Don't Let R Programming Puzzle You! Get personalized R Programming Homework Help and guidance from our expert and master R Programming.

Services

- Analytica Assignment Help
- AWS Assignment Help
- ConnectMath Assignment Help
- ERP Assignment Help
- EViews Assignment Help
- Excel Assignment Help
- Information Technology Assignment Help
- JMP Assignment Help
- Keras Assignment Help Online
- LabVIEW Assignment Help
- LISREL Assignment Help
- MATLAB Assignment Help
- MegaStat Assignment Help
- Minitab Assignment Help
- MYOB Assignment Help
- Power BI Assignment Help
- Programming Assignment Help
- Python Assignment Help
- R Programming Assignment Help
- R Shiny Assignment Help
- SAS Assignment Help
- Software Engineering Assignment Help
- SPSS Amos- SEM Assignment Help
- SPSS Assignment Help
- SQL Assignment Help
- STATA Assignment Help
- Statistics Assignment Experts
- Statistix Assignment Help
- Tableau Assignment Help
- TensorFlow Assignment Help
- XLSTAT Assignment Help

- Advanced Probability Theory Assignment Help
- ANOVA Assignment Help
- Applied Statistics Assignment Help
- Bayesian Statistics Assignment Help
- Black Scholes Theory Assignment Help
- Blockchain Technology Assignment Help
- C++ Assignment Help
- Calculus Assignment Help
- Chi-square Testing Assignment Help
- Cluster Analysis Assignment Help
- CNN Assignment Help
- Computer Architecture Assignment Help
- Confidence Intervals Assignment Help
- Control Charts Assignment Help
- Correlation Analysis Assignment Help
- Cyber Security Assignment Help
- Data Analysis Assignment Help
- Data Classification Assignment Help
- Database Management Assignment Help
- Decision Theory Assignment Help
- Decision Tree Assignment Help
- Descriptive Statistics Assignment Help
- Distribution Theory Assignment Help
- Factor Analysis Assignment Help
- Game Theory Assignment Help
- Hypothesis Testing Assignment Help
- Kalman & Particle Filter Assignment Help
- Linear Algebra Assignment Help
- Linear Discriminant Analysis Assignment Help
- Linear Programming Assignment Help
- Logistics Regression Assignment Help
- Markov Processes Assignment Help
- Mathematical Methods Assignment Help
- MATLAB GUI Assignment Help
- Monte Carlo simulation Assignment Help
- Multivariate Analysis Assignment Help
- Multivariate Statistics Assignment Help
- Neural Networks Assignment Help
- Nonparametric Tests Assignment Help
- Numerical Methods in MATLAB
- Operating System Assignment Help
- Principal Component Analysis Assignment Help
- Probability Assignment Help
- Probability Distributions Assignment Help
- Psychology Statistics Assignment Help
- Regression Analysis Assignment Help
- Sampling Assignment Help
- Statistical Inference Assignment Help
- Stochastic Processes Assignment Help
- Survey Methodology Assignment Help
- Time Series Assignment Help
- Time Series Homework Help

- Artificial Intelligence Assignment Help
- Backpropagation Assignment Help
- Big Data Assignment Help
- Business Analytics Assignment Help
- C Programming Assignment Help
- Chatbot Assignment Help
- Clinical Psychology Assignment Help
- Clinical Trials Assignment Help
- Coding Assignment Help
- Computer Networking Assignment Help
- Computer Science Assignment Help
- Computer Vision Assignment Help Online
- Consumer Behavior Assignment Help
- Control Systems Using MATLAB
- Data Analytics Assignment Help
- Data Flow Diagram Assignment Help
- Data Mining Assignment Help
- Data Science Assignment Help
- Deep Learning Assignment Help
- Derivatives Assignment Help
- Digital Signal Processing in MATLAB
- Econometrics Assignment Help
- Finance Assignment Help
- Finance Insurance Assignment Help
- Financial Risk Analysis Assignment Help
- Financial Statistics Assignment Help
- Fixed Income Markets Assignment Help
- Flask Assignment Help
- Forecasting Financial Time Series
- Image Processing in MATLAB
- Machine Learning Assignment Help
- Math Assignment Help
- MATLAB in Computing
- MyStatLab Help
- Natural Language Processing Assignment Help
- Network Design in MATLAB
- Neural Networks Assignment Help
- Operations Research Assignment Help
- Project Management Assignment Help
- Quantitative Psychology Assignment Help
- Random Forest Assignment Help
- Reinforcement Learning Assignment Help
- Statistics Dissertation Help
- Supervised Learning Assignment Help
- Support Vector Machine Assignment Help
- Take My R Programming Exam
- Unsupervised Learning Assignment Help

