Chapter 2 R for the impatient
2.1 Useful commands
| Command | Description |
|---|---|
| help() | Obtain documentation for a given R command |
| example() | View some examples on the use of a command |
| c(), scan() | Enter data manually to a vector in R |
| seq() | Make arithmetic progression vector |
| rep() | Make vector of repeated values |
| data() | Load (often into a data.frame) built-in dataset |
| View() | View dataset in a spreadsheet-type format |
| str() | Display internal structure of an R object |
| read.csv(), read.table() | Load into a data.frame an existing data file |
| library(), require() | Make available an R add-on package |
| dim() | See dimensions (# of rows/cols) of data.frame |
| length() | Give length of a vector |
| ls() | Lists memory contents |
| rm() | Removes an item from memory |
| names() | Lists names of variables in a data.frame |
| hist() | Command for producing a histogram |
| histogram() | Lattice command for producing a histogram |
| stem() | Make a stem plot |
| table() | List all values of a variable with frequencies |
| xtabs() | Cross-tabulation tables using formulas |
| mosaicplot() | Make a mosaic plot |
| cut() | Groups values of a variable into larger bins |
| mean(), median() | Identify “center” of distribution |
| by() | apply function to a column split by factors |
| summary() | Display 5-number summary and mean |
| var(), sd() | Find variance, sd of values in vector |
| sum() | Add up all values in a vector |
| quantile() | Find the position of a quantile in a dataset |
| barplot() | Produces a bar graph |
| barchart() | Lattice command for producing bar graphs |
| boxplot() | Produces a boxplot |
| bwplot() | Lattice command for producing boxplots |
| plot() | Produces a scatterplot |
| xyplot() | Lattice command for producing a scatterplot |
| lm() | Determine the least-squares regression line |
| anova() | Analysis of variance (can use on results of lm() ) |
| predict() | Obtain predicted values from linear model |
| nls() | estimate parameters of a nonlinear model |
| residuals() | gives (observed - predicted) for a model fit to data |
| sample() | take a sample from a vector of data |
| replicate() | repeat some process a set number of times |
| cumsum() | produce running total of values for input vector |
| ecdf() | builds empirical cumulative distribution function |
| dbinom(), etc. | tools for binomial distributions |
| dpois(), etc. | tools for Poisson distributions |
| pnorm(), etc. | tools for normal distribution |
| sqt(), etc. | tools for student t distributions |
| pchisq(), etc. | tools for chi-square distributions |
| binom.test() | hypothesis test and confidence interval for 1 proportion |
| prop.test() | inference for 1 proportion using normal approx. |
| chisq.test() | carries out a chi-square test |
| fisher.test() | Fisher test for contingency table |
| t.test() | student t test for inference on population mean |
| qqnorm(), qqline() | tools for checking normality |
| addmargins() | adds marginal sums to an existing table |
| prop.table() | compute proportions from a contingency table |
| par() | query and edit graphical settings |
| power.t.test() | power calculations for 1- and 2-samplet |
| anova() | compute analysis of variance table for fitted model |
2.2 Examples
2.3 Arithmetic
# addition
8 + 3
# divide
27 / 5
# calculate the absolute value of cosine of -pi ~3.141592...
cos(-pi)
# absolute value of -2 to the power 3
abs(-2^3)
# Square root of 4068289
sqrt(4068289)
# assigning the result of 8 + 3 to the variable x
x = 8 + 3
# assign number 3 to variable y
y = 3
# adding x and y
x+y
# multiplying x and y
x*y
# assigning the result of x multiplied by y to variable z
z = x * y
# we can wee what's inside y by looking in to values pane in the environment
# or just by calling z
z
# variables are case-sensitive while we have defined the variable x, X (uppercase) is not defined
Z# list the objects in the environment e.g. data, variables, functions, etc.
ls()
# we can remove an object from the environment by rm() function
rm(y)
# now variable/object y is not defined i.e. deleted
ls() # make a variable called "ourdata" containing a vector of numbers
ourdata = c(-3,2,0,1.5,4,1,3,8)
#what is the length of our data?
length(ourdata)
# call the fifth element in ourdata
ourdata[5]
# calculate mean
mean(ourdata)
# calculate median
median(ourdata)
# calculate range
range(ourdata)
# calculate standard deviation
sd(ourdata)
# calculate variance
var(ourdata)
# using summary function to get basic stats
summary(ourdata)
# load the pscych library
install.packages("psych")
library(psych)
describe(ourdata)#loading the data with read.csv() command and assigning to mydata
mydata=read.csv("http://people.fas.harvard.edu/~mparzen/stat104/cars10.csv")
# getting the name of columns
names(mydata)
# getting the dimension of the dataset
dim(mydata)
## Acessing data or a variable
# Using the \$ to choose a column dataframename$colname
# this will bring back a list of values from "mydata" dataframe calling the "mpg" column
# the number in the brackets indicate index of values just to make life easier.
mydata$mpg
# in case you wanted to confirm the list index numbers :)
View(mydata)
# median of MPGs
median(mydata$mpg)
# Using attach () and detach()
attach(mydata)
# median of MPGs just by calling the "attached" dataframe
median(mpg)
# standard deviation of MPGs
sd(mpg)
# it is very important to detach() especially while working with multiple dataframes.
detach(mydata)
# now R might complain that object mpg is not found
sd(mpg)
# we still can ue the $ to access the mpg column
sd(mydata$mpg)
#calling the describe() function on combination of MPG, Price, and Length columns
describe(mydata[,c("mpg","price", "length")])# Assign values to x and y
x <- c(1,2,3)
y <- c(1,4,9)
# making a simple plot
plot(x,y)
# I encourage you to change the values of pch (Appendix 3 has a list of shapes we can use), cex, main, sub, xlab, and ylab:
plot(x,y, xlab = "x", ylab="y", pch = 19, cex=0.8, col = "blue", xlim = c(0,4), ylim=c(0,10), main="Our First Plot!",sub = "STAT 100")
# attaching mydata to the environment
attach(mydata)
#plot price based on weight
plot(weight,price,main="Price of a car versus Weight", pch = 19, cex= 0.8,xlab= "Weight (lbs)", ylab="Price ($)")
# Making Histograms
hist(price, col = "lightgreen") # Transforming the price data
invprice=1/price
logprice=log(price) #simple log by default is the natural log
sqrtprice=sqrt(price)
# ask for a 2 x 2 matrix pattern for plots
par(mfrow=c(2,2))
# make histogram of price and 3 transformed price data points
hist(price,main="Price")
hist(invprice,main="1/Price")
hist(sqrtprice,main="sqrt(Price)")
hist(logprice,main="log(Price)")
# escape from par(mfrow=c(2,2))
dev.off() # making dot plots ~ stacked scatter plot simillar to Stem and Leaf Plots
# loading BHH2 Package, where dotPlot() function resides.
install.packages("BHH2")
library(BHH2)
# Ask for a 2 x 2 matrix pattern in Parameters
par(mfrow=c(2,2))
# making dot plots of price and the 3 transformed price data points
dotPlot(price,main="Price")
dotPlot(invprice,main="1/Price")
dotPlot(sqrtprice,main="sqrt(Price)")
dotPlot(logprice,main="log(Price)")
# escape from par(mfrow=c(2,2))
dev.off()