R is a programming language designed by Ross Ihaka and Robert Gentleman in 1993. R possesses a comprehensive catalog of statistical and graphical methods. It includes machine learning algorithm, linear regression, time series, statistical inference to name a few. The majority of the R libraries are written in R, however for heavy computational task, C, C and Fortran codes are preferred.
R is not merely entrusted by academic, but some large companies also use R语言统计代写, including Uber, Google, Airbnb, Facebook etc.
Data analysis with R is done in a number of steps; programming, transforming, discovering, modeling and communicate the results
* Program: R is a clear and accessible programming tool
* Transform: R is comprised of a collection of libraries designed particularly for data science
* Discover: Investigate the info, refine your hypothesis and analyze them
* Model: R provides a wide array of tools to capture the right model for your data
* Communicate: Integrate codes, graphs, and outputs to your report with R Markdown or build Shiny apps to share with all the world
Data science is shaping just how companies run their businesses. Certainly, staying away from Artificial Intelligence and Machine will lead the company to fail. The big question is which tool/language should you use?
They are many tools you can find to do data analysis. Learning a new language requires a bit of time investment. The image below depicts the training curve when compared to business capability a language offers. The negative relationship implies that there is not any free lunch. If you wish to offer the best insight through the data, you will want to spend some time learning the appropriate tool, which can be R.
On the top left in the graph, you can see Excel and PowerBI. These two tools are pretty straight forward to understand but don’t offer outstanding business capability, particularly in term of modeling. At the center, you can see Python and SAS. SAS is a dedicated tool to perform a statistical analysis for business, but it is not free. SAS is a click and run software. Python, however, is a language having a monotonous learning curve. Python is a fantastic tool to deploy Machine Learning and AI but lacks communication features. Having an identical learning curve, R is a great trade-off between implementation and data analysis.
When it comes to data visualization (DataViz), you’d probably heard about Tableau. Tableau is, certainly, a great tool to learn patterns through graphs and charts. Besides, learning Tableau is not time-consuming. One serious issue with data visualization is that you might end up never finding a pattern or just create lots of useless charts. Tableau is an excellent tool for quick visualization in the data or Business Intelligence. With regards to statistics and decision-making tool, R is more appropriate.
Stack Overflow is a huge community for programming languages. For those who have a coding issue or need to understand one, Stack Overflow has arrived to aid. On the year, the percentage of question-views has grown sharply for R when compared to the other languages. This trend is of course highly correlated with all the booming age of data science but, it reflects the need for R language for data science. In data science, there are 2 tools competing together. R and Python are some of the programming language that defines data science.
Is R difficult? Years back, R was a difficult language to learn. The language was confusing rather than as structured as the other programming tools. To get over this major issue, Hadley Wickham developed a collection of packages called tidyverse. The rule of the game changed to get the best. Data manipulation become trivial and intuitive. Making a graph was not so hard anymore.
The best algorithms for machine learning can be implemented with R. Packages like Keras and TensorFlow allow to produce high-end machine learning technique. R also has a package to execute Xgboost, one the most effective algorithm for Kaggle competition.
R can communicate with another language. It is actually possible to call Python, Java, C in R. The rhibij of big information is also available to R. You can connect R with different databases like Spark or Hadoop.
Finally, R has changed and allowed parallelizing operation to accelerate the computation. In fact, R was criticized for utilizing just one single CPU at the same time. The parallel package lets you to execute tasks in different cores of the machine.