KEMBAR78
Learning R via Python…or the other way around | PDF
Sid Xing, profile picture
Uploaded bySid Xing
2,028 views

Learning R via Python…or the other way around

The document discusses similarities and differences between R and Python programming languages. Both support functional and object-oriented programming paradigms. However, Python is designed more for general application development while R focuses on data analysis functions. Their syntax also differs, with Python emphasizing readability through whitespace and R favoring brevity through nesting. Examples are provided to demonstrate equivalent functionality in each language.

Learning R via Python ...or the other way around Drew Conway Dept. of Politics - NYU January 7, 2010
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources What We’ll Cover Brief review of Python The Zen of Python How are R and Python the same, and how are they different Similar Data Structures Python dictionary R list Example - Testing if an integer is prime Create a function and returning a value Using while-loops Bridging the gap with RPy2 Running a regression with R inside Python Python resources Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Fundamental Elements of Python The Python coder’s creed >>> import this The Zen of Python, by Tim Peters Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren’t special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one-- and preferably only one --obvious way to do it. Although that way may not be obvious at first unless you’re Dutch. Now is better than never. Although never is often better than *right* now. If the implementation is hard to explain, it’s a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea -- let’s do more of those! Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm Array of squared values # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm Array of squared values # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] # In R we define a function in sapply > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm Array of squared values # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] # In R we define a function in sapply > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Object-oriented programming Both languages support robust class structures In R, there are the S3 and S4 classes Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm Array of squared values # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] # In R we define a function in sapply > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Object-oriented programming Both languages support robust class structures In R, there are the S3 and S4 classes Easily call C/Fortran code Due to their high-level, both languages provide functionality to call compiled code from lower-level languages Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm General purpose OOP vs. Functional OOP Python is a high-level language for Array of squared values application development # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) R is a collection of functions for data [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] analysis # In R we define a function in sapply > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Object-oriented programming Both languages support robust class structures In R, there are the S3 and S4 classes Easily call C/Fortran code Due to their high-level, both languages provide functionality to call compiled code from lower-level languages Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm General purpose OOP vs. Functional OOP Python is a high-level language for Array of squared values application development # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) R is a collection of functions for data [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] analysis # In R we define a function in sapply Syntax structure > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Python syntax forces readability through whitespace R emphasizes parsimony and nesting Object-oriented programming Both languages support robust class structures In R, there are the S3 and S4 classes Easily call C/Fortran code Due to their high-level, both languages provide functionality to call compiled code from lower-level languages Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm General purpose OOP vs. Functional OOP Python is a high-level language for Array of squared values application development # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) R is a collection of functions for data [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] analysis # In R we define a function in sapply Syntax structure > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Python syntax forces readability through whitespace R emphasizes parsimony and nesting Object-oriented programming Both languages support robust class Creating a function structures In R, there are the S3 and S4 classes # In Python functions are declared >>> def my func(x): Easily call C/Fortran code ... Due to their high-level, both languages provide functionality to call compiled code from lower-level languages Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm General purpose OOP vs. Functional OOP Python is a high-level language for Array of squared values application development # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) R is a collection of functions for data [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] analysis # In R we define a function in sapply Syntax structure > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Python syntax forces readability through whitespace R emphasizes parsimony and nesting Object-oriented programming Both languages support robust class Creating a function structures In R, there are the S3 and S4 classes # In Python functions are declared >>> def my func(x): Easily call C/Fortran code ... Due to their high-level, both languages # In R functions are assigned > my.func<-function(x) { ... } provide functionality to call compiled code from lower-level languages Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm General purpose OOP vs. Functional OOP Python is a high-level language for Array of squared values application development # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) R is a collection of functions for data [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] analysis # In R we define a function in sapply Syntax structure > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Python syntax forces readability through whitespace R emphasizes parsimony and nesting Object-oriented programming Both languages support robust class Creating a function structures In R, there are the S3 and S4 classes # In Python functions are declared >>> def my func(x): Easily call C/Fortran code ... Due to their high-level, both languages # In R functions are assigned > my.func<-function(x) { ... } provide functionality to call compiled code from lower-level languages For more info see: StackOverlow.com discussion on topic Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Dictionaries In Python the dict type is a data structure that represents a key→value mapping Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Dictionaries In Python the dict type is a data structure that represents a key→value mapping Working with the dict type # Keys and values can be of any data type >>> fruit dict={"apple":1,"orange":[0.23,0.11],"banana":True } Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Dictionaries In Python the dict type is a data structure that represents a key→value mapping Working with the dict type # Keys and values can be of any data type >>> fruit dict={"apple":1,"orange":[0.23,0.11],"banana":True } # Can retrieve the keys and values as Python lists (vector) >>> fruit dict.keys() ["orange","apple","banana"] Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Dictionaries In Python the dict type is a data structure that represents a key→value mapping Working with the dict type # Keys and values can be of any data type >>> fruit dict={"apple":1,"orange":[0.23,0.11],"banana":True } # Can retrieve the keys and values as Python lists (vector) >>> fruit dict.keys() ["orange","apple","banana"] # Or create a (key,value) tuple >>> fruit dict.items() [("orange",[0.23,0.11]),("apple",1),("Banana",True)] Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Dictionaries In Python the dict type is a data structure that represents a key→value mapping Working with the dict type # Keys and values can be of any data type >>> fruit dict={"apple":1,"orange":[0.23,0.11],"banana":True } # Can retrieve the keys and values as Python lists (vector) >>> fruit dict.keys() ["orange","apple","banana"] # Or create a (key,value) tuple >>> fruit dict.items() [("orange",[0.23,0.11]),("apple",1),("Banana",True)] # This becomes especially useful when you master Python ‘‘list comprehension’’ Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Dictionaries In Python the dict type is a data structure that represents a key→value mapping Working with the dict type # Keys and values can be of any data type >>> fruit dict={"apple":1,"orange":[0.23,0.11],"banana":True } # Can retrieve the keys and values as Python lists (vector) >>> fruit dict.keys() ["orange","apple","banana"] # Or create a (key,value) tuple >>> fruit dict.items() [("orange",[0.23,0.11]),("apple",1),("Banana",True)] # This becomes especially useful when you master Python ‘‘list comprehension’’ The Python dictionary is an extremely flexible and useful data structure, making it one of the primary advantages of Python over other languages Luckily, there is a fairly direct mapping between the Python dict and R lists! Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources R Lists Similarly to the Python dictionary, the R list is a key→value mapping Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources R Lists Similarly to the Python dictionary, the R list is a key→value mapping Working with an R list > fruit.list<-list("apple"=1,"orange"=c(0.23,0.11),"banana"=TRUE) > fruit.list $apple [1] 1 $orange [1] 0.55 0.11 $banana [1] TRUE Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources R Lists Similarly to the Python dictionary, the R list is a key→value mapping Working with an R list > fruit.list<-list("apple"=1,"orange"=c(0.23,0.11),"banana"=TRUE) > fruit.list $apple [1] 1 $orange [1] 0.55 0.11 $banana [1] TRUE Notice, however, that R will always treat the value of the key/value pair as a vector; unlike Python, which does not care the value’s data type. Of the many R programming paradigms, the “vectorization of all data” is among the strongest. Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources R Lists Similarly to the Python dictionary, the R list is a key→value mapping Working with an R list > fruit.list<-list("apple"=1,"orange"=c(0.23,0.11),"banana"=TRUE) > fruit.list $apple [1] 1 $orange [1] 0.55 0.11 $banana [1] TRUE Notice, however, that R will always treat the value of the key/value pair as a vector; unlike Python, which does not care the value’s data type. Of the many R programming paradigms, the “vectorization of all data” is among the strongest. Using unlist > unlist(fruist.list) apple orange1 orange2 banana 1.00 0.55 0.11 1.00 Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Testing a Prime in Python We are interested in testing whether integer X is prime, and to do so we will: Declare a function Use a while-loop Return a boolean Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Testing a Prime in Python We are interested in testing whether integer X is prime, and to do so we will: Declare a function Use a while-loop Return a boolean The is prime function def is prime(x): if x%2 == 0: return False else: y=3 while y<x: if x%y==0: return False else: y+=2 return True Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Testing a Prime in Python We are interested in testing whether integer X is prime, and to do so we will: Declare a function Use a while-loop Return a boolean The is prime function def is prime(x): if x%2 == 0: return False else: y=3 while y<x: if x%y==0: return False else: y+=2 return True Notice the number of lines in this simple function. In contrast to R, and many other programming languages, Python’s whitespace and indentation requirements force verbose code; however, the end result is very readable. Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Testing a Prime in R Unlike Python where functions are declared explicitly, R creates functions through assignment. In many ways, this is somewhere between the Python lambda function and an explicit function declaration. Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Testing a Prime in R Unlike Python where functions are declared explicitly, R creates functions through assignment. In many ways, this is somewhere between the Python lambda function and an explicit function declaration. The is.prime function is.prime<-function(x) { if(x%%2==0) {return(FALSE) } else { y<-3 while(y<x) ifelse(x%%y==0,return(FALSE),y<-y+2) } return(TRUE) } Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Testing a Prime in R Unlike Python where functions are declared explicitly, R creates functions through assignment. In many ways, this is somewhere between the Python lambda function and an explicit function declaration. The is.prime function is.prime<-function(x) { if(x%%2==0) {return(FALSE) } else { y<-3 while(y<x) ifelse(x%%y==0,return(FALSE),y<-y+2) } return(TRUE) } Contrast the width of this version of the function with the length of the previous With the ifelse function R allows you to compress many lines of code A good example of the difference between a largely function programming language with objects (R) vs. a largely objected-oriented language with a core set of functions (Python) Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Bridging the Gap with RPy2 Python is widely adopted within the scientific and data communities The combination of SciPy/NumPy/matplotlib represents a powerful set of tools for scientific computing Companies like Enthought are focused on servicing this area Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Bridging the Gap with RPy2 Python is widely adopted within the scientific and data communities The combination of SciPy/NumPy/matplotlib represents a powerful set of tools for scientific computing Companies like Enthought are focused on servicing this area There are, however, many times when even this combination cannot match the analytical power of R—enter RPy2 RPy2 is a Python package that allows you to call R directly from within a Python script This is particularly useful if you want to use one of R’s base functions inside Python Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Bridging the Gap with RPy2 Python is widely adopted within the scientific and data communities The combination of SciPy/NumPy/matplotlib represents a powerful set of tools for scientific computing Companies like Enthought are focused on servicing this area There are, however, many times when even this combination cannot match the analytical power of R—enter RPy2 RPy2 is a Python package that allows you to call R directly from within a Python script This is particularly useful if you want to use one of R’s base functions inside Python For example, creating a function to mimic R’s lm in Python—even using the above combination—would be very time consuming. To avoid this, we will use RPy2 to call lm from within a Python script and plot the data. Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Bridging the Gap with RPy2 Python is widely adopted within the scientific and data communities The combination of SciPy/NumPy/matplotlib represents a powerful set of tools for scientific computing Companies like Enthought are focused on servicing this area There are, however, many times when even this combination cannot match the analytical power of R—enter RPy2 RPy2 is a Python package that allows you to call R directly from within a Python script This is particularly useful if you want to use one of R’s base functions inside Python For example, creating a function to mimic R’s lm in Python—even using the above combination—would be very time consuming. To avoid this, we will use RPy2 to call lm from within a Python script and plot the data. Disclaimer: the syntax can be a bit tricky to understand at first, so allow for a fairly steep learning curve! Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Calling lm from Python with RPy2 Creating a linear model in Python Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Calling lm from Python with RPy2 Creating a linear model in Python # First import RPy2 and create an instance of robjects import rpy2.robjects as robjects r = robjects.r Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Calling lm from Python with RPy2 Creating a linear model in Python # First import RPy2 and create an instance of robjects import rpy2.robjects as robjects r = robjects.r # Create the data by passing a Python list to RPy2, which interprets as an R vector ctl = robjects.FloatVector([4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14]) trt = robjects.FloatVector([4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69]) group = r.gl(2, 10, 20, labels = ["Ctl","Trt"]) weight = ctl + trt Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Calling lm from Python with RPy2 Creating a linear model in Python # First import RPy2 and create an instance of robjects import rpy2.robjects as robjects r = robjects.r # Create the data by passing a Python list to RPy2, which interprets as an R vector ctl = robjects.FloatVector([4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14]) trt = robjects.FloatVector([4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69]) group = r.gl(2, 10, 20, labels = ["Ctl","Trt"]) weight = ctl + trt # RPy2 uses Python dictionary types to index data robjects.globalEnv["weight"] = weight robjects.globalEnv["group"] = group Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Calling lm from Python with RPy2 Creating a linear model in Python # First import RPy2 and create an instance of robjects import rpy2.robjects as robjects r = robjects.r # Create the data by passing a Python list to RPy2, which interprets as an R vector ctl = robjects.FloatVector([4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14]) trt = robjects.FloatVector([4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69]) group = r.gl(2, 10, 20, labels = ["Ctl","Trt"]) weight = ctl + trt # RPy2 uses Python dictionary types to index data robjects.globalEnv["weight"] = weight robjects.globalEnv["group"] = group # Run the models lm_D9 = r.lm("weight ~ group") print(r.anova(lm_D9)) lm_D90 = r.lm("weight ~ group - 1") print(r.summary(lm_D90)) Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Calling lm from Python with RPy2 Creating a linear model in Python # First import RPy2 and create an instance of robjects import rpy2.robjects as robjects r = robjects.r # Create the data by passing a Python list to RPy2, which interprets as an R vector ctl = robjects.FloatVector([4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14]) trt = robjects.FloatVector([4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69]) group = r.gl(2, 10, 20, labels = ["Ctl","Trt"]) weight = ctl + trt # RPy2 uses Python dictionary types to index data robjects.globalEnv["weight"] = weight robjects.globalEnv["group"] = group # Run the models lm_D9 = r.lm("weight ~ group") print(r.anova(lm_D9)) lm_D90 = r.lm("weight ~ group - 1") print(r.summary(lm_D90)) For more info check out the RPy2 documentation (from where this example was stolen!) Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Resources Where to get Python Binaries for several operating systems and chipsets can be downloaded at http://www.python.org/download/ For OS X and Linux some version of Python comes pre-installed Some controversy over what version to use How to use Python Several very good development environments, once again SO has this covered My opinion, OS X: TextMate and Windows: Eclipse with PyDev, Linux: n/a How to Learn Python Several online resources: Dive into Python and Python Rocks For scientific computing: Enthought Webinars For applications to finance: NYC Financial Python User Group Drew Conway Learning R via Python...or the other way around
Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Resources Where to get Python Binaries for several operating systems and chipsets can be downloaded at http://www.python.org/download/ For OS X and Linux some version of Python comes pre-installed Some controversy over what version to use How to use Python Several very good development environments, once again SO has this covered My opinion, OS X: TextMate and Windows: Eclipse with PyDev, Linux: n/a How to Learn Python Several online resources: Dive into Python and Python Rocks For scientific computing: Enthought Webinars For applications to finance: NYC Financial Python User Group The best way to learn any language is to have a problem and solve it using that language! Drew Conway Learning R via Python...or the other way around

