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Page 1238 of 2109
How to subtract one data frame from another in R?
If we have two data frames with same number of columns of same data type and equal number of rows then we might want to find the difference between the corresponding values of the data frames. To do this, we simply need to use minus sign. For example, if we have data-frames df1 and df2 then the subtraction can be found as df1-df2.Consider the below data frame −Examplex1
Read MoreHow to find the cube root of negative values in R?
There is no function in R to find the cube root of negative values, hence we need to create that. The code to create the function is as shown below −CubeRoot
Read MoreHow to combine two matrices to create a block-diagonal matrix in R?
A block-diagonal matrix means that a matrix added to another matrix at the end the last element. For example, if we have a matrix with nine values and the other matrix also has nine values then the second matrix will be added to the first matrix and the elements below first matrix will be zero and the elements above the second matrix will also be zero.ExampleM1
Read MoreHow to convert rows in an R data frame to list?
Sometimes each row needs to be treated differently, therefore, we might want to convert those rows into list. This will help us to perform the operations on our row elements separately. To convert the rows into list, we can use split function by defining the number of rows in the data frame.Consider the below data frame −Exampleset.seed(101) x1
Read MoreHow to generate a normal random vector using the mean of a vector in R?
To create a normal random vector, we can use rnorm function with mean and standard deviation as well as without passing these arguments. If we have a different vector derived from another distribution or simply represent some numbers then we can use the mean of that vector in the rnorm function for mean argument.Exampleset.seed(101) x1
Read MoreHow to take a subset of a matrix in R with finite values only if the matrix contains NA and Inf values?
If we have a matrix that contains NA or Inf values and we want to take the subset of that matrix with finite values then only the rows that do not contain NA or Inf values will be the output. We can do this in R by using rowSums and is.finite function with negation operator !.Exampleset.seed(999) M1
Read MoreHow to find 95% confidence interval for binomial data in R?
The binomial data has two parameters, the sample size and the number of successes. To find the 95% confidence interval we just need to use prop.test function in R but we need to make sure that we put correct argument to FALSE so that the confidence interval will be calculated without continuity correction. In the below examples, we have found the 95% confidence interval for different values of sample size and number of successes.Exampleprop.test(x=25, n=100, conf.level=0.95, correct=FALSE)Output1-sample proportions test without continuity correction data: 25 out of 100, null probability 0.5 X-squared = 25, df = 1, p-value = 5.733e-07 alternative ...
Read MoreHow to find critical value for one-sided and two-sided t test in R?
To find the critical value for t test in R, we need to use qt function. This function requires level of significance and the sample size and returns the tabulated or critical value of t distribution. Below examples shows the calculation of critical value for different situations such as left-side test, right-side test or two-sided test.Left side critical value with sample size 30 and 95% confidence level −Exampleqt(0.05, 30)Output[1] -1.697261Right side critical value with sample size 30 and 95% confidence level −Exampleabs(qt(0.05, 30))Output[1] 1.697261 Exampleqt(0.05, 50)Output[1] -1.675905Exampleabs(qt(0.05, 50))Output[1] 1.675905 Exampleqt(0.01, 50)Output[1] -2.403272Exampleabs(qt(0.01, 50))Output[1] 2.403272 Exampleqt(0.01, 51)Output[1] -2.401718Exampleabs(qt(0.01, 51))Output[1] 2.401718 Exampleqt(0.01, ...
Read MoreHow to create train, test and validation samples from an R data frame?
To create predictive models, it is necessary to create three subsets of a data set for the purpose of training the model, testing the model and checking the validation of the model. These subsets are usually called train, test and validation. For this purpose, we can use different type of sampling methods and the most common is random sampling. In the below example, you can see how it can be done.Consider the mtcars data set in base R −Exampledata(mtcars) str(mtcars)Output'data.frame':32 obs. of 11 variables: $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ... $ ...
Read MoreHow to display the values of two columns of an R data frame separately in a plot?
In general, the scatterplot is used to visualize the relationship between two columns of an R data frame but if we want to display the two columns separately not as a pair then we need to use matplot function. This function will create a plot for all the values in the two columns and represent them by their column number.Consider the below data frame −Exampleset.seed(222) x
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