R Programming Homework Help | Do My R Programming Homework

R is the oldest programming language that is taught in many colleges and universities for computer science, machine learning, and data science students. The language has gained immense popularity and students must understand various concepts to use it effectively. However, R language is challenging and students need time to get a command of this language. This is the reason why many students look for help.

If you struggle to complete the R language homework given by your professors, then you can take our expert's help. They have a lot of experience and knowledge in doing the R language tasks within the given timeline which helps them secure good grades in the examination. We offer best-in-class R Programming homework help. 

You can get in touch with us if you're stressed about finishing your R homework or assignments. We have a team of professionals with years of expertise working on any R programming assignments and homework. Since we started, we have aided hundreds of students, so you might be the next to benefit from our R Homework Help assistance.
 

What is R programming and who needs R Homework Help?

R is an open-source programming language with software packages for graphical illustrations, statistics, mathematical analysis, and coverage. It allows branching and iterations, which include programming exploitation functions. R can also be integrated with procedures that are written in other languages such as C, C++, .Net, Python, and others languages. It is easy, effective, and can handle data. It also has a good storage facility besides offering a wide range of operators for calculations such as lists, matrices, vectors, and arrays.

Many companies use R for data analysis. R programming is also used for machine learning algorithms and statistical inference. R also has an extensive catalog of statistical as well as graphical methods, which will have machine learning algorithms, time series, linear regression, and statistical inference. Libraries written in this language can handle heavy computational tasks. Learn applications of all such algorithms in R by availing of our affordable R Homework Help.
 

Why use R programming for Homework?

Many students are learning this programming language and having knowledge and hands-on practice will help them grab good job opportunities. Indeed, this programming language is used for machine learning, data analysis, statistics, functions, objects, and packages. It is totally independent of the platform and can be used on all operating systems. It is an open-source language that can be installed in any organization. However, it is not just a statistical package, but lets you integrate this with other languages. You can also interact with various other data sources and statistical packages. It is widely used in data science.

Therefore, R is added as a subject in the academic curriculum of many universities. Our pool of tutors can help you solve all your academic worries by offering the best R Programming Homework help.

Key steps for performing data analysis in R Programming Tool

• Program - R is easier to access tool to write the program.
• Transform - It has a lot of libraries which are designed to work for data science.
• Discover - Thoroughly investigate the data, refine the hypothesis and analyse the information
• Model - R offers a wide range of tools that let you capture the right set of data
• Communicate - You can easily integrate code, and graphs and generate the output with R Markdown

Master all such steps by interacting with our R Programming tutors.  We follow the step-by-step approach to enhance your academic understanding. We are the best R Programming Homework Help service.

R Programming features used in Homework & Assignments

A few of the R Programming features used in Homework & Assignments are listed below: 

• R packages - It has many libraries and repositories that can hold thousands of packages.
• Distributed computing - It is a model which has a lot of components of a software system. These can be shared with different components to improve performance and efficiency. Two major packages that are available with R are multidplyr and ddR, which are used for distributed programming.

Some of the popular topics in R Programming on which our programming assignment experts work on a daily basis are listed below:

R packages - Data Science Homework Help

Following are the R packages that are used in data science:

• Dplyr - It is used for data manipulation in R and it holds five functions. These functions help in data manipulation. You can also work with the remote database tables as well as local data frames.
• Ggplot2 - It is the best library that is helpful in data visualization in R. It implements the grammar of graphics and the approach embraced will produce visualizations by showing up the relationship between data attributes through graphical representation.
• Tidyr - It is helpful in creating tidy data. There is a lot of work that is spent on cleaning the data. Tidy has many datasets where every cell will have a value and each row has an observation and the column has a variable. You can install this with ease and start using it.
• Shiny - Shiny is a library used in R to build web applications without extensive use of JavaScript. You can use this with HTML widgets, JavaScript actions and CSS themes. It is used to build dashboards and standalone apps.
• Plotly - Plotly is a graphic library that helps you create graphs, which are highly interactive.
   

R Programming Homework based on real-life applications

R is used for statistical analysis and for data visualization. Scientists, academics, and researchers would use R to analyze the results of experiments.

