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R is an open-source programming language. It is also a free software environment for statistical computing and graphics. Likewise, the S language is also a GNU project. Robert Gentlemen and Ross Ihaka have developed the R programming language in New Zealand. It is used for statistical computations and data analysis. R is the best programming language to perform statistical techniques such as Regression modeling, classical statistical tests, time-series analysis, data mining, classification, clustering, etc.
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These are some of the R programming libraries on which our experts provide help.
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In order to install the R programming language, we'll need an IDE (Integrated Development Environment). Do you know what an IDE is? if you've worked with other programming languages you have heard about IDE.
An IDE is a platform in which we need to install a programming language. For example, in a python programming language, we use Jupyter notebooks as an (IDE). We use the Eclipse IDE for Java development. Similarly, R-Studio is the IDE for the R programming language. It assists you in making your programming effort more manageable. Our specialists will assist you in your R Programming Assignment help including R studio assignment help.
R is a popular programming language used for statistical analysis, data visualization, and machine learning. Here are eight advantages of R:
R is an open-source programming language, which means that anyone can access, use, and modify its code. This makes it easier for users to customize and enhance their analyses as needed.
R is widely used for statistical analysis and modeling, with a vast library of functions and packages available. It is particularly well-suited for exploratory data analysis, linear and nonlinear modeling, and time-series analysis.
R has a wide range of powerful visualization tools, including ggplot2, lattice, and base graphics. These tools make it easy to create informative and visually appealing charts, graphs, and plots.
R can be easily integrated with other programming languages, such as Python and SQL. This allows users to take advantage of the strengths of each language and use them together in their analyses.
R has a large and active community of users, which means that users can get help, share code, and collaborate with others easily.
R supports reproducible research by providing tools for creating reports and documents that contain both code and results. This makes it easy to share and reproduce analyses, which is particularly important for scientific research.
R has a wide range of machine learning libraries and packages, including caret, randomForest, and neuralnet. These tools make it easy to build and deploy machine learning models for a variety of applications.
R is available on a variety of platforms, including Windows, macOS, and Linux. This makes it easy for users to work with R on the platform of their choice.
R is related to a much older programming language called "S." Its base package, thus, does not allow dynamic or 3D graphics. It is possible to produce vibrant, 3D, and animated visuals using ordinary R tools such as Ggplot2 and Plotly.
R is insecure in many ways. Most programming languages, such as Python, include the essential feature of security. As a result, R has several limitations, including the inability to be incorporated in a web application.
R is a complex language to master. The learning curve is very sharp, which makes this language difficult to master. As a result, those who have never programmed before may find it challenging to learn R.
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Adding the shape and color
Many years ago, R was considered a difficult programming language as it was confusing and less structured as compared to other coding languages. Hadley Wickham created various packages to make it faster, easier, and more fun. Now, graph creation is not difficult anymore. With our R Programming assignment help service, anyone can easily implement the best algorithms of machine learning.
R can easily communicate with other programming languages such as Java, Python, C++, etc. Moreover, R provides connectivity with different databases like Hadoop or Spark. Various packages such as TensorFlow and Keras enable the creation of high-quality machine learning techniques. Moreover, it provides a package to perform Xgboost, which is the most suitable algorithm for the Kaggle competition.
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We have a team of highly experienced, qualified, and trained experts to solve your different topics related to R programming. Our Experts will cover various topics involved in the R Programming assignment help service. Some of the essential topics are below-
|Simple Linear Regression
|It is one of the best statistical methods that help us obtain the formulas to predict one variable's value from another variable. It is based on the relationship between the two variables.
|There is only a minor difference between Multiple regression and simple linear regression. If we put the extension on simple linear regression, then it becomes the multiple regression. It helps us to predict the value of one variable from the value of two or more variables. The variable that we will predict is the outcome, target, dependent variable, or criterion variable.
|It is another form of regression analysis, and it is specially designed to overcome the limitations created by the traditional parametric and non-parametric methods. We use it to find the relationship between one or more independent variables.
|In R Programming, logistic regression is a classification algorithm for determining the probability of event success and failure. The dependent variable may be in binary, i.e. (0/1 or True/False or Yes/No), logistic regression is utilized.
|Bayesian statistics use the mathematical language of probability to describe epistemological uncertainty. In this, the probability expresses the degree of belief that is specified in the states of nature.
|The zero truncated is the part of probability theory. The zero-truncated Poisson distribution is known as the certain discrete probability distribution. The set of positive integers works as the support of this distribution. The other name of this distribution is the conditional Poisson distribution or the positive Poisson distribution.
|The non-parametric statistics are don't based solely on parameterized families of probability distributions.
|Exploratory Data Analysis
|It is the best approach that helps us analyze the dataset used to summarize the main characteristics.
|Mapping is one of the major tasks of R programming. As the name suggests, it is used in geographical information systems to plot data on maps. Our experts clear all the doubts of students regarding mapping in R programming in our R help online service.
|Graphics are used for data visualization in R programming. Therefore, the industry uses the graphics in R programming over the tables. But graphics are not that easy to create in R for the statistics students. Therefore, we are here to help the students clear their doubts regarding graphics in R. We offer them full practical assistance to get good command over graphics.
|The T-test is the most popular test in statistics. It is used to compare the two data sets to find the actual difference between them. The T-test is also used with R programming to compare the data at a rapid speed.
|R Packages are responsible for performing almost everything in R. The packages hold the code and the various functions. You can also share your code in the form of R packages with the other programmer. These packages are stored in the library of R. The packages help you to perform statistics operations, plotting, data representation, and even machine learning. There is a special package in R that helps you to perform almost every statistics function. We make sure that you get the best help with R packages, either for statistics or machine learning.
Here are some of the R programming assignment help projects that we cover which are as follows:
|Multiple Linear Regression
|Multinomial Logistic Regression
|Ordered Logistic Regression
Note: These projects are just examples; we cover almost every project/ topic in our R programming assignment help. You can contact us to know more about topics and projects.
row_names = c("row1", "row2", "row3")
col_names = c("col1", "col2", "col3")
M = matrix(c(1:9), nrow = 3, byrow = TRUE, dimnames = list(row_names, col_names))
print("Access the element at 2rd column and 3rd row:")
print("Access only the 1st row:")
print("Access only the 2nd column:")
col1 col2 col3
row1 1 2 3
row2 4 5 6
row3 7 8 9
"Access the element at 2rd column and 3rd row:"
"Access only the 1st row:"
col1 col2 col3
1 2 3
"Access only the 2nd column:"
row1 row2 row3
2 5 8
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