R Labs from ISL Book(Trevor&Hastie)
Priyanka Gagneja
January 20, 2018
Chapter 2 - Statistical Learning
Lets get the basics going. For example:
c() - creates a vector. In a way it concatenates all the inputs in one variable.
- We can also save things using = rather than <-
- ?funcname will always cause R to open a new help file window with additional information about the function funcname.
x <- c(1,3,2,5)
x## [1] 1 3 2 5x = c(1,6,2)
y = c(1,4,3)
# How to open a help page for a function
#?c()length() - checks the length of a vector
length (x)## [1] 3length (y)## [1] 3x+y## [1] 2 10 5- ls() - allows us to look at a list of all of the objects, such as data and functions, that we have saved so far.
- rm() - can be used to delete any that we don’t want.
ls()## [1] "x" "y"rm(x,y)
ls()## character(0)# Removing all at once
#rm(list=ls())Creating matrices: matrix() - creates a matrix of numbers. By default R creates matrices by successively filling in columns. byrow=TRUE option can be used to populate the matrix in order of the rows.
x=matrix(data=c(1,2,3,4) , nrow=2, ncol =2)
x## [,1] [,2]
## [1,] 1 3
## [2,] 2 4# Same output as above without specific option value but then it must be in the same order as in the help file
x=matrix (c(1,2,3,4) ,2,2)
# Above resulted in default by column ordering of values
matrix (c(1,2,3,4) ,2,2,byrow =TRUE)## [,1] [,2]
## [1,] 1 2
## [2,] 3 4Some more examples of functions:
* rnorm() function generates a vector of random normal variables, with first argument n the sample size.
Sometimes we want our code to reproduce the exact same set of random numbers; we can use the set.seed() function to do this. The set.seed() function takes an (arbitrary) integer argument.
sqrt(), cor(), mean(), var() and sd() are some simple other simple functions to calculate square root, pearson correlation cefficient, mean/ averagem variance and standard deviation respectively.
sqrt(x)## [,1] [,2]
## [1,] 1.000000 1.732051
## [2,] 1.414214 2.000000x^2## [,1] [,2]
## [1,] 1 9
## [2,] 4 16x=rnorm (50)
y=x+rnorm (50, mean=50, sd=.1)
cor(x,y)## [1] 0.991476set.seed (1303)
rnorm (50)## [1] -1.1439763145 1.3421293656 2.1853904757 0.5363925179 0.0631929665
## [6] 0.5022344825 -0.0004167247 0.5658198405 -0.5725226890 -1.1102250073
## [11] -0.0486871234 -0.6956562176 0.8289174803 0.2066528551 -0.2356745091
## [16] -0.5563104914 -0.3647543571 0.8623550343 -0.6307715354 0.3136021252
## [21] -0.9314953177 0.8238676185 0.5233707021 0.7069214120 0.4202043256
## [26] -0.2690521547 -1.5103172999 -0.6902124766 -0.1434719524 -1.0135274099
## [31] 1.5732737361 0.0127465055 0.8726470499 0.4220661905 -0.0188157917
## [36] 2.6157489689 -0.6931401748 -0.2663217810 -0.7206364412 1.3677342065
## [41] 0.2640073322 0.6321868074 -1.3306509858 0.0268888182 1.0406363208
## [46] 1.3120237985 -0.0300020767 -0.2500257125 0.0234144857 1.6598706557set.seed (3)
y=rnorm (100)
mean(y)## [1] 0.01103557var(y)## [1] 0.7328675sqrt(var(y))## [1] 0.8560768sd(y)## [1] 0.8560768- seq() - create a sequence of numbers.
- seq(a,b) - makes a vector of integers between a and b.
- seq(0,1,length=10) makes a sequence of 10 numbers that are equally spaced between 0 and 1.
# Typing 3:11 is a shorthand for seq(3,11) for integer arguments.
x=seq (1 ,10)
x## [1] 1 2 3 4 5 6 7 8 9 10x=1:10
x## [1] 1 2 3 4 5 6 7 8 9 10x=seq(-pi ,pi ,length =50)
x## [1] -3.14159265 -3.01336438 -2.88513611 -2.75690784 -2.62867957
## [6] -2.50045130 -2.37222302 -2.24399475 -2.11576648 -1.98753821
## [11] -1.85930994 -1.73108167 -1.60285339 -1.47462512 -1.34639685
## [16] -1.21816858 -1.08994031 -0.96171204 -0.83348377 -0.70525549
## [21] -0.57702722 -0.44879895 -0.32057068 -0.19234241 -0.06411414
## [26] 0.06411414 0.19234241 0.32057068 0.44879895 0.57702722
## [31] 0.70525549 0.83348377 0.96171204 1.08994031 1.21816858
## [36] 1.34639685 1.47462512 1.60285339 1.73108167 1.85930994
## [41] 1.98753821 2.11576648 2.24399475 2.37222302 2.50045130
## [46] 2.62867957 2.75690784 2.88513611 3.01336438 3.14159265Graphics
- plot() - primary way to plot data in R.E.g. plot(x,y) produces a scatterplot of the numbers in x versus the numbers in y.
