Applications-of-R-in-Healthcare
1
Preamble
2
Introduction to R
2.1
Plot using base R
2.2
ggplot2
2.2.1
Histogram
2.2.2
Bar plot
2.2.3
Pie chart
2.2.4
Scatter plot
2.2.5
arrange plot in grids
2.2.6
Line plot
2.2.7
Facet wrap
2.2.8
Polygons
2.2.9
Gantt chart
2.2.10
Heatmap
2.3
ggplot2 extra
2.3.1
Alluvial and Sankey diagram
2.3.2
Survival plot
2.3.3
ggraph and tidygraph
2.3.4
ggparty-decision tree
2.3.5
ggROC- ROC curve
2.3.6
Map
2.3.7
ggwordcloud
2.3.8
gganimate
2.3.9
ggneuro
2.4
plotly
2.4.1
Scatter plot with plotly
2.4.2
Bar plot with plotly
2.4.3
Heatmap
2.4.4
map
3
Data Wrangling
3.1
Data
3.1.1
Vector, Arrays, Matrix
3.1.2
apply, lapply, sapply
3.1.3
Simple function
3.1.4
for loop
3.1.5
Functional
3.2
Data storage
3.2.1
Data frame
3.2.2
Excel data
3.2.3
Foreign data
3.2.4
json format
3.3
Tidy data
3.3.1
Factors
3.3.2
Multiple files
3.3.3
Pivot
3.4
Regular Expressions
3.4.1
base R
3.4.2
stringr
3.5
PDF to xcel
3.5.1
Scanned text or picture
3.6
Web scraping
3.7
Manipulating Medical Images
3.7.1
DICOM and nifti format
3.7.2
Manipulating array of medical images
3.7.3
Combining arrays
3.7.4
Math operation on multidimensional array
3.7.5
Math operation on list
3.7.6
Vectorising nifti object
3.7.7
tar file
3.7.8
Image registration
3.7.9
Rescaling
3.7.10
MNI template
3.7.11
add text
3.7.12
add text
4
Statistics
4.1
Univariable analyses
4.1.1
Parametric tests
4.1.2
Non-parametric tests
4.2
Regression
4.2.1
Brief review of matrix
4.2.2
Linear (least square) regression
4.2.3
Logistic regression
4.3
Special types of regression
4.3.1
Ordinal regression
4.3.2
Survival analysis
4.3.3
Quantile regression
4.3.4
Poisson regression
4.3.5
Conditional logistic regression
4.3.6
Multinomial modelling
4.4
Sample size estimation
4.4.1
Proportion
4.4.2
Logistic regression
4.4.3
Survival studies
4.4.4
Multiple regression
4.5
Interpreting clinical trials
4.5.1
NNT from ARR
4.5.2
NNT from odds ratio
4.5.3
NNT from hazard ratio
4.5.4
NNT from metaananlysis
4.6
Metaanalysis
4.6.1
PRISMA
4.6.2
Conversion of mean and median
4.6.3
Inconsistency I2
4.6.4
Metaanalysis of proportion
4.6.5
Bivariate Metaanalysis
4.6.6
Metaanalysis of clinical trial.
4.6.7
Metaregression
4.7
Data simulation
5
Multivariate Analysis
5.1
Multivariate regression
5.1.1
Penalised regression
5.1.2
MARS
5.1.3
Mixed modelling
5.1.4
Trajectory modelling
5.1.5
Generalized estimating equation (GEE)
5.2
Principal component analysis
5.3
Independent component analysis
5.4
Partial least squares
5.5
Causal inference
6
Machine learning
6.1
Decision tree analysis
6.1.1
Information theory driven
6.1.2
Conditional decision tree
6.1.3
criticisms of decision tree
6.2
Random Forest
6.2.1
Random survival forest
6.3
Gradient Boost Machine
6.3.1
Extreme gradient boost machine
6.4
KNN
6.5
Support vector machine
6.5.1
Survival analysis using random forest
6.6
Non-negative matrix factorisation
6.7
Formal concept analysis
6.8
Evolutionary Algorithm
6.8.1
Simulated Annealing
6.8.2
Genetic Algorithm
6.9
Manifold learning
6.9.1
T-Stochastic Neighbourhood Embedding
6.9.2
Self organising map
6.10
Deep learning
6.10.1
Multiplayer Perceptron
6.10.2
CNN
6.10.3
RNN
6.10.4
Reinforcement learning
7
Machine Learning Part 2
7.1
Bag of words
7.1.1
TFIDF
7.1.2
Extracting data from web
7.2
Wordcloud
7.3
Bigram analysis
7.4
Trigram
7.5
Topic modeling or thematic analysis
7.5.1
Probabilistic topic model
7.5.2
NMF
8
Bayesian Analysis
8.1
Baysian belief
8.1.1
Conditional probability
8.2
Markov model
8.3
INLA, Stan and BUGS
8.3.1
Linear regression
8.3.2
Logistic regression
8.3.3
Mixed model
8.3.4
Bayesian Metaanalysis
8.3.5
Cost
9
Operational Research
9.1
Queueing theory
9.2
Discrete Event Simulations
9.2.1
Simulate capacity of system
9.2.2
Queuing network
9.3
Linear Programming
9.4
Forecasting
9.4.1
Bed requirement
9.4.2
Length of stay
9.4.3
Customer churns
9.5
Process mapping
9.6
Supply chains
9.7
Health economics
9.7.1
Cost
10
Graph Theory
10.1
Special graphs
10.1.1
Laplacian matrix
10.1.2
Bimodal (bipartite) graph
10.2
Centrality Measures
10.2.1
Local centrality measures
10.2.2
Global centrality measures
10.3
Community
10.4
Visualising graph
10.4.1
Visnetwork
10.4.2
Large graph
10.5
Social Media and Network Analysis
10.5.1
Twitter
10.5.2
Youtube
11
Geospatial analysis
11.1
Geocoding
11.1.1
OpenStreetMap
11.1.2
Google Maps API
11.2
Sp and sf objects
11.3
Thematic map
11.3.1
Calculate distance to Hospital-OpenStreetMap
11.4
Spatial regression
11.4.1
New York COVID-19 mortality
11.4.2
Danish Stroke Registry
11.4.3
INLA
11.4.4
Stan
11.5
Machine learning in spatial analysis
11.6
Spatio-temporal regression
12
App
12.1
Brief introduction to Shiny app
13
Appendix
13.1
Brief introduction to Matrix
13.2
Regression
13.2.1
Linear regression
13.2.2
Logistic regression
References
Published with bookdown
Applications of R in Healthcare
References