R Markdown

Petit test pour avoir tout mon code MineAPI_TW et eviter de le perdre

summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

#####(VIDE) n°18_Test RiverDredgebycompany byriver#### response <- GET(“https://amis.mine.gov.tw/api/RiverDredge.aspx?YearMonth=202401”) QualityDetect <- content(response, “text”) %>% fromJSON(flatten = TRUE) print(QualityDetect) df <- as.data.frame(QualityDetect) simple_table <- df[, c(“data.CityName”, “data.CompanyName”, “data.Tele”)] print(simple_table)

#Traduction translated_names <- gtranslate::translate(simple_table[[“data.CompanyName”]], to = “en”) simple_table <- simple_table %>% mutate(data.CompanyName(en) = translated_names) %>% select(1:2, data.CompanyName(en), 3:length(.))

####N°2 GravelQuarry/county##### response <- GET(“https://amis.mine.gov.tw/api/_CHANGJSON.aspx?Firm=%e5%85%a8%e9%83%a8%e7%b8%a3%e5%b8%82”) QualityDetect <- content(response, “text”) %>% fromJSON(flatten = TRUE)

Convert the data to a data frame

df <- as.data.frame(QualityDetect)

####n°4 Sand&GravelPrudction#####

library(httr)
library(xml2)
library(dplyr)

## 
## Attachement du package : 'dplyr'

## Les objets suivants sont masqués depuis 'package:stats':
## 
##     filter, lag

## Les objets suivants sont masqués depuis 'package:base':
## 
##     intersect, setdiff, setequal, union

library(xml2)
library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.1     ✔ readr     2.1.5
## ✔ ggplot2   4.0.0     ✔ stringr   1.5.2
## ✔ lubridate 1.9.4     ✔ tibble    3.3.0
## ✔ purrr     1.1.0     ✔ tidyr     1.3.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

url <- "https://amis.mine.gov.tw/api/CODELIST.aspx?S=1"
response <- GET(url)

# Parse the XML data
xml_data <- read_xml(content(response, "text"))

## No encoding supplied: defaulting to UTF-8.

# Extract the data for each column
years <- xml_data %>%
  xml_find_all("//Years") %>%
  xml_text() %>%
  as.numeric()

months <- xml_data %>%
  xml_find_all("//Months") %>%
  xml_text() %>%
  as.numeric()

sand <- xml_data %>%
  xml_find_all("//sand") %>%
  xml_text() %>%
  as.numeric()

stone <- xml_data %>%
  xml_find_all("//stone") %>%
  xml_text() %>%
  as.numeric()

grading <- xml_data %>%
  xml_find_all("//grading") %>%
  xml_text() %>%
  as.numeric()

other <- xml_data %>%
  xml_find_all("//other") %>%
  xml_text() %>%
  as.numeric()

total <- xml_data %>%
  xml_find_all("//total") %>%
  xml_text() %>%
  as.numeric()

# Combine the columns into a data frame
TOTAL_PROD <- data.frame(years, months, sand, stone, grading, other, total)
# Convert YearMonth to Date format
TOTAL_PROD$YearMonth <- sprintf("%s-%s", years, months)

TOTAL_PROD %>%
  ggplot(aes(
    y = total,
    x = YearMonth
  )) +
  labs(
    title = "Volume d'extraction entre 2021 et 2023 par rivière",
    x = "YearMonth",
    y = "total",
    fill = "Rivières"
  ) +
  geom_col() +
  scale_fill_viridis_d() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    title = element_text(face = "bold.italic")
  ) +
  guides(fill = guide_legend(ncol = 2))

## Ignoring unknown labels:
## • fill : "Rivières"

####n°6 Sand&GravelInventory######## library(httr) library(xml2) library(dplyr) library(xml2)

url <- “https://amis.mine.gov.tw/api/CODELIST.aspx?S=3” response <- GET(url)

Parse the XML data

xml_data <- read_xml(content(response, “text”))

