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spatial.qmd
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spatial.qmd
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---
title: "Spatial analyst"
format:
html:
code-fold: true
code-tools: true
---
So let's pratice !!!
## Spatial analyst:
Now let's start with data contains the information of longitude and latitude of customer's locations. Remember install data [optimize](index.qmd) before starting.
```{r}
#| include: false
#| echo: false
library(readxl)
optimize<-read.csv(r"(C:\\Users\\locca\\Documents\\Xuân Lộc\\VILAS\\Final project\\Optimize_df.csv)")
## Call packages:
pacman::p_load(rio,
here,
janitor,
tidyverse,
dplyr,
magrittr,
ggplot2,
purrr,
lubridate,
knitr,
shiny)
```
```{r}
#| echo: true
#| message: false
#New manufacter:
new_manufacter= data.frame(
Customers = str_c(rep("Manufacter"),1:3),
Latitude =c(21.12256201,21.68421,20.34250),
Longitude = c(105.9150683,105.1940,106.2946),
Total.transactions = c(0,0,0),
Inventory = c(3000,2000,2500))
route<-rbind(new_manufacter,
optimize%>% select(Customers,
Longitude,
Latitude,
Total.transactions) %>%
mutate(Inventory = round(runif(50,100,400)))
)
colnames(route)[4]<-"Demand"
route$Node<-1:nrow(route)
## Adding status:
route$Status <- ifelse(route$Inventory - route$Demand > round(mean(route$Demand)/2),"Control",ifelse(route$Inventory- route$Demand > 0,"Warning","Outstock" ))
```
So we have enough data to pratice. Let show this data in map for clearly understading.
### Map of supply chain management
```{r}
#| fig-cap: "The location of all customers and internal manufacters"
#Prepare labels:
labels<- paste0("<strong> Customers </strong> ",
route$Customers, "<br/> ",
"<strong> Inventory: </strong> ",
route$Inventory, "<br/> ",
"<strong> Demand </strong> ",
route$Demand, "<br/> ",
"<strong> Status </strong> ",
route$Status, "<br/> ") %>%
lapply(htmltools::HTML)
library(leaflet)
library(fontawesome)
#If you don't have, try to install by: devtools::install_github("rstudio/fontawesome")
logos <- awesomeIconList(
Customer = makeAwesomeIcon(
icon = "home",
iconColor = "white",
markerColor = "blue",
library = "fa"),
Manufacter = makeAwesomeIcon(
icon = "beer",
iconColor = "gold",
markerColor = "black",
library = "fa")
)
#Prepare the logos:
route$ticker<-c(rep("Manufacter",3),
rep("Customer",
nrow(route)-3))
leaflet(data = route) %>%
addTiles() %>%
addAwesomeMarkers(
lng = ~Longitude,
lat = ~Latitude,
label = ~labels,
icon = ~logos[ticker]) %>%
setView(lng = mean(route$Longitude),
lat = mean(route$Latitude),
zoom = 7)
```
In the following code, I want to emphasize that the names of colors should have the first letter capitalized. For example, use "Red" instead of "red".
You might be wondering why this is important, and I had the same question :)). After some online research, I found a response to a similar question about changing the color of markers in R using Leaflet [Leaflet change color of markers (R)](https://stackoverflow.com/questions/71726478/leaflet-change-color-of-markers-r).
It was mentioned that capitalizing the first letter allows R to color the markers based on different factors. If you don't capitalize the first letter, R will color the markers randomly (I'm not sure why).
