Programming Articles - Page 1765 of 3366

Mostly, the main title of a plot is short but we might have a long line to write for the main title of the plot. For example, a short version might be “Scatterplot” and a longer version might be “Scatterplot between X and Y”. Therefore, in plot function of R we can use line breaks for the main title as "Scatterplot between X and Y".Exampleset.seed(123) x

Dealing with missing values is one of the initial steps in data analysis and it is also most difficult because we don’t fill the missing values with the appropriate method then the result of the whole analysis might become meaningless. Therefore, we must be very careful about dealing with missing values. Mostly for learning purposes, people use mean to fill the missing values but can use many other values depending on our data characteristic. To fill the missing value with mean of columns, we can use na.aggregate function of zoo package.ExampleConsider the below data frame −x1

Most of the times, the relationship between independent variable and dependent variable is not linear. Therefore, we want to transform the dependent variable or independent variable based on our experiences. Hence, we also want to plot those transformations to visualize the relationship, one such transformation is taking log10 of the dependent variable. To plot this transformation of the dependent variable, we can use scale_y_continuous(trans='log10').ExampleConsider the below data frame −set.seed(10) x

The missing values are represented by NA but if we read them as "NA" then it becomes a level of a factor variable. If we believe that a vector is numeric and we have an "NA" in that vector then it will not be a numeric vector. On the other hand, if we have a vector with NA then it will be a numeric vector.Examplesx1

The slope of the regression line is a very important part of regression analysis, by finding the slope we get an estimate of the value by which the dependent variable is expected to increase or decrease. But the confidence interval provides the range of the slope values that we expect 95% of the times when the sample size is same. To find the 95% confidence for the slope of regression line we can use confint function with regression model object.ExampleConsider the below data frame −set.seed(1) x

When we create a matrix in R, its column names are not defined but we can name them or might import a matrix that might have column names. If the column names are not defined then we simply use column numbers to extract the columns but if we have column names then we can select the column by name as well as its name.Example1M1

A violin plot is similar to a boxplot but looks like a violin and shows the distribution of the data for different categories. It shows the density of the data values at different points. In R, we can draw a violin plot with the help of ggplot2 package as it has a function called geom_violin for this purpose.ExampleConsider the below data frame −set.seed(1) x

In any plot, the vertical lines are generally used to show the thresholds for something, for example, range of the variable under consideration. The package ggplot2 provides geom_vline function to create vertical lines on a plot and we have linetype argument of this function which can be used to draw dotted vertical lines.ExampleConsider the below data frame −set.seed(9) x

The rank function gives the rank of the values in a vector if the vector is sorted but in the same sequence as the original vector and the order function gives the position of the original value in the vector but in the sequence of the sorting in ascending order. The rank function is mostly used for ranking when we deal with ordinal variables, hence, we should use it whenever ranking of values is required, on the other hand, order is frequently used for ordering all numerical values.Examplesset.seed(100) x1

There are so many packages in R and each of these packages have different objectives, thus, the number of functions in these packages are large enough to solve the problems in analysis. A package might have fifteen functions and the other might have hundred, it totally depends on the necessity. We can find the functions inside a package by using lsf.str function but we need to load the package prior to knowing the functions inside.Example1library(BSDA) lsf.str("package:BSDA") CIsim : function (samples = 100, n = 30, mu = 0, sigma = 1, conf.level = 0.95, type = "Mean") Combinations : function ... Read More