- pdf() - to create a pdf file (alternates to manual copy paste).
- jpeg()- to create a jpeg file.
- dev.off() - indicates to R that we are done creating the plot.
- contour() - produces a contour plot used to represent a 3-Dimensional data.
- image() - works the same way as contour(), except that it produces a color-coded plot whose colors depend on the z value.
- persp() - alternate to contour()/image() it results in a 3-D plot as well.The arguments theta and phi control the angles at which the plot is viewed.
x=rnorm (100)
y=rnorm (100)
plot(x,y)
plot(x,y,xlab=" this is the x-axis",ylab=" this is the y-axis",
main = "Plot of X vs Y / Scatterplot ")
pdf (" Figure .pdf ")
plot(x,y,col =" green ")
dev.off ()## png
## 2jpeg ("Figure.jpeg")
x=seq(-pi ,pi ,length =50)
x## [1] -3.14159265 -3.01336438 -2.88513611 -2.75690784 -2.62867957
## [6] -2.50045130 -2.37222302 -2.24399475 -2.11576648 -1.98753821
## [11] -1.85930994 -1.73108167 -1.60285339 -1.47462512 -1.34639685
## [16] -1.21816858 -1.08994031 -0.96171204 -0.83348377 -0.70525549
## [21] -0.57702722 -0.44879895 -0.32057068 -0.19234241 -0.06411414
## [26] 0.06411414 0.19234241 0.32057068 0.44879895 0.57702722
## [31] 0.70525549 0.83348377 0.96171204 1.08994031 1.21816858
## [36] 1.34639685 1.47462512 1.60285339 1.73108167 1.85930994
## [41] 1.98753821 2.11576648 2.24399475 2.37222302 2.50045130
## [46] 2.62867957 2.75690784 2.88513611 3.01336438 3.14159265y=x
f=outer(x,y,function (x,y)cos(y)/(1+x^2))
contour (x,y,f)
contour (x,y,f,nlevels =45, add=T)
fa=(f-t(f))/2
contour (x,y,fa,nlevels =15)
image(x,y,fa)
persp(x,y,fa)
persp(x,y,fa ,theta =30)
persp(x,y,fa ,theta =30, phi =20)
persp(x,y,fa ,theta =30, phi =70)
persp(x,y,fa ,theta =30, phi =40)
dev.off ()## png
## 2Indexing Data
We often wish to examine part of a set of data. Suppose that our data is stored in the matrix A. What are different ways of accessing/selecting the element corresponding to:
- the second row and the third column.
- multiple rows and columns at a time, by providing vectors as the indices.
- all columns of a particular row or all rows of a set of columns.(R treats a single row or column of a matrix as a vector)
- all columns/rows except the one specified(The use of a negative sign - in the index does this).
dim() - outputs the number of rows followed by the number of columns of a given matrix.
A=matrix (1:16 ,4 ,4)
A## [,1] [,2] [,3] [,4]
## [1,] 1 5 9 13
## [2,] 2 6 10 14
## [3,] 3 7 11 15
## [4,] 4 8 12 16A[2,3]## [1] 10A[c(1,3) ,c(2,4) ]## [,1] [,2]
## [1,] 5 13
## [2,] 7 15A[1:3 ,2:4]## [,1] [,2] [,3]
## [1,] 5 9 13
## [2,] 6 10 14
## [3,] 7 11 15A[1:2 ,]## [,1] [,2] [,3] [,4]
## [1,] 1 5 9 13
## [2,] 2 6 10 14A[ ,1:2]## [,1] [,2]
## [1,] 1 5
## [2,] 2 6
## [3,] 3 7
## [4,] 4 8A[1,]## [1] 1 5 9 13A[5]## [1] 5A[-c(1,3) ,]## [,1] [,2] [,3] [,4]
## [1,] 2 6 10 14
## [2,] 4 8 12 16A[-c(1,3) ,-c(1,3,4)]## [1] 6 8dim(A)## [1] 4 4rm(Auto)## Warning in rm(Auto): object 'Auto' not foundLoading Data
- read.table() - imports a data set(reads a text file as a data frame) into R.
- write.table() - is used to export data.
- fix() - used to view it in a spreadsheet like window. However, the window must be closed before further R commands can be entered.
- read.csv() - used to load the csv format file.
- names() - to check the variable names.
There are various ways to deal with the missing data. In this case, only five of the rows contain missing observations, and so we choose to use the na.omit() function to simply remove these rows.
#Auto=read.table ("Auto.data ")
#fix(Auto)
Auto=read.table("D:/Boston College/MS AE Courses/Spring 2018 - Big Data Econometrics/DataSets/Auto.data", header =T,na.strings ="?")
#fix(Auto)
Auto=read.csv("D:/Boston College/MS AE Courses/Spring 2018 - Big Data Econometrics/DataSets/Auto.csv", header =T,na.strings ="?")
