Introduction to RStudio

Transcription

1 Introduction to RStudio Carl Tony Fakhry Jie Chen April 4, 2015

2 Introduction R is a powerful language and environment for statistical computing and graphics. R is freeware and there is lot of help available online if you need to have a discussion with your R peers. Traditional R command line interface is not very user friendly. RStudio is an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management. In short, using RStudio can make using R a lot easier and fun, especially for first time users.

3 Install RStudio To install RStudio go to: You can download R by clicking on Download RStudio

4 Install RStudio Next you click to download RStudio for your desktop

5 Install RStudio Finally, click DOWNLOAD RSTUDIO DESKTOP

6 Starting RSTUDIO Go to your Start menu and in programs start RStudio by clicking on the RStudio icon:

7 GUI Basics When you open the GUI, you will the RStudio screen

8 Console The Console is where you can type code and execute it.

9 Script Open an empty script File New File R Script. You can write a script and then execute it in the console using Ctrl Shift Enter.

10 Benefits of Writing a Script R files are saved with.r extension. It is better to write code in a script that way your work is saved for later.

11 Global Environment The globalenv(), or global environment, is the interactive workspace. This is the environment in which you normally work. The enclosing environment for functions created in it, and the binding of the names to the values in this environment.

12 Graphing with package ggplot2 ggplot2 is a powerful graphing package in R. We will demonstrate many graphing examples using ggplot2, and later with other packages. To make graphs with ggplot2, the data must be in a data frame, and in "long" (as opposed to wide) format.

13 Important Data structures in R Vectors and Lists: Vectors are the basic building blocks in R. Vectors are atomic, you can only put one type of basic data type in a vector. Lists are more flexible, you can put anything in a list. # T h i s w i l l r e t u r n t r u e i s. v e c t o r ( 3 ) i s. a t o m i c ( c ( 1, 2 ) ) # T h i s w i l l r e t u r n F a l s e i s. a t o m i c ( l i s t ( ) ) Matrices are atomic, while data frames are not. # T h i s w i l l r e t u r n t r u e i s. a t o m i c ( mat ( ) ) # T h i s w i l l r e t u r n F a l s e i s. a t o m i c ( d a t a. f r a m e ( ) )

14 Random number generation in R Let s make a random data frame in R using random number generation. # G e n e r a t e a v e c t o r o f 1000 random numbers from a b i n o m i a l w i t h # p r o b a b i l i t y o f s u c c e s s 0. 4 v e c 1 < r b i n o m ( , 1, 0. 4 ) # Generate a v e c t o r of 1000 random numbers between 1 and 4 using # sample f u n c t i o n v e c 2 < s a m p l e ( 1 : 4, 1000, r e p l a c e = T) # Generate a v e c t o r of 1000 random numbers from a standard normal v e c 3 < rnorm ( ) # Generate a v e c t o r of 1000 random numbers from a standard normal # w i t h mean 10 and s t a n d a r d d e v i a t i o n 2 v e c 4 < rnorm ( , mean = 10, s d = 2) # make a m a t r i x u s i n g t h e c b i n d f u n c t i o n mat < c b i n d ( vec1, vec2, vec3, v e c 4 ) # o r do row w i s e b i n d i n g mat < r b i n d ( vec1, vec2, vec3, v e c 4 ) # make a d a t a frame d f < d a t a. f r a m e ( vec1, vec2, vec3, v e c 4 )

15 Graphing with package ggplot2 To make graphs with ggplot2, the data must be in a data frame, and in "long" (as opposed to wide) format. Let s make a random data frame in R using random number generation. # C r e a t e a v e c t o r o f f a c t o r s b e i n g s t o c k s Apple and IBM # and t h e i r c o r r e s p o n d i n g r a t i n g s s t o c k s < f a c t o r ( r e p ( c ( " Apple ", "IBM" ), each = ) ) r a t i n g < c ( rnorm ( ), rnorm ( 2 0 0, mean =. 8 ) ) # Now make t h e d a t a frame d f < d a t a. f r a m e ( s t o c k s, r a t i n g )

