Se carga la base de datos RIASEC
riasec_crudo <- read_tsv(here("datos", "data.csv")) %>%
select(-last_col())
head(riasec_crudo, 20)
## # A tibble: 20 × 93
## R1 R2 R3 R4 R5 R6 R7 R8 I1 I2 I3 I4 I5
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3 4 3 1 1 4 1 3 5 5 4 3 4
## 2 1 1 2 4 1 2 2 1 5 5 5 4 4
## 3 2 1 1 1 1 1 1 1 4 1 1 1 1
## 4 3 1 1 2 2 2 2 2 4 1 2 4 3
## 5 4 1 1 2 1 1 1 2 5 5 5 3 5
## 6 3 5 1 3 1 5 3 4 4 5 4 4 4
## 7 1 4 1 4 1 4 1 2 4 4 1 1 2
## 8 5 1 2 2 2 1 2 1 4 4 4 4 5
## 9 1 1 1 1 1 1 1 1 2 1 1 3 1
## 10 4 2 2 2 2 2 3 2 2 2 2 2 2
## 11 1 1 4 2 1 1 1 1 3 3 1 4 3
## 12 1 4 2 2 3 3 3 2 4 3 2 4 4
## 13 5 3 3 3 3 3 3 3 5 5 4 3 3
## 14 4 2 2 3 1 3 3 2 1 1 1 2 1
## 15 3 5 4 5 4 5 3 5 5 4 3 4 4
## 16 4 5 4 5 5 5 5 3 3 4 2 3 2
## 17 3 3 2 3 2 3 3 2 4 4 3 2 2
## 18 4 2 2 4 2 4 4 3 5 4 4 3 3
## 19 3 4 4 5 3 5 4 4 5 5 5 4 4
## 20 2 2 3 4 3 4 3 3 2 5 4 4 3
## # ℹ 80 more variables: I6 <dbl>, I7 <dbl>, I8 <dbl>, A1 <dbl>, A2 <dbl>,
## # A3 <dbl>, A4 <dbl>, A5 <dbl>, A6 <dbl>, A7 <dbl>, A8 <dbl>, S1 <dbl>,
## # S2 <dbl>, S3 <dbl>, S4 <dbl>, S5 <dbl>, S6 <dbl>, S7 <dbl>, S8 <dbl>,
## # E1 <dbl>, E2 <dbl>, E3 <dbl>, E4 <dbl>, E5 <dbl>, E6 <dbl>, E7 <dbl>,
## # E8 <dbl>, C1 <dbl>, C2 <dbl>, C3 <dbl>, C4 <dbl>, C5 <dbl>, C6 <dbl>,
## # C7 <dbl>, C8 <dbl>, introelapse <dbl>, testelapse <dbl>,
## # surveyelapse <dbl>, TIPI1 <dbl>, TIPI2 <dbl>, TIPI3 <dbl>, TIPI4 <dbl>, …
## [1] 145828 93
glimpse(riasec_crudo[, 1:93])
## Rows: 145,828
## Columns: 93
## $ R1 <dbl> 3, 1, 2, 3, 4, 3, 1, 5, 1, 4, 1, 1, 5, 4, 3, 4, …
## $ R2 <dbl> 4, 1, 1, 1, 1, 5, 4, 1, 1, 2, 1, 4, 3, 2, 5, 5, …
## $ R3 <dbl> 3, 2, 1, 1, 1, 1, 1, 2, 1, 2, 4, 2, 3, 2, 4, 4, …
## $ R4 <dbl> 1, 4, 1, 2, 2, 3, 4, 2, 1, 2, 2, 2, 3, 3, 5, 5, …
## $ R5 <dbl> 1, 1, 1, 2, 1, 1, 1, 2, 1, 2, 1, 3, 3, 1, 4, 5, …
## $ R6 <dbl> 4, 2, 1, 2, 1, 5, 4, 1, 1, 2, 1, 3, 3, 3, 5, 5, …
## $ R7 <dbl> 1, 2, 1, 2, 1, 3, 1, 2, 1, 3, 1, 3, 3, 3, 3, 5, …
## $ R8 <dbl> 3, 1, 1, 2, 2, 4, 2, 1, 1, 2, 1, 2, 3, 2, 5, 3, …
## $ I1 <dbl> 5, 5, 4, 4, 5, 4, 4, 4, 2, 2, 3, 4, 5, 1, 5, 3, …
## $ I2 <dbl> 5, 5, 1, 1, 5, 5, 4, 4, 1, 2, 3, 3, 5, 1, 4, 4, …
## $ I3 <dbl> 4, 5, 1, 2, 5, 4, 1, 4, 1, 2, 1, 2, 4, 1, 3, 2, …
## $ I4 <dbl> 3, 4, 1, 4, 3, 4, 1, 4, 3, 2, 4, 4, 3, 2, 4, 3, …
## $ I5 <dbl> 4, 4, 1, 3, 5, 4, 2, 5, 1, 2, 3, 4, 3, 1, 4, 2, …
## $ I6 <dbl> 5, 4, 1, 2, 5, 3, 4, 4, 1, 3, 3, 3, 3, 1, 3, 3, …
## $ I7 <dbl> 4, 4, 1, 3, 5, 3, 3, 5, 1, 2, 3, 3, 3, 1, 5, 2, …
## $ I8 <dbl> 3, 4, 1, 2, 3, 5, 3, 2, 1, 2, 2, 2, 3, 1, 5, 3, …
## $ A1 <dbl> 5, 2, 1, 5, 3, 5, 1, 3, 1, 3, 4, 3, 5, 4, 3, 5, …
## $ A2 <dbl> 4, 1, 1, 2, 5, 5, 4, 4, 1, 3, 5, 4, 5, 5, 3, 4, …
## $ A3 <dbl> 1, 4, 1, 4, 5, 4, 1, 4, 1, 4, 2, 4, 5, 2, 4, 4, …
## $ A4 <dbl> 2, 2, 2, 5, 5, 5, 2, 3, 1, 3, 4, 4, 5, 1, 4, 4, …
## $ A5 <dbl> 4, 1, 1, 1, 5, 5, 4, 3, 1, 4, 5, 4, 4, 4, 3, 2, …
## $ A6 <dbl> 5, 3, 1, 4, 5, 5, 4, 4, 1, 3, 2, 3, 4, 3, 3, 2, …
## $ A7 <dbl> 2, 4, 3, 4, 1, 3, 1, 2, 1, 2, 2, 3, 5, 4, 4, 3, …
## $ A8 <dbl> 4, 2, 1, 2, 5, 5, 4, 3, 1, 4, 3, 4, 5, 3, 4, 4, …
## $ S1 <dbl> 3, 2, 3, 4, 5, 3, 4, 4, 2, 3, 5, 3, 5, 3, 3, 4, …
## $ S2 <dbl> 5, 3, 1, 2, 4, 5, 4, 3, 3, 4, 4, 3, 5, 5, 3, 3, …
## $ S3 <dbl> 5, 4, 5, 3, 4, 5, 1, 4, 2, 4, 4, 4, 4, 4, 4, 4, …
## $ S4 <dbl> 4, 3, 3, 3, 4, 4, 3, 4, 2, 3, 4, 3, 5, 2, 2, 5, …
## $ S5 <dbl> 5, 4, 5, 2, 5, 5, 1, 4, 3, 4, 4, 4, 5, 5, 4, 3, …
## $ S6 <dbl> 5, 2, 5, 1, 5, 4, 2, 4, 3, 4, 2, 4, 5, 5, 3, 4, …
## $ S7 <dbl> 5, 3, 4, 3, 5, 4, 3, 5, 2, 4, 4, 4, 4, 5, 2, 3, …
## $ S8 <dbl> 5, 1, 4, 2, 5, 4, 2, 3, 3, 4, 2, 4, 4, 3, 3, 3, …
## $ E1 <dbl> 2, 1, 1, 5, 2, 3, 2, 4, 1, 2, 3, 3, 5, 3, 2, 2, …
## $ E2 <dbl> 1, 1, 3, 4, 3, 1, 1, 3, 1, 3, 2, 4, 5, 3, 2, 4, …
## $ E3 <dbl> 4, 1, 3, 3, 2, 1, 3, 3, 1, 3, 5, 4, 5, 5, 2, 3, …
## $ E4 <dbl> 1, 1, 5, 2, 3, 1, 1, 3, 1, 3, 4, 3, 5, 2, 4, 4, …
## $ E5 <dbl> 2, 1, 1, 3, 2, 2, 3, 4, 1, 3, 5, 4, 5, 5, 3, 3, …
## $ E6 <dbl> 2, 1, 4, 3, 4, 1, 2, 3, 1, 4, 4, 3, 5, 4, 2, 4, …
## $ E7 <dbl> 1, 1, 4, 2, 2, 1, 1, 3, 1, 3, 3, 4, 5, 5, 4, 3, …
## $ E8 <dbl> 3, 3, 3, 3, 2, 3, 3, 