speedtest/README.Rmd

---
output: rmarkdown::github_document
editor_options: 
  chunk_output_type: console
---
```{r pkg-knitr-opts, include=FALSE}
hrbrpkghelpr::global_opts()
```

```{r badges, results='asis', echo=FALSE, cache=FALSE}
hrbrpkghelpr::stinking_badges()
```

# speedtest 

Tools to Test and Compare Internet Bandwidth Speeds

## Description

The 'Ookla' 'Speedtest' site <https://beta.speedtest.net/about> provides interactive and programmatic services to test and compare bandwidth speeds from a source node on the Internet to thousands of test servers. Tools are provided to obtain test server lists, identify target servers for testing and performing speed/bandwidth tests.
    
## What's Inside The Tin

The following functions are implemented:

```{r ingredients, results='asis', echo=FALSE, cache=FALSE}
hrbrpkghelpr::describe_ingredients()
```

## Make a CLI utility

While you can run `spd_test()` from an R console, it was desgined to be an easily wrapped into a `bash` (et al) alias or put into a small batch script. Or, you can just type out the following if you're fleet-of-finger/have dexterous digits:

    Rscript --quiet -e 'speedtest::spd_test()'
    
which will look something like:

![](man/figures/spdtst.gif)

## TODO

Folks interested in contributing can take a look at the TODOs and pick as many as you like! Ones with question marks are truly a "I dunno if we shld" kinda thing. Ones with exclamation marks are essentials.

- [ ] Cache config in memory at startup vs pass around to functions?
- [ ] Figure out how to use beta sockets hidden API vs the old Flash API?
- [ ] Ensure the efficacy of relying on the cURL timings for speed measures for the Flash API
- [ ] Figure out best way to capture the results for post-processing
- [ ] Upload results to speedtest (tis only fair)!
- [ ] Incorporate more network or host measures for better statistical determination of the best target!
- [ ] `autoplot` support!
- [ ] RStudio Add-in
- [ ] Shiny app?

## Installation

```{r install-ex, results='asis', echo=FALSE, cache=FALSE}
hrbrpkghelpr::install_block()
```

## Usage

```{r libs, cache=FALSE}
library(speedtest)
library(stringi)
library(hrbrthemes)
library(ggbeeswarm)
library(tidyverse)

# current verison
packageVersion("speedtest")
```

### Download Speed

```{r dl-speeed, cache=TRUE}
config <- spd_config()

servers <- spd_servers(config=config)
closest_servers <- spd_closest_servers(servers, config=config)
only_the_best_severs <- spd_best_servers(closest_servers, config)
```

### Individual download tests

```{r individ-dl, cache=TRUE}
glimpse(spd_download_test(closest_servers[1,], config=config))

glimpse(spd_download_test(only_the_best_severs[1,], config=config))
```

### Individual upload tests

```{r individ-up, cache=TRUE}
glimpse(spd_upload_test(only_the_best_severs[1,], config=config))

glimpse(spd_upload_test(closest_servers[1,], config=config))
```

### Moar download tests

Choose closest, "best" and randomly (there can be, and are, some dups as a result for best/closest), run the test and chart the results. This will show just how disparate the results are from these core/crude tests. Most of the test servers compensate when they present the results. Newer, "socket"-based tests are more accurate but there are no free/hidden exposed APIs yet for most of them.

```{r moar-dl-tests, cache=TRUE}
set.seed(8675309)

bind_rows(

  closest_servers[1:3,] %>%
    mutate(type="closest"),

  only_the_best_severs[1:3,] %>%
    mutate(type="best"),

  filter(servers, !(id %in% c(closest_servers[1:3,]$id, only_the_best_severs[1:3,]$id))) %>%
    sample_n(3) %>%
    mutate(type="random")

) %>%
  group_by(type) %>%
  ungroup() -> to_compare

select(to_compare, sponsor, name, country, host, type)

map_df(1:nrow(to_compare), ~{
  spd_download_test(to_compare[.x,], config=config, summarise=FALSE, timeout=30)
}) -> dl_results_full

mutate(dl_results_full, type=stri_trans_totitle(type)) %>%
  ggplot(aes(type, bw, fill=type)) +
  geom_quasirandom(aes(size=size, color=type), width=0.15, shape=21, stroke=0.25) +
  scale_y_continuous(expand=c(0,5)) +
  scale_size(range=c(2,6)) +
  scale_color_manual(values=c(Random="#b2b2b2", Best="#2b2b2b", Closest="#2b2b2b")) +
  scale_fill_ipsum() +
  labs(x=NULL, y=NULL, title="Download bandwidth test by selected server type",
       subtitle="Circle size scaled by size of file used in that speed test") +
  theme_ipsum_rc(grid="Y") +
  theme(legend.position="none")
```

### Moar upload tests

Choose closest and "best" and filter duplicates out since we're really trying to measure here vs show the disparity:

```{r moar-ul-tests, cache=TRUE}
bind_rows(
  closest_servers[1:3,] %>% mutate(type="closest"),
  only_the_best_severs[1:3,] %>% mutate(type="best")
) %>%
  distinct(.keep_all=TRUE) -> to_compare

select(to_compare, sponsor, name, country, host, type)

map_df(1:nrow(to_compare), ~{
  spd_upload_test(to_compare[.x,], config=config, summarise=FALSE, timeout=30)
}) -> ul_results_full

ggplot(ul_results_full, aes(x="Upload Test", y=bw)) +
  geom_quasirandom(aes(size=size, fill="col"), width=0.1, shape=21, stroke=0.25, color="#2b2b2b") +
  scale_y_continuous(expand=c(0,0.5)) +
  scale_size(range=c(2,6)) +
  scale_fill_ipsum() +
  labs(x=NULL, y=NULL, title="Upload bandwidth test by selected server type",
       subtitle="Circle size scaled by size of file used in that speed test") +
  theme_ipsum_rc(grid="Y") +
  theme(legend.position="none")
```

## speedtest Metrics

```{r cloc, echo=FALSE}
cloc::cloc_pkg_md()
```

## Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.