Merge pull request #1668 from deivuh/r-spanish

[R/es] Added Spanish translation for R

2 files changed, 1524 insertions, 0 deletions

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+---
+language: R
+contributors:
+    - ["e99n09", "http://github.com/e99n09"]
+    - ["isomorphismes", "http://twitter.com/isomorphisms"]
+translators:
+    - ["David Hsieh", "http://github.com/deivuh"]
+lang: es-es    
+filename: learnr-es.r
+---
+
+R es un lenguaje de computación estadística. Tiene muchas librerías para cargar
+y limpiar sets de datos, ejecutar procedimientos estadísticos y generar
+gráficas. También puedes ejecutar comandos `R` dentro de un documento de
+LaTeX.
+
+```r
+
+# Los comentariso inician con símbolos numéricos.
+
+# No puedes hacer comentarios de múltiples líneas
+# pero puedes agrupar múltiples comentarios de esta manera. 
+
+# En Windows puedes utilizar CTRL-ENTER para ejecutar una línea.
+# En Mac utilizas COMMAND-ENTER
+
+
+#############################################################################
+# Cosas que puedes hacer sin entender nada acerca de programación
+#############################################################################
+
+# En esta sección, mostramos algunas cosas chileras / cool que puedes hacer en
+# R sin entender nada de programación. No te preocupes en entender nada 
+# de lo que hace este código. Solo disfruta!
+
+data()	        # Examinar sets de datos pre-cargados
+data(rivers)	# Obtiene este: Lengths of Major North American Rivers"
+ls()	        # Fijarse que "rivers" ahora aparece en el workspace
+head(rivers)	# Echarle un ojo al set de datos
+# 735 320 325 392 524 450
+
+length(rivers)	# ¿Cuántos ríos fueron medidos?
+# 141
+summary(rivers) # ¿Cuáles son algunas estadísticas generales?
+#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
+#  135.0   310.0   425.0   591.2   680.0  3710.0
+
+# Generar una gráfica tallo-y-hoja (Visualización de datos tipo histograma)
+stem(rivers)
+
+# El punto decimal son 2 dígitos a la derecha de | 
+#
+#   0 | 4
+#   2 | 011223334555566667778888899900001111223333344455555666688888999
+#   4 | 111222333445566779001233344567
+#   6 | 000112233578012234468
+#   8 | 045790018
+#  10 | 04507
+#  12 | 1471
+#  14 | 56
+#  16 | 7
+#  18 | 9
+#  20 |
+#  22 | 25
+#  24 | 3
+#  26 |
+#  28 |
+#  30 |
+#  32 |
+#  34 |
+#  36 | 1
+
+stem(log(rivers)) # Fijarse que la data no es normal ni log-normal!
+# Toma eso, fundamentalistas de la curva de campana!
+
+# El punto decimal está a 1 dígito a la izquierda del |
+#
+#  48 | 1
+#  50 |
+#  52 | 15578
+#  54 | 44571222466689
+#  56 | 023334677000124455789
+#  58 | 00122366666999933445777
+#  60 | 122445567800133459
+#  62 | 112666799035
+#  64 | 00011334581257889
+#  66 | 003683579
+#  68 | 0019156
+#  70 | 079357
+#  72 | 89
+#  74 | 84
+#  76 | 56
+#  78 | 4
+#  80 |
+#  82 | 2
+
+# Generar un histograma:
+hist(rivers, col="#333333", border="white", breaks=25) # Juega con los estos parámetros
+hist(log(rivers), col="#333333", border="white", breaks=25) # Generarás más gráficas después
+
+# Aquí hay otro set de datos pre-cargado. R tiene bastantes de éstos.
+data(discoveries)
+plot(discoveries, col="#333333", lwd=3, xlab="Year",
+     main="Number of important discoveries per year")
+plot(discoveries, col="#333333", lwd=3, type = "h", xlab="Year",
+     main="Number of important discoveries per year")
+
+# En lugar de dejar el orden por defecto (por año),
+# podemos ordenar de tal manera que muestre qué es típico:
+sort(discoveries)
+#  [1]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  2  2  2  2
+# [26]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  3  3  3
+# [51]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  4  4  4  4  4  4  4  4
+# [76]  4  4  4  4  5  5  5  5  5  5  5  6  6  6  6  6  6  7  7  7  7  8  9 10 12
+
+stem(discoveries, scale=2)
+#
+#  El punto decimal se encuentra en |
+#
+#   0 | 000000000
+#   1 | 000000000000
+#   2 | 00000000000000000000000000
+#   3 | 00000000000000000000
+#   4 | 000000000000
+#   5 | 0000000
+#   6 | 000000
+#   7 | 0000
+#   8 | 0
+#   9 | 0
+#  10 | 0
+#  11 |
+#  12 | 0
+
+max(discoveries)
+# 12
+summary(discoveries)
+#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
+#    0.0     2.0     3.0     3.1     4.0    12.0
+
+# Tirar los dados varias veces
+round(runif(7, min=.5, max=6.5))
+# 1 4 6 1 4 6 4
+# Tus números será diferente de los míos, a menos que tengamos el mismo valor 
+# de random.seed(31337)
+
+# Dibuja de un Gaussian 9 veces
+rnorm(9)
+# [1]  0.07528471  1.03499859  1.34809556 -0.82356087  0.61638975 -1.88757271
+# [7] -0.59975593  0.57629164  1.08455362
+
+
+
+##################################################
+# Tipos de datos y aritmética básica
+##################################################
+
+# Ahora para la parte de programación orientada a objetos del tutorial.
+# En esta sección conocerás los tipos de datos importantes de R:
+# Enteros, numéricos, caracteres, lógicos, y factores.
+# Hay otros, pero esos son los que menos necesitas para empezar.
+
+# ENTEROS
+# Enteros de almacenamiento largo son escritos con L
+5L # 5
+class(5L) # "integer"
+# (Try ?class para más información en la función class().)
+# En R, cada valor único, como 5L, es considerado un vector de logitud 1
+length(5L) # 1
+# También puedes tener un vector de enteros con longitud > 1:
+c(4L, 5L, 8L, 3L) # 4 5 8 3
+length(c(4L, 5L, 8L, 3L)) # 4
+class(c(4L, 5L, 8L, 3L)) # "integer"
+
+# NUMÉRICOS
+# Un "numérico" es un número de punto flotante de doble precisión.
+5 # 5
+class(5) # "numeric"
+# Nuevamente, todo en R es un vector;
+# puedes hacer un vector numérico con más de un elemento
+c(3,3,3,2,2,1) # 3 3 3 2 2 1
+# También puedes utilizar el notación científica
+5e4 # 50000
+6.02e23 # Número de Avogadro
+1.6e-35 # Logintud Planck 
+# También puedes tener números infinitamente grandes o pequeños
+class(Inf)	# "numeric"
+class(-Inf)	# "numeric"
+# Puede que uses "Inf", por ejemplo, en integrate(dnorm, 3, Inf);
+# esto obvia las tablas de puntos Z.
