Server Side Programming Articles - Page 1487 of 2646

To create a line chart with 3-sigma limits using ggplot2, we first need to calculate the limits then the chart can be created. We can use geom_ribbon function of ggplot2 for this purpose where we can pass lower 3-sigma limit for ymin argument in aes and upper 3-sigma limit for ymin argument in aes, also we need to specify alpha so that the color of lines and the limits can be differentiated.ExampleConsider the below data frame:Live Demo> set.seed(14) > x y df dfOutput x y 1 1 0.6690751 2 2 1.8594771 3 ... Read More

Sometimes we want to change a column or create a new by using other columns of a data frame in R, this is mostly required when we want to create a categorical column but it can be done for numerical columns as well. For example, we might want to create a column based on salary for which if salaries are greater than the salary in another column then adding those salaries otherwise taking the difference between them. This will help us to understand whether the salaries in two columns are equivalent, lesser, or greater. In R, we can use transform ... Read More

When we create a horizontal bar plot using ggplot2 package, the labels of the categorical variable are aligned to the right-side of the axis and if the size of these labels are different then it looks a little ambiguous. Therefore, we might want to set the alignment of the labels to left-side and this can be done by using theme function of ggplot2 package.ExampleConsider the below data frame:> df dfOutput    x y 1 India  14 2 UK     15 3 Russia 12 4 United States of America 18Loading ggplot2 package and creating a horizontal ... Read More

There is no direct function in R to draw a circle but we can make use of plotrix package for this purpose. The plotrix package has a function called draw.cirlce which is can be used to draw a circle but we first need to draw a plot in base R then pass the correct arguments in draw.circle. The first and second arguments of draw.circle takes x and y coordinates, and the third one is for radius, hence these should be properly chosen based on the chart in base R.Loading plotrix package:> library(plotrix)Creating different circles using draw.circle:ExampleLive Demo> plot(1:10, type="n") > ... Read More

First thing we need to understand is diagonal elements are useful only if we have a square matrix, otherwise it would not make sense to set diagonal elements, this is known to almost all mathematicians but some freshman might get confused because we can create diagonal in a non-square matrix which should not be called a diagonal. In R, we can set the diagonal elements of a matrix to missing values/NA by using diag function.Example1Live Demo> M1 M1Output  [, 1] [, 2] [, 3] [, 4] [1, ] 1   5    9   13 [2, ] 2   6 ... Read More

If we have a categorical variable or a group variable then we might want to create a line chart for each of the categories or levels, this will help us to understand the range of multiple levels in a single plot. For this purpose, we can use facet_grid function of ggplot2 package as shown in the below example.ExampleConsider the below data frame:Live Demo> x y df dfOutput x y 1 C -1.55668689 2 A 2.41399136 3 D -0.78520253 4 A -0.43092594 5 C 1.94379390 6 A ... Read More

The type = "h" is a graphing argument in base R which is generally used inside a plot function. It helps to generate the vertical lines in the R environment instead of points. For example, if we want to plot values from 1 to 10 then type = "h" will plot the vertical lines starting from X-axis and the upper end of the lines will represent the actual value.Example1Live Demo> plot(1:10,type="h")Output:Example2Live Demo> plot(rnorm(10),type="h")Output:

The less than probability using normal distribution is the cumulative probability which can be found by using cumulative distribution function of the normal distribution. In R, we have pnorm function that directly calculates the less than probability for a normally distributed random variable that takes Z score, mean and standard deviation.ExamplesLive Demopnorm(0.95,1,0) pnorm(0.95,0,1) pnorm(0.10,0,1) pnorm(0.10,1,5) pnorm(0.10,1,50) pnorm(0.10,25,50) pnorm(0.12,25,50) pnorm(0.12,2,0.004) pnorm(0.12,2,0.5) pnorm(1,2,0.5) pnorm(12,20,3) pnorm(12,12,3) pnorm(12,15,3) pnorm(200,15,3) pnorm(200,201,3) pnorm(200,201,5) pnorm(20,25,5)Output[1] 0 [1] 0.8289439 [1] 0.5398278 [1] 0.4285763 [1] 0.4928194 [1] 0.309242 [1] 0.309383 [1] 0 [1] 8.495668e-05 [1] 0.02275013 [1] 0.003830381 [1] 0.5 [1] 0.1586553 [1] 1 [1] 0.3694413 [1] 0.4207403 [1] 0.1586553

How to change legend values in a bar plot created by using ggplot2 in R?By default, the legend values are taken as the different levels of the categorical variable for which a bar plot is created using ggplot2 package and if we want to change those values then scale_color_manual function of the ggplot2 package can be used where we need to pass the values for color and labels for legend values.ExampleConsider the below data frame:Live Demo> set.seed(1214) > x1 y1 df1 df1Output x1 y1 1 B 4 2 B 5 3 C 5 ... Read More

One of the mostly used time measurement function in R is microbenchmark function of microbenchmark package. We can pass the function to create the plot inside microbenchmark function and this will result in the processing time for each of the plots then a comparison can be done for the difference.Example1Loading microbenchmark package:> library(microbenchmark)Finding the plot generation time:> x1 x2 x3 X XUnit: milliseconds expr min lq mean median uq max neval plot(x1) 12.7488 14.88815 15.65040 15.2515 15.90765 23.9348 100 plot(x2) 20.9810 21.67780 23.92976 22.2116 23.29665 137.2474 100 plot(x3) 93.6965 95.03440 96.67086 95.6717 97.12290 125.3670 100Plots:Example> plot(x1)Output:Example> plot(x2)Output:Example> plot(x3)Output: