adding 2023 dates + trying multiarch container in ghcr

JAlcocerT · Aug 31, 2024 · 0ae5a2d · 0ae5a2d
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diff --git a/.github/workflows/build_shiny.yml b/.github/workflows/build_shiny.yml
@@ -28,4 +28,4 @@ jobs:
       with:
         context: .
         push: true
-        tags: ghcr.io/jalcocert/r-stocks:latest
+        tags: ghcr.io/jalcocert/r-stocks:v1.0
diff --git a/.github/workflows/build_shiny_multiarch.yml b/.github/workflows/build_shiny_multiarch.yml
@@ -0,0 +1,38 @@
+name: CI/CD Build MultiArch Shiny
+
+on:
+  push:
+    branches:
+      - main
+  workflow_dispatch:
+
+jobs:
+  build-and-push:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v2
+
+    - name: Set up QEMU
+      uses: docker/setup-qemu-action@v1
+
+    - name: Set up Docker Buildx
+      uses: docker/setup-buildx-action@v1
+
+    - name: Login to GitHub Container Registry
+      uses: docker/login-action@v1
+      with:
+        registry: ghcr.io
+        username: ${{ github.actor }}
+        password: ${{ secrets.CICD_TOKEN_Shiny }}
+
+    - name: Build and push Docker image
+      uses: docker/build-push-action@v2
+      with:
+        context: .
+        push: true
+        platforms: linux/amd64,linux/arm64
+        tags: |
+          ghcr.io/jalcocert/r-stocks:v1.1
+          ghcr.io/jalcocert/r-stocks:latest
diff --git a/Docker-compose.yml b/Docker-compose.yml
diff --git a/Dockerfile b/Dockerfile
@@ -1,5 +1,4 @@
 # FROM rocker/shiny:3.6.1
-# LABEL maintainer "JAlcocerT <[email protected]>"
 # WORKDIR /srv/shiny-serverRUN apt-get update \
 #     && apt-get install -y libsasl2-dev libssl-devRUN echo \
 #   'options(repos=list(CRAN="https://cloud.r-project.org/"))' > \
@@ -28,7 +27,6 @@
 # #################### Building from shiny docker image ###################3
 
 # FROM rocker/shiny
-# LABEL maintainer "JAlcocerT <[email protected]>"
 
 # WORKDIR /srv/shiny-server
 
@@ -94,6 +92,8 @@
 # ###########################OK##################################
 
 FROM rocker/tidyverse:4 
+LABEL maintainer "JAlcocerT"
+LABEL org.opencontainers.image.source https://github.com/JAlcocerT/R_Stocks
 
 RUN R -e 'install.packages(c("shiny", "plotly", "viridis","dplyr", "tidyr","lubridate","shinythemes","shinyWidgets","DT","bslib","priceR","quantmod"))'
 
@@ -109,17 +109,8 @@ CMD R -e 'shiny::runApp("app.R", port = 3838, host = "0.0.0.0")'
 
 # ####################################################################
 
-# #sudo docker build -t cerdo/shiny2 .
-# #sudo docker run -p3838:3838 -detached cerdo/shiny2
 
 
-
-# #sudo DOCKER_BUILDKIT=1 docker build --no-cache --progress=plain -t fossengineer/rstocks .
-# #sudo docker run -p 3838:3838 --name rstonkss -detached fossengineer/rstocks
-
-# #sudo docker login
-# #sudo docker push fossengineer/rstocks
-
 # ########################casi casi############################
 
 # #https://hub.docker.com/r/rocker/r-ver

diff --git a/EDA/R_Stocks_Analysis.Rmd b/EDA/R_Stocks_Analysis.Rmd
@@ -786,7 +786,7 @@ df_tickers_curr$price_close_eur = df_tickers_curr$price_close*df_tickers_curr$on
 
 library(dplyr)
 
