ggplot2: How to move y axis labels right next to the bars - ggplot2

I am working with following reproducible dataset:
df<- data.frame(name=c(letters[1:10],letters[1:10]),fc=runif(20,-5,5)
,fdr=runif(20),group=c(rep("gene",10),rep("protein",10)))
Code used to plot:
df$sig<- ifelse(df$fdr<0.05 & df$fdr>0 ,"*","")
ggplot(df, aes(x=reorder(name,fc),fc))+geom_col(aes(fill=group),position = "dodge",width = 0.9)+
coord_flip()+
geom_text(aes(label = sig),angle = 90, position = position_stack(vjust = -0.2), color= "black",size=3)+
scale_y_continuous(position = "right")+
scale_fill_manual(values = c("gene"= "#FF002B","protein"="blue"))+
geom_hline(yintercept = 0, colour = "gray" )+
theme(legend.position="none", axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.y=element_text(),
axis.line=element_line(color="gray"),axis.line.y=element_blank(),
axis.ticks.y=element_blank(),
panel.background=element_blank(),panel.border=element_blank(),panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),plot.background=element_blank())
Resulting in following plot:
Instead of having the y-axis labels on left side, I would like to place them right next to the bars. I want to emulate this chart published in nature:
https://www.nature.com/articles/ncomms2112/figures/3

Like this?
df<- data.frame(name=c(letters[1:10],letters[1:10]),fc=runif(20,-5,5)
,fdr=runif(20),group=c(rep("gene",10),rep("protein",10)))
df$sig<- ifelse(df$fdr<0.05 & df$fdr>0 ,"*","")
df$try<-c(1:10,1:10) #assign numbers to letters
x_pos<-ifelse(df$group=='gene',df$try-.2,df$try+.2) #align letters over bars
y_posneg<-ifelse(df$fc>0,df$fc+.5,df$fc-.5) #set up y axis position of letters
ggplot(df, aes(x=try,fc))+geom_col(aes(fill=group),position = "dodge",width = 0.9)+
coord_flip()+
geom_text(aes(y=y_posneg,x=x_pos,label = name),color= "black",size=6)+
scale_y_continuous(position = "right")+
scale_fill_manual(values = c("gene"= "#FF002B","protein"="blue"))+
geom_hline(yintercept = 0, colour = "gray" )+
theme(legend.position="none", axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.y=element_blank(),
axis.line=element_line(color="gray"),axis.line.y=element_blank(),
axis.ticks.y=element_blank(),
panel.background=element_blank(),panel.border=element_blank(),panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),plot.background=element_blank())
Gives:
Or perhaps this?
x_pos<-ifelse(df$group=='gene',df$try-.2,df$try+.2) #align letters over bars
y_pos<-ifelse(df$fc>0,-.2,.2) #set up y axis position of letters
ggplot(df, aes(x=try,fc))+geom_col(aes(fill=group),position = "dodge",width = 0.9)+
coord_flip()+
geom_text(aes(y=y_pos,x=x_pos,label = name),color= "black",size=3)+
scale_y_continuous(position = "right")+
scale_fill_manual(values = c("gene"= "#FF002B","protein"="blue"))+
geom_hline(yintercept = 0, colour = "gray" )+
theme(legend.position="none", axis.title.y=element_blank(),
axis.title.x=element_blank(),
axis.text.y=element_blank(),
axis.line=element_line(color="gray"),axis.line.y=element_blank(),
axis.ticks.y=element_blank(),
panel.background=element_blank(),panel.border=element_blank(),panel.grid.major=element_blank(),
panel.grid.minor=element_blank(),plot.background=element_blank())
Gives:

Related

how to color different datasets separately when overlapping them using geom_smooth and color settings

i have 2 datasets that span full genomes, separated by chromosomes (scaffolds), for 2 group comparisons and i want to overlap them in a single graph.
the way i was doing was as follow:
ggplot(NULL, aes(color = as_factor(scaffold))) +
geom_smooth(data = windowStats_SBvsOC, aes(x = mid2, y = Fst_group1_group5), se=F) +
geom_smooth(data = windowStats_SCLvsSCU, aes(x = mid2, y = Fst_group3_group4), se=F) +
scale_y_continuous(expand = c(0,0), limits = c(0, 1)) +
scale_x_continuous(labels = chrom$chrID, breaks = axis_set$center) +
scale_color_manual(values = rep(c("#276FBF", "#183059"), unique(length(chrom$chrID)))) +
scale_size_continuous(range = c(0.5,3)) +
labs(x = NULL,
y = "Fst (smoothed means)") +
theme_minimal() +
theme(
legend.position = "none",
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
axis.title.y = element_text(),
axis.text.x = element_text(angle = 60, size = 8, vjust = 0.5))
this way, i get each chromosome with alternating colors, and the smoothing is per chromosome. but i wanted the colors to be different between the 2 groups so i can distinguish when they are overlapped like this. is there a way to do it? i can only do it once i remove the color by scaffold, but then the smoothing gets done across the whole genome and i don't want that!
my dataset is big, so i'm attaching it here!
i'm running this in rstudio 2022.02.3, R v.3.6.2 and package ggplot2
EDIT: i've figured out! i just needed to change color = as_factor(scaffold) to group = as_factor(scaffold); and then add the aes(color) to each geom_smooth() function.

