Server Side Programming Articles - Page 1506 of 2646

The intersection of lists means the elements that are unique and common between the lists. For example, if we have a list that contains 1, 2, 3, 3, 3, 2, 1 and the other list that contains 2, 2, 1, 2, 1 then the intersection will return only those elements that are common between the lists and also unique, hence for this example we will get 1 and 2. In R, we can do this by using intersection function along with Reduce function.Consider the below lists −Example Live DemoList1

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

The sum of diagonal elements could be required in matrix analysis therefore, we can convert the matrix into a table and find the sum of diagonal elements. This can be easily done by using sun function by extracting diagonal elements of the table using diag function. For example, if we have a table T then the sum of diagonal elements of T can be found as sum(diag(T)).Example Live DemoTable1

Often, we have multiple values, vectors or columns of an R data frame that needs to be plotted on a single graph so that we can compare them at the same time or they have some kind of relationship among them. Therefore, we can use layout function along with matrix function to divide the plot window as shown in the below exampleConsider the below layout and plot of individual values −Examplelayout(matrix(c(1, 2, 3, 3, 4, 5, 6, 6), nrow=4, ncol=2, byrow=FALSE)) plot(500) plot(525) plot(530) plot(531) plot(540) plot(528)OutputChanging the layout and creating the plots −Examplelayout(matrix(c(1, 2, 3, 3, 4, 5, 6, ... Read More

The fisher test helps us to understand whether there exists a significant non-random relationship among categorical variables or not. It is applied on contingency tables because these tables are used to represent the frequency for categorical variables and we can apply it on a matrix as well as matrices have the similar form. In R, we can use fisher.test function to perform the fisher test.Example Live DemoM1

If a column in an R data frame contain string values that are separated with an underscore and stretches the size of the column values that also contain common values then it would be wise to remove underscore sign from all the values at once along with the values that is common. This will help us to read the data properly as well as analysis will become easy. For this purpose, we can use gsub functionConsider the below data frame −Example Live Demoset.seed(191) ID

If we have a grouping column in an R data frame and we believe that one of the group values is not useful for our analysis then we might want to remove all the rows that contains that value and proceed with the analysis, also it might be possible that the one of the values are repeated and we want to get rid of that. In this situation, we can do subsetting of the data frame using negation and single square brackets.Example Live Demoset.seed(1212) x

We know that a list can multiple elements of different types as well as of different size. For example, a list that contains two elements then one element may contain fifteen elements and the other might have twenty-five elements. In this situation, we might want to fill the first element with ten more elements so that the size of both the elements become equal. This can be done by using lapply function as shown in the below examples.Consider the below list −Example Live Demoset.seed(101) x1

Each value in Bernoulli random variable represents success or a failure for a single trial that makes it different from Binomial random variable because a Binomial random variable represents number of success or failure for a number of trials. To generate a Bernoulli random variable, we can use rbinom function but we need to pass 1 for size argument.Example Live Demorbinom(120, 1, 0.71)Output[1] 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 ... Read More

A data frame in R can have infinite number of unique values and it can also contain many repeated values. Therefore, finding the number of all unique values in the data frame can help us to understand the diversity in the data but this most done in situations where we expect to have repeated elements otherwise it would not make sense. To count the number of occurrences of all unique values, we can use table function along with the unlist as shown in the below examples.Consider the below data frame −Example Live Demox1