eneller/opensky-statistics
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

doc: minor tweaks

Browse Source
This commit is contained in:
eneller
2026-01-21 22:42:00 +01:00
parent 137ddb197b
commit 1ab649f25f
1 changed files with 12 additions and 9 deletions
Download Patch File Download Diff File Expand all files Collapse all files

21
src/main.Rmd
View File

@@ -80,6 +80,7 @@ library(shiny)
# Implementation # Implementation
The following section will demonstrate the implementation of the methodology using R code snippets. The following section will demonstrate the implementation of the methodology using R code snippets.
The full analysis is also available in the `shiny` web interface. The full analysis is also available in the `shiny` web interface.
## API Authentication ## API Authentication
@@ -93,6 +94,7 @@ to [eneller/openSkies](https://github.com/eneller/openSkies/) and made several
[pull request](https://github.com/Rafael-Ayala/openSkies/pull/4). [pull request](https://github.com/Rafael-Ayala/openSkies/pull/4).
Our contributions include refactoring and streamlining authenticated requests to use the `makeAuthenticatedRequest()` function used below Our contributions include refactoring and streamlining authenticated requests to use the `makeAuthenticatedRequest()` function used below
and store the token obtained by initial authentication in a `credentials` object obtained from the new `getCredentials()` function. and store the token obtained by initial authentication in a `credentials` object obtained from the new `getCredentials()` function.
We further adjusted the front-facing functions to accept either _username + password_ or _client ID + secret_ We further adjusted the front-facing functions to accept either _username + password_ or _client ID + secret_
where applicable using the new credentials object. where applicable using the new credentials object.
@@ -210,6 +212,7 @@ if (!is.null(route_df)) {
cat("Insufficient data for altitude analysis\n") cat("Insufficient data for altitude analysis\n")
} }
``` ```
# Results # Results
## Trajectory Metrics ## Trajectory Metrics
@@ -357,6 +360,8 @@ if (length(departures) > 0) {
The following table presents computed metrics for all successfully analyzed flights. The following table presents computed metrics for all successfully analyzed flights.
```{r demo-all-stats-table, purl=FALSE, echo=FALSE} ```{r demo-all-stats-table, purl=FALSE, echo=FALSE}
# FIXME we should display several flights from the same aircraft as stated in the requirements
# not different aircraft's flights
if (!is.null(all_flights_stats)) { if (!is.null(all_flights_stats)) {
display_stats <- all_flights_stats display_stats <- all_flights_stats
display_stats$diffusion_distance_km <- round(display_stats$diffusion_distance_km, 2) display_stats$diffusion_distance_km <- round(display_stats$diffusion_distance_km, 2)
@@ -383,9 +388,6 @@ such as
- standard deviation - standard deviation
- interquartile range - interquartile range
```{r trajectory-summary} ```{r trajectory-summary}
# Statistical Helper Functions
# Get parameter names and labels for trajectory statistics
getTrajectoryParams <- function() { getTrajectoryParams <- function() {
list( list(
params = c("diffusion_distance_km", "straightness", "duration_min", params = c("diffusion_distance_km", "straightness", "duration_min",
@@ -407,12 +409,12 @@ calculateStatsSummary <- function(trajectory_stats_df) {
data.frame( data.frame(
Parameter = p$labels[i], Parameter = p$labels[i],
N = length(x), N = length(x),
Mean = round(mean(x), 4), Mean = round(mean(x), 1),
Variance = round(var(x), 4), Variance = round(var(x), 1),
Std_Dev = round(sd(x), 4), Std_Dev = round(sd(x), 1),
Q1 = round(quantile(x, 0.25), 4), Q1 = round(quantile(x, 0.25), 1),
Median = round(median(x), 4), Median = round(median(x), 1),
Q3 = round(quantile(x, 0.75), 4) Q3 = round(quantile(x, 0.75), 1)
) )
}) })
@@ -424,6 +426,7 @@ calculateStatsSummary <- function(trajectory_stats_df) {
The `calculateStatsSummary()` function computes central tendency and dispersion measures for each trajectory parameter. The `calculateStatsSummary()` function computes central tendency and dispersion measures for each trajectory parameter.
```{r demo-summary-stats, purl=FALSE, echo=FALSE} ```{r demo-summary-stats, purl=FALSE, echo=FALSE}
# FIXME first table column broken
if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) { if (!is.null(all_flights_stats) && nrow(all_flights_stats) >= 2) {
summary_stats <- calculateStatsSummary(all_flights_stats) summary_stats <- calculateStatsSummary(all_flights_stats)
knitr::kable(summary_stats, caption = "Descriptive Statistics Summary") knitr::kable(summary_stats, caption = "Descriptive Statistics Summary")