R for Data Science: Lunchbreak Lessons
Offered By: LinkedIn Learning
Course Description
Overview
Learn R on your lunch break. This weekly series reviews the language features, development tools, and libraries that will make you a more productive R programmer.
Syllabus
Introduction
- Welcome
- Exercise files
- R built-in data sets
- Vector math
- Subsetting
- R data types: Basic types
- R data types: Vector
- R data types: List
- R data types: Factor
- R data types: Matrix
- R data types: Array
- R data types: Data frame
- Data frames: Order and merge
- Data frames: Read and update
- Data frames: rbind
- Dataframes: cbind
- apply and lapply
- mapply
- plot
- Brackets and double-brackets
- mean, rowMeans, and colMeans
- RSQLite
- sqldf
- Aggregate
- Random numbers
- Pipeline
- Working with clipboards
- Style guides
- cut
- split
- askYesNo
- cdplot
- Fun
- boxplot
- Histogram
- Plot to file
- coplot
- cowsay
- table
- Look inside
- barplot
- Pie chart
- unlist
- Joins: Inner and full
- Joins: Left and right
- Sets: Union, intersect, and difference
- Sets: Equal and in
- colors
- ifelse
- spineplot
- browser
- debugonce
- Default mirror
- Dealing with NA
- Using with()
- Simple string matching
- grep
- dotchart
- fourfoldplot
- matplot
- dimnames
- mosaicplot
- stemplot
- stripchart
- sunflower
- Switch
- Switch on factors
- Any/all
- sub, gsub, regex, and backreferences
- agrep and fuzzy matching
- combn finds combinations
- edit, fix, and dataentry
- zeallot
- menu
- person
- txtProgressBar
- zip and tar
- bitwise
- by is like tapply
- Update your R
- Be careful with transpose
- Passwords
- heatmap
- combine
- stopifnot
- weighted.mean
- chartr
- file.choose
- duplicated and unique
- load and save
- floor, round, ceiling, and trunc
- expand.grid
- Professional groups
- Simplify with c
- Logical operators
- char.expand
- complete.cases
- swirl
- tryCatch
- Double colons
- for loop
- The 100th episode
- while loop
- repeat loop
- Create your own swirl lesson
- Logic and flow control
- matrix, row, and column
- cumsum, cumprod, cummax, an dcummin
- issymetric
- file.access
- file.info
- dput and dget
- Sort a data frame by multiple columns
- diag
- crossprod
- upper.tri and lower.tri
- strsplit() splits strings at matched characters
- Use setnames() to change the name of an object
- Change the structure of a vector with stack()
- Use droplevels() to simplify factors
- Use .Rmd for documentation
- Use rep() to create long repetitive vectors
- Use format() to improve readability
- Use pmax() and pmin() to discover the scope of paired vectors
- Use print() for more than you do now
- Use range() and extendrange() to analyze and manipulate groups of numbers
- Evaluate the importance of a number with rank()
- Use saveRDS() and readRDS() to serialize objects
- Use regular expressions with regexpr() and gregexpr()
- message
- regexpr
- diff
- exists
- formulas
- RPres
- lattice: Introduction
- lattice: xyplot
- lattice: cloud and wireframe
- lattice: contourplot
- lattice: barchart
- lattice: splom charts
- lattice: panels
- lattice: stripplot
- whichmin and whichmax
- par: font, size, color
- par: margins
- par: pch and points
- legend
- identical
- Matrix math: Overview of functions
- Matrix math review
- matrix: solve systems
- matrix: solve inverse
- matrix: backsolve and forwardsolve
- Matrix: Determinant
- Arrays and outer
- Matrix: Crossproduct
- Matrix SVD and QR decomposition
- Matrix: Eigenvalues and eigenvectors
- Locator
- on.exit
- missing
- nargs
- tidyverse
- gutenbergr
- Create and clean a natural language corpus
- Remove stopwords from an NLP corpus
- NLP and term-document matrix
- Analyze term-document matrix
- NLP packages: Tidytext
- NLP packages: Quanteda
- NLP packages: Sentiment analysis
- Word clouds
- Hidden features of installr
- Use the Matrix package
- Create a sparse matrix
- Sparse matrices, triangles, and more
- Bootstrap analysis with R
- checkUsage
- Use R on the Raspberry Pi
- list2df()
- Introduction to clustering
- Clustering with kmeans
- Clustering with pam and clara
- Understanding silhouette graphs
- Clustering with fanny
- Clustering with hclust
- Clustering with agnes
- Clustering with diana
- cutree and identify with hclust
- Clustering with mona
- Clustering: dist vs. daisy
- Parameterized R markdown
- Run R on a schedule
- The new forward pipe operator
- Backslash lambda functions
- Dist() in depth
- Scale()
- toJSON
- fromJSON
- Validate JSON
- Plotmath and expression
- Run R in batch mode
- Explore music
- BEEP
- install.packages
- old.packages, new.packages, and update.packages
- library and require
- Excel in R: SUM
- Excel in R: IF
- Excel in R: LOOKUP
- Excel in R: LEFT and RIGHT
- Excel in R: MATCH
- Excel in R: CHOOSE
- Excel in R: DATE
- Excel in R: DAYS
- Excel in R: FIND and FINDB
- Excel in R: INDEX
- Excel in R: COUNT
- Excel in R: AVERAGE
- Excel in R: SUMIF and AVERAGEIF
- Excel in R: COUNTIF
- Excel in R: CONCATENATE
- Excel in R: MAX and MIN
- Excel in R: PROPER
- Excel in R: AND
- Excel in R: LEN
- Excel in R: COUNTA
- Excel in R: NETWORKDAYS
- Excel in R: IFERROR
- Citation
- Vectorize
- Powerpoint from R
- Infix operator
- Kronecker
- Flowcharting
- Glue
- Crayon
- COVID-19
- Apexcharter
- Factorial
- Download files
- Choose
- Beta and gamma
- as.Date()
- as.POSIXlt()
- as.POSIXct()
- Lubridate
- ISOdate()
- system.timezone() and OlsonNames()
- format()
- difftime()
- seq.Date()
- weekdays(), months(), quarters(), Julian()
- Introduction to Plumber
- Plumber request and response objects
- getwd setwd
- Use Visual Studio Code with R
- Tibbles
- Overview of dplyr
- dplyr: mutate
- dplyr: select
- dplyr: filter
- dplyr: slice and friends
- dplyr: summarise
- dplyr: arrange
- dplyr: group_by
- dbplyr translates R to SQL
- dplyr: pull
- dplyr: joins
- R7 OOP: Introduction
- R7 OOP: Properties
- R7 OOPS: Property getters and setters
- R7 OOPS: Validators
- R7 OOP: Class Inheritance
- R7 OOP: Generics and Methods
- Python with RStudio
- Animating plots
- Animating ggplot
- Introduction to Quarto
Taught by
Mark Niemann-Ross
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