Data visualization with R and ggplot2.docx

Data visualization with R and ggplot2.docx

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  Data visualization withR and ggplot2 Last Updated : 20 Dec, 2023    Data visualization with R and ggplot2 in R Programming Language also termed as Grammar of Graphics is a free, open-source, and easy-to-use visualization package widely used in R Programming Language. It is the most powerful visualization package written by Hadley Wickham. It includes several layers on which it is governed. The layers are as follows: Building Blocks of layers with the grammar of graphics  Data: The element is the data set itself  Aesthetics: The data is to map onto the Aesthetics attributes such as x- axis, y-axis, color, fill, size, labels, alpha, shape, line width, line type  Geometrics: How our data being displayed using point, line, histogram, bar, boxplot  Facets: It displays the subset of the data using Columns and rows  Statistics: Binning, smoothing, descriptive, intermediate  Coordinates: the space between data and display using Cartesian, fixed, polar, limits  Themes: Non-data link Dataset Used mtcars(motor trend car road test) comprise fuel consumption and 10 aspects of automobile design and performance for 32 automobiles and come pre-installed with dplyr package in R.
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   R # Printthe top 5 records of the dataset head(mtcars) Output: mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 Now we load dplyr library and print the summary of mtcars dataset using summary function.  R # Installing the package install.packages("dplyr") # Loading package library(dplyr) # Summary of dataset in package summary(mtcars) Output: mpg cyl disp hp Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0 1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5 Median :19.20 Median :6.000 Median :196.3 Median :123.0 Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0 Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0 drat wt qsec vs Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000 Median :3.695 Median :3.325 Median :17.71 Median :0.0000 Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000 Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000 am gear carb Min. :0.0000 Min. :3.000 Min. :1.000
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  1st Qu.:0.0000 1stQu.:3.000 1st Qu.:2.000 Median :0.0000 Median :4.000 Median :2.000 Mean :0.4062 Mean :3.688 Mean :2.812 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000 Max. :1.0000 Max. :5.000 Max. :8.000 ggplot2 in R We devise visualizations on mtcars dataset which includes 32 car brands and 11 attributes using ggplot2 layers. Data Layer: ggplot2 in R the data Layer we define the source of the information to be visualize, let’s use the mtcars dataset in the ggplot2 package.  R library(ggplot2) library(dplyr) ggplot(data = mtcars) + labs(title = "MTCars Data Plot") Output: ggplot2 in R
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  Aesthetic Layer: ggplot2 inR Here we will display and map dataset into certain aesthetics.  R # Aesthetic Layer ggplot(data = mtcars, aes(x = hp, y = mpg, col = disp))+ labs(title = "MTCars Data Plot") Output: ggplot2 in R Geometric layer: ggplot2 in R geometric layer control the essential elements, see how our data being displayed using point, line, histogram, bar, boxplot.  R # Geometric layer ggplot(data = mtcars, aes(x = hp, y = mpg, col = disp)) + geom_point() + labs(title = "Miles per Gallon vs Horsepower",
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  x = "Horsepower", y= "Miles per Gallon") Output: Data visualization with R and ggplot2 Geometric layer: Adding Size, color, and shape and then plotting the Histogram plot  R # Adding size ggplot(data = mtcars, aes(x = hp, y = mpg, size = disp)) + geom_point() + labs(title = "Miles per Gallon vs Horsepower", x = "Horsepower", y = "Miles per Gallon") # Adding shape and color ggplot(data = mtcars, aes(x = hp, y = mpg, col = factor(cyl), shape = factor(am))) +geom_point() + labs(title = "Miles per Gallon vs Horsepower",
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  x = "Horsepower", y= "Miles per Gallon") # Histogram plot ggplot(data = mtcars, aes(x = hp)) + geom_histogram(binwidth = 5) + labs(title = "Histogram of Horsepower", x = "Horsepower", y = "Count") Output: Data visualization with R and ggplot2
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  Data visualization withR and ggplot2
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  Data visualization withR and ggplot2 Facet Layer: ggplot2 in R facet layer is used to split the data up into subsets of the entire dataset and it allows the subsets to be visualized on the same plot. Here we separate rows according to transmission type and Separate columns according to cylinders.  R # Facet Layer # Separate rows according to transmission type p <- ggplot(data = mtcars, aes(x = hp, y = mpg, shape = factor(cyl))) + geom_point() p + facet_grid(am ~ .) + labs(title = "Miles per Gallon vs Horsepower", x = "Horsepower", y = "Miles per Gallon") # Separate columns according to cylinders p <- ggplot(data = mtcars, aes(x = hp, y = mpg, shape = factor(cyl))) + geom_point() p + facet_grid(. ~ cyl) + labs(title = "Miles per Gallon vs Horsepower", x = "Horsepower", y = "Miles per Gallon") Output:
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  Data visualization withR and ggplot2Data visualization with R and ggplot2
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  Data visualization withR and ggplot2 Statistics layer ggplot2 in R this layer, we transform our data using binning, smoothing, descriptive, intermediate  R ggplot(data = mtcars, aes(x = hp, y = mpg)) + geom_point() + stat_smooth(method = lm, col = "red") + labs(title = "Miles per Gallon vs Horsepower") Output: Data visualization with R and ggplot2 Coordinates layer: ggplot2 in R these layers, data coordinates are mapped together to the mentioned plane of the graphic and we adjust the axis and changes the spacing of displayed data with Control plot dimensions.  R
