---
title: "Vectorized version of random walk script"
author: "David Zelený"
date: "December 20, 2015"
output: html_document
---

The original function to generate random walk was based on the `for` loop of steps - in each step, random value (either -1 or 1) was generated, and added to the previous values (check [Creating a loop and function using random walk example](http://www.davidzeleny.net/wiki/doku.php/recol:exercise:random_walk)). The script of `random.walk` function looks like this:
  
```{r}
  random.walk <- function (no.steps)
  {
    steps.rw <- vector (mode = 'numeric', length = no.steps)
    for (k in seq (2, no.steps))
    {
      steps.rw [k] <- steps.rw [k-1] + sample (c(-1,1),1)
    }
    steps.rw
  }
```
    
In this case, we may _vectorize_ it - replace the loop by function(s) which can take as arguments the whole vectors. The solution is to first generate a vector of the length `no.steps`, which contains randomly selected values -1 or 1, and then use this vector in function `cumsum`, which returns the vector of cumulative sums - the vector of the same length as its argument, where each element represents the cumulative sum of elements in the argument.
  
Vectorized option is:
```{r}
  random.walk.vec <- function (no.steps)
  {
  steps.rw <- sample (c(-1,1), size = no.steps-1, replace = T)
  steps.rw <- c(0, steps.rw)
  steps.rw <- cumsum (steps.rw)
  return (steps.rw)
  }
```
We may visually check that both functions produce exactly the same output. We will use `plot` function to draw the results of each random walk procedure applied on 100,000 steps (takes a while). Note the use of `set.seed` function - we need to set up the same random seed before running each function, to make sure that the generator of random values will start always from the same position:
```{r}
  par (mfrow = c(1,2))
  set.seed (123)
  plot (random.walk (100000), type = 'l')
  set.seed (123)
  plot (random.walk (100000), type = 'l')
```
And a comparison of speed using `microbenchmark` function from `library (microbenchmark)` (note that here I decreased the number of steps from 100,000 to 10,000 to save time, since each function is repeated 100 times to obtain average times):
  
```{r}
  library (microbenchmark)
  microbenchmark (random.walk (10000), random.walk.vec (10000))
```
Note that the vectorized version (the second row) is on average 226 times faster than the function using `for` loop! To calculate this number, I took the column `mean` in the table from `microbenchmark`: in average one run of `random.walk (10000)` takes 49341 microseconds = 0.049 seconds, while `random.walk.vec (10000)` takes only 218 microseconds = 0.0002 seconds, 49340/218 = 226. This value is changeable - depends on the speed of your computer, and also on the number of steps the function has to do.
  
  
And because we like figures, we may draw the comparison also as a boxplot using `boxplot.microbenchmark` function from the same library:
```{r}
  boxplot (microbenchmark (random.walk (10000), random.walk.vec (10000)))
```

---
title: "Draw stairs (to heaven)"
author: "David Zelený"
date: "December 20, 2015"
output: slidy_presentation
---

## Aim: Draw this figure
```{r, echo = FALSE}
plot (c(1:10), type = 'S', axes = F, ann = F)
```

##
### But: 
> - only function `plot` is allowed
> - the script must have only **one line**, no more
> - this line must draw the complete figure

##

First, prepare the data:

```{r}
plot (1:10)
```

##
Switch off all the gingles around (`axes` and `ann`otations):
```{r}
plot (1:10, axes = FALSE, ann = FALSE)
```

##
And finally the trick - use argument `type = "S"` to connect dots with stairs:
```{r}
plot (1:10, type = "S", axes = FALSE, ann = FALSE)
```

##
### Thank you for your attention!