the full code above (with the hash lines un-hashed) is "Error: Discrete value supplied to continuous scale."
In your code for the annotation you put x = Event, when on your existing plot Date is on the x-axis, so you just need to make sure that both layers share the same x-axis scale:
ggplot() +
geom_segment(data = cambodia, aes(x = StartDate, xend = EndDate, y = 0, yend = 0, color = Period), linetype = 1, size = 4) +
geom_text(data=cambodia, aes(x=StartDate-100 + (EndDate- StartDate)/2,y=0.05,label=Period,angle=25,hjust=0)) +
scale_color_viridis(discrete = TRUE)+
scale_y_continuous(limits=c(0, 0.5))+
scale_x_continuous(limits=c(-500, 1863), breaks= c(seq(0, 1863, by = 1863), cambodia$StartDate, cambodia$EndDate))+
xlab("Time")+
ylab("Periods of History")+
theme_minimal() +
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.title.y = element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
aspect.ratio = .2,
legend.position="none") +
geom_segment(data = cambodia.events, aes(x = Date, xend = Date, y = 0, yend = .25)) +
geom_text(data = cambodia.events, aes(x = Date, y = .35, label = Event))
Related
I have modified the colors of my x axis labels according to their group.
For that, I have used the following pseudocode:
library(ggsci)
library(ggplot2)
x_cols = pal_jco()(length(unique(melted_df$Group)))
names(x_cols) = unique(melted_df$Group)
ggplot(melted_df, ... + theme(axis.text.x = element_text(colour = x_cols))
I would like to add a legend to the plot (if possible, outside the plot), that explains the colouring of the x axis labels.
melted_df dataframe looks like this:
Here the full code:
#Generate color mapping
x_cols = pal_jco()(length(unique(melted_df$Group)))
names(x_cols) = unique(melted_df$Group)
melted_df$mycolors = sapply(as.character(melted_df$Group), function(x) x_cols[x])
#Plot
ggplot(melted_df, aes(fill=variable, y=value, x=fct_inorder(id))) +
geom_bar(position="stack", stat = "identity") + ggtitle("Barplot") +
theme_bw() +
xlab("samples") + ylab("Counts") +
theme(axis.title.y=element_text(size=10), axis.title.x=element_text(size=10),
plot.title = element_text(face = "bold", size = (15), hjust = 0.5),
axis.text.x = element_text(distinct(samples_melt[c("id", "mycolors")])$mycolors)) +
guides(fill=guide_legend(title="Columns"))
In the absence of a reproducible example, here is how you might do it with the built-in iris data set:
library(ggplot2)
ggplot(iris, aes(Species, Sepal.Length)) +
stat_summary(fun = mean, geom = "col", aes(fill = Species)) +
geom_point(aes(color = Species), alpha = 0, key_glyph = draw_key_text) +
theme_bw(base_size = 20) +
labs(color = "") +
guides(color = guide_legend(override.aes = list(alpha = 1, size = 8))) +
theme(axis.text.x = element_text(color = scales::hue_pal()(3), face = 2))
I addressed the issue using Legend() constructor, provided by ComplexHeatmap library.
I first used the code provided above under the EDIT section, and then I added the following code in order to draw an additional legend explaining the x-axis colouring.
lgd = Legend(labels = names(x_cols), title = "Group", labels_gp = gpar(fontsize = 8), nrow = 1, legend_gp = gpar(fill = x_cols))
draw(lgd, x = unit(1.8, "cm"), y = unit(0.3, "cm"), just = c("left", "bottom"))
I am trying to create a plot like the one below. I'd like the order the points in each category in such a way that they form an s-shape. Is it possible to do this in ggplot?
Similar data available here
What I have so far:
somatic.variants <- read.delim("data/Lawrence.S2.txt", stringsAsFactors=T)
cancer_rates <- tapply(somatic.variants$logn_coding_mutations, somatic.variants$tumor_type, median)
cancer_rates <- cancer_rates[order(cancer_rates, decreasing=F)]
somatic.variants$tumor_type <- factor(somatic.variants$tumor_type, levels = names(cancer_rates))
library(ggplot2)
library(GGally)
ggplot(data = somatic.variants,
mapping = aes(x = tumor_type,
y = log10(n_coding_mutations))) +
geom_point(position = position_dodge2()) +
scale_x_discrete(position = "top") +
scale_y_continuous(labels = c(0,10,100,1000,10000), expand = c(0,0)) +
geom_stripped_cols() +
theme_bw() +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 315, hjust = 1, size = 12),
panel.grid = element_blank()) +
labs(y = "Coding mutations count") +
stat_summary(fun = median,
geom="crossbar",
size = 0.25,
width = 0.9,
group = 1,
show.legend = FALSE,
color = "#FF0000")
This could be achieved by
grouping the data by x-axis categories
arranging by the y-axis value
which ensures that the points are plotted in ascending order of the values for each category.
