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:

In base R, we can save a plot as a png and pass the resolution in the same stage. The procedure to do this is creating the png image with resolution with res argument then creating the plot and using dev.off() to create the file. Check out the below examples to understand how it works.Example1Live Demo> png(file="example1.png",res=100) > plot(1:10) > dev.off()Output:Example2Live Demo> png(file="example2.png",res=200) > plot(1:10) > dev.off()Output

The cex argument is used to increase or decrease the point size in a point chart created in base R. If we want to create a point chart with points of size in increment manner then we can pass a vector of the same size as the vector for which we want to create the point chart. For example, if we have a vector x that contains 10 elements then cex will be set as cex=1:10.Example1Live Demo> x plot(x, cex=1:10, xlim=c(1, 12), ylim=c(-2, 12))Output:ExampleLet’s have a look at another example:Live Demo> y plot(y, cex=1:10, xlim=c(1, 12), ylim=c(-1, 12))Output:Read More

How to create a point chart in R with alternative points having different shape?The pch argument is used to create different points in a base R chart and if we want to have alternative points of different shape then we can use rep function with pch argument but we need to use it for the exact length of the vector. For example, if we want to create a point chart of a vector that contains 10 values having different shapes of alternate points then we can use pch=rep(1:2, 5)).Example1Live Demo> x plot(x, pch=rep(1:2, 5))Output:ExampleLet’s have a look at another example:Live ... Read More