# paquetes
library(lsm)
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, unionlibrary(moments)
library(e1071)##
## Adjuntando el paquete: 'e1071'## The following objects are masked from 'package:moments':
##
## kurtosis, moment, skewnesslibrary(ggplot2)##
## Adjuntando el paquete: 'ggplot2'## The following object is masked from 'package:e1071':
##
## elementinterpretacion= En este chunk se muestra como se cargan las librerias que usaremos en las bases de datos. # base de datos
datosCompleto <- lsm::surveyinterpretacion= Se carga la base de datos con la que vamos a trabajar y a la cual le asignamos el nombre de datos completos.
#head
head(datosCompleto) #A) Por defecto, solo las primeras 6 observaciones ## # A tibble: 6 × 66
## Observation ID Gender Like Age Smoke Height Weight BMI School SES
## <dbl> <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <chr>
## 1 1 SB11201… Female TV 21.4 No 1.58 75 30.0 Priva… Medi…
## 2 2 SB11201… Male Netw… 21.1 Yes 1.6 80 31.2 Public High
## 3 3 SB11201… Male Netw… 20.9 Yes 1.5 64 28.4 Priva… High
## 4 4 SB11201… Male TV 18.4 Yes 1.53 49 20.9 Public Low
## 5 5 SB11201… Female TV 16.6 Yes 1.78 82 25.9 Priva… High
## 6 6 SB11201… Female Netw… 16.0 No 1.65 80 29.4 Public Low
## # ℹ 55 more variables: Enrollment <chr>, Score <dbl>, MotherHeight <chr>,
## # MotherAge <dbl>, MotherCHD <dbl>, FatherHeight <chr>, FatherAge <dbl>,
## # FatherCHD <dbl>, Status <chr>, SemAcum <dbl>, Exam1 <dbl>, Exam2 <dbl>,
## # Exam3 <dbl>, Exam4 <dbl>, ExamAcum <dbl>, Definitive <dbl>, Expense <dbl>,
## # Income <dbl>, Gas <dbl>, Course <chr>, Law <chr>, Economic <chr>,
## # Race <chr>, Region <chr>, EMO1 <dbl>, EMO2 <dbl>, EMO3 <dbl>, EMO4 <dbl>,
## # EMO5 <dbl>, GOAL1 <chr>, GOAL2 <chr>, GOAL3 <chr>, Pre_STAT1 <dbl>, …head(datosCompleto, 3) #B) Solo las primeras 3 observaciones ## # A tibble: 3 × 66
## Observation ID Gender Like Age Smoke Height Weight BMI School SES
## <dbl> <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <chr>
## 1 1 SB11201… Female TV 21.4 No 1.58 75 30.0 Priva… Medi…
## 2 2 SB11201… Male Netw… 21.1 Yes 1.6 80 31.2 Public High
## 3 3 SB11201… Male Netw… 20.9 Yes 1.5 64 28.4 Priva… High
## # ℹ 55 more variables: Enrollment <chr>, Score <dbl>, MotherHeight <chr>,
## # MotherAge <dbl>, MotherCHD <dbl>, FatherHeight <chr>, FatherAge <dbl>,
## # FatherCHD <dbl>, Status <chr>, SemAcum <dbl>, Exam1 <dbl>, Exam2 <dbl>,
## # Exam3 <dbl>, Exam4 <dbl>, ExamAcum <dbl>, Definitive <dbl>, Expense <dbl>,
## # Income <dbl>, Gas <dbl>, Course <chr>, Law <chr>, Economic <chr>,
## # Race <chr>, Region <chr>, EMO1 <dbl>, EMO2 <dbl>, EMO3 <dbl>, EMO4 <dbl>,
## # EMO5 <dbl>, GOAL1 <chr>, GOAL2 <chr>, GOAL3 <chr>, Pre_STAT1 <dbl>, …interpretacion= Este resultado permite observar las primeras observaciones de la base de datos. Primero se muestran las primeras 6 filas y después solamente las primeras 3. Esto sirve para tener una primera idea de como estan organizados los datos y qué información contiene cada variable.