+
+# ARITMÉTICA BÁSICA
+# Puedes hacer aritmética con números
+# Haciendo aritmética en un mix de enteros y numéricos, te da otro numérico
+10L + 66L # 76      # entero mas entero da entero
+53.2 - 4  # 49.2    # entero menos entero da numérico
+2.0 * 2L  # 4       # numérico veces entero da numérico
+3L / 4    # 0.75    # entero sobre numérico da numérico
+3 %% 2	  # 1       # el residuo de dos numéricos es otro numérico
+# La aritmética ilegal rinde un "not-a-number"
+0 / 0 # NaN
+class(NaN) # "numeric"
+# Puedes hacer aritmética con dos vectores con longitud mayor a 1,
+# siempre que la longitud del vector mayor es un entero múltiplo del menor.
+c(1,2,3) + c(1,2,3) # 2 4 6
+
+# CARACTERES
+# No hay diferencia entre strings y caracteres en R
+"Horatio" # "Horatio"
+class("Horatio") # "character"
+class('H') # "character"
+# Ambos eran vectores de caracteres de longitud 1
+# Aquí hay uno más largo:
+c('alef', 'bet', 'gimmel', 'dalet', 'he')
+# =>
+# "alef"   "bet"    "gimmel" "dalet"  "he"
+length(c("Call","me","Ishmael")) # 3
+# Puedes hacer operaciones regex en vectores de caracteres:
+substr("Fortuna multis dat nimis, nulli satis.", 9, 15) # "multis "
+gsub('u', 'ø', "Fortuna multis dat nimis, nulli satis.") # "Fortøna møltis dat nimis, nølli satis."
+# R tiene varios vectores predefinidos de caracteres 
+letters
+# =>
+#  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
+# [20] "t" "u" "v" "w" "x" "y" "z"
+month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"
+
+# LÓGICOS
+# En R, un "logical" es un boolean
+class(TRUE)	# "logical"
+class(FALSE)	# "logical"
+# Ese comportamiento es normal
+TRUE == TRUE	# TRUE
+TRUE == FALSE	# FALSE
+FALSE != FALSE	# FALSE
+FALSE != TRUE	# TRUE
+# El dato faltante (NA) es lógico también
+class(NA)	# "logical"
+# Utiliza | y & para operaciones lógicas
+# OR
+TRUE | FALSE	# TRUE
+# AND
+TRUE & FALSE	# FALSE
+# Puedes probar si x es TRUE (verdadero)
+isTRUE(TRUE)	# TRUE
+# Aquí tenemos un vector lógico con varios elementos:
+c('Z', 'o', 'r', 'r', 'o') == "Zorro" # FALSE FALSE FALSE FALSE FALSE
+c('Z', 'o', 'r', 'r', 'o') == "Z" # TRUE FALSE FALSE FALSE FALSE
+
+# FACTORES
+# La clase factor es para datos de categoría
+# Los factores pueden ser ordenados (como las calificaciones de los niños) 
+# o sin orden (como el género)
+factor(c("female", "female", "male", NA, "female"))
+#  female female male   <NA>   female
+# Levels: female male
+# Los "levels" son los valores que los datos categóricos pueden tener
+# Tomar nota que los datos faltantes no entran a los niveles
+levels(factor(c("male", "male", "female", NA, "female"))) # "female" "male"
+# Si un vector de factores tiene longitud 1, sus niveles también tendrán
+# una longitud de 1 también
+
+length(factor("male")) # 1
+length(levels(factor("male"))) # 1
+# Los factores son comúnmente vistos en marcos de dato, y una estructura de 
+# datos que cubriremos después
+data(infert) # "Infertility after Spontaneous and Induced Abortion"
+levels(infert$education) # "0-5yrs"  "6-11yrs" "12+ yrs"
+
+# NULL
+# "NULL" es uno raro; utilízalo para "limpiar" un vector
+class(NULL)	# NULL
+parakeet = c("beak", "feathers", "wings", "eyes")
+parakeet
+# =>
+# [1] "beak"     "feathers" "wings"    "eyes"
+parakeet <- NULL
+parakeet
+# =>
+# NULL
+
+# COERCIÓN DE TIPO
+# La coerción de tipos es cuando forzas un valor diferente tipo al que puede tomar.
+as.character(c(6, 8)) # "6" "8"
+as.logical(c(1,0,1,1)) # TRUE FALSE  TRUE  TRUE
+# Si pones elementos de diferentes tipos en un vector, coerciones raras pasan:
+c(TRUE, 4) # 1 4
+c("dog", TRUE, 4) # "dog"  "TRUE" "4"
+as.numeric("Bilbo")
+# =>
+# [1] NA
+# Warning message:
+# NAs introduced by coercion
+
+# También tomar nota: Esos solo eran datos de tipos básicos
+# Hay mucho más tipos de datos, como las fechas, series de tiempo, etc.
+
+
+##################################################
+# Variables, ciclos, condiciones (if/else)
+##################################################
+
+# A variable is like a box you store a value in for later use.
+# We call this "assigning" the value to the variable.
+# Having variables lets us write loops, functions, and if/else statements
+
+# VARIABLES
+# Muchas maneras de asignar valores:
+x = 5 # esto es posible
+y <- "1" # esto es preferido
+TRUE -> z # estos funciona pero es raro
+
+# CICLOS
+# Tenemos ciclos 'for'
+for (i in 1:4) {
+  print(i)
+}
+# Tenemos ciclos 'while'
+a <- 10
+while (a > 4) {
+	cat(a, "...", sep = "")
+	a <- a - 1
+}
+# Ten en mente que los ciclos 'for' y 'while' son lentos en R
+# Operaciones con vectores enteros (i.e. una fila o columna completa)
+# o tipos de función apply() (que discutiremos después) son preferidos
+
+# CONDICIONES (IF/ELSE)
+# De nuevo, bastante normal
+if (4 > 3) {
+	print("4 is greater than 3")
+} else {
+	print("4 is not greater than 3")
+}
+# =>
+# [1] "4 is greater than 3"
+
+# FUNCIONES
+# Definidos de la siguiente manera:
+jiggle <- function(x) {
+	x = x + rnorm(1, sd=.1)	#agregar un poco de ruido (controlado)
+	return(x)
+}
+# Llamados como cualquier otra función de R
+jiggle(5)	# 5±ε. luego de set.seed(2716057), jiggle(5)==5.005043
+
+
+
+###########################################################################
+# Estructura de datos: Vectores, matrices, marcos da datos y arreglos
+###########################################################################
+
+# UNIDIMENSIONAL
+
+# Empecemos desde el principio, y con algo que ya conoces: vectores.
