Suppose we have coins of different denominations and a total amount of money amount. We have to define one function to compute the fewest number of coins that we need to make up that amount. When that amount of money cannot be accommodated by any combination of the coins, return -1. So if the input is [1, 2, 5], and the amount is 64, the output is 14. This is formed using 12*5 + 2 + 2 = 64.To solve this, we will follow these steps −if amount = 0, then return 0if minimum of coins array > amount, then ... Read More

Suppose we have coins of different denominations (1, 5, 10, 25) and a total amount of money amount. We have to define one function to compute the fewest number of coins that we need to make up that amount. So if the input is 64, the output is 7. This is formed using 25 + 25 + 10 + 1 + 1 + 1 + 1 = 64.To solve this, we will follow these steps −if amount = 0, then return 0if minimum of coins array > amount, then return -1define one array called dp, of size amount + 1, ... Read More

Suppose we have two lists l1 and l2, we have to make the lists equal by applying this operation repeatedly − Choose a sublist, and replace the whole sublist with its sum. Finally return the size of the longest resulting list possible after applying above operations. If there's no solution, return -1.So, if the input is like l1 = [1, 4, 7, 1, 2, 10] l2 = [5, 6, 1, 3, 10], then the output will be 4, as if we perform this operation as follows −Take l1's sublist [1, 4] we get [5, 7, 1, 2, 10]Take l1's sublist ... Read More

In a plot created by using ggplot package there exists an extra area around all the sides of the plot which uses extra space, thus we might want to get rid of that space by removing that extra margin area. It can be done by setting the scale for both the axes to zero with the help of scale_x_continuous and scale_y_continuous function.Consider the below data frame −Example Live Demoset.seed(151) x

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