R语言入门

注：本文转载自优达学城数据分析进阶课程，这是R语言代码，可以在本地运行。我的优达学城优惠码是C7B2877A

另外这个链接有关于R语言的一些简介：R语言简介

# The goal of this file is to introduce you to the# R programming language. Let's start with by unraveling a# little mystery!# 1. Run the code below to create the vector 'udacious'.# You need to highlight all of the lines of the code and then# run it. You should see "udacious" appear in the workspace.udacious <- c("Chris Saden", "Lauren Castellano",              "Sarah Spikes","Dean Eckles",              "Andy Brown", "Moira Burke",              "Kunal Chawla")# You should see something like "chr[1:7]" in the 'Environment'# or 'Workspace' tab. This is because you created a 'vector' with# 7 names that have a 'type' of character. The arrow-like# '<-' symbol is the assignment operator in R, similar to the# equal sign '=' in other programming languages. The c() is a# generic function that combines arguments, in this case the# names of people, to form a vector.# A 'vector' is one of the data types in R. Vectors must contain# the same type of data, that is the entries must all be of the# same type: character (most programmers call these strings),# logical (TRUE or FALSE), or numeric.# Print out the vector udacious by running this next line of code.udacious# Notice how there are numbers next to the output.# Each number corresponds to the index of the entry in the vector.# Chris Saden is the first entry so [1]# Dean Eckles is the fourth entry so [4]# Kunal Chawla is the seventh entry so [7]# Depending on the size of you window you may see different numbers# in the output.# ANOTHER HELPFUL TIP: You can add values to a vector.# Run each line of code one at a time below to see what is happening.numbers <- c(1:10)numbersnumbers <- c(numbers, 11:20)numbers# 2. Replace YOUR_NAME with your actual name in the vector# 'udacious' and run the code. Be sure to use quotes around it.udacious <- c("Chris Saden", "Lauren Castellano",              "Sarah Spikes","Dean Eckles",              "Andy Brown", "Moira Burke",              "Kunal Chawla", "YOUR_NAME")# Notice how R updates 'udacious' in the workspace.# It should now say something like 'chr[1:8]'.# 3. Run the following two lines of code. You can highlight both lines# of code and run them.mystery = nchar(udacious)mystery# You just created a new vector called mystery. What do you# think is in this vector? (scroll down for the answer)# Mystery is a vector that contains the number of characters# for each of the names in udacious, including your name.# 4. Run this next line of code.mystery == 11# Here we get a logical (or boolean) vector that tells us# which locations or indices in the vector contain a name# that has exactly 11 characters.# 5. Let's use this boolean vector, mystery, to subset our# udacious vector. What do you think the result will be when# running the line of code below?# Think about the output before you run this next line of code.# Notice how there are brackets in the code. Brackets are often# used in R for subsetting.udacious[mystery == 11]# Scroll down for the answer# It's your Udacious Instructors for the course!# (and you may be in the output if you're lucky enough# to have 11 characters in YOUR_NAME) Either way, we# think you're pretty udacious for taking this course.# 6. Alright, all mystery aside...let's dive into some data!# The R installation has a few datasets already built into it# that you can play with. Right now, you'll load one of these,# which is named mtcars.# Run this next command to load the mtcars data.data(mtcars)# You should see mtcars appear in the 'Environment' tab with# <Promise> listed next to it. # The object (mtcars) appears as a 'Promise' object in the# workspace until we run some code that uses the object.# R has stored the mtcars data into a spreadsheet-like object# called a data frame. Run the next command to see what variables# are in the data set and to fully load the data set as an# object in R. You should see <Promise> disappear when you# run the next line of code.# Visit http://cran.r-project.org/doc/manuals/r-release/R-lang.html#Promise-objects# if you want the expert insight on Promise objects. You won't# need to the info on Promise objects to be successful in this course.names(mtcars)# names(mtcars) should output all the variable# names in the data set. You might notice that the car names# are not a variable in the data set. The car names have been saved# as row names. More on this later.# You should also see how many observations (obs.) are in the# the data frame and the number of variables on each observation.# 7. To get more information on the data set and the variables# run the this next line of code.?mtcars# You can type a '?' before any command or a data set to learn# more about it. The details and documentation will appear in# the 'Help' tab.# 8. To print out the data, run this next line as code.mtcars# Scroll up and down in the console to check out the data.# This is the entire data frame printed out.# 9. Run these next two functions, one at a time,# and see if you can figure out what they do.str(mtcars)dim(mtcars)# Scroll down for the answer.# The first command, str(mtcars), gives us the structure of the# data frame. It lists the variable names, the type of each variable# (all of these variables are numerics) and some values for each# variable.# The second command, dim(mtcars), should output '[1] 32 11'# to the console. The [1] indicates that 32 is the first value# in the output.# R uses 1 to start indexing (AND NOT ZERO BASED INDEXING as is true# of many other programming languages.)# 10. Read the documentation for row.names if you're want to know more.?row.names# Run this code to see the current row names in the data frame.row.names(mtcars)# Run this code to change the row names of the cars to numbers.row.names(mtcars) <- c(1:32)# Now print out the data frame by running the code below.mtcars# It's tedious to relabel our data frame with the right car names# so let's reload the data set and print out the first ten rows.data(mtcars)head(mtcars, 10)# The head() function prints out the first six rows of a data frame# by default. Run the code below to see.head(mtcars)# I think you'll know what this does.tail(mtcars, 3)# 11. We've run nine commands so far:#      c, nchar, data, str, dim, names, row.names, head, and tail.# All of these commands took some inputs or arguments.# To determine if a command takes more arguments or to learn# about any default settings, you can look up the documentation# using '?' before the command, much like you did to learn about# the mtcars data set and the row.names# 12. Let's examine our car data more closely. We can access an# an individual variable (or column) from the data frame using# the '$' sign. Run the code below to print out the variable# miles per gallon. This is the mpg column in the data frame.mtcars$mpg# Print out any two other variables to the console.# This is a vector containing the mpg (miles per gallon) of# the 32 cars. Run this next line of code to get the average mpg for# for all the cars. What is it?# Enter this number for the quiz on the Udacity website.# https://www.udacity.com/course/viewer#!/c-ud651/l-729069797/e-804129314/m-830829287mean(mtcars$mpg)