More Related Content

PDF
Access Control via Belnap Logic
byAndrada Astefanoaie
 
PDF
OpenWN-PT: a Brazilian Wordnet for all
byAlexandre Rademaker
 
PDF
Graph-to-Text Generation and its Applications to Dialogue
byJinho Choi
 
PDF
Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal ...
byJinho Choi
 
PDF
Competence-Level Prediction and Resume & Job Description Matching Using Conte...
byJinho Choi
 
PDF
Labelled Variables in Logic Programming: A First Prototipe in tuProlog
byRoberta Calegari
 
PDF
Arabic Phoneme Recognition using Hierarchical Neural Fuzzy Petri Net and LPC ...
byCSCJournals
 
PPTX
Introduction to python
byAyshwarya Baburam
 
Access Control via Belnap Logic
byAndrada Astefanoaie
 
OpenWN-PT: a Brazilian Wordnet for all
byAlexandre Rademaker
 
Graph-to-Text Generation and its Applications to Dialogue
byJinho Choi
 
Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal ...
byJinho Choi
 
Competence-Level Prediction and Resume & Job Description Matching Using Conte...
byJinho Choi
 
Labelled Variables in Logic Programming: A First Prototipe in tuProlog
byRoberta Calegari
 
Arabic Phoneme Recognition using Hierarchical Neural Fuzzy Petri Net and LPC ...
byCSCJournals
 
