README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  fig.height=5, fig.width=8, 
  message=FALSE, warning=FALSE,
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# sfo <a href='https://ramikrispin.github.io/sfo/'><img src='man/figures/sfo.png' align="right" width="150" height="150" /></a>


```{r, include=FALSE}
library(plotly)
library(dplyr)
library(sfo)
p <- sfo_passengers %>%
  group_by(activity_period) %>%
  summarise(total = sum(passenger_count), .groups = "drop") %>%
  mutate(date = as.Date(paste(substr(activity_period, 1, 4), 
                              substr(activity_period, 5, 6), "01", sep = "/"))) %>%
  plot_ly(x = ~ date, y = ~ total,
          type = "scatter", 
          mode = "lines") %>%
  layout(title = "Monthly Air Traffic Passengers in SFO",
         yaxis = list(title = "Number of Passengers"),
         xaxis = list(title = "Source: San Francisco data portal (DataSF)"))

orca(p, "man/figures/total.svg")  
```

<!-- badges: start -->

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/sfo)](https://cran.r-project.org/package=sfo) [![lifecycle](https://img.shields.io/badge/lifecycle-stable-green.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable) [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/license/mit/) [![GitHub commit](https://img.shields.io/github/last-commit/RamiKrispin/sfo)](https://github.com/RamiKrispin/sfo/commit/main)

<!-- badges: end -->
The **sfo** package summarizes the monthly air passengers and landings at San Francisco International Airport (SFO) between 2005 and 2022.
Data source: San Francisco data portal - [DataSF API](https://datasf.org/opendata/)

<img src="man/figures/total.svg" width="90%"/>

## Installation

Install the stable version from CRAN:

``` r
install.packages("sfo")
```

or install the development version from Github:

``` r
# install.packages("devtools")
devtools::install_github("RamiKrispin/sfo", ref = "main")
```

### Datasets

The **sfo** package provides the following two datasets:

* `sfo_passengers` - air traffic passengers statistics
* `sfo_stats` - air traffic landings statistics

More information about the datasets is available in the following [vignette](https://ramikrispin.github.io/sfo/articles/v1_intro.html).

### Examples

The `sfo_passengers` dataset provides monthly summary of the number of passengers in SFO airport by different categories (such as terminal, geo, type, etc.):

```{r }
library(sfo)

data("sfo_passengers")

head(sfo_passengers)
```

The `sfo_stats` dataset provides monthly statistics on the air traffic landing at SFO airport:

```{r }
data("sfo_stats")

head(sfo_stats)
```

#### Total number of passngers

The total number of passengers in most recent month by `activity_type_code` and `geo_region`:

```{r }
library(dplyr)

sfo_passengers %>%
  filter(activity_period == max(activity_period)) %>%
  group_by(activity_type_code, geo_region) %>%
  summarise(total = sum(passenger_count), .groups = "drop")
```


The `sankey_ly` function enables us to plot the distribution of a numeric variable by multiple categorical variables. The following example shows the distribution of the total United Airlines passengers during 2019 by a terminal, travel type (domestic and international), geo, and travel direction (deplaned, enplaned, and transit):

``` r
sfo_passengers %>% 
  filter(operating_airline == "United Airlines",
         activity_period >= 201901 & activity_period < 202001) %>%
  mutate(terminal = ifelse(terminal == "International", "international", terminal)) %>%
  group_by(operating_airline,activity_type_code, geo_summary, geo_region,  terminal) %>%
  summarise(total = sum(passenger_count), .groups = "drop") %>%
  sankey_ly(cat_cols = c("operating_airline", "terminal","geo_summary", "geo_region", "activity_type_code"), 
            num_col = "total",
            title = "Dist. of United Airlines Passengers at SFO During 2019")
```

```{r, include=FALSE}
p <- sfo_passengers %>% 
  filter(operating_airline == "United Airlines",
         activity_period >= 201901 & activity_period < 202001) %>%
  mutate(terminal = ifelse(terminal == "International", "international", terminal)) %>%
  group_by(operating_airline,activity_type_code, geo_summary, geo_region,  terminal) %>%
  summarise(total = sum(passenger_count), .groups = "drop") %>%
  sankey_ly(cat_cols = c("operating_airline", "terminal","geo_summary", "geo_region", "activity_type_code"), 
            num_col = "total",
            title = "Dist. of United Airlines Passengers at SFO During 2019")

orca(p, "man/figures/sankey.svg") 
```

<img src="man/figures/sankey.svg" width="100%"/>


#### Total number of landing

The total number of landings during the most recent month by `activity_type_code` and `aircraft_manufacturer`:

``` r
sfo_stats %>% 
  filter(activity_period == 202212,
         aircraft_manufacturer != "") %>%
  group_by(aircraft_manufacturer) %>%
  summarise(total_landing = sum(landing_count),
            `.groups` = "drop") %>%
  arrange(-total_landing) %>%
  plot_ly(labels = ~ aircraft_manufacturer,
          values = ~ total_landing) %>%
  add_pie(hole = 0.6) %>%
  layout(title = "Landing Distribution by Aircraft Manufacturer during Dec 2022")
```


```{r, include=FALSE}
p <- sfo_stats %>% 
  filter(activity_period == 202212,
         aircraft_manufacturer != "") %>%
  group_by(aircraft_manufacturer) %>%
  summarise(total_landing = sum(landing_count),
            `.groups` = "drop") %>%
  arrange(-total_landing) %>%
  plot_ly(labels = ~ aircraft_manufacturer,
          values = ~ total_landing) %>%
  add_pie(hole = 0.6) %>%
  layout(title = "Landing Distribution by Aircraft Manufacturer During Dec 2022")

orca(p, "man/figures/manufacturer.svg") 
```

<img src="man/figures/manufacturer.svg" width="100%"/>

The following Sankey plot demonstrates the distribution of the number of landing in SFO by region and aircraft type, manufacturer, and body type during Dec 2022:

``` r
sfo_stats %>%
  filter(activity_period == 202212) %>%
  group_by(geo_summary, geo_region, landing_aircraft_type, aircraft_manufacturer, aircraft_body_type) %>%
  summarise(total_landing = sum(landing_count),
  groups = "drop") %>%
  sankey_ly(cat_cols = c("geo_summary", "geo_region", 
                         "landing_aircraft_type", 
                         "aircraft_manufacturer",
                         "aircraft_body_type"),
            num_col = "total_landing",
            title = "Landing Summary by Geo Region and Aircraft Type During Dec 2022")

```


```{r, include=FALSE}
p <- sfo_stats %>%
  filter(activity_period == 202212) %>%
  group_by(geo_region, landing_aircraft_type, aircraft_manufacturer, aircraft_body_type) %>%
  summarise(total_landing = sum(landing_count),
            groups = "drop") %>%
  sankey_ly(cat_cols = c("geo_region", 
                         "landing_aircraft_type", 
                         "aircraft_manufacturer",
                         "aircraft_body_type"),
            num_col = "total_landing",
            title = "Landing Summary by Geo Region and Aircraft Type During Dec 2022")

orca(p, "man/figures/landing_sankey.svg") 
```


<img src="man/figures/landing_sankey.svg" width="100%"/>