INTRODUCCION A R
Luna & Sky
2025-05-30
Tipos de datos, objetos, funciones más comunes y operaciones lógicas
En esta sección aprenderás los tipos fundamentales de datos en R, las funciones básicas para manipularlos y cómo realizar operaciones lógicas para construir condiciones.
Cadenas de texto (Strings)
Las cadenas de texto se crean encerrando letras o palabras entre
comillas (" " o ' '). Se usan para almacenar
nombres, etiquetas o mensajes.
nombre <- "Sky"
print(nombre) # Muestra el objeto como está## [1] "Sky"cat(nombre) # Muestra el texto sin comillas ni formato adicional## Sky- Podemos combinar varios textos usando c()
etiquetas <- c("adulto", "joven", "niño")
print(etiquetas)## [1] "adulto" "joven" "niño"- Unir cadenas de texto
nombre <- "Sky"
apellido <- "Morales"
nombre_completo <- paste(nombre, apellido)
cat(nombre_completo) # Con espacio entre palabras## Sky Moralespaste0(nombre, apellido) # Sin espacios## [1] "SkyMorales"- Repetir cadenas
rep("Hola", 4)## [1] "Hola" "Hola" "Hola" "Hola"- Contar caracteres
nchar("Sky")## [1] 3- Extraer partes del texto
substr("Sky", 3, 8)## [1] "y"- Cambiar mayúscula y minúscula
toupper("Sky") # MAYÚSCULAS## [1] "SKY"tolower("Sky") # minúsculas## [1] "sky"- Reemplazar caracteres
sub("a", "A", "Sky") # Solo el primero## [1] "Sky"gsub("a", "A", "Sky") # Todos los que coincidan## [1] "Sky"Números en R
R trabaja principalmente con:
numeric: números con decimales (por defecto)
integer: números enteros, usando L
a <- 10 # numeric
b <- 2L # integer
class(a)## [1] "numeric"class(b)## [1] "integer"- Operaciones numéricas básicas
a <- 20
b <- 12
# Aritmética
a + b## [1] 32a - b## [1] 8a * b## [1] 240a / b## [1] 1.666667a %% b # Módulo (residuo)## [1] 8a^2 # Potencia## [1] 400- Redondeo de números
x <- 20.64851
ceiling(x) # Al entero superior## [1] 21floor(x) # Al entero inferior## [1] 20trunc(x) # Corta los decimales## [1] 20round(x, 2) # Redondea a 2 decimales## [1] 20.65signif(123456, 3) # 3 cifras significativas## [1] 123000- Datos lógicos(Booleanos)
Los valores lógicos permiten evaluar condiciones. Sus posibles valores son:
TRUE (verdadero)
FALSE (falso)
- Operadores relacionales
x <- 5
y <- 10
x == y # Igual## [1] FALSEx != y # Diferente## [1] TRUEx > y # Mayor que## [1] FALSEx <= y # Menor o igual## [1] TRUE- Operadores logicos
TRUE & FALSE # Y lógico## [1] FALSETRUE | FALSE # O lógico## [1] TRUE!TRUE # Negación## [1] FALSE# Diferencia entre operadores vectoriales y escalares
c(TRUE, FALSE) & c(FALSE, TRUE)## [1] FALSE FALSETRUE && FALSE # Solo evalúa el primer elemento## [1] FALSE- Aplicación lógica en vectores
notas <- c(2.5, 3.6)
notas >= 3.0 # ¿Aprobó?## [1] FALSE TRUEnotas[1] < 3 | notas[2] < 3 # ¿Perdió algún parcial?## [1] TRUEnotas[1] != notas[2] # ¿Son diferentes?## [1] TRUEClases de objetos
En R, existen varios tipos de objetos fundamentales que son la base de todo análisis y programación.
Vectores
Los vectores son la estructura de datos más básica en R. Se trata de secuencias ordenadas en las que todos los elementos deben ser del mismo tipo (números, caracteres, valores lógicos, etc.).
Tipos de vectores
- Vectores de caracteres
Los vectores de texto (también llamados cadenas o strings) son útiles, por ejemplo, para etiquetas de gráficos. Se escriben entre comillas y se agrupan usando la función c():
fruta <- c('manzana', 'pera', 'banano', 'uva')
print(fruta)## [1] "manzana" "pera" "banano" "uva"- Vectores numéricos
Almacenan secuencias de números, enteros o decimales, y permiten operaciones matemáticas:
edad <- c(15, 18, 12, 23)
print(edad)## [1] 15 18 12 23Operaciones entre vectores
- Suma
vector1<- c(15,18,12,23)
vector2<- c(17,88,42,63)
vector1+vector2## [1] 32 106 54 86- Resta
vector1<- c(15,18,12,23)
vector2<- c(17,88,42,63)
vector1-vector2## [1] -2 -70 -30 -40- Multiplicación
vector1<- c(15,18,12,23)
vector2<- c(17,88,42,63)
vector1*vector2## [1] 255 1584 504 1449- División
vector1<- c(15,18,12,23)
vector2<- c(17,88,42,63)
vector1/vector2## [1] 0.8823529 0.2045455 0.2857143 0.3650794- Radicación
vector1<- c(15,18,12,23)
vector2<- c(17,88,42,63)
sqrt(vector1)## [1] 3.872983 4.242641 3.464102 4.795832sqrt(vector2)## [1] 4.123106 9.380832 6.480741 7.937254- Potenciación
vector1<- c(15,18,12,23)
vector2<- c(17,88,42,63)
vector1**vector2## [1] 9.852613e+19 2.910586e+110 2.116471e+45 6.149696e+85vector2^vector1## [1] 2.862423e+18 1.001586e+35 3.012947e+19 2.425675e+41Vectores lógicos
Contienen valores TRUE o FALSE. Se generan normalmente al aplicar condiciones:
# Definir un vector numérico
x <- c(10, 15, 8, 20, 13)
# Crear un vector lógico con la condición x > 13
temp <- x > 13
# Mostrar el resultado
print(temp)## [1] FALSE TRUE FALSE TRUE FALSEVectores de índice
Contienen valores TRUE o FALSE. Se generan normalmente al aplicar condiciones:
- Indice lógico: selecciona solo los elementos para los que la condición es verdadera.
x <- c(10, NA, 20, 30, NA, 40)
x[!is.na(x)] # Elimina los NA## [1] 10 20 30 40- Combinación de condiciones:
(x + 1)[!is.na(x) & x > 0]## [1] 11 21 31 41- Índice con números positivos: selecciona elementos según sus posiciones
x[1:3]## [1] 10 NA 20- Índice con números negativos: excluye los elementos indicados
x[-(1:2)] # Excluye los primeros dos## [1] 20 30 NA 40- Índice con nombres: si un vector tiene nombres asignados, se puede acceder por ellos.
fruta <- c(5, 10, 1, 20)
names(fruta) <- c("naranja", "plátano", "manzana", "pera")
fruta[c("manzana", "naranja")]## manzana naranja
## 1 5Matrices
Las matrices son extensiones bidimensionales de los vectores. Se construyen con la función matrix(), especificando número de filas y columnas:
- Usa matrix(a:d,nrow=c,ncol=c) para crear una matriz, donde a:d genera los números a al d automáticamente, nrow=c son el numero de filas y ncol=c son el numero de columnas.
mi_matriz <- matrix(1:9, nrow = 3, ncol = 3)
print(mi_matriz)## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9Tambien pueden no ser cuadradas
matriz_no_cuadrada <- matrix(1:6, nrow = 2, ncol = 3)
print(matriz_no_cuadrada)## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 6Variables indexadas (Arrays)
Los arrays son estructuras multidimensionales que generalizan las matrices. Se crean a partir de vectores asignando dimensiones con dim():
z <- 1:1500
dim(z) <- c(3, 5, 100)Toda matriz es un array de dos dimensiones, pero no todo array es una matriz
Operaciones basicas
- Suma
A <- matrix(c(1, 2, 3, 4), nrow=2, byrow=TRUE)
B <- matrix(c(5, 6, 7, 8), nrow=2, byrow=TRUE)
A+B## [,1] [,2]
## [1,] 6 8
## [2,] 10 12La función byrow en R es un argumento dentro de la función matrix() que determina cómo se llenan los valores en la matriz:
byrow = TRUE, la matriz se llena por filas (row-wise).byrow = FALSE(Valor por defecto), la matriz se llena por columnas (column-wise).Resta
A <- matrix(c(1, 2, 3, 4), nrow=2)
B <- matrix(c(5, 6, 7, 8), nrow=2)
A-B## [,1] [,2]
## [1,] -4 -4
## [2,] -4 -4B-A## [,1] [,2]
## [1,] 4 4
## [2,] 4 4A-B==B-A## [,1] [,2]
## [1,] FALSE FALSE
## [2,] FALSE FALSE- Multiplicación
A <- matrix(c(1, 2, 3, 4), nrow=2)
B <- matrix(c(5, 6, 7, 8), nrow=2)
A %*% B## [,1] [,2]
## [1,] 23 31
## [2,] 34 46En R, puedes multiplicar matrices que no sean cuadradas siempre que cumplan con la regla de la multiplicación de matrices:
- Si tienes una matriz A de dimención m x n y una matriz B de dimensión n x p, la multiplicación A x B solo es posible si el número de columnas de A coincide con el número de filas de B.
A <- matrix(1:6, nrow = 2, ncol = 3)
B <- matrix(7:12, nrow = 3, ncol = 2)
A %*% B## [,1] [,2]
## [1,] 76 103
## [2,] 100 136- Producto escalar
A <- matrix(1:6, nrow = 2, ncol = 3)
B <- matrix(7:12, nrow = 3, ncol = 2)
6*B## [,1] [,2]
## [1,] 42 60
## [2,] 48 66
## [3,] 54 722*A## [,1] [,2] [,3]
## [1,] 2 6 10
## [2,] 4 8 12- Transposisicón
A <- matrix(1:6, nrow = 2, ncol = 3)
B <- matrix(7:12, nrow = 3, ncol = 2)
t(A %*% B)## [,1] [,2]
## [1,] 76 100
## [2,] 103 136t(A)## [,1] [,2]
## [1,] 1 2
## [2,] 3 4
## [3,] 5 6- Inversa
A <- matrix(c(2, 3, 1, 4), nrow = 2)
solve(A)## [,1] [,2]
## [1,] 0.8 -0.2
## [2,] -0.6 0.4Solve() encuentra la inversa de la matriz A, siempre que sea cuadrada y tenga determinante distinto de cero.
