Sections in this Chapter:

Write & run scripts

In the first Chapter, we are coding within the interactive mode of R/Python. When we are working on the real world projects, using an Integrated development environment (IDE) is a more pragmatic choice. There are not many choices for R IDE, and among them RStudio1 is the best one I have used so far. As for Python, I would recommend either Visual Studio Code2 or PyCharm3. But of course, you could use any text editor to write R/Python scripts.

Let’s write a simple script to print Hello World! in R/Python. I have made a directory chapter2 on my disk, the R script is saved as hello_world.R and the Python script is saved as hello_world.py, inside the directory.

R

chapter2/hello_world.R

1 print("Hello World!")

Python

chapter2/hello_world.py

1 print("Hello World!")

There are a few ways to run the R script. For example, we can run the script from the console with the r -f filename command. Also, we can open the R interactive session and use the source() function. I would recommend the second approach with source() function. As for the Python script, we can run it from the console.

R
1 chapter2 $ls
2 hello_world.R	hello_world.py
3 chapter2 $r -f hello_world.R 
4 > print("Hello World!")
5 [1] "Hello World!"
6 
7 chapter2 $r
8 > source('hello_world.R')
9 [1] "Hello World!"
Python
1 chapter2 $ls
2 hello_world.R	hello_world.py
3 
4 chapter2 $python3.7 hello_world.py 
5 Hello World!

Debugging

Debugging is one of the most important aspects of programming. What is debugging in programming? The programs we write might include errors/bugs and debugging is a step-by-step process to find and remove the errors/bugs in order to get the desired results.

If you are smart enough or the bugs are evident enough then you can debug the program on your mind without using a computer at all. But in general we need some tools/techniques to help us with debugging.

Print

Most of programming languages provide the functionality of printing, which is a natural way of debugging. By trying to place print statements at different positions we may finally catch the bugs. When I use print to debug, it’s feeling like playing the game of minesweeper. In Python, there is a module called logging4 which could be used for debugging like the print function, but in a more elegant fashion.

Browser in R and pdb in Python

In R, there is a function browser() which interrupts the execution and allows the inspection of the current environment. Similarly, there is a module pdb in Python that provides more debugging features. We would only focus on the basic usages of browser() and the set_trace() function in pdb module.
The essential difference between debugging using print() and browser() and set_trace() is that the latter functions allows us to debug in an interactive mode.

Let’s write a function which takes a sorted vector/list v and a target value x as input and returns the leftmost index pos of the sorted vector/list so that v[pos]>=x. Since v is already sorted, we may simply loop through it from left to right to find pos.

R

chapter2/find_pos.R

 1 find_pos=function(v,x){
 2   for (i in 1:length(v)){
 3     if (v[i]>=x){
 4       return(i)
 5     }
 6   }
 7 }
 8 
 9 v=c(1,2,5,10)
10 print(find_pos(v,-1))
11 print(find_pos(v,4))
12 print(find_pos(v,11))

Python

chapter2/find_pos.py

1 def find_pos(v,x):
2   for i in range(len(v)):
3     if v[i]>=x:
4       return i
5 
6 v=[1,2,5,10]
7 print(find_pos(v,-1))
8 print(find_pos(v,4))
9 print(find_pos(v,11))

Now let’s run these two scripts.

R
1 chapter2 $r
2 > source('find_pos.R')
3 [1] 1
4 [1] 3
5 NULL
Python
1 chapter2 $python3.7 find_pos.py 
2 0
3 2
4 None

When x=11, the function returns NULL in R and None in Python because there is no such element in v larger than x.
The implementation above is trivial, but not efficient. If you have some background in data structures and algorithms, you probably know this question can be solved by binary search. The essential idea of binary search comes from divide-and-conquer5. Since v is already sorted, we may divide it into two partitions by cutting it from the middle, and then we get the left partition and the right partition. v is sorted implies that both the left partition and the right partition are also sorted. If the target value x is larger than the rightmost element in the left partition, we can just discard the left partition and search x within the right partition. Otherwise, we can discard the right partition and search x within the left partition. Once we have determined which partition to search, we may apply the idea recursively so that in each step we reduce the size of v by half. If the length of v is denoted as n, in terms of big O notation6, the run time complexity of binary search is $\mathcal{O}(\log{}n)$, compared with $\mathcal{O}(n)$ of the for-loop implementation.

The code below implements the binary search solution to our question (It is more intuitive to do it with recursion but here I write it with iteration since tail recursion optimization7 in R/Python is not supported).

R

chapter2/find_binary_search_buggy.R

 1 binary_search_buggy=function(v,x){
 2   start = 1
 3   end = length(v)
 4   while (start<end){
 5     mid = (start+end) %/% 2 # %/% is the floor division operator
 6     if (v[mid]>=x){
 7       end = mid
 8     }else{
 9       start = mid+1
10     }
11   }
12   return(start)
13 }
14 v=c(1,2,5,10)
15 print(binary_search_buggy(v,-1))
16 print(binary_search_buggy(v,5))
17 print(binary_search_buggy(v,11))

Python

chapter2/find_binary_search_buggy.py

 1 def binary_search_buggy(v,x):
 2   start,end = 0,len(v)-1
 3   while start<end:
 4     mid = (start+end)//2  # // is the floor division operator
 5     if v[mid]>=x:
 6       end = mid
 7     else:
 8       start = mid+1
 9   return start
10 
11 v=[1,2,5,10]
12 print(binary_search_buggy(v,-1))
13 print(binary_search_buggy(v,5))
14 print(binary_search_buggy(v,11))

Now let’s run these two binary search scripts.

R
1 chapter2 $r
2 > source( 'binary_search_buggy.R')
3 [1] 1
4 [1] 3
5 [1] 4
Python
1 chapter2 $python3.7 binary_search_buggy.py
2 0
3 2
4 3

The binary search solutions don’t work as expected when x=11. We write two new scripts.

R

chapter2/find_binary_search_buggy_debug.R

 1 binary_search_buggy=function(v,x){
 2   browser()
 3   start = 1
 4   end = length(v)
 5   while (start<end){
 6     mid = (start+end)
 7     if (v[mid]>=x){
 8       end = mid
 9     }else{
10       start = mid+1
11     }
12   }
13   return(start)
14 }
15 v=c(1,2,5,10)
16 print(binary_search_buggy(v,11))

Python

chapter2/find_binary_search_buggy_debug.py

 1 from pdb import set_trace
 2 def binary_search_buggy(v,x):
 3   set_trace()
 4   start,end = 0,len(v)-1
 5   while start<end:
 6     mid = (start+end)//2
 7     if v[mid]>=x:
 8       end = mid
 9     else:
10       start = mid+1
11   return start
12 
13 v=[1,2,5,10]
14 print(binary_search_buggy(v,11))

Let’s try to debug the programs with the help of browser() and set_trace().