Extract the data for each column

years <- xml_data %>% xml_find_all(“//Years”) %>% xml_text() %>% as.numeric()

months <- xml_data %>% xml_find_all(“//Months”) %>% xml_text() %>% as.numeric()

fprod <- xml_data %>% xml_find_all(“//sstock”) %>% xml_text() %>% as.numeric()

raw <- xml_data %>% xml_find_all(“//mtock”) %>% xml_text() %>% as.numeric()

total<- xml_data %>% xml_find_all(“//total”) %>% xml_text() %>% as.numeric()

Combine the columns into a data frame

TOTAL_STOCK <- data.frame(years, months, fprod, raw, total) # Convert YearMonth to Date format years <- years+1911 TOTAL_STOCK$YearMonth <- sprintf(“%s-%s”, years, months)

p<- TOTAL_STOCK%>% ggplot(aes( y = total, x = YearMonth )) + labs( title = “Stocks de granulats à l’échelle du pays”, x = “YearMonth”, y = “total” ) + geom_col() + scale_fill_viridis_d() + theme( axis.text.x = element_text(angle = 45, hjust = 1), title = element_text(face = “bold.italic”) ) + guides(fill = guide_legend(ncol = 2))

ggplotly(p) ####n°7 Imported Volume#### library(httr) library(jsonlite) library(dplyr) library(lubridate) library(tidyverse)

#⚠️Avant 2006 pas de variation mensuelle, est-ce que ça veut #dire qu’ils avaient des valeurs annuelles qu’ils ont divisées par 12 ?

Define the years and months you want to query

years <- c(1995:2023) # Example years months <- sprintf(“%02d”, 1:12) # All months from 01 to 12

Initialize an empty list to store the data frames

all_data <- list()

Loop over each year and month

for (year in years) { for (month in months) { # Construct the URL for the current year and month url <- sprintf(“https://amis.mine.gov.tw/api/_CHANGJSON.aspx?SandStoneSaleYearMonth=%s%s”, year, month)

# Make the GET request
response <- GET(url)

# Parse the JSON content if the request was successful
if (status_code(response) == 200) {
  QualityDetect <- content(response, "text", encoding = "UTF-8") %>%
    fromJSON(flatten = TRUE)
  
  # Convert the data to a data frame and add a column for the date
  df <- as.data.frame(QualityDetect)
  df$YearMonth <- sprintf("%s-%s", year, month)
  
  # Append the data frame to the list
  all_data[[length(all_data) + 1]] <- df
} else {
  warning(sprintf("Failed to retrieve data for %s-%s", year, month))
}

} }

Combine all data frames into a single data frame

combined_data <- bind_rows(all_data)

Group by YearMonth and summarize the data

summarized_data <- combined_data %>% group_by(YearMonth) %>% summarize( Years = first(Years), Months = first(Months), North = sum(as.numeric(North), na.rm = TRUE), Center = sum(as.numeric(Center), na.rm = TRUE), South = sum(as.numeric(South), na.rm = TRUE), East = sum(as.numeric(East), na.rm = TRUE), Total = sum(as.numeric(Total), na.rm = TRUE) ) # Convert YearMonth to Date format summarized_data <- summarized_data %>% mutate(YearMonth = as.Date(parse_date_time(YearMonth, c(“ym”))))

Reshape the data from wide to long format

long_data <- summarized_data %>% pivot_longer(cols = c(“North”, “Center”, “South”, “East”,“Total”), names_to = “Region”, values_to = “Value”)

#Plot p <- long_data %>% #filter(Years== 110) %>% ggplot(aes(x = YearMonth, y = Value, color = Region, group = Region)) + geom_line() + scale_x_date() + labs(title = “Importation de sable par région”, x = “Year-Month”, y = “Value”, color = “Region”) + theme_minimal()

####n°8 TestSandPrice NORTH/SOUTH/CENTRAL/EAST####

library(httr) library(jsonlite) library(dplyr) library(lubridate) library(tidyverse) library(wesanderson)

Define the years and months you want to query

years <- c(1990:2024) # Example years months <- sprintf(“%02d”, 1:12) # All months from 01 to 12

Initialize an empty list to store the data frames

all_data <- list()

Loop over each year and month

for (year in years) { for (month in months) { # Construct the URL for the current year and month url <- sprintf(“https://amis.mine.gov.tw/api/_CHANGJSON.aspx?SandCostYearMonth=%s%s”, year, month)