Si la matriz no es invertible (det(A) = 0), R devolverá un error.
Determinantes
A <- matrix(c(2, 3, 1, 4), nrow = 2)
det(A)## [1] 5- Propiedades Los eigenvalores (valores propios) y eigenvectores (vectores propios) son propiedades fundamentales de las matrices cuadradas.
A <- matrix(c(4, 2, 2, 3), nrow=2)
eigen(A) ## eigen() decomposition
## $values
## [1] 5.561553 1.438447
##
## $vectors
## [,1] [,2]
## [1,] -0.7882054 0.6154122
## [2,] -0.6154122 -0.7882054Factores
Los factores en R se utilizan para manejar datos categóricos. Se crean a partir de vectores que representan categorías o niveles, como por ejemplo: sexo, tipo de tratamiento, grupo sanguíneo, etc.
Un factor permite que R reconozca explícitamente que los datos son cualitativos, y no valores numéricos o de texto libre. Esto es especialmente útil en análisis estadísticos y en visualizaciones.
sexo <- factor(c("M", "F", "F", "M", "M"))
print(sexo)## [1] M F F M M
## Levels: F Mlevels(sexo) # Muestra los niveles del factor## [1] "F" "M"Listas
Las listas son estructuras de datos que, a diferencia de los vectores, permiten almacenar elementos de diferentes tipos: números, cadenas, vectores, funciones, incluso otras listas. Esto las convierte en herramientas muy versátiles para devolver o almacenar resultados complejos.
mi_lista <- list(nombre = "Ana", edad = 25, notas = c(90, 95, 88))
print(mi_lista)## $nombre
## [1] "Ana"
##
## $edad
## [1] 25
##
## $notas
## [1] 90 95 88Se puede acceder a los elementos de una lista por nombre o posición:
mi_lista$nombre## [1] "Ana"mi_lista[[2]]## [1] 25Manejo de datos, importar, exportar, recodificar. Calculo de nuevas variables, filtrado de datos.
Data frames (hojas de datos)
Un data frame es una estructura similar a una tabla, en la que cada fila representa una observación (por ejemplo, un individuo) y cada columna corresponde a una variable. A diferencia de una matriz, cada columna en un data frame puede ser de un tipo distinto: numérica, lógica, carácter o factor.
Los data frames son ideales para almacenar conjuntos de datos heterogéneos, como los que se usan en encuestas, experimentos o bases de datos reales.
Creacion de una dataframe
se crean cinco vectores (Se crean los vectores que desee), con registro aleatorios de variables en dieciocho depósitos de archivo hipotéticos. Las variables creadas son
nombre<-c("Maura","Camila","Johan","elkin","Alejandro","yandry","Nicolas","Dirley","Willian","Daniela","Steven","Yuliana","Karen","Jose","Santiago","Johan","Santaigo","Daniela")
edad<-c(20,21,21,18,17,19,20,19,18,22,20,19,21,20,19,20,19,20)
promedio<-c(3.5,3.5,3.5,3.3,3.3,3.9,3.7,3.6,3.5,3.2,3.3,3.3,3.5,3.4,3.5,3.9,3.7,3.7)
semestre<-c(7,8,7,3,3,7,7,7,10,7,6,8,7,7,5,5,6,5)
nivelado<-c("si","no","no","no","si","si","no","no","no","no","no","no","no","no","si","no","si","no")
dt<-data.frame(nombre,edad,promedio,semestre,nivelado)
print(dt)## nombre edad promedio semestre nivelado
## 1 Maura 20 3.5 7 si
## 2 Camila 21 3.5 8 no
## 3 Johan 21 3.5 7 no
## 4 elkin 18 3.3 3 no
## 5 Alejandro 17 3.3 3 si
## 6 yandry 19 3.9 7 si
## 7 Nicolas 20 3.7 7 no
## 8 Dirley 19 3.6 7 no
## 9 Willian 18 3.5 10 no
## 10 Daniela 22 3.2 7 no
## 11 Steven 20 3.3 6 no
## 12 Yuliana 19 3.3 8 no
## 13 Karen 21 3.5 7 no
## 14 Jose 20 3.4 7 no
## 15 Santiago 19 3.5 5 si
## 16 Johan 20 3.9 5 no
## 17 Santaigo 19 3.7 6 si
## 18 Daniela 20 3.7 5 noOperaciones
- Agregar columnas
nombre<-c("Maura","Camila","Johan","elkin","Alejandro","yandry","Nicolas","Dirley","Willian","Daniela","Steven","Yuliana","Karen","Jose","Santiago","Johan","Santaigo","Daniela")
edad<-c(20,21,21,18,17,19,20,19,18,22,20,19,21,20,19,20,19,20)
promedio<-c(3.5,3.5,3.5,3.3,3.3,3.9,3.7,3.6,3.5,3.2,3.3,3.3,3.5,3.4,3.5,3.9,3.7,3.7)
semestre<-c(7,8,7,3,3,7,7,7,10,7,6,8,7,7,5,5,6,5)
nivelado<-c("si","no","no","no","si","si","no","no","no","no","no","no","no","no","si","no","si","no")
dt<-data.frame(nombre,edad,promedio,semestre,nivelado)
#Agregar columnas
dt$estrato<-c(1,1,1,1,1,1,2,3,2,3,1,2,2,4,3,2,1,3)
print(dt)## nombre edad promedio semestre nivelado estrato
## 1 Maura 20 3.5 7 si 1
## 2 Camila 21 3.5 8 no 1
## 3 Johan 21 3.5 7 no 1
## 4 elkin 18 3.3 3 no 1
## 5 Alejandro 17 3.3 3 si 1
## 6 yandry 19 3.9 7 si 1
## 7 Nicolas 20 3.7 7 no 2
## 8 Dirley 19 3.6 7 no 3
## 9 Willian 18 3.5 10 no 2
## 10 Daniela 22 3.2 7 no 3
## 11 Steven 20 3.3 6 no 1
## 12 Yuliana 19 3.3 8 no 2
## 13 Karen 21 3.5 7 no 2
## 14 Jose 20 3.4 7 no 4
## 15 Santiago 19 3.5 5 si 3
## 16 Johan 20 3.9 5 no 2
## 17 Santaigo 19 3.7 6 si 1
## 18 Daniela 20 3.7 5 no 3- Agregar filas
nombre<-c("Maura","Camila","Johan","elkin","Alejandro","yandry","Nicolas","Dirley","Willian","Daniela","Steven","Yuliana","Karen","Jose","Santiago","Johan","Santaigo","Daniela")
edad<-c(20,21,21,18,17,19,20,19,18,22,20,19,21,20,19,20,19,20)
promedio<-c(3.5,3.5,3.5,3.3,3.3,3.9,3.7,3.6,3.5,3.2,3.3,3.3,3.5,3.4,3.5,3.9,3.7,3.7)
semestre<-c(7,8,7,3,3,7,7,7,10,7,6,8,7,7,5,5,6,5)
nivelado<-c("si","no","no","no","si","si","no","no","no","no","no","no","no","no","si","no","si","no")
dt<-data.frame(nombre,edad,promedio,semestre,nivelado)
dt$estrato<-c(1,1,1,1,1,1,2,3,2,3,1,2,2,4,3,2,1,3)
#Agregar filas/datos ficticios
valentina<-data.frame(nombre='Valentina',edad=28,promedio=4,semestre=8,nivelado='no',estrato=2)
dt<-rbind(dt,valentina)
print(dt)## nombre edad promedio semestre nivelado estrato
## 1 Maura 20 3.5 7 si 1
## 2 Camila 21 3.5 8 no 1
## 3 Johan 21 3.5 7 no 1
## 4 elkin 18 3.3 3 no 1
## 5 Alejandro 17 3.3 3 si 1
## 6 yandry 19 3.9 7 si 1
## 7 Nicolas 20 3.7 7 no 2
## 8 Dirley 19 3.6 7 no 3
## 9 Willian 18 3.5 10 no 2
## 10 Daniela 22 3.2 7 no 3
## 11 Steven 20 3.3 6 no 1
## 12 Yuliana 19 3.3 8 no 2
## 13 Karen 21 3.5 7 no 2
## 14 Jose 20 3.4 7 no 4
## 15 Santiago 19 3.5 5 si 3
## 16 Johan 20 3.9 5 no 2
## 17 Santaigo 19 3.7 6 si 1
## 18 Daniela 20 3.7 5 no 3
## 19 Valentina 28 4.0 8 no 2- Seleccionar fila
nombre<-c("Maura","Camila","Johan","elkin","Alejandro","yandry","Nicolas","Dirley","Willian","Daniela","Steven","Yuliana","Karen","Jose","Santiago","Johan","Santaigo","Daniela")
edad<-c(20,21,21,18,17,19,20,19,18,22,20,19,21,20,19,20,19,20)
promedio<-c(3.5,3.5,3.5,3.3,3.3,3.9,3.7,3.6,3.5,3.2,3.3,3.3,3.5,3.4,3.5,3.9,3.7,3.7)
semestre<-c(7,8,7,3,3,7,7,7,10,7,6,8,7,7,5,5,6,5)
nivelado<-c("si","no","no","no","si","si","no","no","no","no","no","no","no","no","si","no","si","no")
dt<-data.frame(nombre,edad,promedio,semestre,nivelado)
dt$estrato<-c(1,1,1,1,1,1,2,3,2,3,1,2,2,4,3,2,1,3)
valentina<-data.frame(nombre='Valentina',edad=28,promedio=4,semestre=8,nivelado='no',estrato=2)
dt<-rbind(dt,valentina)
#Seleccionar fila
dato1=dt[7,]
print(dato1)## nombre edad promedio semestre nivelado estrato
## 7 Nicolas 20 3.7 7 no 2- Seleccionar columna
nombre<-c("Maura","Camila","Johan","elkin","Alejandro","yandry","Nicolas","Dirley","Willian","Daniela","Steven","Yuliana","Karen","Jose","Santiago","Johan","Santaigo","Daniela")
edad<-c(20,21,21,18,17,19,20,19,18,22,20,19,21,20,19,20,19,20)
promedio<-c(3.5,3.5,3.5,3.3,3.3,3.9,3.7,3.6,3.5,3.2,3.3,3.3,3.5,3.4,3.5,3.9,3.7,3.7)
semestre<-c(7,8,7,3,3,7,7,7,10,7,6,8,7,7,5,5,6,5)