R
 1 > source('binary_search_buggy_debug.R')
 2 Called from: binary_search_buggy(v, 11)
 3 Browse[1]> ls()
 4 [1] "v" "x"
 5 Browse[1]> n
 6 debug at binary_search_buggy_debug.R#3: start = 1
 7 Browse[2]> n
 8 debug at binary_search_buggy_debug.R#4: end = length(v)
 9 Browse[2]> n
10 debug at binary_search_buggy_debug.R#5: while (start < end) {
11     mid = (start + end)%/%2
12     if (v[mid] >= x) {
13         end = mid
14     }
15     else {
16         start = mid + 1
17     }
18 }
19 Browse[2]> n
20 debug at binary_search_buggy_debug.R#6: mid = (start + end)%/%2
21 Browse[2]> n
22 debug at binary_search_buggy_debug.R#7: if (v[mid] >= x) {
23     end = mid
24 } else {
25     start = mid + 1
26 }
27 Browse[2]> n
28 debug at binary_search_buggy_debug.R#10: start = mid + 1
29 Browse[2]> n
30 debug at binary_search_buggy_debug.R#5: (while) start < end
31 Browse[2]> n
32 debug at binary_search_buggy_debug.R#6: mid = (start + end)%/%2
33 Browse[2]> n
34 debug at binary_search_buggy_debug.R#7: if (v[mid] >= x) {
35     end = mid
36 } else {
37     start = mid + 1
38 }
39 Browse[2]> n
40 debug at binary_search_buggy_debug.R#10: start = mid + 1
41 Browse[2]> n
42 debug at binary_search_buggy_debug.R#5: (while) start < end
43 Browse[2]> start
44 [1] 4
45 Browse[2]> n
46 debug at binary_search_buggy_debug.R#13: return(start)
47 Browse[2]> n
48 [1] 4

In the R code snippet above, we placed the browser() function on the top of the function binary_search_buggy. Then when we call the function we enter into the debugging environment. By calling ls() we see all variables in the current debugging scope, i.e., v,x. Typing n will evaluate the next statement. After typing n a few times, we finally exit from the while loop because start = 4 such that start < end is FALSE. As a result, the function just returns the value of start, i.e., 4. To exit from the debugging environment, we can type Q; to continue the execution we can type c.

The root cause is that we didn’t deal with the corner case when the target value x is larger than the last/largest element in v correctly.

Let’s debug the Python function using pdb module.

Python
 1 chapter2 $python3.7 binary_search_buggy_debug.py 
 2 > chapter2/binary_search_buggy_debug.py(4) binary_search_buggy()
 3 -> start,end = 0,len(v)-1
 4 (Pdb) n
 5 > chapter2/binary_search_buggy_debug.py(5) binary_search_buggy()
 6 -> while start<end:
 7 (Pdb) l
 8   1  	from pdb import set_trace
 9   2  	def binary_search_buggy(v,x):
10   3  	  set_trace()
11   4  	  start,end = 0,len(v)-1
12   5  ->	  while start<end:
13   6  	    mid = (start+end)//2
14   7  	    if v[mid]>=x:
15   8  	      end = mid
16   9  	    else:
17  10  	      start = mid+1
18  11  	  return start
19 (Pdb) b 7
20 Breakpoint 1 at chapter2/binary_search_buggy_debug.py:7
21 (Pdb) c
22 > chapter2/binary_search_buggy_debug.py(7) binary_search_buggy()
23 -> if v[mid]>=x:
24 (Pdb) c
25 > chapter2/binary_search_buggy_debug.py(7) binary_search_buggy()
26 -> if v[mid]>=x:
27 (Pdb) mid
28 2
29 (Pdb) n
30 > chapter2/binary_search_buggy_debug.py(10) binary_search_buggy()
31 -> start = mid+1
32 (Pdb) n
33 > chapter2/binary_search_buggy_debug.py(5) binary_search_buggy()
34 -> while start<end:
35 (Pdb) start
36 3
37 (Pdb) n
38 > chapter2/binary_search_buggy_debug.py(11) binary_search_buggy()
39 -> return start

Similar to R, command n would evaluate the next statement in pdb. Typing command l would show the current line of current execution. Command b line_number would set the corresponding line as a break point; and c would continue the execution until the next breakpoint (if exists).

In R, besides the browser() function there are a pair of functions debug() and undebug() which are also very handy when we try to debug a function; especially when the function is wrapped in a package. More specifically, the debug function would invoke the debugging environment whenever we call the function to debug. See the example below how we invoke the debugging environment for the sd function (standard deviation calculation).

R
 1 > x=c(1,1,2)
 2 > debug(sd)
 3 > sd(x)
 4 debugging in: sd(x)
 5 debug: sqrt(var(if (is.vector(x) || is.factor(x)) x else as.double(x), 
 6     na.rm = na.rm))
 7 Browse[2]> ls()
 8 [1] "na.rm" "x"    
 9 Browse[2]> Q
10 > undebug(sd)
11 > sd(x)
12 [1] 0.5773503

The binary_search solutions are fixed below.

R

chapter2/find_binary_search.R

 1 binary_search=function(v,x){
 2   if (x>v[length(v)]){return(NULL)}
 3   start = 1
 4   end = length(v)
 5   while (start<end){
 6     mid = (start+end)
 7     if (v[mid]>=x){
 8       end = mid
 9     }else{
10       start = mid+1
11     }
12   }
13   return(start)
14 }

Python

chapter2/find_binary_search.py

 1 def binary_search(v,x):
 2   if x>v[-1]: return
 3   start,end = 0,len(v)-1
 4   while start<end:
 5     mid = (start+end)//2
 6     if v[mid]>=x:
 7       end = mid
 8     else:
 9       start = mid+1
10   return start

Benchmarking

By benchmarking, I mean measuring the entire operation time of a piece of program. There is another term called profiling which is related to benchmarking. But profiling is more complex since it commonly aims at understanding the behavior of the program and optimizing the program in terms of time elapsed during the operation.

In R, I like using the microbenchmark package. And in Python, timeit module is a good tool to use when we want to benchmark a small bits of Python code.

As mentioned before, the run time complexity of binary search is better than that of a for-loop search. We can do benchmarking to compare the two algorithms.

R

chapter2/benchmark.R

 1 library(microbenchmark)
 2 source('binary_search.R')
 3 source('find_pos.R')
 4 
 5 v=1:10000
 6 
 7 # call each function 1000 times;
 8 # each time we randomly select an integer as the target value
 9 
10 # for-loop solution
11 set.seed(2019)
12 print(microbenchmark(find_pos(v,sample(10000,1)), times=1000))
13 # binary-search solution
14 set.seed(2019)
15 print(microbenchmark(binary_search(v, sample(10000,1)), times=1000))

In the R code above, times=1000 means we want to call the function 1000 times in the benchmarking process. The sample() function is used to draw samples from a set of elements. Specifically, we pass the argument 1 to sample() to draw a single element. It’s the first time we use set.seed() function in this book. In R/Python, we draw random numbers based on the pseudorandom number generator (PRNG) algorithm8. The sequence of numbers generated by PRNG is completed determined by an initial value, i.e., the seed. Whenever a program involves the usage of PRNG, it is better to set the seed in order to get replicable results (see the example below).

