En esta sección se cargan las librerías necesarias en R que contienen las funciones específicas para el manejo de datos, análisis estadístico y visualización gráfica
library(lsm) # Para descargar una base de datos
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(moments) # Para hallar las medidas de forma
library(e1071)##
## Attaching package: 'e1071'## The following objects are masked from 'package:moments':
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## kurtosis, moment, skewnesslibrary(ggplot2)##
## Attaching package: 'ggplot2'## The following object is masked from 'package:e1071':
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## elementEsta parte es esencial para habilitar las herramientas necesarias sin estos paquetes las funciones avanzadas de estadística y gráficos que se usarán después no estarían disponibles
Se procede a asignar la base de datos survey (proporcionada por el paquete lsm) a una variable llamada datosCompleto para facilitar su manipulación a lo largo del ejercicio
datosCompleto <- lsm::surveyPermite trabajar con un conjunto de datos estructurado y estandarizado para realizar los análisis estadísticos posteriores sin tener que cargar archivos externos manualmente
Se utiliza la función str() para inspeccionar la estructura interna de los datos, permitiendo visualizar los tipos de variables (numéricas, factores, etc) y la cantidad de observaciones
str(datosCompleto) #A) Estructura de los datos## tibble [800 × 66] (S3: tbl_df/tbl/data.frame)
