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R Programming

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Why You Choose R Programming For Data Analysis and Modeling?

If you want to choose one from Python & R to handle data science and machine learning task. Both language are has own strength and importance, and both are great choice for data science and machine learning, R has some unique strength and features so it important for data scientist:

  • R is built for statistics

  • R is a popular language for data science at top tech firms

  • Support Amazing packages

  • Inclusive, growing community of data scientists and statisticians.

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R Programming Assignment Help | R Homework Help

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How to learn Data Science Using R

  1. Get a hold over the language to be used for implementation – practice the basics till you are comfortable.

  2. Practice and become comfortable with data cleaning and processing – this is essential. You need to know about your data well before analyzing it.

  3. Understand and implement one algorithm at a time. You may not understand it completely in the beginning. Give it time. Do not get stuck at one place. Try something else and come back later. Get the intuition of what is going on behind the few lines of code written to implement it. With practice things will keep getting clearer. Keep reading about it from multiple sources.

  4. Go through the quick learning videos from YouTube or other online resources.

  5. Join or purchase any online courses that available in reasonable price, like Edvancer, Eduraka, or get materials from our team. We are also providing good materials that help to learn basic to advance data science. You can contact us at realcode4you@gmail.com or call us at given number(Contact details on website menu).

  6. Make extensive notes when trying to understand / watching videos  – it helps with internalizing the information as well as with the review.

  7. Understand limitations of each algorithm, if any.

  8. Understand the usual application areas of each of the algorithms and why are they used there.

  9. Try understanding how these algorithms differ from each other. Using a single problem statement and solving it using different applicable algorithms should help here.

  10. Remember, the algorithms are just tools to solve problems. Don’t lose sight of the main problem statement during implementation.

  11. Many times, simple implementations are good enough. Build a simple solution first quickly and then iterate - you may want to try different features, tuning the parameters and hyperparameters, different algorithms, stacking different algorithms together and so on. Make sure you try one thing at a time and not everything together since you would want to know what change made the algorithm(s) better or worse.

  12. Explain what you have done to one person who knows about the algorithms in technical terms and to another who does not know the algorithms per se but can follow the problem and its solution logically. Gaps in understanding are best understood when explaining to others.

  13. Learning is an iterative process – your first implementation may not be the best. It can be made better over time. Please be patient.

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.

R Programming Research Paper Writing Help: Format that followed by Experts

Abstract—These instructions give you guidelines for preparing papers for your research project. Use this document as a template if you are using Microsoft Word or later. Paper titles should be written in uppercase and lowercase letters, not all uppercase. Full names of authors are preferred in the author field. Put a space between authors’ initials. Do not cite references in the abstract. Do not delete the blank line immediately above the abstract; it sets the footnote at the bottom of this column.
Keywords—Enter key words or phrases in alphabetical order, separated by commas.

I. INTRODUCTION

Your should place your introduction in this section.

II. LITERATURE REVIEW

Type your literature review in this section. Please define abbreviations and acronyms the first time they are used in the text, even after they have already been defined in the abstract.

III. METHODOLOGY

Type your methodology here.

IV. EMPIRICAL RESULTS AND ANALYSIS

Present and analyze your empirical results here.

V. CONCLUSION

A conclusion section is required. Although a conclusion may review the main points of the paper, do not replicate the abstract as the conclusion. A conclusion might elaborate on the importance of the work or suggest applications and extensions.

 

APPENDIX

Appendixes, if needed, appear before the acknowledgment.

ACKNOWLEDGMENT

Here you can give special thanks to those who assisted you.

 

REFERENCES

All references should be cited in text and listed below. Please use the Harvard, Chicago or APA referencing styles.

WE ARE EXPERTISE IN BELOW TYPES OF DATA VISUALIZATION USING

R PROGRAMMING

Below the list of python machine learning visualizations in which you can also get help to analyze the data. It makes easy to understand the data for any non technical persons. There are many types of visualizations used in data science and machine learning

Below the list of machine learning visualizations in which you can also get help to analyze the data. It makes easy to understand the data for any non technical persons. There are many types of visualizations used in data science and machine learning:

  • Parallel Coordinates chart: Parallel coordinates is a visualization technique used to plot individual data elements across many performance measures. Each of the measures corresponds to a vertical axis and each data element is displayed as a series of connected points along the measure/axes.

