Learning notes of r for python programmer (Temp1)
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R has several basic data types including integers, numerics, characters, complexes, and logicals. Objects in R include vectors, matrices, lists, data frames, factors, and environments. Functions like length(), mode(), class(), and str() can provide properties of R objects. R supports control structures like if/else, for loops, while loops, and repeat loops. R also has rich graphics capabilities for creating plots, histograms and other visualizations using both base and lattice graphics. Common packages used with R include those for statistics, machine learning, and working with time series and financial data.
![Python “a,b,c,d,e”.split(“,”)
(R strsplit)
• strsplit(“a,b,c,d,e”,“,“)
• (Output R-list)
• unlist(strsplit(“a,b,c,d,e”,“,"))[vector_index]](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-11-320.jpg)
![Python Dictionary (R List)
• Constructor
– Rlist <- list(key1=value1, … , key_n = value_n)
• Evaluate
– Rlist$key1 (Python> D[key1])
– Rlist[[1]]
• Sublist
– Rlist[key_i] (output list(key_i=value_i))](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-14-320.jpg)
![Python D[“new_key”]=new_value
• Rlist$new_key = new_value or
• Rlist$new_key <- new_value](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-15-320.jpg)
![Python> del D[key]
• New_Rlist <- Rlist[-key_index] or
• New_Rlist <- Rlist[-vector_of_key_index]](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-16-320.jpg)
![Python List (R vector)
• [Constructor] vector(mode , length)
– vector(mode = "character", length = 10)
• 0:10
– 0:10 == c(0,1,2,3,4,5,6,7,8,9,10)
– Python> range(0,11) )
• seq(0,1,0.1)
– seq(0,1,0.1) == 0:10*0.1
– Matlab> linspace(0,1,0.1)
• rep(0:10, times = 2)](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-19-320.jpg)
![Python List.methods
• vector <- c(vector, other_vector)
– Python> List.append
• vector[-J] or vector[-(I:J)]
– Python> List.pop
• subvector <- vector[vector_of_index]
• which( vector == value )
– Python> List.index(value)](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-20-320.jpg)
![R-Matrix=R-Vector with Dimension
> x <- 1:15
> class(x)
[1] "integer"
> dim(x) <- c(3, 5)
> class(x)
[1] "matrix"](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-26-320.jpg)
![Names on Matrix
• Just as you can name indices in a vector you
can (and should!) name columns and rows in a
matrix with colnames(X) and rownames(X).
• E.g.
– colname(R-matrix) <- c(name_1,name_2,…)
– colname(R-matrix) [i] <- name_i](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-27-320.jpg)
![swtich
• Switch (statement, list)
• Example:
> y <- "fruit"
> switch(y, fruit = "banana", vegetable =
"broccoli", meat = "beef")
[1] "banana"](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-37-320.jpg)
![Time to Timestamp
[Python> time.mktime(…)]
• as.numeric(POSIXlt Object)
• E.g.
– as.numeric(Sys.time())](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-49-320.jpg)
![pairs
• E.g.
– R> pairs(iris[,c(1,3,5)])
– R> example(pairs)](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-61-320.jpg)
![MISC. Code2 (Saving Graph)
• windows(record=TRUE, width=7, height=7)
• Last_30_TXF<-last(TXF,30)plt
• chartSeries(Last_30_TXF)
• savePlot(paste("Last30_",unlist(strsplit(filena
me,"."))[1],sep=""),type = "jpeg",device =
dev.cur(),restoreConsole = TRUE)](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-63-320.jpg)
![grep
• Pattern_Index <- grep(Pattern, Search_Vector)
• E.g. (quantmod中的 Cl function)
return(x[, grep("Close", colnames(x))])](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-79-320.jpg)
![adaboost.M1’s Training Data Form
• Label Column must be a factor object
(in source code)
fit <- rpart(formula = formula, weights =
data$pesos, data = data[, -1], maxdepth =
maxdepth)
flearn <- predict(fit, data = data[, -1], type =
"class")](https://image.slidesharecdn.com/learningnotesofrforpythonprogrammer-120930145414-phpapp01/85/Learning-notes-of-r-for-python-programmer-Temp1-88-320.jpg)
Learning notes of r for python programmer (Temp1)
- 1. Learning Notes of R For Python Programmer
- 2. R Basic Scalar Types • R basic scalar data types – integer ( 1L,2L,3L,…) – numeric ( 1,2,3,…) – character – complex – logical (TRUE, FALSE) • and(&) , or(|), not(!)
