Server Side Programming Articles - Page 1615 of 2646

The cumulative sum is used to determine the total sum of a variable or group and helps us to understand the changes in the values of that variable or group over time. While creating the cumulative, we must be sure that the total sum and the cumulative sum of the last value (depending on the direction of sum) are same. We can use mutate function of dplyr package to find the cumulative and create a column for it.ExampleConsider the below data frame −x1

When we create a plot using plot function, the axes labels are automatically created based on the values of the variables that is being plotted. It is possible to set a limit to the labels for both the axes, X-axis and Y-axis and we can do this by using xlim and ylim options. For example, if we have the variable limits from 0 to 50 for variable that is going to be plotted on X-axis then it can be done as xlim = c(0,50).Exampleset.seed(99) x

A rank variable is created to convert a numerical variable into ordinal variable. This is useful for non-parametric analysis because if the distribution of the numerical variable is not normal or there are assumptions of parametric analysis that cannot be followed by the numerical variable then the raw variable values are not analyzed directly. To create a rank variable using mutate function, we can use dense_rank argument.ExampleConsider the below data frame −set.seed(7) x1

A string can be short or long, also we can have a vector or list of strings as well in R. Extraction of partial string is common when we want to use the strings for single or multiple comparisons. If we want to extract first two characters from a string, we can use substr function and the syntax is substr(“String_object Or String”,start=1,stop=2)Examplesx1

A boxplot shows the minimum, first quartile, median, third quartile, and maximum. When we create a boxplot with ggplot2 it shows the boxplot without horizontal lines on the minimum and maximum, if we want to create the horizontal lines we can use stat_boxplot(geom= 'errorbar') with ggplot function of ggplot2.ExampleConsider the below data frame −set.seed(101) Gender

A scatterplot is the plot that has one dependent variable plotted on Y-axis and one independent variable plotted on X-axis. Sometimes the pair of dependent and independent variable are grouped with some characteristics, thus, we might want to create the scatterplot with different colors of the group based on characteristics. For this purpose, we can use colour argument in ggplot function.ExampleConsider the below data frame −set.seed(123) x

The bars of a bar plot are generally vertical from bottom to top but we can reverse them as well. Although, this is not a normal practice but we can do it if we want to. For this purpose, we will have to reverse the values on the Y-axis, as a result the bars will be reversed. It can be achieved by using scale_y_continuous.ExampleConsider the below data frame −Salary_Group

A list can contain many elements and each of them can be of different type but if they are numerical then we can perform some mathematical operations on them such as addition, multiplication, subtraction, division, etc. To do this, we can use Reduce function by mentioning the mathematical operation and the list name as Reduce(“Mathematical_Operation”, List_name).Examplex1

Mostly, the main title of a plot is short but we might have a long line to write for the main title of the plot. For example, a short version might be “Scatterplot” and a longer version might be “Scatterplot between X and Y”. Therefore, in plot function of R we can use line breaks for the main title as "Scatterplot between X and Y".Exampleset.seed(123) x

Dealing with missing values is one of the initial steps in data analysis and it is also most difficult because we don’t fill the missing values with the appropriate method then the result of the whole analysis might become meaningless. Therefore, we must be very careful about dealing with missing values. Mostly for learning purposes, people use mean to fill the missing values but can use many other values depending on our data characteristic. To fill the missing value with mean of columns, we can use na.aggregate function of zoo package.ExampleConsider the below data frame −x1