- ANOVA Homework Help
- Computer Science Homework Help
- Data Mining Homework Help
- Java Homework Help
- Live Exam Help
- MyMathlab Quiz Help
- Neural Networks Homework Help
- Online Calculus Exam Help
- Online Computer Engineering Exam Help
- Online Math Exam Help
- Online Programming Exam Helper
- Online Python Exam Help
- Online Statistics Exam Helper
- Pay Someone To Take Statistics Exam
- Proctored Exam Help
- R Programming Homework Help
- Statistics Homework Help
- Take My GED Test Online
- Take My Online Exam
- Take My Psychology Quiz
- Take My Statistics Quiz
- Take my Statistics Test

- Accounting Assignment Writers in the US
- Accounting Dissertation Help
- Accounting Research Paper Help
- Activity Based Accounting Assignment Help
- Auditing Assignment Help
- Balance Sheet Analysis Assignment Help
- Behavioral Finance Assignment Help
- Business Valuation Assignment Help
- Capital Budgeting Assignment Help
- Cost Accounting Assignment Help
- Demand Forecast Assignment Help
- Economics Cost Curves Assignment Help
- Financial Accounting Assignment Help
- Financial Accounting Exam Help
- Financial Reporting Assignment Help
- Financial Statement Analysis Assignment Help
- Forensic Accounting Assignment Help
- Fund Accounting Assignment Help
- International Finance Assignment Help
- Managerial Accounting Assignment Help
- Mergers and Acquisitions Assignment Help
- Online Economics Exam Help
- Online Finance Exam Help
- Public Economics Assignment Help
- Solve My Accounting Paper
- Statistics Research Paper Help
- Take My Cost Accounting Exam
- Take My Managerial Accounting Exam
- Tax Accounting Assignment Help

- Python Assignment Help Australia
- Python Assignment Help Canada
- Python Assignment Help UK
- Python Assignment Help USA
- Statistics Assignment Help Australia
- Statistics Assignment Help Canada
- Statistics Assignment Help Hong Kong
- Statistics Assignment Help Ireland
- Statistics Assignment Help New Zealand
- Statistics Assignment Help Qatar
- Statistics Assignment Help Saudi Arabia
- Statistics Assignment Help Singapore
- Statistics Assignment Help UK
- Statistics Assignment Help USA
- Statistics Project Help

On Time Delivery

Plagiarism Free Service

24/7 Support

Affordable Pricing

PhD Holder Experts

100% Confidentiality

Live Review

Our Mission Client Satisfaction

4.9
Jean

07-11-2022

My R programming homework received an extremely good mark. It was amazing and just what I wanted. The work quality is excellent.

5.0
Jean

20-02-2023

5.0
Michel

07-11-2022

You did a fantastic job on my R programming homework! I appreciate the support. Everything was well-written, succinct, and entirely original.

4.9
Michel

20-02-2023

4.7
Shree

07-11-2022

That was it. I loved this professional method of answering the question on the R Programming Homework because it complied with my directions and was good.

5.0
Shree

20-02-2023

If you are studying statistics or data analysis, then certainly R programming would be in your curriculum. It is the programming language that is in huge demand. Many students who are assigned the task of the program in this language will find it challenging and end up submitting poor-quality homework as a result of which they lose valuable grades. If you are assigned to work on R programming homework, then you can seek the help of our **R programming homework help **experts. They are available round the clock to offer you the required help. The solutions given by our team will help you secure good grades in the examination.

Our **R programming homework help **experts have years of experience in the subject. You are always welcome to ask us for the best services at the most affordable prices. When you entrust us with your homework, our professionals will provide the ideal solution. We deliver accurate solutions to R programming coursework - Homework, assignments, projects, technical writing, exams & Quizzes. Our Statistics Homework Help Experts can help you with all of your R programming homework help and **R programming assignment help**.

R is an open-source programming language that has a catalogue of various statistical and graphical methods. The language will also have machine learning algorithms, time series, linear regression, and statistical interference. The libraries that are available in R are also written in this language. However, for complicated computational tasks, you would need C, C++ and Fortran codes. R is used by many big companies to develop applications.

