Why is it so difficult to make things in ggplot2 , i like the way it helps in customisation but the curve is steep nevertheless
Here is my sample dataframe
df <- gene HSC CMP
ENSG00000158292.6 1.8102636 2.456869
ENSG00000162496.6 2.6796705 6.203838
ENSG00000117115.10 3.4509115 5.555739
ENSG00000159423.14 3.6809277 5.063446
ENSG00000053372.4 5.7089974 6.851090
If i have plot a boxplot i can simply write this boxplot(df[,-1],col=c("red","blue"))
I get a boxplot but when im trying with ggplot2 im having difficult time
ex <- melt(df, id.vars=c("HSC", "CMP"))
ggplot(data = ex,
aes(x = CMP, y = HSC)) +
geom_boxplot()
I get a single boxplot what i want is i get a box plot for HSC and CMP which i got when i use simple base R boxplot .
Any help or suggestion would be highly appreciated with my ggplot2 code
3 answers
Devon got there before me but as he mentioned the id.vars needs to be set to 'gene'
Here's a boxplot with scatterplot overlay for anyone else arriving here from Google.
I do agree that ggplot can be difficult to work with. Many functions redundant in the sense that they do the same thing as other but have different names, and conflicts frequently arise. That said, if you can master ggplot, then you can produce very nice graphics for publications.
require(reshape2)
require(ggplot2)
ex <- melt(df, id.vars=c("gene"))
colnames(ex) <- c("gene","group","exprs")
ggplot(data=ex, aes(x=group, y=exprs)) +
geom_boxplot(position=position_dodge(width=0.5), outlier.shape=17, outlier.colour="red", outlier.size=0.1, aes(fill=group)) +
#Choose which colours to use; otherwise, ggplot2 choose automatically
#scale_color_manual(values=c("red3", "white", "blue")) + #for scatter plot dots
scale_fill_manual(values=c("red", "royalblue")) + #for boxplot
#Add the scatter points (treats outliers same as 'inliers')
geom_jitter(position=position_jitter(width=0.3), size=3.0, colour="black") +
#Set the size of the plotting window
theme_bw(base_size=24) +
#Modify various aspects of the plot text and legend
theme(
legend.position="none",
legend.background=element_rect(),
plot.title=element_text(angle=0, size=14, face="bold", vjust=1),
axis.text.x=element_text(angle=45, size=14, face="bold", hjust=1.10),
axis.text.y=element_text(angle=0, size=14, face="bold", vjust=0.5),
axis.title=element_text(size=14, face="bold"),
#Legend
legend.key=element_blank(), #removes the border
legend.key.size=unit(1, "cm"), #Sets overall area/size of the legend
legend.text=element_text(size=12), #Text size
title=element_text(size=12)) + #Title text size
#Change the size of the icons/symbols in the legend
guides(colour=guide_legend(override.aes=list(size=2.5))) +
#Set x- and y-axes labels
xlab("Stem cell class") +
ylab("Expression") +
#ylim(0, 0) +
ggtitle("My plot")
Don't worry. I did the same a few years ago trying to work with ggplot.
Im using your code to make boxplots for normalised vs as the data that is not normalised ,what i have to do not to fill those box with data points or dots i tried to remove "aes(fill=group)" still i dont get it i see my hoxplot but it looks filled up with dotpoints..any suggestion ?
Hello my friend. If you do not want the scatterplot overlayed onto the boxplot, just comment out:
geom_jitter(position=position_jitter(width=0.3), size=3.0, colour="black") +
No problem, good luck with it.
That's nice, but a violin plot would be better ;-)
Coincidentally, I just produced a violin plot for other data ;)
ggplot(violinMatrix, aes(x=Sample, y=Expression)) + geom_violin() + theme(axis.text.x = element_text(angle=45, hjust=1))
lol
ex = melt(df, id.vars="gene")
ggplot(ex, aes(x=variable, y=value)) + geom_boxplot()
Your melt() command produced nonsensical output.
This is good but I would use gather from tidyr. The package tidyr is the evolution of reshape2, and it contains more functions to massage data and reshape it for ggplot2/tidyverse.
Agreed and that's what I teach our students, but I don't want to complicate things when answering a simple "why does X not work" question :)
options(stringsAsFactors = F)
df= read.csv("test.txt", sep="\t")
library(reshape2)
library(ggplot2)
df_melt=melt(df,id.vars="gene")
ggplot(df_melt, aes(variable,value)) +
stat_boxplot(geom="errorbar", width=.5)+
geom_boxplot(aes(fill=variable))+
theme_bw()+
theme(axis.title.x=element_blank(), axis.title.y=element_blank())+
stat_summary(fun.y=median, colour="red", geom="line", aes(group = 1))+
geom_jitter(position = position_jitter(0.2))
Input:
> df
gene HSC CMP
1 ENSG00000158292.6 1.810264 2.456869
2 ENSG00000162496.6 2.679670 6.203838
3 ENSG00000117115.10 3.450912 5.555739
4 ENSG00000159423.14 3.680928 5.063446
5 ENSG00000053372.4 5.708997 6.851090
You are talented cpad
Nice, but what is the point of connecting the two medians with a red line ? I don't mean to be rude here but unless I'm missing something, that line is just "polluting" the data.
There were several requests in SO to connect group means. In addition, there were requests to view data as well (jitter here). search for "connecting means in ggplot" yields several SO requests. Some of them include for boxplots as well. Lines and colors can be customized, as you are aware.
Yeah I guess this can make sense for time series analysis or things like that...
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Scatter Plot for two sample condition
written by 1769mkc
Thank you for such cool neat code ...