# Make the GET request
response <- GET(url)

# Parse the JSON content if the request was successful
if (status_code(response) == 200) {
  QualityDetect <- content(response, "text", encoding = "UTF-8") %>%
    fromJSON(flatten = TRUE)
  
  # Convert the data to a data frame and add a column for the date
  df <- as.data.frame(QualityDetect)
  df$YearMonth <- sprintf("%s-%s", year, month)
  
  # Append the data frame to the list
  all_data[[length(all_data) + 1]] <- df
} else {
  warning(sprintf("Failed to retrieve data for %s-%s", year, month))
}

} }

Combine all data frames into a single data frame

combined_PRICE <- bind_rows(all_data)

Convert YearMonth to Date format + SandPrice to number

combined_PRICE <- combined_PRICE %>% mutate(YearMonth = as.Date(parse_date_time(YearMonth, c(“ym”)))) %>% mutate(SandPrice = as.numeric(SandPrice))

Filter out rows where the SandPrice is 0

combined_PRICE <- combined_PRICE %>% filter(SandPrice != 0)

#Plot combined_PRICE %>% #filter(Area %in% c(“North”, “Central”, “South”, “East”)) %>% ggplot(aes(x = YearMonth, y = SandPrice, color = Area)) + geom_line() + #geom_point()+ labs(title = “Coût de la tonne de sable à l’achat par région”, x = “Date”, y = “NTD/tonne”, color = “Area”) + theme(axis.text.x = element_text(angle = 45, hjust = 1), title = element_text(face=“bold.italic” )) + guides(fill = guide_legend(ncol = 2))

####n°10 TestSandPrice PER COUNTY####

library(httr) library(jsonlite) library(dplyr) library(lubridate) library(tidyverse) library(wesanderson) library(plotly)

Define the years and months you want to query

years <- c(1990:2024) # Example years months <- sprintf(“%02d”, 1:12) # All months from 01 to 12

Initialize an empty list to store the data frames

all_data <- list()

Loop over each year and month

for (year in years) { for (month in months) { # Construct the URL for the current year and month url <- sprintf(“https://amis.mine.gov.tw/api/_CHANGJSON.aspx?SandCostCityYearMonth=%s%s”, year, month)

# Make the GET request
response <- GET(url)

# Parse the JSON content if the request was successful
if (status_code(response) == 200) {
  QualityDetect <- content(response, "text", encoding = "UTF-8") %>%
    fromJSON(flatten = TRUE)
  
  # Convert the data to a data frame and add a column for the date
  df <- as.data.frame(QualityDetect)
  df$YearMonth <- sprintf("%s-%s", year, month)
  
  # Append the data frame to the list
  all_data[[length(all_data) + 1]] <- df
} else {
  warning(sprintf("Failed to retrieve data for %s-%s", year, month))
}

} }

Combine all data frames into a single data frame

combined_PRICE <- bind_rows(all_data)

Convert YearMonth to Date format + SandPrice to number

combined_PRICE <- combined_PRICE %>% mutate(YearMonth = as.Date(parse_date_time(YearMonth, c(“ym”)))) %>% mutate(SandPrice = as.numeric(SandPrice))

Filter out rows where the SandPrice is 0

combined_PRICE <- combined_PRICE %>% filter(SandPrice != 0)

#Plot p <- combined_PRICE %>% filter(CityName %in% c(“台北市”, “高雄市”, “花蓮縣”,“台中市”,“新北市”)) %>% filter(Years %in% c(“105”)) %>% ggplot(aes(x = YearMonth, y = SandPrice, color = CityName)) + geom_line() + #geom_point()+ labs(title = “Coût de la tonne de sable à l’achat par région”, x = “Date”, y = “NTD/tonne”, color = “City Name”) + scale_color_manual(values=wes_palette(“Darjeeling1”))+ theme(axis.text.x = element_text(angle = 45, hjust = 1), title = element_text(face=“bold.italic” )) + guides(fill = guide_legend(ncol = 2))

ggplotly(p)

