I have the following data frame:
A B C D Xax
0.451 0.333 0.034 0.173 0.22
0.491 0.270 0.033 0.207 0.34
0.389 0.249 0.084 0.271 0.54
0.425 0.819 0.077 0.281 0.34
0.457 0.429 0.053 0.386 0.53
0.436 0.524 0.049 0.249 0.12
0.423 0.270 0.093 0.279 0.61
0.463 0.315 0.019 0.204 0.23
I need to plot all these columns in the same plot(on the x-axis I want the variable Xax and the y-axis the variables A,B,C and D) and also to draw the regression line for each variable alone.
I tried this:
pl<-ggplot(data=df) + geom_point(aes(x=Xax,y=A,size=10)) +
geom_point(aes(x=Xax,y=B,size=10)) +
geom_point(aes(x=Xax,y=C,size=10)) +
geom_point(aes(x=Xax,y=D,size=10)) +
geom_smooth(method = "lm", se=FALSE, color="black")
But it's only plotting the first one(Xax and A)
The easiest is to convert your data to a "tall" format.
s <-
"A B C G Xax
0.451 0.333 0.034 0.173 0.22
0.491 0.270 0.033 0.207 0.34
0.389 0.249 0.084 0.271 0.54
0.425 0.819 0.077 0.281 0.34
0.457 0.429 0.053 0.386 0.53
0.436 0.524 0.049 0.249 0.12
0.423 0.270 0.093 0.279 0.61
0.463 0.315 0.019 0.204 0.23
"
d <- read.delim(textConnection(s), sep="")
library(ggplot2)
library(reshape2)
d <- melt(d, id.vars="Xax")
# Everything on the same plot
ggplot(d, aes(Xax,value, col=variable)) +
geom_point() +
stat_smooth()
# Separate plots
ggplot(d, aes(Xax,value)) +
geom_point() +
stat_smooth() +
facet_wrap(~variable)
To select columns to plot, I added 2 lines to Vincent Zoonekynd's answer:
#convert to tall/long format(from wide format)
col_plot = c("A","B")
dlong <- melt(d[,c("Xax", col_plot)], id.vars="Xax")
#"value" and "variable" are default output column names of melt()
ggplot(dlong, aes(Xax,value, col=variable)) +
geom_point() +
geom_smooth()
Google "tidy data" to know more about tall(or long)/wide format.
Using tidyverse
df %>% tidyr::gather("id", "value", 1:4) %>%
ggplot(., aes(Xax, value))+
geom_point()+
geom_smooth(method = "lm", se=FALSE, color="black")+
facet_wrap(~id)
DATA
df<- read.table(text =c("
A B C G Xax
0.451 0.333 0.034 0.173 0.22
0.491 0.270 0.033 0.207 0.34
0.389 0.249 0.084 0.271 0.54
0.425 0.819 0.077 0.281 0.34
0.457 0.429 0.053 0.386 0.53
0.436 0.524 0.049 0.249 0.12
0.423 0.270 0.093 0.279 0.61
0.463 0.315 0.019 0.204 0.23"), header = T)
A very simple solution:
df <- read.csv("df.csv",sep=",",head=T)
x <- cbind(df$Xax,df$Xax,df$Xax,df$Xax)
y <- cbind(df$A,df$B,df$C,df$D)
matplot(x,y,type="p")
please note it just plots the data and it does not plot any regression line.
Source: Stackoverflow.com