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Which is better for data analysis: R or Python? Is R still a better data analysis language than Python? Has anyone else used Python with Pandas, to a large extent, in data analysis projects?
The vast majority of people who answer this question will do so out of bias, not fact.  (And in turn, the bias comes from which language one learns first.)  This is true whether they answer R or Python.  I hope I am reasonably neutral, having written a book on R and a 151-page tutorial on Python.  I will come to R's defense here, though, because even those who said nice things about R made incorrect statements, in my opinion.For those who object to R on "computer science" grounds, I would note the following:R is object-oriented.  Functions are first-class objects, and can be assigned, modified etc.  You actually have a choice of three levels of OOP.R is a functional programming language i.e. (almost) no side effects.Operator overloading is much easier in R than in Python.Python has cleaner syntax than R, but not THAT much cleaner. Really, in terms of syntax, they are quite similar, basically both of them being C-family languages.Parallelism in R has been much further developed than in Python.Using Rcpp, interfacing R to C/C++ is much easier than interfacing Python to C/C++.In my experience, R is easier than Python for "data munging," taming bad or irregular data, transforming data, filtering data, etc.  If you add NumPy in your definition of Python, that brings the two closer, but if you then bring in R packages such as plyr and data.table, things strongly tip in R's favor.  By the way, data.table is blinding fast.These days, I do a lot of tasks---nonnumeric tasks, e.g. text processing---in R that I used to do in Python.  I'm not saying they are easier in R; the coding effort is about the same, but it's easier for me not to keep switching languages.As noted, there are over 5,000 packages available for R.  For example, when I needed a fast nearest-neighbor function, I went to the R package repository, CRAN, and found that not only was there one there, there were two to choose from.  When I needed code to find distances from rows of one matrix to rows of another, again it was right there on CRAN.Really, you should just program in whatever language you are most comfortable with.  But don't write off one simply because you first learned the other.
What are some good resources for learning R?
There are online courses that might get you diving into real-world examples with this great open source language straight away:EdX The Analytics Edge.  It doesn't cover all theoretical background extensively but is great to get hands-on experience on real datasets with R as the programming language. Other free online education options, but I don't know the quality of  those ones e.g. https://www.edx.org/course/kix/k... or Coursera's R Programming course.Kaggle Competitions e.g. the Titanic dataset (Titanic: Machine Learning from Disaster).  Some people documented their workflow for this problem set in R. You can first try  it in Excel, to get a feel for the dataset and then proceed to R.Everyone has their own way of learning, the way I learn might not be productive for you, but here is how I would approach it (from retrospective).Summary:pick a resource/book: e.g. http://cran.r-project.org/doc/ma... in order to understand the basic syntax (language)pick a dataset (your own, one from the R base package...) comment: if your own data isn't preprocessed/cleaned, you could encounter problems earlier on... but writing and running scripts mainly is about finding ways to solve a problem/debugging. I do believe however that you need some basics (along with some perseveration) to be able to solve the encountered problemsunderstand the dataset: what are the variables in my dataset? what are the types of variables? how is the data represented in my code (as a data.frame, list...)? Using the function str or class is very useful in this regard. Do I need to change some things about my dataset to make it more accessible (e.g. providing labels, variable names, converting to another data type...)understand an existing dataset by knowing how it can be regenerated in R (dput -function)apply the concepts in the book/resource to the dataset; what does the command do? How is the data returned?make it interactive: can I change some things in the arguments provided and how do those changes affect the outcome (e.g. data[,1:2] versus data[,1:4]); can I find out alternative ways to obtain the same goal  (e.g. data[,1:2] versus data[,c('columnname1','columnname2')])make it fun and try to relate to what you do as much as possible (e.g. by using datasets that interest you and trying to challenge yourself)! This is a bit linked to the making it interactive part.learn how to approach/solve problems you encounter; simplify/dissect a problem; write down steps in words in order to understand what you want as output and how you think you can obtain such output from the input at handdon't hesitate in asking help. R has a great user community, you can e.g. use the r mailing list (https://stat.ethz.ch/mailman/lis...). I also use the R-Talkstats forum (www.talkstats.com) learning R is also learning how to take advantage of all the resources available (but don't let them overwhelm you and don't make them distract you from really actively using/doing R). I learned a lot by browsing Quick-R (http://www.statmethods.net/), stackoverflow (http://stackoverflow.com/questio...), R-bloggers (http://www.r-bloggers.com/), R-journal (http://journal.r-project.org/cur...), R data mining (RDataMining.com: R and Data Mining)...use the R reference card for a nice overview of some basic functions http://cran.r-project.org/doc/co...use an alternative R editor/environment that "pleases" the eye, or Rstudio's IDE (an IDE is an environment which combines/Integrates all the R components, i.e. editor, workspace, command prompt...; http://rstudio.org/)persevereExtended version...First of all... a good way to learn is indeed by using your own dataset. The downside is... it can be quite discouraging... from importing/reading your data files to doing your first analyses and plotting results. For me R wasn't an easy language to