[Getting and Cleaning data] Week 2

For more detail, down the html file here.

Week 2

Reading data from MySQL

What is MySQL? SQL is short for Structured Query Language and MySQL is the world’s most popular database.(Further information can be found in wiki page and mysql page)

• Free and widely used open source database software
• Widely used in internet bases application
• Data are structured in
  
  • Databases
  • Tables within databases
  • Fields with tables
• Each row is called a record

And why using it is the important point to keep in mind here is that as a data scientist what role that you will have is likely to collect data from a database, and maybe later you’re going to put some data back in it. But usually, the basic data collection has already been formed before you get there, so you usually be handed a database and trying having to get data out of it.

Now we will focus on how to access MySQL database using R. Firstly you need to install R package RMySQL. The instruction can be found in my blog How to install RMySQL package on Windows. FOn a Mac, general way install.packages("RMySQL") is OK. Then we will access the database and collect some information about it.

• step 1: connect a database using dbConnect function.

library(RMySQL)
ucscDb <- dbConnect(MySQL(), user="genome", host="genome-mysql.cse.ucsc.edu")

• step 2: apply a query to the database using dbGetQuery function. (Here the result contains all the databases in this sever.)

result <- dbGetQuery(ucscDb, "show databases;")

• step 3: disconnect the connection using dbDisconnect function.(It is very important that whenever you’ve done analysis data or collecting data from MySQL server that you disconnect from the server.)

dbDisconnect(ucscDb)

Or you can use dbSendQuery function in step 2. The difference for dbGetQuery and dbSendQuery is that

1 dbSendQuery only submits and synchronously executes the SQL statement to the database engine. It does not extracts any records — for that you need to use the function dbFetch, and then you must call dbClearResult when you finish fetching the records you need.
2 dbGetQuery comes with a default implementation that calls dbSendQuery, then if dbHasCompleted is TRUE, it uses fetch to return the results. on.exit is used to ensure the result set is always freed by dbClearResult. Subclasses should override this method only if they provide some sort of performance optimisation.

The above codes gives all databases in the connection. If we want to focus on a specific database, we need to use the argument dbname for the name of database.

• step 1: connect a specific database using dbConnect function with argument dbname .

hg19 <- dbConnect(MySQL(), user="genome", dbname="hg19", host="genome-mysql.cse.ucsc.edu")

• step 2: list all tables in this database hg19

allTables <- dbListTables(hg19)

• step 3: View the length and head rows of hg19

length(allTables) # NO. of tables or data frames in database hg19
allTables[1:4]

• step 4: list the column name (fields) of a specific table ‘affyU133Plus2’.

dbListFields(hg19, "affyU133Plus2") 

• step 5: give the number of records in the table ‘affyU133Plus2’

num <- dbGetQuery(hg19, "select count(*) from affyU133Plus2")
num

• step 6: get a full data frame out of the table (Too much data for R)

oldw <- getOption("warn")
options(warn = -1)
affyData <- dbReadTable(hg19, "affyU133Plus2")
head(affyData)
options(warn = oldw)

• step 7: sent query using dbSentQuery function.

oldw <- getOption("warn")
options(warn = -1)
query <- dbSendQuery(hg19, "select * from affyU133Plus2 where misMatches between 1 and 3")
options(warn = oldw)

• step 8: retrieve the results via fetch.(subset via feature dimension)

affyMis <- fetch(query)
quantile(affyMis$misMatches)

• step 9: automatically queries and retrieves the last 10 entries. note: you must clear the query afterwards.(subset via observation dimension.)

affyMisSmall <- fetch(query, n=10) 
dbClearResult(query)

• step 10: close the connection

dbDisconnect(hg19)

Reading data from HDF5

What is HDF? HDF stands for hierarchical data format and HDF5 is a data model, library, and file format for storing and managing data.(More details can br found in here)

• Used for storing large data sets
• Supports storing a range of data types
• Heirarchical data format
• groups containing zero or more data sets and metadata
  
  • have a group header with group name and list of attributes
  • have a group symbol table with a list of objects in group
• datasets multidimensional array of data elements with metadata
  
  • have a header with name, datatye, dataspace, and storage layout
  • have a data array with the data

Now we begin to play with HDF5.