somatic.variants <- read.delim("https://gist.githubusercontent.com/wudustan/57deecdaefa035c1ecabf930afde295a/raw/1594d51a1e3b52f674ff746caace3231fd31910a/Lawrence.S2.txt", stringsAsFactors=T)
cancer_rates <- tapply(somatic.variants$logn_coding_mutations, somatic.variants$tumor_type, median)
cancer_rates <- cancer_rates[order(cancer_rates, decreasing=F)]
somatic.variants$tumor_type <- factor(somatic.variants$tumor_type, levels = names(cancer_rates))
library(ggplot2)
library(GGally)
library(dplyr)
somatic.variants <- somatic.variants %>%
group_by(tumor_type) %>%
arrange(n_coding_mutations)
ggplot(data = somatic.variants,
mapping = aes(x = tumor_type,
y = log10(n_coding_mutations))) +
geom_point(position = position_dodge2(.9), size = .25) +
scale_x_discrete(position = "top") +
scale_y_continuous(labels = c(0,10,100,1000,10000), expand = c(0,0)) +
geom_stripped_cols() +
theme_bw() +
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 315, hjust = 1, size = 12),
panel.grid = element_blank()) +
labs(y = "Coding mutations count") +
stat_summary(fun = median,
geom="crossbar",
size = 0.25,
width = 0.9,
group = 1,
show.legend = FALSE,
color = "#FF0000")
#> Warning: Removed 29 rows containing non-finite values (stat_summary).
I have made a barplot similar to the one below using ggplot2.
I cannot get the percentages on top of the bars to be centered and not overlapping of other bars and numbers. Sample code is below.
library(tidyverse)
cat1=c("cat1","cat1","cat1","cat1","cat1","cat1","cat1","cat1","cat1","cat1","cat1","cat1",
"cat2","cat2","cat2","cat2","cat2","cat2","cat2","cat2","cat2","cat2","cat2","cat2",
"cat3","cat3","cat3","cat3","cat3","cat3","cat3","cat3","cat3","cat3","cat3","cat3",
"cat4","cat4","cat4","cat4","cat4","cat4","cat4","cat4","cat4","cat4","cat4","cat4")
cat2=c("c1","c2","c3","c4","c5","c6","c7","c8","c9","c10","c11","c12",
"c1","c2","c3","c4","c5","c6","c7","c8","c9","c10","c11","c12",
"c1","c2","c3","c4","c5","c6","c7","c8","c9","c10","c11","c12",
"c1","c2","c3","c4","c5","c6","c7","c8","c9","c10","c11","c12")
count1=round(rnorm(48,10))
fakeperc=rnorm(48,9)
df1=cbind(count1,fakeperc)
df2=cbind(cat1,cat2)
finaldf=as.data.frame(cbind(df1,df2))
finaldf$cat1=as.factor(finaldf$cat1)
finaldf$fakeperc=as.numeric(finaldf$fakeperc)
#finaldf$cat1=factor(finaldf$cat1,levels = c("cat1","cat2","cat3","cat4"))
finaldf$cat2 = factor(finaldf$cat2,
levels = c("c1","c2","c3","c4","c5","c6","c7","c8","c9","c10","c11","c12"))
a=ggplot(data=finaldf,aes(x=cat1, y=count1,
fill=cat2,group=cat2)) +
geom_bar(stat='identity',color='black',width=.65,position=position_dodge(width=.9))+
scale_y_discrete(limits=0:50,breaks=c(0,10,20,30,40,50))+
scale_fill_brewer(palette="Set3") +
theme_classic() +
geom_text(data = finaldf,
aes(x=cat1,y=count1,group=cat2,
label=format(paste(round(fakeperc),"%",sep = ""))),inherit.aes = F,
color='black',position=position_dodge(.9),vjust=-.5,size=3)
a
When trying to add either nudge_y or nudge_x to the geom_text call, nothing happens. I suspect this is because there is already a position_dodge call. I am open any and all solutions to make these percentages non-overlapping and legible.
What do you think of this?
