Index
A
- advanced operations, data types
- analytics workflow
- revisiting / Revisiting analytics workflow
- data access / Revisiting the analytics workflow
- data processing / Revisiting the analytics workflow
- normalization / Revisiting the analytics workflow
- data analysis / Revisiting the analytics workflow
- insights / Revisiting the analytics workflow
- API call / The API call and JSON monster
- Application Programming Interfaces (APIs) / Data access
- arrays / Arrays
- artificial intelligence (AI)
- about / Machine learning
C
- category trend
- analysis / Category trend analysis
- check-in data, obtaining / Getting the data – the usual hurdle
- API end point / The required end point
- venue data, extracting / Getting data for a city – geometry to the rescue
- data, analysis / Analysis – the fun part
- descriptive statistics / Basic descriptive statistics – the usual
- clustering algorithm
- Elbow method / Elbow method
- Silhouette method / Silhouette method
- code flow, data types
- constructs, looping / Looping constructs
- conditional constructs / Conditional constructs
- code snippet
- reference link / Environment setup
- Comma Separated Values (CSV) / Data processing and normalization
- commit frequency distribution
- Comprehensive R Archival Network (CRAN) / Getting started with R
- Comprehensive R Archive Network (CRAN) / Managing packages
- computer-mediated communication (CMC) / Understanding social media
- Cross Industry Standard Process for Data Mining (CRISP-DM)
- about / Analytics workflow
- business context / Analytics workflow
- data acquisition / Analytics workflow
- data preparation / Analytics workflow
- modeling / Analytics workflow
- analysis / Analytics workflow
- evaluation / Analytics workflow
- deployment / Analytics workflow
D
- daily commit frequency
- analyzing / Analyzing daily commit frequency
- Dart language / Analyzing languages with the most open issues
- data access challenges / Data access challenges
- data analytics
- about / Data analytics
- workflow / Analytics workflow
- DataFrame / DataFrames
- data science
- reference link / Data Science and StackExchange
- about / Data Science and StackExchange, Demographics and data science
- data structures, data types
- vectors / Vectors
- arrays / Arrays
- matrices / Matrices
- lists / Lists
- DataFrame / DataFrames
- data types
- about / Data types
- data structures / Data structures
- functions / Functions
- code flow, controlling / Controlling code flow
- advanced operations / Advanced operations
- data, visualizing / Visualizing data
- demographics
- about / Demographics and data science
- reference link / Demographics and data science
- diameter, English football social network
- page distances / Page distances
- density / Density
- transitivity / Transitivity
- coreness / Coreness
- document summarization
- about / Document summarization
- extraction-based summarization / Document summarization
- abstraction-based summarization / Document summarization
E
- English Football Club's brand page engagements
- English Football Clubs brand page engagements
- data, obtaining / Getting the data
- data, curating / Curating the data
- post counts per page, visualizing / Visualizing post counts per page
- post counts, visualizing by post type per page / Visualizing post counts by post type per page
- average likes, visualizing by post type per page / Visualizing average likes by post type per page
- average shares, visualizing by post type per page / Visualizing average shares by post type per page
- page engagement over time, visualizing / Visualizing page engagement over time
- user engagement, visualizing with page over time / Visualizing user engagement with page over time
- posts, trending by user likes per page / Trending posts by user likes per page
- posts, trending by user shares per page / Trending posts by user shares per page
- influential users, on popular page posts / Top influential users on popular page posts
- English football social network
- analyzing / Analyzing an English football social network
- descriptive statistics / Basic descriptive statistics
- network, visualizing / Visualizing the network
- network properties, analyzing / Analyzing network properties
- diameter / Diameter
- node properties, analyzing / Analyzing node properties
- network communities, analyzing / Analyzing network communities
- English Premier League (EPL) / Analyzing an English football social network
- Exchangeable Image File Format (EXIF) / Understanding more about EXIF
- Extensible Markup Language (XML) / Data processing and normalization
- Extract-Transform-Load (ETL) / Analytics workflow
F
- Facebook data
- accessing / Accessing Facebook data
- Graph API / Understanding the Graph API
- Rfacebook / Understanding Rfacebook
- Netvizz application / Understanding Netvizz
- data access challenges / Data access challenges
- Facebook Graph API
- reference link / Understanding the Graph API
- Flickr
- about / A Flickr-ing world
- used, for identifying interesting photos / Are your photos interesting?
