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Page 1995 of 2109
How to create sets using vector values in R?
A set in mathematics is defined as the collection of unique elements and the order of the elements does not matter. In R, we can create sets using set_power function of sets package. For example, if we have a vector x that contains A, B, C then the sets using the vector x can be created by using set_power(x).Loading sets package −library(sets)Examplesx1
Read MoreHow to create a random sample of values between 0 and 1 in R?
The continuous uniform distribution can take values between 0 and 1 in R if the range is not defined. To create a random sample of continuous uniform distribution we can use runif function, if we will not pass the minimum and maximum values the default will be 0 and 1 and we can also use different range of values.Examplesrunif(5) [1] 0.8667731 0.7109824 0.4466423 0.1644701 0.5611908 runif(10) [1] 0.5923782 0.8793613 0.6912947 0.2963916 0.6076762 0.7683766 0.1143595 [8] 0.4782710 0.1143383 0.4540217 runif(50) [1] 0.841674685 0.325249762 0.640847906 0.203868249 0.495230429 0.897175830 [7] 0.744447459 0.490173680 0.254711280 0.144844443 0.867749180 0.004405166 [13] 0.539785687 0.739637398 0.062214554 0.648021581 0.768686809 0.305543906 [19] 0.757496413 0.527085302 0.633331579 0.700118363 0.857950259 0.929350618 [25] 0.167015719 0.775870043 0.430343200 0.528408273 0.600575697 0.612206968 [31] 0.065904791 0.061135682 0.082027863 0.193586800 0.013956337 0.156875620 [37] 0.837501421 0.971202297 0.930835689 0.292126061 0.599263353 0.826630821 [43] 0.509235736 0.741715013 0.224485511 0.113099235 0.395143355 0.375654137 [49] 0.973050494 0.107550270 round(runif(50),2) [1] 0.51 0.70 0.90 0.45 0.41 0.74 0.31 0.40 0.10 0.05 0.18 0.05 0.63 0.34 0.57 [16] 0.06 0.73 0.37 0.79 0.85 0.82 0.41 0.32 0.34 0.37 0.14 0.21 0.11 0.43 0.86 [31] 0.83 0.09 0.88 0.04 0.62 0.64 0.15 0.75 0.78 0.16 0.67 0.97 0.79 0.64 0.56 [46] 0.40 0.07 0.69 0.82 0.63 round(runif(50),4) [1] 0.2951 0.2916 0.9049 0.2669 0.7613 0.2080 0.4739 0.1110 0.6155 0.5429 [11] 0.4490 0.2941 0.8262 0.7719 0.7896 0.7634 0.6260 0.7812 0.7600 0.6852 [21] 0.9142 0.0165 0.2324 0.0821 0.0814 0.4009 0.3315 0.8843 0.9684 0.1966 [31] 0.4841 0.5795 0.7898 0.1865 0.6929 0.8599 0.0492 0.8275 0.7431 0.3122 [41] 0.8480 0.3327 0.4872 0.0503 0.1887 0.0296 0.6011 0.1162 0.7776 0.6874 round(runif(50),5) [1] 0.40368 0.33585 0.03557 0.06047 0.95041 0.18260 0.70011 0.75148 0.12414 [10] 0.01310 