+vec <- c(8, 9, 10, 11)
+vec	#  8  9 10 11
+# Preguntamos por elementos específicos poniendo un subconjunto en corchetes
+# (Toma nota de que R empieza los conteos desde 1)
+vec[1]		# 8
+letters[18]	# "r"
+LETTERS[13]	# "M"
+month.name[9]	# "September"
+c(6, 8, 7, 5, 3, 0, 9)[3]	# 7
+# También podes buscar por los índices de componentes específicos,
+which(vec %% 2 == 0)	# 1 3
+# obtener la primera o las últimas entradas de un vector,
+head(vec, 1)	# 8
+tail(vec, 2)	# 10 11
+# o averiguar si cierto valor se encuentra dentro de un vector
+any(vec == 10) # TRUE
+# Si un índice "se pasa", obtendrás un NA:
+vec[6]	# NA
+# Puedes encontrar la longitud de un vector con length()
+length(vec)	# 4
+# Puedes realizar operaciones con vectores enteros o con subconjuntos de vectores
+vec * 4	# 16 20 24 28
+vec[2:3] * 5	# 25 30
+any(vec[2:3] == 8) # FALSE
+# y R tiene muchas funciones pre-definidas para resumir vectores
+mean(vec)	# 9.5
+var(vec)	# 1.666667
+sd(vec)		# 1.290994
+max(vec)	# 11
+min(vec)	# 8
+sum(vec)	# 38
+# Otras funciones pre-definidas:
+5:15	# 5  6  7  8  9 10 11 12 13 14 15
+seq(from=0, to=31337, by=1337)
+# =>
+#  [1]     0  1337  2674  4011  5348  6685  8022  9359 10696 12033 13370 14707
+# [13] 16044 17381 18718 20055 21392 22729 24066 25403 26740 28077 29414 30751
+
+# BIDIMENCIONAL (TODO EN UNA CLASE)
+
+# Puedes hacer una matriz de las entradas todos de un mismo tipo como:
+mat <- matrix(nrow = 3, ncol = 2, c(1,2,3,4,5,6))
+mat
+# =>
+#      [,1] [,2]
+# [1,]    1    4
+# [2,]    2    5
+# [3,]    3    6
+# A diferencia de un vector, una clase matriz es una 'matriz', 
+# sin importar qué contiene
+class(mat) # => "matrix"
+# Consulta la primera fila
+mat[1,]	# 1 4
+# Realiza una operación en la primera columna
+3 * mat[,1]	# 3 6 9
+# Consulta por una celda específica
+mat[3,2]	# 6
+
+# Transpone una matriz entera
+t(mat)
+# =>
+#      [,1] [,2] [,3]
+# [1,]    1    2    3
+# [2,]    4    5    6
+
+# Multiplicación de matrices
+mat %*% t(mat)
+# =>
+#      [,1] [,2] [,3]
+# [1,]   17   22   27
+# [2,]   22   29   36
+# [3,]   27   36   45
+
+# cbind() une vectores como columnas para hacer una matriz
+mat2 <- cbind(1:4, c("dog", "cat", "bird", "dog"))
+mat2
+# =>
+#      [,1] [,2]
+# [1,] "1"  "dog"
+# [2,] "2"  "cat"
+# [3,] "3"  "bird"
+# [4,] "4"  "dog"
+class(mat2)	# matrix
+# De nuevo, ten en cuenta lo que sucedió
+# Debido a que las matrices deben de contener todas las entradas del mismo tipo,
+# todo fue convertido a la clase caracter
+c(class(mat2[,1]), class(mat2[,2]))
+
+# rbind() une vectores como filas para hacer una matriz
+mat3 <- rbind(c(1,2,4,5), c(6,7,0,4))
+mat3
+# =>
+#      [,1] [,2] [,3] [,4]
+# [1,]    1    2    4    5
+# [2,]    6    7    0    4
+# Ah, todo es de la misma clase. No hay coerciones. Mucho mejor.
+
+# BIDIMENSIONAL (DIFERENTES CLASES)
+
+# Para columnas de tipos diferentes, utiliza un data frame
+# Esta estructura de datos es muy útil para programación estadística,
+# una versión de ésta fue agregada a Python en el paquete "pandas".
+
+students <- data.frame(c("Cedric","Fred","George","Cho","Draco","Ginny"),
+                       c(3,2,2,1,0,-1),
+                       c("H", "G", "G", "R", "S", "G"))
+names(students) <- c("name", "year", "house") # name the columns
+class(students)	# "data.frame"
+students
+# =>
+#     name year house
+# 1 Cedric    3     H
+# 2   Fred    2     G
+# 3 George    2     G
+# 4    Cho    1     R
+# 5  Draco    0     S
+# 6  Ginny   -1     G
+class(students$year)	# "numeric"
+class(students[,3])	# "factor"
+# encontrar las dimensiones
+nrow(students)	# 6
+ncol(students)	# 3
+dim(students)	# 6 3
+# La función data.frame() convierte vectores de caracteres en vectores 
+# de factores por defecto; deshabilita este atributo
+# stringsAsFactors = FALSE cuando vayas a crear el data.frame
+?data.frame
+
+# Hay otras formas de hacer subconjuntos de data frames
+students$year	# 3  2  2  1  0 -1
+students[,2]	# 3  2  2  1  0 -1
+students[,"year"]	# 3  2  2  1  0 -1
+
+# Una versión aumentada de la estructura data.frame es el data.table
+# Si estás trabajando huge o panel data, o necesitas unificar algunos 
+# subconjuntos de datos, data.table puede ser una buena elección.
+# Aquí un tour:
+install.packages("data.table") # Descarga el paquete de CRAN
+require(data.table) # Cárgalo
+students <- as.data.table(students)
+students # Tomar en cuenta la diferencia de la impresión
+# =>
+#      name year house
+# 1: Cedric    3     H
+# 2:   Fred    2     G
+# 3: George    2     G
+# 4:    Cho    1     R
+# 5:  Draco    0     S
+# 6:  Ginny   -1     G
+students[name=="Ginny"] # obtener filas con name == "Ginny"
+# =>
+#     name year house
+# 1: Ginny   -1     G
+students[year==2] # obtener filas con year == 2
+# =>
+#      name year house
+# 1:   Fred    2     G
+# 2: George    2     G
+# data.table hace que la unificación de dos sets de datos sea fácil
+# Hagamos otro data.table para unifiar a los estudiantes
+founders <- data.table(house=c("G","H","R","S"),
+                       founder=c("Godric","Helga","Rowena","Salazar"))
+founders
+# =>
+#    house founder
+# 1:     G  Godric
+# 2:     H   Helga
+# 3:     R  Rowena
+# 4:     S Salazar
+setkey(students, house)
+setkey(founders, house)
+students <- founders[students] # Unifica los dos sets de datos comparando "house"
+setnames(students, c("house","houseFounderName","studentName","year"))
+students[,order(c("name","year","house","houseFounderName")), with=F]
+# =>
+#    studentName year house houseFounderName
+# 1:        Fred    2     G           Godric
+# 2:      George    2     G           Godric
+# 3:       Ginny   -1     G           Godric
+# 4:      Cedric    3     H            Helga
+# 5:         Cho    1     R           Rowena
+# 6:       Draco    0     S          Salazar
+
+# data.table hace que sea fácil obtener resúmenes de las tablas
+students[,sum(year),by=house]
+# =>
+#    house V1
+# 1:     G  3
+# 2:     H  3
+# 3:     R  1
+# 4:     S  0
+
+# Para eliminar una columna de un data.frame o data.table, 
+# asignarle el valor NULL.