# I think you meant count1 to be numeric
finaldf$count1 <- as.numeric(finaldf$count1)
ggplot(data = finaldf,
aes(x = cat1,
y = count1,
fill = cat2,
group = cat2)) +
geom_col(color = 'black',
width = 0.65,
position = position_dodge(width = 0.9)) +
geom_text(data = finaldf,
aes(x = cat1,
y = count1,
group = cat2,
label = scales::percent(fakeperc/100, accuracy = 0.01)),
inherit.aes = FALSE,
color = 'black',
position = position_dodge(0.9),
hjust = -0.1,
size = 3) +
scale_y_continuous(limits = c(0,50), breaks = c(0,10,20,30,40,50)) +
scale_fill_brewer(palette = "Set3") +
theme_classic() +
coord_flip()
I cleaned up a bit the code (according to my taste)
I changed scale_y_numeric to scale_y_continuous (since count1 should be numeric)
I used coord_flip() to make it more readable
I used scales::percent to write percentage numbers
(don't know why you set up limits from 0 to 50 but I left them as I suppposed they were intended)
If you don't want to use coor_flip:
finaldf$count1 <- as.numeric(finaldf$count1)
ggplot(data = finaldf,
aes(x = cat1,
y = count1,
fill = cat2,
group = cat2)) +
geom_col(color = 'black',
width = 0.65,
position = position_dodge(width = 0.9)) +
geom_text(data = finaldf,
aes(x = cat1,
y = count1,
group = cat2,
label = scales::percent(fakeperc/100, accuracy = 0.01)),
inherit.aes = FALSE,
color = 'black',
position = position_dodge(0.9),
hjust = -0.1,
angle = 90,
size = 3) +
scale_y_continuous(limits = c(0,50), breaks = c(0,10,20,30,40,50)) +
scale_fill_brewer(palette = "Set3") +
theme_classic()
Is this what you are looking for:
library(ggplot2)
#Code
ggplot(data=finaldf,aes(x=cat2, y=count1,
fill=cat2,group=cat2)) +
geom_bar(stat='identity',color='black',
position=position_dodge(width=1))+
scale_fill_brewer(palette="Set3") +
theme_bw() +
geom_text(aes(x=cat2,y=count1,group=cat2,
label=format(paste(round(fakeperc),"%",sep = ""))),inherit.aes = F,
color='black',position=position_dodge(1),
size=3,vjust=-0.5)+
facet_wrap(.~cat1,scales = 'free_x',nrow = 1,strip.position = 'bottom')+
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = 'top',
strip.background = element_blank(),
panel.spacing = unit(2, "lines"),
panel.grid = element_blank())+
guides(fill = guide_legend(nrow = 1))
Output:
I am trying to create a boxplot with a log y axis as I have some very small values and then some much higher values which do not work well in a boxplot with a continuous y axis. However, I have negative values which obviously do not work with a log scale. I was wondering if there was a way around this so that I can display my data on a boxplot which is still easy to interpret but has a more appropriate scale on the y axis.
p <- ggplot(data = Elstow.monthly.fluxes, aes(x = Month1, y = CH4.Flux)) + stat_boxplot(geom = "errorbar", linetype = 1, width = 0.5) + geom_boxplot() +
xlab(expression("Month")) + ylab(expression(~CH[4]~Flux~(µg~CH[4]~m^{-2}~d^{-1}))) +
scale_y_continuous(breaks = seq(-5000,40000,5000), limits = c(-5000,40000))+
theme(axis.text.x = element_text(colour = "black")) + theme(axis.text.y = element_text(colour =
"black")) +
theme(panel.background = element_rect("white", "black")) +
theme(panel.border = element_rect(colour = "black", fill=NA, size=0.5)) +
theme(axis.text = element_text(size = 12))+ theme(axis.title = element_text(size = 14))+
theme(axis.title.y = element_text(margin = margin(t = 0, r = 15, b = 0, l = 0))) +
theme(axis.title.x = element_text(margin = margin(t = 15, r = 0, b = 0, l = 0))) +
geom_hline(yintercept = 0, linetype ="dashed", colour = "black")
While you could indeed use the secondary axis to get the labels you want as Zhiqiang suggests, you could also use a transformation that fits your needs.
Consider the following skewed boxplots:
df <- data.frame(
x = rep(letters[1:2], each = 500),
y = rlnorm(1000) - 2
)
ggplot(df, aes(x, y)) +
geom_boxplot()
Instead, you could use the pseudo-log transformation to visualise your data:
ggplot(df, aes(x, y)) +
geom_boxplot() +
scale_y_continuous(trans = scales::pseudo_log_trans())
Alternatively, you could make any transformation you want. I personally like the inverse hyperbolic sine transformation, which is very much like the pseudo-log:
asinh_trans <- scales::trans_new(
"inverse_hyperbolic_sine",
transform = function(x) {asinh(x)},
inverse = function(x) {sinh(x)}
)
ggplot(df, aes(x, y)) +
geom_boxplot() +
scale_y_continuous(trans = asinh_trans)
I have a silly solution: trick the secondary axis to re-scale y axis. I do not have your data, just made up some numbers for the purpose of demonstration.
