The use of set.seed is to make sure that we get the same results for randomization. If we randomly select some observations for any task in R or in any statistical software it results in different values all the time and this happens because of randomization. If we want to ... Read More

This can be done by using list function.Example> df1

We can do this by using filter and grepl function of dplyr package.ExampleConsider the mtcars data set.> data(mtcars) > head(mtcars) mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 ... Read More

This can be done by using theme argument in ggplot2Example> df df x y 1 long text label a -0.8080940 2 long text label b 0.2164785 3 long text label ... Read More

The easiest way to do it is by using select_if function of dplyr package but we can also do it through lapply.Using dplyr> df df X1 X2 X3 X4 X5 1 1 11 21 a k 2 2 12 22 b l 3 3 13 23 c m 4 ... Read More

We can do this by setting the column to NULLExample> library(data.table) > df data_table data_table[, x:=NULL] > data_table numbers 1: 1 2: 2 3: 3 4: 4 5: 5 6: 6 7: 7 8: 8 9: 9 10: 10To delete two columns> df Data_table Data_table numbers 1: 0 2: 1 3: 2 4: 3 5: 4 6: 5 7: 6 8: 7 9: 8 10: 9

This can be done by using scale function.Example> data data x y 1 49.57542 2.940931 2 49.51565 2.264866 3 50.70819 2.918803 4 49.09796 2.416676 5 49.90089 2.349696 6 49.03445 3.883145 7 51.29564 4.072614 8 49.11014 3.526852 9 49.41255 3.320530 10 49.42131 3.033730 > standardized_data standardized_data x y [1,] -0.1774447 -0.20927607 [2,] -0.2579076 -1.28232321 [3,] 1.3476023 -0.24439768 [4,] -0.8202493 -1.04137095 [5,] 0.2607412 -1.14768085 [6,] -0.9057468 1.28619932 [7,] 2.1384776 1.58692277 [8,] -0.8038439 0.72069363 [9,] -0.3967165 0.39321942 [10,] -0.3849124 -0.06198639 attr(,"scaled:center") x y 49.707220 3.072784 attr(,"scaled:scale") x y 0.7427788 0.6300430

It can be done by using weekdays function.Example< df = data.frame(date=c("2020-07-01", "2020-08-10", "2020-11-15")) < df$day

We can use rm remove all or few objects.Example< x>-rnorm(100, 0.5) < y>-1:100 < z>-rpois(100, 5) < a>-rep(1:5, 20)To remove all objects> rm(list=ls()) ls() character(0)To remove all except a> rm(list=setdiff(ls(), "a")) > ls() [1] "a"To remove all except x and a> rm(list=ls()[! ls() %in% c("x", "a")]) ls() [1] "a" "x"Read More

Consider the below data frames −> glucose fructose glucose$sweetener fructose$sweetener sweeteners library(ggplot2) > ggplot(sweeteners, aes(length, fill = sweetener)) + geom_density(alpha = 0.2)