+students$houseFounderName <- NULL
+students
+# =>
+#    studentName year house
+# 1:        Fred    2     G
+# 2:      George    2     G
+# 3:       Ginny   -1     G
+# 4:      Cedric    3     H
+# 5:         Cho    1     R
+# 6:       Draco    0     S
+
+# Elimina una fila poniendo un subconjunto
+# Usando data.table:
+students[studentName != "Draco"]
+# =>
+#    house studentName year
+# 1:     G        Fred    2
+# 2:     G      George    2
+# 3:     G       Ginny   -1
+# 4:     H      Cedric    3
+# 5:     R         Cho    1
+# Usando data.frame:
+students <- as.data.frame(students)
+students[students$house != "G",]
+# =>
+#   house houseFounderName studentName year
+# 4     H            Helga      Cedric    3
+# 5     R           Rowena         Cho    1
+# 6     S          Salazar       Draco    0
+
+# MULTI-DIMENSIONAL (TODOS LOS ELEMENTOS DE UN TIPO)
+
+# Arreglos crean una tabla de dimensión n
+# Todos los elementos deben de ser del mismo tipo
+# Puedes hacer una tabla bi-dimensional (como una matriz)
+array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4))
+# =>
+#      [,1] [,2] [,3] [,4]
+# [1,]    1    4    8    3
+# [2,]    2    5    9    6
+# Puedes utilizar un arreglo para hacer una matriz tri-dimensional también
+array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2))
+# =>
+# , , 1
+#
+#      [,1] [,2]
+# [1,]    2    8
+# [2,]  300    9
+# [3,]    4    0
+#
+# , , 2
+#
+#      [,1] [,2]
+# [1,]    5   66
+# [2,]   60    7
+# [3,]    0  847
+
+# LISTAS (MULTI-DIMENSIONAL, POSIBLEMENTE DESIGUALES, DE DIFERENTES TIPOS)
+
+# Finalmente, R tiene listas (de vectores)
+list1 <- list(time = 1:40)
+list1$price = c(rnorm(40,.5*list1$time,4)) # aleatorio
+list1
+# Puedes obtener elementos de una lista de la siguiente manera
+list1$time # Una manera
+list1[["time"]] # Otra manera
+list1[[1]] # Y otra manera
+# =>
+#  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
+# [34] 34 35 36 37 38 39 40
+# Puedes crear una lista de subconjuntos como cualquier otro vector
+list1$price[4]
+
+# Las listas no son la estructura de datos más eficiente para trabajar en R;
+# a menos de que tengas una buena razón, deberías de quedarte con data.frames
+# Las listas son usualmente devueltas por funciones que realizan regresiones 
+# lineales
+
+##################################################
+# La familia de funciones apply()
+##################################################
+
+# Te recuerdas de mat?
+mat
+# =>
+#      [,1] [,2]
+# [1,]    1    4
+# [2,]    2    5
+# [3,]    3    6
+# Utiliza apply(X, MARGIN, FUN) paraaplicar una función FUN a la matriz X
+# sobre las filas (MAR = 1) o las columnas (MAR = 2)
+# Eso es, R aplica FUN sobre cada fila (o columna) de X, mucho más rápido que
+# lo que haría un ciclo 'for' o 'loop'
+apply(mat, MAR = 2, jiggle)
+# =>
+#      [,1] [,2]
+# [1,]    3   15
+# [2,]    7   19
+# [3,]   11   23
+# Otras funciones: ?lapply, ?sapply
+
+# No te sientas muy intimidado; todos están de acuerdo que son confusas
+
+# El paquete plyr busca reemplazar (y mejorar) la familiar *apply()
+install.packages("plyr")
+require(plyr)
+?plyr
+
+
+
+#########################
+# Carga de datos
+#########################
+
+# "pets.csv" es un archivo en internet
+# (pero puede ser tan fácil como tener el archivo en tu computadora)
+pets <- read.csv("http://learnxinyminutes.com/docs/pets.csv")
+pets
+head(pets, 2) # primeras dos filas
+tail(pets, 1) # última fila
+
+# Para guardar un data frame o una matriz como un archivo .csv
+write.csv(pets, "pets2.csv") # para hacer un nuevo archivo .csv
+# definir el directorio de trabajo con setwd(), búscalo con getwd()
+
+# Prueba ?read.csv ?write.csv para más información
+
+
+#########################
+# Gráficas
+#########################
+
+# FUNCIONES PREDEFINIDAS DE GRAFICACIÓN
+# Gráficos de dispersión!
+plot(list1$time, list1$price, main = "fake data")
+# Regresiones!
+linearModel <- lm(price  ~ time, data = list1)
+linearModel # Muestra el resultado de la regresión
+# Grafica la línea de regresión
+abline(linearModel, col = "red")
+# Obtiene una veridad de diagnósticos
+plot(linearModel)
+# Histogramas!
+hist(rpois(n = 10000, lambda = 5), col = "thistle")
+# Barras!
+barplot(c(1,4,5,1,2), names.arg = c("red","blue","purple","green","yellow"))
+
+# GGPLOT2
+# Pero éstas no son las gráficas más bonitas de R
+# Prueba el paquete ggplot2 para mayor variedad y mejores gráficas
+install.packages("ggplot2")
+require(ggplot2)
+?ggplot2
+pp <- ggplot(students, aes(x=house))
+pp + geom_histogram()
+ll <- as.data.table(list1)
+pp <- ggplot(ll, aes(x=time,price))
+pp + geom_point()
+# ggplot2 tiene una excelente documentación 
+# (disponible en http://docs.ggplot2.org/current/)
+
+
+
+```
+
+## ¿Cómo obtengo R?
+
+* Obtén R y R GUI de [http://www.r-project.org/](http://www.r-project.org/)
+* [RStudio](http://www.rstudio.com/ide/) es otro GUI
diff --git a/r.html.markdown~ b/r.html.markdown~
new file mode 100644
index 00000000..ee9e7c90
--- /dev/null
+++ b/r.html.markdown~
@@ -0,0 +1,807 @@
+---
+language: R
+contributors:
+    - ["e99n09", "http://github.com/e99n09"]
+<<<<<<< HEAD
+=======
+    - ["isomorphismes", "http://twitter.com/isomorphisms"]
+    - ["kalinn", "http://github.com/kalinn"]
+>>>>>>> 6e38442b857a9d8178b6ce6713b96c52bf4426eb
+filename: learnr.r
+---
+
+R is a statistical computing language. It has lots of libraries for uploading and cleaning data sets, running statistical procedures, and making graphs. You can also run `R` commands within a LaTeX document.
+
+```r
+
+# Comments start with number symbols.
