...

src/content/blog/cs/visualizing-time-series-data-in-r.md

@@ -0,0 +1,122 @@
+---
+title: "Visualizing Time Series Data in R: A Beginner's Guide"
+description: "An introductory guide to visualizing time series data in R. Discover foundational techniques, from basic plots to custom visuals using ggplot2, and gain insights into your data."
+tags: ["R", "dataviz", "Data Visualization", "Time Series"]
+authors: ["Datanautes"]
+date: "2023-09-21"
+isLocalized: false
+---
+
+Time series analysis helps us understand trends and predict future values in areas like finance, weather, and sales. Using R, a key tool for statistics, we can visualize this data for better understanding. This post will introduce basic visualization techniques for time series data in R.
+
+Before anything, make sure to install and load `forecast`:
+```r
+install.packages('forecast', dependencies = TRUE)
+library(forecast)
+
+```
+
+### **1. Starting with the Basics: The Initial Plot**
+
+The first step in any time series analysis is to simply visualize the raw data. In R, the `plot()` function serves this purpose:
+
+```r
+plot(AirPassengers, xlab="Time", ylab="Number of Passengers", 
+  main="Monthly Airline Passengers Data")
+
+```
+
+![Monthly Airline Passengers Data](@assets/images/monthly-airline-passengers-data.png)
+
+This basic visualization gives us an understanding of the general trends and patterns.
+
+### **2. Unraveling the Mystery: Decomposition**
+
+Once we have our basic plot, we can break our time series down into its core components: trend, seasonality, and residuals.
+
+```r
+decomposition <- decompose(AirPassengers)
+plot(decomposition)
+```
+
+![decomposition of additive time series](@assets/images/decomposition-of-additive-time-series.png)
+
+This decomposition allows us to see the underlying trend, any seasonality component, and the residuals (or noise) separately.
+
+### **3. Digging Deeper: Understanding Correlation with ACF and PACF**
+
+The Auto-correlation function (ACF) and the partial auto-correlation function (PACF) are tools to measure and visualize the correlation in time series data:
+
+```r
+acf(AirPassengers)
+pacf(AirPassengers)
+```
+
+![acf](@assets/images/acf.png)
+
+![pacf](@assets/images/pacf.png)
+
+
+### **4. Diagnosing our Models**
+
+After fitting a model, such as ARIMA, it's important to visualize the residuals to understand the model's fit:
+
+```r
+fit <- auto.arima(AirPassengers)
+# Setting up the plotting window to 2x1 for the first two plots
+par(mfrow=c(1,2))
+
+# Plot residuals
+plot(fit$residuals, main="Residuals over Time")
+
+# ACF of residuals
+acf(fit$residuals, main="ACF of Residuals")
+
+# Reset graphical parameters to default
+par(mfrow=c(1,1))
+```
+![Residuals vs. ACF of residuals](@assets/images/residuals-asc-of-residuals.png)
+
+```r
+# Histogram of residuals
+hist(fit$residuals, main="Histogram of Residuals")
+```
+
+![Histogram of residuals](@assets/images/histogram-of-residuals.png)
+
+### **5. Gazing into the Future: Forecast Visualizations**
+
+To understand the potential future values and their prediction intervals, we can visualize forecasts:
+
+```r
+future <- forecast(fit, h=24)
+plot(future)
+```
+
+![Forecast](@assets/images/forecast.png)
+
+### **6. Comparing and Contrasting: Multiple Time Series Visualization**
+
+For datasets where multiple time series need to be compared. Here is a (more) generic example: 
+
+```r
+ts.plot(ts1, ts2, ts3, col=c("blue", "red", "green"))
+```
+
+![Comparing and Contrasting: Multiple Time Series Visualization](@assets/images/mock-comp.png)
+
+### **7. Aesthetic and Custom Visuals with `ggplot2`**
+
+For those who crave more customized visuals, the `ggplot2` package in R is a treasure:
+
+```r
+library(ggplot2)
+autoplot(AirPassengers) +
+  labs(title = "Monthly Airline Passengers Data", 
+      x = "Time", y = "Number of Passengers") +
+  theme_minimal()
+```
+![Custom Visuals with ggplot2](@assets/images/time-ser-via-ggplot2.png)
+
+
+In conclusion, visualizing time series data in R can range from basic plots to more advanced, custom visuals. The tools and functions in R make it a versatile choice for time series analysis. As you dive deeper into this realm, always remember: the essence of visualization is clarity. Choose elements that make your data shine and tell its story effectively.

