Solutions - Bayesian Networks in R: With Applications in Systems Biology - page 124

(b) > summary(iris)
> dim(iris)
(c) > par(mfrow = c(1, 3))
> hist(iris[iris[, "Species"] == "setosa",
+ "Sepal.Length"],
+ xlab = "length", main = "Setosa Sepal Length")
> hist(iris[iris[, "Species"] == "versicolor",
+ "Sepal.Length"],

+ xlab = "length",
+ main = "Versicolor Sepal Length")
> hist(iris[iris[, "Species"] == "virginica",
+ "Sepal.Length"],

+ xlab = "length",
+ main = "Virginica Sepal Length")
(d) > boxplot(Sepal.Length ˜ Species, data = iris)

1.4 Consider again the iris data set from Exercise 1.3 .
(a) Write the data frame holding iris data frame into a space-separated
text file named “iris.txt”, and read it back into a second data frame called
iris2 .
(b) Check that iris and iris2 are identical.
(c) Repeat the previous two steps with a file compressed with bzip2 named
“iris.txt.bz2”.
(d) Save iris directly (e.g., without converting it to a text table) into a file
called “iris.rda”, and read it back.
(e) List all R objects in the global environment, and remove all of them apart
from iris .
(f) Exit the R saving the contents of the current session.

(a) > write.table(iris, file = "iris.txt",
+ row.names = FALSE)
> iris2 = read.table("iris.txt", header = TRUE)
(b) > identical(iris, iris2)
(c) > bzfd = bzfile("iris.txt.bz2", open = "w")
> write.table(iris, file = bzfd, row.names = FALSE)
> close(bzfd)
> bzfd = bzfile("iris.txt.bz2", open = "r")
> iris2 = read.table(bzfd, header = TRUE)
> close(bzfd)
> identical(iris, iris2)
(d) > save(iris, file = "iris.rda")
> load("iris.rda")
(e) > ls()
> l = ls()
> rm(list = l[l != "iris"])