2, 1, 3, 2, 4, 4, 4, 4, 3, …
## $ C1 <dbl> 1, 1, 1, 3, 4, 3, 4, 2, 3, 4, 1, 3, 3, 4, 4, 4, …
## $ C2 <dbl> 3, 1, 3, 2, 2, 2, 2, 4, 1, 4, 1, 2, 5, 4, 4, 4, …
## $ C3 <dbl> 1, 2, 2, 3, 2, 1, 2, 2, 4, 3, 1, 2, 4, 5, 4, 5, …
## $ C4 <dbl> 1, 1, 2, 3, 4, 1, 2, 2, 3, 4, 1, 2, 4, 3, 3, 5, …
## $ C5 <dbl> 1, 1, 1, 2, 5, 3, 4, 4, 1, 4, 1, 2, 3, 4, 4, 5, …
## $ C6 <dbl> 3, 2, 2, 2, 5, 3, 3, 2, 2, 4, 3, 2, 4, 4, 4, 5, …
## $ C7 <dbl> 1, 1, 4, 2, 2, 1, 3, 2, 2, 3, 1, 3, 5, 5, 4, 4, …
## $ C8 <dbl> 1, 1, 1, 2, 2, 3, 1, 2, 2, 3, 1, 3, 5, 3, 4, 4, …
## $ introelapse <dbl> 3, 25, 29, 6, 576, 1014, 91, 26, 4, 28, 42, 16, …
## $ testelapse <dbl> 110, 236, 242, 466, 186, 269, 302, 200, 115, 216…
## $ surveyelapse <dbl> 109, 367, 294, 414, 209, 189, 210, 229, 294, 433…
## $ TIPI1 <dbl> 5, 5, 6, 5, 6, 2, 1, 7, 6, 2, 6, 6, 6, 7, 6, 5, …
## $ TIPI2 <dbl> 4, 5, 4, 2, 7, 2, 2, 6, 2, 2, 5, 5, 3, 2, 4, 2, …
## $ TIPI3 <dbl> 2, 7, 3, 7, 1, 3, 5, 5, 6, 7, 7, 6, 7, 2, 5, 5, …
## $ TIPI4 <dbl> 3, 4, 7, 7, 7, 5, 5, 4, 5, 2, 1, 6, 5, 2, 2, 6, …
## $ TIPI5 <dbl> 2, 7, 5, 5, 4, 3, 3, 6, 6, 7, 7, 3, 7, 6, 4, 7, …
## $ TIPI6 <dbl> 7, 6, 1, 7, 7, 6, 6, 3, 6, 6, 5, 2, 1, 1, 3, 4, …
## $ TIPI7 <dbl> 5, 6, 7, 6, 7, 7, 2, 7, 5, 6, 5, 5, 7, 5, 6, 5, …
## $ TIPI8 <dbl> 6, 4, 1, 2, 5, 5, 4, 5, 4, 2, 1, 4, 1, 3, 4, 3, …
## $ TIPI9 <dbl> 6, 6, 5, 3, 4, 2, 7, 5, 5, 6, 7, 4, 6, 5, 5, 5, …
## $ TIPI10 <dbl> 5, 1, 3, 6, 7, 1, 2, 3, 6, 1, 2, 2, 1, 2, 3, 3, …
## $ VCL1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, …
## $ VCL2 <dbl> 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, …
## $ VCL3 <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, …
## $ VCL4 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, …
## $ VCL5 <dbl> 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, …
## $ VCL6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ VCL7 <dbl> 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
## $ VCL8 <dbl> 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, …
## $ VCL9 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ VCL10 <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ VCL11 <dbl> 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, …
## $ VCL12 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ VCL13 <dbl> 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, …
## $ VCL14 <dbl> 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, …
## $ VCL15 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, …
## $ VCL16 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ education <dbl> 2, 2, 2, 1, 3, 3, 3, 3, 2, 3, 4, 1, 4, 1, 2, 3, …
## $ urban <dbl> 2, 2, 1, 3, 3, 2, 2, 1, 2, 3, 2, 3, 3, 1, 3, 2, …
## $ gender <dbl> 1, 1, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, …
## $ engnat <dbl> 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 2, 2, 2, …
## $ age <dbl> 14, 29, 23, 17, 18, 28, 20, 17, 31, 19, 41, 17, …
## $ hand <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ religion <dbl> 7, 7, 7, 0, 4, 2, 2, 7, 4, 10, 1, 1, 10, 2, 10, …
## $ orientation <dbl> 1, 3, 1, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1, 0, 1, …
## $ race <dbl> 1, 4, 4, 1, 1, 5, 1, 1, 1, 1, 4, 5, 3, 4, 1, 0, …
## $ voted <dbl> 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 2, 1, 2, 2, 2, …
## $ married <dbl> 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ familysize <dbl> 1, 3, 1, 1, 4, 2, 3, 0, 3, 6, 3, 5, 3, 3, 3, 2, …
## $ uniqueNetworkLocation <dbl> 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 2, 2, …
## $ country <chr> "US", "US", "US", "CN", "PH", "IN", "US", "PH", …
## $ source <dbl> 2, 1, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, …
## $ major <chr> NA, "Nursing", NA, NA, "education", "Literature"…
## [1] "R1" "R2" "R3"
## [4] "R4" "R5" "R6"
## [7] "R7" "R8" "I1"
## [10] "I2" "I3" "I4"
## [13] "I5" "I6" "I7"
## [16] "I8" "A1" "A2"
## [19] "A3" "A4" "A5"
## [22] "A6" "A7" "A8"
## [25] "S1" "S2" "S3"
## [28] "S4" "S5" "S6"
## [31] "S7" "S8" "E1"
## [34] "E2" "E3" "E4"
## [37] "E5" "E6" "E7"
