If integer vector is read as strings and we want to find the position of the minimum value in that vector then we need to use as.numeric along with the vector to read it as a numeric vector then use which function for finding the position of the minimum value. For example, if we have a vector x that contains first ten integers as strings then to find the position of the minimum we can use which(as.numeric(x)==min(as.numeric(x))).Example1x1

While dealing with text data, we sometimes need to extract values between two words. These words can be close to each other, at the end sides or on random sides. If we want to extract the strings between two words then str_extract_all function of stringr package can be used.Loading stringr package −library(stringr)Example1 Live Demox1

In data analysis, we encounter many problems with a lot of variations among them. One such problem is we have some information in a place that needs to be checked through a different place and these places can be data frames. Therefore, we can to find a data frame’s column value based on a column value of another data frame. In R, we can easily do it with the help of which function.Example Live DemoConsider the below data frame −set.seed(12121) x1

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

The difference between as.matrix and matrix function is that nrow argument or ncol argument are not helpful with as.matrix function but with matrix function we can use them. Therefore, we can actual define a matrix with matrix function but if we have a data frame or data table then it can be converted to matrix by using as.matrix function.Examples of creating matrix with as.matrix and matrix functionExample1 Live DemoM

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