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---
output: rmarkdown::github_document
---
# speedtest
Tools to Test and Compare Internet Bandwidth Speeds
## Description
The 'Ookla' 'Speedtest' site <http://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:
- `spd_best_servers`: Find "best" servers (latency-wise) from master server list
- `spd_closest_servers`: Find "closest" servers (geography-wise) from master server list
- `spd_compute_bandwidth`: Compute bandwidth from bytes transferred and time taken
- `spd_config`: Retrieve client configuration information for the speedtest
- `spd_download_test`: Perform a download speed/bandwidth test
- `spd_servers`: Retrieve a list of SpeedTest servers
- `spd_upload_test`: Perform an upload speed/bandwidth test
## 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
- [ ] CLI wrapper
- [ ] Shiny app?
## Installation
```{r eval=FALSE}
devtools::install_github("hrbrmstr/speedtest")
```
```{r message=FALSE, warning=FALSE, error=FALSE, include=FALSE}
options(width=120)
```
## Usage
```{r message=FALSE, warning=FALSE, error=FALSE}
library(speedtest)
library(stringi)
library(hrbrthemes)
library(ggbeeswarm)
library(tidyverse)
# current verison
packageVersion("speedtest")
```
### Download Speed
```{r message=FALSE, warning=FALSE, error=FALSE, cache=FALSE}
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 message=FALSE, warning=FALSE, error=FALSE}
glimpse(spd_download_test(closest_servers[1,], config=config))
glimpse(spd_download_test(only_the_best_severs[1,], config=config))
```
### Individual download tests
```{r message=FALSE, warning=FALSE, error=FALSE}
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 message=FALSE, warning=FALSE, error=FALSE, cache=FALSE}
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)
```
```{r message=FALSE, warning=FALSE, error=FALSE, cache=TRUE}
map_df(1:nrow(to_compare), ~{
spd_download_test(to_compare[.x,], config=config, summarise=FALSE, timeout=30)
}) -> dl_results_full
```
```{r message=FALSE, warning=FALSE, error=FALSE}
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), labels=c(sprintf("%s", seq(0,150,50)), "200 Mb/s"), limits=c(0,200)) +
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 message=FALSE, warning=FALSE, error=FALSE, 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
```
```{r message=FALSE, warning=FALSE, error=FALSE}
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), breaks=seq(0,16,4),
labels=c(sprintf("%s", seq(0,12,4)), "16 Mb/s"), limits=c(0,16)) +
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")
```
## Code of Conduct
Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.