## [40] "E8" "C1" "C2"
## [43] "C3" "C4" "C5"
## [46] "C6" "C7" "C8"
## [49] "introelapse" "testelapse" "surveyelapse"
## [52] "TIPI1" "TIPI2" "TIPI3"
## [55] "TIPI4" "TIPI5" "TIPI6"
## [58] "TIPI7" "TIPI8" "TIPI9"
## [61] "TIPI10" "VCL1" "VCL2"
## [64] "VCL3" "VCL4" "VCL5"
## [67] "VCL6" "VCL7" "VCL8"
## [70] "VCL9" "VCL10" "VCL11"
## [73] "VCL12" "VCL13" "VCL14"
## [76] "VCL15" "VCL16" "education"
## [79] "urban" "gender" "engnat"
## [82] "age" "hand" "religion"
## [85] "orientation" "race" "voted"
## [88] "married" "familysize" "uniqueNetworkLocation"
## [91] "country" "source" "major"
## R1 R2 R3 R4 R5
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.00
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.00
## Median :3.000 Median :2.000 Median :1.000 Median :2.000 Median :1.00
## Mean :2.573 Mean :2.107 Mean :1.749 Mean :2.294 Mean :1.75
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:2.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
## R6 R7 R8 I1
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:3.000
## Median :2.000 Median :2.000 Median :2.000 Median :4.000
## Mean :2.198 Mean :2.017 Mean :1.967 Mean :3.435
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## I2 I3 I4 I5
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000
## Median :4.000 Median :3.000 Median :3.000 Median :3.000
## Mean :3.336 Mean :3.105 Mean :3.003 Mean :2.857
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## I6 I7 I8 A1 A2
## Min. :0.000 Min. :0.00 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :3.00 Median :2.000 Median :2.000 Median :3.000
## Mean :2.987 Mean :2.76 Mean :2.515 Mean :2.444 Mean :2.784
## 3rd Qu.:4.000 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000
## A3 A4 A5 A6
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :3.000 Median :3.000
## Mean :2.995 Mean :2.932 Mean :3.057 Mean :3.205
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## A7 A8 S1 S2
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:3.000 1st Qu.:3.000
## Median :3.000 Median :3.000 Median :4.000 Median :4.000
## Mean :2.661 Mean :2.812 Mean :3.414 Mean :3.609
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## S3 S4 S5 S6
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000 1st Qu.:2.000
## Median :3.000 Median :3.000 Median :4.000 Median :3.000
## Mean :3.249 Mean :3.074 Mean :3.535 Mean :3.072
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## S7 S8 E1 E2
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :3.000 Median :2.000 Median :2.000
## Mean :3.236 Mean :2.886 Mean :2.188 Mean :2.372
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## E3 E4 E5 E6
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :2.000 Median :3.000 Median :3.000
## Mean :2.779 Mean :2.386 Mean :2.994 Mean :2.638
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## E7 E8 C1 C2
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :3.000 Median :2.000 Median :2.000
## Mean :2.478 Mean :2.655 Mean :2.295 Mean :2.381
## 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## C3 C4 C5 C6
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :2.000 Median :3.000
## Mean :2.377 Mean :2.473 Mean :2.512 Mean :2.622
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## C7 C8 introelapse testelapse
## Min. :0.000 Min. :0.000 Min. : 0 Min. : 2
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.: 5 1st Qu.: 176
## Median :2.000 Median :2.000 Median : 14 Median : 235
## Mean :2.193 Mean :2.251 Mean : 1489 Mean : 1256
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.: 51 3rd Qu.: 338
## Max. :5.000 Max. :5.000 Max. :7625973 Max. :19308511
## surveyelapse TIPI1 TIPI2 TIPI3
## Min. : 2 Min. :0.00 Min. :0.000 Min. :0.000
## 1st Qu.: 147 1st Qu.:3.00 1st Qu.:2.000 1st Qu.:5.000
## Median : 192 Median :5.00 Median :4.000 Median :6.000
## Mean : 3559 Mean :4.71 Mean :3.897 Mean :5.535
## 3rd Qu.: 268 3rd Qu.:6.00 3rd Qu.:5.000 3rd Qu.:7.000
## Max. :34040115 Max. :7.00 Max. :7.000 Max. :7.000
## TIPI4 TIPI5 TIPI6 TIPI7
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.000
