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R Programming Homework Help | Do My R Programming Homework

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 team. 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 domain. 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 create the best answer to your homework problem and assist you in receiving the best scores by adhering to all of the important rules of the leading colleges. R Programming Homework Help, we are able to help you with all of your R programming homework help and R programming assignment help. It is achievable to get in contact with us via e-mail and live chat. We've been a really Energetic company and we provide the best customer care. Our prices are certainly aggressive and low cost. We can assure you that we'll provide you with the very best quality support you ever had. We also have a great team dealing with us who are responsible for updating and informing us about any new launch or update in languages. That’s why we're sure that we would be the authorities who can help you with all of your assignments and homework.

What is R programming?

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. 
 

Why use R programming language 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 port the code that is written on one platform to another platform without any hassle. It also supports cross-platform interoperability.

Increase job opportunities
Learning this language for data scientists is helpful. Having R programming experience will make your profile stand out from others. 
 

R packages

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. 

 

Data manipulation

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.
Data visualization

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.

 

Data modelling

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  

 

Reasons to choose our R homework help

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 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. Though our service is cost-effective, our solutions are top-notch. 

Round-the-clock support
If you have any queries regarding the homework help, you can ring, live chat or email us. 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.

 

Example of A Simple R Programming Code Written By Our Expert

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.
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