- data, preparing / Preparing the data
- classifier, building / Building the classifier
- challenges / Challenges
- Flickr, challenges
- API response objects / Challenges
- standard packages, lacking / Challenges
- libraries, lacking / Challenges
- data quality / Challenges
- Flickr APIs
- reference link / Accessing Flickr's data
- Flickr data
- accessing / Accessing Flickr's data
- Flickr app, creating / Creating the Flickr app
- R packages, connecting / Connecting to R
- about / Getting started with Flickr data, Understanding Flickr data
- Exchangeable Image File Format (EXIF) / Understanding more about EXIF
- Foursquare
- about / Foursquare – the app and data
- APIs / Foursquare APIs – show me the data
- application, creating / Creating an application – let me in
- data access / Data access – the twist in the story
- JavaScript Object Notation (JSON), handling, in R programming language / Handling JSON in R – the hidden art
- analytics workflow, revisiting / Revisiting the analytics workflow
- Foursquare, APIs
- venues / Foursquare APIs – show me the data
- users / Foursquare APIs – show me the data
- check-ins / Foursquare APIs – show me the data
- tips / Foursquare APIs – show me the data
- events / Foursquare APIs – show me the data
- Foursquare data analysis
- challenges for / Challenges for Foursquare data analysis
- data extraction / Challenges for Foursquare data analysis
- API changes / Challenges for Foursquare data analysis
- privacy / Challenges for Foursquare data analysis
- functions, data types
- built-in functions / Built-in functions
- user-defined functions / User-defined functions
G
- Gibbs sampling / The modeling part
- GitHub
- about / Understanding GitHub
- data, accessing / Accessing GitHub data
- rgithub package, used for data access / Using the rgithub package for data access
- application, registering / Registering an application on GitHub
- API, used for accessing data / Accessing data using the GitHub API
- graph analysis
- about / Follower graph analysis
- challenges / Challenges
- graph analysis, challenges
- accessibility / Challenges
- privacy / Challenges
- API changes / Challenges
- data / Challenges
- Graph API
- about / Understanding the Graph API
- nodes / Understanding the Graph API
- edges / Understanding the Graph API
- fields / Understanding the Graph API
- graphical user interface (GUI) / Environment setup
H
- hashtags / Trend analysis
- Hierarchical clustering
- initialization / Sentiment analysis in R
- merge / Sentiment analysis in R
- compute / Sentiment analysis in R
- HTML scraping
- from links / HTML scraping from the links – the bigger monster
- Hyperlink-Induced Topic Search (HITS) / HITS authority score
- Hyper Text Markup Language (HTML) / Data processing and normalization
I
- igraph
- reference link / Follower graph analysis
- Integrated Development Environment (IDE) / Environment setup
- interestingness algorithm / Understanding interestingness – similarities
- clustering algorithm / Finding K
- inverse document frequency (IDF) / Understanding LexRank
- Inverse Document Frequency (TF-IDF) / Features
J
- JavaScript Object Notation (JSON) / Data processing and normalization
- about / Handling JSON in R – the hidden art
- category data, obtaining / Getting category data – introduction to JSON parsing and data extraction
- JSON monster / The API call and JSON monster
K
- key performance indicators (KPIs) / Social media analytics
L
- language trends
- analyzing / Analyzing language trends
- top trending languages, visualizing / Visualizing top trending languages
- top trending languages, visualizing over time / Visualizing top trending languages over time
- languages, analyzing with issues / Analyzing languages with the most open issues
- languages, analyzing with issues over time / Analyzing languages with the most open issues over time
- languages, analyzing with repositories / Analyzing languages with the most helpful repositories
- languages, analyzing with highest popularity score / Analyzing languages with the highest popularity score
- language correlations, analyzing / Analyzing language correlations
- user trends, analyzing / Analyzing user trends
- top contributing users, visualizing / Visualizing top contributing users
- user activity metrics, analyzing / Analyzing user activity metrics
- Latent Dirichlet Allocation (LDA) / Topic modeling
- LexRank algorithm / Understanding LexRank
- reference link / Understanding LexRank
- lexRankr package
- reference link / Summarizing articles with lexRankr
- Linux, GitHub repository
- reference link / Analyzing repository activity
- Linux operating system
- reference link / Analyzing weekly code modification history
- lists / Lists
M
- machine learning (ML)
- about / Machine learning
- techniques / Machine learning techniques
- supervised learning techniques / Supervised learning
- unsupervised learning techniques / Unsupervised learning
