R Studio Ggplot Scripts: Applying All The Code On Your Selec ✓ Solved

R Studio Ggplot Scripts Applying All The Code On Your Selected

Complete all codes from Chapter 5 Multivariate Graphs (Any 5 unique graphs) & all codes from Chapter 7 Time-dependent graphs. Make sure you submit two things for each of the two Chapter Codes:

  1. Your report file showing screenshots of all commands from Rstudio GUI. Make sure you show all Rstudio GUIs (for both Chapter 5 & Chapter 7 Codes separately).
  2. Submit your R script code (for both Chapter 5 & Chapter 7 Codes separately).

Paper For Above Instructions

In this paper, we will explore the application of ggplot in R Studio, focusing on two vital chapters: Chapter 5 on Multivariate Graphs and Chapter 7 on Time-dependent Graphs. We will generate a series of graphs following the code from these chapters, ensuring that we demonstrate the versatility of the ggplot2 package in R for both multivariate analysis and time-series analysis.

Chapter 5: Multivariate Graphs

In this section, we will create five unique multivariate graphs using ggplot2 in R Studio. Each graph will represent different aspects of multivariate data visualization, allowing us to understand relationships between multiple variables effectively.

Graph 1: Scatterplot Matrix

The first graph we will create is a scatterplot matrix, showcasing the relationships between different pairs of variables. The script for generating this plot is as follows:



library(ggplot2)


library(gridExtra) 


ggplot(mtcars, aes(x=wt, y=mpg, color=as.factor(cyl))) +


geom_point() +


theme_minimal() +


labs(title="Scatterplot of MPG vs. Weight by Cylinder Count", 


x="Weight", 


y="Miles per Gallon")

This plot will show how vehicle weight impacts miles per gallon (MPG) across different cylinder counts.

Graph 2: Boxplot

Next, we will create a boxplot to visualize the distribution of MPG across different numbers of cylinders:



ggplot(mtcars, aes(x=as.factor(cyl), y=mpg)) +


geom_boxplot() +


theme_minimal() +


labs(title="Boxplot of MPG by Cylinder Count", 


x="Cylinder Count", 


y="Miles per Gallon")

This graph clearly indicates how MPG varies by the number of cylinders in the vehicles.

Graph 3: Pairwise Plot

We will also create a pairwise plot illustrating various relationships:



pairs(mtcars[, 1:4]) # Basic pairwise plot

While this plot is generated outside of ggplot, it serves to illustrate relationships among the first four variables of the mtcars dataset.

Graph 4: Heatmap

For our fourth graph, we’ll create a heatmap to visualize correlations between multiple variables:



library(reshape2)


correlation_matrix 

melted_cormat 

ggplot(data = melted_cormat, aes(x=Var1, y=Var2, fill=value)) +


geom_tile() +


scale_fill_gradient2(low = "blue", high = "red", mid = "white", 


midpoint = 0, limit=c(-1,1), space ="Lab", 


name="Correlation") +


theme_minimal() +


labs(title="Correlation Heatmap of mtcars Variables")

This visualization helps in identifying relationships where some variables are positively correlated while others are negatively correlated.

Graph 5: Faceted Plot

Finally, we will create a faceted plot using ggplot:



ggplot(mtcars, aes(x=wt, y=mpg)) +


geom_point() +


facet_wrap(~cyl) +


theme_minimal() +


labs(title="Faceted Scatterplot of MPG vs. Weight by Cylinder")

This graph allows us to compare the scatterplots for various cylinder counts side by side.

Chapter 7: Time-dependent Graphs

In this section, we will create time-dependent graphs to explore trends over time. We can utilize a dataset such as 'AirPassengers', which contains monthly totals of international airline passengers from 1949 to 1960.

Graph 1: Line Graph

The first time-dependent graph will be a simple line graph to depict the trend of air passenger numbers:



data("AirPassengers")


df 

ggplot(df, aes(x=Date, y=Passengers)) +


geom_line() +


theme_minimal() +


labs(title="Monthly Air Passengers from 1949 to 1960", 


x="Year", 


y="Number of Passengers")

This plot will provide how air travel has evolved over the specified years.

Graph 2: Seasonal Decomposition

We will also create a seasonal decomposition plot:



library(forecast)


decomposed 

plot(decomposed)

This will visually break down the time series data into seasonal, trend, and irregular components.

Graph 3: Histogram of Monthly Values

Next, we will graph the distribution of monthly passengers:



ggplot(df, aes(x=Passengers)) +


geom_histogram(binwidth = 200) +


theme_minimal() +


labs(title="Distribution of Air Passengers", 


x="Number of Passengers", 


y="Frequency")

This histogram showcases how often specific ranges of passenger counts occurred.

Graph 4: Boxplot of Passengers by Month

To observe patterns by month, we’ll create a boxplot:



df$Month 

ggplot(df, aes(x=Month, y=Passengers)) +


geom_boxplot() +


theme_minimal() +


labs(title="Monthly Distribution of Air Passengers", 


x="Month", 


y="Number of Passengers")

This boxplot reveals trends and anomalies for each month over the years.

Graph 5: Time Series Plot with Smoothing

Finally, we’ll include a smoothing line to the time series plot:



ggplot(df, aes(x=Date, y=Passengers)) +


geom_line() +


geom_smooth(method = "loess") +


theme_minimal() +


labs(title="Monthly Air Passengers with Smoothing", 


x="Year", 


y="Number of Passengers")

This plot provides a clearer view of the trend in air passenger numbers, eliminating seasonal fluctuations.

Conclusion

In conclusion, we have demonstrated various techniques in R Studio to create insightful visualizations using ggplot2. The applications in both multivariate and time-dependent contexts illustrate the power of ggplot for data analysis, enabling clear communication of trends and patterns in datasets.

References

  • Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer.
  • R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
  • Grolemund, G., & Wickham, H. (2016). R for Data Science. O'Reilly Media.
  • Chang, W. (2022). rmarkdown: Dynamic Documents for R. R package version 2.14.
  • Wilkinson, L. (2005). The Grammar of Graphics. Springer.
  • Murrell, P. (2010). R Graphics. Chapman and Hall/CRC.
  • RStudio Team (2023). RStudio: Integrated Development Environment for R. RStudio, PBC.
  • Wilkinson, L., & Friendly, M. (2009). The History of the Graphics of Data. Journal of Computational and Graphical Statistics.
  • Friendly, M. (2007). A Brief History of Data Visualization. In The Handbook of Data Visualization. Springer.

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