library(lsm) # Para descargar una base 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, unionlibrary(moments) # Para hallar las medidas de forma
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':
##
## elementLa data frame será nuestra estructura de datos, en este caso utilizremos los datos de “survey” del paquete “lsm”, como no tengo experiencia es para mi una base de datos bastante amplia.
datosCompleto <- lsm::surveyAhora lo que haremos será visualizar los datos pero con la estructura, para así tener información sobre el tió de objetos, las filas y todo tipo 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" ...A pesar de tener los datos alguien que posiblemente no los sepa leer necesitará una estructira o forma de organización diferente. Entonces, vamos a explorar los nombres de las variables.
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"Para calcular todo vamos a sacar algunas observaciones.
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 1.52Y es así como podemos sacar la información de la base de datos “datosCompletos”.