src/content/blog/cs/working-with-non-numbered-sections-in-latex.md

@@ -55,7 +55,7 @@ This is a non-numbered subsection.
 ```
 ### Result: 
 
-![](/images/working-with-non-numbered-sections-in-latex.png)
+![](@assets/images/working-with-non-numbered-sections-in-latex.png)
 
 ## Wrapping Up
 

src/content/blog/da/visualizing-time-series-data-in-r.md

@@ -0,0 +1,122 @@
+---
+title: "Visualizing Time Series Data in R: A Beginner's Guide"
+description: "An introductory guide to visualizing time series data in R. Discover foundational techniques, from basic plots to custom visuals using ggplot2, and gain insights into your data."
+tags: ["R", "dataviz", "Data Visualization", "Time Series"]
+authors: ["Datanautes"]
+date: "2023-09-21"
+isLocalized: false
+---
+
+Time series analysis helps us understand trends and predict future values in areas like finance, weather, and sales. Using R, a key tool for statistics, we can visualize this data for better understanding. This post will introduce basic visualization techniques for time series data in R.
+
+Before anything, make sure to install and load `forecast`:
+```r
+install.packages('forecast', dependencies = TRUE)
+library(forecast)
+
+```
+
+### **1. Starting with the Basics: The Initial Plot**
+
+The first step in any time series analysis is to simply visualize the raw data. In R, the `plot()` function serves this purpose:
+
+```r
+plot(AirPassengers, xlab="Time", ylab="Number of Passengers", 
+  main="Monthly Airline Passengers Data")
+
+```
+
+![Monthly Airline Passengers Data](@assets/images/monthly-airline-passengers-data.png)
+
+This basic visualization gives us an understanding of the general trends and patterns.
+
+### **2. Unraveling the Mystery: Decomposition**
+
+Once we have our basic plot, we can break our time series down into its core components: trend, seasonality, and residuals.
+
+```r
+decomposition <- decompose(AirPassengers)
+plot(decomposition)
+```
+
+![decomposition of additive time series](@assets/images/decomposition-of-additive-time-series.png)
+
+This decomposition allows us to see the underlying trend, any seasonality component, and the residuals (or noise) separately.
+
+### **3. Digging Deeper: Understanding Correlation with ACF and PACF**
+
+The Auto-correlation function (ACF) and the partial auto-correlation function (PACF) are tools to measure and visualize the correlation in time series data:
+
+```r
+acf(AirPassengers)
+pacf(AirPassengers)
+```
+
+![acf](@assets/images/acf.png)
+
+![pacf](@assets/images/pacf.png)
+
+
+### **4. Diagnosing our Models**
+
+After fitting a model, such as ARIMA, it's important to visualize the residuals to understand the model's fit:
+
+```r
+fit <- auto.arima(AirPassengers)
+# Setting up the plotting window to 2x1 for the first two plots
+par(mfrow=c(1,2))
+
+# Plot residuals
+plot(fit$residuals, main="Residuals over Time")
+
+# ACF of residuals
+acf(fit$residuals, main="ACF of Residuals")
+
+# Reset graphical parameters to default
+par(mfrow=c(1,1))
+```
+![Residuals vs. ACF of residuals](@assets/images/residuals-asc-of-residuals.png)
+
+```r
+# Histogram of residuals
+hist(fit$residuals, main="Histogram of Residuals")
+```
+
+![Histogram of residuals](@assets/images/histogram-of-residuals.png)
+
+### **5. Gazing into the Future: Forecast Visualizations**
+
+To understand the potential future values and their prediction intervals, we can visualize forecasts:
+
+```r
+future <- forecast(fit, h=24)
+plot(future)
+```
+
+![Forecast](@assets/images/forecast.png)
+
+### **6. Comparing and Contrasting: Multiple Time Series Visualization**
+
+For datasets where multiple time series need to be compared. Here is a (more) generic example: 
+
+```r
+ts.plot(ts1, ts2, ts3, col=c("blue", "red", "green"))
+```
+
+![Comparing and Contrasting: Multiple Time Series Visualization](@assets/images/mock-comp.png)
+
+### **7. Aesthetic and Custom Visuals with `ggplot2`**
+
+For those who crave more customized visuals, the `ggplot2` package in R is a treasure:
+
+```r
+library(ggplot2)
+autoplot(AirPassengers) +
+  labs(title = "Monthly Airline Passengers Data", 
+      x = "Time", y = "Number of Passengers") +
+  theme_minimal()
+```
+![Custom Visuals with ggplot2](@assets/images/time-ser-via-ggplot2.png)
+
+
+In conclusion, visualizing time series data in R can range from basic plots to more advanced, custom visuals. The tools and functions in R make it a versatile choice for time series analysis. As you dive deeper into this realm, always remember: the essence of visualization is clarity. Choose elements that make your data shine and tell its story effectively.

src/content/blog/da/working-with-non-numbered-sections-in-latex.md

@@ -55,7 +55,7 @@ This is a non-numbered subsection.
 ```
 ### Result: 
 
-![](/images/working-with-non-numbered-sections-in-latex.png)
+![](@assets/images/working-with-non-numbered-sections-in-latex.png)
 
