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R Programming Visualization Help

At Realcode4you you can get all R programming Visualization Related Help, you can help in R programming Visualization Assignment help, R Programming Visualization Homework Help, R Programming Visualization Coursework Help, etc. 

Visualization techniques used to deliver insights in data using visual cues such as graphs, charts, maps, and many others. The popular data visualization tools that are available are Tableau, Plotly, R, Google Charts, Infogram, and Kibana.

R is a language that is designed for statistical computing, graphical data analysis, and scientific research. It support many visualization libraries. Top R Libraries for Data Visualization that are commonly used these days. There many R programming visualization libraries; ggplot2, Plotly, Lattice, Dygraphs, RGLLeaflet

There are many types of R Programming Graphs or Plot in which you can get Assignment Help, Project Help, Homework Help and coursework Help: Bar Plot, Histogram, Scatter Plot, Line Plot, Pie Chart, Time Series Analysis, Box Plot, etc.

Hire Expert To Get Help In Time Series Analysis

Time Series in R is used to see how an object behaves over a period of time. In R, it can be easily done by ts() function with some parameters. Time series takes the data vector and each data is connected with timestamp value as given by the user.

Syntax:

objectName <- ts(data, start, end, frequency)

 

At Realcode4you you can get all types of time series analysis help like: 

Univariate Analysis: Univariate analysis is the technique of comparing and analyzing the dependency of a single predictor and a response variable. The prefix "uni" means one, emphasizing the fact that the analysis only accounts for one variable's effect on a dependent variable. In simple way we can say analysis using single variable or visualization using single variable called univariate analysis. 

Multivariate Analysis: Multivariate analysis deals with the statistical analysis of data collected on more than one dependent variable. Multivariate techniques are popular because they help organizations to turn data into knowledge and thereby improve their decision making.

Forecasting: Time series forecasting occurs when you make scientific predictions based on historical time stamped data. It involves building models through historical analysis and using them to make observations and drive future strategic decision-making.

Hire Expert To Get Help In Classification Algorithms

The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups.

There are many types of Classification Techniques:

  • Decision Trees – These are organised in the form of sets of questions and answers in the tree structure.

  • Naive Bayes Classifiers – A probabilistic machine learning model that is used for classification.

  • K-NN Classifiers – Based on the similarity measures like distance, it classifies new cases.

  • Support Vector Machines – It is a non-probabilistic binary linear classifier that builds a model to classify a case into one of the two categories.

If you need any help of the above ML Algorithms then hire expert to get instant help. Here you can get all supervised learning algorithms related help without any plagiarism issue.

Hire Expert To Get Help In Regression Algorithms

Regression analysis is a group of statistical processes used in R programming and statistics to determine the relationship between dataset variables. Generally, regression analysis is used to determine the relationship between the dependent and independent variables of the dataset.

Types of Regression 

Linear Regression: A linear regression is a statistical model that analyzes the relationship between a response variable (often called y) and one or more variables and their interactions (often called x or explanatory variables). or Linear Regression model is one of the widely used among three of the regression types. In linear regression, the relationship is estimated between two variables i.e., one response variable and one predictor variable. Linear regression produces a straight line on the graph.

Logistic Regression: Logistic Regression is another widely used regression analysis technique and predicts the value with a range. Moreover, it is used for predicting the values for categorical data.

Multiple regression: Multiple regression is another type of regression analysis technique that is an extension of the linear regression model as it uses more than one predictor variables to create the model. 

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If you need help Research Paper Implementation & Report Writing then here you can get professional or researchers which delivered more then 2000+ Papers and have good idea to writing research paper and report Writing. Expert follow all standards and procedure which used to writing the report. Here you can also get help in Master Thesis Project and papers. Research Paper Writing is also important part for which pursuing Master Degree or PHD from top institution. 

Get Help In R Programming Libraries Related Project

R Programming support large number of in-built libraries which make coding easy. Below the some useful R programming libraries which play vital role in recent time:

Dplyr: dplyr is a grammar of data manipulation, providing a consistent set of verbs that help you solve the most common data manipulation challenges; mutate()select()filter()summarise()arrange().

Ggplot2: ggplot2 is a plotting package that provides helpful commands to create complex plots from data in a data frame. It provides a more programmatic interface for specifying what variables to plot, how they are displayed, and general visual properties.

Esquisse: One of the most important packages in R is the Esquisse package. Esquisse package helps to explore and visualize your data interactively.