#tails
tail(datosCompleto) #C) Por defecto, solo las últimas 6 observaciones ## # A tibble: 6 × 66
## Observation ID Gender Like Age Smoke Height Weight BMI School SES
## <dbl> <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <chr>
## 1 795 AC31201… Female <NA> NA <NA> 1.64 NA NA <NA> Low
## 2 796 AC31201… Female TV 13.5 <NA> 1.71 78 26.7 Public <NA>
## 3 797 AC31201… <NA> Netw… 15.8 No 1.68 53 18.8 Priva… Medi…
## 4 798 AC31201… Male <NA> 15.7 No NA 83 NA <NA> Low
## 5 799 AC31201… Female TV NA No 1.76 73 23.6 Priva… Low
## 6 800 AC31201… Male TV 16.6 No 1.62 70 26.7 Priva… <NA>
## # ℹ 55 more variables: Enrollment <chr>, Score <dbl>, MotherHeight <chr>,
## # MotherAge <dbl>, MotherCHD <dbl>, FatherHeight <chr>, FatherAge <dbl>,
## # FatherCHD <dbl>, Status <chr>, SemAcum <dbl>, Exam1 <dbl>, Exam2 <dbl>,
## # Exam3 <dbl>, Exam4 <dbl>, ExamAcum <dbl>, Definitive <dbl>, Expense <dbl>,
## # Income <dbl>, Gas <dbl>, Course <chr>, Law <chr>, Economic <chr>,
## # Race <chr>, Region <chr>, EMO1 <dbl>, EMO2 <dbl>, EMO3 <dbl>, EMO4 <dbl>,
## # EMO5 <dbl>, GOAL1 <chr>, GOAL2 <chr>, GOAL3 <chr>, Pre_STAT1 <dbl>, …tail(datosCompleto, 2) #D) Solo las últimas 2 observaciones## # A tibble: 2 × 66
## Observation ID Gender Like Age Smoke Height Weight BMI School SES
## <dbl> <chr> <chr> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <chr>
## 1 799 AC31201… Female TV NA No 1.76 73 23.6 Priva… Low
## 2 800 AC31201… Male TV 16.6 No 1.62 70 26.7 Priva… <NA>
## # ℹ 55 more variables: Enrollment <chr>, Score <dbl>, MotherHeight <chr>,
## # MotherAge <dbl>, MotherCHD <dbl>, FatherHeight <chr>, FatherAge <dbl>,
## # FatherCHD <dbl>, Status <chr>, SemAcum <dbl>, Exam1 <dbl>, Exam2 <dbl>,
## # Exam3 <dbl>, Exam4 <dbl>, ExamAcum <dbl>, Definitive <dbl>, Expense <dbl>,
## # Income <dbl>, Gas <dbl>, Course <chr>, Law <chr>, Economic <chr>,
## # Race <chr>, Region <chr>, EMO1 <dbl>, EMO2 <dbl>, EMO3 <dbl>, EMO4 <dbl>,
## # EMO5 <dbl>, GOAL1 <chr>, GOAL2 <chr>, GOAL3 <chr>, Pre_STAT1 <dbl>, …interpretacion= Aqui se observan las ultimas observaciones de la base de datos. Primero aparecen las ultimas 6 filas y despues las ultimas 2. Esto permite comprobar como terminan los datos y verificar que la base este completa.
str(datosCompleto) ## 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" ...interpretacion= En este chunk se conoce la estructura de la base de datos. Se puede ver cuantas observaciones y variables tiene. #nombres variables
names(datosCompleto)## [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"interpretacion= Se muestra el nombre de todas las variables que estan en la base de datos
#explorar tamaño
length(datosCompleto) ## [1] 66dim(datosCompleto) ## [1] 800 66ncol(datosCompleto) ## [1] 66nrow(datosCompleto) ## [1] 800Interpretacion= Estas funciones permiten conocer el tamaño de la base de datos. muestra las filas y columnas, indica el numero de variables e indica el numero de observaciones, muestra la cantidad de elementos. #muestral
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 1.52Interpretacion= Se crea una base llamada muestra, seleccionando las primeras 10 filas y las columnas 2 a 7 de la base original. Se trabaja solo con una pequeña parte de la muestra.