```{r}
#| fig-cap: "The mini map by clustering the locations"
#Setting the level of status
route$Status<-fct_relevel(route$Status,"Control","Warning","Outstock")
#Prepare palette for labeling control/warning/outstock:
palPwr <- leaflet::colorFactor(palette = c("Lightgreen","Yellow","Red"),
domain = route$Status,
ordered = T)
#Prepare font for labeling
font<-labelOptions(noHide = T,
direction = "bottom",
style = list(
"font-family" = "serif",
"font-style" = "ilatic",
"box-shadow" = "3px 3px rgba(0,0,0,0.25)",
"font-size" = "10px",
"border-color" = "rgba(0,0,0,0.5)"
))
#Plot map with leaflet:
library(leaflet.extras)
leaflet(data = route) %>%
addProviderTiles("CartoDB.Positron") %>%
addCircleMarkers(radius = 10, # size of the dots
fillOpacity = .7, # alpha of the dots
stroke = FALSE, # no outline
label = ~labels,
lng = ~Longitude,
lat = ~Latitude,
color = ~palPwr(route$Status),
clusterOptions = markerClusterOptions(),
labelOptions = font) %>%
leaflet::addLegend(position = "bottomright",
values = ~Status, # data frame column for legend
opacity = .7,
pal = palPwr, # palette declared earlier
title = "Status") %>% # legend titleƯ
addResetMapButton()
```
Also for adjusting the base map, you can base on the preview of base map in [Leaflet preview](https://leaflet-extras.github.io/leaflet-providers/preview/) and copy the name of provider to paste in the argument {addProviderTitles}. For instance, I use provider = `CartoDB.Positron`.
## Routing the vehicle's path for Supply Chain Plan:
To set up the connection between RStudio and GitHub, you can use the `source()` function and assign the URL link of the GitHub repository that contains the R script you need. Remember to click on "Raw" to move to another page and then copy that URL.
I found the original code in Viktor Plamenov's project on [GitLab](https://gitlab.com/vikplamenov/vrpoptima/-/tree/main/R?ref_type=heads). I found it convenient to use, so I copied and uploaded it to my private GitHub repository. You can use this URL for your work.
The author created the package {vrpoptima} for easily install and using it. You can install by package {remote}, another details you can read in this link [remotes](https://remotes.r-lib.org/reference/install_gitlab.html)
```{r}
#| include: false
#| warning: false
#| message: false
# Repo must in format username/repo[@ref].
remotes::install_gitlab("vikplamenov/vrpoptima")
```
```{r}
#| warning: false
#| message: false
library(vrpoptima)
colnames(optimize)[2:3]<-c("lat","lon")
colnames(new_manufacter)[2:3]<-c("lat","lon")
mat_optimize<-as.matrix(optimize[,2:3])
dist_optimize<-as.matrix(geodist::geodist(mat_optimize,measure = 'haversine')/1000)
mat_WH<-as.matrix(new_manufacter[,2:3])
```
Next, just simply add the criteria and run the code illustrated below.
```{r}
#| warning: false
#| echo: false
#Optimizing:
(solution <- VehicleRouting(visit_points = mat_optimize,
num_agents = nrow(new_manufacter),
agent_points = as.matrix(new_manufacter[,2:3]),
cost_type = 2,
max_tour_distance = 250000,
max_tour_visits = 30,
distance_metric = 'Geodesic',
distance_matrix = dist_optimize,
min_tour = 2,
population_size = 96,
num_generations = 1000,
distance_truncation = TRUE,
seed = 42)
)
```
Finally, plot the results of optimization by two functions:
* `PlotToursCombined` function: use to display of the combined routes created with the genetic program.
* `PlotToursIndividual` function: use to display of the individual routes created with the genetic program.
```{r}
#Plot the results:
routes <- solution$routes
rownames(routes) <- 1:nrow(routes)
routes_list = RoutesDataPrep(routes = solution$routes,
visit_points = mat_optimize,
agent_points = as.matrix(new_manufacter[,2:3]))
# Display all routes at the same time
PlotToursCombined(solution = solution,
routes_list = routes_list,
agent_locations = as.matrix(new_manufacter[,2:3]),
orientation = "vertical")
# Display all the inidividual routes on a single figure block
PlotToursIndividual(solution = solution,
routes_list = routes_list)
```
# References:
Thanks to all authors of documentaions below that help me complete this pratice.
* [leaflet](https://www.jla-data.net/eng/leaflet-in-r-tips-and-tricks/) by Jindra Lacko.
* [How to Use Git/GitHub with R](https://rfortherestofus.com/2021/02/how-to-use-git-github-with-r) by David Keyes.
* [Multiple Depot in VRP](https://gitlab.com/vikplamenov/vrpoptima/-/blob/main/R/VehicleRouting.R?ref_type=heads) by Viktor Plamenov.
* [htmltools with R](https://3mw.albert-rapp.de/) by Albert Rapp.
* [Dataui](https://timelyportfolio.github.io/dataui/articles/dataui_reactable.html)