+
+# You can't make multi-line comments,
+# but you can stack multiple comments like so.
+
+# in Windows you can use CTRL-ENTER to execute a line.
+# on Mac it is COMMAND-ENTER
+
+
+
+#############################################################################
+# Stuff you can do without understanding anything about programming
+#############################################################################
+
+# In this section, we show off some of the cool stuff you can do in
+# R without understanding anything about programming. Do not worry
+# about understanding everything the code does. Just enjoy!
+
+data()	        # browse pre-loaded data sets
+data(rivers)	# get this one: "Lengths of Major North American Rivers"
+ls()	        # notice that "rivers" now appears in the workspace
+head(rivers)	# peek at the data set
+# 735 320 325 392 524 450
+
+length(rivers)	# how many rivers were measured?
+# 141
+summary(rivers) # what are some summary statistics?
+#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
+#  135.0   310.0   425.0   591.2   680.0  3710.0
+
+# make a stem-and-leaf plot (a histogram-like data visualization)
+stem(rivers)
+
+#  The decimal point is 2 digit(s) to the right of the |
+#
+#   0 | 4
+#   2 | 011223334555566667778888899900001111223333344455555666688888999
+#   4 | 111222333445566779001233344567
+#   6 | 000112233578012234468
+#   8 | 045790018
+#  10 | 04507
+#  12 | 1471
+#  14 | 56
+#  16 | 7
+#  18 | 9
+#  20 |
+#  22 | 25
+#  24 | 3
+#  26 |
+#  28 |
+#  30 |
+#  32 |
+#  34 |
+#  36 | 1
+
+stem(log(rivers)) # Notice that the data are neither normal nor log-normal!
+# Take that, Bell curve fundamentalists.
+
+#  The decimal point is 1 digit(s) to the left of the |
+#
+#  48 | 1
+#  50 |
+#  52 | 15578
+#  54 | 44571222466689
+#  56 | 023334677000124455789
+#  58 | 00122366666999933445777
+#  60 | 122445567800133459
+#  62 | 112666799035
+#  64 | 00011334581257889
+#  66 | 003683579
+#  68 | 0019156
+#  70 | 079357
+#  72 | 89
+#  74 | 84
+#  76 | 56
+#  78 | 4
+#  80 |
+#  82 | 2
+
+# make a histogram:
+hist(rivers, col="#333333", border="white", breaks=25) # play around with these parameters
+hist(log(rivers), col="#333333", border="white", breaks=25) # you'll do more plotting later
+
+# Here's another neat data set that comes pre-loaded. R has tons of these.
+data(discoveries)
+plot(discoveries, col="#333333", lwd=3, xlab="Year",
+     main="Number of important discoveries per year")
+plot(discoveries, col="#333333", lwd=3, type = "h", xlab="Year",
+     main="Number of important discoveries per year")
+
+# Rather than leaving the default ordering (by year),
+# we could also sort to see what's typical:
+sort(discoveries)
+#  [1]  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  2  2  2  2
+# [26]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  3  3  3
+# [51]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  4  4  4  4  4  4  4  4
+# [76]  4  4  4  4  5  5  5  5  5  5  5  6  6  6  6  6  6  7  7  7  7  8  9 10 12
+
+stem(discoveries, scale=2)
+#
+#  The decimal point is at the |
+#
+#   0 | 000000000
+#   1 | 000000000000
+#   2 | 00000000000000000000000000
+#   3 | 00000000000000000000
+#   4 | 000000000000
+#   5 | 0000000
+#   6 | 000000
+#   7 | 0000
+#   8 | 0
+#   9 | 0
+#  10 | 0
+#  11 |
+#  12 | 0
+
+max(discoveries)
+# 12
+summary(discoveries)
+#   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
+#    0.0     2.0     3.0     3.1     4.0    12.0
+
+# Roll a die a few times
+round(runif(7, min=.5, max=6.5))
+# 1 4 6 1 4 6 4
+# Your numbers will differ from mine unless we set the same random.seed(31337)
+
+# Draw from a standard Gaussian 9 times
+rnorm(9)
+# [1]  0.07528471  1.03499859  1.34809556 -0.82356087  0.61638975 -1.88757271
+# [7] -0.59975593  0.57629164  1.08455362
+
+
+
+##################################################
+# Data types and basic arithmetic
+##################################################
+
+# Now for the programming-oriented part of the tutorial.
+# In this section you will meet the important data types of R:
+# integers, numerics, characters, logicals, and factors.
+# There are others, but these are the bare minimum you need to
+# get started.
+
+# INTEGERS
+# Long-storage integers are written with L
+5L # 5
+class(5L) # "integer"
+# (Try ?class for more information on the class() function.)
+# In R, every single value, like 5L, is considered a vector of length 1
+length(5L) # 1
+# You can have an integer vector with length > 1 too:
+c(4L, 5L, 8L, 3L) # 4 5 8 3
+length(c(4L, 5L, 8L, 3L)) # 4
+class(c(4L, 5L, 8L, 3L)) # "integer"
+
+# NUMERICS
+# A "numeric" is a double-precision floating-point number
+5 # 5
+class(5) # "numeric"
+# Again, everything in R is a vector;
+# you can make a numeric vector with more than one element
+c(3,3,3,2,2,1) # 3 3 3 2 2 1
+# You can use scientific notation too
+5e4 # 50000
+6.02e23 # Avogadro's number
+1.6e-35 # Planck length
+# You can also have infinitely large or small numbers
+class(Inf)	# "numeric"
+class(-Inf)	# "numeric"
+# You might use "Inf", for example, in integrate(dnorm, 3, Inf);
+# this obviates Z-score tables.
+
+# BASIC ARITHMETIC
+# You can do arithmetic with numbers
+# Doing arithmetic on a mix of integers and numerics gives you another numeric
+10L + 66L # 76      # integer plus integer gives integer
+53.2 - 4  # 49.2    # numeric minus numeric gives numeric
+2.0 * 2L  # 4       # numeric times integer gives numeric
+3L / 4    # 0.75    # integer over numeric gives numeric
+3 %% 2	  # 1       # the remainder of two numerics is another numeric
+# Illegal arithmetic yeilds you a "not-a-number":
+0 / 0 # NaN
+class(NaN) # "numeric"
+# You can do arithmetic on two vectors with length greater than 1,
+# so long as the larger vector's length is an integer multiple of the smaller
+c(1,2,3) + c(1,2,3) # 2 4 6
+# Since a single number is a vector of length one, scalars are applied 
+# elementwise to vectors
+(4 * c(1,2,3) - 2) / 2 # 1 3 5
+# Except for scalars, use caution when performing arithmetic on vectors with 
+# different lengths. Although it can be done, 
+c(1,2,3,1,2,3) * c(1,2) # 1 4 3 2 2 6
+# Matching lengths is better practice and easier to read
+c(1,2,3,1,2,3) * c(1,2,1,2,1,2) 
+
+# CHARACTERS
+# There's no difference between strings and characters in R
+"Horatio" # "Horatio"
+class("Horatio") # "character"
+class('H') # "character"
+# Those were both character vectors of length 1
+# Here is a longer one:
+c('alef', 'bet', 'gimmel', 'dalet', 'he')
+# =>
+# "alef"   "bet"    "gimmel" "dalet"  "he"
+length(c("Call","me","Ishmael")) # 3
+# You can do regex operations on character vectors:
+substr("Fortuna multis dat nimis, nulli satis.", 9, 15) # "multis "
+gsub('u', 'ø', "Fortuna multis dat nimis, nulli satis.") # "Fortøna møltis dat nimis, nølli satis."