## 1st Qu.:2.000 1st Qu.:5.000 1st Qu.:3.000 1st Qu.:5.000
## Median :4.000 Median :6.000 Median :5.000 Median :6.000
## Mean :4.053 Mean :5.737 Mean :4.468 Mean :5.593
## 3rd Qu.:6.000 3rd Qu.:7.000 3rd Qu.:6.000 3rd Qu.:7.000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :7.000
## TIPI8 TIPI9 TIPI10 VCL1
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :0.0000
## 1st Qu.:1.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:1.0000
## Median :3.000 Median :5.000 Median :2.000 Median :1.0000
## Mean :3.068 Mean :4.997 Mean :2.862 Mean :0.9055
## 3rd Qu.:5.000 3rd Qu.:6.000 3rd Qu.:4.000 3rd Qu.:1.0000
## Max. :7.000 Max. :7.000 Max. :7.000 Max. :1.0000
## VCL2 VCL3 VCL4 VCL5
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:1.0000
## Median :1.0000 Median :0.0000 Median :1.0000 Median :1.0000
## Mean :0.7638 Mean :0.2924 Mean :0.9079 Mean :0.8417
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## VCL6 VCL7 VCL8 VCL9
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.00000
## Mean :0.0832 Mean :0.1584 Mean :0.2806 Mean :0.05836
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.00000
## VCL10 VCL11 VCL12 VCL13
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.000
## 1st Qu.:1.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.000
## Median :1.0000 Median :0.0000 Median :0.000 Median :0.000
## Mean :0.9157 Mean :0.1337 Mean :0.123 Mean :0.448
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:1.000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.000
## VCL14 VCL15 VCL16 education
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.:2.000
## Median :1.0000 Median :1.0000 Median :1.0000 Median :2.000
## Mean :0.7084 Mean :0.8821 Mean :0.9666 Mean :2.376
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :4.000
## urban gender engnat age
## Min. :0.000 Min. :0.000 Min. :0.000 Min. :1.300e+01
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.700e+01
## Median :2.000 Median :2.000 Median :1.000 Median :2.100e+01
## Mean :2.212 Mean :1.669 Mean :1.316 Mean :1.178e+05
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:3.100e+01
## Max. :3.000 Max. :3.000 Max. :2.000 Max. :2.147e+09
## hand religion orientation race
## Min. :0.000 Min. : 0.000 Min. :0.000 Min. :0.000
## 1st Qu.:1.000 1st Qu.: 2.000 1st Qu.:1.000 1st Qu.:3.000
## Median :1.000 Median : 6.000 Median :1.000 Median :4.000
## Mean :1.137 Mean : 5.481 Mean :1.363 Mean :3.288
## 3rd Qu.:1.000 3rd Qu.: 7.000 3rd Qu.:1.000 3rd Qu.:4.000
## Max. :3.000 Max. :12.000 Max. :5.000 Max. :5.000
## voted married familysize uniqueNetworkLocation
## Min. :0.000 Min. :0.000 Min. :0.000e+00 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000e+00 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :3.000e+00 Median :1.000
## Mean :1.621 Mean :1.275 Mean :1.256e+05 Mean :1.309
## 3rd Qu.:2.000 3rd Qu.:1.000 3rd Qu.:3.000e+00 3rd Qu.:2.000
## Max. :2.000 Max. :3.000 Max. :2.147e+09 Max. :2.000
## country source major
## Length:145828 Min. :0.0000 Length:145828
## Class :character 1st Qu.:0.0000 Class :character
## Mode :character Median :0.0000 Mode :character
## Mean :0.4202
## 3rd Qu.:1.0000
## Max. :2.0000
Valores faltantes y nulos
# Valores faltantes en la variable country
sum(is.na(riasec_crudo$country))
## [1] 12
# Valores faltantes en la variable major
sum(is.na(riasec_crudo$major))
## [1] 52274
# Verificar niveles en la variable target education y comprobar la existencia de
# valores faltantes o nulos
riasec_crudo %>%
count(education)
Filtrado por rango etario y limpieza de valores faltantes y
nulos
riasec_en_proceso <- riasec_crudo %>%
# Limpieza de los valores faltantes
filter(
!is.na(age),
# Delimitación del rango etario
age >= 13,
age <= 90,
# Exclusión de los valores nulos en education
education != 0
) %>%
# Categorización y factorización
mutate(
# Creación a partir de la variable target education la variable
# "educ_4_niveles" y tranformación de la misma a factor
educ_4_niveles = factor(