nivelado<-c("si","no","no","no","si","si","no","no","no","no","no","no","no","no","si","no","si","no")
dt<-data.frame(nombre,edad,promedio,semestre,nivelado)
dt$estrato<-c(1,1,1,1,1,1,2,3,2,3,1,2,2,4,3,2,1,3)
valentina<-data.frame(nombre='Valentina',edad=28,promedio=4,semestre=8,nivelado='no',estrato=2)
dt<-rbind(dt,valentina)
#Seleccionar columna
dato2=dt[,2]
print(dato2)## [1] 20 21 21 18 17 19 20 19 18 22 20 19 21 20 19 20 19 20 28- Seleccionar un elemento especifico
nombre<-c("Maura","Camila","Johan","elkin","Alejandro","yandry","Nicolas","Dirley","Willian","Daniela","Steven","Yuliana","Karen","Jose","Santiago","Johan","Santaigo","Daniela")
edad<-c(20,21,21,18,17,19,20,19,18,22,20,19,21,20,19,20,19,20)
promedio<-c(3.5,3.5,3.5,3.3,3.3,3.9,3.7,3.6,3.5,3.2,3.3,3.3,3.5,3.4,3.5,3.9,3.7,3.7)
semestre<-c(7,8,7,3,3,7,7,7,10,7,6,8,7,7,5,5,6,5)
nivelado<-c("si","no","no","no","si","si","no","no","no","no","no","no","no","no","si","no","si","no")
dt<-data.frame(nombre,edad,promedio,semestre,nivelado)
dt$estrato<-c(1,1,1,1,1,1,2,3,2,3,1,2,2,4,3,2,1,3)
valentina<-data.frame(nombre='Valentina',edad=28,promedio=4,semestre=8,nivelado='no',estrato=2)
dt<-rbind(dt,valentina)
#Seleccionar un elemento especifico
dato3=dt[5,2]
print(dato3)## [1] 17- Seleccionar varias filas
nombre<-c("Maura","Camila","Johan","elkin","Alejandro","yandry","Nicolas","Dirley","Willian","Daniela","Steven","Yuliana","Karen","Jose","Santiago","Johan","Santaigo","Daniela")
edad<-c(20,21,21,18,17,19,20,19,18,22,20,19,21,20,19,20,19,20)
promedio<-c(3.5,3.5,3.5,3.3,3.3,3.9,3.7,3.6,3.5,3.2,3.3,3.3,3.5,3.4,3.5,3.9,3.7,3.7)
semestre<-c(7,8,7,3,3,7,7,7,10,7,6,8,7,7,5,5,6,5)
nivelado<-c("si","no","no","no","si","si","no","no","no","no","no","no","no","no","si","no","si","no")
dt<-data.frame(nombre,edad,promedio,semestre,nivelado)
dt$estrato<-c(1,1,1,1,1,1,2,3,2,3,1,2,2,4,3,2,1,3)
valentina<-data.frame(nombre='Valentina',edad=28,promedio=4,semestre=8,nivelado='no',estrato=2)
dt<-rbind(dt,valentina)
#Seleccionar varias filas
dato4<-dt[c(3,5,6,7,4),]
print(dato4)## nombre edad promedio semestre nivelado estrato
## 3 Johan 21 3.5 7 no 1
## 5 Alejandro 17 3.3 3 si 1
## 6 yandry 19 3.9 7 si 1
## 7 Nicolas 20 3.7 7 no 2
## 4 elkin 18 3.3 3 no 1- Seleccionar varias columnas
nombre<-c("Maura","Camila","Johan","elkin","Alejandro","yandry","Nicolas","Dirley","Willian","Daniela","Steven","Yuliana","Karen","Jose","Santiago","Johan","Santaigo","Daniela")
edad<-c(20,21,21,18,17,19,20,19,18,22,20,19,21,20,19,20,19,20)
promedio<-c(3.5,3.5,3.5,3.3,3.3,3.9,3.7,3.6,3.5,3.2,3.3,3.3,3.5,3.4,3.5,3.9,3.7,3.7)
semestre<-c(7,8,7,3,3,7,7,7,10,7,6,8,7,7,5,5,6,5)
nivelado<-c("si","no","no","no","si","si","no","no","no","no","no","no","no","no","si","no","si","no")
dt<-data.frame(nombre,edad,promedio,semestre,nivelado)
dt$estrato<-c(1,1,1,1,1,1,2,3,2,3,1,2,2,4,3,2,1,3)
valentina<-data.frame(nombre='Valentina',edad=28,promedio=4,semestre=8,nivelado='no',estrato=2)
dt<-rbind(dt,valentina)
#Seleccionar varias columnas
dato4<-dt[,c(1,4,2)]
print(dato4)## nombre semestre edad
## 1 Maura 7 20
## 2 Camila 8 21
## 3 Johan 7 21
## 4 elkin 3 18
## 5 Alejandro 3 17
## 6 yandry 7 19
## 7 Nicolas 7 20
## 8 Dirley 7 19
## 9 Willian 10 18
## 10 Daniela 7 22
## 11 Steven 6 20
## 12 Yuliana 8 19
## 13 Karen 7 21
## 14 Jose 7 20
## 15 Santiago 5 19
## 16 Johan 5 20
## 17 Santaigo 6 19
## 18 Daniela 5 20
## 19 Valentina 8 28- Seleccionar varias filas y columnas
nombre<-c("Maura","Camila","Johan","elkin","Alejandro","yandry","Nicolas","Dirley","Willian","Daniela","Steven","Yuliana","Karen","Jose","Santiago","Johan","Santaigo","Daniela")
edad<-c(20,21,21,18,17,19,20,19,18,22,20,19,21,20,19,20,19,20)
promedio<-c(3.5,3.5,3.5,3.3,3.3,3.9,3.7,3.6,3.5,3.2,3.3,3.3,3.5,3.4,3.5,3.9,3.7,3.7)
semestre<-c(7,8,7,3,3,7,7,7,10,7,6,8,7,7,5,5,6,5)
nivelado<-c("si","no","no","no","si","si","no","no","no","no","no","no","no","no","si","no","si","no")
dt<-data.frame(nombre,edad,promedio,semestre,nivelado)
dt$estrato<-c(1,1,1,1,1,1,2,3,2,3,1,2,2,4,3,2,1,3)
#Agregar filas/datos ficticios
valentina<-data.frame(nombre='Valentina',edad=28,promedio=4,semestre=8,nivelado='no',estrato=2)
dt<-rbind(dt,valentina)
#Seleccionar varias filas y columnas
dato5<-dt[c(1,6,2,9),c(1,4,2)]
print(dato5)## nombre semestre edad
## 1 Maura 7 20
## 6 yandry 7 19
## 2 Camila 8 21
## 9 Willian 10 18- Seleccionar varias filas y columnas en secuencia
nombre<-c("Maura","Camila","Johan","elkin","Alejandro","yandry","Nicolas","Dirley","Willian","Daniela","Steven","Yuliana","Karen","Jose","Santiago","Johan","Santaigo","Daniela")
edad<-c(20,21,21,18,17,19,20,19,18,22,20,19,21,20,19,20,19,20)
promedio<-c(3.5,3.5,3.5,3.3,3.3,3.9,3.7,3.6,3.5,3.2,3.3,3.3,3.5,3.4,3.5,3.9,3.7,3.7)
semestre<-c(7,8,7,3,3,7,7,7,10,7,6,8,7,7,5,5,6,5)
nivelado<-c("si","no","no","no","si","si","no","no","no","no","no","no","no","no","si","no","si","no")
dt<-data.frame(nombre,edad,promedio,semestre,nivelado)
dt$estrato<-c(1,1,1,1,1,1,2,3,2,3,1,2,2,4,3,2,1,3)
valentina<-data.frame(nombre='Valentina',edad=28,promedio=4,semestre=8,nivelado='no',estrato=2)
dt<-rbind(dt,valentina)
#Seleccionar varias filas y columnas en secuencia
dato6<-dt[1:6,2:5]
print(dato6)## edad promedio semestre nivelado
## 1 20 3.5 7 si
## 2 21 3.5 8 no
## 3 21 3.5 7 no
## 4 18 3.3 3 no
## 5 17 3.3 3 si
## 6 19 3.9 7 si- Filtro de datos
library(dplyr)##
## Adjuntando el paquete: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionnombre<-c("Maura","Camila","Johan","elkin","Alejandro","yandry","Nicolas","Dirley","Willian","Daniela","Steven","Yuliana","Karen","Jose","Santiago","Johan","Santaigo","Daniela")
edad<-c(20,21,21,18,17,19,20,19,18,22,20,19,21,20,19,20,19,20)
promedio<-c(3.5,3.5,3.5,3.3,3.3,3.9,3.7,3.6,3.5,3.2,3.3,3.3,3.5,3.4,3.5,3.9,3.7,3.7)
semestre<-c(7,8,7,3,3,7,7,7,10,7,6,8,7,7,5,5,6,5)
nivelado<-c("si","no","no","no","si","si","no","no","no","no","no","no","no","no","si","no","si","no")
dt<-data.frame(nombre,edad,promedio,semestre,nivelado)
dt$estrato<-c(1,1,1,1,1,1,2,3,2,3,1,2,2,4,3,2,1,3)
valentina<-data.frame(nombre='Valentina',edad=28,promedio=4,semestre=8,nivelado='no',estrato=2)
dt<-rbind(dt,valentina)
#Filtro de datos
#Para utilizar el filtro de datos debemostener descargado el paquete dplyr
filtrado<-dt%>%filter(promedio>3.5)
print(filtrado)## nombre edad promedio semestre nivelado estrato
## 1 yandry 19 3.9 7 si 1
## 2 Nicolas 20 3.7 7 no 2
## 3 Dirley 19 3.6 7 no 3
## 4 Johan 20 3.9 5 no 2
## 5 Santaigo 19 3.7 6 si 1
## 6 Daniela 20 3.7 5 no 3
## 7 Valentina 28 4.0 8 no 2Funciones
Las funciones también son tratadas como objetos, lo que significa que pueden ser guardadas en el entorno de trabajo. Esto permite ampliar fácilmente las funcionalidades del lenguaje, ya que los usuarios pueden definir sus propias funciones para automatizar tareas y reutilizar código.
Cálculo de medidas de tendencias central, posición, disperción y forma
Estadistica descriptiva
R ofrece herramientas poderosas para realizar estadística descriptiva, permitiendo explorar y resumir conjuntos de datos mediante indicadores como el promedio, la mediana, la desviación estándar, así como los cuartiles. Estas medidas ayudan a obtener una visión general del comportamiento de los datos.
