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)

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

This can be easily done by using subset function.Example> df df x y z a 1 1 6 11 16 2 2 7 12 17 3 3 8 13 18 4 4 9 14 19 5 5 10 15 20To remove only one column> df ... Read More