Explore
For Enterprise
Join for Free
Log In

Kunst und Geisteswissenschaften
Wirtschaft
Informatik
Datenverarbeitung
Informationstechnologie
Gesundheit
Mathematik und Logik
Persönliche Entwicklung
Physik und Ingenieurwesen
Sozialwissenschaften
Sprachen lernen
Abschlüsse und Zertifikate

Alle Bereiche erkunden

Blättern
Suche
Für Unternehmen
Anmelden
Kostenlose Teilnahme

Textual Data Formats

Loading...

R-Programmierung

Johns Hopkins University

4.6 (14,325 ratings) | 410K Students Enrolled

Kurs 2 von 10 in Datenverarbeitung Spezialisierung

Lehrplan anzeigen

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

Skills You'll Learn

Data Analysis, Debugging, R Programming, Rstudio

Bewertungen

4.6 (14,325 ratings)

5 stars
9,915 ratings
4 stars
3,202 ratings
3 stars
779 ratings
2 stars
241 ratings
1 star
188 ratings

AK

May 27, 2017

This was very engaging, however, the level of expectation and effort needed is much greater than course 1 - ToolBox.\n\nThis is perhaps the best course on R Programming designed for a small duration.

GB

Feb 03, 2016

Excelente opportunity to learn a lot. The course is very well prepared introduce you to R programing. Dont feel bad if you dont get it at te first moment. It will be a process of leaning worth trying

Aus der Unterrichtseinheit

Week 1: Background, Getting Started, and Nuts & Bolts

This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.

Introduction1:20

Overview and History of R16:07

Getting Help13:53

R Console Input and Evaluation4:46

Data Types - R Objects and Attributes4:43

Data Types - Vectors and Lists6:27

Data Types - Matrices3:24

Data Types - Factors4:31

Data Types - Missing Values2:10

Data Types - Data Frames2:44

Data Types - Names Attribute1:49

Data Types - Summary0:43

Reading Tabular Data5:51

Reading Large Tables7:08

Textual Data Formats4:58

Connections: Interfaces to the Outside World4:35

Subsetting - Basics4:13

Subsetting - Lists4:45

Subsetting - Matrices2:50

Subsetting - Partial Matching1:42

Subsetting - Removing Missing Values3:59

Vectorized Operations3:46

Unterrichtet von

Roger D. Peng, PhD
Associate Professor, Biostatistics
Jeff Leek, PhD
Associate Professor, Biostatistics
Brian Caffo, PhD
Professor, Biostatistics

There are other types of formats that you can save data in

beyond the tabular format, beyond, or the CSV file or text file.

These are also textual formats, but they are a little bit different for,

from the tabular data that we've talked about before.

And the two main functions for writing out data and f, are dumping and dputing.

So, and, and the idea behind these types of formats is they're text formats, but

they're not really, they're not really formatted in a way that's,

in the same as like a table because they contain a little bit more meta-data.

So data about, for example,

the type of the data in, in each class, object for example.

So if you, if you dump or dput a data frame.

It will include in the output, that the class of each column, of the data frame,

so that you don't have to specify it when you read it in.

And so the advantage of, of doing, using this type of mechanism to store data or

to read, or to read data, is that you don't have,

it's still a textual format, which can be useful, but it also contains metadata, so

that you don't have to specify it every single time you read it in.

Because that, if you don't, ca,

if the metadata do not get carried with the data set itself, then it, they ca,

they can get lost if you, if they get transferred somewhere else and

if you don't remember what the metadata are, for example the classes of

the different columns, then you kind of have to reconstruct that from scratch.

So that's one advantage of using, using a function like dump or

dput to, to output data from R.

And similarly the, the, the, the functions for reading data using, fr,

that haven't been outputted from dump or dput are source and, dget.

So in general, the textual formats are very nice formats for storing data

because, there's a number of different types of, different advantages to them.

First of all, they're editable, so you can, if you want to you can edit them.

I wouldn't say this is something that I would advice, but

because of you wanted something that's reproducible.

But, for example if something gets corrupted then you can look at the file

to see if it's possible to recover it.

So textual formats can be a little longer lived, if you're going to be storing data

for a long time, sometimes it's useful to, if it's possible to use a type of textual

format so that you can avoid problem, potential problems with corruption.

Textual formats can also work better if you're using like a version control

program, like subversion or git, where you're tracking changes between documents.

and, and those types of programs tend to be much more useful with

textual data rather than binary data, so that you can track changes meaningfully.

Textual formats adhere to the general kind of Unix philosophy, which is to

store all kinds of data, which generally stores all kinds of data in text.

But the one downside of textual formats is that they tend not to be space efficient,

so they tend to, they tend to take up a lot of space, and so,

it often need to be compressed.

So, d, the dput function takes an arbitrary R object, and

it will, use, it will take most types of R objects except for some more exotic ones,

and it will create some R code that will essentially reconstruct the object in R.

So here's I'm creating a small data frame,

it's got two columns, the first column is called A the second column is called B,

and then I'm going to dput this data frame.

And you'll see the out,

if you discall dput it'll just output the results to the console.

And you can see that what I've done is that.

What it does, it, it's re, it's constructed some R code.

For example, it's creating this list that has these two elements in it.

And you can see that each element has has the data that's in it.

And it has the names embedded here, it's got the row names here.

And it has the class of the object which, in this case, is the data frame.

And so, all the metadata here like the row names and

the names and the class are all included in the output.

Now, of course, you generally don't want to print this to the console,

that's not particularly useful, you probably want to save it to a file.

So you can dput the file to a file and then later on, you can read it into R

using dget, and when you dget the object, you will get this object and

you will see that it's, you have kind of reconstructed the object from before.

So the dput function,

essentially writes R code, which can be used to reconstruct an R object.

The dump function is a lot like dget however,

the difference is that dget can only be used on a single R object.

Whereas dump can be used on multiple R objects and so what you do is what

you pass a dump is the character vector which contains the names of the objects.

So here I created two objects one called x,

the other called y and when I pass the dump.

Is are the names of those objects?

The names are X and Y.

And I give it a file, that I want to store the da, the objects in.

And then I can remove them if I want to, but to read those objects back into R,

I can call the source function on that file and you'll see that the Y object and

the X object have been reconstructed.

Durchsuchen Sie unseren Katalog

Melden Sie sich kostenlos an und erhalten Sie individuelle Empfehlungen, Aktualisierungen und Angebote.

Coursera arbeitet mit erstklassigen Universitäten und Organisationen zusammen, um Online-Kurse anzubieten und dadurch universellen Zugriff auf die weltweit beste Ausbildung zu ermöglichen.

© 2019 Coursera Inc. Alle Rechte vorbehalten.

Aus dem App Store herunterladen

Erhältlich bei Google Play

Coursera
Info
Leitung
Jobs
Katalog
Zertifikate
MasterTrack™ Certificates
Abschlüsse
Für Unternehmen
Für Regierungen

Community
Learners
Partners
Developers
Beta Testers
Translators

Verbinden
Blog
Facebook
LinkedIn
Twitter
Instagram
Youtube
Tech-Blog

mehr
Bedingungen
Datenschutz
Hilfe
Zugänglichkeit
Presse
Kontakt
Verzeichnis
Partnerunternehmen