- 3. R Basic Scalar Types Constructors • RScalarType(0) == NULL – length(xxx(0)) == 0 • RScalarType(1) – integer 0L/ 0 – numeric 0 – character “” – complex 0+0i – logical FALSE
- 4. R Basic Object Types • R basic data structure types – (row) vector (In R, everything is vector) – matrix – list – data.frame – factor – environment • In R the “base" type is a vector, not a scalar.
- 5. R Object
- 6. Find R Object’s Properties • length(object) • mode(object) / class(object)/ typeof(obj) • attributes(object) • attr(object, name) • str(object)
- 7. Python type(obj) • R> class(obj) • R> mode(obj) class mode typeof • R> typeof(obj) 1 "numeric" "numeric" "double" 1:10 “integer" "numeric" “integer" “1” "character" "character" "character" class "function" "function" "builtin"
- 8. Python dir(obj) • attributes(obj) • str(object) • ls() (Python> dir() ) • The function attributes(object) returns a list of all the non-intrinsic attributes currently defined for that object.
- 9. R attr(object, name) • The function attr(object, name) can be used to select a specific attribute. • When it is used on the left hand side of an assignment it can be used either to associate a new attribute with object or to change an existing one. • For example • > attr(z, "dim") <- c(10,10) – allows R to treat z as if it were a 10-by-10 matrix.
- 10. R character
- 11. Python “a,b,c,d,e”.split(“,”) (R strsplit) • strsplit(“a,b,c,d,e”,“,“) • (Output R-list) • unlist(strsplit(“a,b,c,d,e”,“,"))[vector_index]
- 12. R paste • paste(“a”,”b”,sep=“”) – Python> “a”+”b” “ab”
- 14. Python Dictionary (R List) • Constructor – Rlist <- list(key1=value1, … , key_n = value_n) • Evaluate – Rlist$key1 (Python> D[key1]) – Rlist[[1]] • Sublist – Rlist[key_i] (output list(key_i=value_i))
- 15. Python D[“new_key”]=new_value • Rlist$new_key = new_value or • Rlist$new_key <- new_value
- 16. Python> del D[key] • New_Rlist <- Rlist[-key_index] or • New_Rlist <- Rlist[-vector_of_key_index]
- 17. Python Dict.keys() • vector_of_Rlist_keys <- names(Rlist) • ( output “vector_of_Rlist_keys” is a R-vector)
- 19. Python List (R vector) • [Constructor] vector(mode , length) – vector(mode = "character", length = 10) • 0:10 – 0:10 == c(0,1,2,3,4,5,6,7,8,9,10) – Python> range(0,11) ) • seq(0,1,0.1) – seq(0,1,0.1) == 0:10*0.1 – Matlab> linspace(0,1,0.1) • rep(0:10, times = 2)
- 20. Python List.methods • vector <- c(vector, other_vector) – Python> List.append • vector[-J] or vector[-(I:J)] – Python> List.pop • subvector <- vector[vector_of_index] • which( vector == value ) – Python> List.index(value)
- 21. R which • which( vector == value ) – Python> List.index(value) • which( vector < v) or which( vector > v) • which(arg, arr.in=TRUE) • http://fortheloveof.co.uk/2010/04/11/r- select-specific-elements-or-find-their-index- in-a-vector-or-a-matrix/
- 22. R vector • length(vector) – Python> len(List) • names(vector) • rownames(vector)
- 23. Python> element in List • R> element %in% R-Vector • R> !(element %in% R-Vector) (not in)
- 24. R matrix R-Vector with Dimension
- 25. R-Matrix • Constructor: – matrix( ?? , nrow = ?? , ncol = ?? ) – as.matrix( ?? )
- 26. R-Matrix=R-Vector with Dimension > x <- 1:15 > class(x) [1] "integer" > dim(x) <- c(3, 5) > class(x) [1] "matrix"
- 27. Names on Matrix • Just as you can name indices in a vector you can (and should!) name columns and rows in a matrix with colnames(X) and rownames(X). • E.g. – colname(R-matrix) <- c(name_1,name_2,…) – colname(R-matrix) [i] <- name_i
- 28. Functions on Matrix • If X is a matrix apply(X, 1, f) is the result of applying f to each row of X; apply(X, 2, f) to the columns. – Python> map(func,py-List)
- 29. Add Columns and Rows • cbind E.g. > cbind(c(1,2,3),c(4,5,6)) • rbind E.g. > rbind(c(1,2,3),c(4,5,6))
- 30. Data Frame in R Explicitly like a list
- 31. Explicitly like a list • When can a list be made into a data.frame? – Components must be vectors (numeric, character, logical) or factors. – All vectors and factors must have the same lengths.