learn... Challenging myself was what made it so fun... going from print('damn... another error message...')torep(print('yes... in your face R, I solved it!!!'), times = 10) #no offence intended, I love you, now give me some significant p's!!!I think some important things to remember when learning R...it has a great community with a lot of helpful resources (e.g. mailing list: http://tolstoy.newcastle.edu.au/R/, manuals: http://cran.r-project.org/doc/ma..., http://cran.r-project.org/doc/ma..., other: http://www.statmethods.net/, http://www.r-bloggers.com/); take advantage of it (but don't abuse it)!you can find a lot of info in the doc files of R itself; using?functionname (e.g.?substring), has been priceless; but!! take the time to carefully read the doc-files; especially the arguments to prto your function and the values that the function returns (what are the "variable types" that you pras arguments, what are the "variables types" that are returned, is the data coerced in other "variable types")as you can derive from the above point... there will always be some basic syntax understanding that is needed (so use the resources that are available, even if it is only the introductory chapters). E.g. what is a function, what different variable types exist -- lists, vectors, matrices, data frames... how to access an element in a list versus how to access it in a data frame...there are a lot of alternative ways to reach your goal: e.g. getting help?substring or help(substring); from pre-existing functions to writing your own functions (print('damn, I just spent 4 hours on writing a function, that I would have found in 10 minutes if I wouldn't have been too lazy reading an introductory R chapter'); data[1] versus data[,'columnname1']don't like reading help files on a function and just interested in the application? Use example(functionname) in the command prompt (e.g. example(mean) and try figuring out what is happening to the data in the application. Be warned however... some example can be quite advanced. You can also use this example-function on datasets, which mostly result in some data visualization examples of the example data at hand, e.g. example(mtcars) Try figuring out what arguments are provided to result in such a plot, play around with the arguments yourself and see how they change the resulting plot...write down what you start with (use as input for a function) and what you want to end up with (output)... I find it helpful when writing down what I want my script to do (so the between steps between input-output); I first write comment code and try to formulate ideas in a stepwise manner (what is the 1st, 2nd, 3rd... step that needs to be done; algorithm).evaluate error/warning messages: what does the error state? can you make sense of the error? what part of the code/function might have generated the error?try to visualize data in an early stage! Visualizing data makes it more accessible/understandable as well as more fun to work with. Plotting is mostly a chapter that is covered later, but I would recommend playing around with the variables in a plot in an early stage. If you don't have any ad hoc hypotheses, try to derive some hypotheses based on those plots and find out what analyses you could use to test those hypotheses. When you figure out which analyses you want to try out, look into the specific chapter of interest, or search for more information in the help pages.Side notes...many resources don't make it easier to learn... searching for a resource that fits your learning needs, can be quite tedious and discouraging in itself, maybe even resulting in you not installing the software. Pick a book/resource and stick to it, there's no such thing as a perfect resource, just like there isn't such a thing as a perfect way to learn R... But almost every book/resource will have the basic information to get you started. if you're into "linear" learning and like to read through scripts that you haven't written yourself and datasets you don't completely relate to, go for it. If not... stick to the basics and process to the advanced by experimenting with your own dataset. If you don't have your own dataset, a nice way to begin is by exploring datasets that are already in the "base" package of R. You can get a list of these when entering data in the command prompt. Subsequently you can get more info on a dataset by typing?datasetname in the command prompt (e.g.?mtcars). Another option is finding some open source datasets that interest you and start out with those Where can I find large datasets open to the public?, http://www.rdatamining.com/it's not only about learning a programming language, it's also about learning to understand data and understanding data in the first place can make it easier to learn the programming language... e.g. what I found useful was the str -function... there are existing datasets in R... e.g. mtcars which is a data.frame object (just type mtcars in the command prompt) and airmiles which is a time series object. By using str you can find out about the structure of your data file (in addition to just opening your data file), and by using dput you can find out which R-code can be used to regenerate such datasets. The dput-function in addition is useful if you want someone else to be able to regenerate the data you are running your script on and make it easier for someone to help you fix your code/problem.I don't use Rstudio myself, but I will be trying it out since it looks like a nice integrated way to get easy access to all things you need when using R (easy access to workspace, command prompt, editor, help files...) and as a beginner could really be quite helpful and ease the learning process; in addition I read you can also integrate Rstudio with a Version Control System (although that's a whole new topic). Anyways... when you spend a lot of time programming and figuring things out, it's nice to have an interface that "pleases" the eye and keeps you motivated in using it (although this of course is something subjective). In the pictures you can see my personalised version of an R editor (in Textwrangler; http://www.barebones.com/product...) as compared to R's editor (in addition you can also personalise your R Console)
What are the coolest R packages? Why?
