#r tutorials

[quickinsights]

R for data science - Quickinsights.org

R for data science. Explore R's data wrangling, visualization, and statistical modeling capabilities, positioning it as a powerful toolbox f

[superaakash24]

R Tutorial for Beginners - Learn R Programming from Scratch - DataFlair

R tutorial - An amazing collection of 90+ tutorials to excel the R Programming Language. Learn R Programming with plethora of code examples and use cases. A complete R tutorial series for beginners and advanced learners.

[awaytodata]

Tutorial: How to import multiple (large) data files in R at once?

I had a project where I had to import around 100 different files 30 times (from 30 folders), in order to do that, I used a function called fread from data.table package (a faster alternative to read.csv that can work with big files), dirLister (a free and simple tool), and sheets.google.com (any Excel-ish software will do).

So, let’s assume we’ve got a folder with the following files:

I suggest to download and install dirLister, after launching the DirLister.exe file, navigate to the folder in Input tab.

Now switch to the Output tab, and choose where you want to save the filenames file, I chose Plain text (.txt) as an Output format and the same folder, then pressed Set as default options and Start.

The file with a list of folder file names will open (if you tick “Open outpute file/folder after list generation”), otherwise check the Output folder.

You can now copy the generated list in a Google Sheets file, where you are going to need a function called CONCATENATE.

id,name,host_id,host_name,neighbourhood_group,neighbourhood,latitude,longitude,room_type,price,minimum_nights,number_of_reviews,last_review,reviews_per_month,calculated_host_listings_count,availability_365

Considering these are the column names in the .csv files, let’s say we only want to select columns id, host_id, price, and availability_365, so the respective column numbers will be: 1, 3, 9, 16.  

NOTE: I’d advise to only import the columns that you need for your analysis as there are limits to either R or RAM. The rule is simple, the less data you load, the more files you can import.

So I pasted the list in column C, filled the first 3 cells in column A, and then filled the first row, used CONCATENATE in column F to connect all the other columns in one cell, then stretched the cells to the end of column C. 

=CONCATENATE(A1:D1)

Open R and write the following: install.packages("data.table") #if you don’t have the package preinstalled library(data.table) setwd("C://Users//ww//Documents//Data//InsideAirbnb") You can copy and paste column F from Google Sheets to R now. Here’s what I got:

file01<- fread("GermanyBerlin_listings.csv", select=c(1,3,10,16)) file02<- fread("Italy_Florencelistings.csv", select=c(1,3,10,16)) file03<- fread("ItalyBergamo_listings.csv", select=c(1,3,10,16)) file04<- fread("ItalyBologna_listings.csv", select=c(1,3,10,16)) file05<- fread("ItalyMilan_listings.csv", select=c(1,3,10,16)) file06<- fread("ItalyNaples_listings.csv", select=c(1,3,10,16)) file07<- fread("ItalyPuglia_listings.csv", select=c(1,3,10,16)) file08<- fread("ItalyRome_listings.csv", select=c(1,3,10,16)) file09<- fread("ItalySicily_listings.csv", select=c(1,3,10,16)) file10<- fread("ItalyVenice_listings.csv", select=c(1,3,10,16)) file11<- fread("SpainBarcelona_listings.csv", select=c(1,3,10,16)) file12<- fread("SpainEuskadi_listings.csv", select=c(1,3,10,16)) file13<- fread("SpainGirona_listings.csv", select=c(1,3,10,16)) file14<- fread("SpainMadrid_listings.csv", select=c(1,3,10,16)) file15<- fread("SpainMalaga_listings.csv", select=c(1,3,10,16)) file16<- fread("SpainMallorca_listings.csv", select=c(1,3,10,16)) file17<- fread("SpainMenorca_listings.csv", select=c(1,3,10,16)) file18<- fread("SpainSevilla_listings.csv", select=c(1,3,10,16)) file19<- fread("SpainValencia_listings.csv", select=c(1,3,10,16)) file20<- fread("TrentinoItaly_listings.csv", select=c(1,3,10,16)) file21<- fread("UKBristol_listings.csv", select=c(1,3,10,16)) file22<- fread("UKEdinburgh_listings.csv", select=c(1,3,10,16)) file23<- fread("UKGreaterManchester_listings.csv", select=c(1,3,10,16)) file24<- fread("UKLondon_listings.csv", select=c(1,3,10,16)) file25<- fread("UKManchester_listings.csv", select=c(1,3,10,15))

allfiles <- rbind(file01, file02, file03, file04, file05, file06, file07, file08, file09, file10, file11, file12, file13, file14, file15, file16, file17, file18, file19, file20, file21, file22, file23, file24, file25)

Use rbind then to unite all the files into one dataset. This is the way I did it for over 30 times, I hope that this semiautomatic method helped you and I would be happy to know of other more efficient ways.

[ktoutloud]

There are many different techniques that be used to model physical, social, economic, and conceptual systems. The purpose of this post is to show how the Kermack-McKendrick (1927) formulation of the SIR […]

[ktoutloud]

[ktoutloud]

R tutorial for statistics. Contains sample R code to solve college statistics textbook exercises with R.

[python-django-tutorial-blog]

R: Is ‘R’ a language like other programming languages?

With the growing volume of Big Data there is need for better decision making and data analysis. Analysis of data helps us to arrive at accurate and meaningful information and makes sense out of scattered data in the huge database.

INTRODUCTION TO ‘R’

R is a programming language and an amazing tool used for Data analysis and Statistical Graphics. It adds an easy visualization to your data by showing it in Graphical forms. It is free, and widely used by professional statisticians and market analysts. It's free, open source, powerful and highly extensible.

HISTORY OF ‘R’

The R language is a terminology of S which was designed in the 1980s by John Chambers at Bell labs and has been in widespread use in the statistical community since. It was grown up by Robert Gentleman and Ross Ihaka of the University of Auckland. R has been with us since 1993.

WHY R?

R can alone be used for complex analysis and very useful for those who are tired of using Excel for analysis. The emergence of data sciences leads R to an unmarkable growth. We have almost everything built-in in R to reduce programming overhead. R provides Statistical and Programming features both. Many mathematical operations (mean, median, matrix, bargraphs, histograms, etc.) can be computed in a single command.

APPLICATION AREAS OF ‘R’

Bioinformatics

Operations Research

Statistics

Artificial Intelligence

Linear Algebra

Machine learning

Data Mining

Data visualization

Analysis of Big Data

Making predictions

  R has tremendous growth over the last few years. R has picked up the pace of popularity because of its application areas. Beside above areas R can be used in many other fields.

HOW ‘R’ DIFFERENT FROM OTHER PROGRAMMING LANGUAGES?

R has a lot of prepackaged stuff that's already available. Because it's a programmable environment that uses command-line scripting, you can store a series of complex data-analysis steps in R. That lets you re-use your analysis work on similar data more easily. We are not required to deal with complex parts like pointers as we need to do in C, C++, or any other programming languages. There is no need to specify data types against variables. Other languages are difficult to learn and take a longer period to learn.