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17/10/2019 Data Handling: Import, Cleaning and Visualisation

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Data Handling: Import, Cleaning and VisualisationLecture 5:

Programming with Data

Prof. Dr. Ulrich Matter

17/10/2019

Updates

Part I: Data (Science) fundamentals

Date Topic

19.09.19 Introduction: Big Data/Data Science, course overview

26.09.19 An introduction to data and data processing

26.09.19 Exercises/Workshop 1: Tools, working with text files

03.10.19 Data storage and data structures

10.10.19 'Big Data‘ from the Web

10.10.19 Exercises/Workshop 2: Computer code and data storage

17.10.19 Programming with data

Next Week (24.10.2019)

No lecture in the morning! (no rooms available)

The exercise session in the afternoon is not affected by this (will takeplace as scheduled)!

Recap: "Big Data" from the Web

Limitations of rectangular data

Only two dimensions.

Hard to represent hierarchical structures.

Observations (rows)

Characteristics/variables (columns)

Might introduce redundancies.

Machine-readability suffers (standard parsers won't recognize it).

XML: JSON:

<person> <firstName>John</firstName> <lastName>Smith</lastName> <age>25</age> <address> <streetAddress>21 2nd Street</streetAddress> <city>New York</city> <state>NY</state> <postalCode>10021</postalCode> </address> <phoneNumber> <type>home</type> <number>212 555-1234</number> </phoneNumber> <phoneNumber> <type>fax</type> <number>646 555-4567</number> </phoneNumber> <gender> <type>male</type> </gender></person>

{"firstName": "John", "lastName": "Smith", "age": 25, "address": { "streetAddress": "21 2nd Street", "city": "New York", "state": "NY", "postalCode": "10021" }, "phoneNumber": [ { "type": "home", "number": "212 555-1234" }, { "type": "fax", "number": "646 555-4567" } ], "gender": { "type": "male" }}

XML: JSON:

<person> <firstName>John</firstName> <lastName>Smith</lastName> </person>

{"firstName": "John", "lastName": "Smith", }

Parsing XML in R

The following examples are based on the example code shown above (thetwo text-files persons.json and persons.xml)

# load packages library(xml2) # parse XML, represent XML document as R object xml_doc <- read_xml("persons.xml") xml_doc

## {xml_document} ## <person> ## [1] <firstName>John</firstName> ## [2] <lastName>Smith</lastName> ## [3] <age>25</age> ## [4] <address>\n <streetAddress>21 2nd Street</streetAddress>\n <city>New York</## [5] <phoneNumber>\n <type>home</type>\n <number>212 555-1234</number>\n</phoneN## [6] <phoneNumber>\n <type>fax</type>\n <number>646 555-4567</number>\n</phoneNu## [7] <gender>\n <type>male</type>\n</gender>

Parsing JSON in R

# load packages library(jsonlite) # parse the JSON-document shown in the example above json_doc <- fromJSON("persons.json") # check the structure str(json_doc)

## List of 6 ## $ firstName : chr "John" ## $ lastName : chr "Smith" ## $ age : int 25 ## $ address :List of 4 ## ..$ streetAddress: chr "21 2nd Street" ## ..$ city : chr "New York" ## ..$ state : chr "NY" ## ..$ postalCode : chr "10021" ## $ phoneNumber:'data.frame': 2 obs. of 2 variables: ## ..$ type : chr [1:2] "home" "fax" ## ..$ number: chr [1:2] "212 555-1234" "646 555-4567" ## $ gender :List of 1 ## $ type: chr "male"

<!DOCTYPE html> <html> <head> <title>hello, world</title> </head> <body> <h2> hello, world </h2> </body> </html>

HTML documents: code and data!

HTML documents/webpages consist of 'semi-structured data':

A webpage can contain a HTML-table (structured data)…

…but likely also contains just raw text (unstructured data).

Characteristics of HTML

1. Annotate/'mark up' data/text (with tags)

2. Nesting principle

3. Expresses what is what in a document.

Defines structure and hierarchy

Defines content (pictures, media)

head and body are nested within the html document

Within the head, we define the title, etc.

Doesn't explicitly 'tell' the computer what to do

HTML is a markup language, not a programming language.

HTML document as a 'tree'

HTML (DOM) tree diagram (by Lubaochuan 2014, licensed under the Creative Commons Attribution-Share Alike 4.0 International license).

Parsing a Webpage with R

# install package if not yet installed# install.packages("rvest") # load the package library(rvest)

# parse the webpage, show the content swiss_econ_parsed <- read_html("https://en.wikipedia.org/wiki/Economy_of_Switzerlandswiss_econ_parsed

## {html_document} ## <html class="client-nojs" lang="en" dir="ltr"> ## [1] <head>\n<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">\n## [2] <body class="mediawiki ltr sitedir-ltr mw-hide-empty-elt ns-0 ns-subject mw-e

Parsing a Webpage with R

Now we can easily separate the data/text from the html code. For example,we can extract the HTML table containing the data we are interested in as adata.frames.

tab_node <- html_node(swiss_econ_parsed, xpath = "//*[@id='mw-content-text']/div/tabtab <- html_table(tab_node) tab

## Year GDP (billions of CHF) US Dollar Exchange ## 1 1980 184 1.67 Francs ## 2 1985 244 2.43 Francs ## 3 1990 331 1.38 Francs ## 4 1995 374 1.18 Francs ## 5 2000 422 1.68 Francs ## 6 2005 464 1.24 Francs ## 7 2006 491 1.25 Francs ## 8 2007 521 1.20 Francs ## 9 2008 547 1.08 Francs ## 10 2009 535 1.09 Francs ## 11 2010 546 1.04 Francs ## 12 2011 659 0.89 Francs ## 13 2012 632 0 94 Francs

Basic Programming Concepts

Repeatedly execute a sequence of commands.