You can perform data analysis using the R language in a series of steps such as programming, transforming, discovering, modelling and communicating the output. If you are stuck finding the solution for R problems or requirements, our R programming homework help can help you with the best solutions.

**Program**– It is used as a programming tool to write the code.**Transform**– R has a collection of libraries that are designed to be used in data science.**Discover**- You can use this to thoroughly investigate the data filter the hypothesis and analyze the data.**Model**– R has a wide range of tools that allow you to capture the best model for your data.**Communicate**– It will be integrating the code, graphs, and outputs to the report using the R Markdown and Shiny apps and share the result with the world.

Reasons why students use R programming for statistical computing and graphics:

**Open-source -**R can be downloaded without paying a single penny using a General Public license. There are sources from where you can learn about various concepts in R. There are R packages that are available under this license, and thus can be used for commercial apps.**Run on different platforms -**Distributions of R can be found on various platforms such as Linux, Mac and Windows. You can effortlessly transfer the code from one platform to another without any difficulty. Additionally, it supports seamless communication between different platforms.**Increase job opportunities -**Learning this language for data scientists is helpful. Having R programming experience will make your profile stand out from others.

Following are the R packages that are available to be used in data analysis:

**DBI -**It is the standard that is used for communication between R and Relational database management systems. Packages that are connected to R will be based on the DBI package.**ODBC -**You can use the ODBC driver along with the ODBC package to connect R to the database. The RStudio products will come with professional drivers to use with the popular databases.**RMySQL, RPostgreSQL, RSQLite -**You can read the data from the database using these packages. You can choose the right one that fits you to retrieve data from the database.**XLConnect, XLSX -**These are the packages in R, which allow you to read and write Microsoft files from the R programming. You can even export the data to the excel sheet with ease.**Foreign -**You can read the SAS dataset from R. Foreign will help you with the functions which can be used to load files from different programs to R.**Haven -**It allows you to read as well as write data from SPSS, SAS and STATA with ease.

**Tidyverse -**It has a collection of R packages that work well with data science to share philosophy, grammar, and data structures. The collection has various packages, with data import, tidying and visualization.**Dplyr -**It has all the shortcuts that allow you subset, submit, rearrange, and join the datasets together. It is the package that is best to be used for data manipulation**Tidyr -**It has various tools that allow you to change the layout of datasets. The spread and gather functions can be used to convert data into a tidy format.

**Ggplot2 -**It is the most famous R package that is used for making beautiful graphics. You can use the grammar of graphics to layer and customize the plots.**Ggvis -**It allows you to use interactive and web-based graphics that are built with the help of the grammar of graphics.

**Tidymodels -**It has a collection of packages that are used for modelling and machine learning with the help of tidyverse principles.**Car -**The Anova function in this package allows you to use type II and type II ANOVA tables.- Reporting results
**Shiny -**It is used to make highly interactive apps and is an ideal way to explore data and share information with non-programmers.

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

Machine and Deep Learning in R | Lists and Data Frames |

Functional Programming | Probability Distributions |

Applied Statistics with R | Grouping, Loops, and Conditionals |

Manipulation of Vectors | User-Defined Functions |

Objects, Models and Attributes | Developing Statistical Models |

Arrays and Matrices | Graphics and Procedures |

List and Data Frames | Packages and OS Facilities |

File Handling |

A quadratic model is a second-degree polynomial that takes the form of ‘y = a + bx + cx^2’. To graph a quadratic model in R Programming, you can use the ‘ggplot2’ package.

First, you need to create a data frame that contains the values of ‘x’ and ‘y’ for your quadratic model. You can do this using the ‘data.frame’ function.

Next, you can use ‘ggplot’ to create a scatterplot of the data and add a quadratic regression line using the ‘stat_smooth’ function with the ‘method’ argument set to ‘lm’.

R Programming offers a range of pre-existing datasets that are available for analysis and modeling. To access these datasets, you can employ the 'data' function followed by the dataset's name. Additionally, you can bring in datasets from external origins, like CSV files, by utilizing functions such as 'read.csv' or 'read.table'.

The mode of a variable is the value that occurs most frequently in a dataset. To find the mode of a variable in R Programming, you can use the 'Mode' function from the 'DescTools' package.

