This is an exercising memo for R introduction in ISLR2
Setup
R introduction memo
- dev.off(): indidates to R that we are done creating the plot.
- image(): color-coded contour plot
- persp(): three-dimensional plot
- index of matrix: A[ row index, column index].
- no index for the columns or rows indicates all columns or rows (省略すると全項目指定)。
- negative sing - in th index tells R to keep all rows or colulmns.
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> A <- matrix(1:16, 4, 4)
> A
[,1] [,2] [,3] [,4]
[1,] 1 5 9 13
[2,] 2 6 10 14
[3,] 3 7 11 15
[4,] 4 8 12 16
> A[c(1, 3), c(2,4)]
[,1] [,2]
[1,] 5 13
[2,] 7 15
> c(1,3)
[1] 1 3
> A[3,]
[1] 3 7 11 15
> A[,3]
[1] 9 10 11 12
> A[-c(1,3),]
[,1] [,2] [,3] [,4]
[1,] 2 6 10 14
[2,] 4 8 12 16
> A[-c(1,3),-c(1,3,4)]
[1] 6 8
> dim(A)
[1] 4 4
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If cannot find variables from object, add object name before $ as delimiter.
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> names(Auto)
[1] "mpg" "cylinders" "displacement" "horsepower" "weight"
[6] "acceleration" "year" "origin" "name"
> plot(Auto$cylinders, Auto$mpg)
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Or call attach()
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> attach(Auto)
> plot(cylinders, mpg)
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as.factor() function converts quantitative values into qualitative values.
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cylinders
[1] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 4 6 6 6 4 4 4 4 4 4 6 8 8 8 8 4 4 4 4 6 6 6 6
[38] 6 8 8 8 8 8 8 8 6 4 6 6 4 4 4 4 4 4 4 4 4 4 4 4 4 8 8 8 8 8 8 8 8 8 3 8 8
[75] 8 8 4 4 4 4 4 4 4 4 4 8 8 8 8 8 8 8 8 8 8 8 8 6 6 6 6 6 4 8 8 8 8 6 4 4 4
[112] 3 4 6 4 8 8 4 4 4 4 8 4 6 8 6 6 6 6 4 4 4 4 6 6 6 8 8 8 8 8 4 4 4 4 4 4 4
[149] 4 4 4 4 6 6 6 6 8 8 8 8 6 6 6 6 6 8 8 4 4 6 4 4 4 4 6 4 6 4 4 4 4 4 4 4 4
[186] 4 4 8 8 8 8 6 6 6 6 4 4 4 4 6 6 6 6 4 4 4 4 4 8 4 6 6 8 8 8 8 4 4 4 4 4 8
[223] 8 8 8 6 6 6 6 8 8 8 8 4 4 4 4 4 4 4 4 6 4 3 4 4 4 4 4 8 8 8 6 6 6 4 6 6 6
[260] 6 6 6 8 6 8 8 4 4 4 4 4 4 4 4 5 6 4 6 4 4 6 6 4 6 6 8 8 8 8 8 8 8 8 4 4 4
[297] 4 5 8 4 8 4 4 4 4 4 6 6 4 4 4 4 4 4 4 4 6 4 4 4 4 4 4 4 4 4 4 5 4 4 4 4 4
[334] 6 3 4 4 4 4 4 4 6 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 6 6 6 6 8 6 6 4 4 4
[371] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 6 6 4 6 4 4 4 4 4 4 4 4
> cylinders <- as.factor(cylinders)
> cylinders
[1] 8 8 8 8 8 8 8 8 8 8 8 8 8 8 4 6 6 6 4 4 4 4 4 4 6 8 8 8 8 4 4 4 4 6 6 6 6
[38] 6 8 8 8 8 8 8 8 6 4 6 6 4 4 4 4 4 4 4 4 4 4 4 4 4 8 8 8 8 8 8 8 8 8 3 8 8
[75] 8 8 4 4 4 4 4 4 4 4 4 8 8 8 8 8 8 8 8 8 8 8 8 6 6 6 6 6 4 8 8 8 8 6 4 4 4
[112] 3 4 6 4 8 8 4 4 4 4 8 4 6 8 6 6 6 6 4 4 4 4 6 6 6 8 8 8 8 8 4 4 4 4 4 4 4
[149] 4 4 4 4 6 6 6 6 8 8 8 8 6 6 6 6 6 8 8 4 4 6 4 4 4 4 6 4 6 4 4 4 4 4 4 4 4
[186] 4 4 8 8 8 8 6 6 6 6 4 4 4 4 6 6 6 6 4 4 4 4 4 8 4 6 6 8 8 8 8 4 4 4 4 4 8
[223] 8 8 8 6 6 6 6 8 8 8 8 4 4 4 4 4 4 4 4 6 4 3 4 4 4 4 4 8 8 8 6 6 6 4 6 6 6
[260] 6 6 6 8 6 8 8 4 4 4 4 4 4 4 4 5 6 4 6 4 4 6 6 4 6 6 8 8 8 8 8 8 8 8 4 4 4
[297] 4 5 8 4 8 4 4 4 4 4 6 6 4 4 4 4 4 4 4 4 6 4 4 4 4 4 4 4 4 4 4 5 4 4 4 4 4
[334] 6 3 4 4 4 4 4 4 6 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 6 6 6 6 8 6 6 4 4 4
[371] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 6 6 4 6 4 4 4 4 4 4 4 4
Levels: 3 4 5 6 8
>
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If the variable plotted on the x-axis is qualitative, then boxplots will automatically generated by the plot() function.
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> jpeg("~/boxplot.jpeg")
> plot(cylinders, mpg, col = "red", varwidth = T, horizontal = T, xlab = "cylinders", ylab = "mpg")
> dev.off()
quartz
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pairs() create a scatterplot matrix.
identify() provides useful method on interactive mode on plot.
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> plot(horsepower, mpg)
> identify(horsepower, mpg, name)
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Then click the points, then hit Escape will cause R to print the values.
summary(Auto) provides metrics like median, 1st/3rd Qu.
For finishing,
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> savehistory()
> q()
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we can load history by loadhistory()