R
1 > set.seed(2019)
2 > rnorm(1)
3 [1] 0.7385227
4 > rnorm(1)
5 [1] -0.5147605
R
1 > set.seed(2019)
2 > rnorm(1)
3 [1] 0.7385227
4 > set.seed(2019)
5 > rnorm(1)
6 [1] 0.7385227

Now let’s run the R script to see the benchmarking result.

R
1 > source('benchmark.R')
2 Unit: microseconds
3                           expr  min       lq     mean   median       uq     max neval
4  find_pos(v, sample(10000, 1)) 3.96 109.5385 207.6627 207.5565 307.8875 536.171 1000
5 
6 Unit: microseconds
7                                expr   min     lq    mean median     uq     max neval
8  binary_search(v, sample(10000, 1)) 5.898 6.3325 14.2159 6.6115 7.3635 6435.57 1000

The binary_search solution is much more efficient based on the benchmarking result.
Doing the same benchmarking in Python is a bit of complicated.

Python

chapter2/benchmark.py

 1 from binary_search import binary_search
 2 from find_pos import find_pos
 3 import timeit
 4 import random
 5 
 6 v=list(range(1,10001))
 7 
 8 def test_for_loop(n):
 9   random.seed(2019)
10   for _ in range(n):
11     find_pos(v,random.randint(1,10000))
12 
13 def test_bs(n):
14   random.seed(2019)
15   for _ in range(n):
16     binary_search(v,random.randint(1,10000))
17 
18 # for-loop solution
19 print(timeit.timeit('test_for_loop(1000)', setup='from __main__ import test_for_loop',number=1))
20 # binary_search solution
21 print(timeit.timeit('test_bs(1000)', setup='from __main__ import test_bs',number=1))

The most interesting part of the Python code above is from __main__ import. Let’s ignore it for now, and we would revisit it later.

Below is the benchmarking result in Python (the unit is second).

Python
1 chapter2 $python3 benchmark.py
2 0.284618441
3 0.00396658900000002

Vectorization

In parallel computing, automatic vectorization9 means a program in a scalar implementation is converted to a vector implementation which process multiple pairs of operands simultaneously by compilers that feature auto-vectorization. For example, let’s calculate the element-wise sum of two arrays x and y of the same length in C programming language.

C
1 int x[4] = {1,2,3,4};
2 int y[4] = {0,1,2,3};
3 int z[4];
4 for (int i=0;i<4;i++){
5     z[i]=x[i]+y[i];
6 }

The C code above might be vectorized by the compiler so that the actual number of iterations performed could be less than 4. If 4 pairs of operands are processed at once, there would be only 1 iteration. Automatic vectorization may make the program runs much faster in some languages like C. However, when we talk about vectorization in R/Python, it is different from automatic vectorization. Vectorization in R/Python usually refers to the human effort paid to avoid for-loops. First, let’s see some examples of how for-loops may slow your programs in R/Python.

R

chapter2/vectorization_1.R

 1 library(microbenchmark)
 2 
 3 # generate n standard normal r.v
 4 rnorm_loop = function(n){
 5 x=rep(0,n)
 6 for (i in 1:n) {x[i]=rnorm(1)}
 7 }
 8 
 9 rnorm_vec = function(n){
10 x=rnorm(n)
11 }
12 
13 n=100
14 # for loop
15 print(microbenchmark(rnorm_loop(n),times=1000))
16 # vectorize
17 print(microbenchmark(rnorm_vec(n),times=1000))

Running the R code results in the following result on my local machine.

R
1 > source('vectorization_1.R')
2 Unit: microseconds
3           expr     min      lq     mean   median      uq     max neval
4  rnorm_loop(n) 131.622 142.699 248.7603 145.3995 270.212 16355.6  1000
5 Unit: microseconds
6          expr   min    lq     mean median    uq      max neval
7  rnorm_vec(n) 6.696 7.128 10.87463  7.515 8.291 2422.338  1000
Python
 1 import timeit
 2 import numpy as np
 3 
 4 def rnorm_for_loop(n):
 5   x=[0]*n # create a list with n 0s
 6   np.random.seed(2019)
 7   for _ in range(n):
 8     np.random.normal(0,1,1)
 9 
10 def rnorm_vec(n):
11   np.random.seed(2019)
12   x = np.random.normal(0,1,n)
13  
14 print("for loop")
15 print(f'{timeit.timeit("rnorm_for_loop(100)", setup="from __main__ import rnorm_for_loop",number=1000):.6f} seconds')
16 print("vectorized")
17 print(f'{timeit.timeit("rnorm_vec(100)", setup="from __main__ import rnorm_vec",number=1000):.6f} seconds')

Please note that in this Python example we are using the random submodule of numpy module instead of the built-in random module since random module doesn’t provide the vectorized version of random number generation function. Running the Python code results in the following result on my local machine.

Also, the timeit.timeit measures the total time to run the main statements number times, but not the average time per run.

Python
1 chapter2 $python3.7 vectorization_1.py 
2 for loop
3 0.258466 seconds
4 vectorized
5 0.008213 seconds

In either R or Python, the vectorized version of random normal random variable (r.v.) is significantly faster than the scalar version. It is worth noting the usage of the print(f'') statement in the Python code, which is different from the way how we print the object of Complex class in \autoref{ch:chp1. In the code above, we use the f-string10 which is a literal string prefixed with 'f' containing expressions inside \{\ which would be replaced with their values. f-string was a feature introduced since Python 3.6. If you are familiar with Scala, you may find that this feature is quite similar with the string interpolation mechanism introduced since Scala 2.10.

It’s also worth noting that lots of built-in functions in R are already vectorized, such as the basic arithmetic operators, comparison operators, ifelse(), element-wise logical operators &,|. But the logical operators &&, || are not vectorized.

In addition to vectorization, there are also some built-in functions which may help to avoid the usages of for-loops. For example, in R we might be able use the apply family of functions to replace for-loops; and in Python the map() function can also be useful. In the Python pandas module, there are also many usages of map/apply methods. But in general the usage of apply/map functions has little or nothing to do with performance improvement. However, appropriate usages of such functions may help with the readability of the program. Compared with the apply family of functions in R, I think the do.call() function is more useful in practice. We would spend some time in do.call() later.

Application – Biham–Middleton–Levine (BML) traffic model

Considering the importance of vectorization in scientific programming, let’s try to get more familiar with vectorization thorough the Biham–Middleton–Levine (BML) traffic model11. The BML model is very important in modern studies of traffic flow since it exhibits a sharp phase transition from free flowing status to a fully jammed status. A simplified BML model could be characterized as follows:

The BML model specified above is implemented in both R/Python as follows to illustrate the usage of vectorization.