Introduction to python
byAyshwarya Baburam
 

What's hot

DOCX
Mit203 analysis and design of algorithms
bysmumbahelp
 
PDF
IJCTT-V4I9P137
byAmitesh Bhardwaj
 
PPTX
Python libraries
byVenkat Projects
 
PPTX
Language models
byMaryam Khordad
 
PDF
4.languagebasics
bySINGH PROJECTS
 
PDF
An Efficient Search Engine for Searching Desired File
byIDES Editor
 
PDF
LaTeX 3 Paper
byHirwanto Iwan
 
PDF
P4 P Update January 2009
byvsainteluce
 
PDF
Ebay News 2001 4 19 Earnings
byQuarterlyEarningsReports
 
PDF
Ebay News 2000 10 19 Earnings
byQuarterlyEarningsReports
 
PDF
Zero to Hero - Introduction to Python3
byChariza Pladin
 
PDF
Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF ...
byDBOnto
 
PPTX
Csr2011 june17 15_15_kaminski
byCSR2011
 
PDF
2P-Kt: logic programming with objects & functions in Kotlin
byGiovanni Ciatto
 
DOC
Pré Descobrimento Do Brasil
byecsette
 
PDF
A First Analysis of String APIs: the Case of Pharo
byESUG
 
PDF
Analysis of Similarity Measures between Short Text for the NTCIR-12 Short Tex...
byKozoChikai
 
PDF
Ai unit-4
byTribhuvan University
 
PDF
RProtoBuf: protocol buffers for R
byRomain Francois
 
PDF
Algorithms and Their Explanations
byMarco Benini
 
Mit203 analysis and design of algorithms
bysmumbahelp
 
IJCTT-V4I9P137
byAmitesh Bhardwaj
 
Python libraries
byVenkat Projects
 
Language models
byMaryam Khordad
 
4.languagebasics
bySINGH PROJECTS
 
An Efficient Search Engine for Searching Desired File
byIDES Editor
 
LaTeX 3 Paper
byHirwanto Iwan
 
P4 P Update January 2009
byvsainteluce
 
Ebay News 2001 4 19 Earnings
byQuarterlyEarningsReports
 
Ebay News 2000 10 19 Earnings
byQuarterlyEarningsReports
 
Zero to Hero - Introduction to Python3
byChariza Pladin
 
Parallel Materialisation of Datalog Programs in Centralised, Main-Memory RDF ...
byDBOnto
 
Csr2011 june17 15_15_kaminski
byCSR2011
 
2P-Kt: logic programming with objects & functions in Kotlin
byGiovanni Ciatto
 
Pré Descobrimento Do Brasil
byecsette
 
A First Analysis of String APIs: the Case of Pharo
byESUG
 
Analysis of Similarity Measures between Short Text for the NTCIR-12 Short Tex...
byKozoChikai
 
Ai unit-4
byTribhuvan University
 
RProtoBuf: protocol buffers for R
byRomain Francois
 
Algorithms and Their Explanations
byMarco Benini
 

Similar to Learning R via Python…or the other way around

PDF
An Analytics Toolkit Tour
byRory Winston
 
PDF
R Vs Python – The most trending debate of aspiring Data Scientists
byabhishekdf3
 
PDF
Python vs R: Key Differences and Practical Uses
bysahilbansaldf
 
PDF
Python vs R Programming language
bysahilbansaldf
 
PDF
Overview of Python and R Features .
byTechVidvan
 
PDF
Python vs. r for data science
byHugo Shi
 
PDF
R for Pythonistas (PyData NYC 2017)
byChristopher Roach
 
PDF
Python and R for quantitative finance
byLuca Sbardella
 
PDF
R Programming - part 1.pdf
byRohanBorgalli
 
PPTX
R vs python. Which one is best for data science
byStat Analytica
 
PPTX
Which programming language to learn R or Python - MeasureCamp XII
byMaggie Petrova
 
PDF
Migrating from matlab to python
byActiveState
 
PDF
R vs python
byPrwaTech
 
PDF
Introduction to r bddsil meetup
byNimrod Priell
 
PDF
Learning notes of r for python programmer (Temp1)
byChia-Chi Chang
 
PPTX
R language
byLearningTech
 
PPTX
Big data analytics with R tool.pptx
bysalutiontechnology
 
PDF
Study of R Programming
byIRJET Journal
 
PPTX
R Programming Language
byNareshKarela1
 
PPTX
1_Introduction.pptx
byranapoonam1
 
An Analytics Toolkit Tour
byRory Winston
 
R Vs Python – The most trending debate of aspiring Data Scientists
byabhishekdf3
 
Python vs R: Key Differences and Practical Uses
bysahilbansaldf
 
Python vs R Programming language
bysahilbansaldf
 
Overview of Python and R Features .
byTechVidvan
 
Python vs. r for data science
byHugo Shi
 
R for Pythonistas (PyData NYC 2017)
byChristopher Roach
 
Python and R for quantitative finance
byLuca Sbardella
 
R Programming - part 1.pdf
byRohanBorgalli
 
R vs python. Which one is best for data science
byStat Analytica
 
Which programming language to learn R or Python - MeasureCamp XII
byMaggie Petrova
 