Generación de datos
- Creación de vectores aleatorios
vector_aleatorio <- runif(5, min = 0, max = 100)
print(vector_aleatorio)## [1] 97.202451 5.500916 11.775960 76.587030 27.727220- Creación de matrices aleatorias
matriz_aleatoria <- matrix(rnorm(9), nrow = 3)
print(matriz_aleatoria)## [,1] [,2] [,3]
## [1,] -0.2555551 0.03830648 -0.4713651
## [2,] -0.5862424 -0.30875015 0.7249840
## [3,] -0.4339612 1.56363478 -0.6913806Medidas de tendencia central
Media (Promedio)
Suma todos los valores y los divide por la cantidad total
datos <- c(10, 20, 30, 40, 50)
mean(datos)## [1] 30Mediana
Es el valor central del conjunto de datos cuando están ordenados
datos <- c(10, 20, 30, 40, 50)
median(datos)## [1] 30Moda
La moda es el valor que mas se repite
moda <- function(x) {
t <- table(x) # Cuenta las frecuencias de los valores
return(as.numeric(names(t[t == max(t)]))) # Extrae el valor más frecuente
}
datos<-c(2, 4, 3, 5, 2, 6, 3, 2, 5, 4, 1, 3, 2)
moda(datos)## [1] 2Medidas de dispersión
Varianza y Desviación Estándar
datos<-c(2, 4, 3, 5, 2, 6, 3, 2, 5, 4, 1, 3, 2)
var(datos)## [1] 2.192308sd(datos)## [1] 1.480644La varianza mide qué tan dispersos están los datos con respecto a la media.
La desviación estándar es la raíz cuadrada de la varianza y se usa para interpretar la dispersión en las mismas unidades que los datos.
Rango y Rango Intercuartil (IQR)
datos<-c(2, 4, 3, 5, 2, 6, 3, 2, 5, 4, 1, 3, 2)
range(datos) # Muestra el valor mínimo y máximo## [1] 1 6IQR(datos) # Calcula la diferencia entre el Q3 y Q1## [1] 2Resumen estadístico Completo
datos<-c(2, 4, 3, 5, 2, 6, 3, 2, 5, 4, 1, 3, 2)
summary(datos)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.231 4.000 6.000Gráficos básicos, ventanas, personalizar, exportar
Gráficos en R
R permite crear gráficos con funciones muy simples. Por ejemplo, el clásico gráfico de dispersión, histogramas y diagramas de caja.
¿Qué es un gráfico en R?
Un gráfico es una representación visual de datos. R es uno de los lenguajes más potentes para visualización porque permite crear gráficos de alta calidad, desde los más básicos hasta los más avanzados. Los gráficos en R se usan para explorar, entender y presentar resultados de forma clara y efectiva.
Tipos de gráficos
- Gráfico de dispersión
x <- 1:10
y <- x^2
plot(x, y,
main = "Relación entre X y Y",
xlab = "X",
ylab = "X al cuadrado")
- Histograma
hist(rnorm(100), main = "Histograma", col = "blue")
- Diagrama de caja
boxplot(rnorm(100), main = "Diagrama de caja", col = "orange")
- Gráfico de líneas
#Datos de ejemplo
x <- 1:10
y <- c(2, 4, 5, 7, 6, 8, 9, 12, 11, 13)
plot(x, y,
type = "l", # 'l' para línea
main = "Gráfico de líneas",
xlab = "Días",
ylab = "Temperatura",
col = "blue",
lwd = 2) # grosor de línea
- Gráfico de barras
# Datos de ejemplo
categorias <- c("A", "B", "C", "D")
valores <- c(10, 25, 15, 30)
barplot(valores,
names.arg = categorias,
main = "Gráfico de barras",
col = "tomato",
ylab = "Frecuencia")
- Gráfico de puntos
#Datos de ejemplo
x <- rnorm(50)
y <- x + rnorm(50, sd = 0.5)
plot(x, y, main = "Gráfico de dispersión", col = "darkred", pch = 19)
- Gráfico de pastel
porcentajes <- c(40, 20, 15, 25)
etiquetas <- c("Math", "Science", "Arts", "Sports")
pie(porcentajes, labels = etiquetas, col = rainbow(4), main = "Gráfico de pastel")
- Boxplot (diagrama de caja y bigotes)
grupo1 <- rnorm(20, mean = 5)
grupo2 <- rnorm(20, mean = 7)
boxplot(grupo1, grupo2,
names = c("Grupo 1", "Grupo 2"),
col = c("orange", "cyan"),
main = "Boxplot comparativo")
Personalización del gráfico
# Datos de ejemplo
x <- 1:10
y <- x^2
plot(x, y,
type = "o",
col = "darkgreen",
lwd = 2, # Grosor de línea
pch = 16, # Tipo de punto
cex = 1.5, # Tamaño de puntos
main = "Personalización de gráfico",
xlab = "Valores de X",
ylab = "Cuadrado de X")
# Agregar línea horizontal y leyenda
abline(h = mean(y), col = "blue", lty = 2)
legend("topleft", legend = c("Datos", "Media de Y"),
col = c("darkgreen", "blue"), lty = c(1, 2), pch = c(16, NA))
### Visualización basica de un dataframe
data("mtcars")
hist(mtcars$mpg, main="Distribución de MPG", xlab="MPG", col="green")
plot(mtcars$hp, mtcars$mpg, main="MPG vs HP", xlab="Caballos de fuerza (HP)", ylab="Millas por galón (MPG)", col="blue", pch=16)
Cuando estamos trabajando con una data y queremos tener una
visualizacion grafica de ciertas variables utilizamos
mtcars$mpg, donde mtcars es la data
$mpg es para escoger la variable de la data que queremos
estudiar, donde $ nos permite escogerla.
Gráficos con GGPLOT2
ggplot2 es un sistema de gráficos más avanzado y personalizable en R.
- ¿Como cargar el paquete?
#install.packages("ggplot2")
library(ggplot2)Tipos de graficos
- Gráfico de Dispersión (Scatter Plot)
library(ggplot2)
datos <- data.frame(
altura = c(160, 165, 170, 175, 180),
peso = c(55, 60, 65, 72, 80)
)
ggplot(datos, aes(x = altura, y = peso)) +
geom_point(color = "blue") +
ggtitle("Altura vs Peso") +
xlab("Altura (cm)") +
ylab("Peso (kg)")
- Histograma
df <- data.frame(valor = rnorm(100))
ggplot(df, aes(x = valor)) +
geom_histogram(fill = "#9B59B6", color = "black", bins = 20) +
ggtitle("Histograma") +
theme(plot.title = element_text(color = "#8E44AD"))
- Gráfico de Líneas
datos <- data.frame(
altura = c(160, 165, 170, 175, 180),
peso = c(55, 60, 65, 72, 80)
)
ggplot(datos, aes(x = altura, y = peso)) +
geom_line(color = "#27AE60") +
geom_point(color = "blue") +
ggtitle("Altura vs Peso") +
xlab("Altura (cm)") +
ylab("Peso (kg)")
- Gráfico de Barras
datos_fruta <- data.frame(
fruta = c("Manzana", "Banana", "Pera", "Uva"),
ventas = c(30, 45, 25, 50)
)
ggplot(datos_fruta, aes(x = fruta, y = ventas, fill = fruta)) +
geom_bar(stat = "identity") +
ggtitle("Ventas por tipo de fruta")
- Boxplot
df <- data.frame(grupo = rep(c("G1", "G2"), each = 20),
valores = c(rnorm(20, 10, 2), rnorm(20, 15, 3)))
ggplot(df, aes(x = grupo, y = valores, fill = grupo)) +
geom_boxplot() +
ggtitle("Boxplot por Grupo") +
xlab("Grupo") + ylab("Valores")
Se visualiza y se analiza diferencias entre distribuciones de dos grupos o conjuntos de datos.
- Gráfico de Pastel (Pie Chart)
df <- data.frame(categoria = c("A", "B", "C"), valor = c(30, 45, 25))
ggplot(df, aes(x = "", y = valor, fill = categoria)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y")
Estadística descriptiva con dplyr
Se mostrará como usar el paquete dplyr de R para hacer un análisis descriptivo usando una base de datos real.
Las variables de la base de datos que vamos a utilizar en el ejemplo de este capítulo se muestran a continuación.
bwt:Peso del bebé al nacer, redondeado a la onza más cercana. gestacion:Duración del embarazo en días, calculado a partir del primer día del último período menstrual normal. paridad:Indicador de si el bebé es el primogénito o desconocido. Altura:Altura de la madre, en pulgadas. Edad:Edad de la madre en el momento de la concepción, en años. Peso:Peso de la madre antes del embarazo, en libras. Fumar:Estado de tabaquismo Indicador de si la madre fuma o no.