education,
levels = c(1, 2, 3, 4),
# Renombrar los niveles de la variable "educ_4_niveles"
labels = c("1_primario", "2_secundario", "3_universitario", "4_posgrado"),
ordered = FALSE
)
)
# Verificación de niveles en la variable educ_4_niveles
table(riasec_en_proceso$educ_4_niveles)
##
## 1_primario 2_secundario 3_universitario 4_posgrado
## 22629 62599 39084 20294
Filtro de calidad en las palabaras falsas VCL y en los tiempos de
respuesta
# Verificar la cantidad de palabras falsas marcadas como verdaderas
table(riasec_en_proceso$VCL6 + riasec_en_proceso$VCL9 + riasec_en_proceso$VCL12)
##
## 0 1 2 3
## 115570 21795 5293 1948
# Eliminar las observaciones que marcaron mas de una palabra falsa como verdadera
riasec_en_proceso <- riasec_en_proceso %>%
drop_na(VCL6, VCL9, VCL12) %>%
filter((VCL6 + VCL9 + VCL12) < 2)
table(riasec_en_proceso$VCL6 + riasec_en_proceso$VCL9 + riasec_en_proceso$VCL12)
##
## 0 1
## 115570 21795
# Verificar la distribución de los tiempos de respuesta
tiempos_its <- riasec_en_proceso %>%
select(introelapse, testelapse, surveyelapse) %>%
summary()
tiempos_its
## introelapse testelapse surveyelapse
## Min. : 0 Min. : 2 Min. : 2
## 1st Qu.: 5 1st Qu.: 176 1st Qu.: 148
## Median : 15 Median : 236 Median : 193
## Mean : 1498 Mean : 1288 Mean : 3631
## 3rd Qu.: 51 3rd Qu.: 339 3rd Qu.: 269
## Max. :7625973 Max. :19308511 Max. :34040115
# Se realiza un filtrado de calidad en las tres variables de tiempo de respuesta:
# "introelapse", "testelapse" y "surveyelapse" calculando los límites superiores
c_intro <- quantile(riasec_en_proceso$introelapse, 0.75) +
(3 * IQR(riasec_en_proceso$introelapse))
c_test <- quantile(riasec_en_proceso$testelapse, 0.75) +
(3 * IQR(riasec_en_proceso$testelapse))
c_survey <- quantile(riasec_en_proceso$surveyelapse, 0.75) +
(3 * IQR(riasec_en_proceso$surveyelapse))
# Filtrado en los límites inferiores
riasec_en_proceso <- riasec_en_proceso %>%
filter(introelapse >= 2 &
introelapse <= c_intro) %>% # Por arriba de 3s y un mínimo lógico de 2s
filter(testelapse >= 96 &
testelapse <= c_test) %>% # 48 items * 2s = 96s
filter(surveyelapse >= 84 &
surveyelapse <= c_survey) # 42 items * 2s = 84s
riasec_en_proceso %>%
select(introelapse, testelapse, surveyelapse) %>%
summary()
## introelapse testelapse surveyelapse
## Min. : 2.00 Min. : 96.0 Min. : 84.0
## 1st Qu.: 5.00 1st Qu.:174.0 1st Qu.:148.0
## Median : 12.00 Median :227.0 Median :189.0
## Mean : 25.91 Mean :261.2 Mean :215.3
## 3rd Qu.: 32.00 3rd Qu.:310.0 3rd Qu.:254.0
## Max. :189.00 Max. :828.0 Max. :632.0
Análisis y tratamiento de los ítems RIASEC y
TIPI
# Verificar la presencia de valores nulos (respuesta = 0) en ítems RIASEC
sum(riasec_en_proceso %>% select(R1:C8 )==0)
## [1] 11900
# Verificar la presencia de valores nulos (respuesta = 0) en ítems TIPI
sum(riasec_en_proceso %>% select(starts_with("TIPI")) == 0)
## [1] 6062
# Verificar si los items TIPI: 2, 4, 6, 8 y 10 están invertidos
# Seleccionar solo los ítems TIPI
tipi_items <- riasec_en_proceso %>% select(TIPI1:TIPI10)
# Calcular matriz de correlación
cor_matrix <- cor(tipi_items, use = "pairwise.complete.obs")
# Mostrar matriz de correlación
print(round(cor_matrix, 2))
## TIPI1 TIPI2 TIPI3 TIPI4 TIPI5 TIPI6 TIPI7 TIPI8 TIPI9 TIPI10
## TIPI1 1.00 0.09 0.17 -0.13 0.29 -0.48 0.23 -0.07 0.16 -0.11
## TIPI2 0.09 1.00 -0.01 0.22 0.03 -0.03 -0.16 0.12 -0.15 0.06
## TIPI3 0.17 -0.01 1.00 -0.10 0.17 0.05 0.18 -0.37 0.32 0.01
## TIPI4 -0.13 0.22 -0.10 1.00 -0.10 0.17 0.05 0.20 -0.45 0.09
## TIPI5 0.29 0.03 0.17 -0.10 1.00 -0.07 0.20 0.02 0.19 -0.18
## TIPI6 -0.48 -0.03 0.05 0.17 -0.07 1.00 0.04 0.05 0.06 0.13
## TIPI7 0.23 -0.16 0.18 0.05 0.20 0.04 1.00 -0.05 0.17 -0.06
## TIPI8 -0.07 0.12 -0.37 0.20 0.02 0.05 -0.05 1.00 -0.21 0.09
## TIPI9 0.16 -0.15 0.32 -0.45 0.19 0.06 0.17 -0.21 1.00 0.02
## TIPI10 -0.11 0.06 0.01 0.09 -0.18 0.13 -0.06 0.09 0.02 1.00
# Graficar matriz de correlación
corrplot(cor_matrix, method = "color", type = "upper",
tl.col = "black", tl.srt = 45)