Migrating from matlab to python
byActiveState
 
R vs python
byPrwaTech
 
Introduction to r bddsil meetup
byNimrod Priell
 
Learning notes of r for python programmer (Temp1)
byChia-Chi Chang
 
R language
byLearningTech
 
Big data analytics with R tool.pptx
bysalutiontechnology
 
Study of R Programming
byIRJET Journal
 
R Programming Language
byNareshKarela1
 
1_Introduction.pptx
byranapoonam1
 

More from Sid Xing

PPTX
Advanced dimensional modelling
bySid Xing
 
PDF
Informatica PowerCenter 8.6.1 for windows installation
bySid Xing
 
DOC
Oracle biee 使用administration创建仓库文件
bySid Xing
 
PDF
Oracle oracle database 11g product family
bySid Xing
 
PDF
Oracle sql & plsql
bySid Xing
 
PPTX
Multidimensional analysis
bySid Xing
 
PPT
ERwin overview introduction
bySid Xing
 
Advanced dimensional modelling
bySid Xing
 
Informatica PowerCenter 8.6.1 for windows installation
bySid Xing
 
Oracle biee 使用administration创建仓库文件
bySid Xing
 
Oracle oracle database 11g product family
bySid Xing
 
Oracle sql & plsql
bySid Xing
 
Multidimensional analysis
bySid Xing
 
ERwin overview introduction
bySid Xing
 

Recently uploaded

PPTX
Agentic AI Solutions and Services | Aeologic
byankitaeologic
 
PDF
The invisible key: Securing the new attack vector of OAuth tokens
byGianluca Varisco
 
PDF
From Bots to Brains: Getting Started with Agentic Automation in UiPath
byUiPathCommunity
 
PDF
NSSHOEU-J 4x35mm² Cable Specification.pdf
byAnhui Feichun Special Cable Co., Ltd.
 
PDF
UiPath DevConnect 2025: Introduction to Agentic Automation
byDianaGray10
 
PDF
Session 2 - Agentic Orchestration with UiPath Maestro
byDianaGray10
 
PDF
What is Google Cloud Platform (GCP)? A 5-Minute Guide for Beginners (2025)
byTeleglobal International
 
PPTX
BioVault.net ADW 2025 CODATA: SyftBox: a General Purpose Solution for Data Vi...
byMadhava Jay
 
PDF
A fool with a tool is still a fool - Plone Conference 2025
byAndreas Jung
 
PDF
Rivian's Push Notification Sub Stream with Mega Filter by Marcus Kim & Saahil...
byScyllaDB
 
PDF
Cassandra vs. ScyllaDB: Evolutionary Differences
byScyllaDB
 
PDF
Atlantix is an AI-first venture studio, building companies with AI at the core
byadmin625551
 
PDF
UnityNet Digital Sovereignty Checklist 2025-10-13
byLHelferty
 
PDF
Unlock This Brilliant App Freezur That Builds Brainrot Videos That Captivate ...
bySOFTTECHHUB
 
PPTX
UiPath Automation Suite Installation (Hands-On) [2/3]
bysuhanisingh58689
 
PDF
NDP Act GAID Simplified: From Confusion to Compliance
byMuiz Adeleke
 
PDF
Expert Python App Development Company for Modern Enterprises
bySynapseIndia
 
PDF
How to Measure Microsoft 365 Copilot Success and Manage AI Risk: Advanced Str...
byNikki Chapple
 
PDF
Ethical Initiatives in AI and accountability.pdf
byShiwani Gupta
 
PDF
Session 4 - Specialized AI Associate Series: UiPath Document Understanding O...
byDianaGray10
 
Agentic AI Solutions and Services | Aeologic
byankitaeologic
 
The invisible key: Securing the new attack vector of OAuth tokens
byGianluca Varisco
 
From Bots to Brains: Getting Started with Agentic Automation in UiPath
byUiPathCommunity
 
NSSHOEU-J 4x35mm² Cable Specification.pdf
byAnhui Feichun Special Cable Co., Ltd.
 
UiPath DevConnect 2025: Introduction to Agentic Automation
byDianaGray10
 
Session 2 - Agentic Orchestration with UiPath Maestro
byDianaGray10
 
What is Google Cloud Platform (GCP)? A 5-Minute Guide for Beginners (2025)
byTeleglobal International
 
BioVault.net ADW 2025 CODATA: SyftBox: a General Purpose Solution for Data Vi...
byMadhava Jay
 
A fool with a tool is still a fool - Plone Conference 2025
byAndreas Jung
 
Rivian's Push Notification Sub Stream with Mega Filter by Marcus Kim & Saahil...
byScyllaDB
 
Cassandra vs. ScyllaDB: Evolutionary Differences
byScyllaDB
 
Atlantix is an AI-first venture studio, building companies with AI at the core
byadmin625551
 
UnityNet Digital Sovereignty Checklist 2025-10-13
byLHelferty
 
Unlock This Brilliant App Freezur That Builds Brainrot Videos That Captivate ...
bySOFTTECHHUB
 
UiPath Automation Suite Installation (Hands-On) [2/3]
bysuhanisingh58689
 
NDP Act GAID Simplified: From Confusion to Compliance
byMuiz Adeleke
 
Expert Python App Development Company for Modern Enterprises
bySynapseIndia
 
How to Measure Microsoft 365 Copilot Success and Manage AI Risk: Advanced Str...
byNikki Chapple
 
Ethical Initiatives in AI and accountability.pdf
byShiwani Gupta
 
Session 4 - Specialized AI Associate Series: UiPath Document Understanding O...
byDianaGray10
 

