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Data Types - Missing Values

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Linguagem R

Universidade Johns Hopkins

4.6 (15,066 classificações) | 420K alunos inscritos

Curso 2 de 10 no Ciência de Dados Programa de cursos integrados

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.

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Habilidades que você aprenderá

Data Analysis, Debugging, R Programming, Rstudio

Avaliações

4.6 (15,066 classificações)

5 stars
10,460 ratings
4 stars
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3 stars
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2 stars
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1 star
205 ratings

WH

Feb 03, 2016

"R Programming" forces you to dive in deep.\n\nThese skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!

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

Na lição

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

Ministrado por

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

Transcrição

So there's a special type of object that we haven't talked too much about yet. And these are missing values. Missing values in R are denoted by either NA or NAN which we talked about before. NAN is used for undefined mathematical operations. And NA is pretty much used for everything else. And so, there's a function in R called is.na which is used to test objects to see if they are NA. To see if they are missing values in that object. There's another function called is.nan which is used to test for NANs. So, NA values can have a class, too. So you can have missing integer val, values or you can have missing character values or missing numeric values etc. And so even though it looks like it's all NAs, the NAs can have different classes potentially. And then it's an NA, an NAN value is considered to be also NA, so for example, an NAN value, a NAN value, is missing. Is considered to be missing. So, but the reverse is not true. So an NA value is not necessarily, an NAN value. I've got a few different types of missing values listed here. So, here I created a vector x which is 1,2, NA, 10, and 3. So, now, this is a numeric vector. And the NA value in here's going to be a numeric missing value. So when I call is.na on x, what it returns is a, is a logical vector. And the logical vector indicates whether each element of the vector x is missing or not. And so, there's only one missing element in this vector, and so that's the third element. So you can see that the, that the logical vector that's returned. The first two are false, the third is true, and the fourth and the fifth are false. So the, the, the element that's true indicated where the missing value is. If I call is.NaN on this vector, you'll see that vector that's returned is all false. Because there aren't any NaN values, or their aren't any MAN values in this vector so everything's false. Of course, if I create a vector that has an end, a NAN value and an, and an NA value in it. You'll see that is.na returns true for both of them. But is.nan only returns true for the for the value that's actually NAN.

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