Lubridate: Lubridate is an R package that makes it easier to work with dates and times. Below is a concise tour of some of the things lubridate can do for you.

tidyverse: The tidyverse is a set of packages that work in harmony because they share common data representations and API design. The tidyverse package is designed to make it easy to install and load core packages from the tidyverse in a single command

And More others

 

If you face any issue any of the above R programming libraries then our expert ready to help you. Hire R programming expert to get help in R programming related task.

Power BI Visualization Assignment Help Using R Programming

Power BI is a Data Visualization and Business Intelligence tool that converts data from different data sources to interactive dashboards and BI reports. Power BI suite provides multiple software, connector, and services - Power BI desktop, Power BI service based on Saas, and mobile Power BI apps available for different platforms. Here we have providing the visualization using Power BI with the help of R programming. If you need attractive visualization as per your need and requirement then here realcode4you expert and get instant help.

Application Of R Programming

There are many applications of R Programming:

Finance: R provides an advanced statistical suite that is able to carry out all the necessary financial tasks. Finance industries are also provide the time-series statistical processes of R. It also provides facilities for financial data mining through its packages like quantmod, pdfetch, TFX, pwt, etc. R makes it easy for you to extract data from online assets. With the help of RShiny, you can also demonstrate your financial products through vivid and engaging visualizations.

Banking: Bank of America makes use of R for financial reporting. With the help of R, the data scientists at BOA are able to analyze financial losses and make use of R’s visualization tools.

Healthcare: R is most widely used for performing pre-clinical trials and analyzing the drug-safety data. It also provides a suite for performing exploratory data analysis and vivid visualization tools to its users.

Social Media: R is used for social media analytics, for segmenting potential customers and targeting them for selling your products. mining user sentiment is another popular category in social media analytics. With the help of R, companies are able to model statistical tools that analyze user sentiments, allowing them to improve their experiences.

E-Commerce: E-commerce industry is one of the most important and useful sectors that utilize Data Science. R is one of the standard tools that is being used in e-commerce. Various statistical procedures like linear modeling are necessary to analyze the purchases made by the customers as well as in predicting product sales.

Manufacturing: Some Manufacturing like Ford, Modelez, and John Deere use R to analyze customer sentiment. This helps them optimize their product according to trending consumer interests and also to match their production volume to varying market demand.

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R Programming Services

Realcode4you has rich experience in developing dynamic websites, custom web applications and Desktop Applications in R, Machine Learning, Python. we  are a specialist in Small-Team of Programming Expert and Software Development . So if you are company looking to create highly flexible & fast to market products OR looking for better integration of your existing technology, you should consider R programming to develop AI based applications. In order to successfully implement your product development strategy, you may also need an experienced R Programming Partner who understands nitty gritties of R and can add value to design, development, deployment & support of your product. 

With a dedicated team of R Programming experts, Realcode4you delivers world class solutions, BI & analytics services. Realcode4you is well known consulting company for wide range of customers in domain of Ecommerce, Automobile, Healthcare, Media & Publishing, Consumer services & BFSI sector. Realcode4you also provides Python web development services including Python apps development, Python programming, Python integration framework, designing, etc.

 

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R Programming Sample Code

---
title: "Analysis of life in the Good Place"
author: "Eleanor Shellstrop"
date: "July 1, 2021"
mainfont: "Source Sans Pro"
monofont: "Consolas"
fontsize: "11pt"

 

output: 
  pdf_document: 
    dev: cairo_pdf
    highlight: tango
    latex_engine: xelatex
  html_document: 
    fig_caption: yes
---

# Executive summary

Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt. Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur? Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur?


# Data background

Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.


# Data cleaning

Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

```{r load-libraries-data, warning=FALSE, message=FALSE}
library(tidyverse)
library(forcats)  # For factors
library(scales)  # For nicer scales

set.seed(1234)  # Make all random draws the same
example_data <- data_frame(x1 = rnorm(10000),
                           x2 = rnorm(10000),
                           y1 = sample(1:100, 10000, replace = TRUE),
                           y2 = sample(LETTERS[1:4], 10000, replace = TRUE),
                           y3 = sample(LETTERS[10:11], 10000, replace = TRUE),
                           year = sample(2010:2017, 10000, replace = TRUE)) %>%
  arrange(y2, year)

# write_csv(example_data, "data/example_data.csv")
```

To make life easier, I created a custom ggplot theme that I can use in all my figures:

```{r create-theme}
my_beautiful_fancy_theme <- theme_minimal(base_family = "Source Sans Pro") +
  theme(legend.position = "bottom",
        panel.background = element_rect(fill = "transparent", colour = NA),
        plot.background = element_rect(fill = "transparent", colour = NA),
        axis.title.x = element_text(margin = margin(t = 15)),
        axis.title.y = element_text(margin = margin(r = 15)),
        strip.text = element_text(family = "Source Sans Pro", face = "bold",
                                  size = rel(1.3)))
```


