Programming Articles - Page 921 of 3363

The error “Calling var(x) on a factor x is defunct” occurs when we try to apply a numerical function on factor data.For example, if we have a factor column in a data frame then applying numerical functions on that column would result in the above error. To deal with this problem, we can use as.numeric function along with the numerical function as shown in the below examples.Example 1Following snippet creates a sample data frame −x

The summation of column values if missing values exist in the R data frame can be found with the help of summarise_each function of dplyr package where we can remove missing values by setting na.rm argument to TRUE.Since, we we will have groups in the data frame hence group_by function of the same package will help the summarise_each function to perform the summation by group. Check out the below Examples to understand how it works.Example 1Following snippet creates a sample data frame −Grp

We are given a positive integer type array, let's say, arr[] of any given size. The task is to rearrange an array in such a manner that all the elements present at an odd index should value greater than the element presents at an even index and print the result.Let us see various input output scenarios for this −Input − int arr[] = {2, 1, 5, 4, 3, 7, 8}Output − Array before Arrangement: 2 1 5 4 3 7 8 Rearrangement of an array such that every odd indexed element is greater than it previous is: 1 4 2 5 3 ... Read More

To create a base R plot without axes but keeping the frame of the plot, we can set axes argument to FALSE and frame.plot argument to TRUE.For example, if we have a vector called V and we want to create a plot of V without axes but with the frame of the plot then, we can use the command given below −plot(V,axes=FALSE,frame.plot=TRUE)Check out the below example to understand how it works.ExampleConsider the following snippet −x

The autocorrelation plot or ACF plot is a display of serial correlation in data that changes over time. The ACF plot can be easily created by using acf function.For example, if we have a vector called V then we can create its autocorrelation plot by using the command acf(V). If we want to extract autocorrelation values then we would need to save the plot values in an object by using the below command. This will not create the plot.Autocorrelation_x

If we have frequency data then we first need to find the total data or complete data by repeating the values up to the frequency corresponding to each value after that we can apply var function on this complete data.For Example, if we have a data frame called df that contains two columns say X and Frequency then we can find the total data by using the command given below −Total_data

We are given a positive integer type array, let's say, arr[] of any given size such that elements in an array should value greater than 0 but less than the size of an array. The task is to rearrange an array in such a manner that if arr[i] is ‘i’, if ‘i’ is present in an array else it will set the arr[i] element with the value -1 and print the final result.Let us see various input output scenarios for this −Input − int arr[] = {0, 8, 1, 5, 4, 3, 2, 9 }Output − Rearrangement of an array such that ... Read More

We are given a positive integer type array, let's say, arr[] of any given size such that elements in an array should value greater than 0 but less than the size of an array. The task is to rearrange an array in such a manner that if arr[j] is ‘j’ then arr[j] becomes ‘i’ and print the final result.Let us see various input output scenarios for this −Input − int arr[] = {3, 4, 1, 2, 0}Outpu t− Array before Arrangement: 3 4 1 2 0 Rearrangement of an array such that arr[j] becomes i if arr[i] is j is: 4 ... Read More

We are given a positive integer type array, let's say, arr[] of any given size such that elements in an array should value greater than 0 but less than the size of an array. The task is to rearrange an array in such a manner that arr[i] becomes arr[arr[i]] within the given O(1) space only and print the final result.Let us see various input output scenarios for this −Input − int arr[] = {0 3 2 1 5 4 }Output − Array before Arrangement: 0 3 2 1 5 4 Rearrangement of an array so that arr[i] becomes arr[arr[i]] with O(1) extra ... Read More

To find the row mean of all matrices stored in an R list, we can use sapply function along with rowMeans function.For example, if we have a list called LIST that contains some matrices then the row means for each matrix can be found by using the following command −sapply(LIST,rowMeans)Check out the below example to understand how it works.ExampleFollowing snippet creates the matrices −M1