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R part iii
R part iii
R part iii
R part iii
R part iii
R part iii
R part iii
R part iii
R part iii
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R part iii

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Praxis Weekend Business Analytics

Praxis Weekend Business Analytics

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  • 1. Data available in R > data() > data("AirPassengers") > head(AirPassengers) [1] 112 118 132 129 121 135 > tail(AirPassengers) [1] 622 606 508 461 390 432 > str(AirPassengers) Time-Series [1:144] from 1949 to 1961: 112 118 132 129 121 135 148 148 136 119 ... > class(AirPassengers) [1] "ts" > help(ts) • The command data() loads data-sets available in R • head() and tail() command displays first few or last few values • str() shows the structure of an R object • class() shows the class of an R object • What does “ts” stand for?
  • 2. Try runif() and plot() commands …. runif(10) [1] 0.14350413 0.54293576 0.62881627 0.30278850 0.28030129 0.03784996 0.49483957 [8] 0.23571517 0.40072956 0.20327478 > plot(runif(10)) The runif() command generates U(0,1)10 random numbers between 0 and 1. These numbers have been plotted by the plot() function.
  • 3. A dataset in R: iris > head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2 setosa 3 4.7 3.2 1.3 0.2 setosa 4 4.6 3.1 1.5 0.2 setosa 5 5.0 3.6 1.4 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa > tail(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width 145 6.7 3.3 5.7 2.5 146 6.7 3.0 5.2 2.3 147 6.3 2.5 5.0 1.9 148 6.5 3.0 5.2 2.0 149 6.2 3.4 5.4 2.3 150 5.9 3.0 5.1 1.8 Species virginica virginica virginica virginica virginica virginica The iris dataset contains measurement of 150 flowers, 50 each from 3 species : iris setosa, versicolor and virginica.
  • 4. Data frame in R: iris > str(iris) 'data.frame': 150 obs. of 5 variables: $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ... $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ... $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ... $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ... $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ... > class(iris) [1] "data.frame" • As you see, iris is not a simple vector but a composite “data frame” object made up of several component vectors as you can see in the output of class(iris) • You can think of a data frame as a matrix-like object - each row for each observational unit (here, a flower) - each column for each measurement made on the unit • But the str() function gives you more concise description on iris.
  • 5. Use of $ operator: iris > iris$Sepal.Length [1] 5.1 4.9 4.7 4.6 5.7 5.1 [21] 5.4 5.1 4.6 5.1 4.4 5.1 [41] 5.0 4.5 4.4 5.0 6.6 5.2 [61] 5.0 5.9 6.0 6.1 6.0 5.7 [81] 5.5 5.5 5.8 6.0 5.1 5.7 [101] 6.3 5.8 7.1 6.3 7.7 6.0 [121] 6.9 5.6 7.7 6.3 6.0 6.9 [141] 6.7 6.9 5.8 6.8 5.0 5.4 4.6 5.0 4.4 4.9 5.4 4.8 4.8 4.3 5.8 5.7 5.4 5.1 4.8 5.0 5.0 5.2 5.2 4.7 4.8 5.4 5.2 5.5 4.9 5.0 5.5 4.9 5.1 4.8 5.1 4.6 5.3 5.0 7.0 6.4 6.9 5.5 6.5 5.7 6.3 4.9 5.6 6.7 5.6 5.8 6.2 5.6 5.9 6.1 6.3 6.1 6.4 6.6 6.8 6.7 5.4 6.0 6.7 6.3 5.6 5.5 5.5 6.1 5.8 5.0 5.6 5.7 5.7 6.2 6.5 7.6 4.9 7.3 6.7 7.2 6.5 6.4 6.8 5.7 5.8 6.4 6.5 7.7 6.7 7.2 6.2 6.1 6.4 7.2 7.4 7.9 6.4 6.3 6.1 7.7 6.3 6.4 6.7 6.7 6.3 6.5 6.2 5.9 Note that $-operator extracts individual components of a data frame. Try summary() and IQR() commands on iris$Sepal.Length and study the data
  • 6. summary() command: iris > summary(iris$Sepal.Length) Min. 1st Qu. Median Mean 3rd Qu. Max. 4.300 5.100 5.800 5.843 6.400 7.900 > summary(iris$Species) setosa versicolor virginica 50 50 50 > summary(iris) Sepal.Length Sepal.Width Petal.Length Min. :4.300 Min. :2.000 Min. :1.000 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 Median :5.800 Median :3.000 Median :4.350 Mean :5.843 Mean :3.057 Mean :3.758 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 Max. :7.900 Max. :4.400 Max. :6.900 Petal.Width Min. :0.100 1st Qu.:0.300 Median :1.300 Mean :1.199 3rd Qu.:1.800 Max. :2.500 Species setosa :50 versicolor:50 virginica :50 • Note the different output formats of using summary() • Species is summarized (by frequency distribution) as it is a categorical variable • The entire data frame iris is summarized by combining the summaries of its components
  • 7. class() command: iris > class(iris$Sepal.Length) [1] "numeric" > class(iris$Species) [1] "factor" > class(iris) [1] "data.frame" • Note that each R object has a class (“numeric”, “factor” etc.) • summary() is referred to as a generic function • When summary() is applied, R figures out the appropriate method and calls it
  • 8. More on summary() command > methods(summary) [1] summary.aov [4] summary.connection [7] summary.default [10] summary.glm [13] summary.loess* [16] summary.mlm [19] summary.PDF_Dictionary* [22] summary.POSIXlt [25] summary.princomp* [28] summary.stepfun [31] summary.tukeysmooth* summary.aovlist summary.data.frame summary.ecdf* summary.infl summary.manova summary.nls* summary.PDF_Stream* summary.ppr* summary.srcfile summary.stl* summary.aspell* summary.Date summary.factor summary.lm summary.matrix summary.packageStatus* summary.POSIXct summary.prcomp* summary.srcref summary.table Non-visible functions are asterisked • Objects of class “factor” are handled by summary.factor() • “data.frame”s are handled by summary.data.frame() • Numeric vectors are handled by summary.default()
  • 9. Try the following …. • • • • • • • • • attach() and detach() with iris xx <- 1:12 and then dim(xx) <- c(3,4) apply nrow(xx) and ncol(xx) dim(xx) <- c(2,2,3) yy <- matrix(1:12, nrows=3, byrow=TRUE rownames(yy) <- LETTERS[1:3] use colnames() zz <- cbind(A=1:4, B=5:8, C=9:12) rbind(zz,0)

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