R

chapter2/BML.R

 1 library(R6)
 2 BML = R6Class(
 3   "BML",
 4   public = list(
 5     # alpha is the parameter of the uniform distribution to control particle distribution's density
 6     # m*n is the dimension of the lattice
 7     alpha = NULL,
 8     m = NULL,
 9     n = NULL,
10     lattice = NULL,
11     initialize = function(alpha, m, n) {
12       self$alpha = alpha
13       self$m = m
14       self$n = n
15       self$initialize_lattice()
16     },
17     initialize_lattice = function() {
18       # 0 -> empty site
19       # 1 -> blue particle
20       # 2 -> red particle
21       u = runif(self$m * self$n)
22       # the usage of L is to make sure the elements in particles are of type integer;
23       # otherwise they would be created as double
24       particles = rep(0L, self$m * self$n)
25       # doing inverse transform sampling
26       particles[(u > self$alpha) &
27                   (u <= (self$alpha + 1.0) / 2)] = 1L
28       particles[u > (self$alpha + 1.0) / 2] = 2L
29       self$lattice = array(particles, c(self$m, self$n))
30     },
31     odd_step = function() {
32       blue.index = which(self$lattice == 1L, arr.ind = TRUE)
33       # make a copy of the index
34       blue.up.index = blue.index
35       # blue particles move 1 site up
36       blue.up.index[, 1] = blue.index[, 1] - 1L
37       # periodic boundary condition
38       blue.up.index[blue.up.index[, 1] == 0L, 1] = self$m
39       # find which moves are feasible
40       blue.movable = self$lattice[blue.up.index] == 0L
41       # move blue particles one site up
42       # drop=FALSE prevents the 2D array degenerates to 1D array
43       self$lattice[blue.up.index[blue.movable, , drop = FALSE]] = 1L
44       self$lattice[blue.index[blue.movable, , drop = FALSE]] = 0L
45     },
46     even_step = function() {
47       red.index = which(self$lattice == 2L, arr.ind = TRUE)
48       # make a copy of the index
49       red.right.index = red.index
50       # red particles move 1 site right
51       red.right.index[, 2] = red.index[, 2] + 1L
52       # periodic boundary condition
53       red.right.index[red.right.index[, 2] == (self$n + 1L), 2] = 1
54       # find which moves are feasible
55       red.movable = self$lattice[red.right.index] == 0L
56       # move red particles one site right
57       self$lattice[red.right.index[red.movable, , drop = FALSE]] = 2L
58       self$lattice[red.index[red.movable, , drop = FALSE]] = 0L
59     }
60   )
61 )

Now we can create a simple BML system on a $5\times5$ lattice using the R code above.

R
 1 > source('BML.R')
 2 > set.seed(2019)
 3 > bml=BML$new(0.4,5,5)
 4 > bml$lattice
 5      [,1] [,2] [,3] [,4] [,5]
 6 [1,]    2    0    2    1    1
 7 [2,]    2    2    1    0    1
 8 [3,]    0    0    0    2    2
 9 [4,]    1    0    0    0    0
10 [5,]    0    1    1    1    0
11 > bml$odd_step()
12 > bml$lattice
13      [,1] [,2] [,3] [,4] [,5]
14 [1,]    2    0    2    1    0
15 [2,]    2    2    1    0    1
16 [3,]    1    0    0    2    2
17 [4,]    0    1    1    1    0
18 [5,]    0    0    0    0    1
19 > bml$even_step()
20 > bml$lattice
21      [,1] [,2] [,3] [,4] [,5]
22 [1,]    0    2    2    1    0
23 [2,]    2    2    1    0    1
24 [3,]    1    0    0    2    2
25 [4,]    0    1    1    1    0
26 [5,]    0    0    0    0    1

In the initialization step, we used the inverse transform sampling approach12 to generate the status of each site. Inverse transform sampling method is basic but powerful approach to generate r.v. from any probability distribution given its cumulative distribution function (CDF). Reading the wiki page is enough to master this sampling method.

Python
 1 import numpy as np
 2 
 3 class BML:
 4     def __init__(self, alpha, m, n):
 5         self.alpha = alpha
 6         self.shape = (m, n)
 7         self.initialize_lattice()
 8 
 9     def initialize_lattice(self):
10         u = np.random.uniform(0.0, 1.0, self.shape)
11         # instead of using default list, we use np.array to create the lattice
12         self.lattice = np.zeros_like(u, dtype=int)
13         # the parentheses below can't be ignored
14         self.lattice[(u > self.alpha) & (u <= (1.0+self.alpha)/2.0)] = 1
15         self.lattice[u > (self.alpha+1.0)/2.0] = 2
16 
17     def odd_step(self):
18         # please note that np.where returns a tuple which is immutable
19         blue_index = np.where(self.lattice == 1)
20         blue_index_i = blue_index[0] - 1
21         blue_index_i[blue_index_i < 0] = self.shape[0]-1
22         blue_movable = self.lattice[(blue_index_i, blue_index[1])] == 0
23         self.lattice[(blue_index_i[blue_movable],
24                       blue_index[1][blue_movable])] = 1
25         self.lattice[(blue_index[0][blue_movable],
26                       blue_index[1][blue_movable])] = 0
27 
28     def even_step(self):
29         red_index = np.where(self.lattice == 2)
30         red_index_j = red_index[1] + 1
31         red_index_j[red_index_j == self.shape[1]] = 0
32         red_movable = self.lattice[(red_index[0], red_index_j)] == 0
33         self.lattice[(red_index[0][red_movable],
34                       red_index_j[red_movable])] = 2
35         self.lattice[(red_index[0][red_movable],
36                       red_index[1][red_movable])] = 0

The Python implementation is also given.

R
 1 >>> import numpy as np
 2 >>> np.random.seed(2019)
 3 >>> from BML import BML
 4 >>> bml=BML(0.4,5,5)
 5 >>> bml.lattice
 6 array([[2, 0, 1, 1, 2],
 7        [0, 2, 2, 2, 1],
 8        [1, 0, 0, 2, 0],
 9        [2, 0, 1, 0, 2],
10        [1, 1, 0, 2, 1]])
11 >>> bml.odd_step()
12 >>> bml.lattice
13 array([[2, 0, 0, 1, 2],
14        [1, 2, 2, 2, 1],
15        [0, 0, 1, 2, 0],
16        [2, 1, 0, 0, 2],
17        [1, 0, 1, 2, 1]])
18 >>> bml.even_step()
19 >>> bml.lattice
20 array([[0, 2, 0, 1, 2],
21        [1, 2, 2, 2, 1],
22        [0, 0, 1, 0, 2],
23        [2, 1, 0, 0, 2],
24        [1, 0, 1, 2, 1]])

Please note that although we have imported numpy in BML.py, we import it again in the code above in order to set the random seed. If we change the line to from BML import *, we don’t need to import numpy again. But it is not recommended to import * from a module.

Embarrassingly parallelism in R/Python

According to the explanation of wikipedia13, single-threading is the processing of one command at a time, and its opposite is multithreading. A process is the instance of a computer program executed by one or many threads14. Multithreading is not the same as parallelism. In a single processor environment, the processor can switch execution between threads, which results in concurrent execution. However, it is possible a process with multithreads runs on on a multiprocessor environment and each of the threads on a separate processor, which results in parallel execution.

Both R and Python are single-threaded. In Python, there is a threading package15, which supports multithreading on a single core. It may suit some specific tasks. For example, in web scraping multithreading on a single core may be able to speed up the program if the download time is in excess of the CPU processing time.