Learning R via Python…or the other way around

  • 1.
    Learning R viaPython ...or the other way around Drew Conway Dept. of Politics - NYU January 7, 2010
  • 2.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources What We’ll Cover Brief review of Python The Zen of Python How are R and Python the same, and how are they different Similar Data Structures Python dictionary R list Example - Testing if an integer is prime Create a function and returning a value Using while-loops Bridging the gap with RPy2 Running a regression with R inside Python Python resources Drew Conway Learning R via Python...or the other way around
  • 3.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Fundamental Elements of Python The Python coder’s creed >>> import this The Zen of Python, by Tim Peters Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren’t special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one-- and preferably only one --obvious way to do it. Although that way may not be obvious at first unless you’re Dutch. Now is better than never. Although never is often better than *right* now. If the implementation is hard to explain, it’s a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea -- let’s do more of those! Drew Conway Learning R via Python...or the other way around
  • 4.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Drew Conway Learning R via Python...or the other way around
  • 5.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm Array of squared values # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] Drew Conway Learning R via Python...or the other way around
  • 6.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm Array of squared values # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] # In R we define a function in sapply > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Drew Conway Learning R via Python...or the other way around
  • 7.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm Array of squared values # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] # In R we define a function in sapply > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Object-oriented programming Both languages support robust class structures In R, there are the S3 and S4 classes Drew Conway Learning R via Python...or the other way around
  • 8.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm Array of squared values # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] # In R we define a function in sapply > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Object-oriented programming Both languages support robust class structures In R, there are the S3 and S4 classes Easily call C/Fortran code Due to their high-level, both languages provide functionality to call compiled code from lower-level languages Drew Conway Learning R via Python...or the other way around
  • 9.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm General purpose OOP vs. Functional OOP Python is a high-level language for Array of squared values application development # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) R is a collection of functions for data [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] analysis # In R we define a function in sapply > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Object-oriented programming Both languages support robust class structures In R, there are the S3 and S4 classes Easily call C/Fortran code Due to their high-level, both languages provide functionality to call compiled code from lower-level languages Drew Conway Learning R via Python...or the other way around
  • 10.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm General purpose OOP vs. Functional OOP Python is a high-level language for Array of squared values application development # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) R is a collection of functions for data [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] analysis # In R we define a function in sapply Syntax structure > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Python syntax forces readability through whitespace R emphasizes parsimony and nesting Object-oriented programming Both languages support robust class structures In R, there are the S3 and S4 classes Easily call C/Fortran code Due to their high-level, both languages provide functionality to call compiled code from lower-level languages Drew Conway Learning R via Python...or the other way around
  • 11.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm General purpose OOP vs. Functional OOP Python is a high-level language for Array of squared values application development # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) R is a collection of functions for data [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] analysis # In R we define a function in sapply Syntax structure > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Python syntax forces readability through whitespace R emphasizes parsimony and nesting Object-oriented programming Both languages support robust class Creating a function structures In R, there are the S3 and S4 classes # In Python functions are declared >>> def my func(x): Easily call C/Fortran code ... Due to their high-level, both languages provide functionality to call compiled code from lower-level languages Drew Conway Learning R via Python...or the other way around
  • 12.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm General purpose OOP vs. Functional OOP Python is a high-level language for Array of squared values application development # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) R is a collection of functions for data [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] analysis # In R we define a function in sapply Syntax structure > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Python syntax forces readability through whitespace R emphasizes parsimony and nesting Object-oriented programming Both languages support robust class Creating a function structures In R, there are the S3 and S4 classes # In Python functions are declared >>> def my func(x): Easily call C/Fortran code ... Due to their high-level, both languages # In R functions are assigned > my.func<-function(x) { ... } provide functionality to call compiled code from lower-level languages Drew Conway Learning R via Python...or the other way around
  • 13.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources How are R and Python alike, and how are they different? Similarities Differences Functional programming paradigm General purpose OOP vs. Functional OOP Python is a high-level language for Array of squared values application development # In Python we use a lambda nested in map >>> map(lambda x: x**2,range(1,11)) R is a collection of functions for data [1, 4, 9, 16, 25, 36, 49, 64, 81, 100] analysis # In R we define a function in sapply Syntax structure > sapply(1:10,function(x){return(x**2)}) [1] 1 4 9 16 25 36 49 64 81 100 Python syntax forces readability through whitespace R emphasizes parsimony and nesting Object-oriented programming Both languages support robust class Creating a function structures In R, there are the S3 and S4 classes # In Python functions are declared >>> def my func(x): Easily call C/Fortran code ... Due to their high-level, both languages # In R functions are assigned > my.func<-function(x) { ... } provide functionality to call compiled code from lower-level languages For more info see: StackOverlow.com discussion on topic Drew Conway Learning R via Python...or the other way around
  • 14.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Dictionaries In Python the dict type is a data structure that represents a key→value mapping Drew Conway Learning R via Python...or the other way around