url<-'https://raw.githubusercontent.com/fhernanb/datos/master/babies.txt'
dt_web <- read.table(url, header=TRUE, sep='\t') #Para que no me borre el nombre de la variable
dt_web<- dt_web[!is.na(dt_web$gestation),] #Limpiar la data de los valores NA
dt_web<- dt_web[!is.na(dt_web$height),]
dt_web<- dt_web[!is.na(dt_web$weight),]
print(dt_web)## bwt gestation parity age height weight smoke
## 1 120 284 First born 27 62 100 Not
## 2 113 282 First born 33 64 135 Not
## 3 128 279 First born 28 64 115 Yes
## 5 108 282 First born 23 67 125 Yes
## 6 136 286 First born 25 62 93 Not
## 7 138 244 First born 33 62 178 Not
## 8 132 245 First born 23 65 140 Not
## 9 120 289 First born 25 62 125 Not
## 10 143 299 First born 30 66 136 Yes
## 11 140 351 First born 27 68 120 Not
## 12 144 282 First born 32 64 124 Yes
## 13 141 279 First born 23 63 128 Yes
## 14 110 281 First born 36 61 99 Yes
## 15 114 273 First born 30 63 154 Not
## 16 115 285 First born 38 63 130 Not
## 17 92 255 First born 25 65 125 Yes
## 18 115 261 First born 33 60 125 Yes
## 19 144 261 First born 33 68 170 Not
## 20 119 288 First born 43 66 142 Yes
## 21 105 270 First born 22 56 93 Not
## 22 115 274 First born 27 67 175 Yes
## 23 137 287 First born 25 66 145 Not
## 24 122 276 First born 30 68 182 Not
## 25 131 294 First born 23 65 122 Not
## 26 103 261 First born 27 65 112 Yes
## 27 146 280 First born 26 58 106 Not
## 28 114 266 First born 20 65 175 Yes
## 29 125 292 First born 32 65 125 Not
## 30 114 274 First born 28 66 132 Yes
## 31 122 270 First born 26 61 105 Not
## 32 93 278 First born 34 61 146 Not
## 33 130 268 First born 30 66 123 Not
## 34 119 275 First born 23 60 105 Not
## 35 113 281 First born 24 65 120 Not
## 36 134 283 First born 22 67 130 Not
## 37 107 279 First born 24 63 115 Not
## 38 134 288 First born 23 63 92 Yes
## 39 122 267 First born 27 65 101 Yes
## 41 129 293 First born 30 61 160 Not
## 42 110 278 First born 23 63 177 Not
## 44 111 270 First born 27 61 119 Not
## 45 87 248 First born 37 65 130 Yes
## 46 143 274 First born 27 63 110 Yes
## 47 155 294 First born 32 66 150 Not
## 48 110 272 First born 25 60 90 Not
## 49 122 275 First born 26 66 147 Not
## 50 145 291 First born 26 63 119 Yes
## 51 115 258 First born 26 62 130 Not
## 52 108 283 First born 31 65 148 Yes
## 53 102 282 First born 28 61 110 Not
## 54 143 286 First born 31 64 126 Not
## 55 146 267 First born 30 67 132 Not
## 56 124 275 First born 22 60 130 Not
## 57 124 278 First born 26 70 145 Yes
## 58 145 257 First born 33 65 140 Not
## 59 106 273 First born 28 60 116 Not
## 60 75 232 First born 33 61 110 Not
## 61 107 273 First born 24 61 96 Not
## 62 124 288 First born 22 67 118 Not
## 63 122 280 First born 23 65 125 Yes
## 64 101 245 First born 23 63 130 Yes
## 65 128 283 First born 28 63 125 Yes
## 66 104 282 First born 36 65 115 Yes
## 67 97 246 First born 37 63 150 Not
## 68 137 274 First born 26 69 137 Yes
## 69 103 273 First born 31 63 170 Yes
## 70 142 276 First born 38 63 170 Not
## 71 130 289 First born 27 66 130 Not
## 72 156 292 First born 26 63 118 Not
## 73 133 284 First born 25 66 125 Yes
## 74 120 274 First born 24 62 120 Not
## 75 91 270 First born 24 60 149 Yes
## 76 127 274 First born 21 62 110 Not
## 77 153 286 First born 26 63 107 Yes
## 78 121 276 First born 39 63 130 Not
## 79 120 277 First born 27 63 126 Not
## 80 99 272 First born 27 62 103 Yes
## 81 149 293 First born 35 65 116 Not
## 82 129 280 First born 23 64 104 Not
## 83 139 292 First born 25 68 135 Not
## 84 114 274 First born 33 67 148 Yes
## 85 138 287 First born 30 66 145 Not
## 87 138 294 First born 32 65 117 Not
## 88 131 296 First born 37 63 143 Not
## 89 125 305 First born 22 70 196 Yes
## 91 128 281 First born 33 59 117 Not
## 92 134 268 First born 28 62 112 Not
## 93 114 271 First born 27 60 104 Not
## 95 85 278 First born 23 61 103 Yes
## 96 135 282 First born 22 64 100 Not
## 97 87 255 First born 28 61 100 Yes
## 98 125 302 First born 37 62 162 Not
## 100 105 254 First born 29 64 137 Not
## 101 120 279 First born 27 60 121 Yes
## 102 119 274 First born 33 64 120 Not
## 104 107 280 First born 36 65 117 Yes
## 105 119 273 First born 24 61 108 Yes
## 106 133 279 First born 37 66 140 Not
## 107 155 287 First born 33 66 143 Not
## 108 126 273 First born 22 65 150 Not
## 109 129 303 First born 27 64 125 Not
## 110 137 274 First born 29 65 154 Not
## 112 125 302 First born 28 65 125 Not
## 113 91 255 First born 19 67 136 Yes
## 115 95 279 First born 22 66 145 Yes
## 116 118 276 First born 29 64 114 Not
## 117 141 278 First born 33 66 109 Yes
## 118 131 283 First born 25 67 215 Not
## 119 121 264 First born 32 66 145 Not
## 120 100 243 First born 39 65 170 Yes
## 121 131 288 First born 24 61 103 Not
## 122 118 284 First born 26 66 133 Not
## 123 152 288 First born 35 67 130 Not
## 124 121 284 First born 34 69 155 Not
## 125 117 276 First born 31 69 150 Not
## 126 115 283 First born 25 61 150 Yes
## 127 112 277 First born 23 65 110 Not
## 128 94 267 First born 30 62 120 Yes
## 129 109 272 First born 35 66 154 Not
## 130 132 225 First born 28 67 148 Not
## 131 117 278 First born 25 62 103 Not
## 132 101 266 First born 20 67 110 Yes
## 133 112 294 First born 25 64 125 Yes
## 134 128 283 First born 24 60 100 Not
## 135 128 279 First born 25 66 147 Yes
## 136 117 258 First born 31 64 120 Not
## 137 134 278 First born 24 69 135 Not
## 138 127 284 First born 28 65 145 Not
## 139 93 269 First born 21 65 104 Yes
## 140 122 275 First born 27 65 165 Not
## 141 100 265 First born 39 62 107 Yes
## 142 147 293 First born 32 65 123 Not
## 143 120 299 First born 25 65 110 Not
## 144 144 277 First born 30 63 127 Not
## 145 105 268 First born 32 61 115 Yes
## 146 136 276 First born 23 66 155 Not
## 147 102 262 First born 24 63 125 Not
## 148 160 300 First born 29 71 175 Yes
## 149 113 275 First born 24 68 140 Yes
## 150 126 282 First born 38 66 250 Not
## 151 126 271 First born 29 68 148 Not
## 152 115 278 First born 29 61 128 Not
## 154 119 284 First born 20 66 132 Not
## 156 123 318 First born 21 64 152 Not
## 157 118 282 First born 22 68 135 Yes
## 158 133 287 First born 24 60 104 Yes
## 160 134 290 First born 22 60 121 Not
## 161 144 288 First born 21 67 111 Not
## 162 111 273 First born 43 62 138 Not
## 163 125 262 First born 36 66 190 Not
## 164 135 296 First born 30 63 123 Not
## 165 134 289 First born 22 63 125 Not
## 166 116 289 First born 22 65 160 Yes
## 167 129 291 First born 29 69 123 Not
## 168 113 301 First born 26 67 105 Yes
## 169 131 295 First born 23 65 123 Yes
## 170 126 293 First born 29 59 110 <NA>
## 171 121 272 First born 22 62 109 Not
## 172 121 271 First born 25 68 118 Yes
## 173 138 287 First born 24 65 115 Not
## 174 136 278 First born 23 61 105 Not
## 175 120 279 First born 30 66 131 Not
## 176 122 278 First born 31 72 155 Yes
## 177 134 267 First born 30 66 170 Not
## 178 101 280 First born 25 65 123 Yes
## 179 112 288 First born 32 62 125 Not
## 180 132 290 First born 25 64 120 Not
## 181 136 285 First born 23 62 175 Not
## 182 113 277 First born 23 65 192 Yes
## 183 96 271 First born 23 64 116 Not
## 184 124 277 First born 29 63 220 Not
## 185 113 306 First born 21 62 150 Not
## 187 137 258 First born 25 63 117 Not
## 188 133 268 First born 24 61 93 Not
## 189 107 244 First born 20 58 97 Not
## 190 96 265 First born 28 59 135 Yes
## 191 142 278 First born 35 66 136 Yes
## 192 136 275 First born 22 63 110 Not
## 193 75 239 First born 26 63 124 Yes
## 195 104 295 First born 26 65 155 Yes
## 196 130 274 First born 30 63 150 Not
## 197 90 290 First born 22 63 168 Not
## 198 118 276 First born 22 66 147 Yes
## 199 123 320 First born 22 66 117 Not
## 200 137 291 First born 34 61 110 Not
## 201 101 268 First born 19 63 140 Not
## 202 142 275 First born 25 64 132 Not
## 203 98 282 First born 20 63 97 Yes
## 204 124 283 First born 23 63 112 Not
## 206 109 281 First born 23 61 105 Not
## 207 150 285 First born 22 61 110 Yes
## 208 119 282 First born 26 68 150 Yes
## 209 131 280 First born 38 65 125 Not
## 210 101 272 First born 29 63 150 Yes
## 211 113 246 First born 19 62 138 Yes
## 212 127 270 First born 25 62 150 Not
## 213 97 260 First born 23 61 99 Yes
## 214 117 282 First born 28 64 115 Not
## 215 150 290 First born 21 65 125 Not
## 216 85 234 First born 33 67 130 Not
## 217 128 288 First born 27 70 145 Not
## 218 105 233 First born 34 61 130 Not
## 219 90 269 First born 26 67 125 <NA>
## 220 115 274 First born 22 65 130 Yes
## 221 107 290 First born 28 62 135 Not
## 222 121 275 First born 24 63 121 Yes
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## 292 98 280 First born 35 64 122 Yes
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## 305 136 290 First born 26 66 135 Not
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## 852 134 297 First born 27 67 170 Yes
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## 865 113 285 First born 26 66 140 Not
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## 868 116 277 First born 41 64 124 Yes
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## 872 125 283 First born 29 65 125 Not
## 873 164 286 Unknown 32 66 143 Not
## 874 133 297 First born 36 61 125 Not
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## 877 122 306 Unknown 22 62 100 Not
## 878 121 271 Unknown 34 63 129 Yes
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## 885 123 282 First born 30 63 118 Not
