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