• Fintech - Companies that deal with financial services would use R since money and statistics will go hand-in-hand. Banks will make use of R language to create different risk models and carry out different types of risk analysis. It is also helpful for detecting fraud, modeling mortgages, volatility modeling, client assessment, and loan-stress test simulations.
• Research - R is widely used in statistical analysis and calculations. Various techniques that are used including time-series analysis, statistical testing, clustering, classification, and non-linear and linear modeling are implemented using R and its libraries. It is also helpful in deep and machine-learning research. It is used for machine learning and its libraries for supervised as well as unsupervised learning.
• IT sector - R is used in the IT sector by companies of different sizes for business intelligence. R is used to build data-handling products along with statistical computing tools.
   

Why do students hire us for their R homework help?

Following are the benefits that every student can reap by hiring us:

• Experienced experts - We have a team of experts who have enough knowledge of R to write the homework. Be the topic is simple or complicated, our team works on this and delivers an immaculate solution that impresses your professors.
• Round-the-clock support - We are accessible to students around the clock. They can order the homework or contact us to track the progress of the homework at any time.
• On-time delivery - We assure you to deliver the solution before the timeline. This helps you to check the solution that is given by our team before you submit it to the professors.
• 100% plagiarism free - Our solutions are free from plagiarism. Everything is written from scratch and after thorough research. Our team will check the solution before sending it to you in the best plagiarism tool to ensure that the solution is unique.

We know that delivering unique solutions will help you secure good grades. We avoid duplicity that can take a toll on your grades.

Example of A Simple R Programming Code Written By Our Expert

Code for: Summary Analysis of Heart Failure

Solution:

```{r}
# reading the dataset
data1 <- read.csv("heart_failure_clinical_records_dataset.csv")
# displaying the first few rows of the dataset
head(data1)
```

# Boxplots

```{r}
# boxplots of the numerical (non-boolean) variables based on sex
boxplot(data1$creatinine_phosphokinase~data1$sex,main="Boxplot of creatinine phosphokinase (CPK) based on sex (0-woman,1-man)")
boxplot(data1$ejection_fraction~data1$sex,main="Boxplot of ejection fraction based on sex (0-woman,1-man)")
boxplot(data1$platelets~data1$sex,main="Boxplot of platelets based on sex (0-woman,1-man)")
boxplot(data1$serum_creatinine~data1$sex,main="Boxplot of serum creatinine based on sex (0-woman,1-man)")
boxplot(data1$serum_sodium~data1$sex,main="Boxplot of serum sodium based on sex (0-woman,1-man)")
boxplot(data1$time~data1$sex,main="Boxplot of time: follow-up period based on sex (0-woman,1-man)")
boxplot(data1$platelets~data1$anaemia,main="Boxplot of platelets based on anaemia: decrease of red blood cells or hemoglobin")
boxplot(data1$ejection_fraction~data1$smoking,main="Boxplot of ejection fraction based on smoking (0-non-smoker,1-smoker)")
```

# Histograms

```{r}
hist(data1$creatinine_phosphokinase,main="Histogram of creatinine phosphokinase (CPK)")
hist(data1$ejection_fraction,main="Histogram of ejection fraction")
hist(data1$platelets,main="Histogram of platelets")
hist(data1$serum_creatinine,main="Histogram of serum creatinine")
hist(data1$serum_sodium,main="Histogram of serum sodium")
hist(data1$time,main="Histogram of time: follow-up period")
```

# Q-Q Plots

```{r}
library(stats)
qqnorm(data1$creatinine_phosphokinase,main="Q-Q plot of creatinine phosphokinase (CPK)")
qqline(data1$creatinine_phosphokinase,col="red")
qqnorm(data1$ejection_fraction,main="Q-Q plot of ejection fraction")
qqline(data1$ejection_fraction,col="red")
qqnorm(data1$platelets,main="Q-Q plot of platelets")
qqline(data1$platelets,col="red")
qqnorm(data1$serum_creatinine,main="Q-Q plot of serum creatinine")
qqline(data1$serum_creatinine,col="red")
qqnorm(data1$serum_sodium,main="Q-Q plot of serum sodium")
qqline(data1$serum_sodium,col="red")
qqnorm(data1$time,main="Q-Q plot of time: follow-up period")
qqline(data1$time,col="red")
```

# Pairwise scatterplots and Correlation Coefficients

```{r}
library(GGally)
ggpairs(data1[,-c(2,4,6,10,11,13)]) 
```

# Covariance Matrix

```{r}
cov(data1[,-c(2,4,6,10,11,13)])
```

# Covariance and Correlation Matrix of the scaled data (using z score)

```{r}
cov(scale(data1[,-c(2,4,6,10,11,13)]))
cor(scale(data1[,-c(2,4,6,10,11,13)]))
```
 

If you want help with R programming homework, you can call us anytime.