Tibbles are created when we analyze data using dplyr package and if the data size is large then only 10 values are printed in R. If we want to display the complete output of tibble then View function needs to be used. For example, if we want to perform calculation of counts then we should add View() at the end of the code with pipe operator.Example Live DemoConsider the below data frame −Group%View()Output

Dealing with time data is not an easy task, it is sometimes difficult even in built-in analytical softwares, thus it won’t be easy in R as well. Mostly, we record time on a 12-hour time scale but in some situations, we need 24-hour time scale. Therefore, if we want to convert 12-hour time scale to 24-hour time scale then format function can be used with as.POSIXct. Look at the below examples, to understand it better.Example1 Live DemoTime1

A matrix can also contain missing values and those missing values can be placed in a matrix by randomization as well, hence we cannot be sure about the positions of those values that are referred to as NA and the non-missing values. If we want to find the positions of the non-missing values in a matrix then apply function can be used where we can use which function to exclude NA values. Check out the below examples to understand how it works.Example1 Live DemoM1

To multiply a rows or columns of a matrix, we need to use %*% symbol that perform the multiplication for matrices in R. If we have a matrix M with 5 rows and 5 columns then row 1 of M can be multiplied with column 1 of M using M[1,]%*%M[,1], similarly, we can multiply other rows and columns.Example Live DemoM

Usually, plots are created with solid lines but sometimes we need to use dashed line so that the points can represent a threshold or something critical, the main objective here is to get the attention of the reader on these points. In base R, the plots are created with plot function and we can use abline function with lty =2 to draw dashed lines.Example1 Live Demox

A dendrogram display the hierarchical relationship between objects and it is created by using hierarchical clustering. In base R, we can use hclust function to create the clusters and the plot function can be used to create the dendrogram. For example, if we want to create the dendrogram for mtcars data without X−axis labels then it can be done as shown below −hc=hclust(dist(mtcars)) plot(hc, xlab="", sub="")Example Live Demohead(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 6 160 110 3.90 2.875 ... Read More

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

Suppose we want to find the total number of combinations of two numbers, say, 1 and 2 and then multiply each of the combination values. This will result in the following combinations −1 1 1 2 2 1 2 2And the multiplication will also have a third column as shown below −Multiplication 1 1 1 1 2 2 2 1 2 2 2 4Example1 Live Demofor (i in 1:2) for (j in 1:2) cat(i, j, i*j, "") Output1 1 1 1 2 2 2 1 2 2 2 4Example2 Live Demofor (i in 1:5) for (j in 1:5) cat(i, j, i*j, "") ... Read More

MANOVA refers to multivariate analysis of variance, in this method we have more than one dependent variable and multiple independent variables. We want to compare each level of the independent variable combination for each of the dependent variables. To convert MANOVA data frame for two-dependent variables into a count table, we can use cast function of reshape package but we need to melt the data frame first so that the casting can be done appropriately.Example Live DemoConsider the below data frame −Gender

We know that the word “and” can be written as “&”. If we have vectors that contain string values separated with word “and” then we can replace it with “&”. To do this, we can use stri_replace_last function of stringi package. For example, if we have a string vector that contain only one element defined as x