-df_tickers_inflation <- df_tickers %>% filter(ref_date <= '2021-12-31')
+df_tickers_inflation <- df_tickers %>% filter(ref_date <= '2023-12-31')
 
 ```
 
@@ -806,18 +806,18 @@ library(priceR)
 
 #show_countries()
 
-Year <- 2000:2021
+Year <- 2000:2023
 nominal_prices <- rep(1,length(Year))
 
 df_inflation <- data.frame(Year, nominal_prices)
-df_inflation$in_202x_dollars_factor <- adjust_for_inflation(nominal_prices, Year, "US", to_date = 2021) #PriceR
+df_inflation$in_202x_dollars_factor <- adjust_for_inflation(nominal_prices, Year, "US", to_date = 2023) #PriceR
 
 
 df_inflation <- mutate(df_inflation, inflation_level = ( lag(in_202x_dollars_factor)/in_202x_dollars_factor -1)*100 )
 
 
 plot_ly(df_inflation, type = 'scatter', mode = 'lines') %>%
-      add_trace(x = ~Year, y = ~in_202x_dollars_factor, name="Inflation factor to Y2021") %>%
+      add_trace(x = ~Year, y = ~in_202x_dollars_factor, name="Inflation factor to Y2023") %>%
                     layout(title = '<b>US Historical Inflation<b>', xaxis = list(title = 'Year'), 
                            yaxis = list(title = 'a'),
                            legend = list(title=list(text='<b> Legend </b>')))  %>%
@@ -829,11 +829,11 @@ plot_ly(df_inflation, type = 'scatter', mode = 'lines') %>%
 
 
 plot_ly(df_inflation) %>%
-    add_trace(x = ~Year, y = ~in_202x_dollars_factor, type = 'scatter', mode = "lines", yaxis = "y2",name="$ Factor Y2021") %>%
+    add_trace(x = ~Year, y = ~in_202x_dollars_factor, type = 'scatter', mode = "lines", yaxis = "y2",name="$ Factor Y2023") %>%
       add_trace(x = ~Year, y = ~inflation_level, type = 'bar',name='Yearly Inflation US')%>%
                     layout(title = '<b>US Inflation Data<b>', xaxis = list(title = 'Date'), 
                            yaxis = list(title = 'Inflation (%Y)'), legend = list(title=list(text='<b> Legend </b>')),
-                           yaxis2 = list(overlaying = "y", side = "right",automargin = T,title = "Factor Y2021",range = c(0,3))
+                           yaxis2 = list(overlaying = "y", side = "right",automargin = T,title = "Factor Y2023",range = c(0,3))
                           )
 
 
@@ -887,13 +887,13 @@ plot_ly(df_inflation) %>%
 #       
 #        # Specifying second error message
 #           error = function(e){         
-#             print(paste("There was an error message for 2022 too. Then considering one year less for inflation:","2021"))
+#             print(paste("There was an error message for 2022 too. Then considering one year less for inflation:","2023"))
 #             
 #             a=a+1
-#             Year <- 2000:2021
+#             Year <- 2000:2023
 #             nominal_prices <- rep(1,length(Year))
 #             df_inflation <- data.frame(Year, nominal_prices)
-#             df_inflation$in_202x_dollars_factor <- adjust_for_inflation(nominal_prices, Year, "US", to_date = 2021) #PriceR
+#             df_inflation$in_202x_dollars_factor <- adjust_for_inflation(nominal_prices, Year, "US", to_date = 2023) #PriceR
 #           })
 #   },
 #   finally = {            
@@ -1469,12 +1469,12 @@ for(i in (n+1):nrow(divs_agg_ticker)) {
 
 library(tidyr) #for drop_na
 
-Year <- 2000:2021
+Year <- 2000:2023
 #nominal_prices <- rnorm(10, mean=10, sd=3)
 nominal_prices <- rep(1,length(Year))
 
 df_inflation <- data.frame(Year, nominal_prices)
-df_inflation$in_2020_dollars_factor <- adjust_for_inflation(nominal_prices, Year, "US", to_date = 2021) #PriceR
+df_inflation$in_2020_dollars_factor <- adjust_for_inflation(nominal_prices, Year, "US", to_date = 2023) #PriceR
 
 
 
@@ -1764,7 +1764,7 @@ ending_date="2022-01-01"
 
 
 #inflation & money input (priceR)
-Year <- (year(initial_date)-1):(year(ending_date)-1) #2000 to 2021
+Year <- (year(initial_date)-1):(year(ending_date)-1) #2000 to 2023
 #nominal_prices <- rnorm(10, mean=10, sd=3)
 nominal_prices <- rep(1,length(Year))
 
@@ -1868,7 +1868,7 @@ ending_date="2022-01-01"
 
 
 #inflation & money input (priceR)
-Year <- (year(initial_date)-1):(year(ending_date)-1) #2000 to 2021
+Year <- (year(initial_date)-1):(year(ending_date)-1) #2000 to 2023
 #nominal_prices <- rnorm(10, mean=10, sd=3)
 nominal_prices <- rep(1,length(Year))
 
@@ -1992,9 +1992,9 @@ divs_agg <- setNames( aggregate(div ~ year(date) + ticker, data = divs, FUN = su
 