These are may favourite packages of R, which I used frequently. The best thing about R packages, which I like most they increase the power of R by enhancing the existing base R functionalities.List of top R Packages-infer - An R package for tidyverse-friendly statistical inferencejanitor - Simple tools for data cleaning in REsquisse - R studio add-in to make plots with ggplot2DataExplorer - Automate data exploration and treatmentSparklyr - R interface for Apache SparkDrake - An R-focused pipeline toolkit for reproducibility and high-performance computingggplot2 - An Implementation of the Grammer of Graphics.DALEX - Descriptive mAchine Learning Explanation.R Packages Popular in 2018-r2d3- R interface to D3 Visualizationspromises - Abstraction for promise-based asynchronous programming.tinytex - A lightweight and easy to maintain LaTaX distributionList of awesome packages that will changes the way you use R-magrittrpipeR - Multi-paradigm piprline Implementations.lambda.r - Functional programming and simple pattern matching in RData Manipulation Packages-dplyr - Fast data frames manipulation and database queryreshape2- Flexible rearrange, reshape and aggregate data.broom - Convert statistical analysis objects into tidy data frames.stringi - ICU based string processing packages.Graphics Displays-ggfortify - A unified interface to ggplot2 popular statistical packages using one line of code.lattice - A powerful and elegant high-level data visualization system.rgl - 3D visualization device system for Rr2d3 - R interface to D3 Visualization.HTML Widgets-DiagrammerR- Create JS graphc diagrams and flowcharts in R.formattable - Formattable Data structures.plotly - Interactive ggplot2 and Shiny plotting with plot.ly.Web Technologies List-shiny - Easy interative web applications with ROpenCPU - HTTP API for Rhttr - User-friendly RCurl wrapper.Parallel Computing-foreach - Executing the loop in parallel.SparkR - R fronyend for Spark.spaklyr - R interface for Apache Spark from RStudio.These are top and coolest R packages in each domain. Learn here to Install & Use the Packages in R.Upvote, if you like it.A Readers Appreciations Means alot to a Writer. So, don’t forget to upvote.
Is Python better than R?
Some real important differences to consider when you are choosing R or Python over one another:Machine Learning has 2 phases. Model Building and Prediction phase. Typically, model building is performed as a batch process and predictions are done realtime. The model building process is a compute-intensive process while the prediction happens in a jiffy. Therefore, the performance of an algorithm in Python or R doesn't really affect the turn-around time of the user. Python 1, R 1.Production: The real difference between Python and R comes in being production-ready. Python, as such is a full-fledged programming language and many organizations use it in their production systems. R is a statistical programming software favored by many academia and due to the rise in data science and availability of libraries and being open-source, the industry has started using R. Many of these organizations have their production systems either in Java, C++, C#, Python, etc. So, ideally, they would like to have the prediction system in the same language to reduce the latency and maintenance issues. Python 2, R 1.Libraries: Both languages have enormous and reliable libraries. R has over 5000 libraries catering to many domains while Python has some incredible packages like Pandas, NumPy, SciPy, Scikit Learn, Matplotlib. Python 3, R 2.Development: Both the language are interpreted languages. Many say that python is easy to learn, it's almost like reading English (to put it on a lighter note) but R requires more initial studying effort. Also, both of them have good IDEs (Spyder, etc for Python and RStudio for R). Python 4, R 2.Speed: R software initially had problems with large computations (say, like n by n matrix multiplications). But, this issue is addressed with the introduction of R by Revolution Analytics. They have re-written computation-intensive operations in C which is blazingly fast. Python being a high-level language is relatively slow. Python 4, R 3.Visualizations: In data science, we frequently tend to plot data to showcase patterns to users. Therefore, visualizations become an important criterion in choosing software and R completely kills Python in this regard. Thanks to Hadley Wickham for an incredible ggplot2 package. R wins hands down. Python 4, R 4.Dealing with Big Data: One of the constraints of R is it stores the data in system memory (RAM). So, RAM capacity becomes a constraint when you are handling Big Data. Python does well, but I would say, as both R and Python have HDFS connectors, leveraging Hadoop infrastructure would give substantial performance improvement. So, Python 5, R 5.So, both languages are equally good. Therefore, depending upon your domain and the place you work, you have to smartly choose the right language. The technology world usually prefers using a single language. Business users (marketing analytics, retail analytics) usually go with statistical programming languages like R, since they frequently do quick prototyping and build visualizations (which is faster done in R than Python).If you like my answer, please upvote :)
What is the name of a fruit that does not have an “E” or “R” in it?
