Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Like this presentation? Why not share!

- Simple big data, in Python by Gael Varoquaux 6212 views
- Step By Step How To Install Oracle XE by Achmad Solichin 33188 views
- AmI 2015 - Python basics by Luigi De Russis 850 views
- Introduction to Machine Learning wi... by Matt Hagy 2980 views
- Python business intelligence (PyDat... by Stefan Urbanek 17532 views
- R -> Python by Kazufumi Ohkawa 3068 views

3,218 views

3,082 views

3,082 views

Published on

Published in:
Technology

No Downloads

Total views

3,218

On SlideShare

0

From Embeds

0

Number of Embeds

453

Shares

0

Downloads

0

Comments

0

Likes

13

No embeds

No notes for slide

- 1. Learning R via Python...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 ResourcesWhat 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 ResourcesFundamental 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 ResourcesHow 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 ResourcesHow 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 ResourcesHow 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 ResourcesHow 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 ResourcesHow 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 ResourcesHow 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 ResourcesHow 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 ResourcesHow 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 ResourcesHow 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 ResourcesHow 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 ResourcesPython 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 ResourcesPython 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 ResourcesPython 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 ResourcesPython 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 ResourcesPython 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 ResourcesPython 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 ResourcesR 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 ResourcesR 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 ResourcesR 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 ResourcesR 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 ResourcesTesting 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 ResourcesTesting 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 ResourcesTesting 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 ResourcesTesting 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 ResourcesTesting 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 ResourcesTesting 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 ResourcesBridging 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 ResourcesBridging 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 ResourcesBridging 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 ResourcesBridging 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 ResourcesCalling 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 ResourcesCalling 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 ResourcesCalling 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 ResourcesCalling 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 ResourcesCalling 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 ResourcesCalling 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 ResourcesPython 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 ResourcesPython 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

No public clipboards found for this slide

×
### Save the most important slides with Clipping

Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.

Be the first to comment