- 33. Python os and R
- 34. Python os.method • getwd() (Python> os.getcwd() ) • setwd(Path) (Python> os.chdir(Path))
- 35. Control Structures and Looping
- 36. if • if ( statement1 ) • statement2 • else if ( statement3 ) • statement4 • else if ( statement5 ) • statement6 • else • statement8
- 37. swtich • Switch (statement, list) • Example: > y <- "fruit" > switch(y, fruit = "banana", vegetable = "broccoli", meat = "beef") [1] "banana"
- 38. for • for ( name in vector ) statement1 • E.g. >.for ( ind in 1:10) { print(ind) }
- 39. while • while ( statement1 ) statement2
- 40. repeat • repeat statement • The repeat statement causes repeated evaluation of the body until a break is specifically requested. • When using repeat, statement must be a block statement. You need to both perform some computation and test whether or not to break from the loop and usually this requires two statements.
- 41. Functions in R
- 42. Create Function in R • name <- function(arg_1, arg_2, ...) expression • E.g. – ADD <- function(a,b) a+b – ADD <- function(a,b) {c<-a+b} – ADD <- function(a,b) {c<-a+b;c} – ADD <- function(a,b) {c<-a+b; return(c)} – (All these functions are the same functions)
- 43. Function Return R-List • To return more than one item, create a list using list() • E.g. – MyFnTest1 <- function(a,b) {c<-a+b;d<-a-b; list(r1=c,r2=d)} – MyFnTest1 <- function(a,b) {c<-a+b;d<-a-b; return(list(r1=c,r2=d))} – (These two functions are the same, too)
- 44. Python map(func,Py-List) • apply series methods (to be continued.)
- 45. R Time Objects
- 46. R Basic Time Objects • Basic Types – Date – POSIXct – POSIXlt • Constructors: – as.Date – as. POSIXct – as. POSIXlt
- 47. as.POSIXct/ as.POSIXlt • as. POSIXct( timestamp , origin , tz , …) • E.g. – as. POSIXct( timestamp , origin="1970-01- 01",tz="CST“, …)
- 48. strftime / strptime • "POSIXlt“/"POSIXct“ to Character – strftime(x, format="", tz = "", usetz = FALSE, ...) • Character to "POSIXlt“ – strptime(x, format, tz = "") • E.g. – strptime(… ,"%Y-%m-%d %H:%M:%S", tz="CST")
- 49. Time to Timestamp [Python> time.mktime(…)] • as.numeric(POSIXlt Object) • E.g. – as.numeric(Sys.time())
- 50. R Graph
- 51. Types of Graphics • Base • Lattice
- 52. Base Graphics • Use function such as – plot – barplot – contour – boxplot – pie – pairs – persp – image
- 53. Plot Arguments • type = ??? • axes = FALSE : suppresses axes • xlab = “str” : label of x-axis • ylab = “str” : label of y-axis • sub = “str” : subtitle appear under the x-axis • main = “str” : title appear at top of plot • xlim = c(lo,hi) • ylim = c(lo,hi)
- 54. Plot’s type arg • type = – “p” : plots points – “l” : plots a line – “n” : plots nothing, just creates the axes for later use – “b” : plots both lines and points – “o” : plot overlaid lines and points – “h” : plots histogram-like vertical lines – “s” : plots step-like lines
- 55. Plot Example • R> plot(x=(1:20),y=(11:30),pch=1:20,col=1:20,mai n="plot",xlab="x-axis",ylab="y- axis",ylim=c(0,30)) • R> example(points)
- 56. pch • 0:18: S-compatible vector symbols. • 19:25: further R vector symbols. • 26:31: unused (and ignored). • 32:127: ASCII characters. • 128:255 native characters only in a single-byte locale and for the symbol font. (128:159 are only used on Windows.) • Ref: http://stat.ethz.ch/R-manual/R-devel/library/graphics/html/points.html http://rgraphics.limnology.wisc.edu/
- 57. cex • a numerical vector giving the amount by which plotting characters and symbols should be scaled relative to the default. This works as a multiple of par("cex"). NULL and NA are equivalent to 1.0. Note that this does not affect annotation: see below. • E.g. – points(c(6,2), c(2,1), pch = 5, cex = 3, col = "red") – points(c(6,2), c(2,1), pch = 5, cex = 10, col = "red")
- 58. points, lines, text, abline
- 59. arrows
- 60. par/layout (Matlab> subplot) • par(mfrow=c(m,n)) – Matlab> subplot(m,n,?)