What is Julia's equivalent ggplot code of R's?

I would like to plot a sophisticated graph in Julia. The code below is in Julia's version using ggplot.
using CairoMakie, DataFrames, Effects, GLM, StatsModels, StableRNGs, RCall
#rlibrary ggplot2
rng = StableRNG(42)
growthdata = DataFrame(; age=[13:20; 13:20],
sex=repeat(["male", "female"], inner=8),
weight=[range(100, 155; length=8); range(100, 125; length=8)] .+ randn(rng, 16))
mod_uncentered = lm(#formula(weight ~ 1 + sex * age), growthdata)
refgrid = copy(growthdata)
filter!(refgrid) do row
return mod(row.age, 2) == (row.sex == "male")
end
effects!(refgrid, mod_uncentered)
refgrid[!, :lower] = #. refgrid.weight - 1.96 * refgrid.err
refgrid[!, :upper] = #. refgrid.weight + 1.96 * refgrid.err
df= refgrid
ggplot(df, aes(x=:age, y=:weight, group = :sex, shape= :sex, linetype=:sex)) +
geom_point(position=position_dodge(width=0.15)) +
geom_ribbon(aes(ymin=:lower, ymax=:upper), fill="gray", alpha=0.5)+
geom_line(position=position_dodge(width=0.15)) +
ylab("Weight")+ xlab("Age")+
theme_classic()
However, I would like to modify this graph a bit more. For example, I would like to change the scale of the y axis, the colors of the ribbon, add some error bars, and also change the text size of the legend and so on. Since I am new to Julia, I am not succeding in finding the equivalent language code for these modifications. Could someone help me translate this R code below of ggplot into Julia's language?
t1= filter(df, sex=="male") %>% slice_max(df$weight)
ggplot(df, aes(age, weight, group = sex, shape= sex, linetype=sex,fill=sex, colour=sex)) +
geom_line(position=position_dodge(width=0.15)) +
geom_point(position=position_dodge(width=0.15)) +
geom_errorbar(aes(ymin = lower, ymax = upper),width = 0.1,
linetype = "solid",position=position_dodge(width=0.15))+
geom_ribbon(aes(ymin = lower, ymax = upper, fill = sex, colour = sex), alpha = 0.2) +
geom_text(data = t1, aes(age, weight, label = round(weight, 1)), hjust = -0.25, size=7,show_guide = FALSE) +
scale_y_continuous(limits = c(70, 150), breaks = seq(80, 140, by = 20))+
theme_classic()+
scale_colour_manual(values = c("orange", "blue")) +
guides(color = guide_legend(override.aes = list(linetype = c('dotted', 'dashed'))),
linetype = "none")+
xlab("Age")+ ylab("Average marginal effects") + ggtitle("Title") +
theme(
axis.title.y = element_text(color="Black", size=28, face="bold", hjust = 0.9),
axis.text.y = element_text(face="bold", color="black", size=16),
plot.title = element_text(hjust = 0.5, color="Black", size=28, face="bold"),
legend.title = element_text(color = "Black", size = 13),
legend.text = element_text(color = "Black", size = 16),
legend.position="bottom",
axis.text.x = element_text(face="bold", color="black", size=11),
strip.text = element_text(face= "bold", size=15)
)
As I commented before, you can use R-strings to run R code. To be clear, this isn't like your post's approach where you piece together many Julia objects that wrap many R objects, this is RCall converting a Julia Dataframe to an R dataframe then running your R code.
Running an R script may not seem very Julian, but code reuse is very Julian. Besides, you're still using an R library and active R session either way, and there might even be a slight performance benefit from reducing how often you make wrapper objects and switch between Julia and R.
## import libraries for Julia and R; still good to do at top
using CairoMakie, DataFrames, Effects, GLM, StatsModels, StableRNGs, RCall
R"""
library(ggplot2)
library(dplyr)
"""
## your Julia code without the #rlibrary or ggplot lines
rng = StableRNG(42)
growthdata = DataFrame(; age=[13:20; 13:20],
sex=repeat(["male", "female"], inner=8),
weight=[range(100, 155; length=8); range(100, 125; length=8)] .+ randn(rng, 16))
mod_uncentered = lm(#formula(weight ~ 1 + sex * age), growthdata)