       #divs_agg %>% group_by(ticker) %>% top_n(1, Year_div)
       #divs_agg %>% group_by(ticker) %>% top_n(2, Year_div)
-      #divs_agg[divs_agg$Year_div== 2021,]%>% group_by(ticker)
+      #divs_agg[divs_agg$Year_div== 2023,]%>% group_by(ticker)
 
-DCA_df_divs <- left_join(DCA_df, divs_agg[divs_agg$Year_div== 2021,]%>% group_by(ticker), by=c('ticker'='ticker'))       
+DCA_df_divs <- left_join(DCA_df, divs_agg[divs_agg$Year_div== 2023,]%>% group_by(ticker), by=c('ticker'='ticker'))       
 #DCA_df_divs <- left_join(DCA_df, divs_agg %>% group_by(ticker) %>% top_n(1, Year_div), by=c('ticker'='ticker')) 
 DCA_df_divs <- left_join(DCA_df_divs, hist_price %>% group_by(ticker) %>% top_n(1, ym), by=c('ticker'='ticker')) 
 
@@ -2166,7 +2166,7 @@ ending_date="2022-01-01"
 
 
 #inflation & money input (priceR)
-Year <- (year(initial_date)-1):(year(ending_date)-1) #2000 to 2021
+Year <- (year(initial_date)-1):(year(ending_date)-1) #2000 to 2023
 #nominal_prices <- rnorm(10, mean=10, sd=3)
 nominal_prices <- rep(1,length(Year))
 
@@ -2331,13 +2331,13 @@ historical_yield <- list()
 
 historical_CF <- list()
 
-# j=2021
+# j=2023
 # DCA_df_divs <- left_join(DCA_df, divs_agg %>% group_by(ticker) %>% filter( Year_div == j)  , by=c('ticker'='ticker'))
 # DCA_df_divs$div_cash_flow <- DCA_df_divs$Stocks*DCA_df_divs$div
 # (sum(DCA_df_divs$div_cash_flow)/sum(hist_price_infl_filtered[hist_price_infl_filtered$Year <= j ,]$money_input_infl)+1)*100-100
 
 
- for ( j in 2000:2021 ) {
+ for ( j in 2000:2023 ) {
   #divs_agg %>% group_by(ticker) %>% filter( Year_div == j)  
    #divs_agg %>% group_by(ticker) %>% top_n(1, Year_div)
    
@@ -2369,7 +2369,7 @@ historical_CF <- list()
       
   historical_yield =unlist(historical_yield)
   historical_CF =unlist(historical_CF)
-  yearz =  2000:2021
+  yearz =  2000:2023
   
   dfz = data.frame(historical_yield,historical_CF,yearz)
   
@@ -2435,15 +2435,15 @@ invest = 1000 #it will assume that all given tickers are included in the hist_pr
 
 
 initial_date="2005-01-01" #year(initial_date)
-ending_date="2021-12-31"
+ending_date="2023-12-31"
 
   initial_investment_date <- "2005-01-01"
   final_investment_date <- "2008-12-31"
 
 
 
 #inflation & money input (priceR)
-Year <- (year(initial_date)-1):(year(ending_date)) #2018 to 2021
+Year <- (year(initial_date)-1):(year(ending_date)) #2018 to 2023
 
 #nominal_prices <- rnorm(10, mean=10, sd=3)
 nominal_prices <- rep(1,length(Year))

diff --git a/EDA/R_Stocks_Shiny.Rmd b/EDA/R_Stocks_Shiny.Rmd
@@ -155,7 +155,7 @@ ending_date = today()     #IMPORTANT: Historical inflation data on the current y
 
 
 #ending_date="2023-01-14"
-ending_date="2021-12-31" 
+ending_date="2023-12-31" 
 
 
 
@@ -373,7 +373,7 @@ df_inflation <- mutate(df_inflation, inflation_level = ( lag(in_202x_dollars_fac
 
 theme <- bs_theme(
   # Controls the default grayscale palette
-  bg = "#202123", fg = "#B8BCC2",
+  bg = "#202323", fg = "#B8BCC2",
   # Controls the accent (e.g., hyperlink, button, etc) colors
   primary = "#EA80FC", secondary = "#48DAC6",
   base_font = c("Grandstander", "sans-serif"),
@@ -574,9 +574,9 @@ if (interactive()) {
                                         min = min_year, max = max_year-1, value = c(2010, 2020)),
                             sliderInput("year_yoc",
                                 "YoC looking from the year (TODAY*)",
-                                min = min_year, max = max_year-1, value = 2021,width = "90%"),
+                                min = min_year, max = max_year-1, value = 2023,width = "90%"),
                             sliderInput("money_in_portf",
-                                "Monetary input - Monthly (value of 2021)",
+                                "Monetary input - Monthly (value of 2023)",
                                 min = 100, max = 5000, value = 1000,width = "90%"),
                             selectInput(
                                         'selected_tickers_portf',
@@ -732,7 +732,7 @@ if (interactive()) {
                          
                             sliderInput("years_inflation",
                                         label = h3("Years"),
-                                        min = min_year, max = max_year-1, value = c(2000, 2021)),
+                                        min = min_year, max = max_year-1, value = c(2000, 2023)),