# Se observan correlaciones negativas entre los pares de items correspondientes
# al mismo factor, evidenciando que no están invertidos.
# TIPI1 y TIPI6 = -0.48
# TIPI2 y TIPI7 = -0.16
# TIPI3 y TIPI8 = -0.37
# TIPI4 y TIPI9 = -0.45
# TIPI5 y TIPI10 = -0.18
Limpieza de observaciones con ceros (errores u omisiones) en los
ítems RIASEC y TIPI
#Limpieza de ceros (errores u omisiones en las respuestas) en ítems RIASEC y TIPI
riasec_en_proceso <- riasec_en_proceso %>%
filter(if_all(c(R1:C8, TIPI1:TIPI10), ~ .x != 0))
# Invertir los ítems pares del TIPI (escala 1-7)
riasec_en_proceso <- riasec_en_proceso %>%
mutate(
TIPI2 = 8 - TIPI2,
TIPI4 = 8 - TIPI4,
TIPI6 = 8 - TIPI6,
TIPI8 = 8 - TIPI8,
TIPI10 = 8 - TIPI10
)
Creación de los factores para realizar los modelos de variables
latentes
# Se crean los Factores RIASEC (6) y TIPI (5)
riasec_total_limpia <- riasec_en_proceso %>%
# Se crean los Factores RIASEC (Promedio de sus 8 ítems)
mutate(
R_f = rowMeans(select(., R1:R8)),
I_f = rowMeans(select(., I1:I8)),
A_f = rowMeans(select(., A1:A8)),
S_f = rowMeans(select(., S1:S8)),
E_f = rowMeans(select(., E1:E8)),
C_f = rowMeans(select(., C1:C8))
) %>%
# Se crean los Factores TIPI (Promedio de sus 2 ítems)
mutate(
extraversion = (TIPI1 + TIPI6) / 2,
amabilidad = (TIPI7 + TIPI2) / 2,
responsab = (TIPI3 + TIPI8) / 2,
estabilidad = (TIPI9 + TIPI4) / 2,
apertura = (TIPI5 + TIPI10) / 2
)
## [1] 101023 105
Filtro de registros repetidos y limpieza de la variable country
# Aplicación de filtros
riasec_prueba_1 <- riasec_total_limpia %>%
# Filtrar calidad de UniqueNetworkLocation: dejar solo registros únicos
filter(uniqueNetworkLocation == 1) %>%
# Mantener países angloparlantes
filter(country %in% c("US", "CA", "GB", "AU", "NZ", "IE", "SG"))
Filtro de calidad sobre el nivel educativo universitario y
posgrado
riasec_prueba_2 <- riasec_prueba_1 %>%
filter(!(
# Borrar menores de 23 que declaran nivel educativo:posgrado
(age < 23 & educ_4_niveles == "4_posgrado") |
# Borrar menores de 21 que declaran nivel educativo: universitario
(age < 21 & educ_4_niveles == "3_universitario")
))
## [1] 48613 105
LImpieza y filtro de edad 13-70
riasec_experimento <- riasec_prueba_2 %>%
filter(
# Se acota el rango de edad viable
age >= 13 & age <= 70,
)
## [1] 48508 105
sum(is.na(riasec_experimento))
## [1] 15279
# Filtro por país (mantener solo US, según Del Giúdice, 2012)
riasec_experimento <- riasec_experimento %>%
filter(country == "US") %>%
#
mutate(
educ_4_niveles = droplevels(as.factor(educ_4_niveles)),
country = droplevels(as.factor(country))
)
## [1] 36020 105
Recategorización de variables sociodemográficas (conversión a
factor)
riasec_preparada <- riasec_experimento %>%
# Tratamiento de ceros como NA en variables sociodemográficas
mutate(across(
c(gender, urban, voted, married, familysize),
~ na_if(., 0)
)) %>%
# Conversión a factores y modificación de categorías
mutate(
gender = factor(gender, levels = c(1, 2), labels = c("hombre", "mujer")),
urban = factor(urban, levels = c(1, 2, 3), labels = c("rural", "suburbano", "urbano")),
voted = factor(voted, levels = c(1, 2), labels = c("si", "no")),
# Estado civil colapsar categorías
married = factor(married, levels = c(1, 2, 3), labels = c("nunca", "casado", "prev_casado")),
married = fct_collapse(married,
"nunca" = "nunca",
