When we create a histogram using hist function in R, often the Y-axis labels are smaller than the one or more bars of the histogram. Therefore, the histogram does not look appealing and it becomes a little difficult to match the Y-axis values with the bars size.To solve this problem, we can use ylim argument of hist function in which the range can be supplied to plot on the Y-axis labels.ExampleConsider the below data and its histogram − Live Demoset.seed(101) x

If an R data frame has numerical columns then it is also possible that there exist zeros in few or all columns and we might be interested in finding the number of non-zero values in a column. This will help us to compare the columns based on the number on non-zero values and it can be done by using colSums.ExampleConsider the below data frame − Live Demox1

When the variables are not continuous but could be ranked then we do not use pearson correlation coefficient to find the linear relationship, in this case spearman correlation coefficient comes into the scene. Since the spearman correlation coefficient considers the rank of values, the correlation test ignores the same ranks to find the p-values as a result we get the warning “Cannot compute exact p-value with ties”. This can be avoided by using exact = FALSE inside the cor.test function.ExampleConsider the below vectors and perform spearman correlation test to check the relationship between them − Live Demox1

A row of an R data frame can have multiple ways in columns and these values can be numerical, logical, string etc. It is easy to find the values based on row numbers but finding the row numbers based on a value is different. If we want to find the row number for a particular value in a specific column then we can extract the whole row which seems to be a better way and it can be done by using single square brackets to take the subset of the row.ExampleConsider the below data frame − Live Demox1

A boxplot shows the median as a measure of center along with other values but we might want to compare the means as well. Therefore, showing mean with a point is likely to be preferred if we want to compare many boxplots. This can be done by using points(mean(“Vector_name”)), if we are plotting the columns of an R data frame then we will reference them instead of vector name.ExampleConsider the below data and the boxplot − Live Demox

If a data frame has all numerical columns then we might be interested in finding the mean of all values in that data frame but this cannot be done directly because a data frame object is not numeric. Therefore, to find the mean of all values in an R data frame, we need to convert it to a matrix first then use the mean function.ExampleConsider the below data frame − Live Demox1

The calculation of quantiles in R is very simple, we just need to use quantile function and it returns all the quantiles that are 0%, 25%, 50%, 75% and 100%. If we want to avoid the printing the name of these quantiles then we can use names=FALSE with the quantile function. For example, if we have a vector called x then the quantiles without names can be found as quantile(x,names=FALSE).Example Live Demox1

Generally, a matrix is created with given values but if we want to create the matrix with random values then we will use the usual method with the matrix function. Random selection in R can be done in many ways depending on our objective, for example, if we want to randomly select values from normal distribution then rnorm function will be used and to store it in a matrix, we will pass it inside matrix function.Example Live DemoM1

The matrices that have same number of columns can be combined by rows. For example, if we have five matrices list, each having six columns then those matrices can be converted into a single matric by joining the rows of those matrices. It can be done by using do.call(rbind,”List_of_matrices_object_name”).ExampleConsider the below matrices and their list − Live DemoM1

Random sampling helps us to reduce the biasedness in the analysis. If we have data in groups then we might want to find a random sample based on groups. For example, if we have a data frame with a group variable and each group contains ten values then we might want to create a random sample where we will have two values randomly selected from each group. This can be done by using sample function inside .SDExampleConsider the below data.table −library(data.table) Group