  • 15.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Dictionaries In Python the dict type is a data structure that represents a key→value mapping Working with the dict type # Keys and values can be of any data type >>> fruit dict={"apple":1,"orange":[0.23,0.11],"banana":True } Drew Conway Learning R via Python...or the other way around
  • 16.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Dictionaries In Python the dict type is a data structure that represents a key→value mapping Working with the dict type # Keys and values can be of any data type >>> fruit dict={"apple":1,"orange":[0.23,0.11],"banana":True } # Can retrieve the keys and values as Python lists (vector) >>> fruit dict.keys() ["orange","apple","banana"] Drew Conway Learning R via Python...or the other way around
  • 17.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Dictionaries In Python the dict type is a data structure that represents a key→value mapping Working with the dict type # Keys and values can be of any data type >>> fruit dict={"apple":1,"orange":[0.23,0.11],"banana":True } # Can retrieve the keys and values as Python lists (vector) >>> fruit dict.keys() ["orange","apple","banana"] # Or create a (key,value) tuple >>> fruit dict.items() [("orange",[0.23,0.11]),("apple",1),("Banana",True)] Drew Conway Learning R via Python...or the other way around
  • 18.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Dictionaries In Python the dict type is a data structure that represents a key→value mapping Working with the dict type # Keys and values can be of any data type >>> fruit dict={"apple":1,"orange":[0.23,0.11],"banana":True } # Can retrieve the keys and values as Python lists (vector) >>> fruit dict.keys() ["orange","apple","banana"] # Or create a (key,value) tuple >>> fruit dict.items() [("orange",[0.23,0.11]),("apple",1),("Banana",True)] # This becomes especially useful when you master Python ‘‘list comprehension’’ Drew Conway Learning R via Python...or the other way around
  • 19.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Dictionaries In Python the dict type is a data structure that represents a key→value mapping Working with the dict type # Keys and values can be of any data type >>> fruit dict={"apple":1,"orange":[0.23,0.11],"banana":True } # Can retrieve the keys and values as Python lists (vector) >>> fruit dict.keys() ["orange","apple","banana"] # Or create a (key,value) tuple >>> fruit dict.items() [("orange",[0.23,0.11]),("apple",1),("Banana",True)] # This becomes especially useful when you master Python ‘‘list comprehension’’ The Python dictionary is an extremely flexible and useful data structure, making it one of the primary advantages of Python over other languages Luckily, there is a fairly direct mapping between the Python dict and R lists! Drew Conway Learning R via Python...or the other way around
  • 20.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources R Lists Similarly to the Python dictionary, the R list is a key→value mapping Drew Conway Learning R via Python...or the other way around
  • 21.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources R Lists Similarly to the Python dictionary, the R list is a key→value mapping Working with an R list > fruit.list<-list("apple"=1,"orange"=c(0.23,0.11),"banana"=TRUE) > fruit.list $apple [1] 1 $orange [1] 0.55 0.11 $banana [1] TRUE Drew Conway Learning R via Python...or the other way around
  • 22.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources R Lists Similarly to the Python dictionary, the R list is a key→value mapping Working with an R list > fruit.list<-list("apple"=1,"orange"=c(0.23,0.11),"banana"=TRUE) > fruit.list $apple [1] 1 $orange [1] 0.55 0.11 $banana [1] TRUE Notice, however, that R will always treat the value of the key/value pair as a vector; unlike Python, which does not care the value’s data type. Of the many R programming paradigms, the “vectorization of all data” is among the strongest. Drew Conway Learning R via Python...or the other way around
  • 23.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources R Lists Similarly to the Python dictionary, the R list is a key→value mapping Working with an R list > fruit.list<-list("apple"=1,"orange"=c(0.23,0.11),"banana"=TRUE) > fruit.list $apple [1] 1 $orange [1] 0.55 0.11 $banana [1] TRUE Notice, however, that R will always treat the value of the key/value pair as a vector; unlike Python, which does not care the value’s data type. Of the many R programming paradigms, the “vectorization of all data” is among the strongest. Using unlist > unlist(fruist.list) apple orange1 orange2 banana 1.00 0.55 0.11 1.00 Drew Conway Learning R via Python...or the other way around
  • 24.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Testing a Prime in Python We are interested in testing whether integer X is prime, and to do so we will: Declare a function Use a while-loop Return a boolean Drew Conway Learning R via Python...or the other way around
  • 25.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Testing a Prime in Python We are interested in testing whether integer X is prime, and to do so we will: Declare a function Use a while-loop Return a boolean The is prime function def is prime(x): if x%2 == 0: return False else: y=3 while y<x: if x%y==0: return False else: y+=2 return True Drew Conway Learning R via Python...or the other way around
  • 26.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Testing a Prime in Python We are interested in testing whether integer X is prime, and to do so we will: Declare a function Use a while-loop Return a boolean The is prime function def is prime(x): if x%2 == 0: return False else: y=3 while y<x: if x%y==0: return False else: y+=2 return True Notice the number of lines in this simple function. In contrast to R, and many other programming languages, Python’s whitespace and indentation requirements force verbose code; however, the end result is very readable. Drew Conway Learning R via Python...or the other way around
  • 27.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Testing a Prime in R Unlike Python where functions are declared explicitly, R creates functions through assignment. In many ways, this is somewhere between the Python lambda function and an explicit function declaration. Drew Conway Learning R via Python...or the other way around
  • 28.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Testing a Prime in R Unlike Python where functions are declared explicitly, R creates functions through assignment. In many ways, this is somewhere between the Python lambda function and an explicit function declaration. The is.prime function is.prime<-function(x) { if(x%%2==0) {return(FALSE) } else { y<-3 while(y<x) ifelse(x%%y==0,return(FALSE),y<-y+2) } return(TRUE) } Drew Conway Learning R via Python...or the other way around
  • 29.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Testing a Prime in R Unlike Python where functions are declared explicitly, R creates functions through assignment. In many ways, this is somewhere between the Python lambda function and an explicit function declaration. The is.prime function is.prime<-function(x) { if(x%%2==0) {return(FALSE) } else { y<-3 while(y<x) ifelse(x%%y==0,return(FALSE),y<-y+2) } return(TRUE) } Contrast the width of this version of the function with the length of the previous With the ifelse function R allows you to compress many lines of code A good example of the difference between a largely function programming language with objects (R) vs. a largely objected-oriented language with a core set of functions (Python) Drew Conway Learning R via Python...or the other way around
  • 30.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Bridging the Gap with RPy2 Python is widely adopted within the scientific and data communities The combination of SciPy/NumPy/matplotlib represents a powerful set of tools for scientific computing Companies like Enthought are focused on servicing this area Drew Conway Learning R via Python...or the other way around