So now this seems to be an interesting question which will require our knowledge.There are few fruits that does not have an “E” or “R” in it which are:1)AVOCADOS:It is a unique fruit which mainly contains carbohydrate and is high in fats.It is nutritious and loaded with fiber.2)BANANAS:Banana is high in calcium and is used in most of the things like bakery items, sweet recipes and milkshakes.It may vary in size and color but it’s texture remains same.3)DAMSON PLUM:Mostly people have not heard of this fruit but it is delicious in taste and is used for various culinary purposes like in jam or fruit preserves.It is very beneficial for our health.4)FIG:It is also known as “ANJEER” and has various health benefits like -treatment of indigestion, piles, diabetes, cough, bronchitis, and asthma.It has a unique and amazing taste.5)GUAVA:It has hard covering from outside with little seeds inside.It is very nutritious and healthy as it helps our body in controlling the blood sugar levels, regulating blood pressure and treating diarrhea.It further helps in strengthening the immune system and digestive system.6)JAMBOLAN:It is purple in color and has a big seed inside with the soft pulp.It is tasty, healthy and nutritious with many benefits.7)KIWI:Kiwi is a unique fruit which looks like a small coconut from outside but is soft so that makes it a different fruit.It has different taste and is very juicy.It is also very good for our skin.8)KUMQUAT:It has a similar look as that of orange but it is not.The texture is different and the benefits also.It has a juicy pulp.9)LONGAN:It belongs to a lychee family as you can see that it looks similar to it.It is small, round, sweet and juicy fruit which helps you gaining the glowing skin with other skin benefits as well.It is healthy for our body.10)LOQUAT:It has a number of health benefits which makes it a one of the healthiest fruit.It has numeral benefits which will make keep our body fit and healthy.11)MANGO:As we all know that mango is known as a king of fruits.It is yellow in color with soft pulp inside .It is delicious in taste and is used in ice-creams, milkshakes, drinks and bakery items.It is very healthy and is mostly available in summers therefore helping us to beat the heat.12)PAPAYA:It is mostly big in size with the tough coating from outside and sweet and soft from inside with small black seeds.It has a great taste which is also very nutritious and healthy for our body.13)VOAVANGA:It is generally referred as an unique fruit and mostly people don’t know about it.It has a very different texture as compared to other fruits and from inside also it is visible in an exclusive way.But as it is said that each and every fruit ha it’s own benefits so we should try and eat every possible fruit because they will keep our body fit and healthy.
Which is a better initiative to learn data science: Python or R?
“Let me tell you the most important thing before using any Programming Language.”Primary Need- What is Requirement?Secondary Need- Your Interest( or more comfortable with)Similar case with R and Python also. Both programming languages are best for the data science. We can’t say exactly which is better.Read more about R language.Now, I will let you know every concept one by one. Firstly I will pryou simple definition of R and PythonR- It’s an open source programming language. Also, available as Free Software under the terms of the Free Software Foundation’s GNU.One of the most important things about R is that it provides the best publication quality post.Learn why R is importantPython- It’s very simple language. That easily anyone can learn. One of the important thing about python is that it didn’t require too much time in investment. Also, its syntax is easily readable.Moreover, because of its simplicity, it is an ideal teaching language. Also, allows newcomers to pick it up quickly. As we have seen that developers spend their time in thinking about the problem they’re trying to solve, and less time thinking about language complexities.Here, I have link for the best books to R.Now, I will tell you what is the case with data science for R and python:Why is Python great for data science?It was released in 1989.IPython / Jupiter’s notebook IDE is excellent.There’s a very large ecosystem for python.Why is R great for data science?It was created after python in 1992.In this programming language, Rcpp helps to make it very easy to extend with C++.In R, we use R studio to call a mature and excellent IDE.For R interview questions and answers you can follow this below mentioned link:R Interview Question and AnswersIntroduction to R and Python for data science wars:There is an interview link for freshers as well experienced people. You can call follow this below mention link for this particular:125 R Interview Questions and Answers for Freshers & ExperiencedIntroduction to R and Python for data analysis warsNow, at the end, we will discuss pros and cons of both R and PythonOnce you done with interview preparation then you can check how much you have grasp with R quiz:R Programming Online Quiz Questions and AnswersHere, I have one more link to R Quiz:R Multiple Choice Questions and AnswersMoreover, if you feel any query, feel free to ask in comment section. Hope, I will sort out your problem.;""
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