Now let’s talk about embarrassingly parallelism by multi-processing. Embarrassingly parallel problem is one where little or no effort is needed to separate the problem into a number of parallel tasks16. In R there are various packages supporting multi-processing on multiple CPU cores, for example, the parallel package, which is my favorite one. In Python, there are also some available modules, such as multiprocessing, joblib and concurrent.futures. Let’s see an application of embarrassingly parallelism to calculate $\pi$ using Monte Carlo simulation17.

Application – Monte Carlo simulation to estimate $\pi$ via parallelization

Monte Carlo simulation provides a simple and straightforward way to estimate $\pi$. We know the area of a circle with radius 1 is just $\pi$. Thus, we can convert the original problem of $\pi$ calculation to a new problem, i.e., how to calculate the area of a circle with radius 1. We also know the area of a square with side length 2 is equal to 4. Thus, $\pi$ can be calculated as 4$r_{c/s}$ where $r_{c/s}$ denotes the ratio of areas of a circle with radius 1 and a square with side length 2. Now the problem is how to calculate the ratio $r_{c/s}$? When we randomly throw $n$ points into the square and $m$ of these pionts fall into the inscribed circle, then we can estimate the ratio as $m/n$. As a result, a natural estimate of $\pi$ is $4m/n$. This problem is an embarrassingly parallel problem by its nature. Let’s see how we implement the idea in R/Python.

Generate points within a square and count how many times these points fall into the inscribed circle
Generate points within a square and count how many times these points fall into the inscribed circle
R

chapter2/pi.R

 1 library(parallel)
 2 count_inside_point = function(n){
 3   m = 0
 4   for (i in 1:n){
 5     p_x = runif(1, -1, 1)
 6     p_y = runif(1, -1, 1)
 7     if (p_x^2 + p_y^2 <=1){
 8       m = m+1
 9     }
10   }
11   m
12 }
13 
14 # now let's use the mcapply for parallelization
15 generate_points_parallel = function(n){
16   # detectCores() returns the number of cores available
17   # we assign the task to each core
18   unlist(mclapply(X = rep(n %/% detectCores(), detectCores()),FUN=count_inside_point))
19 }
20 
21 # now let's use vectorization
22 generate_points_vectorized = function(n){
23   p = array(runif(n*2,-1,1), c(n,2))
24   sum((p[,1]^2+p[,2]^2)<=1)
25 }
26 
27 pi_naive = function(n) cat('naive: pi -', 4*count_inside_point(n)/n, '\n')
28 
29 pi_parallel = function(n) cat('parallel: pi -', 4*sum(generate_points_parallel(n))/n, '\n')
30 
31 pi_vectorized = function(n) cat('vectorized: pi -', 4*sum(generate_points_vectorized(n))/n, '\n')

In the above R code snippet, we use the function mclapply which is not currently available on some operation systems18. When it is not available, we may consider to use parLapply instead.

Python

chapter2/pi.py

 1 # now let's try the parallel approach
 2 # each process uses the same seed, which is not desired
 3 def generate_points_parallel(n):
 4     pool = mp.Pool()
 5     # we ask each process to generate n//mp.cpu_count() points
 6     return pool.map(count_inside_point, [n//mp.cpu_count()]*mp.cpu_count())
 7 
 8 # set seed for each process
 9 # first, let's define a helper function
10 def helper(args):
11     n, s = args
12     seed(s)
13     return count_inside_point(n)
14 
15 def generate_points_parallel_set_seed(n):
16     pool = mp.Pool() # we can also specify the number of processes by Pool(number)
17     # we ask each process to generate n//mp.cpu_count() points
18     return pool.map(helper, list(zip([n//mp.cpu_count()]*mp.cpu_count(), range(mp.cpu_count()))))
19 
20 # another optimization via vectorization
21 def generate_points_vectorized(n):
22     p = uniform(-1, 1, size=(n,2))
23     return np.sum(np.linalg.norm(p, axis=1) <= 1)
24 
25 def pi_naive(n):
26     print(f'pi: {count_inside_point(n)/n*4:.6f}')
27 
28 def pi_parallel(n):
29     print(f'pi: {sum(generate_points_parallel_set_seed(n))/n*4:.6f}')
30 
31 def pi_vectorized(n):
32     print(f'pi: {generate_points_vectorized(n)/n*4:.6f}')

In the above Python code snippet, we defined a function generate_points_parallel which returns a list of number of inside points. But the problem of this function is that each process in the pool shares the same random state. As a result, we will obtain the a list of duplicated numbers by calling this function. To overcome the issue, we defined another function generate_points_parallel_set_seed. Let’s see the results of our estimation for $\pi$ running on a laptop.

R
 1 > source('pi.R')
 2 > system.time(pi_naive(1e6))
 3 naive: pi - 3.144592 
 4    user  system elapsed 
 5   8.073   1.180   9.333 
 6 > system.time(pi_parallel(1e6))
 7 parallel: pi - 3.1415 
 8    user  system elapsed 
 9   4.107   0.560   4.833 
10 > system.time(pi_vectorized(1e6))
11 vectorized: pi - 3.141224 
12    user  system elapsed 
13   0.180   0.031   0.214 
Python
 1 >>> import timeit
 2 >>> from pi import pi_naive, pi_parallel, pi_vectorized
 3 >>> print(f'naive - {timeit.timeit("pi_naive(1000000)", setup="from __main__ import pi_naive",number=1):.6f} seconds')
 4 pi: 3.141056
 5 naive - 4.363822 seconds
 6 >>> print(f'parallel - {timeit.timeit("pi_parallel(1000000)", setup="from __main__ import pi_parallel",number=1):.6f} seconds')
 7 pi: 3.141032
 8 parallel - 2.204697 seconds
 9 >>> print(f'vectorized - {timeit.timeit("pi_vectorized(1000000)", setup="from __main__ import pi_vectorized",number=1):.6f} seconds')
10 pi: 3.139936
11 vectorized - 0.148950 seconds

We see the winner in this example is vectorization, and the parallel solution is better than the naive solution. However, when the problem cannot be vectorized we may use parallelization to achieve better performance.

Scope of variables

We have seen how to define variables in R/Python in Chapter 1, and we have known that a variable is an identifier to a location in memory. What is the scope of a variable and why does it matter? Let’s first have a look at the code snippets below.

R
1 > x=1
2 > var_func_1 = function(){print(x)}
3 > var_func_1()
4 [1] 1
5 > var_func_2 = function(){x=x+1;print(x)}
6 > var_func_2()
7 [1] 2
8 > x
9 [1] 1
Python
 1 >>> x=1
 2 >>> def var_func_1():print(x)
 3 >>> var_func_1()
 4 1
 5 >>> def var_func_2():x+=1
 6 ... 
 7 >>> var_func_2()
 8 Traceback (most recent call last):
 9   File "<stdin>", line 1, in <module>
10   File "<stdin>", line 1, in var_func_2
11 UnboundLocalError: local variable 'x' referenced before assignment

The results of the code above seem strange before knowing the concept of variable scope. Inside a function, a variable may refer to a function argument/parameter or it could be formally declared inside the function which is called a local variable. But in the code above, x is neither a function argument nor a local variable. How does the print() function know where the identifier x points to?