  • 31.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Bridging the Gap with RPy2 Python is widely adopted within the scientific and data communities The combination of SciPy/NumPy/matplotlib represents a powerful set of tools for scientific computing Companies like Enthought are focused on servicing this area There are, however, many times when even this combination cannot match the analytical power of R—enter RPy2 RPy2 is a Python package that allows you to call R directly from within a Python script This is particularly useful if you want to use one of R’s base functions inside Python Drew Conway Learning R via Python...or the other way around
  • 32.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Bridging the Gap with RPy2 Python is widely adopted within the scientific and data communities The combination of SciPy/NumPy/matplotlib represents a powerful set of tools for scientific computing Companies like Enthought are focused on servicing this area There are, however, many times when even this combination cannot match the analytical power of R—enter RPy2 RPy2 is a Python package that allows you to call R directly from within a Python script This is particularly useful if you want to use one of R’s base functions inside Python For example, creating a function to mimic R’s lm in Python—even using the above combination—would be very time consuming. To avoid this, we will use RPy2 to call lm from within a Python script and plot the data. Drew Conway Learning R via Python...or the other way around
  • 33.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Bridging the Gap with RPy2 Python is widely adopted within the scientific and data communities The combination of SciPy/NumPy/matplotlib represents a powerful set of tools for scientific computing Companies like Enthought are focused on servicing this area There are, however, many times when even this combination cannot match the analytical power of R—enter RPy2 RPy2 is a Python package that allows you to call R directly from within a Python script This is particularly useful if you want to use one of R’s base functions inside Python For example, creating a function to mimic R’s lm in Python—even using the above combination—would be very time consuming. To avoid this, we will use RPy2 to call lm from within a Python script and plot the data. Disclaimer: the syntax can be a bit tricky to understand at first, so allow for a fairly steep learning curve! Drew Conway Learning R via Python...or the other way around
  • 34.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Calling lm from Python with RPy2 Creating a linear model in Python Drew Conway Learning R via Python...or the other way around
  • 35.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Calling lm from Python with RPy2 Creating a linear model in Python # First import RPy2 and create an instance of robjects import rpy2.robjects as robjects r = robjects.r Drew Conway Learning R via Python...or the other way around
  • 36.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Calling lm from Python with RPy2 Creating a linear model in Python # First import RPy2 and create an instance of robjects import rpy2.robjects as robjects r = robjects.r # Create the data by passing a Python list to RPy2, which interprets as an R vector ctl = robjects.FloatVector([4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14]) trt = robjects.FloatVector([4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69]) group = r.gl(2, 10, 20, labels = ["Ctl","Trt"]) weight = ctl + trt Drew Conway Learning R via Python...or the other way around
  • 37.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Calling lm from Python with RPy2 Creating a linear model in Python # First import RPy2 and create an instance of robjects import rpy2.robjects as robjects r = robjects.r # Create the data by passing a Python list to RPy2, which interprets as an R vector ctl = robjects.FloatVector([4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14]) trt = robjects.FloatVector([4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69]) group = r.gl(2, 10, 20, labels = ["Ctl","Trt"]) weight = ctl + trt # RPy2 uses Python dictionary types to index data robjects.globalEnv["weight"] = weight robjects.globalEnv["group"] = group Drew Conway Learning R via Python...or the other way around
  • 38.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Calling lm from Python with RPy2 Creating a linear model in Python # First import RPy2 and create an instance of robjects import rpy2.robjects as robjects r = robjects.r # Create the data by passing a Python list to RPy2, which interprets as an R vector ctl = robjects.FloatVector([4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14]) trt = robjects.FloatVector([4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69]) group = r.gl(2, 10, 20, labels = ["Ctl","Trt"]) weight = ctl + trt # RPy2 uses Python dictionary types to index data robjects.globalEnv["weight"] = weight robjects.globalEnv["group"] = group # Run the models lm_D9 = r.lm("weight ~ group") print(r.anova(lm_D9)) lm_D90 = r.lm("weight ~ group - 1") print(r.summary(lm_D90)) Drew Conway Learning R via Python...or the other way around
  • 39.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Calling lm from Python with RPy2 Creating a linear model in Python # First import RPy2 and create an instance of robjects import rpy2.robjects as robjects r = robjects.r # Create the data by passing a Python list to RPy2, which interprets as an R vector ctl = robjects.FloatVector([4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14]) trt = robjects.FloatVector([4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69]) group = r.gl(2, 10, 20, labels = ["Ctl","Trt"]) weight = ctl + trt # RPy2 uses Python dictionary types to index data robjects.globalEnv["weight"] = weight robjects.globalEnv["group"] = group # Run the models lm_D9 = r.lm("weight ~ group") print(r.anova(lm_D9)) lm_D90 = r.lm("weight ~ group - 1") print(r.summary(lm_D90)) For more info check out the RPy2 documentation (from where this example was stolen!) Drew Conway Learning R via Python...or the other way around
  • 40.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Resources Where to get Python Binaries for several operating systems and chipsets can be downloaded at http://www.python.org/download/ For OS X and Linux some version of Python comes pre-installed Some controversy over what version to use How to use Python Several very good development environments, once again SO has this covered My opinion, OS X: TextMate and Windows: Eclipse with PyDev, Linux: n/a How to Learn Python Several online resources: Dive into Python and Python Rocks For scientific computing: Enthought Webinars For applications to finance: NYC Financial Python User Group Drew Conway Learning R via Python...or the other way around
  • 41.
    Introduction Review of Python Similar Data Structures Example - Testing a Prime Bridging the Gap with RPy2 Python Resources Python Resources Where to get Python Binaries for several operating systems and chipsets can be downloaded at http://www.python.org/download/ For OS X and Linux some version of Python comes pre-installed Some controversy over what version to use How to use Python Several very good development environments, once again SO has this covered My opinion, OS X: TextMate and Windows: Eclipse with PyDev, Linux: n/a How to Learn Python Several online resources: Dive into Python and Python Rocks For scientific computing: Enthought Webinars For applications to finance: NYC Financial Python User Group The best way to learn any language is to have a problem and solve it using that language! Drew Conway Learning R via Python...or the other way around