The ideal count of clusters is the number that most accurately portrays the inherent structure of the data. In R Programming, you can ascertain the optimal cluster count using methods like the elbow method or the silhouette method.

The elbow method involves plotting the within-cluster sum of squares (WCSS) against the number of clusters and selecting the number of clusters at the "elbow" of the curve.

The silhouette method involves calculating the silhouette score for each point in the dataset for a range of cluster sizes and selecting the number of clusters that maximize the average silhouette score.

The multivariate normal distribution is a generalization of the normal distribution to multiple dimensions. To fit a multivariate normal distribution in R Programming, you can use the `mvtnorm` package.

Principal Component Analysis (PCA) is a method used to decrease the dimensions of a dataset by mapping it onto a space with fewer dimensions. To choose the number of principal components in R Programming, you can use the scree plot or the cumulative proportion of variance method.

The scree plot involves plotting the eigenvalues of the principal components against their corresponding indices and selecting the number of principal components at the "elbow" of the curve.

The cumulative proportion of variance method involves calculating the proportion of variance explained by each principal component and selecting the number of principal components that explain a sufficiently large proportion of the total variance.

A time series object is a data structure that contains data with time stamps. Before running a smoothing analysis on a raw baseline in R Programming, it is important to convert the data into a time series object because time series analysis requires data to be in a specific format.

To convert a raw baseline to a time series object, you can use the `ts` function.

To get a count of a variable in R Programming, you can use the table function.

You can also use the count function from the ‘dplyr’ package.

In R Programming, there exist several approaches to rectify data issues, contingent upon the type of problem encountered. Here are some prevalent challenges along with their corresponding solutions:

__Missing values__: Missing values can be imputed using techniques such as mean imputation or regression imputation. You can use the ‘complete.cases’ function to identify rows with missing values, and the ‘na.omit’ function to remove them.__Outliers__: Outliers can be detected using techniques such as box plots or the Grubbs test. Outliers can be removed or transformed using techniques such as ‘winsorization’ or log transformation.__Inconsistent data__: Inconsistent data can be detected using techniques such as regular expressions or logical tests. Inconsistent data can be corrected using techniques such as string substitution or recoding.

We are the best online R programming homework help providers offering professional homework support to students across the globe. A few of the perks every student can reap by availing of our service include:

**Access to expert statistics tutors -**We have a team of R programmers who work on your homework. They first understand the requirement, do the research and give the solution from scratch.**Plagiarism free -**The solutions that are given by our team are free from plagiarism. We also run a plagiarism test before sending you the solution along with the report to improve your confidence levels.**Pocket-friendly pricing -**We understand the budget of students and designed our pricing structure by keeping students in mind. Students do not need to burn holes to avail of our service. While our service is budget-friendly, our solutions maintain a high standard of quality.**Round-the-clock support -**whether it's through phone calls, live chats, or emails—for any questions you may have about our homework assistance. Our support team is prompt in addressing your queries.

Our support team will respond as soon as they can. You can also track the progress of your assignment anytime or pass on additional requirements to the tutor to add to your solution.

**Code for: **Keyword Sentiment Analyzer

**Solution:**

```
```{r setup, include=FALSE}
library(tidyverse)
library(tidytext)
library(glue)
library(stringr)
```
## Defining the sentiment analyser function
```{r}
sentiment <- function(doc){
# tokenize
tokens <- data_frame(text = doc) %>% unnest_tokens(word, text)
# we will use the "bing" positive-negative words list
d <- get_sentiments("bing")
# checking if the tokens are there in the "bing" list or not
check <- tokens$word %in% d$word
# FALSE in the check vector means that this particular word is not their in the "bing" list
pn <- c() #vector for "positive" = positive word, "negative" = negative word, NA if word not in "bing" list
for (i in 1:length(check))
{if (check[i] == TRUE)
{pos <- match(tokens$word[i],d$word)
pn[i] <- d$sentiment[pos]}
}
# number of positive tokens
positive.count <- length(which(pn=="positive"))
# number of negative tokens
negative.count <- length(which(pn=="negative"))
# calculating final score
final.score <- positive.count - negative.count
return (final.score)
}
```
## Testing the function on a sample document
```{r}
sample <- "This dinner is WONDERFUL"
sentiment(sample)
```
```

If you need help in completing the R programming homework, then you can take the help of our experts who work day in and day out to finish the task on time.