When we create a scatterplot or any other plot and the values are presented in scientific form in the original data then the axes values of the plot are also plotted in scientific form. This makes the plot ambiguous, therefore, reading the plot or interpreting it becomes difficult. Hence, we need to convert the scientific form of the axes labels to numbers and it can be done by using scale_x_continuous(labels =comma) and scale_y_continuous(labels=comma) for both the axes.ExampleConsider the below data frame −set.seed(101) x

A bar plot is one of the most commonly used plots for categorical data and it can be easily done in R with the help of ggplot2. When we create a bar plot using ggplot2, there exists some space between bars and the X-axis and the largest bar and top area of the plot. This can be reduced or increased by using scale_y_continuous function.ExampleConsider the below data frame −x

If we want to provide more information about the data, we have in columns of an R data frames then we might want to use prefixes. These prefixes help everyone to understand the data, for example, we can use data set name as a prefix, the analysis objective as a prefix, or something that is common among all the columns. To add a prefix to columns of an R data frame, we can use paste function to separate the prefix with the original column names.ExampleConsider the below data frame −Exampleset.seed(100) Rate

Sometimes, we want to use some columns of an R data frame for analysis, therefore, it is better to get a list of all the columns that we need. In this way, we don’t have to worry about the column operations, if required because we will be having only necessary columns. To get the list of all columns except one or more columns can be done with the help of single square brackets.ExampleConsider the below data frame −set.seed(100) x1

Scatterplot helps us to identify the linear relationship between two variables and it is the first step of determining a predictive model. Before using any predictive modeling technique we must draw a scatterplot between independent and dependent variables to check what kind of relationship exists between them. A scatterplot generally represented by circular points on the plot area but we can have different types of points such as square, rectangle, diamond, etc. In ggplot2, pch argument of geom_point can help us to create scatterplot with these types of points.ExampleConsider the below data frame −set.seed(123) x

Comparison of columns of an R data frame can be done in many ways and one of the ways is having one or more columns of means. In this way, we can compare column of raw data with the column of means and also the column of means with another column of means. We can use apply function to create a new column with means of row values for each or some of the columns of an R data frame.ExampleConsider the below data framex1

This error occurs when we do not pass the argument for independent variable as a data frame. The predict function will predict the value of the dependent variable for the provided values of the independent variable and we can also use the values of the independent variable using which the model is created.ExampleConsider the below data frame −set.seed(1) x

Aesthetics is an essential part of a plot whether it is a scatterplot or any other plot. When we create a scatterplot with ggplot function of ggplot2 package, the border of the points is black if we fill the points with the sequence of a color but we can change these borders to any other color by using colour argument.ExampleConsider the below data frameRate

Making comparisons is bit easier through horizontal bar graphs as compared to the vertical bar graphs in cases where the labels for the categories have large names. Because a large name for the labels of a vertical bar graph is likely to mix with the other labels and therefore, the reading of these labels become difficult for the viewer. To solve this problem, we can draw a bar graph and flip it with coord_flip() in ggplot2.ExampleConsider the below data frame −Size

When we create a chart using plot function and add a legend to that chart, the output of the chart has the legend that is covered with borders. But this breaks the flow of the chart and the area covered by the order make the chart unattractive. Therefore, we can use bty="n" with the legend function and it will remove the border of the legend.ExampleCreating the chart with legend that has border −plot(x=1:10, y=10:1) legend(1, 2, "This is a scatterplot between x and y")OutputCreating the chart with legend which does not have a border −Exampleplot(x=1:10, y=10:1) legend(1, 2, "This is ... Read More