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## 894 88 273 First born 20 66 110 Yes
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## 898 129 281 First born 31 67 155 Not
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## 900 85 255 First born 24 68 159 Not
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## 902 124 275 First born 28 61 116 Not
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## 907 116 291 First born 26 66 153 Not
## 908 127 272 First born 20 64 130 Yes
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## 913 58 245 First born 34 64 156 Yes
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## 915 110 277 Unknown 19 62 160 Not
## 916 129 278 First born 27 63 128 Not
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## 918 128 279 First born 27 66 135 Not
## 919 142 284 Unknown 31 66 137 Yes
## 920 115 268 Unknown 31 64 125 Not
## 921 108 274 First born 28 66 175 <NA>
## 922 108 283 First born 35 62 108 Not
## 923 139 281 First born 27 63 137 Not
## 924 115 275 First born 25 61 155 Yes
## 925 136 288 First born 23 62 217 Not
## 926 163 289 Unknown 25 64 126 Yes
## 927 131 285 First born 26 64 130 Not
## 928 77 238 First born 38 67 135 Yes
## 929 124 283 Unknown 33 67 156 Yes
## 930 104 270 Unknown 26 62 115 Not
## 931 102 267 Unknown 24 61 109 Yes
## 932 94 268 First born 30 62 105 Yes
## 933 158 295 Unknown 37 70 137 Not
## 934 112 275 Unknown 21 68 143 Yes
## 935 119 286 First born 26 64 123 Yes
## 936 97 279 First born 29 68 178 Yes
## 937 99 252 First born 21 64 120 Not
## 938 115 264 Unknown 23 67 134 Yes
## 939 139 284 First born 37 61 121 Not
## 940 144 304 Unknown 27 58 102 Yes
## 941 99 270 First born 22 63 115 Yes
## 942 105 280 Unknown 22 63 116 Not
## 943 89 275 First born 34 66 170 Not
## 944 129 270 First born 43 67 160 Not
## 945 119 270 Unknown 20 64 109 Not
## 946 114 291 First born 35 60 112 Not
## 947 106 289 First born 28 67 120 Yes
## 948 122 292 Unknown 34 65 133 Not
## 949 136 261 First born 24 65 110 Not
## 950 121 286 Unknown 22 69 130 Yes
## 951 112 282 First born 26 65 122 Not
## 952 112 266 First born 26 64 122 Not
## 953 123 314 First born 22 61 121 Yes
## 954 139 286 First born 33 65 125 Yes
## 955 125 290 First born 36 59 105 Not
## 956 105 295 Unknown 20 64 112 Yes
## 957 130 276 First born 41 68 130 Not
## 958 146 294 First born 22 66 145 Yes
## 959 133 290 First born 21 64 145 Not
## 960 147 296 Unknown 19 67 124 Not
## 961 109 269 First born 23 63 113 Not
## 962 122 286 First born 23 64 120 Yes
## 963 135 260 First born 43 65 135 Not
## 965 117 272 First born 32 66 118 Not
## 966 138 284 First born 30 66 133 Yes
## 967 120 283 First born 28 64 122 Yes
## 968 119 273 First born 35 65 125 Yes
## 969 118 278 Unknown 19 62 126 Not
## 970 105 330 First born 23 64 112 Yes
## 971 113 306 Unknown 21 65 137 Not
## 973 148 291 Unknown 21 63 115 Not
## 974 140 281 Unknown 22 69 135 Not
## 975 134 287 Unknown 33 67 131 Not
## 976 120 280 First born 31 61 111 Not
## 977 123 296 Unknown 26 64 110 Yes
## 978 102 275 First born 43 64 160 Not
## 979 55 204 First born 35 65 140 Not
## 980 103 276 Unknown 19 63 149 Yes
## 981 123 283 First born 21 65 110 Not
## 982 105 270 Unknown 27 65 134 Yes
## 983 138 289 First born 33 65 155 Not
## 984 128 281 First born 28 63 150 Not
## 985 139 285 First born 30 65 129 Yes
## 986 104 288 Unknown 27 61 122 Yes
## 987 159 296 Unknown 27 64 112 Not
## 988 118 276 First born 29 62 130 Yes
## 989 99 285 First born 25 69 128 Yes
## 990 144 281 First born 20 63 120 Not
## 991 121 270 First born 25 62 108 Yes
## 992 117 265 Unknown 24 66 98 Not
## 993 119 293 Unknown 23 65 127 Not
## 994 105 281 Unknown 19 61 130 Not
## 995 125 283 First born 37 63 145 Yes
## 996 119 259 First born 37 62 130 Not
## 997 101 273 First born 39 60 113 Not
## 998 105 277 Unknown 25 64 156 Not
## 999 110 281 First born 27 60 110 Not
## 1000 100 270 Unknown 21 65 132 Yes
## 1001 98 284 First born 29 68 140 Not
## 1002 127 276 First born 37 64 159 Not
## 1003 117 324 First born 22 62 164 Yes
## 1004 122 278 First born 37 68 114 Not
## 1005 122 273 Unknown 23 64 130 Yes
## 1006 118 281 Unknown 36 66 140 Yes
## 1007 137 303 Unknown 23 66 127 Yes
## 1008 120 275 First born 32 63 115 Yes
## 1009 143 285 First born 27 68 185 Not
## 1010 108 270 First born 29 67 124 Yes
## 1011 131 284 Unknown 19 61 114 Yes
## 1012 110 277 First born 36 61 116 Not
## 1013 105 276 First born 20 62 112 Yes
## 1015 125 255 First born 23 63 133 Not
## 1016 78 258 Unknown 24 66 115 Yes
## 1017 114 289 First born 36 60 115 Not
## 1018 111 278 First born 29 65 145 Yes
## 1019 103 250 First born 40 59 140 Not
## 1020 114 276 First born 26 62 127 Not
## 1021 75 247 First born 36 64 120 Yes
## 1022 169 296 First born 33 67 185 Not
## 1023 94 271 First born 36 61 130 Yes
## 1024 150 287 First born 36 62 135 Not
## 1025 144 248 First born 30 70 145 Not
## 1026 144 291 First born 28 67 130 Not
## 1027 143 313 First born 20 68 150 Not
## 1028 145 304 Unknown 25 63 109 Yes
## 1029 121 285 First born 34 64 110 Not
## 1030 105 256 First born 31 66 142 Not
## 1031 134 286 First born 25 64 125 Not
## 1032 129 294 Unknown 21 65 132 Not
## 1033 114 276 First born 24 63 110 Not
## 1034 97 265 First born 30 61 110 Not
## 1035 160 292 First born 28 64 120 Not
## 1036 65 237 First born 31 67 130 Not
## 1037 145 288 First born 28 64 116 Not
## 1038 95 273 First born 23 60 90 Not
## 1039 139 293 Unknown 21 69 130 Not
## 1040 123 288 First born 27 63 125 Not
## 1041 109 283 First born 23 65 112 Yes
## 1042 110 268 First born 34 64 127 Not
## 1043 122 296 Unknown 24 65 132 Not
## 1044 115 307 First born 34 65 128 Yes
## 1046 108 279 Unknown 19 64 115 Not
## 1047 120 287 First born 23 67 116 Yes
## 1048 131 269 First born 36 68 145 Not
## 1049 136 283 Unknown 24 63 119 Not
## 1050 125 290 First born 32 63 135 Not
## 1051 96 285 Unknown 20 66 117 Yes
## 1052 102 282 Unknown 29 65 125 Yes
## 1053 102 288 Unknown 18 65 117 Not
## 1054 112 277 Unknown 22 67 120 Not
## 1055 135 272 First born 30 65 130 Not
## 1056 91 266 First born 23 60 120 Yes
## 1057 129 276 First born 31 63 125 Not
## 1058 155 290 First born 26 66 129 Yes
## 1059 109 274 First born 33 69 144 Yes
## 1060 80 262 Unknown 31 61 100 Yes
## 1061 125 273 First born 30 64 145 Not
## 1062 94 284 First born 24 63 104 Yes
## 1063 148 281 First born 27 63 110 Yes
## 1064 73 277 First born 29 65 145 Not
## 1065 123 267 Unknown 19 66 132 Yes
## 1066 65 232 First born 24 66 125 Yes
## 1067 118 279 Unknown 21 64 108 Not
## 1068 102 283 First born 39 60 119 Not
## 1069 120 280 First born 24 61 118 Not
## 1070 108 270 Unknown 21 65 130 Yes
## 1071 122 280 Unknown 45 62 128 Not
## 1072 103 268 First born 32 62 97 Yes
## 1073 105 312 First born 41 61 115 Yes
## 1074 126 273 Unknown 25 68 135 Not
## 1075 145 316 First born 22 67 142 Not
## 1076 139 293 First born 34 66 131 Not
## 1077 124 290 First born 26 65 165 Not
## 1078 121 282 First born 30 65 122 Not
## 1079 126 299 Unknown 21 60 114 Not
## 1080 119 286 Unknown 33 67 137 Not
## 1081 114 277 Unknown 19 63 107 Not
## 1082 118 272 First born 23 64 113 Not
## 1083 127 295 First born 36 65 145 Not
## 1084 117 290 Unknown 22 67 110 Not
## 1085 137 277 First born 41 65 126 Not
## 1086 133 292 First born 29 65 135 Not
## 1087 100 264 First born 28 60 111 Yes
## 1088 107 273 Unknown 26 65 135 Not
## 1089 115 276 Unknown 20 62 105 Yes
## 1090 91 292 Unknown 26 61 113 Yes
## 1091 112 287 First born 27 64 110 Yes
## 1092 125 289 Unknown 31 61 120 Not
## 1093 157 291 First born 33 65 121 Not
## 1094 108 256 Unknown 26 67 130 Not
## 1095 130 279 First born 31 62 122 Not
## 1096 135 289 First born 25 64 127 Not
## 1097 123 277 First born 24 66 122 Not
## 1098 100 281 First born 24 61 115 Not
## 1099 124 277 Unknown 23 64 104 Not
## 1100 174 284 First born 39 65 163 Not
## 1101 129 278 First born 26 67 146 Not
## 1102 119 275 First born 27 59 113 Yes
## 1103 126 272 Unknown 35 61 120 Yes
## 1104 128 267 First born 37 61 142 Not
## 1105 116 282 Unknown 19 64 124 Not
## 1106 100 285 First born 18 68 127 Yes
## 1107 96 285 First born 37 66 135 Yes
## 1108 131 279 Unknown 20 68 122 Yes
## 1109 110 292 First born 35 62 127 Not
## 1110 108 278 First born 28 63 125 Yes
## 1111 129 275 First born 24 65 135 Not
## 1112 141 285 First born 23 67 150 Not
## 1113 110 276 First born 31 70 155 Not
## 1114 118 273 First born 21 63 120 Not
## 1115 111 267 Unknown 24 60 115 Not
## 1116 160 297 First born 20 68 136 Not
## 1117 120 280 First born 30 60 115 Not
## 1118 121 281 First born 29 63 108 Not
## 1119 113 282 First born 30 64 118 Yes
## 1120 117 270 First born 23 58 115 Not
## 1121 158 267 First born 35 64 125 Not
## 1122 128 277 First born 39 61 120 Not
## 1123 158 289 First born 30 66 140 Not
## 1124 133 289 First born 22 65 123 Yes
## 1125 163 298 First born 37 61 98 Not
## 1126 128 282 Unknown 19 66 118 Not
## 1127 126 271 Unknown 21 60 105 Not
## 1128 127 283 First born 42 62 154 Yes
## 1129 134 287 First born 40 63 118 Not
## 1130 140 274 First born 41 63 122 Not
## 1131 102 285 First born 29 63 117 Yes
## 1132 100 252 First born 24 61 150 Not
## 1133 120 295 First born 29 59 100 Yes
## 1134 98 279 Unknown 18 65 115 Yes
## 1135 130 246 First born 19 62 118 Not
## 1136 104 280 First born 41 63 118 Yes
## 1137 122 285 First born 31 62 102 Yes
## 1138 137 276 Unknown 25 64 127 Not
## 1139 114 285 Unknown 20 61 104 Not
## 1140 63 236 Unknown 