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C>f1J^) zxLrZ2+$8CRDPSjMwnLzYdV2Js%mw6@nT@qpM&{l&>!hJ}Iq^#$&#_B#91IE7zJL6?s@59vEYYi@?{A$`ZxaMeqTiEtDxyX4g=j}G@1 z*FTQRxtFZ&Ys8f;{fehZ-q|V5z0~hs5VkkPDTrhIzP)pW8loyk7MS8dkf#$fpT54jXlv_BV9&*C1rsn2`voF%Kq45dOgK{sm znx*+HS;{y?*otN>{WVL+;2-}|g&HKaZ`~*Gk5_+sOSs1svE9A~Us`(QAOE84%*p0= YS(kaWJYPO!8T>nX`1qlW1D4nR2VtxoUjP6A literal 0 HcmV?d00001 diff --git a/src/content/blog/cs/visualizing-time-series-data-in-r.md b/src/content/blog/cs/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/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. \ No newline at end of file diff --git a/src/content/blog/cs/working-with-non-numbered-sections-in-latex.md b/src/content/blog/cs/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/cs/working-with-non-numbered-sections-in-latex.md +++ b/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 diff --git a/src/content/blog/da/visualizing-time-series-data-in-r.md b/src/content/blog/da/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/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. \ No newline at end of file diff --git a/src/content/blog/da/working-with-non-numbered-sections-in-latex.md b/src/content/blog/da/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/da/working-with-non-numbered-sections-in-latex.md +++ b/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 diff --git a/src/content/blog/de/visualizing-time-series-data-in-r.md b/src/content/blog/de/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/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. \ No newline at end of file diff --git a/src/content/blog/de/working-with-non-numbered-sections-in-latex.md b/src/content/blog/de/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/de/working-with-non-numbered-sections-in-latex.md +++ b/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 diff --git a/src/content/blog/en/visualizing-time-series-data-in-r.md b/src/content/blog/en/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..70e20a1 --- /dev/null +++ b/src/content/blog/en/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: true +--- + +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. \ No newline at end of file diff --git a/src/content/blog/en/working-with-non-numbered-sections-in-latex.md b/src/content/blog/en/working-with-non-numbered-sections-in-latex.md index 0af4bde..de39c64 100644 --- a/src/content/blog/en/working-with-non-numbered-sections-in-latex.md +++ b/src/content/blog/en/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) +![Result: Non-numbered Sections in LaTeX / Overleaf](@assets/images/working-with-non-numbered-sections-in-latex.png) ## Wrapping Up diff --git a/src/content/blog/es/visualizing-time-series-data-in-r.md b/src/content/blog/es/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/src/content/blog/es/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. \ No newline at end of file diff --git a/src/content/blog/es/working-with-non-numbered-sections-in-latex.md b/src/content/blog/es/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/es/working-with-non-numbered-sections-in-latex.md +++ b/src/content/blog/es/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 diff --git a/src/content/blog/fr/visualizing-time-series-data-in-r.md b/src/content/blog/fr/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/src/content/blog/fr/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. \ No newline at end of file diff --git a/src/content/blog/fr/working-with-non-numbered-sections-in-latex.md b/src/content/blog/fr/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/fr/working-with-non-numbered-sections-in-latex.md +++ b/src/content/blog/fr/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 diff --git a/src/content/blog/it/visualizing-time-series-data-in-r.md b/src/content/blog/it/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/src/content/blog/it/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. \ No newline at end of file diff --git a/src/content/blog/it/working-with-non-numbered-sections-in-latex.md b/src/content/blog/it/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/it/working-with-non-numbered-sections-in-latex.md +++ b/src/content/blog/it/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 diff --git a/src/content/blog/ja/visualizing-time-series-data-in-r.md b/src/content/blog/ja/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/src/content/blog/ja/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. \ No newline at end of file diff --git a/src/content/blog/ja/working-with-non-numbered-sections-in-latex.md b/src/content/blog/ja/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/ja/working-with-non-numbered-sections-in-latex.md +++ b/src/content/blog/ja/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 diff --git a/src/content/blog/ko/visualizing-time-series-data-in-r.md b/src/content/blog/ko/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/src/content/blog/ko/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. \ No newline at end of file diff --git a/src/content/blog/ko/working-with-non-numbered-sections-in-latex.md b/src/content/blog/ko/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/ko/working-with-non-numbered-sections-in-latex.md +++ b/src/content/blog/ko/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 diff --git a/src/content/blog/no/visualizing-time-series-data-in-r.md b/src/content/blog/no/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/src/content/blog/no/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. \ No newline at end of file diff --git a/src/content/blog/no/working-with-non-numbered-sections-in-latex.md b/src/content/blog/no/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/no/working-with-non-numbered-sections-in-latex.md +++ b/src/content/blog/no/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 diff --git a/src/content/blog/pt/visualizing-time-series-data-in-r.md b/src/content/blog/pt/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/src/content/blog/pt/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. \ No newline at end of file diff --git a/src/content/blog/pt/working-with-non-numbered-sections-in-latex.md b/src/content/blog/pt/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/pt/working-with-non-numbered-sections-in-latex.md +++ b/src/content/blog/pt/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 diff --git a/src/content/blog/ru/visualizing-time-series-data-in-r.md b/src/content/blog/ru/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/src/content/blog/ru/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. \ No newline at end of file diff --git a/src/content/blog/ru/working-with-non-numbered-sections-in-latex.md b/src/content/blog/ru/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/ru/working-with-non-numbered-sections-in-latex.md +++ b/src/content/blog/ru/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 diff --git a/src/content/blog/sv/visualizing-time-series-data-in-r.md b/src/content/blog/sv/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/src/content/blog/sv/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. \ No newline at end of file diff --git a/src/content/blog/sv/working-with-non-numbered-sections-in-latex.md b/src/content/blog/sv/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/sv/working-with-non-numbered-sections-in-latex.md +++ b/src/content/blog/sv/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 diff --git a/src/content/blog/tr/visualizing-time-series-data-in-r.md b/src/content/blog/tr/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/src/content/blog/tr/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. \ No newline at end of file diff --git a/src/content/blog/tr/working-with-non-numbered-sections-in-latex.md b/src/content/blog/tr/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/tr/working-with-non-numbered-sections-in-latex.md +++ b/src/content/blog/tr/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 diff --git a/src/content/blog/zh-cn/visualizing-time-series-data-in-r.md b/src/content/blog/zh-cn/visualizing-time-series-data-in-r.md new file mode 100644 index 0000000..959a2ce --- /dev/null +++ b/src/content/blog/zh-cn/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. \ No newline at end of file diff --git a/src/content/blog/zh-cn/working-with-non-numbered-sections-in-latex.md b/src/content/blog/zh-cn/working-with-non-numbered-sections-in-latex.md index 8976aa2..6a0163d 100644 --- a/src/content/blog/zh-cn/working-with-non-numbered-sections-in-latex.md +++ b/src/content/blog/zh-cn/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