- matrices / Matrices
N
- N-grams
- reference link / Features
- Natural Language Processing (NLP) / Features
- Netvizz application / Understanding Netvizz
- reference link / Understanding Netvizz
- network communities
- analyzing / Analyzing network communities
- cliques / Cliques
- communities / Communities
- network communities, English football social network
- cliques / Cliques
- communities / Communities
- news articles
- summarizing / Summarizing news articles
- document summarization / Document summarization
- LexRank algorithm / Understanding LexRank
- summarizing, with lexRankr / Summarizing articles with lexRankr
- news data
- about / News data – news is everywhere
- accessing / Accessing news data
- applications, creating for data access / Creating applications for data access
- data extraction / Data extraction – not just an API call
- API call / The API call and JSON monster
- JSON monster / The API call and JSON monster
- news data analysis
- challenges / Challenges to news data analysis
- API sources, lacking / Challenges to news data analysis
- web scraping / Challenges to news data analysis
- text analysis, subjectivity / Challenges to news data analysis
- nltk library
- reference link / Features
- node properties
- analyzing / Analyzing node properties
- degree / Degree
- closeness / Closeness
- betweenness / Betweenness
- node properties, English football social network
- degree / Degree
- closeness / Closeness
- betweenness / Betweenness
- correlation, visualizing among centrality measures / Visualizing correlation among centrality measures
- eigenvector centrality / Eigenvector centrality
- PageRank / PageRank
- HITS authority score / HITS authority score
- page neighbours / Page neighbours
O
- Open Authentication (OAuth) / Registering an application
P
- package topicmodels
- reference link / Analysis of topics
- PageRank
- reference link / PageRank
- parts of speech (POS) / Data processing and normalization
- Parts of Speech (POS) / Features
R
- Read-Evaluate-Print-Loop (REPL) / Environment setup
- recommendation engine
- about / Recommendation engine – let's open a restaurant, Recommendation engine – the clichés
- issues, framing / Framing the recommendation problem
- restaurant recommender, building / Building our restaurant recommender
- repository activity
- analyzing / Analyzing repository activity
- weekly commit frequency, analyzing / Analyzing weekly commit frequency
- commit frequency distribution, analyzing / Analyzing commit frequency distribution versus day of the week
- daily commit frequency, analyzing / Analyzing daily commit frequency
- weekly commit frequency comparison, analyzing / Analyzing weekly commit frequency comparison
- weekly code modification history, analyzing / Analyzing weekly code modification history
- trending repositories, retrieving / Retrieving trending repositories
- repository metrics
- analyzing / Analyzing repository metrics
- repository metric distributions, visualizing / Visualizing repository metric distributions
- repository metric correlations, analyzing / Analyzing repository metric correlations
- relationship, analyzing between stargazer and repository counts / Analyzing relationship between stargazer and repository counts
- relationship, analyzing between stargazer and fork counts / Analyzing relationship between stargazer and fork counts
- relationship, analyzing between total forks and repository count / Analyzing relationship between total forks, repository count, and health
- relationship, analyzing between total forks and repository health / Analyzing relationship between total forks, repository count, and health
- repository trends
- analyzing / Analyzing repository trends
- trending repositories analyzing, created over time / Analyzing trending repositories created over time
- trending repositories analyzing, updated over time / Analyzing trending repositories updated over time
- repository metrics, analyzing / Analyzing repository metrics
- Rfacebook / Understanding Rfacebook
- rgithub package
- reference link / Using the rgithub package for data access
- R package
- environment, setting up / Environment setup
- reference link / Environment setup
- R programming language
- about / Getting started with R, Next steps
- environment setup / Environment setup
- help in / Getting help
- packages, managing / Managing packages
- R programming language 3.3.1
- URL, for downloading / Environment setup
- RStudio
- reference link / Environment setup
S
- sentimental rankings
- about / The sentimental rankings
- tips data, extracting / Extracting tips data – the go to step
- textual data / The actual data
- tips data, analysis / Analysis of tips
- final rankings / The final rankings
- sentiment analysis / Features
- about / Sentiment analysis
- key concepts / Key concepts of sentiment analysis
- subjectivity / Subjectivity
- sentiment polarity / Sentiment polarity