#Nouveau type de plot : plot année-type avec évolutions par villes p <- combined_PRICE %>% #filter(CityName %in% c(“台北市”, “高雄市”, “花蓮縣”, “台中市”, “新北市”)) %>% ggplot(aes(x = month(YearMonth, label = TRUE), y = SandPrice, color = as.numeric(year(YearMonth)), # Couleur basée sur l’année (numérique) group = interaction(CityName, year(YearMonth)))) + geom_line() + scale_color_gradientn( colors = RColorBrewer::brewer.pal(n = 9, name = “Blues”) # Palette dégradée ) + labs(title = “Coût de la tonne de sable à l’achat par région sur une année”, x = “Mois”, y = “NTD/tonne”, color = “Année”) + theme(axis.text.x = element_text(angle = 45, hjust = 1), title = element_text(face = “bold.italic”)) + facet_wrap(~ CityName, ncol = 4) # Optionnel : afficher chaque année dans un panneau séparé

ggplotly(p) ###(OK)##n°15_Test Main Companies##### response <- GET(“https://amis.mine.gov.tw/api/QUALITYDETECT.aspx”) QualityDetect <- content(response, “text”) %>% fromJSON(flatten = TRUE) print(QualityDetect) df <- as.data.frame(QualityDetect) simple_table <- df[, c(“data.CityName”, “data.CompanyName”, “data.Tele”)] print(simple_table)

print(simple_table)

####TestLoopSALES##### library(httr) library(jsonlite) library(dplyr) library(lubridate) library(tidyverse)

#⚠️Avant 2006 pas de variation mensuelle, est-ce que ça veut #dire qu’ils avaient des valeurs annuelles qu’ils ont divisées par 12 ?

Define the years and months you want to query

years <- c(1995:2023) # Example years months <- sprintf(“%02d”, 1:12) # All months from 01 to 12

Initialize an empty list to store the data frames

all_data <- list()

Loop over each year and month

# Make the GET request
response <- GET(url)

# Parse the JSON content if the request was successful
if (status_code(response) == 200) {
  QualityDetect <- content(response, "text", encoding = "UTF-8") %>%
    fromJSON(flatten = TRUE)
  
  # Convert the data to a data frame and add a column for the date
  df <- as.data.frame(QualityDetect)
  df$YearMonth <- sprintf("%s-%s", year, month)
  
  # Append the data frame to the list
  all_data[[length(all_data) + 1]] <- df
} else {
  warning(sprintf("Failed to retrieve data for %s-%s", year, month))
}

} }

Combine all data frames into a single data frame

combined_data <- bind_rows(all_data)

Group by YearMonth and summarize the data

Reshape the data from wide to long format

long_data <- summarized_data %>% pivot_longer(cols = c(“North”, “Center”, “South”, “East”), names_to = “Region”, values_to = “Value”)

#Plot long_data %>% filter(Years== 110) %>% ggplot(aes(x = YearMonth, y = Value, color = Region, group = Region)) + geom_line() + scale_x_date() + labs(title = “Achat de sable par région”, x = “Year-Month”, y = “Value”, color = “Region”) + theme_minimal()

####n°18 TestLoop RIVERDREDGE#####

MineAPI_TW

Gregoire Rame

2025-12-18

R Markdown

Convert the data to a data frame

Parse the XML data

Extract the data for each column

Combine the columns into a data frame

Define the years and months you want to query

Initialize an empty list to store the data frames

Loop over each year and month

Combine all data frames into a single data frame

Group by YearMonth and summarize the data

Reshape the data from wide to long format

Define the years and months you want to query

Initialize an empty list to store the data frames

Loop over each year and month

Combine all data frames into a single data frame

Convert YearMonth to Date format + SandPrice to number

Filter out rows where the SandPrice is 0

Define the years and months you want to query

Initialize an empty list to store the data frames

Loop over each year and month

Combine all data frames into a single data frame

Convert YearMonth to Date format + SandPrice to number

Filter out rows where the SandPrice is 0

Define the years and months you want to query

Initialize an empty list to store the data frames

Loop over each year and month

Combine all data frames into a single data frame

Group by YearMonth and summarize the data

Reshape the data from wide to long format