Programming Articles - Page 1731 of 3366

The cor function in R helps us to find the correlation matrix from a data frame or a matrix but the output of it always a matrix as intended. We might want to convert that matrix into a data frame which consists of all combination of variables with their correlation value. It can be done by reading the correlation matrix with as.table and converting that table into a data frame with as.data.frame.ExampleConsider the below data frame − Live Demox1

If an R data frame contains all numerical columns and we want to find the difference between row values then we will lose first row of the data frame because that will not be subtracted from any row. This can be done by using head function and minus sign. It will work as subtracting the second last row from the last row, then subtracting third last row from the second last row and so on.ExampleConsider the below data frame − Live Demox1

The sumproduct function in Excel multiply each value of two or more arrays with the corresponding values then add the sum of the result. For example, if we have 1, 2 in A1, A2 in Excel and 2, 2 in B1 and B2 then sumproduct will multiply 1*2 and 2*2 then take the summation of those two multiplications. In R, we have crossprod function for the same.Examples Live Demox1

ExampleThere exists a possibility that one of the variables is recorded in an opposite manner and we want to create a scatterplot using that variable. Therefore, we would need to reverse that variable while plotting. Suppose that variable is an independent variable, hence it will be plotted on X-axis. Thus, to reverse the X-axis labels we can use scale_x_reverse function of ggplot2 package.Consider the below data frame −Example Live Demox

Weighted mean is the average which is determined by finding the sum of the products of weights and the values then dividing this sum by the sum of total weights. If the weights are in proportion then the total sum of the weights should be 1. In base R, we have a function weighted.mean to find the weighted mean in which we just need to pass the vector of values and the vector of weights.Examples Live Demox1

When we create a plot and put a title above it, the alignment of the title is left-adjusted by default and that lies on the edge of the plot area. But sometimes, we want to display the title on the above side of the Y-axis labels, therefore, we can use theme function and set the hjust argument accordingly.ExampleConsider the below data frame − Live Demox

If we have summary data for a group variable then we might want to look at the errors or say differences between mean and standard deviations visually, therefore, we can create a bar plot with error bars of standard deviations. This can be done by using geom_errorbar function of ggplot2 package.ExampleConsider the below data frame − Live DemoGroup

When we draw a scatterplot, there might be some crucial points that we would want to display, hence we create a vertical or horizontal based on our objective. These vertical or horizontal lines can be drawn by using geom_vline or geom_hline function of ggplot2 but to add some value with them we can use geom_text function.ExampleConsider the below data frame − Live Demo> x y df dfOutput      x          y 1 1.2474363 -0.15892165 2 1.7511870 -1.18938250 3 -1.3001612 -0.32313571 4 -1.4220049 1.52915756 5 0.4355646 0.18282983 6 0.3128323 0.16467130 7 1.5099580 1.15199751 8 -0.4907705 -1.98635182 9 -1.4249190 ... Read More

Mostly, the bar plot is created with frequency or count on the Y-axis in any way, whether it is manual or by using any software or programming language but sometimes we want to use percentages. It can be done by using scales package in R, that gives us the option labels=percent_format() to change the labels to percentage.ExampleConsider the below data frame − Live Demo> x df dfOutput x 1 2 2 3 3 3 4 1 5 2 6 4 7 4 8 4 9 2 10 3 11 3 12 4 13 3 14 4 15 4 16 1 17 3 ... Read More

To replace a value in an R vector, we can use replace function. It is better to save the replacement with a new object, even if you name that new object same as the original, otherwise the replacements will not work with further analysis. As you can see in the object x5(in examples), when we replaced 5 with 3, the previous replacement of -1 with 0 returned as in original vector. Therefore, we should save it in a new object.Examples Live Demo> x1 x1Output[1] 1 2 3 4 5 6 7 8 9 10 > replace(x1, x1==5, 10)Output[1] 1 2 3 ... Read More