0.42343 0.05846 0.21341 0.05454 0.77823 0.66151 0.61406 0.59459 [19] 0.50299 0.96780 0.43033 0.64652 0.39697 0.05897 0.47169 0.79828 0.74154 [28] 0.56074 0.97303 0.35301 0.36110 0.67452 0.14553 0.45195 0.05780 0.90489 [37] 0.96745 0.28014 0.02089 0.77789 0.04797 0.03550 0.40495 0.08924 0.59908 [46] 0.89074 0.48498 0.47335 0.59422 0.00719 round(runif(100),2) [1] 0.10 0.06 0.51 0.89 0.80 0.68 0.97 0.58 0.60 0.79 0.96 0.48 0.29 0.16 0.42 [16] 0.35 0.46 0.18 0.46 0.34 0.48 0.35 0.72 0.10 0.50 0.93 0.30 0.54 0.85 0.19 [31] 0.12 0.10 0.47 0.66 0.43 0.09 0.44 0.86 0.99 0.31 0.10 0.61 0.20 0.15 0.02 [46] 0.25 0.33 0.75 0.98 0.23 0.21 0.70 0.42 0.24 0.87 0.84 0.99 0.06 0.75 0.48 [61] 0.84 0.35 0.48 0.62 0.40 0.25 0.07 0.08 0.75 0.40 0.83 0.95 0.00 0.87 0.27 [76] 0.53 0.21 0.41 0.28 0.83 0.90 0.26 0.50 0.19 0.70 0.93 0.24 0.45 0.33 0.84 [91] 0.15 0.81 0.62 0.17 0.08 0.76 0.74 0.11 0.20 0.49 round(runif(150),1) [1] 0.6 0.3 0.3 0.3 0.9 0.7 0.1 0.1 0.1 0.9 0.4 0.6 1.0 0.0 0.4 1.0 0.1 1.0 [19] 0.8 0.0 0.9 0.9 0.7 0.7 0.7 0.7 0.3 0.7 0.1 0.1 0.9 0.0 0.1 1.0 0.9 1.0 [37] 0.9 0.6 0.0 0.4 0.4 1.0 0.2 0.4 0.2 0.8 0.3 0.9 0.8 0.6 0.3 0.3 0.4 0.7 [55] 0.2 0.9 1.0 0.9 0.8 0.7 0.9 1.0 0.5 0.8 0.6 0.8 0.6 0.8 0.3 0.3 1.0 0.6 [73] 0.9 0.3 0.0 1.0 0.5 0.6 0.7 0.7 0.6 0.3 0.4 0.0 0.3 0.1 0.6 0.2 0.1 0.7 [91] 0.9 0.8 0.3 0.2 0.5 0.6 0.6 0.1 0.0 0.9 0.4 0.6 0.3 0.2 0.9 0.6 0.0 0.2 [109] 0.3 0.3 0.3 0.7 0.4 0.8 0.5 0.9 0.6 0.5 0.3 1.0 0.6 0.7 0.9 0.1 0.8 1.0 [127] 0.3 1.0 0.2 0.9 0.2 0.3 0.5 0.4 0.1 0.6 0.6 0.0 0.3 0.3 0.0 0.3 0.3 1.0 [145] 0.6 0.5 0.1 0.7 0.6 0.4 round(runif(75),1) [1] 0.7 0.3 0.7 0.9 0.8 0.1 0.4 0.2 0.5 0.4 0.1 0.7 0.1 0.6 1.0 0.3 0.4 0.7 0.2 [20] 0.2 0.3 0.4 0.4 0.0 0.1 0.2 0.3 0.5 0.1 1.0 0.3 0.5 0.3 0.7 0.1 0.6 0.6 0.6 [39] 0.5 0.7 0.5 0.8 0.1 1.0 0.7 0.4 0.6 0.1 0.5 0.5 0.9 0.3 0.8 0.9 0.3 0.9 0.7 [58] 0.6 0.8 0.4 0.4 0.7 0.4 0.1 0.2 0.6 0.6 0.9 0.3 0.6 0.5 0.9 0.2 0.3 0.2 round(runif(75),3) [1] 0.712 0.355 0.130 0.768 0.134 0.681 0.273 0.663 0.849 0.851 0.842 0.430 [13] 0.371 0.903 0.148 0.879 0.812 0.330 0.567 0.646 0.199 0.159 0.056 0.448 [25] 0.637 0.204 0.101 0.389 0.797 0.030 0.021 0.167 0.440 0.359 0.670 0.435 [37] 0.807 0.669 0.738 0.546 0.535 0.969 0.055 0.201 0.436 0.336 0.841 0.548 [49] 0.901 0.850 0.369 0.770 0.678 0.922 0.252 0.132 0.635 0.544 0.291 0.715 [61] 0.601 0.399 0.585 0.161 0.423 0.244 0.451 0.397 0.951 0.382 0.123 0.959 [73] 0.252 0.330 0.238
Read MoreHow to set the alignment of labels in horizontal bar plot to left in R?