"alguna_vez" = c("casado", "prev_casado")),
# Control de outliers en tamaño familiar
familysize = as.integer(ifelse(familysize > 6, 6, familysize))
)
## [1] 34311 107
Binarización del target para modelos binarios
#Binarización del target para modelos binarios
riasec_prep <- riasec_preparada %>%
mutate(
educ_binario = case_when(
educ_4_niveles %in% c("1_primario", "2_secundario") ~ "basico",
educ_4_niveles %in% c("3_universitario", "4_posgrado") ~ "superior",
TRUE ~ NA_character_
),
# Se transforma a factor
educ_binario = factor(educ_binario, levels = c("basico", "superior"))
) %>%
# Acomodar las columnas para mantener el orden
relocate(educ_binario, .after = educ_4_niveles)
table(riasec_prep$educ_binario)
##
## basico superior
## 18602 15709
riasec_definitiva <- riasec_prep
## [1] 34311 108
Base limpia definitiva para modelar
glimpse(riasec_definitiva[, -106])
## Rows: 34,311
## Columns: 107
## $ R1 <dbl> 3, 1, 2, 1, 3, 3, 1, 5, 3, 1, 3, 1, 3, 5, 3, 1, …
## $ R2 <dbl> 4, 1, 1, 1, 4, 1, 5, 1, 1, 1, 1, 1, 2, 3, 3, 4, …
## $ R3 <dbl> 3, 2, 1, 1, 4, 1, 5, 1, 1, 2, 2, 1, 1, 3, 2, 1, …
## $ R4 <dbl> 1, 4, 1, 1, 5, 1, 1, 2, 2, 1, 2, 1, 2, 3, 2, 1, …
## $ R5 <dbl> 1, 1, 1, 1, 3, 1, 5, 1, 1, 1, 2, 1, 3, 3, 4, 1, …
## $ R6 <dbl> 4, 2, 1, 1, 5, 1, 1, 1, 2, 1, 2, 1, 3, 4, 2, 1, …
## $ R7 <dbl> 1, 2, 1, 1, 4, 2, 5, 1, 2, 1, 2, 1, 2, 3, 4, 1, …
## $ R8 <dbl> 3, 1, 1, 1, 4, 1, 1, 1, 1, 1, 2, 1, 2, 3, 2, 4, …
## $ I1 <dbl> 5, 5, 4, 2, 5, 3, 1, 5, 5, 4, 4, 2, 4, 4, 4, 4, …
## $ I2 <dbl> 5, 5, 1, 1, 5, 3, 1, 1, 3, 3, 2, 4, 3, 4, 5, 3, …
## $ I3 <dbl> 4, 5, 1, 1, 5, 2, 1, 1, 5, 1, 2, 3, 3, 5, 2, 1, …
## $ I4 <dbl> 3, 4, 1, 3, 4, 4, 1, 3, 5, 4, 2, 1, 2, 4, 2, 5, …
## $ I5 <dbl> 4, 4, 1, 1, 4, 3, 1, 4, 5, 1, 2, 1, 3, 4, 3, 1, …
## $ I6 <dbl> 5, 4, 1, 1, 5, 2, 1, 4, 1, 4, 3, 3, 4, 4, 3, 1, …
## $ I7 <dbl> 4, 4, 1, 1, 5, 1, 1, 5, 5, 1, 2, 1, 3, 3, 3, 4, …
## $ I8 <dbl> 3, 4, 1, 1, 4, 1, 5, 1, 1, 4, 2, 1, 2, 5, 2, 1, …
## $ A1 <dbl> 5, 2, 1, 1, 4, 5, 1, 3, 2, 1, 3, 1, 2, 4, 3, 5, …
## $ A2 <dbl> 4, 1, 1, 1, 3, 3, 1, 3, 1, 3, 2, 1, 2, 4, 4, 2, …
## $ A3 <dbl> 1, 4, 1, 1, 4, 3, 1, 4, 1, 5, 3, 2, 2, 5, 3, 3, …
## $ A4 <dbl> 2, 2, 2, 1, 5, 5, 5, 3, 1, 2, 3, 1, 2, 5, 2, 5, …
## $ A5 <dbl> 4, 1, 1, 1, 3, 4, 1, 4, 1, 4, 3, 1, 2, 5, 4, 3, …
## $ A6 <dbl> 5, 3, 1, 1, 5, 5, 1, 1, 2, 3, 3, 4, 3, 5, 3, 5, …
## $ A7 <dbl> 2, 4, 3, 1, 5, 3, 1, 4, 1, 5, 3, 1, 2, 5, 2, 1, …
## $ A8 <dbl> 4, 2, 1, 1, 3, 3, 1, 4, 1, 4, 3, 1, 3, 4, 2, 1, …
## $ S1 <dbl> 3, 2, 3, 2, 4, 2, 1, 5, 3, 3, 5, 2, 4, 3, 5, 3, …
## $ S2 <dbl> 5, 3, 1, 3, 4, 4, 5, 4, 5, 5, 5, 3, 3, 5, 4, 3, …
## $ S3 <dbl> 5, 4, 5, 2, 5, 5, 5, 3, 5, 4, 2, 4, 3, 4, 3, 3, …
## $ S4 <dbl> 4, 3, 3, 2, 4, 4, 1, 3, 2, 5, 3, 1, 3, 3, 4, 3, …
## $ S5 <dbl> 5, 4, 5, 3, 5, 5, 1, 4, 5, 4, 4, 4, 4, 5, 4, 2, …
## $ S6 <dbl> 5, 2, 5, 3, 4, 3, 5, 5, 2, 5, 3, 1, 3, 4, 4, 5, …
## $ S7 <dbl> 5, 3, 4, 2, 5, 3, 1, 5, 5, 3, 3, 3, 4, 4, 4, 4, …
## $ S8 <dbl> 5, 1, 4, 3, 4, 3, 1, 4, 5, 4, 1, 2, 4, 4, 3, 4, …
## $ E1 <dbl> 2, 1, 1, 1, 3, 2, 1, 3, 1, 3, 3, 3, 2, 4, 2, 1, …
## $ E2 <dbl> 1, 1, 3, 1, 4, 3, 1, 3, 3, 4, 2, 3, 2, 1, 3, 1, …
## $ E3 <dbl> 4, 1, 3, 1, 5, 3, 1, 3, 1, 2, 3, 2, 3, 3, 2, 1, …
## $ E4 <dbl> 1, 1, 5, 1, 4, 4, 1, 3, 2, 5, 2, 1, 3, 3, 4, 1, …
## $ E5 <dbl> 2, 1, 1, 1, 5, 3, 1, 3, 4, 3, 4, 4, 3, 4, 2, 1, …
## $ E6 <dbl> 