  • Density Plot: Density Plot is a type of data visualization tool. It is a variation of the histogram that uses ‘kernel smoothing’ while plotting the values. It is a continuous and smooth version of a histogram inferred from a data.

  • Column Chart: A column chart is a data visualization where each category is represented by a rectangle, with the height of the rectangle being proportional to the values being plotted. Column charts are also known as vertical bar charts.

  • Bar Graph: A bar plot or bar chart is a graph that represents the category of data with rectangular bars with lengths and heights that is proportional to the values which they represent. The bar plots can be plotted horizontally or vertically

  • Stacked Bar Graph: A stacked bar chart is also known as a stacked bar graph. It is a graph that is used to compare parts of a whole. In a stacked bar chart each bar represents the whole, and the segments or parts in the bar represent categories of that whole

  • Grouped Bar Chart: A grouped barplot is used when you have several groups, and subgroups of these groups. The example in this post shows how to build a grouped barplor using the bar() function of matplotlib library.

  • Area Chart: An area chart or area graph displays graphically quantitative data. It is based on the line chart. The area between axis and line are commonly emphasized with colors, textures and hatchings. Commonly one compares two or more quantities with an area chart.

  • Dual Axis Chart: A dual axis chart (also called a multiple axes chart) uses two axes to easily illustrate the relationships between two variables with different magnitudes and scales of measurement. The relationship between two variables is referred to as correlation.

  • Line Graph: A line chart or line plot or line graph or curve chart is a type of chart which displays information as a series of data points called 'markers' connected by straight line segments. It is a basic type of chart common in many fields. 

  • Candle set chart: A typical candlestick chart is composed of a series of bars, known as candles, which vary in height and color. Candlestick charts are one of the most popular chart types for day traders. Learn how to read these charts and apply them to your trading

  • Box and whisker plot: A Box and Whisker Plot (or Box Plot) is a convenient way of visually displaying the data distribution through their quartiles.

  • Mekko Chart: A Mekko chart (sometimes also called marimekko chart) is a two-dimensional stacked chart. In addition to the varying segment heights of a regular stacked chart, a Mekko chart also has varying column widths. Column widths are scaled such that the total width matches the desired chart width. 

  • Pie Chart: A pie chart, sometimes called a circle chart, is a way of summarizing a set of nominal data or displaying the different values of a given variable (e.g. percentage distribution).

  • Bubble Chart: Bubble chart displaying the relationship between poverty and violent and property crime rates by state. Larger bubbles indicate higher percentage of state residents at or below the poverty level. 

  • Scatter Plot Chart: A scatter plot (aka scatter chart, scatter graph) uses dots to represent values for two different numeric variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point.

  • Grouped Scatter Chart: Display scatter plot of two variables. Adding a grouping variable to the scatter plot is possible. In this we group more than two variable that called group scatter chart.

  • Scatter Plot Matrix: A scatter plot matrix is a grid (or matrix) of scatter plots used to visualize bivariate relationships between combinations of variables. Each scatter plot in the matrix visualizes the relationship between a pair of variables, allowing many relationships to be explored in one chart.

  • Radar Chart: A radar chart is a way of showing multiple data points and the variation between them. They are often useful for comparing the points of two or more different data sets.

  • Radial Bar Chart: A Radial/Circular bar chart is a bar chart displayed on a polar coordinate system. The difference between radial column chart is that base axis of series is y axis of a radar chart making columns circular. You can easily adjust start/end angles of a chart by setting startAngle and endAngle of your RadarChart component.

  • Donut chart: A donut chart is essentially a Pie Chart with an area of the centre cut out. A donut chart (also spelled doughnut) is functionally identical to a pie chart, with the exception of a blank center and the ability to support multiple statistics at once.