+# R has several built-in character vectors:
+letters
+# =>
+#  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
+# [20] "t" "u" "v" "w" "x" "y" "z"
+month.abb # "Jan" "Feb" "Mar" "Apr" "May" "Jun" "Jul" "Aug" "Sep" "Oct" "Nov" "Dec"
+
+# LOGICALS
+# In R, a "logical" is a boolean
+class(TRUE)	# "logical"
+class(FALSE)	# "logical"
+# Their behavior is normal
+TRUE == TRUE	# TRUE
+TRUE == FALSE	# FALSE
+FALSE != FALSE	# FALSE
+FALSE != TRUE	# TRUE
+# Missing data (NA) is logical, too
+class(NA)	# "logical"
+# Use | and & for logic operations.
+# OR
+TRUE | FALSE	# TRUE
+# AND
+TRUE & FALSE	# FALSE
+# Applying | and & to vectors returns elementwise logic operations
+c(TRUE,FALSE,FALSE) | c(FALSE,TRUE,FALSE) # TRUE TRUE FALSE
+c(TRUE,FALSE,TRUE) & c(FALSE,TRUE,TRUE) # FALSE FALSE TRUE
+# You can test if x is TRUE
+isTRUE(TRUE)	# TRUE
+# Here we get a logical vector with many elements:
+c('Z', 'o', 'r', 'r', 'o') == "Zorro" # FALSE FALSE FALSE FALSE FALSE
+c('Z', 'o', 'r', 'r', 'o') == "Z" # TRUE FALSE FALSE FALSE FALSE
+
+# FACTORS
+# The factor class is for categorical data
+# Factors can be ordered (like childrens' grade levels) or unordered (like gender)
+factor(c("female", "female", "male", NA, "female"))
+#  female female male   <NA>   female
+# Levels: female male
+# The "levels" are the values the categorical data can take
+# Note that missing data does not enter the levels
+levels(factor(c("male", "male", "female", NA, "female"))) # "female" "male"
+# If a factor vector has length 1, its levels will have length 1, too
+length(factor("male")) # 1
+length(levels(factor("male"))) # 1
+# Factors are commonly seen in data frames, a data structure we will cover later
+data(infert) # "Infertility after Spontaneous and Induced Abortion"
+levels(infert$education) # "0-5yrs"  "6-11yrs" "12+ yrs"
+
+# NULL
+# "NULL" is a weird one; use it to "blank out" a vector
+class(NULL)	# NULL
+parakeet = c("beak", "feathers", "wings", "eyes")
+parakeet
+# =>
+# [1] "beak"     "feathers" "wings"    "eyes"
+parakeet <- NULL
+parakeet
+# =>
+# NULL
+
+# TYPE COERCION
+# Type-coercion is when you force a value to take on a different type
+as.character(c(6, 8)) # "6" "8"
+as.logical(c(1,0,1,1)) # TRUE FALSE  TRUE  TRUE
+# If you put elements of different types into a vector, weird coercions happen:
+c(TRUE, 4) # 1 4
+c("dog", TRUE, 4) # "dog"  "TRUE" "4"
+as.numeric("Bilbo")
+# =>
+# [1] NA
+# Warning message:
+# NAs introduced by coercion
+
+# Also note: those were just the basic data types
+# There are many more data types, such as for dates, time series, etc.
+
+
+
+##################################################
+# Variables, loops, if/else
+##################################################
+
+# A variable is like a box you store a value in for later use.
+# We call this "assigning" the value to the variable.
+# Having variables lets us write loops, functions, and if/else statements
+
+# VARIABLES
+# Lots of way to assign stuff:
+x = 5 # this is possible
+y <- "1" # this is preferred
+TRUE -> z # this works but is weird
+
+# LOOPS
+# We've got for loops
+for (i in 1:4) {
+  print(i)
+}
+# We've got while loops
+a <- 10
+while (a > 4) {
+	cat(a, "...", sep = "")
+	a <- a - 1
+}
+# Keep in mind that for and while loops run slowly in R
+# Operations on entire vectors (i.e. a whole row, a whole column)
+# or apply()-type functions (we'll discuss later) are preferred
+
+# IF/ELSE
+# Again, pretty standard
+if (4 > 3) {
+	print("4 is greater than 3")
+} else {
+	print("4 is not greater than 3")
+}
+# =>
+# [1] "4 is greater than 3"
+
+# FUNCTIONS
+# Defined like so:
+jiggle <- function(x) {
+	x = x + rnorm(1, sd=.1)	#add in a bit of (controlled) noise
+	return(x)
+}
+# Called like any other R function:
+jiggle(5)	# 5±ε. After set.seed(2716057), jiggle(5)==5.005043
+
+
+
+###########################################################################
+# Data structures: Vectors, matrices, data frames, and arrays
+###########################################################################
+
+# ONE-DIMENSIONAL
+
+# Let's start from the very beginning, and with something you already know: vectors.