# Individual figures

## Figure 1: Lollipop chart

First, I was interested in blah because blah, so I created a lollipop chart to show blah. Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

```{r lollipop, fig.height=4, fig.width=6}
example_data_summarized <- example_data %>%
  group_by(y2, y3) %>%
  summarize(n = n())

figure1 <- ggplot(example_data_summarized, aes(x = n, y = fct_rev(y2), color = y3)) +
  geom_pointrange(aes(xmin = 0, xmax = n), position = position_dodge(width = 0.5),
                  size = 1, fatten = 5) +
  labs(x = "Total number of things", y = NULL) +
  guides(color = guide_legend(title = NULL)) +
  scale_color_manual(values = c("#FF4266", "#82B09C")) +
  my_beautiful_fancy_theme + 
  theme(panel.grid.minor = element_blank(),
        panel.grid.major.y = element_blank())

figure1

ggsave(figure1, filename = "output/figure1.pdf", device = cairo_pdf,
       width = 6, height = 4, units = "in", bg = "transparent")
```


## Figure 2: Changes over time

Next, I wanted to see how things have changed over time, so I created a blah because blah. I found blah. Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. 

```{r change-over-time, fig.width=10, fig.height=3}
example_data_time <- example_data %>%
  pivot_longer(cols = c(x1, x2), names_to = "x_names", values_to = "value") %>%
  group_by(x_names, year, y2) %>%
  summarize(x_avg = mean(value),
            error = sd(value) / sqrt(length(value))) %>%
  ungroup() %>%
  mutate(upper = x_avg + (1.96 * error),
         lower = x_avg - (1.96 * error)) %>%
  mutate(x_names = recode(x_names, 
                          x1 = "X1 (average)",
                          x2 = "X2 (average)"))

figure2 <- ggplot(example_data_time, aes(x = year, y = x_avg, color = x_names)) +
  geom_hline(yintercept = 0, size = 0.75, color = "#CC3340", linetype = "dotted") +
  geom_ribbon(aes(ymin = lower, ymax = upper, fill = x_names, color = NULL), alpha = 0.2) +
  geom_line(size = 1) + 
  scale_color_manual(values = c("#FA6900", "#69D1E8")) + 
  scale_y_continuous(labels = percent) +
  guides(color = guide_legend(title = NULL), fill = "none") +
  labs(x = NULL, y = "Whatever this is measuring") +
  facet_wrap(~ y2, nrow = 1) + 
  my_beautiful_fancy_theme + 
  theme(panel.grid.minor = element_blank())

figure2

ggsave(figure2, filename = "output/figure2.pdf", device = cairo_pdf,
       width = 16, height = 3, units = "in", bg = "transparent")
```


## Figure 3: Relationships

I was also interested in the relationship between blah and blah, so I blahed. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

```{r relationships, fig.width=6, fig.height=4}
 

# There are a lot of points here and they're all random and pointless, so I
# simplify this graphic by just taking a subset of them

example_data_subset <- example_data %>%
  sample_n(500)
 
figure3 <- ggplot(example_data_subset, aes(x = y1, y = x2, color = y2)) +
  geom_point(size = 1, alpha = 0.75) + 
  geom_smooth(method = "lm", color = "#85144A", size = 2) +
  labs(x = "Some variable", y = "Some other variable") +
  guides(color = guide_legend(title = NULL)) +
  scale_color_manual(values = c("#188146", "#004259", "#B00DC9", "#FFE01C")) +
  facet_wrap(~ y3) +
  my_beautiful_fancy_theme + 
  theme(panel.grid.minor = element_blank())

figure3

ggsave(figure3, filename = "output/figure3.pdf", device = cairo_pdf,
       width = 6, height = 4, units = "in", bg = "transparent")
```


# Final figure

I took these three graphs and combined them and enhanced them in Illustrator. I chose the colors, fonts, alignment, etc. because blah and the final figure represents truth because of blah. 

![Final fancy visualization](final_graphics/final_graphic.pdf)

 

Output Final Graphics

 

 

 

 

 

 

 

 

 

 

 

 

 

 


 

final_graphic.png

What Is R Programming?

R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible.

Useful ML Algorithms in R

Linear Regression

lm() under the stats package is used for training a Linear Regression Model on Training Data in R. It models a linear relationship between features, X, and continuous target y.

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