24 58 99 Not
## 1141 98 318 First born 23 63 107 Not
## 1142 99 268 First born 32 63 124 Yes
## 1143 89 238 Unknown 26 64 136 Not
## 1144 117 283 First born 22 65 142 Yes
## 1145 143 281 First born 29 67 132 Not
## 1146 106 279 First born 29 63 125 Yes
## 1147 99 246 First born 35 62 106 Not
## 1148 156 300 First born 27 65 120 Yes
## 1149 72 266 Unknown 25 66 200 Yes
## 1150 75 266 First born 37 61 113 Yes
## 1151 97 285 First born 35 61 112 Yes
## 1152 106 264 First born 41 64 114 Not
## 1153 91 225 First born 18 68 117 Yes
## 1154 117 269 Unknown 28 61 99 Not
## 1155 117 284 First born 25 66 177 Yes
## 1156 112 291 First born 23 66 145 Not
## 1157 112 270 First born 29 61 124 Not
## 1158 141 293 First born 28 61 125 Not
## 1159 131 259 First born 19 63 134 Not
## 1160 130 290 First born 19 65 123 Yes
## 1161 132 270 First born 26 67 140 Not
## 1162 114 265 First born 23 67 130 Yes
## 1163 160 291 First born 34 64 110 Yes
## 1164 106 283 First born 24 63 119 Not
## 1165 84 260 Unknown 20 64 104 Yes
## 1166 112 268 Unknown 25 59 103 Not
## 1167 139 311 First born 37 66 135 Not
## 1168 104 267 First born 30 63 180 Not
## 1169 130 294 First born 32 63 110 Yes
## 1170 71 254 First born 19 61 145 Yes
## 1171 82 270 First born 21 65 150 Yes
## 1172 119 280 Unknown 21 64 128 Not
## 1173 123 353 First born 26 63 115 Not
## 1174 115 278 First born 27 59 95 Not
## 1175 124 289 Unknown 21 67 145 Yes
## 1176 138 292 First born 25 65 130 Yes
## 1177 88 276 First born 25 63 103 Yes
## 1179 128 241 Unknown 17 64 126 Not
## 1180 82 274 First born 31 64 101 Yes
## 1181 100 274 First born 24 63 113 Not
## 1182 114 271 First born 32 61 130 Not
## 1183 97 269 First born 20 65 137 Yes
## 1184 126 298 First born 24 61 112 Not
## 1185 122 275 Unknown 20 65 127 Not
## 1186 152 295 First born 39 62 140 Not
## 1187 116 274 First born 21 62 110 Yes
## 1188 132 302 First born 36 63 145 Yes
## 1189 84 260 Unknown 37 66 140 Not
## 1190 119 277 Unknown 18 61 89 Yes
## 1192 106 312 First born 24 62 135 Yes
## 1194 139 291 First born 24 65 160 Not
## 1195 103 273 First born 36 65 158 Yes
## 1196 112 299 First born 24 67 145 Yes
## 1197 96 276 First born 33 64 127 Yes
## 1198 102 281 Unknown 19 67 135 Yes
## 1199 120 300 First born 34 63 150 Yes
## 1200 102 338 First born 19 64 170 Not
## 1201 97 255 Unknown 22 63 107 Yes
## 1202 113 285 First born 22 70 145 Not
## 1203 130 297 First born 32 58 130 Not
## 1204 97 260 Unknown 25 63 115 Yes
## 1205 116 273 First born 31 61 120 Not
## 1206 114 266 First born 29 64 113 Not
## 1207 127 242 First born 17 61 135 Yes
## 1208 87 247 Unknown 18 66 125 Yes
## 1209 141 281 First born 29 54 156 Yes
## 1210 144 283 Unknown 25 66 140 Not
## 1211 116 273 First born 33 66 130 Yes
## 1212 75 265 First born 21 65 103 Yes
## 1213 138 286 Unknown 28 68 120 Not
## 1214 99 271 First born 39 69 151 Not
## 1215 118 293 First born 21 63 103 Not
## 1217 97 266 First born 24 62 109 Not
## 1218 146 319 First born 28 66 145 Not
## 1219 81 285 First born 19 63 150 Yes
## 1220 110 321 First born 28 66 180 Not
## 1221 135 284 Unknown 19 60 95 Not
## 1222 114 290 Unknown 21 65 120 Yes
## 1223 124 288 Unknown 21 64 116 Yes
## 1224 115 262 Unknown 23 64 136 Yes
## 1225 143 281 First born 28 65 135 Yes
## 1226 113 287 Unknown 29 70 145 Yes
## 1227 109 244 Unknown 21 63 102 Yes
## 1228 103 278 First born 30 60 87 Yes
## 1229 118 276 First born 34 64 116 Not
## 1230 127 290 First born 27 65 121 Not
## 1231 132 270 First born 27 65 126 Not
## 1232 113 275 Unknown 27 60 100 Not
## 1233 128 265 First born 24 67 120 Not
## 1234 130 291 First born 30 65 150 Yes
## 1235 125 281 Unknown 21 65 110 Not
## 1236 117 297 First born 38 65 129 NotFunciones principales de dpylr
- Filtrar filas con
filter()Selecciona observaciones que cumplen una condición específica.
library(dplyr)
url<-'https://raw.githubusercontent.com/fhernanb/datos/master/babies.txt'
dt_web <- read.table(url, header=TRUE, sep='\t') #Para que no me borre el nombre de la variable
dt_web<- dt_web[!is.na(dt_web$gestation),] #Limpiar la data de los valores NA
dt_web<- dt_web[!is.na(dt_web$height),]
dt_web<- dt_web[!is.na(dt_web$weight),]
dt_web %>% filter(age > 35)## bwt gestation parity age height weight smoke
## 1 110 281 First born 36 61 99 Yes
## 2 115 285 First born 38 63 130 Not
## 3 119 288 First born 43 66 142 Yes
## 4 87 248 First born 37 65 130 Yes
## 5 104 282 First born 36 65 115 Yes
## 6 97 246 First born 37 63 150 Not
## 7 142 276 First born 38 63 170 Not
## 8 121 276 First born 39 63 130 Not
## 9 131 296 First born 37 63 143 Not
## 10 125 302 First born 37 62 162 Not
## 11 107 280 First born 36 65 117 Yes
## 12 133 279 First born 37 66 140 Not
## 13 100 243 First born 39 65 170 Yes
## 14 100 265 First born 39 62 107 Yes
## 15 126 282 First born 38 66 250 Not
## 16 111 273 First born 43 62 138 Not
## 17 125 262 First born 36 66 190 Not
## 18 131 280 First born 38 65 125 Not
## 19 154 292 First born 42 65 116 Yes
## 20 99 262 First born 38 59 110 Yes
## 21 138 294 First born 40 64 125 Not
## 22 142 284 First born 39 66 132 Not
## 23 102 280 First born 38 67 140 Not
## 24 119 275 First born 42 67 156 Yes
## 25 131 308 First born 40 65 160 Not
## 26 113 282 Unknown 36 59 140 Not
## 27 91 264 First born 36 60 100 Yes
## 28 117 293 First born 39 60 120 Yes
## 29 93 246 First born 37 65 130 Not
## 30 122 281 First born 42 63 103 Yes
## 31 116 280 First born 40 62 159 Not
## 32 126 274 First born 39 62 122 Not
## 33 145 315 First born 39 67 143 Yes
## 34 154 292 First born 40 66 145 Not
## 35 143 279 First born 39 65 129 Yes
## 36 146 263 First born 39 53 110 Yes
## 37 138 257 First born 38 67 138 Not
## 38 120 269 Unknown 40 63 130 Not
## 39 85 258 First born 41 67 137 Not
## 40 109 273 First born 37 65 138 Yes
## 41 109 272 First born 41 66 154 Yes
## 42 126 262 First born 37 66 135 Yes
## 43 112 252 First born 37 64 162 Not
## 44 71 277 First born 40 69 135 Not
## 45 129 289 First born 37 63 132 Not
## 46 174 281 First born 37 67 155 Not
## 47 105 265 First born 43 65 124 Not
## 48 133 275 First born 36 65 137 Yes
## 49 108 281 First born 41 66 171 Not
## 50 115 269 First born 41 63 165 Yes
## 51 128 271 First born 41 65 135 Yes
## 52 117 265 First born 40 68 134 Yes
## 53 111 274 First born 36 67 159 Not
## 54 116 265 First born 36 63 120 Not
## 55 142 284 First born 37 68 155 <NA>
## 56 154 271 First born 36 69 160 Yes
## 57 79 268 First born 36 61 108 Not
## 58 143 294 First born 44 65 145 Not
## 59 151 298 First born 37 64 135 <NA>
## 60 119 288 First born 37 62 128 Not
## 61 122 291 First born 40 64 155 Not
## 62 146 306 First born 38 63 112 Not
## 63 117 256 First born 37 65 132 Yes
## 64 112 255 First born 39 60 115 Not
## 65 110 269 First born 38 61 102 Not
## 66 150 286 First born 38 67 175 Not
## 67 86 284 First born 39 65 174 Yes
## 68 113 259 First born 38 64 128 Not
## 69 110 280 First born 39 67 125 Not
## 70 148 286 First born 38 68 160 Not
## 71 110 269 First born 38 63 145 Yes
## 72 113 277 First born 38 64 108 Not
## 73 107 268 First born 37 58 112 Yes
## 74 120 279 First born 38 64 124 Not
## 75 108 291 First born 39 65 135 Not
## 76 123 271 First born 41 64 162 Not
## 77 141 277 First born 38 66 162 Not
## 78 109 275 Unknown 37 63 112 Yes
## 79 135 284 First born 39 67 141 Not
## 80 113 287 First born 36 63 118 Not
## 81 109 291 First born 39 64 107 Not
## 82 136 291 First born 41 66 191 Not
## 83 116 277 First born 41 64 124 Yes
## 84 133 297 First born 36 61 125 Not
## 85 72 271 First born 39 61 136 Not
## 86 150 284 First born 40 67 130 Not
## 87 77 238 First born 38 67 135 Yes
## 88 158 295 Unknown 37 70 137 Not
## 89 139 284 First born 37 61 121 Not
## 90 129 270 First born 43 67 160 Not
## 91 125 290 First born 36 59 105 Not
## 92 130 276 First born 41 68 130 Not
## 93 135 260 First born 43 65 135 Not
## 94 102 275 First born 43 64 160 Not
## 95 125 283 First born 37 63 145 Yes
## 96 119 259 First born 37 62 130 Not
## 97 101 273 First born 39 60 113 Not
## 98 127 276 First born 37 64 159 Not
## 99 122 278 First born 37 68 114 Not
## 100 118 281 Unknown 36 66 140 Yes
## 101 110 277 First born 36 61 116 Not
## 102 114 289 First born 36 60 115 Not
## 103 103 250 First born 40 59 140 Not
## 104 75 247 First born 36 64 120 Yes
## 105 94 271 First born 36 61 130 Yes
## 106 150 287 First born 36 62 135 Not
## 107 131 269 First born 36 68 145 Not
## 108 102 283 First born 39 60 119 Not
## 109 122 280 Unknown 45 62 128 Not
## 110 105 312 First born 41 61 115 Yes
## 111 127 295 First born 36 65 145 Not
## 112 137 277 First born 41 65 126 Not
## 113 174 284 First born 39 65 163 Not
## 114 128 267 First born 37 61 142 Not
## 115 96 285 First born 37 66 135 Yes
## 116 128 277 First born 39 61 120 Not
## 117 163 298 First born 37 61 98 Not
## 118 127 283 First born 42 62 154 Yes
## 119 134 287 First born 40 63 118 Not
## 120 140 274 First born 41 63 122 Not
## 121 104 280 First born 41 63 118 Yes
## 122 75 266 First born 37 61 113 Yes
## 123 106 264 First born 41 64 114 Not
## 124 139 311 First born 37 66 135 Not
## 125 152 295 First born 39 62 140 Not
## 126 132 302 First born 36 63 145 Yes
## 127 84 260 Unknown 37 66 140 Not
## 128 103 273 First born 36 65 158 Yes
## 129 99 271 First born 39 69 151 Not
## 130 117 297 First born 38 65 129 Not- Funcion glimpse
La función glimpse del paquete dplyr nos da un resumen
de las variables de la base de datos.
