Trimmed mean is the mean that find the mean of values by excluding a small percentage of smallest and largest values. If we have a 5% trimmed mean that means 2.5% of smallest values and 2.5% of largest values are trimmed from the data and then the mean of the remaining data is calculated.In R, we can simply use trim argument inside mean function to find the trimmed mean. Check out the below Examples to understand how it can be done.Example 1Following snippet creates a sample data frame −x

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

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

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

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 median 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 following command −Total_data

If we have multiple vectors and we want to combine the vector elements alternatively then we can use rbind function along with c function.For Example, if we have three vectors say X, Y, and Z as −X = 1, 2, 3 Y = 4, 5, 6 Z = 7, 8, 9Then, we can combine the elements in these vectors alternatively by using the below command −c(rbind(X,Y,Z,)) Example 1To concatenate elements of equal size vectors add the following code to the above snippet −x1

To find the sample size for t test, we can use pwr.t.test function of pwr package, wherever we can pass the arguments for alternative hypothesis such as one-sided or two-sided, significance level, power of the test and difference for two samples.Check out the below examples to understand how it works.Example 1Consider the following code to find sample size for t test −library("pwr") pwr.t.test(power=0.80, d=1, sig.level=0.05, alternative="two.sided")OutputIf you execute the above given code, it generates the following Output for the two-sample t test power calculation −   n = 16.71472    d = 1 sig.level = 0.05    power = 0.8 alternative ... Read More