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#This is a literate programming version of the full tutorial.
#The full tutorial contains a little more information (e.g., a note on how to install R).
# R Basics
## Vectors
#Vectors are a core data structure in R, and are created with `c()`. Elements in a vector must be of the same type.
numbers = c(23, 13, 5, 7, 31)
names = c("edwin", "alice", "bob")
#Elements are indexed starting at 1, and are accessed with `[]` notation.
numbers[1] # 23
names[1] # edwin
## Data frames
#[Data frames]( are like matrices, but with named columns of different types (similar to [database tables](
books = data.frame(
title = c("harry potter", "war and peace", "lord of the rings"),
author = c("rowling", "tolstoy", "tolkien"),
num_pages = c("350", "875", "500")
#You can access columns of a data frame with `$`.
books$title # c("harry potter", "war and peace", "lord of the rings")
books$author[1] # "rowling"
#You can also create new columns with `$`.
books$num_bought_today = c(10, 5, 8)
books$num_bought_yesterday = c(18, 13, 20)
books$total_num_bought = books$num_bought_today + books$num_bought_yesterday
## read.table
#Suppose you want to import a TSV file into R as a data frame.
### tsv file without header
#For example, consider the [`data/students.tsv`]( file (with columns describing each student's age, test score, and name).
#We can import this file into R using [`read.table()`](
students = read.table(
header = F,
sep = "\t",
col.names = c("age", "score", "name")
#* `header = F` means that the file does not contain a header (`F` is shorthand for `FALSE`)
#* `sep = "\t"` means that the file is tab-delimited
#* `col.names = c("age", "score", "name")` tells R the column names
#We can now access the different columns in the data frame with `students$age`, `students$score`, and `students$name`.
### csv file with header
#For an example of a file in a different format, look at the [`data/studentsWithHeader.tsv`]( file.
#Here we have the same data, but now the file is comma-delimited and contains a header. We can import this file with
students = read.table("data/students.tsv", header = T, sep = ",")
#By setting `header = T`, we tell R that the first line of the file contains column names, so we can immediately access `students$age` and so on. (Note: there is also a `read.csv` function that uses `sep = ","` by default.)
## help
#There are many more options that `read.table` can take. For a full list of these, just type `help(read.table)` (or equivalently, `?read.table`) at the prompt to access documentation.
#This works for other functions as well.
# ggplot2
#With these R basics in place, let's dive into the ggplot2 package.
## Installation
#One of R's greatest strengths is its excellent set of [packages]( To install a package, you can use the `install.packages()` function.
#To load a package into your current R session, use `library()`.
## Scatterplots with qplot()
#Let's look at how to create a scatterplot in ggplot2. We'll use the `iris` data frame that's automatically loaded into R.
#What does the data frame contain? We can use the `head` function to look at the first few rows.
head(iris) # by default, head displays the first 6 rows
head(iris, n = 10) # we can also explicitly set the number of rows to display
#(The data frame actually contains three types of species: setosa, versicolor, and virginica.)
#Let's plot `Sepal.Length` against `Petal.Length` using ggplot2's `qplot()` function.
qplot(Sepal.Length, Petal.Length, data = iris)
# Plot Sepal.Length vs. Petal.Length, using data from the `iris` data frame.
#To see where each species is located in this graph, we can color each point by adding a `color = Species` argument.
qplot(Sepal.Length, Petal.Length, data = iris, color = Species) # dude!
#Similarly, we can let the size of each point denote sepal width, by adding a `size = Sepal.Width` argument.
qplot(Sepal.Length, Petal.Length, data = iris, color = Species, size = Petal.Width)
# We see that Iris setosa flowers have the narrowest petals.
qplot(Sepal.Length, Petal.Length, data = iris, color = Species, size = Petal.Width, alpha = I(0.7))
# By setting the alpha of each point to 0.7, we reduce the effects of overplotting.
#Finally, let's fix the axis labels and add a title to the plot.
qplot(Sepal.Length, Petal.Length, data = iris, color = Species,
xlab = "Sepal Length", ylab = "Petal Length",
main = "Sepal vs. Petal Length in Fisher's Iris data")
## Other common geoms
#In the scatterplot examples above, we implicitly used a *point* **geom**, the default when you supply two arguments to `qplot()`.
# These two invocations are equivalent.
qplot(Sepal.Length, Petal.Length, data = iris, geom = "point")
qplot(Sepal.Length, Petal.Length, data = iris)
#But we can easily use other types of geoms to create other kinds of plots.
### Barcharts: geom = "bar"
movies = data.frame(
director = c("spielberg", "spielberg", "spielberg", "jackson", "jackson"),
movie = c("jaws", "avatar", "schindler's list", "lotr", "king kong"),
minutes = c(124, 163, 195, 600, 187)
# Plot the number of movies each director has.
qplot(director, data = movies, geom = "bar", ylab = "# movies")
# By default, the height of each bar is simply a count.
# But we can also supply a different weight.
# Here the height of each bar is the total running time of the director's movies.
qplot(director, weight = minutes, data = movies, geom = "bar", ylab = "total length (min.)")
### Line charts: geom = "line"
qplot(Sepal.Length, Petal.Length, data = iris, geom = "line", color = Species)
# Using a line geom doesn't really make sense here, but hey.
# `Orange` is another built-in data frame that describes the growth of orange trees.
qplot(age, circumference, data = Orange, geom = "line",
colour = Tree,
main = "How does orange tree circumference vary with age?")
# We can also plot both points and lines.
qplot(age, circumference, data = Orange, geom = c("point", "line"), colour = Tree)
# Next Steps
#In this post, I skipped over a lot of aspects of R and ggplot2.
#For example,
#* There are many geoms (and other functionalities) in ggplot2 that I didn't cover, e.g., [boxplots]( and [histograms](
#* I didn't talk about ggplot2's layering system, or the [grammar of graphics]( it's based on.
#So I'll end with some additional resources on R and ggplot2.
#* I don't use it myself, but [RStudio]( is a popular IDE for R.
#* The [official ggplot2 documentation]( is great and has lots of examples. There's also an excellent [book](
#* [plyr]( is another fantastic R package that's also by Hadley Wickham (the author of ggplot2).
#* The [official R introduction]( is okay, but definitely not great. I haven't found any R tutorials I really like, but I've heard good things about [The Art of R Programming](
#Edwin Chen :: [@edchedch](!/edchedch) :: [](
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