Server Side Programming Articles - Page 1507 of 2646

In general, we can say that the homogeneity of variance test is the type of test that compares the variance of two or more variables and finds the significant difference between or among them if exists. For a two-way anova, one of the most commonly used homogeneity of variance test is Levene’s Test and it can be easily done with the help of leveneTest function of car package in base R.Consider the below data frame −Example Live Demoset.seed(151) x1F) group 6 0.6593 0.6835 13

In data analysis, sometimes we need to count the number of values that are greater than or less than a certain value, and this certain value could be a threshold. For example, we might have a vector that contain values for blood pressure of people and we might want check how many values are greater than 120. In this type of situation, we can use length function as shown in the below examples.Example Live Demox11])Output[1] 9 Example Live Demox25])Output[1] 93Example Live Demox35])Output[1] 42Example Live Demox40])Output[1] 108Example Live Demox51])Output[1] 107Example Live Demox65])Output[1] 31Example Live Demox71])Output[1] 21Example Live Demox84])Output[1] 19Example Live Demox9118])Output[1] 11Example Live Demox105000])Output[1] 68Read More

The matrix can be created by using matrix function in R and if we want to create a matrix by replicating a vector then we just need to focus on the replication. For example, if we have a vector V and we want to create matrix by replicating V two times then the matrix can be created as matrix(replicate(2,V),nrow=2).Example1 Live DemoV1

The bar plot is created with geom_bar function but there always exist some space between the bars and the X-axis labels. If we want to reduce that space or completely remove it we need to use scale_y_continuous function by defining expand argument for former and scale_y_continuous(expand=c(0,0)) for latter.Example Live DemoConsider the below data frame −set.seed(888) x

When we multiply a matrix with a vector the output is a vector. Suppose we have a matrix M and vector V then they can be multiplied as M%*%V. To understand the step-by-step multiplication, we can multiply each value in the vector with the row values in matrix and find out the sum of that multiplication.Example1 Live DemoM1

The replicates of a data frame in R can be created with the help of sapply function, to set the number of times we want to repeat the data frame we can use rep.int,times argument. For example, if we have a data frame df and we want to create 5 replicates of df and add them in the original then sapply(df,rep.int,times=5) can be used.Example Live DemoConsider the below data frame −set.seed(151) x1

To find the day of the year from dates, we can use yday function of lubridate package. For example, if we have a date or a date of vectors then we simply need to pass that date or the vector inside yday function by using the below syntax −yday(“date”)oryday(“vector_of_date”)Loading lubridate package −library(lubridate)Examplesdate1

To find the row means we can use rowMeans function but if we have some missing values in the data frame then na.rm=TRUE argument can be used in the same way as it is used while calculating the means for columns. For example, if we have a data frame df that contains two columns x and y each having some missing values then the row means can be calculated as rowMeans(df,na.rm=TRUE).ExampleConsider the below data frame − Live Demoset.seed(1515) x1

One of the most important aspects of a boxplot is Y-axis labels because these labels help us to understand the limit of the variable. Since R generate these labels automatically in a good way, we stick with that but we can change that using coord_cartesian function with ylim as shown in the below example.Example Live DemoConsider the below data frame −set.seed(1212) x

To replace missing values with median, we can use the same trick that is used to replace missing values with mean. For example, if we have a data frame df that contain columns x and y where both of the columns contains some missing values then the missing values can be replaced with median as df$x[is.na(df$x)]