+vec <- c(8, 9, 10, 11)
+vec	#  8  9 10 11
+# We ask for specific elements by subsetting with square brackets
+# (Note that R starts counting from 1)
+vec[1]		# 8
+letters[18]	# "r"
+LETTERS[13]	# "M"
+month.name[9]	# "September"
+c(6, 8, 7, 5, 3, 0, 9)[3]	# 7
+# We can also search for the indices of specific components,
+which(vec %% 2 == 0)	# 1 3
+# grab just the first or last few entries in the vector,
+head(vec, 1)	# 8
+tail(vec, 2)	# 10 11
+# or figure out if a certain value is in the vector
+any(vec == 10) # TRUE
+# If an index "goes over" you'll get NA:
+vec[6]	# NA
+# You can find the length of your vector with length()
+length(vec)	# 4
+# You can perform operations on entire vectors or subsets of vectors
+vec * 4	# 16 20 24 28
+vec[2:3] * 5	# 25 30
+any(vec[2:3] == 8) # FALSE
+# and R has many built-in functions to summarize vectors
+mean(vec)	# 9.5
+var(vec)	# 1.666667
+sd(vec)		# 1.290994
+max(vec)	# 11
+min(vec)	# 8
+sum(vec)	# 38
+# Some more nice built-ins:
+5:15	# 5  6  7  8  9 10 11 12 13 14 15
+seq(from=0, to=31337, by=1337)
+# =>
+#  [1]     0  1337  2674  4011  5348  6685  8022  9359 10696 12033 13370 14707
+# [13] 16044 17381 18718 20055 21392 22729 24066 25403 26740 28077 29414 30751
+
+# TWO-DIMENSIONAL (ALL ONE CLASS)
+
+# You can make a matrix out of entries all of the same type like so:
+mat <- matrix(nrow = 3, ncol = 2, c(1,2,3,4,5,6))
+mat
+# =>
+#      [,1] [,2]
+# [1,]    1    4
+# [2,]    2    5
+# [3,]    3    6
+# Unlike a vector, the class of a matrix is "matrix", no matter what's in it
+class(mat) # => "matrix"
+# Ask for the first row
+mat[1,]	# 1 4
+# Perform operation on the first column
+3 * mat[,1]	# 3 6 9
+# Ask for a specific cell
+mat[3,2]	# 6
+
+# Transpose the whole matrix
+t(mat)
+# =>
+#      [,1] [,2] [,3]
+# [1,]    1    2    3
+# [2,]    4    5    6
+
+# Matrix multiplication
+mat %*% t(mat)
+# =>
+#      [,1] [,2] [,3]
+# [1,]   17   22   27
+# [2,]   22   29   36
+# [3,]   27   36   45
+
+# cbind() sticks vectors together column-wise to make a matrix
+mat2 <- cbind(1:4, c("dog", "cat", "bird", "dog"))
+mat2
+# =>
+#      [,1] [,2]
+# [1,] "1"  "dog"
+# [2,] "2"  "cat"
+# [3,] "3"  "bird"
+# [4,] "4"  "dog"
+class(mat2)	# matrix
+# Again, note what happened!
+# Because matrices must contain entries all of the same class,
+# everything got converted to the character class
+c(class(mat2[,1]), class(mat2[,2]))
+
+# rbind() sticks vectors together row-wise to make a matrix
+mat3 <- rbind(c(1,2,4,5), c(6,7,0,4))
+mat3
+# =>
+#      [,1] [,2] [,3] [,4]
+# [1,]    1    2    4    5
+# [2,]    6    7    0    4
+# Ah, everything of the same class. No coercions. Much better.
+
+# TWO-DIMENSIONAL (DIFFERENT CLASSES)
+
+# For columns of different types, use a data frame
+# This data structure is so useful for statistical programming,
+# a version of it was added to Python in the package "pandas".
+
+students <- data.frame(c("Cedric","Fred","George","Cho","Draco","Ginny"),
+                       c(3,2,2,1,0,-1),
+                       c("H", "G", "G", "R", "S", "G"))
+names(students) <- c("name", "year", "house") # name the columns
+class(students)	# "data.frame"
+students
+# =>
+#     name year house
+# 1 Cedric    3     H
+# 2   Fred    2     G
+# 3 George    2     G
+# 4    Cho    1     R
+# 5  Draco    0     S
+# 6  Ginny   -1     G
+class(students$year)	# "numeric"
+class(students[,3])	# "factor"
+# find the dimensions
+nrow(students)	# 6
+ncol(students)	# 3
+dim(students)	# 6 3
+# The data.frame() function converts character vectors to factor vectors
+# by default; turn this off by setting stringsAsFactors = FALSE when
+# you create the data.frame
+?data.frame
+
+# There are many twisty ways to subset data frames, all subtly unalike
+students$year	# 3  2  2  1  0 -1
+students[,2]	# 3  2  2  1  0 -1
+students[,"year"]	# 3  2  2  1  0 -1
+
+# An augmented version of the data.frame structure is the data.table
+# If you're working with huge or panel data, or need to merge a few data
+# sets, data.table can be a good choice. Here's a whirlwind tour:
+install.packages("data.table") # download the package from CRAN
+require(data.table) # load it
+students <- as.data.table(students)
+students # note the slightly different print-out
+# =>
+#      name year house
+# 1: Cedric    3     H
+# 2:   Fred    2     G
+# 3: George    2     G
+# 4:    Cho    1     R
+# 5:  Draco    0     S
+# 6:  Ginny   -1     G
+students[name=="Ginny"] # get rows with name == "Ginny"
+# =>
+#     name year house
+# 1: Ginny   -1     G
+students[year==2] # get rows with year == 2
+# =>
+#      name year house
+# 1:   Fred    2     G
+# 2: George    2     G
+# data.table makes merging two data sets easy
+# let's make another data.table to merge with students
+founders <- data.table(house=c("G","H","R","S"),
+                       founder=c("Godric","Helga","Rowena","Salazar"))
+founders
+# =>
+#    house founder
+# 1:     G  Godric
+# 2:     H   Helga
+# 3:     R  Rowena
+# 4:     S Salazar
+setkey(students, house)
+setkey(founders, house)
+students <- founders[students] # merge the two data sets by matching "house"
+setnames(students, c("house","houseFounderName","studentName","year"))
+students[,order(c("name","year","house","houseFounderName")), with=F]
+# =>
+#    studentName year house houseFounderName
+# 1:        Fred    2     G           Godric
+# 2:      George    2     G           Godric
+# 3:       Ginny   -1     G           Godric
+# 4:      Cedric    3     H            Helga
+# 5:         Cho    1     R           Rowena
+# 6:       Draco    0     S          Salazar
+
+# data.table makes summary tables easy
+students[,sum(year),by=house]
+# =>
+#    house V1
+# 1:     G  3
+# 2:     H  3
+# 3:     R  1
+# 4:     S  0
+
+# To drop a column from a data.frame or data.table,
+# assign it the NULL value
+students$houseFounderName <- NULL
+students
+# =>
+#    studentName year house
+# 1:        Fred    2     G
+# 2:      George    2     G
+# 3:       Ginny   -1     G
+# 4:      Cedric    3     H
+# 5:         Cho    1     R
+# 6:       Draco    0     S
+
+# Drop a row by subsetting
+# Using data.table:
+students[studentName != "Draco"]
+# =>
+#    house studentName year
+# 1:     G        Fred    2
+# 2:     G      George    2
+# 3:     G       Ginny   -1
+# 4:     H      Cedric    3
+# 5:     R         Cho    1
+# Using data.frame:
+students <- as.data.frame(students)
+students[students$house != "G",]
+# =>
+#   house houseFounderName studentName year
+# 4     H            Helga      Cedric    3
+# 5     R           Rowena         Cho    1
+# 6     S          Salazar       Draco    0
+
+# MULTI-DIMENSIONAL (ALL ELEMENTS OF ONE TYPE)
+
+# Arrays creates n-dimensional tables
+# All elements must be of the same type
+# You can make a two-dimensional table (sort of like a matrix)
+array(c(c(1,2,4,5),c(8,9,3,6)), dim=c(2,4))
+# =>
+#      [,1] [,2] [,3] [,4]
+# [1,]    1    4    8    3
+# [2,]    2    5    9    6
+# You can use array to make three-dimensional matrices too
+array(c(c(c(2,300,4),c(8,9,0)),c(c(5,60,0),c(66,7,847))), dim=c(3,2,2))
+# =>
+# , , 1
+#
+#      [,1] [,2]
+# [1,]    2    8
+# [2,]  300    9
+# [3,]    4    0
+#
+# , , 2
+#
+#      [,1] [,2]
+# [1,]    5   66
+# [2,]   60    7
+# [3,]    0  847
+
+# LISTS (MULTI-DIMENSIONAL, POSSIBLY RAGGED, OF DIFFERENT TYPES)
+
+# Finally, R has lists (of vectors)
+list1 <- list(time = 1:40)
+list1$price = c(rnorm(40,.5*list1$time,4)) # random
+list1
+# You can get items in the list like so
+list1$time # one way
+list1[["time"]] # another way
+list1[[1]] # yet another way
+# =>
+#  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
+# [34] 34 35 36 37 38 39 40
+# You can subset list items like any other vector
+list1$price[4]
+
+# Lists are not the most efficient data structure to work with in R;
+# unless you have a very good reason, you should stick to data.frames
+# Lists are often returned by functions that perform linear regressions
+
+##################################################
+# The apply() family of functions
+##################################################
+
+# Remember mat?