## $ Observation : num [1:800] 1 2 3 4 5 6 7 8 9 10 ...
## $ ID : chr [1:800] "SB11201910010435" "SB11201910004475" "SB11201910011427" "SB11201910041975" ...
## $ Gender : chr [1:800] "Female" "Male" "Male" "Male" ...
## $ Like : chr [1:800] "TV" "Network" "Network" "TV" ...
## $ Age : num [1:800] 21.4 21.1 20.9 18.4 16.6 ...
## $ Smoke : chr [1:800] "No" "Yes" "Yes" "Yes" ...
## $ Height : num [1:800] 1.58 1.6 1.5 1.53 1.78 1.65 1.73 1.53 1.64 1.52 ...
## $ Weight : num [1:800] 75 80 64 49 82 80 90 55 50 78 ...
## $ BMI : num [1:800] 30 31.2 28.4 20.9 25.9 ...
## $ School : chr [1:800] "Private" "Public" "Private" "Public" ...
## $ SES : chr [1:800] "Medium" "High" "High" "Low" ...
## $ Enrollment : chr [1:800] "Credit" "Scholarship" "Scholarship" "Credit" ...
## $ Score : num [1:800] 81 78 77 70 68 65 54 50 36 35 ...
## $ MotherHeight: chr [1:800] "Short_M" "Normal_M" "Normal_M" "Tall_M" ...
## $ MotherAge : num [1:800] 41 45 45 45 46 46 47 48 48 48 ...
## $ MotherCHD : num [1:800] 0 0 0 0 1 0 0 0 0 1 ...
## $ FatherHeight: chr [1:800] "Normal_F" "Short_F" "Tall_F" "Short_F" ...
## $ FatherAge : num [1:800] 40 43 44 45 45 46 46 48 48 49 ...
## $ FatherCHD : num [1:800] 1 1 1 2 1 1 1 1 1 1 ...
## $ Status : chr [1:800] "Distinguished" "Distinguished" "Distinguished" "Regular" ...
## $ SemAcum : num [1:800] 4.25 2.8 4.15 3.2 3.45 2.75 2.7 4.35 4.3 2.8 ...
## $ Exam1 : num [1:800] 1.5 2.3 3.4 2.5 3.1 3.8 5 4 2.5 2.4 ...
## $ Exam2 : num [1:800] 5 4.9 3.6 4.2 3.5 4.4 3 2.3 3.3 2.6 ...
## $ Exam3 : num [1:800] 5 3.7 2 5 5 4.2 3.5 4.6 3.8 4.3 ...
## $ Exam4 : num [1:800] 4.5 3.3 1.9 2.5 3 5 3.6 4.3 1.9 5 ...
## $ ExamAcum : num [1:800] 16 14.2 10.9 14.2 14.6 17.4 15.1 15.2 11.5 14.3 ...
## $ Definitive : num [1:800] 4 3.55 2.73 3.55 3.65 ...
## $ Expense : num [1:800] 48.9 72.1 85.2 56.6 64.6 63 40.8 65.4 37.3 63 ...
## $ Income : num [1:800] 1.61 2.07 2.84 1.55 2.32 2.1 1.69 2.18 1.71 2.1 ...
## $ Gas : num [1:800] 27.4 24.2 22.3 23.1 27.3 ...
## $ Course : chr [1:800] "Face-to-Face" "Virtual" "Face-to-Face" "Virtual" ...
## $ Law : chr [1:800] "Agree" "Agree" "Agree" "Agree" ...
## $ Economic : chr [1:800] "Regular" "Good" "Regular" "Bad" ...
## $ Race : chr [1:800] "Ethnic" "Ethnic" "Ethnic" "Ethnic" ...
## $ Region : chr [1:800] "North" "Center" "North" "Center" ...
## $ EMO1 : num [1:800] 1 4 3 4 2 3 2 3 4 2 ...
## $ EMO2 : num [1:800] 2 4 1 2 1 1 4 1 2 2 ...
## $ EMO3 : num [1:800] 2 1 3 3 2 4 2 4 3 3 ...
## $ EMO4 : num [1:800] 1 2 3 1 4 2 3 2 1 1 ...
## $ EMO5 : num [1:800] 4 1 2 2 2 2 1 1 2 2 ...
## $ GOAL1 : chr [1:800] "Strongly agree" "Undecided" "Agree" "Agree" ...
## $ GOAL2 : chr [1:800] "Agree" "Disagree" "Disagree" "Undecided" ...
## $ GOAL3 : chr [1:800] "Strongly agree" "Disagree" "Agree" "Strongly agree" ...
## $ Pre_STAT1 : num [1:800] 2 1 5 4 1 4 4 2 2 2 ...
## $ Pre_STAT2 : num [1:800] 4 1 1 3 4 1 2 3 3 5 ...
## $ Pre_STAT3 : num [1:800] 2 1 3 1 1 5 4 3 3 2 ...
## $ Pre_STAT4 : num [1:800] 5 1 1 2 2 3 2 3 2 4 ...
## $ Post_STAT1 : num [1:800] 4 5 5 3 5 2 3 3 2 5 ...
## $ Post_STAT2 : num [1:800] 5 1 2 2 3 3 2 3 2 3 ...
## $ Post_STAT3 : num [1:800] 2 3 3 4 3 5 5 4 5 4 ...
## $ Post_STAT4 : num [1:800] 2 3 3 5 4 4 3 5 5 1 ...
## $ Pre_IDARE1 : chr [1:800] "Quite a bit" "Quite a bit" "Quite a bit" "Little" ...
## $ Pre_IDARE2 : chr [1:800] "Little" "Little" "Little" "Nothing" ...
## $ Pre_IDARE3 : chr [1:800] "Quite a bit" "A lot" "Quite a bit" "Quite a bit" ...
## $ Pre_IDARE4 : chr [1:800] "Quite a bit" "Nothing" "Quite a bit" "Quite a bit" ...
## $ Pre_IDARE5 : chr [1:800] "Little" "Quite a bit" "Little" "Nothing" ...
## $ Post_IDARE1 : chr [1:800] "A lot" "A little" "Nothing" "Quite a bit" ...
## $ Post_IDARE2 : chr [1:800] "A lot" "Nothing" "Quite a bit" "A little" ...
## $ Post_IDARE3 : chr [1:800] "A little" "Quite a bit" "Nothing" "A lot" ...
## $ Post_IDARE4 : chr [1:800] "Quite a bit" "A lot" "Nothing" "Quite a bit" ...
## $ Post_IDARE5 : chr [1:800] "A lot" "Quite a bit" "Nothing" "A lot" ...
## $ PSICO1 : chr [1:800] "Frequently" "Frequently" "Sometimes" "Almost always" ...
## $ PSICO2 : chr [1:800] "Almost always" "Sometimes" "Sometimes" "Frequently" ...
## $ PSICO3 : chr [1:800] "Frequently" "Sometimes" "Sometimes" "Frequently" ...
## $ PSICO4 : chr [1:800] "Almost always" "Frequently" "Frequently" "Almost never" ...
## $ PSICO5 : chr [1:800] "Almost always" "Frequently" "Sometimes" "Sometimes" ...Es un paso fundamental para conocer los datos, ya que permite identificar qué variables se pueden sumar o promediar (numéricas) y cuáles solo se pueden contar (categóricas)
Se utiliza la función names() para listar los encabezados de las columnas del data frame, lo cual ayuda a identificar rápidamente las etiquetas de cada variable
names(datosCompleto) #A) Muestra los nombres de las columnas (variables).## [1] "Observation" "ID" "Gender" "Like" "Age"
## [6] "Smoke" "Height" "Weight" "BMI" "School"
## [11] "SES" "Enrollment" "Score" "MotherHeight" "MotherAge"
## [16] "MotherCHD" "FatherHeight" "FatherAge" "FatherCHD" "Status"
## [21] "SemAcum" "Exam1" "Exam2" "Exam3" "Exam4"
## [26] "ExamAcum" "Definitive" "Expense" "Income" "Gas"
## [31] "Course" "Law" "Economic" "Race" "Region"
## [36] "EMO1" "EMO2" "EMO3" "EMO4" "EMO5"
## [41] "GOAL1" "GOAL2" "GOAL3" "Pre_STAT1" "Pre_STAT2"
## [46] "Pre_STAT3" "Pre_STAT4" "Post_STAT1" "Post_STAT2" "Post_STAT3"
## [51] "Post_STAT4" "Pre_IDARE1" "Pre_IDARE2" "Pre_IDARE3" "Pre_IDARE4"
## [56] "Pre_IDARE5" "Post_IDARE1" "Post_IDARE2" "Post_IDARE3" "Post_IDARE4"
## [61] "Post_IDARE5" "PSICO1" "PSICO2" "PSICO3" "PSICO4"
## [66] "PSICO5"FUNCIÓN CORCHETE
Muestra1 <- datosCompleto[1:10,2:7] # A) Un nuevo data frame
Muestra1## # A tibble: 10 × 6
## ID Gender Like Age Smoke Height
## <chr> <chr> <chr> <dbl> <chr> <dbl>
## 1 SB11201910010435 Female TV 21.4 No 1.58
## 2 SB11201910004475 Male Network 21.1 Yes 1.6
## 3 SB11201910011427 Male Network 20.9 Yes 1.5
## 4 SB11201910041975 Male TV 18.4 Yes 1.53
## 5 SB11201910013623 Female TV 16.6 Yes 1.78
## 6 SB11201910038122 Female Network 16.0 No 1.65
## 7 SB11201910037905 Female TV 19.3 Yes 1.73
## 8 SB11201910038140 Female TV 18.6 Yes 1.53
## 9 SB11201910038005 Female TV 17.0 Yes 1.64
## 10 SB11201910037919 Male TV 19.7 Yes 1.52