- 61. pairs • E.g. – R> pairs(iris[,c(1,3,5)]) – R> example(pairs)
- 62. MISC. Code1 (Saving Graph) • postscript("myfile.ps") • plot(1:10) • dev.off()
- 63. MISC. Code2 (Saving Graph) • windows(record=TRUE, width=7, height=7) • Last_30_TXF<-last(TXF,30)plt • chartSeries(Last_30_TXF) • savePlot(paste("Last30_",unlist(strsplit(filena me,"."))[1],sep=""),type = "jpeg",device = dev.cur(),restoreConsole = TRUE)
- 64. 可使用的顏色種類 • R> colors() 可以查出所有顏色 • 可搭配grep找尋想要的色系, 如 • R> grep("red",colors()) • Reference: • http://research.stowers-institute.org/efg/R/Color/Chart/
- 65. R xts
- 66. Tools for xts • diff • lag
- 67. My XTS’ Tools • Integration_of_XTS • Indexing_of_XTS • XTS_Push_Events_Back • Get_XTS_Local_Max • Get_XTS_Local_Min
- 71. Model Formulae • formula(x, ...) • as.formula(object, env = parent.frame()) • E.g. – R> example(formula)
- 73. MISC. 1 Updating fitted models • http://cran.r-project.org/doc/manuals/R- intro.html#Updating-fitted-models
- 74. R Packages
- 75. • library() • search() • loadedNamespaces() • getAnywhere(Package_Name) • http://cran.r-project.org/doc/manuals/R- intro.html#Namespaces
- 78. Regular Expression Python Re Module
- 79. grep • Pattern_Index <- grep(Pattern, Search_Vector) • E.g. (quantmod中的 Cl function) return(x[, grep("Close", colnames(x))])
- 80. • hits <- grep( pattern, x ) • Ref: Lecture5v1
- 81. R LibSVM (e1071) http://www.csie.ntu.edu.tw/~cjlin/lib svm/R_example
- 83. R CR Tree Method (rpart) Classification and Regression Tree
- 85. • http://www.statsoft.com/textbook/classificati on-and-regression-trees/ • http://www.stat.cmu.edu/~cshalizi/350/lectur es/22/lecture-22.pdf • http://www.stat.wisc.edu/~loh/treeprogs/gui de/eqr.pdf
- 86. R Adaboost Package (adabag)
- 87. adaboost.M1 • 此函數的演算法使用 Freund and Schapire‘s Adaboost.M1 algorithm • 其中 weak learner 的部分使用 CR Tree 也就 是R中的 rpart package
- 88. adaboost.M1’s Training Data Form • Label Column must be a factor object (in source code) fit <- rpart(formula = formula, weights = data$pesos, data = data[, -1], maxdepth = maxdepth) flearn <- predict(fit, data = data[, -1], type = "class")
- 89. R IDE Tools
- 91. Reference • http://en.wikipedia.org/wiki/R_(programming_language) • http://jekyll.math.byuh.edu/other/howto/R/RE.shtml (Emacs) • http://stat.ethz.ch/ESS/
- 92. Reference
- 94. • http://www.nd.edu/~steve/Rcourse/Lecture2 v1.pdf • http://addictedtor.free.fr/graphiques/ • http://www.evc- cit.info/psych018/r_intro/r_intro4.html • http://www.r-tutor.com/r-introduction/data- frame • http://msenux.redwoods.edu/math/R/datafra me.php