The easiest way to create a regression model with interactions is inputting the variables with multiplication sign that is * but this will create many other combinations that are of higher order. If we want to create the interaction of two variables combinations then power operator can be used as shown in the below examples.Example1 Live Demox1

To understand the difference ordered factors and unordered factors, it is better to understand them by creating the factor vectors by using ordered argument with TRUE and FALSE options. For example, if we have a vector x then it can be ordered or unordered as factor(x,ordered=TRUE) and factor(x,ordered=FALSE).Example1 Live Demox1

To combine array of vectors we can use rbind function. For example, if we have multiple vectors say x, y, z of same size or different sizes but the total number of elements are even then we can use rbind(x,y,z) to combine those vectors. Check out the examples to understand how it works.Example1 Live Demox1

To convert factor levels into character then we can use as.character function by accessing the column of the data frame that contain factor values. For example, if we have a data frame df which contains a factor column named as Gender then this column can be converted into character column as as.character(df$Gender).Example Live DemoConsider the below data frame −set.seed(121) x1

The boxplot can be created by using boxplot function in base R but the Y−axis labels are generated based on the vector we pass through the function. If we want to remove the axis labels then axes = FALSE argument can be used. For example, if we have a vector x then the boxplot for x without axes labels can be created by using boxplot(x,axes=FALSE).Example Live DemoConsider the below vector x and creating boxplot −set.seed(777) x

The t test is used to find the p−value for the correlation coefficient and on the basis of that we decide whether there exists a statistically significant relationship between two variables or not. In R, we can perform this test by using function cor.test. For example, if we have a vector x and y then we can find the p−value using cor.test(x,y).Example1 Live Demoset.seed(444) x1

In base R, we use boxplot function to create the boxplots but if we have categorical vector and the corresponding numerical vector then the boxplot can be easily created. For this purpose, we should save those vectors in a data frame and use the $ operator and las = 2 argument to create the boxplot as shown in the below example.ExampleConsider the below vectors:Countries Rate

In R, for the calculation of power we can simply use power operator ^ and this will be also used in case of generating a power sequence. For example, if we want to generate a power sequence from 1 to 5 of 2 then we can use the code 2^(1:5) this will result 2 4 8 16 32.Example Live Demo2^(0:2)Output[1] 1 2 4Example Live Demo2^(0:10)Output[1] 1 2 4 8 16 32 64 128 256 512 1024 Example Live Demo2^(0:50)Output[1] 1.000000e+00 2.000000e+00 4.000000e+00 8.000000e+00 1.600000e+01 [6] 3.200000e+01 6.400000e+01 1.280000e+02 2.560000e+02 5.120000e+02 [11] 1.024000e+03 2.048000e+03 4.096000e+03 8.192000e+03 1.638400e+04 [16] 3.276800e+04 6.553600e+04 1.310720e+05 2.621440e+05 5.242880e+05 [21] ... Read More

To convert columns of an R data frame from integer to numeric we can use lapply() function. For example, if we have a data frame df that contains all integer columns then we can use the code lapply(df,as.numeric) to convert all of the columns data type into numeric data type.Example1Consider the below data frame − Live Demoset.seed(871) x1

The point chart can be created by using geom_point function and if we want to create a point chart for single vector then we should pass the vector in both the places inside aes function. Also, by default the points are complete black circles and if we want to change the points to empty points then shape argument can be used.ExampleConsider the below data frame − Live Demoset.seed(171) x