#fix(Auto)
dim(Auto)## [1] 397 9Auto [1:4 ,]## mpg cylinders displacement horsepower weight acceleration year origin
## 1 18 8 307 130 3504 12.0 70 1
## 2 15 8 350 165 3693 11.5 70 1
## 3 18 8 318 150 3436 11.0 70 1
## 4 16 8 304 150 3433 12.0 70 1
## name
## 1 chevrolet chevelle malibu
## 2 buick skylark 320
## 3 plymouth satellite
## 4 amc rebel sstAuto=na.omit(Auto)
dim(Auto)## [1] 392 9names(Auto)## [1] "mpg" "cylinders" "displacement" "horsepower"
## [5] "weight" "acceleration" "year" "origin"
## [9] "name"str(Auto)## 'data.frame': 392 obs. of 9 variables:
## $ mpg : num 18 15 18 16 17 15 14 14 14 15 ...
## $ cylinders : int 8 8 8 8 8 8 8 8 8 8 ...
## $ displacement: num 307 350 318 304 302 429 454 440 455 390 ...
## $ horsepower : int 130 165 150 150 140 198 220 215 225 190 ...
## $ weight : int 3504 3693 3436 3433 3449 4341 4354 4312 4425 3850 ...
## $ acceleration: num 12 11.5 11 12 10.5 10 9 8.5 10 8.5 ...
## $ year : int 70 70 70 70 70 70 70 70 70 70 ...
## $ origin : int 1 1 1 1 1 1 1 1 1 1 ...
## $ name : Factor w/ 304 levels "amc ambassador brougham",..: 49 36 231 14 161 141 54 223 241 2 ...
## - attr(*, "na.action")=Class 'omit' Named int [1:5] 33 127 331 337 355
## .. ..- attr(*, "names")= chr [1:5] "33" "127" "331" "337" ...Data Descriptives
plot() - produces scatterplots of the quantitative scatterplot variables and box plot if the variable on x-axis is categorial.
Simply typing the variable names will produce an error message, because R does not know to look in the Auto data set for those variables. To refer to a variable, we must type the data set and the variable name joined with a $ symbol.
Alternatively, we can use the attach() function in order to tell R to make the variables in this data frame available by name.as.factor() - converts quantitative variables into qualitative variables.
hist() - can be used to plot a histogram.
pairs() - creates a scatterplot matrix i.e. a scatterplot for every scatterplot pair of variables for any given data set. We can also produce scatterplots matrix for just a subset of the variables.
identify() - In conjunction with the plot() function, indentify() provides a useful interactive method for identifying the value for a particular variable for points on a plot.
The numbers printed under the identify() function correspond to the rows for the selected points.summary() - produces a numerical summary of each variable in a particular data set. For qualitative variables such as name, R will list the number of observations that fall in each category.
Before exiting R, we may want to save a record of all of the commands that we typed in the most recent session; this can be accomplished using the savehistory() function. Next time we enter R, we can load that history using the loadhistory() function.
#plot(cylinders , mpg)
plot(Auto$cylinders , Auto$mpg )
attach (Auto)
plot(cylinders , mpg)
cylinders =as.factor (cylinders )
plot(cylinders , mpg)
plot(cylinders , mpg , col ="red ")
plot(cylinders , mpg , col ="red", varwidth =T)
plot(cylinders , mpg , col ="red", varwidth =T,horizontal =T)
plot(cylinders , mpg , col ="red", varwidth =T, xlab=" cylinders ",ylab ="MPG ")
hist(mpg)
hist(mpg ,col =2)
hist(mpg ,col =2, breaks =15)
pairs(Auto)
pairs(~mpg + displacement + horsepower + weight + acceleration , Auto)
plot(horsepower ,mpg)
identify(horsepower ,mpg ,name)
## integer(0)boxplot(Auto$mpg~Auto$cylinders)
summary(Auto)## mpg cylinders displacement horsepower
## Min. : 9.00 Min. :3.000 Min. : 68.0 Min. : 46.0
## 1st Qu.:17.00 1st Qu.:4.000 1st Qu.:105.0 1st Qu.: 75.0
## Median :22.75 Median :4.000 Median :151.0 Median : 93.5
## Mean :23.45 Mean :5.472 Mean :194.4 Mean :104.5
## 3rd Qu.:29.00 3rd Qu.:8.000 3rd Qu.:275.8 3rd Qu.:126.0
## Max. :46.60 Max. :8.000 Max. :455.0 Max. :230.0
##
## weight acceleration year origin
## Min. :1613 Min. : 8.00 Min. :70.00 Min. :1.000
## 1st Qu.:2225 1st Qu.:13.78 1st Qu.:73.00 1st Qu.:1.000
## Median :2804 Median :15.50 Median :76.00 Median :1.000
## Mean :2978 Mean :15.54 Mean :75.98 Mean :1.577
## 3rd Qu.:3615 3rd Qu.:17.02 3rd Qu.:79.00 3rd Qu.:2.000
## Max. :5140 Max. :24.80 Max. :82.00 Max. :3.000
##
## name
## amc matador : 5
## ford pinto : 5
## toyota corolla : 5
## amc gremlin : 4
## amc hornet : 4
## chevrolet chevette: 4
## (Other) :365summary(mpg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.00 17.00 22.75 23.45 29.00 46.60