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Here's some code for creating a chart with colored symbols available in R:
# Make an empty chart
plot(1, 1, xlim=c(1,5.5), ylim=c(0,7), type="n", ann=FALSE)
# Plot digits 0-4 with increasing size and color
text(1:5, rep(6,5), labels=c(0:4), cex=1:5, col=1:5)
# Plot symbols 0-4 with increasing size and color
points(1:5, rep(5,5), cex=1:5, col=2:6, pch=0:4)
text((1:5)+0.4, rep(5,5), cex=0.6, (0:4))
# Plot symbols 5-9 with labels
points(1:5, rep(4,5), cex=2, pch=(5:9), col=3:7)
text((1:5)+0.4, rep(4,5), cex=0.6, (5:9))
# Plot symbols 10-14 with labels
points(1:5, rep(3,5), cex=2, pch=(10:14), col=4:8)
text((1:5)+0.4, rep(3,5), cex=0.6, (10:14))
# Plot symbols 15-19 with labels
points(1:5, rep(2,5), cex=2, pch=(15:19), col=5:9)
text((1:5)+0.4, rep(2,5), cex=0.6, (15:19))
# Plot symbols 20-25 with labels
points((1:6)*0.8+0.2, rep(1,6), cex=2, pch=(20:25), col=5:10)
text((1:6)*0.8+0.5, rep(1,6), cex=0.6, (20:25))
Here's how to import data files from different places:
#specify the name and address of the remote file
datafilename <- "http://personality-project.org/r/datasets/maps.mixx.epi.bfi.data"
#datafilename <- "Desktop/epi.big5.txt" #read from local directory or
# datafilename <- file.choose() # use the OS to find the file
#in all cases
person.data <- read.table(datafilename,header=TRUE) #read the data file
#Alternatively, to read in a comma delimited file:
#person.data <- read.table(datafilename,header=TRUE,sep=",")
names(person.data) #list the names of the variables
Hey all, here are R codes I use for regression plots, residual plots, and log scatterplots (using the data "weights" from Matt's book "Practicing Statistics" in Chapter three.
> weight<-read.table(file.choose(), header=TRUE,sep="\t")
> head(weight)
Species body brain
1 African elephant 6654.000 5712.0
2 African giant pouched rat 1.000 6.6
3 Arctic Fox 3.385 44.5
4 Arctic ground squirrel 0.920 5.7
5 Asian elephant 2547.000 4603.0
6 Baboon 10.550 179.5
> attach(weight)
> plot(body,brain)
> weight.lm<-lm(body~brain)
> summary(weight.lm)
Call:
lm(formula = body ~ brain)
Residuals:
Min 1Q Median 3Q Max
-1552.25 -8.00 47.36 55.10 1553.42
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -56.85555 42.97805 -1.323 0.191
brain 0.90291 0.04453 20.278 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 323.5 on 60 degrees of freedom
Multiple R-squared: 0.8727, Adjusted R-squared: 0.8705
F-statistic: 411.2 on 1 and 60 DF, p-value: < 2.2e-16
> abline( -56.85555,.90291)
##How to find individual residual numbers
> residuals<-weight.lm$res
> residuals[1]
1
1553.417
##Now, these are the R codes I used to make a residual plot.
> pred.weight<-weight.lm$fitted
> residuals<-weight.lm$res
> plot(pred.weight,residuals)
> abline(0,0)
##Log of Body and Brain weight
>plot(log10(body)~log10(brain))
body.log<-lm(log10(body)~log10(brain))
> summary(body.log)
Call:
lm(formula = log10(body) ~ log10(brain))
Residuals:
Min 1Q Median 3Q Max
-0.94050 -0.25955 0.04097 0.28572 0.90971
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.08968 0.07995 -13.63 <2e-16 ***
log10(brain) 1.22496 0.04638 26.41 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.3849 on 60 degrees of freedom
Multiple R-squared: 0.9208, Adjusted R-squared: 0.9195
F-statistic: 697.4 on 1 and 60 DF, p-value: < 2.2e-16
## Residual plot of the log of body and brain weight
> pred.weight<-body.log$fitted
> residuals<-body.log$res
> plot(pred.weight,residuals)
>abline(0,0)