First convert y values as logy = log(y + 5000). When generating the graph, transform the values back to the original scale. I borrow the second axis to display the values. I am pretty sure others may have more elegant ways to do this.
I was lazy for not trying to find the right way to remove the primary y axis tick labels, just used breaks = c(0).
df<-data.frame(y = runif(33, min=-5000, max=40000),
x = rep(c("Aug", "Sep", "Oct"),33))
library(tidyverse)
df$logy = log(df$y+5000)
p <- ggplot(data = df, aes(x = x, y = logy)) +
stat_boxplot(geom = "errorbar", linetype = 1, width = 0.5) +
geom_boxplot() +
xlab(expression("Month")) +
ylab(expression(~CH[4]~Flux~(µg~CH[4]~m^{-2}~d^{-1}))) +
scale_y_continuous(sec.axis = sec_axis(~(exp(.) -5000),
breaks = c(-4000, 0, 5000, 10000, 20000, 40000)),
breaks = c(0))+
theme(axis.text.x = element_text(colour = "black")) +
theme(axis.text.y = element_text(colour = "black")) +
theme(panel.background = element_rect("white", "black")) +
theme(panel.border = element_rect(colour = "black", fill=NA, size=0.5)) +
theme(axis.text = element_text(size = 12))+
theme(axis.title = element_text(size = 14))+
theme(axis.title.y = element_text(margin = margin(t = 0, r = 15, b = 0, l = 0))) +
theme(axis.title.x = element_text(margin = margin(t = 15, r = 0, b = 0, l = 0))) +
geom_hline(yintercept = log(5000), linetype ="dashed", colour = "black")
p
coord_trans() is applied after the statistics are calculated (unlike scale). This can be combined with the pseudo_log_trans to cope with negatives.
library(plotly)
set.seed(1234)
dat <- data.frame(cond = factor(rep(c("A","B"), each=200)), rating = c(rnorm(200),rnorm(200, mean=500)))
pseudoLog <- scales::pseudo_log_trans(base = 10)
p <- ggplot(dat, aes(x=cond, y=rating)) + geom_boxplot() + coord_trans(y=pseudoLog)
I've got a data frame with three variables: location, weather, and wc.
I'd like to make a heatmap using geom_tile in ggplot2(), so that weather is on the y axis, location on the x axis, and wc being the fill. I found some code on https://learnr.wordpress.com/2010/01/26/ggplot2-quick-heatmap-plotting/, producing exactly the kind of plot I'm looking for.
The problem's that while I can make the basic plot (p), I can't get the code for the finishing touches to work (there's a visual of the end product on the webpage). As far as I can tell some of the code is for older versions of ggplot2(), but I can't figure out what the updated equivalent is.
Any help would be much appreciated.
Starting point (df):
df1 <- data.frame(location=c("az","az","az","bi","bi","ca","ca","ca"),weather=c(1,2,3,2,3,1,2,3),wc=c(2,1,1,2,1,2,2,1))
Current code:
p <- ggplot(df1, aes(location,weather)) + geom_tile(aes(fill = wc),colour = "white") +
scale_fill_gradient(low = "white", high = "steelblue")
p + theme_grey(base_size = base_size) + labs(x = "", y = "") +
scale_x_discrete(expand = c(0, 0)) + scale_y_discrete(expand = c(0, 0)) +
opts(legend.position = "none", axis.ticks = theme_blank(), axis.text.x = theme_text(size = base_size * 0.8, angle = 330, hjust = 0, colour = "grey50"))
Here is an updated version:
base_size <- 9
ggplot(df1, aes(location,weather)) + geom_tile(aes(fill = wc),colour = "white") +
scale_fill_gradient(low = "white", high = "steelblue") +
theme_grey(base_size = base_size) + labs(x = "", y = "") +
scale_x_discrete(expand = c(0, 0)) + scale_y_discrete(expand = c(0, 0)) +
theme(legend.position = "none",
axis.ticks = element_blank(),
axis.text.x = element_text(size = base_size * 0.8,
angle = 330,
hjust = 0,
colour = "grey50"))
all arguments under opts should be inside theme
theme_text() is replaced with element_text()
theme_blank() is replaced with element_blank()