Server Side Programming Articles - Page 1621 of 2646

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

There are many ways to initialize a data frame in R but initializing with matrix is the best among them because creating the data frame with matrix help us to avoid entering the wrong number of columns and the wrong number of rows. After initializing the matrix, we can simply use as.data.frame to convert the matrix into a data frame and that’s it.Examples Live Demodf1

Reading transparent Venn Diagrams is a little difficult due to transparency in all areas, therefore, we should create Venn Diagrams that has different color in each of the area. This will help us to easily read the diagram and also the diagram will be more appealing to viewers. We can do this by using venn.diagram function of VennDiagram package.Installing and Loading VennDiagram package −Exampleinstall.packages(“VennDiagram”) library(VennDiagram)Creating the colored Venn Diagram (Note: The colored diagram will be saved in default folder for R in your system, mostly it is documents folder) −venn.diagram(list(x=1:10, y=7:12), fill=c("yellow", "black"), + filename = "Example1.tiff")Outputvenn.diagram(list(x=1:10, y=7:12, z=9:16), fill=c("yellow", ... Read More

A plot created with ordinal values on X-axis needs to be ordered for plotting, otherwise, the plot will have continuous values on the X-axis that includes ordinal values. If we want to convert those values to text then scale_x_discrete should be used with the number of breaks, these number of breaks are the actual number of labels we want to use in our plot.ExampleConsider the below data frame −x

Two data frames can be same if the column names, row names and all the values in the data frame are exactly same. We might to check this for data frames that we expect to be same, for example, if we have two data sets each one of have same number of rows, same number of columns, same data type for each of the columns, and the data view shows that values are same then it is worth checking whether the complete data sets are same or not. To do this checking in R, we can use identical function.Examples Live Demodf1Read More