refgrid = copy(growthdata)
filter!(refgrid) do row
return mod(row.age, 2) == (row.sex == "male")
end
effects!(refgrid, mod_uncentered)
refgrid[!, :lower] = #. refgrid.weight - 1.96 * refgrid.err
refgrid[!, :upper] = #. refgrid.weight + 1.96 * refgrid.err
df= refgrid
## convert Julia's df and run your R code in R-string
## - note that $df is interpolation of Julia's df into R-string,
## not R's $ operator like in rdf$weight
## - call the R dataframe rdf because df is already an R function
R"""
rdf <- $df
t1= filter(rdf, sex=="male") %>% slice_max(rdf$weight)
ggplot(rdf, aes(age, weight, group = sex, shape= sex, linetype=sex,fill=sex, colour=sex)) +
geom_line(position=position_dodge(width=0.15)) +
geom_point(position=position_dodge(width=0.15)) +
geom_errorbar(aes(ymin = lower, ymax = upper),width = 0.1,
linetype = "solid",position=position_dodge(width=0.15))+
geom_ribbon(aes(ymin = lower, ymax = upper, fill = sex, colour = sex), alpha = 0.2) +
geom_text(data = t1, aes(age, weight, label = round(weight, 1)), hjust = -0.25, size=7,show_guide = FALSE) +
scale_y_continuous(limits = c(70, 150), breaks = seq(80, 140, by = 20))+
theme_classic()+
scale_colour_manual(values = c("orange", "blue")) +
guides(color = guide_legend(override.aes = list(linetype = c('dotted', 'dashed'))),
linetype = "none")+
xlab("Age")+ ylab("Average marginal effects") + ggtitle("Title") +
theme(
axis.title.y = element_text(color="Black", size=28, face="bold", hjust = 0.9),
axis.text.y = element_text(face="bold", color="black", size=16),
plot.title = element_text(hjust = 0.5, color="Black", size=28, face="bold"),
legend.title = element_text(color = "Black", size = 13),
legend.text = element_text(color = "Black", size = 16),
legend.position="bottom",
axis.text.x = element_text(face="bold", color="black", size=11),
strip.text = element_text(face= "bold", size=15)
)
"""
The result is the same as your post's R code:
I used Vega-Lite (https://github.com/queryverse/VegaLite.jl) which is also grounded in the "Grammar of Graphics", and LinearRegression (https://github.com/ericqu/LinearRegression.jl) which provides similar features as GLM, although I think it is possible to get comparable results with the other plotting and linear regression packages. Nevertheless, I hope that this gives you a starting point.
using LinearRegression: Distributions, DataFrames, CategoricalArrays
using DataFrames, StatsModels, LinearRegression
using VegaLite
growthdata = DataFrame(; age=[13:20; 13:20],
sex=categorical(repeat(["male", "female"], inner=8), compress=true),
weight=[range(100, 155; length=8); range(100, 125; length=8)] .+ randn(16))
lm = regress(#formula(weight ~ 1 + sex * age), growthdata)
results = predict_in_sample(lm, growthdata, req_stats="all")
fp = select(results, [:age, :weight, :sex, :uclp, :lclp, :predicted]) |> #vlplot() +
#vlplot(
mark = :errorband, color = :sex,
y = { field = :uclp, type = :quantitative, title="Average marginal effects"},
y2 = { field = :lclp, type = :quantitative },
x = {:age, type = :quantitative} ) +
#vlplot(
mark = :line, color = :sex,
x = {:age, type = :quantitative},
y = {:predicted, type = :quantitative}) +
#vlplot(
:point, color=:sex ,
x = {:age, type = :quantitative, axis = {grid = false}, scale = {zero = false}},
y = {:weight, type = :quantitative, axis = {grid = false}, scale = {zero = false}},
title = "Title", width = 400 , height = 400
)
which gives:
You can change the style of the elements by changing the "config" as indicated here (https://www.queryverse.org/VegaLite.jl/stable/gettingstarted/tutorial/#Config-1).
As the Julia Vega-Lite is a wrapper to Vega-Lite additional documentation can be found on the Vega-lite website (https://vega.github.io/vega-lite/)

Is there any other way to find percentage and plot a group bar-chart without using matplotlib?

