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R Programming Articles
Page 90 of 174
How to save the summary statistics into a data frame in R?
When we find the summary statistics of a data frame then the output is returned as a table and each of the column records the minimum, first quartile, median, median, third quartile, and maximum with their names. If we want to save this summary as a data frame then it is better to calculate it with apply function and store it as data.frame.ExampleConsider the below data frame −x1
Read MoreHow to solve the simultaneous linear equations in R?
The data in simultaneous equations can be read as matrix and then we can solve those matrices to find the value of the variables. For example, if we have three equations as −x + y + z = 6 3x + 2y + 4z = 9 2x + 2y – 6z = 3then we will convert these equations into matrices and solve them using solve function in R.Example1> A AOutput [, 1] [, 2] [, 3] [1, ] 1 1 2 [2, ] 3 2 4 [3, ] 2 3 -6> b bOutput[, 1] ...
Read MoreHow to remove a character in an R data frame column?
To remove a character in an R data frame column, we can use gsub() function which will replace the character with blank. For example, if we have a data frame called df that contains a character column say x which has a character ID in each value then it can be removed by using the command gsub("ID", "", as.character(df$x)).Example1Consider the below data frame −> x1 x2 df1 df1Output x1 x2 1 Male1 8 2 Female1 4 3 Male1 9 4 Male1 2 5 Male1 7 6 Female1 5 7 Male1 3 8 ...
Read MoreHow to convert more than one column in R data frame to from integer to numeric in a single line code?
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 −set.seed(871) x1
Read MoreData Manipulation in R with data.table
Data manipulation is a crucial step in the data analysis process, as it allows us to prepare and organize our data in a way that is suitable for the specific analysis or visualization. There are many different tools and techniques for data manipulation, depending on the type and structure of the data, as well as the specific goals of the manipulation. The data.table package is an R package that provides an enhanced version of the data.frame class in R. It’s syntax and features make it easier and faster to manipulate and work with large datasets. The date.table is one ...
Read MoreHow to find the R version you are using?
Most of the times, we need to use packages in R and some packages are restricted to different versions in R, generally to newer versions. Therefore, we might need to find which version of R we are using. To find the R version, we can directly use the command R.Version().ExampleR.version.stringOutput[1] "R version 4.0.2 (2020−06−22)"
Read MoreHow to create a vector with repeated values in R?
There are two methods to create a vector with repeated values in R but both of them have different approaches, first one is by repeating each element of the vector and the second repeats the elements by a specified number of times. Both of these methods use rep function to create the vectors.ExampleConsider the below examples −> x1 x1 [1] 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 [39] 4 ...
Read MoreHow to remove all rows having NA in R?
To remove all rows having NA, we can use na.omit() function. For Example, if we have a data frame called df that contains some NA values then we can remove all rows that contains at least one NA by using the command na.omit(df).That means if we have more than one column in the data frame then rows that contains even one NA will be removed. Check out the below Examples to understand how it works.Example 1Consider the below data frame −x1
Read MoreAn Introduction to RMarkdown: Creating Reports and Presentations
RMarkdown is a powerful tool that helps users seamlessly combine code, text, and visualizations together to create dynamic reports and presentations. Today we will explore to know what are the capabilities of RMarkdown and where it can be applied. We will also cover the basics of RMarkdown, about its syntax, and how to create reports and presentations using this versatile tool. Additionally, we will discuss its advantages, integration with popular data analysis tools and provide examples to demonstrate its practical use. Introduction to RMarkdown RMarkdown is an open-source authoring framework that enables the creation of dynamic documents by integrating text, ...
Read MoreAdvanced Techniques in Statistical Inference with R
Introduction Statistical inference is the process of drawing conclusions or making predictions about a population based on sample data. While traditional methods like hypothesis testing and confidence intervals are widely used, advanced techniques have emerged to tackle more complex problems and provide more robust results. In this article, we will explore how R, a powerful statistical programming language, can be utilized to implement these advanced techniques effectively. We will explore some of the advanced techniques in statistical inference and demonstrate how to implement them using the popular programming language R. We will cover topics such as Bayesian inference, resampling methods, ...
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