Programming Articles - Page 1764 of 3366

Suppose we have two different series A1 and A2 of size b and a respectively. We have to find the mean and variance of combined series.So, if the input is like A1 = [24, 46, 35, 79, 13, 77, 35] and A2 = [66, 68, 35, 24, 46], then the output will be Mean = [44.1429, 47.8], sd = [548.694, 294.56], combined mean = 45.6667, d1_square = 2.322, d2_square = 4.5511, combined_var = 446.056To solve this, we will follow these steps −Define a function mean() . This will take arrreturn average of arr elementsDefine a function sd() . This will ... Read More

Often, we simulate random values from different distributions in R. The base R provides some inbuilt functions for the same and if we want to repeat the simulation a fixed number of times then we write these inbuilt functions again and again. But we can do multiple simulations using a single line of code with the help of replicate function, that means if we want to simulate ten uniform random variables ten times then it can be done by using replicate function.Examplesreplicate(10, runif(5, 2, 5)) [, 1] [, 2] [, 3] [, 4] [, 5] [, 6] [, 7] [, ... Read More

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