emp_attrited = pd.DataFrame(df[df['Attrition'] == 'Yes'])
emp_not_attrited = pd.DataFrame(df[df['Attrition'] == 'No'])
print(emp_attrited.shape)
print(emp_not_attrited.shape)
att_dep = emp_attrited['Department'].value_counts()
percentage_att_dep = (att_dep/237)*100
print("Attrited")
print(percentage_att_dep)
not_att_dep = emp_not_attrited['Department'].value_counts()
percentage_not_att_dep = (not_att_dep/1233)*100
print("\nNot Attrited")
print(percentage_not_att_dep)
fig = plt.figure(figsize=(20,10))
ax1 = fig.add_subplot(221)
index = np.arange(att_dep.count())
bar_width = 0.15
rect1 = ax1.bar(index, percentage_att_dep, bar_width, color = 'black', label = 'Attrited')
rect2 = ax1.bar(index + bar_width, percentage_not_att_dep, bar_width, color = 'green', label = 'Not Attrited')
ax1.set_ylabel('Percenatage')
ax1.set_title('Comparison')
xTickMarks = att_dep.index.values.tolist()
ax1.set_xticks(index + bar_width)
xTickNames = ax1.set_xticklabels(xTickMarks)
plt.legend()
plt.tight_layout()
plt.show()
The first block represents how the dataset is split into 2 based upon Attrition
The second block represents the calculation of percentage of Employees in each Department who are attrited and not attrited.
The third block is to plot the given as a grouped chart.
You can do:
(df.groupby(['Department'])
['Attrited'].value_counts(normalize=True)
.unstack('Attrited')
.plot.bar()
)

Integrate default color into personalized theme ggplot

I created my own theme and now I also want to standardize the color set that is used. I tried to do this with the list solution, provided in the answer of Viktor in this feed:
Associate a color palette with ggplot2 theme
df <- mtcars
uwvPalet <- c("#0078D2","#003282","#C4D600")
theme_uwv <- function(base_size = 22, base_family = "Verdana"){theme_hc(base_size = base_size, base_family = base_family)%+replace%theme(plot.title = element_text(color = rgb(0, 120, 210)), complete = TRUE)}
theme_uwv2 <- list(theme_uwv, scale_color_manual(values = uwvPalet))
ggplot(df, aes(fill = cyl, x = am, y = mpg)) + geom_bar(position = "dodge", stat="identity") + theme_uwv2()
Unfortunately, I get the error:
Error in theme_uwv2() : could not find function "theme_uwv2"
Anyone know how I can fix this?
The following worked for me. theme_uwv2 needed the value returned from theme_uwv() as a list element, not the function itself. Also, you were making a plot where the fill was the dominant colour variable, so I've substituted scale_color_manual() with scale_fill_manual() for demonstration purposes.
library(ggplot2)
library(ggthemes)
df <- mtcars
uwvPalet <- c("#0078D2","#003282","#C4D600")
theme_uwv <- function(base_size = 22, base_family = "Verdana"){
theme_hc(base_size = base_size, base_family = base_family) %+replace%
theme(plot.title = element_text(color = rgb(0, 120, 210, maxColorValue = 255)),
complete = TRUE)}
theme_uwv2 <- list(theme_uwv(), scale_fill_manual(values = uwvPalet))
ggplot(df, aes(fill = as.factor(cyl), x = am, y = mpg)) +
geom_col(position = "dodge") +
ggtitle("test") +
theme_uwv2

Problem with alignment of geom_point and geom_errorbar

I am trying to plot how different predictors associate with stroke and underlying phenotypes (i.e. cholesterol). In my data, I originally had working ggplot code in which shapes denoted the different variables (stroke, HDL cholesterol and total cholesterol) and colour denoted type (i.e. disease (stroke) or phenotype (HDL/total cholesterol). To make it more intuitive, I want to swap shape and colour around but now that I do this, I am having issues with position dodge and the alignment of geom_point and geom_error
stroke_graph <- ggplot(stroke,aes(y=as.numeric(stroke$test),
x=Clock,
shape = Type,
colour = Variable)) +
geom_point(data=stroke, aes(shape=Type, colour=Variable), show.legend=TRUE,
position=position_dodge(width=0.5), size = 3) +
geom_errorbar(aes(ymin = as.numeric(stroke$LCI), ymax= as.numeric(stroke$UCI)),
position = position_dodge(0.5), width = 0.05,
colour ="black")+
ylab("standardised beta/log odds")+ xlab ("")+
geom_hline(yintercept = 0, linetype = "dotted")+
theme(axis.text.x = element_text(size = 10, vjust = 0.5), legend.position = "none",
plot.title = element_text(size = 12))+
scale_y_continuous(limit = c(-0.402, 0.7))+ scale_shape_manual(values=c(15, 17, 18))+
theme(legend.position="right") + labs(shape = "Variable") + guides(shape = guide_legend(reverse=TRUE)) +
coord_flip()
stroke_graph + ggtitle("Stroke and Associated Phenotypes") + theme(plot.title = element_text(hjust = 0.5))
Graph now: 1
Previously working graph - only difference in code is swapping "Type" and "Variable": 2