I'm working with a dataset where I have one continous variable (V1) and want to see how that variable differs depending on demographics such as sex, age group etc.
I would like to do one graph that contains multiple boxplots - so that V1 is on the Y-axis and all my demographic variables (sex, age groups etc.) are on the x-axis with their corresponding p-values. Anyonw know how to do this in R?
I've added two photos to illustrate my dataset and the output I want.
Thanks!
Output example
Data example
It would be nice to have actual data and the code you already have so we can replicate what you have and work what you want. That being said, this link might be what you are looking for:
https://statisticsglobe.com/draw-multiple-boxplots-in-one-graph-in-r#example-2-drawing-multiple-boxplots-using-ggplot2-package
Scroll down about half way to Example 4: Drawing Multiple Boxplots for Each Group Side-by-Side
Related
I have the following code to plot a large dataset (450k) in ggplot2
x<-ggplot()+
geom_point(data=data_Male,aes(x=a,y=b),color="Turquoise",position=position_jitter(w=0.2,h=1),alpha=0.1,size=.5,show.legend=TRUE)+
geom_point(data=data_Female,aes(x=a,y=b),color="#FF9999",position=position_jitter(w=0.2,h=1),alpha=0.1,size=.5,show.legend=TRUE)+
theme_bw()
x<-x+geom_smooth(data=data_Male,aes(x=a,y=b,alpha="Male"),method="lm",colour="Blue",linetype=1,se=T)+
geom_smooth(data=data_Female,aes(x=a,y=b,alpha="Female"),method="lm",colour="Dark Red",linetype=5,se=T)+
geom_smooth(data=data_All,aes(x=a,y=b,alpha="All"),method="lm",colour="Black",linetype=3,se=T)+
scale_fill_discrete(name="Key",labels=c("Female","Male","All"))+
scale_colour_discrete(name="Plot Colour",labels=c("Female","Male","All"))+
scale_alpha_manual(name="Key",
values=c(1,1,1),
breaks=c("Female","Male","All"),
guide=guide_legend(override.aes=list(linetype=c(5,1,3),name="Key",
shape=c(16,16,NA),
color=c("Dark Red","Blue","Black"),
fill=c("#FF9999","Turquoise",NA))))
How can I change the order in which points are plotted? I have seen answered questions here dealing with a single dataframe but I am working with several dataframes so I cannot re-order the rows or ask ggplot to plot by certain criteria from within the dataframe. You can see an example of the kind of problem that this causes in the attached picture: the Female points are plotted on top of the Male points. Ideally I would like to be able to plot all the points in a random order, so that one "cloud" of points is not plotted on top of the other, obscuring it (N.B. the image shown doesn't include the "All" line).
Any help would be appreciated. Thank you.
I belive this is not possible. The following should work though:
You'd have to paste the two data frames together to df. The new data frame will appear sorted by male and female.
You can then suffle the new data frame:
set.seed(42)
rows <- sample(nrow(df))
male_female_mixed <- df[rows, ]
Then you can plot male_female_mixed
I have a data frame (pLog) containing the number of reads per nucleotide for a chip-seq experiment done for a E. coli genome (4.6MB). I want to be able to plot on the X axis the chromosomal position and on the Y axis the number of reads. To make it easier, I binned the data in windows of 100bp. That makes the data frame of 46,259 rows and 2 columns. One column is named "position" and has a number representing a chromosomal position (1,101,201,....) and the other column is named "values" and contains the number of reads found on that bin e.g.(210,511,315,....). I have been using ggplot for all my analysis and I would like to use it for this plot, if possible.
I am trying for the graph to look something like this:
but I haven't been able to plot it.
This is how my data looks like
I tried
ggplot(pLog,aes(position))+
geom_histogram(binwidth=50)
ggsave(file.jpg)
And this is how it looks like :(
Many thanks!
You cannot use geom_histogram(), try geom_line:
pLog=data.frame(position=seq(1,100000,by=100),
value=rnbinom(10000,mu=100,size=20))
ggplot(pLog,aes(x=position,y=value))+geom_line(alpha=0.7,col="steelblue")
Most likely you need to play around to get the visualization you need
I'm having real trouble creating a simple bar plot. I am looking at the parasite load in mackerel, and want to create a bar plot that shows the difference in mean parasite load in male and female fish (with error bars).
At the moment, my data includes a column titled as SEX, under which fish are classed as M or F. Another column shows the number of parasites in each individual.
Any help is appreciated
You do not provide any data so we cannot respond precisely, but I will illustrate with other data and then say what I think you need. Using the built-in iris data:
Tab = aggregate(iris$Sepal.Length, list(iris$Species), mean)
barplot(Tab$x, names.arg=Tab$Group.1)
So you probably need something like:
Tab = aggregate(DAT$parasites, list(DAT$SEX), mean)
barplot(Tab$x, names.arg=Tab$Group.1)
I'm struggling with something I'm trying to do in R.
I have two datasets with the same (categorical) columns but different values. I want to compare the count of each combination of columns (e.g. male and married, female and single etc) visually.
This is easy enough to do with ggplot's geom_bar for each dataset, and I know I can put the counts for each dataset next to each other by binding them and setting position = "dodge".
My question is whether there's an easy to way to plot the difference between the two counts for each pair of variables. And whether there's a way of changing the default 'count' method in geom_count (ironic I know) to other things (like proportion or, maybe in this case, a predefined set of values for the difference).
Thanks
I have some items that have different eligibility criteria - specifically in this example two variables each with a min and max the values are allowed to take. I would like to see the coverage of the products by plotting rectangles for each product on a chart that shows the area between the mins and maxs.
How would you go about
converting the records most elegantly to that required by geom_polygon() and
ensuring the shapes produced appear as rectangles
Example
library(data.table)
library(ggplot2)
df<-data.table(Product=letters[1:10], minX=1:10, maxX=5:14, minY= 10:1, maxY=14:5)
df.t<-data.table(rbind( df[,list(Product,X=minX,Y=minY)],
df[,list(Product,X=minX,Y=maxY)],
df[,list(Product,X=maxX,Y=minY)],
df[,list(Product,X=maxX,Y=maxY)]))[
order(Product,X,Y)]
ggplot(df.t,aes(x=X,y=Y,group=Product,fill=Product))+geom_polygon()
NB In this reduced example there are only two criteria, however I have a range of criteria columns and would not want to repeat the exercise above for different combinations.
Use your original data frame df and then geom_rect() as you already have minimal and maximal values for the x and y.
ggplot(df,aes(xmin=minX,xmax=maxX,ymin=minY,ymax=maxY,fill=Product))+geom_rect()