  • Bullet Graph: A bullet graph is a variation of a bar graph developed to replace dashboard gauges and meters. A bullet graph is useful for comparing the performance of a primary measure to one or more other measures

  • Funnel Chart: A funnel chart is a specialized chart type that demonstrates the flow of users through a business or sales process. Funnel charts show values across multiple stages in a process. For example, you could use a funnel chart to show the number of sales prospects at each stage.

  • TreeMap: A treemap chart provides a hierarchical view of your data and makes it easy to spot patterns, such as which items are a store's best sellers. Treemapping is a data visualization technique that is used to display hierarchical data using nested rectangles;

  • Dendo gram: A dendrogram (or tree diagram) is a network structure. It is constituted of a root node that gives birth to several nodes connected by edges or branches.

  • Heat Map: A heat map is a two-dimensional representation of data in which values are represented by colors. A simple heat map provides an immediate visual summary of information. More elaborate heat maps allow the viewer to understand complex data sets.

  • Violin Chart: A violin plot is a method of plotting numeric data. It is similar to a box plot, with the addition of a rotated kernel density plot on each side.

  • Area graph: An area chart or area graph displays graphically quantitative data. It is based on the line chart. The area between axis and line are commonly emphasized with colors, textures and hatchings. Commonly one compares two or more quantities with an area chart.

  • stacked Area graph: Stacked Area Graphs work in the same way as simple Area Graphs do, except for the use of multiple data series that start each point from the point left by the previous data series. 

Best Machine Learning Sample Projects 2023

Project 1(OpenCV): Domain- Entertainment

Company X owns a movie application and repository which caters movie streaming to millions of users who on subscription basis. Company wants to automate the process of cast and crew information in each scene from a movie such that when a user pauses on the movie and clicks on cast information button, the app will show details of the actor in the scene. Company has an in-house computer vision and multimedia experts who need to detect faces from screen shots from the movie scene. The data labelling is already done. Since there higher time complexity is involved in the

OpenCV Assignment Help, Face Detection Using OpenCV.jpg

Project 2: Statistical Analysis to Reducing Gender Inequality in Wages and Employment

Germany’s government is interested in reducing gender inequality, especially gender wage gaps and gender gaps in employment. They are considering the introduction of a set of
policies that incentivize firms to shrink gender inequality in working conditions. First, the policy forces firms to internally publish salaries of all workers, so discrepancies in salaries can be detected by workers themselves. Second, firms are incentivized to encourage salary negotiations. Third, firms are incentivized to offer childcare where needed for their employees to fulfill their duties.
The government has been made aware that, in parts of the United States, exactly these policies have been introduced and now asks you to evaluate the effectiveness of these policies in reducing gender inequality in wages and employment. The dataset genderinequality (provided in RData and csv formats) contains data on individuals in the U.S., some working in firms to which the new policies apply (these are the treated workers) and some working at firms to which the new policies do not apply (these are the untreated workers). The policies have been introduced in 2007 and we have panel data on workers for the years 2005 and 2010, i.e. before and after the introduction of the new policies.

Statistical Analysis to Reducing Gender Inequality in Wages and Employment.jpg

Project 3: ReneWind

Renewable energy sources play an important role in the global energy mix, as the effort to reduce the environmental impact of energy production increases. Wind energy is one of the most developed technologies worldwide and the U.S Department of Energy has put together a guide to achieving operational efficiency using predictive maintenance practices. Predictive maintenance means failure patterns are predictable and if component failure can be predicted accurately and the component is replaced before it fails, the costs of operation and maintenance will be much lower. ReneWind is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors. ReneWind is a company working on improving the machinery/processes involved in the production of wind energy using machine learning and has collected data of generator failure of wind turbines using sensors.

Hire Machine Learning Expert, Get Help In Machine Learning Assignment, Best Machine learni

Project 4: TRAIN&AHEAD PROJECT

Trade&Ahead is a financial consultancy firm who provide their customers with personalized investment strategies. They have hired you as a Data Scientist and provided you with data comprising stock price and some financial indicators for a few companies listed under the New York Stock Exchange. They have assigned you the tasks of analyzing the data, grouping the stocks based on the attributes provided, and sharing insights about the characteristics of each group. 

Tread & Ahead.jpg

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