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  ggplot(data = mtcars,aes(x = wt, y = mpg)) + geom_point() + stat_smooth(method = lm, col = "red") + scale_y_continuous("Miles per Gallon", limits = c(2, 35), expand = c(0, 0)) + scale_x_continuous("Weight", limits = c(0, 25), expand = c(0, 0)) + coord_equal() + labs(title = "Miles per Gallon vs Weight", x = "Weight", y = "Miles per Gallon") Output: Data visualization with R and ggplot2 Coord_cartesian() to proper zoom in:  R # Add coord_cartesian() to proper zoom in ggplot(data = mtcars, aes(x = wt, y = hp, col = am)) + geom_point() + geom_smooth() + coord_cartesian(xlim = c(3, 6)) Output:
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  Data visualization withR and ggplot2 Theme Layer: ggplot2 in R layer controls the finer points of display like the font size and background color properties. Example 1: Theme layer – element_rect() function  R ggplot(data = mtcars, aes(x = hp, y = mpg)) + geom_point() + facet_grid(. ~ cyl) + theme(plot.background = element_rect(fill = "blue", colour = "gray")) + labs(title = "Miles per Gallon vs Horsepower") Output:
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  Data visualization withR and ggplot2 Example 2:  R ggplot(data = mtcars, aes(x = hp, y = mpg)) + geom_point() + facet_grid(am ~ cyl) + theme_gray()+ labs(title = "Miles per Gallon vs Horsepower") Output:
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  Data visualization withR and ggplot2 ggplot2 in R provides various types of visualizations. More parameters can be used included in the package as the package gives greater control over the visualizations of data. Many packages can integrate with the ggplot2 package to make the visualizations interactive and animated. Contour plot for the mtcars dataset  R # Install and load required packages install.packages("ggplot2") library(ggplot2) # Create a 2D density contour plot for the mtcars dataset ggplot(mtcars, aes(x = wt, y = mpg)) + stat_density_2d(aes(fill = ..level..), geom = "polygon", color = "white") + scale_fill_viridis_c() + labs(title = "2D Density Contour Plot of mtcars Dataset", x = "Weight (wt)", y = "Miles per Gallon (mpg)", fill = "Density") +
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  theme_minimal() Output: Data visualization withR and ggplot2 In ggplot2 in R stat_density_2d to generate the 2D density contour plot. The aesthetics x and y specify the variables on the x-axis and y-axis, respectively. The fill aesthetic is set to ..level.. to map fill color to density levels. Creating a panel of different plots  R library(ggplot2) library(gridExtra) # Selecting specific columns from mtcars dataset selected_cols <- c("mpg", "disp", "hp", "drat") selected_data <- mtcars[, selected_cols] # Create histograms for individual variables hist_plot_mpg <- ggplot(selected_data, aes(x = mpg)) + geom_histogram(binwidth = 2, fill = "blue", color = "white") +
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  labs(title = "Histogram:Miles per Gallon", x = "Miles per Gallon", y = "Frequency") hist_plot_disp <- ggplot(selected_data, aes(x = disp)) + geom_histogram(binwidth = 50, fill = "red", color = "white") + labs(title = "Histogram: Displacement", x = "Displacement", y = "Frequency") hist_plot_hp <- ggplot(selected_data, aes(x = hp)) + geom_histogram(binwidth = 20, fill = "green", color = "white") + labs(title = "Histogram: Horsepower", x = "Horsepower", y = "Frequency") hist_plot_drat <- ggplot(selected_data, aes(x = drat)) + geom_histogram(binwidth = 0.5, fill = "orange", color = "white") + labs(title = "Histogram: Drat", x = "Drat", y = "Frequency") # Arrange the plots in a grid grid.arrange(hist_plot_mpg, hist_plot_disp, hist_plot_hp, hist_plot_drat, ncol = 2) Output: Data visualization with R and ggplot2
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  The ggplot2 andgridExtra packages to create histograms for four different variables (“Miles per Gallon,” “Displacement,” “Horsepower,” and “Drat”) from the mtcars dataset. Each histogram is visually represented in a distinctive color (blue, red, green, and orange) with white borders. The resulting grid of histograms provides a quick visual overview of the distribution of these car-related variables. Save and extract R plots: To save and extract plots in R, you can use the ggsave function from the ggplot2 package. Here’s an example of how to save and extract plots:  R # Create a plot plot <- ggplot(data = mtcars, aes(x = hp, y = mpg)) + geom_point() + labs(title = "Miles per Gallon vs Horsepower") # Save the plot as an image file (e.g., PNG) ggsave("plot.png", plot) # Save the plot as a PDF file ggsave("plot.pdf", plot) # Extract the plot as a variable for further use extracted_plot <- plot plot Output:
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  Data visualization withR and ggplot2 In this demonstration, I used ggplot to construct a plot and the ggsave function to save it as a PDF file (plot.pdf) and a PNG image file (plot.png). By including the correct file extension, you can indicate the intended file format. You may easily give the ggplot object to a variable, as demonstrated with extracted_plot, to extract the plot as a variable for later usage. Be sure to substitute your unique plot and desired file names for the plot code and file names (plot.png and plot.pdf). Summer-time is here and so is the time to skill-up! More than 5,000 learners have now completed their journey from basics of DSA to advanced level development programs such as Full-Stack, Backend Development, Data Science.