• step 1: Install rhdf5 package, which is installed through bioconductor.(This will install packages from Bioconductor, which is used for genomics but also has good “big data” packages)

#source("http://bioconductor.org/biocLite.R")
#biocLite("rhdf5")

• step 2: create a new hdf5 file

library(rhdf5)
created <- h5createFile("./data/example.h5")

• step 3: create groups(dataset + metadata) in the hdf5 file

created <- h5createGroup("./data/example.h5", "foo")
created <- h5createGroup("./data/example.h5", "baa")
created <- h5createGroup("./data/example.h5", "foo/foobaa")

• step 3: list the groups of hdf5 file

h5ls("./data/example.h5")

• step 4: write content to groups

# matrix
A <- matrix(1:10, 5, 2)
# write the matrix to a particular group
h5write(A, "./data/example.h5", "foo/A")
# multidimension array
B <- array(seq(0.1, 2, by=0.2), dim = c(5, 2, 2))
# add attributes
attr(B, "scale") <- "liter"
# write the array to a particular group
h5write(B, "./data/example.h5", "foo/foobaa/B")

• step 5: list the content in hdf5 file

h5ls("./data/example.h5")

• step 6: write a data set in the top level group

# data frame
df <- data.frame(1L:5L, seq(0, 1, length.out = 5), c("ab", "cde", "fghi", "a", "s"), stringsAsFactors = FALSE)
# write the data frame to a particular group
h5write(df, "./data/example.h5", "df")
h5ls("./data/example.h5")

• step 7: read content from hdf5 file

# read HDF5 data
readA <- h5read("./data/example.h5", "foo/A")
readB <- h5read("./data/example.h5", "foo/foobaa/B")
readdf <- h5read("./data/example.h5", "df")
readA

• step 8: change content in hdf5 file

h5write(c(12, 13, 15), "./data/example.h5", "foo/A", index = list(1:3, 1))
h5read("./data/example.h5", "foo/A")

Reading data from website

Webscraping: Programatically extracting data from HTML code of website

• it can be a great way to get data
• Many website have information you may want to programatically read
• In some cases this is against the terms of service for the website
• Attempting to read too many pages too quickly can get your IP address blocked

(一) readLines function

• step 1: open a connection of a url using usl function

con <- url("http://scholar.google.com/citations?user=HI-I6C0AAAAJ&hl=en")

• step 2: read out the data from website

htmlCode <- readLines(con)

• step 3: close connection

close(con)

• step 4: view the content

#htmlCode

（二） XML package

• step 1: give url to parse

library(XML)
url <- "http://scholar.google.com/citations?user=HI I6C0AAAAJ&hl=en"

• step 2: parse the url and use the internal nodes to get the complete structure out

html <- htmlTreeParse(url, useInternalNodes = TRUE)

• step 3: get the title of the page

xpathSApply(html, "//title", xmlValue)

• step 4: get the number of citation

xpathSApply(html, "//td[@id='col-citedby']", xmlValue)

（maybe the last script is no longer right, but it gives us a good view to see how to extract information from website.）

（三）httr package

• step 1: get url

library(httr)
url <- "http://scholar.google.com/citations?user=HI I6C0AAAAJ&hl=en"
html2 <- GET(url)

• step 2: extract the content from that page

content2 <- content(html2, as = "text")

• step 3: parse out the content

parseHtml <- htmlParse(content2, asText = TRUE)

• step 4: extract the title of the page

xpathSApply(parseHtml, "//title", xmlValue)

• step 5: accessing website via password

pg1 <- GET("http://httpbin.org/basic-auth/user/passwd")

• step 6: view

pg1

• step 7: via password

pg2 <- GET("http://httpbin.org/basic-auth/user/passwd", authenticate("user", "passwd"))
pg2

• step 8: get name of it

names(pg2)

Reading data from API

What is API?
API stands for application programming interfaces through which softwares can interact with each other. For example, most internet companies, like twitter or Facebook will have an application programming interface, where you can download data. For example, you can get data about which users are tweeting or what they are tweeting about, where you can get information about what people are posting on Facebook.

• step 1: create an account, not a user account but an account with the api or with the development team out of each particular organization. Then you can create a new application which will give you some number being used to authenticate the application through R and access data later.(on website)

• step 2: start the autherization processing for your application

library(httr)
myapp <- oauth_app("twitter",
                   key="YourConsumerKey",
                 secret="YourConsumerSecret")

• step 3: sign in the application

sig = sign_oauth1.0(myapp, 
                    token="YourAccessToken", 
                    token_secret="YourAccessTokenSecret")

• step 4: get content

homeTL = GET("https://api.twitter.com/1.1/statuses/home_timeline.json", sig)
# use the content function to extract the information
json1 <- content(homeTL)
json2 <- jsonlite::fromJSON(jsonlite::toJSON(json1))
json2[1,1:4]