---
title: "Examples of using runstats package "
author: "Marta Karas"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Examples of using runstats package}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  # fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# runstats

Package `runstats` provides methods for fast computation of running sample statistics for time series. The methods utilize Convolution Theorem to compute convolutions via Fast Fourier Transform (FFT). Implemented running statistics include:

1. mean, 
2. standard deviation, 
3. variance,
4. covariance, 
5. correlation,
6. euclidean distance. 

### Installation

```{r, eval = FALSE}
devtools::install_github("martakarass/runstats")
```


### Usage 

```{r, eval = FALSE}
library(runstats)

## Example: running correlation
x0 <- sin(seq(0, 2 * pi * 5, length.out = 1000))
x  <- x0 + rnorm(1000, sd = 0.1)
pattern <- x0[1:100]
out1 <- RunningCor(x, pattern)
out2 <- RunningCor(x, pattern, circular = TRUE)

## Example: running mean
x <- cumsum(rnorm(1000))
out1 <- RunningMean(x, W = 100)
out2 <- RunningMean(x, W = 100, circular = TRUE)
```


### Running statistics

To better explain the details of running statistics, package's function `runstats.demo(func.name)` allows to visualize how the output of each running statistics method is generated. To run the demo, use `func.name` being one of the methods' names:

1. `"RunningMean"`,
2. `"RunningSd"`,
3. `"RunningVar"`,
4. `"RunningCov"`,
5. `"RunningCor"`,
6. `"RunningL2Norm"`.

```{r, eval = FALSE}
## Example: demo for running correlation method  
runstats.demo("RunningCor")
```

![](https://i.imgur.com/JQObAu7.gif)

```{r, eval = FALSE}
## Example: demo for running mean method 
runstats.demo("RunningMean")
```

![](https://i.imgur.com/FWah3aG.gif)


### Performance

We use `rbenchmark` to measure elapsed time of `RunningCov` execution, for different lengths of time-series `x` and fixed length of the shorter pattern `y`.

```{r, echo=FALSE, include=FALSE}
library(rbenchmark)
library(ggplot2)
```

```{r, eval = FALSE}
library(rbenchmark)

set.seed (20181010)
x.N.seq <- 10^(3:7)
x.list  <- lapply(x.N.seq, function(N) runif(N))
y <- runif(100)

## Benchmark execution time of RunningCov 
out.df <- data.frame()
for (x.tmp in x.list){
  out.df.tmp <- benchmark("runstats" = runstats::RunningCov(x.tmp, y),
                          replications = 10,
                          columns = c("test", "replications", "elapsed",
                                      "relative", "user.self", "sys.self"))
  out.df.tmp$x_length <- length(x.tmp)
  out.df.tmp$pattern_length <- length(y)
  out.df <- rbind(out.df, out.df.tmp)
}

```


```{r, echo = FALSE, eval = FALSE}

out.df.path <- "../inst/benchmark_results/2018-12-17-performance_rbenchmark_suite1.csv"
write.csv(out.df, out.df.path, row.names = FALSE, quote = FALSE)
```

```{r, echo = FALSE, eval = TRUE}
out.df.path <- "../inst/benchmark_results/2018-12-17-performance_rbenchmark_suite1.csv"
out.df <- read.csv(out.df.path)
options("scipen" = 10, "digits" = 4)
```


```{r}
knitr::kable(out.df)
```

##### Compare with a conventional method 

To compare `RunStats` performance with "conventional" loop-based way of computing running covariance in `R`, we use `rbenchmark` package to measure elapsed time of `RunStats::RunningCov` and running covariance implemented with `sapply` loop, for different lengths of time-series `x` and fixed length of the shorter time-series `y`.

```{r, eval = FALSE}
## Conventional approach 
RunningCov.sapply <- function(x, y){
  l_x <- length(x)
  l_y <- length(y)
  sapply(1:(l_x - l_y + 1), function(i){
    cov(x[i:(i+l_y-1)], y)
  })
}

set.seed (20181010)

out.df2 <- data.frame()
for (x.tmp in x.list[c(1,2,3,4)]){
  out.df.tmp <- benchmark("conventional" = RunningCov.sapply(x.tmp, y),
                          "runstats" = runstats::RunningCov(x.tmp, y),
                          replications = 10,
                          columns = c("test", "replications", "elapsed",
                                      "relative", "user.self", "sys.self"))
  out.df.tmp$x_length <- length(x.tmp)
  out.df2 <- rbind(out.df2, out.df.tmp)
}

```

```{r, echo = FALSE, eval = FALSE}
out.df.path <-"../inst/benchmark_results/2018-12-17-performance_rbenchmark_suite2.csv"
write.csv(out.df2, out.df.path, row.names = FALSE, quote = FALSE)
```

```{r, echo = FALSE, eval = TRUE}
out.df.path <- "../inst/benchmark_results/2018-12-17-performance_rbenchmark_suite2.csv"
out.df2 <- read.csv(out.df.path)
```


Benchmark results

```{r, fig.width=10, fig.height=5}
library(ggplot2)

plt1 <- 
  ggplot(out.df2, aes(x = x_length, y = elapsed, color = test)) + 
  geom_line() + geom_point(size = 3) + scale_x_log10() + 
  theme_minimal(base_size = 14) + 
  labs(x = "Vector length of x",
       y = "Elapsed [s]", color = "Method", 
       title = "Running covariance rbenchmark") + 
  theme(legend.position = "bottom")
plt2 <- 
  plt1 + 
  scale_y_log10() + 
  labs(y = "Log of elapsed [s]")

cowplot::plot_grid(plt1, plt2, nrow = 1, labels = c('A', 'B'))
```

Platform information

```{r}
sessioninfo::platform_info()
```