The scope of a variable determines where the variable is available/accessible (can be referenced). Both R and Python apply lexical/static scoping for variables, which set the scope of a variable based on the structure of the program. In static scoping, when an ‘unknown’ variable referenced, the function will try to find it from the most closely enclosing block. That explains how the print() function could find the variable x.

In the R code above, x=x+1 the first x is a local variable created by the = operator; the second x is referenced inside the function so the static scoping rule applies. As a result, a local variable x which is equal to 2 is created, which is independent with the x outside of the function var_func_2(). However, in Python when a variable is assigned a value in a statement the variable would treated as a local variable and that explains the UnboundLocalError.

Is it possible to change a variable inside a function which is declared outside the function? Based on the static scoping rule only, it’s impossible. But there are workarounds in both R/Python. In R, we need the help of environment; and in Python we can use the keyword global.

So what is an environment in R? An environment is a place where objects are stored. When we invoke the interactive R session, an environment named as .GlobalEnv is created automatically. We can also use the function environment() to get the present environment. The ls() function can take an environment as the argument to list all objects inside the environment.

R
 1 $r
 2 > typeof(.GlobalEnv)
 3 [1] "environment"
 4 > environment()
 5 <environment: R_GlobalEnv>
 6 > x=1
 7 > ls(environment())
 8 [1] "x"
 9 > env_func_1=function(x){
10 +   y=x+1
11 +   print(environment())
12 +   ls(environment())
13 + }
14 > env_func_1(2)
15 <environment: 0x7fc59d165a20>
16 [1] "x" "y"
17 > env_func_2=function(){print(environment())}
18 > env_func_2()
19 <environment: 0x7fc59d16f520>

The above code shows that each function has its own environment containing all function arguments and local variables declared inside the function. In order to change a variable declared outside of a function, we need the access of the environment enclosing the variable to change. There is a function parent_env(e) that returns the parent environment of the given environment e in R. Using this function, we are able to change the value of x declared in .GlobalEnv inside a function which is also declared in .GlobalEnv. The global keyword in Python works in a totally different way, which is simple but less flexible.

R
1 > x=1
2 > env_func_3=function(){
3 +   cur_env=environment()
4 +   par_env=parent.env(cur_env)
5 +   par_env$x=2
6 + }
7 > env_func_3()
8 > x
9 [1] 2
Python
1 >>> def env_func_3():
2 ...   global x
3 ...   x = 2
4 ... 
5 >>> x=1
6 >>> env_func_3()
7 >>> x
8 2

I seldomly use the global keyword in Python, if any. But the environment in R could be very handy in some occasions. In R, environment could be used as a purely mutable version of the list data structure.

R
1 # list is not purely mutable
2 > x=list(1)
3 > tracemem(x)
4 [1] "<0x7f829183f6f8>"
5 > x$a=2
6 > tracemem(x)
7 [1] "<0x7f828f4d05c8>"
Python
1 # environment is purely mutable
2 > x=new.env()
3 > x
4 <environment: 0x7f8290aee7e8>
5 > x$a=2
6 > x
7 <environment: 0x7f8290aee7e8>

Actually, the object of an R6 class type is also an environment.

R
1 > # load the Complex class that we defined in chapter 1
2 > x = Complex$new(1,2)
3 > typeof(x)
4 [1] "environment"

In Python, we can assign values to multiple variables in one line.

R
1 >>> x,y = 1,2
2 >>> x
3 1
4 >>> y
5 2
Python
1 >>> x,y=(1,2)
2 >>> print(x,y)
3 1 2
4 >>> (x,y)=(1,2)
5 >>> print(x,y)
6 1 2
7 >>> [x,y]=(1,2)
8 >>> print(x,y)
9 1 2

Even though in the left snippet above there aren’t parentheses embracing 1,2 after the = operator, a tuple is created first and then the tuple is unpacked and assigned to x, y. Such mechanism doesn’t exist in R, but we can define our own multiple assignment operator with the help of environment.

R

chapter2/multi_assignment.R

 1 `%=%` = function(left, right) {
 2   # we require the RHS to be a list strictly
 3   stopifnot(is.list(right))
 4   # dest_env is the desitination environment enclosing the variables on LHS
 5   dest_env = parent.env(environment())
 6   left = substitute(left)
 7 
 8   recursive_assign = function(left, right, dest_env) {
 9     if (length(left) == 1) {
10       assign(x = deparse(left),
11              value = right,
12              envir = dest_env)
13       return()
14     }
15     if (length(left) != length(right) + 1) {
16       stop("LHS and RHS must have the same shapes")
17     }
18 
19     for (i in 2:length(left)) {
20       recursive_assign(left[[i]], right[[i - 1]],  dest_env)
21     }
22   }
23 
24   recursive_assign(left, right, dest_env)
25 }

Before going into the script deeper, first let’s see the usage of the multiple assignment operator we defined.

R
 1 > source('multi_assignment.R')
 2 > c(x,y,z) %=% list(1,"Hello World!",c(2,3))
 3 > x
 4 [1] 1
 5 > y
 6 [1] "Hello World!"
 7 > z
 8 [1] 2 3
 9 > list(a,b) %=% list(1,as.Date('2019-01-01'))
10 > a
11 [1] 1
12 > b
13 [1] "2019-01-01"

In the \%=\% operator defined above, we used two functions substitute, deparse which are very powerful but less known by R novices. To better understand these functions as well as some other less known R functions, the Rchaeology19 tutorial is worth reading.

It is also interesting to see that we defined the function recursive_assign inside the \%=\% function. Both R and Python support the concept of first class functions. More specifically, a function in R/Python is an object, which can be

The essential idea behind the recursive_assign function is a depth-first search (DFS), which is a fundamental graph traversing algorithm20. In the context of the recursive_assign function, we use DFS to traverse the parse tree of the left argument created by calling substitute(left).

Evaluation strategy

R is a lazy programming language since it uses lazy evaluation by default21. Lazy evaluation strategy delays the evaluation of an expression until its value is needed. When an expression in R is evaluated, it follows an outermost reduction order in general. But Python is not a lazy programming language and the expression in Python follows an innermost reduction order.

The code snippets below illustrate the innermost reduction vs. outermost reduction.

R
1 > divide = function(x, y) {stopifnot(y!=0); return(x/y)}
2 > power = function(x, p){if (p==0) 1 else x^p}
3 > power(divide(1,0),2)
4 Error in divide(1, 0) : y != 0 is not TRUE
5 > power(divide(1,0),0)
6 [1] 1
Python
1 >>> def divide(x, y): return x/y
2 >>> def power(x, p): return 1 if p==0 else x**p
3 >>> power(divide(1,0),0)
4 Traceback (most recent call last):
5   File "<stdin>", line 1, in <module>
6   File "<stdin>", line 1, in divide
7 ZeroDivisionError: division by zero

Because of the outermost reduction order, the R code snippet evaluate the function power first and since if second argument is 0 the first argument is not required to evaluate. Thus, the function call returns 1. But the Python code snippet first evaluates the function call of divide and an exception is raised because of division by zero.

