R, Notes for Professionals

R, Notes for Professionals.

Фрагмент из книги:
In R, data objects are manipulated using named data structures. The names of the objects might be called "variables" although that term does not have a specific meaning in the official R documentation. R names are case sensitive and may contain alphanumeric characters(a-z,A-z,0-9), the dot/period(.) and underscore(_). To create names for the data structures, we have to follow the following rules.

R, Notes for Professionals


Types of data structures.
There are no scalar data types in R. Vectors of length-one act like scalars.
• Vectors: Atomic vectors must be sequence of same-class objects.: a sequence of numbers, or a sequence of logicals or a sequence of characters, v <- c(2, 3, 7, 10), v2 <- c("a", "b", "c") are both vectors.

• Matrices: A matrix of numbers, logical or characters, a <- matrix(data = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), nrov/ = 4, ncol = 3, byrow = F). Like vectors, matrix must be made of same-class elements. To extract elements from a matrix rows and columns must be specified: a[ 1,2] returns [1 ] 5 that is the element on the first row, second column.

• Lists: concatenation of different elements mylist <- list (course = 'stat', date = '04/07/2009', num_isc = 7, num_cons = 6, num_mat = as.character(c(45020, 45679, 46789, 43126, 42345, 47568, 45674)), results = c(30, 19, 29, NA, 25, 26 ,27) ). Extracting elements from a list can be done by name (if the list is named) or by index. In the given example mylistSresults and mylist [ [6] ] obtains the same element. Warning: if you try mylist [6], R won't give you an error, but it extract the result as a list. While mylist[ [6] ] [2] is permitted (it gives you 19), mylist[6] [2] gives you an error.

• data.frame: object with columns that are vectors of equal length, but (possibly) different types. They are not matrices, exam <- data.frame(matr = as.character(c(45020, 45679, 46789, 43126, 42345, 47568, 45674)), res_S = c(30, 19, 29, NA, 25, 26, 27), res_0 = c(3, 3, 1, NA, 3, 2, NA), res.TOT = c(30,22,30,NA,28,28,27)). Columns can be read by name examSmatr, exam[, 'matr' ] or by index exam[1 ], exam[, 1 ]. Rows can also be read by name exam[' rowname', ] or index exam[1, ]. Dataframes are actually just lists with a particular structure (rownames-attribute and equal length components)

Contents.
About.
Chapter 1: Getting started with R Language.
Chapter 2: Variables.
Chapter 3: Arithmetic Operators.
Chapter 4: Matrices.
Chapter 5: Formula.
Chapter 6: Reading and writing strings.
Chapter 7: String manipulation with stringi package.
Chapter 8: Classes.
Chapter 9: Lists.
Chapter 10: Hashmaps.
Chapter 11: Creating vectors.
Chapter 12: Date and Time.
Chapter 13: The Date class.
Chapter 14: Date-time classes (POSIXct and POSIXlt).
Chapter 15: The character class.
Chapter 16: Numeric classes and storage modes.
Chapter 17: The logical class.
Chapter 18: Data frames.
Chapter 19: Split function.
Chapter 20: Reading and writing tabular data in plain-text files (CSV, TSV, etc.).
Chapter 21: Pipe operators (%>% and others).
Chapter 22: Linear Models (Regression).
Chapter 23: data.table.
Chapter 24: Pivot and unpivot with data.table.
Chapter 25: Bar Chart.
Chapter 26: Base Plotting.
Chapter 27: boxplot.
Chapter 28: ggplot2.
Chapter 29: Factors.
Chapter 30: Pattern Matching and Replacement.
Chapter 31: Run-length encoding.
Chapter 32: Speeding up tough-to-vectorize code.
Chapter 33: Introduction to Geographical Maps.
Chapter 34: Set operations.
Chapter 35: tidyverse.
Chapter 36: Rcpp.
Chapter 37: Random Numbers Generator.
Chapter 38: Parallel processing.
Chapter 39: Subsetting.
Chapter 40: Debugging.
Chapter 41: Installing packages.
Chapter 42: Inspecting packages.
Chapter 43: Creating packages with devtools.
Chapter 44: Using pipe assignment in your own package %<>%: How to?.
Chapter 45: Arima Models.
Chapter 46: Distribution Functions.
Chapter 47: Shiny.
Chapter 48: spatial analysis.
Chapter 49: sqldf.
Chapter 50: Code profiling.
Chapter 51: Control flow structures.
Chapter 52: Column wise operation.
Chapter 53: JSON.
Chapter 54: RODBC.
Chapter 55: lubridate.
Chapter 56: Time Series and Forecasting.
Chapter 57: strsplit function.
Chapter 58: Web scraping and parsing.
Chapter 59: Generalized linear models.
Chapter 60: Reshaping data between long and wide forms.
Chapter 61: RMarkdown and knitr presentation.
Chapter 62: Scope of variables.
Chapter 63: Performing a Permutation Test.
Chapter 64: xgboost.
Chapter 65: R code vectorization best practices.
Chapter 66: Missing values.
Chapter 67: Hierarchical Linear Modeling.
Chapter 68: *apply family of functions (functionals).
Chapter 69: Text mining.
Chapter 70: ANOVA.
Credits.
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2021-12-05 15:09:01