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Introduction to R and Statistics

Introduction to R and Statistics. Thomas INGICCO. G. Courbet, Le désespéré (Autoportrait) G. Courbet, The desperate man (Self-portrait). R is a freeware…. … but before all it is a language with its own grammar made of:.

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Introduction to R and Statistics

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  1. Introduction to R and Statistics Thomas INGICCO G. Courbet, Le désespéré (Autoportrait) G. Courbet, The desperate man (Self-portrait)

  2. R is a freeware…

  3. … but before all itis a languagewithitsowngrammar made of:

  4. … but before all itis a languagewithitsowngrammar made of: To create an object which will contain data or informations, we use "<-" : aa <- NULL aa <- "A sentence" bb <- 10:34 cc <- matrix(10:34, nc=5, nr=5) To see the content of the object, we type its name: aa Hash symbol (#) allows you to comment your script: aa# This is a comment Semicolon allows you to separate the commands on the same line: aa ; bb ; cc # We look at the content of the three objects R is case sensitive aa Aa# R being a language, when you make a mistake, it tells you Spaces are not important bbb<-10 : 34

  5. … but R isalso a calculator: 25/5 5^2 25^0.5 sqrt(25) # This is a function sqrt ?sqrt help(sqrt)

  6. … but R isalso a calculator: 25/5 5^2 25^0.5 sqrt(25) # This is a function sqrt ?sqrt help(sqrt) sqrt(sum(bb)) # Functions are matriochkas

  7. … but R isalso a calculator: 25/5 5^2 25^0.5 sqrt(25) # This is a function sqrt ?sqrt help(sqrt) sqrt(sum(bb)) # Functions are matriochkas bb[-3] bb+bb bb+bb[-3] bb+cc

  8. … but before all itis a languagewithitsowngrammar made of:

  9. … but before all itis a languagewithitsowngrammar made of: • Special arguments • - NA – Not Available, absence of data • NULL – Emptyobject • TRUE or T – Logical argument • FALSE or F– Logical argument • Modes – nature of your data • - Numeric – numbers (51, 32, 47mm) • Character – chain of characters (« y », « a+b+c ») • Factor – qualitative values (« Red », « Orange ») • Logical – specificattributes (TRUE, FALSE, NA)

  10. … but before all itis a languagewithitsowngrammar made of: • Classes – how youpresentyour data • - Vector – series of values of 1 dimension • Matrix – series of values of 2 dimensions • Arrays – series of values of n dimensions • Data Frame – series of values in columns • List – series of objects • Table – Contingency table ls() # Check the list of the created objects # Vector is.vector(bb)

  11. … but before all itis a languagewithitsowngrammar made of: • Classes – how youpresentyour data • - Vector – series of values of 1 dimension • Matrix – series of values of 2 dimensions • Arrays – series of values of n dimensions • Data Frame – series of values in columns • List – series of objects • Table – Contingency table # Matrix class(cc) cc <- matrix(10:34, nc=5, nr=5) # Remember ccc <- c(10:34) ccc

  12. … but before all itis a languagewithitsowngrammar made of: • Classes – how youpresentyour data • - Vector – series of values of 1 dimension • Matrix – series of values of 2 dimensions • Arrays – series of values of n dimensions • Data Frame – series of values in columns • List – series of objects • Table – Contingency table # Matrix class(cc) cc <- matrix(10:34, nc=5, nr=5) # Remember ccc <- c(10:34) ccc is.vector(ccc) dim(ccc)<-c(5,5) ccc

  13. … but before all itis a languagewithitsowngrammar made of: • Classes – how youpresentyour data • - Vector – series of values of 1 dimension • Matrix – series of values of 2 dimensions • Arrays – series of values of n dimensions • Data Frame – series of values in columns • List – series of objects • Table – Contingency table # Matrix class(cc) cc <- matrix(10:34, nc=5, nr=5) # Remember ccc <- c(10:34) ccc is.vector(ccc) dim(ccc)<-c(5,5) ccc is.matrix(ccc)

  14. … but before all itis a languagewithitsowngrammar made of: • Classes – how youpresentyour data • - Vector – series of values of 1 dimension • Matrix – series of values of 2 dimensions • Arrays – series of values of n dimensions • Data Frame – series of values in columns • List – series of objects • Table – Contingency table # Matrix class(cc) cc <- matrix(10:34, nc=5, nr=5) # Remember ccc <- c(10:34) ccc is.vector(ccc) dim(ccc)<-c(5,5) ccc is.matrix(ccc) matrix(1:6, 3, 2) matrix(1:6, 3, 2, byrow=T)

  15. … but before all itis a languagewithitsowngrammar made of: • Classes – how youpresentyour data • - Vector – series of values of 1 dimension • Matrix – series of values of 2 dimensions • Arrays – series of values of n dimensions • Data Frame – series of values in columns • List – series of objects • Table – Contingency table # Data.frame dd<-read.table("K:/Cours/Philippines/Statistics-210/Lecture-4/Ceramics.txt", header=TRUE) # Opening Data; return is possible in a function; a function has arguments class(dd) dd$Type dd[,9] dd[3,8:11]

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