 ## Wrapping Up
 

src/content/blog/de/visualizing-time-series-data-in-r.md

@@ -0,0 +1,122 @@
+---
+title: "Visualizing Time Series Data in R: A Beginner's Guide"
+description: "An introductory guide to visualizing time series data in R. Discover foundational techniques, from basic plots to custom visuals using ggplot2, and gain insights into your data."
+tags: ["R", "dataviz", "Data Visualization", "Time Series"]
+authors: ["Datanautes"]
+date: "2023-09-21"
+isLocalized: false
+---
+
+Time series analysis helps us understand trends and predict future values in areas like finance, weather, and sales. Using R, a key tool for statistics, we can visualize this data for better understanding. This post will introduce basic visualization techniques for time series data in R.
+
+Before anything, make sure to install and load `forecast`:
+```r
+install.packages('forecast', dependencies = TRUE)
+library(forecast)
+
+```
+
+### **1. Starting with the Basics: The Initial Plot**
+
+The first step in any time series analysis is to simply visualize the raw data. In R, the `plot()` function serves this purpose:
+
+```r
+plot(AirPassengers, xlab="Time", ylab="Number of Passengers", 
+  main="Monthly Airline Passengers Data")
+
+```
+
+![Monthly Airline Passengers Data](@assets/images/monthly-airline-passengers-data.png)
+
+This basic visualization gives us an understanding of the general trends and patterns.
+
+### **2. Unraveling the Mystery: Decomposition**
+
+Once we have our basic plot, we can break our time series down into its core components: trend, seasonality, and residuals.
+
+```r
+decomposition <- decompose(AirPassengers)
+plot(decomposition)
+```
+
+![decomposition of additive time series](@assets/images/decomposition-of-additive-time-series.png)
+
+This decomposition allows us to see the underlying trend, any seasonality component, and the residuals (or noise) separately.
+
+### **3. Digging Deeper: Understanding Correlation with ACF and PACF**
+
+The Auto-correlation function (ACF) and the partial auto-correlation function (PACF) are tools to measure and visualize the correlation in time series data:
+
+```r
+acf(AirPassengers)
+pacf(AirPassengers)
+```
+
+![acf](@assets/images/acf.png)
+
+![pacf](@assets/images/pacf.png)
+
+
+### **4. Diagnosing our Models**
+
+After fitting a model, such as ARIMA, it's important to visualize the residuals to understand the model's fit:
+
+```r
+fit <- auto.arima(AirPassengers)
+# Setting up the plotting window to 2x1 for the first two plots
+par(mfrow=c(1,2))
+
+# Plot residuals
+plot(fit$residuals, main="Residuals over Time")
+
+# ACF of residuals
+acf(fit$residuals, main="ACF of Residuals")
+
+# Reset graphical parameters to default
+par(mfrow=c(1,1))
+```
+![Residuals vs. ACF of residuals](@assets/images/residuals-asc-of-residuals.png)
+
+```r
+# Histogram of residuals
+hist(fit$residuals, main="Histogram of Residuals")
+```
+
+![Histogram of residuals](@assets/images/histogram-of-residuals.png)
+
+### **5. Gazing into the Future: Forecast Visualizations**
+
+To understand the potential future values and their prediction intervals, we can visualize forecasts:
+
+```r
+future <- forecast(fit, h=24)
+plot(future)
+```
+
+![Forecast](@assets/images/forecast.png)
+
+### **6. Comparing and Contrasting: Multiple Time Series Visualization**
+
+For datasets where multiple time series need to be compared. Here is a (more) generic example: 
+
+```r
+ts.plot(ts1, ts2, ts3, col=c("blue", "red", "green"))
+```
+
+![Comparing and Contrasting: Multiple Time Series Visualization](@assets/images/mock-comp.png)
+
+### **7. Aesthetic and Custom Visuals with `ggplot2`**
+
+For those who crave more customized visuals, the `ggplot2` package in R is a treasure:
+
+```r
+library(ggplot2)
+autoplot(AirPassengers) +
+  labs(title = "Monthly Airline Passengers Data", 
+      x = "Time", y = "Number of Passengers") +
+  theme_minimal()
+```
+![Custom Visuals with ggplot2](@assets/images/time-ser-via-ggplot2.png)
+
+
+In conclusion, visualizing time series data in R can range from basic plots to more advanced, custom visuals. The tools and functions in R make it a versatile choice for time series analysis. As you dive deeper into this realm, always remember: the essence of visualization is clarity. Choose elements that make your data shine and tell its story effectively.

src/content/blog/de/working-with-non-numbered-sections-in-latex.md

@@ -55,7 +55,7 @@ This is a non-numbered subsection.
 ```
 ### Result: 
 
-![](/images/working-with-non-numbered-sections-in-latex.png)
+![](@assets/images/working-with-non-numbered-sections-in-latex.png)
 
 ## Wrapping Up