introduction to r and statistics

15
Introduction to R and Statistics Thomas INGICCO G. Courbet, Le désespéré (Autoportrait) G. Courbet, The desperate man (Self-portrait)

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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:. - PowerPoint PPT Presentation

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Page 1: Introduction to R and  Statistics

Introduction to R and StatisticsThomas INGICCO

G. Courbet, Le désespéré (Autoportrait)G. Courbet, The desperate man (Self-portrait)

Page 2: Introduction to R and  Statistics

R is a freeware…

Page 3: Introduction to R and  Statistics

… but before all it is a language with its own grammar made of:

Page 4: Introduction to R and  Statistics

… but before all it is a language with its own grammar made of:

To create an object which will contain data or informations, we use "<-" :aa <- NULLaa <- "A sentence"bb <- 10:34cc <- 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 sensitiveaa Aa # R being a language, when you make a mistake, it tells you

Spaces are not importantbbb<-10 : 34

Page 5: Introduction to R and  Statistics

… but R is also a calculator:

25/5

5^2

25^0.5sqrt(25) # This is a function

sqrt

?sqrthelp(sqrt)

Page 6: Introduction to R and  Statistics

… but R is also a calculator:

25/5

5^2

25^0.5sqrt(25) # This is a function

sqrt

?sqrthelp(sqrt)

sqrt(sum(bb)) # Functions are matriochkas

Page 7: Introduction to R and  Statistics

… but R is also a calculator:

25/5

5^2

25^0.5sqrt(25) # This is a function

sqrt

?sqrthelp(sqrt)

sqrt(sum(bb)) # Functions are matriochkas

bb[-3]bb+bbbb+bb[-3]bb+cc

Page 8: Introduction to R and  Statistics

… but before all it is a language with its own grammar made of:

Page 9: Introduction to R and  Statistics

Modes – nature of your data- Numeric – numbers (51, 32, 47mm)- Character – chain of characters (« y », « a+b+c »)- Factor – qualitative values (« Red », « Orange »)- Logical – specific attributes (TRUE, FALSE, NA)

Special arguments- NA – Not Available, absence of data- NULL – Empty object- TRUE or T – Logical argument- FALSE or F– Logical argument

… but before all it is a language with its own grammar made of:

Page 10: Introduction to R and  Statistics

Classes – how you present your 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

… but before all it is a language with its own grammar made of:

ls() # Check the list of the created objects

# Vectoris.vector(bb)

Page 11: Introduction to R and  Statistics

Classes – how you present your 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

… but before all it is a language with its own grammar made of:

# Matrixclass(cc)cc <- matrix(10:34, nc=5, nr=5) # Remember

ccc <- c(10:34)ccc

Page 12: Introduction to R and  Statistics

Classes – how you present your 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

… but before all it is a language with its own grammar made of:

# Matrixclass(cc)cc <- matrix(10:34, nc=5, nr=5) # Remember

ccc <- c(10:34)ccc

is.vector(ccc)dim(ccc)<-c(5,5)ccc

Page 13: Introduction to R and  Statistics

Classes – how you present your 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

… but before all it is a language with its own grammar made of:

# Matrixclass(cc)cc <- matrix(10:34, nc=5, nr=5) # Remember

ccc <- c(10:34)ccc

is.vector(ccc)dim(ccc)<-c(5,5)cccis.matrix(ccc)

Page 14: Introduction to R and  Statistics

Classes – how you present your 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

… but before all it is a language with its own grammar made of:

# Matrixclass(cc)cc <- matrix(10:34, nc=5, nr=5) # Remember

ccc <- c(10:34)ccc

is.vector(ccc)dim(ccc)<-c(5,5)cccis.matrix(ccc)

matrix(1:6, 3, 2)matrix(1:6, 3, 2, byrow=T)

Page 15: Introduction to R and  Statistics

Classes – how you present your 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

… but before all it is a language with its own grammar made of:

# Data.framedd<-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$Typedd[,9]dd[3,8:11]