Although Python is an eager language, it is possible to simulate the lazy evaluation behavior. For example, the code inside a generator22 is evaluated when the generator is consumed but not evaluated when it is created. We can create a sequence of Fibonacci numbers with the help of generator.

Python
 1 >>> def fib():
 2 ...     '''
 3 ...     a generator to create Fibonacci numbers less than 10
 4 ...     '''
 5 ...     a, b = 0, 1
 6 ...     while a<10:
 7 ...         a, b = b, a+b
 8 ...         yield a
 9 ... 
10 >>> f = fib()
11 >>> print(f)
12 <generator object fib at 0x116dfc570>
13 >>> for e in f: print(e)
14 ... 
15 1
16 1
17 2
18 3
19 5
20 8
21 13
22 >>> 

In the code snippet above, we create a generator which generates the sequence of Fibonacci numbers less than 10. When the generator is created, the sequence is not generated immediately. When we consume the generator in a loop, each element is then generated as needed. It is also common to consume the generator with the next function.

Python
 1 >>> f = fib()
 2 >>> next(f)
 3 1
 4 >>> next(f)
 5 1
 6 >>> next(f)
 7 2
 8 >>> next(f)
 9 3
10 >>> next(f)
11 5

Speed up with C/C++ in R/Python

The purpose of this section is not to teach C/C++. If you know how to write C/C++ then it may help to improve the performance of R/Python program.

It is recommended to use vectorization technique to speed up your program whenever possible. But what if it’s infeasible to vectorize the algorithm? One potential option is to use C/C++ in the R/Python code. According to my limited experience, it is much easier to use C/C++ in R with the help of Rcpp package than in Python. In Python, more options are available but not as straightforward as the solution of Rcpp. In this section, let’s use Cython23. Actually, Cython itself is a programming language written in Python and C. In Rcpp we can write C++ code directly and use it in native R code. With the help of Cython, we can write python-like code which is able to be compiled to C or C++ and automatically wrapped into python-importable modules. Cython could also wrap independent C or C++ code into python-importable modules.

Let’s see how to calculate Fibonacci numbers with these tools. The first step is to install Rcpp package in R and the Cython module (use pip) in Python. Once they are installed, we can write the code in C++ directly for R. As for Cython, we write the python-like code which would be compiled to C/C++ later.

C

chapter2/Fibonacci.cpp

 1 #include <Rcpp.h>
 2 using namespace Rcpp;
 3 
 4 // [[Rcpp::export]]
 5 int Fibonacci(int n) {
 6   if (n==1 or n==2){
 7     return 1;
 8   } 
 9   return Fibonacci(n-1)+Fibonacci(n-2);
10 }

Cython

chapter2/Fibonacci.pyx

 1 #!python
 2 #cython: language_level=3
 3 
 4 cdef int Fibonacci_c(int n):
 5     if n==1 or n==2:
 6         return 1
 7     return Fibonacci_c(n-1) + Fibonacci_c(n-2)
 8 
 9 def Fibonacci_static(n):
10     return Fibonacci_c(n)
11 
12 def Fibonacci(n):
13     if n==1 or n==2:
14         return 1
15     return Fibonacci(n-1) + Fibonacci(n-2)

It’s worth noting the extension of the Cython file above is pyx, not py. And in Fibonacci.pyx, the function we defined is quite following the native Python syntax. But in Cython we can add static typing declarations which are often helpful to improve the performance, although not mandatory. The keyword cdef makes the function Fibonacci_c invisible to Python and thus we have to define the Fibonacci_static function as a wrapper which can be imported into Python. The function Fibonacci is not static typed for benchmarking.

Let’s also implement the same functions in native R/Python for benchmarking purpose.

R

chapter2/Fibonacci.R

1 Fibonacci_native = function(n){
2   if (n==1 || n==2){
3     1
4   }else {
5     Fibonacci_native(n-1) + Fibonacci_native(n-2)
6   }
7 }

Python

chapter2/Fibonacci.py

1 def Fibonacci_native(n):
2     if n==1 or n==2:
3         return 1
4     return Fibonacci_native(n-1) + Fibonacci_native(n-2)

Now let’s compare the performance of different implementations.

R
 1 > library(microbenchmark)
 2 > library(Rcpp)
 3 > source("Fibonacci_native.R")
 4 > sourceCpp('Fibonacci.cpp')
 5 > # the native implementation in R
 6 > microbenchmark(Fibonacci_native(20), times=1000)
 7 Unit: milliseconds
 8                  expr      min       lq     mean   median       uq      max neval
 9  Fibonacci_native(20) 3.917138 4.056468 4.456824 4.190078 4.462815 26.30846 1000
10 > the C++ implementation
11 > microbenchmark(Fibonacci(20), times=1000)
12 Unit: microseconds
13           expr    min     lq     mean median     uq     max neval
14  Fibonacci(20) 14.251 14.482 15.81708 14.582 14.731 775.095  1000

The native implementation in R is much slower the C++ implementation (see the difference of units).

Python
 1 >>> import pyximport
 2 >>> pyximport.install() 
 3 (None, <pyximport.pyximport.PyxImporter object at 0x1056f9f98>)
 4 >>> from Fibonacci import Fibonacci_static, Fibonacci
 5 >>> from Fibonacci_native import Fibonacci_native
 6 >>> import timeit
 7 >>> print(f'{timeit.timeit("Fibonacci_native(20)", setup="from __main__ import Fibonacci_native",number=1000):.6f} seconds')
 8 1.927359 seconds
 9 >>> print(f'{timeit.timeit("Fibonacci(20)", setup="from __main__ import Fibonacci",number=1000):.6f} seconds')
10 0.316726 seconds
11 >>> print(f'{timeit.timeit("Fibonacci_static(20)", setup="from __main__ import Fibonacci_static",number=1000):.6f} seconds')
12 0.012741 seconds

The results show the static typed implementation is the fastest, and the native implementation in pure Python is the slowest. Again the time measured by timeit.timeit is the total time to run the main statement 1000 times. The average time per run of Fibonacci_static function is close to the average time per run of Fibonacci in R.

A first impression of functional programming

Functional (programming) languages define programs as mathematical functions and treat them as first-class24. In pure functional programming, a function takes input and returns output without making any side effect. A side effect means a state variable outside the function is modified. For example, printing the value of a variable inside a function is a side effect.

Both R and Python are not purely functional languages per se, although R behaves more functional than Python25. If you wonder why the return function could be ignored to return a value in an R function, now you get the explanation – a function should return the output automatically from a FP perspective.

I don’t think it makes any sense to debate on if we should choose OOP or FP using R/Python. And both languages support multiple programming paradigms (FP, OOP etc.). If you want to do purist FP, neither R nor Python would be the best choice. For example, purist FP should avoid loop because loop always involves an iteration counter whose value changes over iterations, which is obviously a side effect. Thus, recursion is very important in FP. But tail recursion optimization is not supported in R and Python although it can be implemented via trampoline26.

Let’s introduce a few tools that could help to get a first impression of FP in R and Python.

Anonymous function

We have seen anonymous functions before. Anonymous functions are commonly used in FP. To recap, let’s see how to define a useless anonymous function.

