A histogram plot is an alternative to Density plot for visualizing the distribution of a continuous variable. This chart represents the distribution of a continuous variable by dividing into bins and counting the number of observations in each bin. This article describes how to create Histogram plots using the ggplot2 R package. ## GGPlot Density Plot

A density plot is an alternative to Histogram used for visualizing the distribution of a continuous variable. The peaks of a Density Plot help to identify where values are concentrated over the interval of the continuous variable. Compared to Histograms, Density Plots are better at finding the distribution shape because they are re not affected by the number of bins used (each bar used in a typical histogram). This article describes how to create density plots using the ggplot2 R package. ## GGPlot Error Bars

Error Bars are used to visualize the variability of the plotted data. Error Bars can be applied to graphs such as, Dot Plots, Barplots or Line Graphs, to provide an additional layer of detail on the presented data. Generally, Error bars are used to show either the standard deviation, standard error, confidence intervals or interquartile range. The length of an Error Bar helps reveal the uncertainty of a data point. This article describes how to add error bars into a plot using the ggplot2 R package. You will learn how to create bar plots and line plots with error bars ## GGPlot Barplot

Barplot is used to show discrete, numerical comparisons across categories. One axis of the chart shows the specific categories being compared and the other axis represents a discrete value scale.This article describes how to create a barplot using the ggplot2 R package.You will learn how to: 1) Create basic and grouped barplots; 2) Add labels to a barplot; 3) Change the bar line and fill colors by group ## GGPlot Line Plot

In a line plot, observations are ordered by x value and connected by a line. This article describes how to create a line plot using the ggplot2 R package. You will learn how to: 1) Create basic and grouped line plots; 2) Add points to a line plot; 3) Change the line types and colors by group. ## GGPlot Stripchart

Stripcharts are also known as one dimensional scatter plots. These plots are suitable compared to box plots when sample sizes are small. This article describes how to create and customize Stripcharts using the ggplot2 R package. ## GGPlot Dot Plot

A Dot Plot is used to visualize the distribution of the data. This chart creates stacked dots, where each dot represents one observation. Summary statistics are usually added to dotplots for indicating, for example, the median of the data and the interquartile range. This article describes how to create and customize Dot Plots using the ggplot2 R package. ## GGPlot Violin Plot

A Violin Plot is used to visualize the distribution of the data and its probability density. This chart is a combination of a Box plot and a Density Plot that is rotated and placed on each side, to display the distribution shape of the data. A Violin Plot shows more information than a Box Plot. For example, in a violin plot, you can see whether the distribution of the data is bimodal or multimodal. This article describes how to create and customize violin plots using the ggplot2 R package. ## GGPlot Boxplot

Boxplots are used to visualize the distribution of a grouped continuous variable through their quartiles. You will learn how to create and customize boxplots using the ggplot2 R package. A Scatter plot is used to display the relationship between two continuous variables x and y. This article describes how to create scatter plots in R using the ggplot2 package. You will learn how to: 1) Color points by groups; 2) Create bubble charts; 3) Add regression line to a scatter plot.