Known or unknown number of iterations.

Types: 'for-loop' and 'while-loop'.

'for-loop': number of iterations typically known.

'while-loop: number of iterations typically not known.

for-loop

for-loop in R

# number of iterations n <- 100# start loopfor (i in 1:n) { # BODY}

for-loop in R

# vector to be summed up numbers <- c(1,2,3,4,5)# initiate total total_sum <- 0# number of iterations n <- length(numbers)# start loopfor (i in 1:n) { total_sum <- total_sum + numbers[i]}

Nested for-loops

# matrix to be summed up numbers_matrix <- matrix(1:20, ncol = 4) numbers_matrix

## [,1] [,2] [,3] [,4] ## [1,] 1 6 11 16 ## [2,] 2 7 12 17 ## [3,] 3 8 13 18 ## [4,] 4 9 14 19 ## [5,] 5 10 15 20

Nested for-loops

# number of iterations for outer loop m <- ncol(numbers_matrix)# number of iterations for inner loop n <- nrow(numbers_matrix)# start outer loop (loop over columns of matrix)for (j in 1:m) { # start inner loop # initiate total total_sum <- 0 for (i in 1:n) { total_sum <- total_sum + numbers_matrix[i, j] } print(total_sum) }

while-loop

while-loop in R

# initiate variable for logical statement x <- 1# start loopwhile (x == 1) { # BODY}

while-loop in R

# initiate starting value total <- 0# start loopwhile (total <= 20) { total <- total + 1.12}

Booleans and logical statements

2+2 == 4

## [1] TRUE

3+3 == 7

## [1] FALSE

## [1] TRUE

condition <- TRUE if (condition) { print("This is true!")} else { print("This is false!")}

## [1] "This is true!"

condition <- FALSE if (condition) { print("This is true!")} else { print("This is false!")}

## [1] "This is false!"

R functions

'Take a variable/parameter value as input and provide value asoutput'

For example, .

R functions take 'parameter values' as input, process those valuesaccording to a predefined program, and 'return' the results.

· f : X → Y

· X Y

· 2 × X = Y

R functions

Many functions are provided with R.

More can be loaded by installing and loading packages.

# install a package install.packages("<PACKAGE NAME>")# load a package library(<PACKAGE NAME>)

Tutorial: A Function to Compute the Mean

Preparation

1. Open a new R-script and save it in your code-directory as my_mean.R.

2. In the first few lines, use # to write some comments describing what thisscript is about.

Preparation

###################################### Mean Function:# Computes the mean, given a # numeric vector.

Preparation

3. Also in the comment section, describe the function argument (input)and the return value (output)

Preparation

###################################### Mean Function:# Computes the mean, given a # numeric vector.# x, a numeric vector# returns the arithmetic mean of x (a numeric scalar)

Preparation

3. Also in the comment section, describe the function argument (input)and the return value (output)

4. Add an example (with comments), illustrating how the function issupposed to work.

Preparation

# Example:# a simlpe numeric vector, for which we want to compute the mean# a <- c(5.5, 7.5)# desired functionality and output:# my_mean(a)# 6.5

1. Know the concepts/context!

Programming a function in R means telling R how to transform a giveninput (x).

Before we think about how we can express this transformation in the Rlanguage, we should be sure that we understand the transformation perse.

1. Know the concepts/context!

Here, we should be aware of how the mean is defined:

Programming a function in R means telling R how to transform a giveninput (x).

Before we think about how we can express this transformation in the Rlanguage, we should be sure that we understand the transformation perse.

= ( ) =x̄ 1

+ +⋯+x1 x2 xn

2. Split the problem into several smaller problems

From looking at the mathematical definition of the mean ( ), we recognizethat there are two main components to computing the mean:

: the sum of all the elements in vector

and , the number of elements in vector .

· ∑ni=1

· n x

3. Address each problem step-by-step

In R, there are two built-in functions that deliver exactly these twocomponents:

sum() returns the sum of all the values in i ts arguments (i.e., if x is anumeric vector, sum(x) returns the sum of all elements in x).

length() returns the total number of elements in a given vector (thevector's 'length').

4. Putting the pieces together

With the following short line of code we thus get the mean of the elementsin vector a.

sum(a)/length(a)

5. Define the function

All that is left to do is to pack all this into the function body of our newlydefined my_mean() function:

# define our own function to compute the mean, given a numeric vector my_mean <- function(x) { x_bar <- sum(x) / length(x) return(x_bar)}

6. Test it with the pre-defined example

# test it a <- c(5.5, 7.5) my_mean(a)

## [1] 6.5

6. Test it with other implementations

Here, compare it with the built-in mean() function:

b <- c(4,5,2,5,5,7) my_mean(b) # our own implementation

## [1] 4.666667

mean(b) # the built_in function

## [1] 4.666667

References

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