+mat
+# =>
+#      [,1] [,2]
+# [1,]    1    4
+# [2,]    2    5
+# [3,]    3    6
+# Use apply(X, MARGIN, FUN) to apply function FUN to a matrix X
+# over rows (MAR = 1) or columns (MAR = 2)
+# That is, R does FUN to each row (or column) of X, much faster than a
+# for or while loop would do
+apply(mat, MAR = 2, jiggle)
+# =>
+#      [,1] [,2]
+# [1,]    3   15
+# [2,]    7   19
+# [3,]   11   23
+# Other functions: ?lapply, ?sapply
+
+# Don't feel too intimidated; everyone agrees they are rather confusing
+
+# The plyr package aims to replace (and improve upon!) the *apply() family.
+install.packages("plyr")
+require(plyr)
+?plyr
+
+
+
+#########################
+# Loading data
+#########################
+
+# "pets.csv" is a file on the internet
+# (but it could just as easily be be a file on your own computer)
+pets <- read.csv("http://learnxinyminutes.com/docs/pets.csv")
+pets
+head(pets, 2) # first two rows
+tail(pets, 1) # last row
+
+# To save a data frame or matrix as a .csv file
+write.csv(pets, "pets2.csv") # to make a new .csv file
+# set working directory with setwd(), look it up with getwd()
+
+# Try ?read.csv and ?write.csv for more information
+
+
+
+#########################
+# Statistical Analysis
+#########################
+
+# Linear regression!
+linearModel <- lm(price  ~ time, data = list1)
+linearModel # outputs result of regression
+# =>
+# Call:
+# lm(formula = price ~ time, data = list1)
+# 
+# Coefficients:
+# (Intercept)         time  
+#      0.1453       0.4943  
+summary(linearModel) # more verbose output from the regression
+# =>
+# Call:
+# lm(formula = price ~ time, data = list1)
+#
+# Residuals:
+#     Min      1Q  Median      3Q     Max 
+# -8.3134 -3.0131 -0.3606  2.8016 10.3992 
+#
+# Coefficients:
+#             Estimate Std. Error t value Pr(>|t|)    
+# (Intercept)  0.14527    1.50084   0.097    0.923    
+# time         0.49435    0.06379   7.749 2.44e-09 ***
+# ---
+# Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
+#
+# Residual standard error: 4.657 on 38 degrees of freedom
+# Multiple R-squared:  0.6124,	Adjusted R-squared:  0.6022 
+# F-statistic: 60.05 on 1 and 38 DF,  p-value: 2.44e-09
+coef(linearModel) # extract estimated parameters
+# =>
+# (Intercept)        time 
+#   0.1452662   0.4943490 
+summary(linearModel)$coefficients # another way to extract results
+# =>
+#              Estimate Std. Error    t value     Pr(>|t|)
+# (Intercept) 0.1452662 1.50084246 0.09678975 9.234021e-01
+# time        0.4943490 0.06379348 7.74920901 2.440008e-09
+summary(linearModel)$coefficients[,4] # the p-values 
+# =>
+#  (Intercept)         time 
+# 9.234021e-01 2.440008e-09 
+
+# GENERAL LINEAR MODELS
+# Logistic regression
+set.seed(1)
+list1$success = rbinom(length(list1$time), 1, .5) # random binary
+glModel <- glm(success  ~ time, data = list1, 
+	family=binomial(link="logit"))
+glModel # outputs result of logistic regression
+# =>
+# Call:  glm(formula = success ~ time, 
+#	family = binomial(link = "logit"), data = list1)
+#
+# Coefficients:
+# (Intercept)         time  
+#     0.17018     -0.01321  
+# 
+# Degrees of Freedom: 39 Total (i.e. Null);  38 Residual
+# Null Deviance:	    55.35 
+# Residual Deviance: 55.12 	 AIC: 59.12
+summary(glModel) # more verbose output from the regression
+# =>
+# Call:
+# glm(formula = success ~ time, 
+#	family = binomial(link = "logit"), data = list1)
+
+# Deviance Residuals: 
+#    Min      1Q  Median      3Q     Max  
+# -1.245  -1.118  -1.035   1.202   1.327  
+# 
+# Coefficients:
+#             Estimate Std. Error z value Pr(>|z|)
+# (Intercept)  0.17018    0.64621   0.263    0.792
+# time        -0.01321    0.02757  -0.479    0.632
+# 
+# (Dispersion parameter for binomial family taken to be 1)
+#
+#     Null deviance: 55.352  on 39  degrees of freedom
+# Residual deviance: 55.121  on 38  degrees of freedom
+# AIC: 59.121
+# 
+# Number of Fisher Scoring iterations: 3
+
+
+#########################
+# Plots
+#########################
+
+# BUILT-IN PLOTTING FUNCTIONS
+# Scatterplots!
+plot(list1$time, list1$price, main = "fake data")
+# Plot regression line on existing plot
+abline(linearModel, col = "red")
+# Get a variety of nice diagnostics
+plot(linearModel)
+# Histograms!
+hist(rpois(n = 10000, lambda = 5), col = "thistle")
+# Barplots!
+barplot(c(1,4,5,1,2), names.arg = c("red","blue","purple","green","yellow"))
+
+# GGPLOT2
+# But these are not even the prettiest of R's plots
+# Try the ggplot2 package for more and better graphics
+install.packages("ggplot2")
+require(ggplot2)
+?ggplot2
+pp <- ggplot(students, aes(x=house))
+pp + geom_histogram()
+ll <- as.data.table(list1)
+pp <- ggplot(ll, aes(x=time,price))
+pp + geom_point()
+# ggplot2 has excellent documentation (available http://docs.ggplot2.org/current/)
+
+
+
+```
+
+## How do I get R?
+
+* Get R and the R GUI from [http://www.r-project.org/](http://www.r-project.org/)
+* [RStudio](http://www.rstudio.com/ide/) is another GUI