library(dplyr)
url<-'https://raw.githubusercontent.com/fhernanb/datos/master/babies.txt'
dt_web <- read.table(url, header=TRUE, sep='\t') #Para que no me borre el nombre de la variable
dt_web<- dt_web[!is.na(dt_web$gestation),] #Limpiar la data de los valores NA
dt_web<- dt_web[!is.na(dt_web$height),]
dt_web<- dt_web[!is.na(dt_web$weight),]
glimpse(dt_web)## Rows: 1,185
## Columns: 7
## $ bwt <int> 120, 113, 128, 108, 136, 138, 132, 120, 143, 140, 144, 141, …
## $ gestation <int> 284, 282, 279, 282, 286, 244, 245, 289, 299, 351, 282, 279, …
## $ parity <chr> "First born", "First born", "First born", "First born", "Fir…
## $ age <int> 27, 33, 28, 23, 25, 33, 23, 25, 30, 27, 32, 23, 36, 30, 38, …
## $ height <int> 62, 64, 64, 67, 62, 62, 65, 62, 66, 68, 64, 63, 61, 63, 63, …
## $ weight <int> 100, 135, 115, 125, 93, 178, 140, 125, 136, 120, 124, 128, 9…
## $ smoke <chr> "Not", "Not", "Yes", "Yes", "Not", "Not", "Not", "Not", "Yes…Extraer una sola variable con pull y calcular la media,
varianza y desviación estándar del peso de los bebés.
dt_web |> pull(bwt) |> mean() ## [1] 119.5207dt_web |> pull(bwt) |> var()## [1] 336.8849dt_web |> pull(bwt) |> sd()## [1] 18.35443dt_web |> pull(bwt) |> quantile(probs=c(0.25, 0.40, 0.90))## 25% 40% 90%
## 108.0 116.0 142.6Estadistica inferencial en R
¿Que es la estadistica inferencial y por qué es importante?
La estadística inferencial es la parte de la estadística que permite sacar conclusiones sobre una población grande a partir del estudio de una muestra pequeña es fundamental porque nos permite tomar decisiones o llegar a conclusiones sin necesidad de estudiar a toda una población, lo cual muchas veces es costoso, lento o directamente imposible. Gracias a ella, podemos estimar promedios, proporciones o diferencias entre grupos con un nivel de confianza, evaluar hipótesis, y predecir comportamientos en áreas como la salud, la economía, la sociología o la educación.
Conceptos claves
Población: Conjunto total de elementos o individuos que comparten una característica y es lo que se estudia en general.
Muestra: Subconjunto representativo de la población que se selecciona para estudiar.
Parámetro: Medida numérica que describe una característica de la población.
Estadístico: Medida numérica que describe una característica de una muestra.
Error de muestreo: Diferencia entre el valor del parámetro poblacional y el estadístico muestral.
Nivel de significancia (α): Probabilidad de rechazar la hipótesis nula cuando esta es verdadera; comúnmente se usa α = 0.05.
Distribución muestral: Distribución de los valores de un estadístico calculado a partir de múltiples muestras de una misma población.
Pruebas de normalidad
Shapiro-Wilk: Prueba estadística que evalúa si una muestra proviene de una distribución normal.
Kolmogorov-Smirnov: Prueba que compara la distribución de una muestra con una distribución teórica, como la normal.
Correlación
Pearson: Mide la relación lineal entre dos variables cuantitativas.
Spearman: Mide la relación entre rangos de dos variables; útil para datos no normales.
Kendall: Evalúa la concordancia entre rangos de dos variables.
Prueba de hipótesis:
Procedimiento estadístico para decidir si aceptar o rechazar una afirmación sobre una población.
Hipótesis Nula: Afirmación inicial que se pone a prueba, normalmente indica “no hay efecto”.
Hipótesis alterna: Afirmación contraria a la nula; indica la existencia de un efecto o diferencia.
Valor-p: Probabilidad de obtener un resultado igual o más extremo que el observado, si la hipótesis nula fuera cierta.
Intervalo de confianza: Rango de valores dentro del cual se espera que esté el parámetro poblacional con cierta probabilidad (confianza).
Pruebas para datos paramétricos
Pruebas que suponen que los datos siguen una distribución específica, usualmente normal (como t de Student, ANOVA).
Prueba para datos no paramétricos
Pruebas que no requieren supuestos sobre la distribución de los datos (como U de Mann-Whitney, prueba de Wilcoxon).
Pruebas de hipotesis
- Plantear hipótesis nula
- Plantear hipótesis alterna
- Verificar si los datos tienen una distribución normal
- Aplicar prueba de hipótesis (paramétrica o no paramétrica)
- Inferir sobre los datos
- Conclusiones
Prueba de normalidad
¿Para qué sirve la prueba de normalidad y por qué es importante? Sirve para elegir el tipo correcto de prueba estadística, para interpretar bien el valor p y los resultados de esa prueba y su importancia es porque si aplicas una prueba que asume normalidad y tus datos no son normales, tus resultados pueden ser incorrectos o engañosos.
Pruebas para verificar su normalidad
- Gráfico Q-Q plot
- Prueba de Shapiro
- Prueba de Kolmoworov
Gráfico Q-Q plot
Es un gráfico que compara cuantiles teóricos de una distribución (normal en este caso) con los cuantiles observados de los datos. Si los puntos en el gráfico se ajustan aproximadamente a una línea diagonal, sugiere que los datos siguen una distribución normal.
Interpretación del valor p
- p > 0.05 → No se rechaza la normalidad (los datos pueden considerarse normales y se acepta la hipotesis nula).
- p ≤ 0.05 → Se rechaza la normalidad (los datos no son normales y se acepta la hipotesis alterna).
Prueba de Shapiro-Wilk
La prueba de Shapiro-Wilk es una prueba estadística que evalúa la hipótesis nula de que una muestra proviene de una población con una distribución normal.
Prueba Kolmogorov-Smirnov
Se compara cómo se distribuyen los datos reales con lo que se esperaría si siguieran una distribución normal. Un valor p alto indica que no hay evidencia suficiente para decir que los datos no son normales, es decir, podrían venir de una distribución normal.
Prueba t de Student
Es una prueba estadística utilizada para comparar las medias de dos grupos de datos y determinar si hay una diferencia significativa entre ellas. ¿Cuándo se aplica?: se aplica cuando se cumplen ciertos supuestos, como: -Normalidad de los datos -Homogeneidad de varianzas -Datos independientes
Prueba ANOVA
Es una prueba técnica estadística utilizada para comparar las medias de tres o más grupos independientes. La idea principal es analizar si hay diferencias significativas en las medias de los grupos, considerando tanto las variaciones dentro de cada grupo como las variaciones entre los grupos.
Prueba de correlación de Pearson
La prueba de correlación de Pearson mide la fuerza y dirección de la relación lineal entre dos variables cuantitativas. Un valor cercano a 1 o -1 indica una fuerte correlación positiva o negativa, respectivamente, mientras que un valor cercano a 0 indica poca o ninguna relación lineal.
Prueba de la Suma de Rangos de Wilcoxon
Ésta prueba se utiliza para evaluar si hay diferencias significativas entre dos grupos independientes en una variable ordinal o continua. Es una alternativa no paramétrica a la prueba t de Student para muestras independientes.
Prueba de Kruskal-Wallis
Ésta prueba se utiliza para evaluar si hay diferencias significativas entre tres o más grupos independientes en una variable ordinal o continua. Es una alternativa no paramétrica al análisis de varianza (ANOVA) para muestras independientes
Prueba de correlación de Spearman
La prueba de correlación de Spearman se utiliza para evaluar la relación monotónica entre dos variables, especialmente cuando las relaciones no son lineales.
Prueba Chi-Cuadrado
Es una prueba estadística que se utiliza para determinar si existe una relación significativa entre dos variables categóricas. Se basa en comparar las frecuencias observadas en una tabla de contingencia con las frecuencias esperadas bajo el supuesto de que las variables son independientes.