                             selectInput(
                                         'selected_countries',
                                         'Select Countries',
@@ -1426,17 +1426,17 @@ df_inflation$money_input_infl_year <- input$money_in_portf/df_inflation$in_202x_
                         
           
           # plot_ly(df_inflation) %>%
-          # add_trace(x = ~Year, y = ~in_202x_dollars_factor, type = 'scatter', mode = "lines", yaxis = "y2",name="$ Factor Y2021") %>%
+          # add_trace(x = ~Year, y = ~in_202x_dollars_factor, type = 'scatter', mode = "lines", yaxis = "y2",name="$ Factor Y2023") %>%
           #   add_trace(x = ~Year, y = ~inflation_level, type = 'bar',name='Yearly Inflation US')%>%
           #           layout(title = '<b>US Inflation Data<b>', xaxis = list(title = 'Date'), 
           #                  yaxis = list(title = 'Inflation (%Y)'), legend = list(title=list(text='<b> Legend </b>')),
-          #                  yaxis2 = list(overlaying = "y", side = "right",automargin = T,title = "Factor Y2021",range = c(0,3))
+          #                  yaxis2 = list(overlaying = "y", side = "right",automargin = T,title = "Factor Y2023",range = c(0,3))
           #                 )
           # 
           
 
 plot_ly(df_inflationinteractive(), type = 'scatter', mode = 'lines') %>%
-      add_trace(x = ~Year, y = ~in_202x_dollars_factor, name="Inflation factor to Y2021") %>%
+      add_trace(x = ~Year, y = ~in_202x_dollars_factor, name="Inflation factor to Y2023") %>%
                     layout(title = '<b>US Historical Inflation<b>', xaxis = list(title = 'Year'), 
                            yaxis = list(title = 'a'),
                            legend = list(title=list(text='<b> Legend </b>')))  %>%

diff --git a/README.md b/README.md
@@ -21,7 +21,7 @@
 
 Using public financial data together with in R to create an interactive Shiny App to check how noisy the stock market can be and definitely [not a tool to guide your personal finances](https://github.com/JAlcocerT/R_Stocks#important---no-investment-advice).
 
-* Further Description at: <https://fossengineer.com/project-shiny-R-Stocks/>
+* Further Description at: <https://jalcocert.github.io/JAlcocerT/R-Stocks/>
 
 ## Repisotory Structure
 
@@ -31,8 +31,8 @@ Using public financial data together with in R to create an interactive Shiny Ap
     * Dividend Analysis: yfR, QuantMod, PriceR, *QuandDl (optional)*
     * Portfolio Analysis: yfR, QuantMod, PriceR
 * **The R-Stocks App:**
-    * [Docker and the R-Stocks Shiny App](https://fossengineer.com/building-r-shiny-apps-container-image-with-docker/)
-    * Versioning of the Docker Images: <https://hub.docker.com/repository/docker/fossengineer/r_stocks>
+    * [Docker and the R-Stocks Shiny App](https://jalcocert.github.io/JAlcocerT/building-r-shiny-apps-container-image-with-docker/)
+    * Versioning of the [Docker Container Images at GHCR](https://github.com/users/JAlcocerT/packages/container/package/r-stocks)
 
 ## 🎯 Features and RoadMap
 
@@ -83,20 +83,17 @@ I am listing here the ones that served as major pilars for making the project co
   &nbsp;
 
 * <https://github.com/ropensci/yfR>
-    * [Discovering the yfR package](https://fossengineer.com/r-yfR-package-guide/)
 * <https://github.com/stevecondylios/priceR>
-    * [Querying currency pairs with PriceR](https://fossengineer.com/r-priceR-package-guide/)
 * <https://github.com/joshuaulrich/quantmod>
-    * [My guide for QuantMod Package](https://fossengineer.com/r-Quantmod-package-guide/)
 * <https://github.com/quandl/quandl-r>
-    * [Using the quandl R Package](https://fossengineer.com/r-quandl-package-guide/)
 
 </details>
 
 * Other Data sources:
     * For the Flexdashboard: <https://github.com/datasets/s-and-p-500>
     * <https://finance.yahoo.com/quote/GC=F/> 
     * <https://www.macrotrends.net/1333/historical-gold-prices-100-year-chart>
+    * FED <https://fred.stlouisfed.org/series/FEDFUNDS>
 
 
 #### Visualizations
@@ -122,7 +119,7 @@ I need to mention the importance of additional **open source tools** that made t
 
 * Support extra evening code sessions:
 
-[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/FossEngineer)
+[![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://ko-fi.com/Z8Z1QPGUM)
 
 ## IMPORTANT! - NO INVESTMENT ADVICE