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 MoreHow to check the difference between plot generation time in base R?
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:
Read MoreHow to generate passwords with varying lengths in R?
To generate passwords, we can use stri_rand_strings function of stringi package. If we want to have passwords of varying length then we need to create the passwords using the particular size separately. For example, for a size or length of the password equals to 8, we can use the argument length in the stri_rand_strings function.Loading stringi package:> library(stringi)Example1> stri_rand_strings(n=5, length=8, pattern="[0-9a-zA-Z]") [1] "YkIEDYQz" "t42JCzYO" "rOE9YN8U" "2lu9AonY" "6lDUxScX"Example2> stri_rand_strings(n=20, length=8, pattern="[0-9a-zA-Z]") [1] "glH3ysoX" "X0Sgvg3F" "P3YOePTa" "45GOb2hA" "tLCwszus" "CerCi1ks" [7] "UtFwzrSc" "pG8AJCQX" "NTCdMRHj" "5thI1wKb" "Ic8Rol1Y" "JakWa1Wd" [13] "9AfeXo7T" "SFJVn9XV" "lIRhLbJ9" "DNFyAbkJ" "jV4jJRZk" "IthkzfEU" [19] "talj9nBq" "Nak9Tidh"Example3> ...
Read MoreCount of distinct sums that can be obtained by adding prime numbers from given arrays in C++
We are given two arrays containing prime and non prime numbers. The goal is to find the count of distinct sums of pairs of prime numbers in each array. We will do this by making a pair of two primes from each array, take their sum and add them to set sums. In the end the size of the set is the number of distinct sums of primes. Let's understand with examples. Input Arr1[] = { 1, 2, 3 } Arr2[] = { 2, 3, 4} Output Distinct Sums of primes :3 Explanation Prime pairs ...
Read MoreCount numbers whose difference with N is equal to XOR with N in C++
We are a number N. The goal is to find numbers between 0 and N whose difference with N is equal to XOR with N.We will do this by traversing no. from i=0 to i
Read MoreCount pieces of the circle after N cuts in C++
We are given an integer N which represents the number of cuts applied on a 2D-circle. Each circle divides the circle in two halves. Goal is to find the pieces of the circle after N cuts.Number of pieces= 2 * no. of cutsLet’s understand with examples.Input − N=1Output − Pieces of circle: 2Explanation −Input − N=3Output − Pieces of circle: 6Explanation −Approach used in the below program is as followsWe take N for a number of cuts.Take pieces=1*N.Print the result..Example#include using namespace std; int main(){ int N=2; Int pieces=2*N; cout
Read MoreHow to Handle Large CSV files with Pandas?
In this post, we will go through the options handling large CSV files with Pandas.CSV files are common containers of data, If you have a large CSV file that you want to process with pandas effectively, you have a few options.Pandas is an in−memory toolYou need to be able to fit your data in memory to use pandas with it. If you can process portions of it at a time, you can read it into chunks and process each chunk. Alternatively, if you know that you should have enough memory to load the file, there are a few hints to ...
Read MoreHow to apply manually created x-axis labels in a histogram created by hist function in R?
When we generate a histogram in R using hist function, the x-axis labels are automatically generated but we might want to change them to values defined by researchers or by any other authority. Therefore, firstly we need to create the histogram by ignoring the labels and then axis function can be used for new values.Consider the below vector x and create a histogram of x by ignoring x-axis labels −Exampleset.seed(1999) x
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