16 Histograms # B a s i c h i s t o g r a m from t h e v e c t o r " r a t i n g ". Each b i n i s. 5 wide. # These both do t h e same t h i n g : q p l o t ( d f $ r a t i n g, b i n w i d t h =.5) g g p l o t ( df, a e s ( x=r a t i n g ) ) + geom_h i s t o g r a m ( b i n w i d t h =.5) # Draw w i t h b l a c k o u t l i n e, w h i t e f i l l g g p l o t ( df, a e s ( x=r a t i n g ) ) + geom_h i s t o g r a m ( b i n w i d t h =.5, c o l o u r=" b l a c k ", f i l l =" w h i t e " ) # D e n s i t y c u r v e g g p l o t ( df, a e s ( x=r a t i n g ) ) + geom_d e n s i t y ( f i l l =" d a r k g o l d e n r o d 1 " ) # H i s t o g r a m o v e r l a i d w i t h k e r n e l d e n s i t y c u r v e g g p l o t ( df, a e s ( x=r a t i n g ) ) + geom_h i s t o g r a m ( a e s ( y =.. d e n s i t y.. ), binwidth =.5, c o l o u r=" b l a c k ", f i l l =" w h i t e " ) + geom_d e n s i t y ( ) # O v e r l a y w i t h t r a n s p a r e n t d e n s i t y p l o t # H i s t o g r a m o v e r l a i d w i t h k e r n e l d e n s i t y c u r v e g g p l o t ( df, a e s ( x=r a t i n g ) ) + geom_h i s t o g r a m ( a e s ( y =.. d e n s i t y.. ), binwidth =.5, c o l o u r=" b l a c k ", f i l l =" w h i t e " ) + geom_d e n s i t y ( f i l l =" y e l l o w " ) # O v e r l a y w i t h t r a n s p a r e n t d e n s i t y p l o t

17 Histograms

18 Bar Graph # L e t i m p o r t a d a t a s e t c a l l e d t i p s from package r e s h a p e 2 l i b r a r y ( r e s h a p e 2 ) # v i e w y o u r d a t a t i p s # P l o t b a r g r a p h o f c o u n t s o f d a y s g g p l o t ( d a t a=t i p s, a e s ( x=day ) ) + geom_b a r ( s t a t=" b i n ", c o l o u r=" r e d ", f i l l =" g r e e n " ) + g g t i t l e ( " Counts o f d a y s " ) Counts of days 75 count Fri Sat Sun Thur day

19 Facets Suppose you want to split up your data by one or more variables and plot the subsets of data together. # b a s i c p l o t p e r c e n t a g e o f t i p s t i p s_p e r c e n t < t i p s $ t i p / t i p s $ t o t a l_ b i l l p l t < g g p l o t ( t i p s, a e s ( x=t o t a l_ b i l l, y=t i p s_p e r c e n t ) ) + geom_p o i n t ( s h a p e =1) + g g t i t l e ( " B a s i c P l o t " ) p l t Basic Plot tips_percent total_bill

20 Facets Suppose you want to split up your data by one or more variables and plot the subsets of data together. # D i v i d e by l e v e l s o f " s e x ", i n t h e v e r t i c a l d i r e c t i o n p l t + f a c e t_g r i d ( s e x. ) + g g t i t l e ( "By Sex, V e r t i c a l l y " ) # D i v i d e by l e v e l s o f " s e x ", i n t h e h o r i z o n t a l d i r e c t i o n p l t + f a c e t_g r i d (. s e x ) + g g t i t l e ( "By Sex, H o r i z o n t a l l y " ) # D i v i d e w i t h " s e x " v e r t i c a l, " day " h o r i z o n t a l p l t + f a c e t_g r i d ( s e x day ) + g g t i t l e ( " P e r c e n t a g e s p e r Sex and Days " )

21 Facets 0.6 By Sex, Vertically Female By Sex, Horizontally Male tips_percent Female Male tips_percent total_bill total_bill

22 Facets By Sex, Vertically tips_percent Female Male total_bill

23 Facets Suppose you want to split up your data by one or more variables and plot the subsets of data together. # A h i s t o g r a m o f b i l l s i z e s h i s t o < g g p l o t ( t i p s, a e s ( x=t o t a l_ b i l l ) ) + geom_h i s t o g r a m ( b i n w i d t h =2, c o l o u r = " w h i t e ", f i l l = " b l u e " ) # H i s t o g r a m o f t o t a l_ b i l l, d i v i d e d by s e x and smoker h i s t o + f a c e t_g r i d ( s e x smoker ) + g g t i t l e ( " Yes /No Smokers p e r Sex " ) No Yes/No Smokers per Gender Yes count Female Male total_bill