R
1 > function(x) x*x
2 function(x) x*x
Python
1 >>> lambda x: x**2
2 <function <lambda> at 0x1033d5510>

Usually, we define anonymous functions because we want to pass them to other functions. And thus, the functions defined above are useless as they are not passed to any other function and after the definition they are deleted automatically from the memory.

Of course, we can assign names to the anonymous functions – but what is the point to name anonymous functions?

R
1 > f1 = function(x) x*x
2 > f1
3 function(x) x*x
Python
1 >>> f1 = lambda x: x**2
2 >>> f1(2)
3 4

Map

Map could be used to avoid loops. The first argument to map is a function.

In R, we use Map which is shipped with base R; and in Python we use map which is also a built-in function.

R
 1 > Map(function(x) x*x, c(1:3))
 2 [[1]]
 3 [1] 1
 4 
 5 [[2]]
 6 [1] 4
 7 
 8 [[3]]
 9 [1] 9
10 > Map(function(x, y) x*y, c(1:3), c(4:6))
11 [[1]]
12 [1] 4
13 
14 [[2]]
15 [1] 10
16 
17 [[3]]
18 [1] 18
Python
1 >>> map(lambda e: e**2, [1,2,3])
2 <map object at 0x1033e3160>
3 >>> list(map(lambda x: x**2, [1,2,3]))
4 [1, 4, 9]
5 >>> list(map(lambda x, y: x*y, [1,2,3], [4,5,6]))
6 [4, 10, 18]

There are a few things to note from the code snippets above. First, the returned value from Map in R is a list rather than a vector. That is because Map is just a wrapper of mapply function in R, and the SIMPLIFY argument is set to FALSE. If we want to get a vector value returned, just use mapply instead.

R
1 > mapply(function(x, y) x*y, c(1:3), c(4:6), SIMPLIFY=TRUE)
2 [1]  4 10 18

The returned value from map in Python is a map object which is an iterator. The generator function we have seen before is also an iterator. To use an iterator, we could convert it to a list, or get the next value using the next function, or just use a for loop.

Python
 1 >>> # use next on an iterator
 2 >>> o=map(lambda x, y: x*y, [1,2,3], [4,5,6])
 3 >>> next(o)
 4 4
 5 >>> next(o)
 6 10
 7 >>> o=map(lambda x, y: x*y, [1,2,3], [4,5,6])
 8 >>> # use for loop on an iterator
 9 >>> for e in o:print(e)
10 ... 
11 4
12 10
13 18
14 >>> # convert an iterator to a list
15 >>> o=map(lambda x, y: x*y, [1,2,3], [4,5,6])
16 >>> list(o)
17 [4, 10, 18]

Filter

Similar to map, the first argument to Filter in R and filter in Python is also a function. This function is used to get the elements which satisfy a certain condition specified in the first argument. For example, we can use it to get the even numbers.

R
1 > Filter(function(x) x%%2==0, c(0:10))
2 [1]  0  2  4  6  8 10
Python
1 >>> list(filter(lambda e: e%2==0, range(11)))
2 [0, 2, 4, 6, 8, 10]

Reduce

Reduce behaves a bit different from Map and Filter. The first argument is a function with exactly two arguments. The second argument is a sequence, on each element of which the first function argument will be applied from left to right. And there is one optional argument called initializer. When the initializer is provided, it would be used as the first argument of the function argument of Reduce. The examples below depict how it works.

R
1 > Reduce(function(x, y) x+2*y, c(1,2,3))
2 [1] 11
3 > Reduce(function(x, y) x+2*y, c(1,2,3), 1)
4 [1] 13
Python
1 >>> from functools import reduce
2 >>> reduce(lambda x, y:x+2*y, [1,2,3])
3 11
4 >>> reduce(lambda x, y:x+2*y, range(1,4), 1)
5 13

Please note that in order to use reduce in Python, we have to import it from the functools module.

Utilizing these functions flexibly may help to make the code more concise, at the cost of readability.

Miscellaneous

We have introduced the basics of R/Python programming so far. There are much more to learn to become an advanced user of R/Python. For example, the appropriate usages of iterator, generator, decorator could improve both the conciseness and readability of your Python code. The generator27 is commonly seen in machine learning programs to prepare training/testing samples. decorator is a kind of syntactic sugar to allow the modification of a function’s behavior in a simple way. In R there are no built-in iterator, generator, decorator, but you may find some third-party libraries to mimic these features; or you may try to implement your own.

One advantage of Python over R is that there are some built-in modules containing high-performance data structures or commonly-used algorithms implemented efficiently. For example, I enjoy using the deque structure in the Python collections module28, but there is no built-in counterpart in R. We have written our own binary search algorithm earlier in this Chapter, which can also be replaced by the functions in the built-in module bisect29 in Python.

Another important aspect of programming is testing. Unit testing is a typical software testing method that is commonly adopted in practice. In R there are two third-party packages testthat and RUnit. In Python, the built-in unittest is quite powerful. Also, the third-party module pytest30 is very popular.


1 https://www.rstudio.com/products/rstudio

2 https://code.visualstudio.com/

3 https://www.jetbrains.com/pycharm

4 https://docs.python.org/3/library/logging.html

5 https://en.wikipedia.org/wiki/Divide-and-conquer_algorithm

6 https://en.wikipedia.org/wiki/Big_O_notation

7 https://en.wikipedia.org/wiki/Tail_call

8 https://en.wikipedia.org/wiki/Pseudorandom_number_generator

9 https://en.wikipedia.org/wiki/Automatic_vectorization

10 https://www.python.org/dev/peps/pep-0498

11 https://en.wikipedia.org/wiki/Biham-Middleton-Levine_traffic_model

12 https://en.wikipedia.org/wiki/Inverse_transform_sampling

13 https://en.wikipedia.org/wiki/Thread_(computing)

14 https://en.wikipedia.org/wiki/Process_(computing)

15 https://docs.python.org/3.7/library/threading.html

16 https://en.wikipedia.org/wiki/Embarrassingly_parallel

17 https://en.wikipedia.org/wiki/Monte_Carlo_method

18 https://stat.ethz.ch/R-manual/R-devel/library/parallel/doc/parallel.pdf

19 https://cran.r-project.org/web/packages/rockchalk/vignettes/Rchaeology.pdf

20 https://en.wikipedia.org/wiki/Depth-first_search

21 Floréal Morandat, Brandon Hill, Leo Osvald, and Jan Vitek. Evaluating the design of the r language. In European Conference on Object-Oriented Programming, pages 104–131. Springer, 2012.

22 https://www.python.org/dev/peps/pep-0255

23 https://en.wikipedia.org/wiki/Cython

24 https://en.wikipedia.org/wiki/List_of_programming_languages_by_type#Functional_languages

25 John M Chambers et al. Object-oriented programming, functional programming and r. Statistical Science, 29(2):167–180, 2014.

26 https://en.wikipedia.org/wiki/Tail_call#Through_trampolining

27 https://docs.python.org/3/howto/functional.html

28 https://docs.python.org/3/library/collections.html

29 https://docs.python.org/3.7/library/bisect.html

30 https://docs.pytest.org/en/latest