- opinion summarization / Opinion summarization
- features / Features
- Parts of Speech (POS) / Features
- N-grams / Features
- in R programming language / Sentiment analysis in R
- sentiment trend analysis
- about / Sentiment trend analysis
- data, obtaining / Getting the data – not again
- basic descriptive statistics / Basic descriptive statistics – the usual
- numerical sentiment trends / Numerical sentiment trends
- emotion-based sentiment trends / Emotion-based sentiment trends
- SentiNet / Sentiment analysis in R
- SentiWordNet / Sentiment analysis in R
- social media
- about / Understanding social media
- advantages / Advantages and significance
- disadvantages / Disadvantages and pitfalls
- analytics / Social media analytics
- analytics workflow / A typical social media analytics workflow
- data access / Data access
- data processing / Data processing and normalization
- normalization / Data processing and normalization
- data analysis / Data analysis
- insights / Insights
- opportunities / Opportunities
- challenges / Challenges
- social media, advantages
- cost savings / Advantages and significance
- networking / Advantages and significance
- ease of use / Advantages and significance
- global audience / Advantages and significance
- prompt feedback / Advantages and significance
- grievance redressal / Advantages and significance
- entertainment / Advantages and significance
- visibility / Advantages and significance
- social media, challenges
- big data / Challenges
- accessibility issues / Challenges
- unstructured / Challenges
- noisy data / Challenges
- social media, disadvantages
- privacy concerns / Disadvantages and pitfalls
- security issues / Disadvantages and pitfalls
- addiction / Disadvantages and pitfalls
- negativity / Disadvantages and pitfalls
- risks / Disadvantages and pitfalls
- social network
- analyzing / Analyzing your personal social network
- descriptive statistics / Basic descriptive statistics
- mutual interests, analyzing / Analyzing mutual interests
- network graph, building / Build your friend network graph
- network graph, visualizing / Visualizing your friend network graph
- node properties, analyzing / Analyzing node properties
- network communities, analyzing / Analyzing network communities
- StackExchange
- about / Understanding StackExchange, Data Science and StackExchange, Challenges
- reference link / Understanding StackExchange
- data access / Data access
- data dump / Data access, The StackExchange data dump
- data explorer / Data access
- APIs / Data access
- data dump, accessing / Accessing data dumps
- data dump, contents / Contents of data dumps
- data, overview in data dump / Quick overview of the data in data dumps
- posts / Posts
- users / Users
- with data dumps / Getting started with data dumps
- StackExchange data dump
- reference link / Accessing data dumps
- supervised learning techniques
- about / Supervised learning
- classification / Supervised learning
- regression / Supervised learning
- Support Vector Machines (SVM) / Sentiment analysis in R
- syuzhet
- reference link / Sentiment analysis in R
T
- text analytics / Text analytics
- The Guardian
- URL / Accessing news data
- The New York Times
- URL / Accessing news data
- tips data
- descriptive statistics / Basic descriptive statistics
- topic modeling
- about / Topic modeling
- data extraction / Getting to the data
- basic descriptive analysis / Basic descriptive analysis
- example / Topic modeling for Mr. Trump's phases
- data, cleaning / Cleaning the data
- data, pre-processing / Pre-processing the data
- package topicmodels / The modeling part
- analysis / Analysis of topics
- trend
- analysis / Trend analysis
- trending repositories
- retrieving / Retrieving trending repositories
- analyzing, created over time / Analyzing trending repositories created over time
- analyzing, updated over time / Analyzing trending repositories updated over time
- Twitter
- about / Understanding Twitter
- Application Programming Interfaces (APIs) / APIs
- URL, for documentation / APIs
- application, registering / Registering an application
- connecting, to R programming language used / Connecting to Twitter using R
- Tweets, extracting / Extracting sample Tweets
U
- unsupervised learning techniques
- clustering / Unsupervised learning
- association rule mining / Unsupervised learning
- dimensionality reduction / Unsupervised learning
- unsupervised learning techniques about / Unsupervised learning
- US Geological Survey (USGS) / Trend analysis
V
- vectors / Vectors
- venue graph / Venue graph – where do people go next?
W
- weekly code modification history
- analyzing / Analyzing weekly code modification history
- weekly commit frequency
- analyzing / Analyzing weekly commit frequency
- weekly commit frequency comparison
- analyzing / Analyzing weekly commit frequency comparison
- WordNet / Sentiment analysis in R