24 One-way Anova # G e n e r a t e some random d a t a p a i n < s a m p l e ( 1 : 1 0, 1000, r e p l a c e = T) m e d i c i n e < c ( r e p ( "A", ), r e p ( "B", ), r e p ( "C", ) ) m i g r a i n e < d a t a. f r a m e ( pain, m e d i c i n e ) # Run Anova Test t e s t oneway. t e s t ( p a i n m e d i c i n e, v a r. e q u a l=true) # B o x p l o t g g p l o t ( m i g r a i n e, a e s ( x=m e d i c i n e, y=p a i n ) ) + geom_b o x p l o t ( c o l o r=" b l a c k ", f i l l =" y e l l o w " ) + g g t i t l e ( " B o x p l o t s " ) 10.0 Boxplots 7.5 pain A B C medicine

25 Linear Regression There two standard ways for running a regression in R. r 1 < rnorm ( ) r2 < rnorm ( 100, mean = 2, s d = 2) r 3 < rnorm ( 1 00, mean = 3, s d =1) # L i n e a r r e g r e s s i o n u s i n g lm o r glm f u n c t i o n > r e g 1 < lm ( r 1 r 2 + r 3 ) ) > r e g 1 < glm ( r 1 r 2 + r 3, f a m i l y=" g a u s s i a n " ) # Get summary s t a t i s t i c s u s i n g summary ( lm ) summary ( r e g 1 )

26 Regression 3d plot #P l o t 3d r e s u l t s l i b r a r y ( c a r ) s c a t t e r 3 d ( r 1 r 2 + r 3 )

27 Logistic Regression # L e t g e t new d a t a d a t a ( m t c a r s ) d f < s u b s e t ( mtcars, s e l e c t=c (mpg, am, v s ) ) #L o g i s t i c r e g r e s s i o n u s i n g glm ( ) r e g 2 < glm ( v s mpg, d a t a=df, f a m i l y=b i n o m i a l ) # Get summary s t a t i s t i c s e. g Wald S t a t i s t i c s f o r c o e f f i c i e n t s summary ( r e g 2 ) # P l o t t i n g p l o t ( d f $mpg, d f $ v s ) c u r v e ( p r e d i c t ( l o g r. vm, d a t a. f r a m e (mpg=x ), t y p e=" r e s p o n s e " ), add=true) # U s i n g g g p l o t 2, and p l o t t i n g t h e s t a n d a r d e r r o r s g g p l o t ( df, a e s ( x=mpg, y=v s ) ) + geom_p o i n t ( ) + s t a t_smooth ( method=" glm ", f a m i l y=" b i n o m i a l ", s e=true)

28 Logistic Regression df$vs vs df$mpg mpg

29 Functions in R # S y n t a x f o r f u n c t i o n s i n R i s t o g i v e t h e f u n c t i o n # a name, then pass the parameters to the f u n c t i o n # r e s e r v e d word. i n c r e a s e v a l u e s < f u n c t i o n ( number ){ # I n c r e a s e t h e number p a r a m t e r by 1 number < number + 1 # r e t u r n t h e v a l u e t h a t was i n c r e a s e d r e t u r n ( number ) } # Let s t e s t t h i s f u n c t i o n newnumber < i n c r e a s e v a l u e s ( 5 ) # T h i s w i l l p r i n t 6 newnumber

30 Important Concepts in R: Vectorization You can use functions such as sapply and apply to apply functions to entire data structures such as vectors and data frames. # F i r s t, s a p p l y, p a s s a f u n c t i o n and v e c t o r # T h i s w i l l i n c r e a s e a l l t h e v a l u e s o f a # v e c t o r by 1 z e r o e s < r e p ( 0, 1 0 ) s a p p l y ( z e r o e s, i n c r e a s e v a l u e s ) # I n c r e a s e t h e v a l u e s i n t h e z e r o # m a t r i x by 1 mat < m a t r i x ( 0, n c o l = 3, nrow = 3) a p p l y ( mat, 1, i n c r e a s e v a l u e s )

31 Questions?