Server Side Programming Articles - Page 1723 of 2646

There is no function in base R to simulate discrete uniform random variable like we have for other random variables such as Normal, Poisson, Exponential etc. but we can simulate it using rdunif function of purrr package.The rdunif function has the following syntax −> rdunif(n, b , a)Here, n = Number of random values to returnb = Maximum value of the distribution, it needs to be an integer because the distribution is discretea = Minimum value of the distribution, it needs to be an integer because the distribution is discreteExampleLet’s say you want to simulate 10 ages between 21 to ... Read More

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"

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)

This can be done by using square brackets.Example> data data    X1       X2       X3       X4       X5 1 4.371434 6.631030 5.585681 3.951680 5.174490 2 4.735757 4.376903 4.100580 4.512687 4.085132 3 4.656816 5.326476 6.188766 4.824059 5.401279 4 3.487443 4.253042 5.277751 6.121441 4.925158 5 5.174943 3.704238 5.813336 5.224412 4.990136 6 3.461819 5.102038 6.094579 5.536754 6.311731 7 4.772712 6.445479 5.254032 4.430560 7.183776 8 5.366510 5.232044 5.422526 3.746559 4.810256 9 4.786759 4.665812 4.634238 6.511210 4.959757 10 6.731195 5.083179 4.969842 4.976357 4.939117Let’s say we want to remove rows 4, 7, and 9. We will do it as follows −> data data       X1    X2       X3       X4       X5 1 4.371434 6.631030 5.585681 3.951680 5.174490 2 4.735757 4.376903 4.100580 4.512687 4.085132 3 4.656816 5.326476 6.188766 4.824059 5.401279 5 5.174943 3.704238 5.813336 5.224412 4.990136 6 3.461819 5.102038 6.094579 5.536754 6.311731 8 5.366510 5.232044 5.422526 3.746559 4.810256 10 6.731195 5.083179 4.969842 4.976357 4.939117

This can be done with the help of seq_along, split, and ceiling.Example> x x [1] 6 5 6 3 11 4 5 6 7 4 6 4 3 4 7 1 8 4 2 4 5 7 8 5 9 [26] 3 5 5 5 1 7 5 8 1 8 3 8 4 5 7 5 8 4 4 2 5 6 3 9 4 [51] 6 3 3 2 5 6 5 4 5 5 2 3 3 12 11 6 4 5 6 7 5 2 2 5 8 [76] 3 8 8 7 3 7 6 6 4 1 6 8 3 6 6 6 6 4 8 6 4 5 4 2 5 > max y chunks chunks $`1` [1] 6 5 6 3 11 4 5 6 7 4 6 4 3 4 7 1 8 4 2 4 $`2` [1] 5 7 8 5 9 3 5 5 5 1 7 5 8 1 8 3 8 4 5 7 $`3` [1] 5 8 4 4 2 5 6 3 9 4 6 3 3 2 5 6 5 4 5 5 $`4` [1] 2 3 3 12 11 6 4 5 6 7 5 2 2 5 8 3 8 8 7 3 $`5` [1] 7 6 6 4 1 6 8 3 6 6 6 6 4 8 6 4 5 4 2 5

This can be done with the help of tidyr package.Example> library(tidyr) > data = data.frame(attr = c(1,5,12,17), type=c('class_and_memory','class_and_memory_2')) > data %>% + separate(type, c("class", "memory"), "_and_") attr class memory 1 1 class memory 2 5 class memory_2 3 12 class memory 4 17 class memory_2

We can achieve this by using gsub functionExample> x x [1] "14870e" "16578e" "302e47" "e95748" > gsub("e", "", x) [1] "14870" "16578" "30247" "95748"

This can be done by using reshape function.Example> data dataname salarygroup Errors1 firstName 1 58 2 firstName 2 50 3 firstName 3 47 4 firstName 4 29 5 firstName 5 36 6 LastName 1 34 7 LastName 2 40 8 LastName 3 54 9 LastName 4 38 10 LastName 5 41 > reshape(data, idvar = "name", timevar = "salarygroup", direction = "wide")name Errors.1 Errors.2 Errors.3 Errors.4 Errors.51 firstName 58 50 47 29 36 6 LastName 34 40 54 38 41