2, 1, 4, 1, 4, 3, 1, 4, 2, 4, 2, 1, 3, 3, 3, 1, …
## $ E7 <dbl> 1, 1, 4, 1, 4, 5, 1, 3, 1, 1, 4, 1, 3, 3, 2, 1, …
## $ E8 <dbl> 3, 3, 3, 1, 5, 3, 1, 3, 2, 4, 2, 1, 4, 4, 3, 1, …
## $ C1 <dbl> 1, 1, 1, 3, 4, 4, 1, 4, 4, 1, 3, 4, 2, 2, 4, 4, …
## $ C2 <dbl> 3, 1, 3, 1, 4, 3, 1, 4, 4, 2, 3, 3, 3, 4, 4, 1, …
## $ C3 <dbl> 1, 2, 2, 4, 4, 3, 1, 4, 5, 1, 2, 4, 3, 3, 4, 1, …
## $ C4 <dbl> 1, 1, 2, 3, 4, 5, 1, 2, 4, 1, 3, 4, 3, 3, 3, 1, …
## $ C5 <dbl> 1, 1, 1, 1, 3, 4, 1, 3, 5, 1, 3, 4, 2, 3, 2, 1, …
## $ C6 <dbl> 3, 2, 2, 2, 5, 4, 1, 1, 3, 1, 2, 3, 2, 2, 4, 1, …
## $ C7 <dbl> 1, 1, 4, 2, 4, 4, 1, 2, 4, 1, 4, 3, 2, 3, 2, 4, …
## $ C8 <dbl> 1, 1, 1, 2, 4, 3, 1, 3, 5, 1, 3, 3, 3, 3, 4, 3, …
## $ introelapse <dbl> 3, 25, 29, 4, 3, 2, 3, 6, 6, 64, 8, 27, 13, 2, 2…
## $ testelapse <dbl> 110, 236, 242, 115, 173, 332, 108, 230, 299, 225…
## $ surveyelapse <dbl> 109, 367, 294, 294, 113, 316, 190, 183, 204, 177…
## $ TIPI1 <dbl> 5, 5, 6, 6, 6, 6, 7, 7, 5, 5, 6, 4, 3, 5, 5, 6, …
## $ TIPI2 <dbl> 4, 3, 4, 6, 4, 3, 7, 2, 4, 2, 7, 2, 5, 5, 5, 7, …
## $ TIPI3 <dbl> 2, 7, 3, 6, 7, 7, 7, 7, 7, 7, 6, 7, 5, 6, 7, 7, …
## $ TIPI4 <dbl> 5, 4, 1, 3, 5, 1, 7, 6, 7, 3, 5, 3, 7, 6, 3, 2, …
## $ TIPI5 <dbl> 2, 7, 5, 6, 6, 6, 7, 7, 6, 6, 7, 3, 6, 6, 4, 4, …
## $ TIPI6 <dbl> 1, 2, 7, 2, 3, 2, 7, 2, 7, 2, 3, 4, 3, 4, 3, 1, …
## $ TIPI7 <dbl> 5, 6, 7, 5, 6, 7, 7, 7, 7, 7, 6, 5, 6, 6, 6, 7, …
## $ TIPI8 <dbl> 2, 4, 7, 4, 7, 2, 7, 6, 6, 7, 7, 6, 7, 3, 6, 4, …
## $ TIPI9 <dbl> 6, 6, 5, 5, 5, 4, 7, 7, 4, 6, 6, 1, 6, 6, 5, 4, …
## $ TIPI10 <dbl> 3, 7, 5, 2, 4, 7, 7, 7, 4, 4, 7, 2, 5, 6, 4, 1, …
## $ VCL1 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ VCL2 <dbl> 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, …
## $ VCL3 <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, …
## $ VCL4 <dbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ VCL5 <dbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ VCL6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, …
## $ VCL7 <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, …
## $ VCL8 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, …
## $ VCL9 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ VCL10 <dbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ VCL11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ VCL12 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
## $ VCL13 <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, …
## $ VCL14 <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, …
## $ VCL15 <dbl> 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, …
## $ VCL16 <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ education <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 3, 3, 2, 3, 2, …
## $ urban <fct> suburbano, suburbano, rural, suburbano, urbano, …
## $ gender <fct> hombre, hombre, mujer, mujer, hombre, hombre, ho…
## $ engnat <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, …
## $ age <dbl> 14, 29, 23, 31, 34, 23, 35, 21, 24, 17, 39, 32, …
## $ hand <dbl> 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ religion <dbl> 7, 7, 7, 4, 7, 7, 12, 4, 4, 7, 1, 7, 1, 0, 6, 0,…
## $ orientation <dbl> 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 2, 1, 1, 0, …
## $ race <dbl> 1, 4, 4, 1, 5, 3, 4, 3, 5, 4, 4, 4, 4, 4, 4, 0, …
## $ voted <fct> no, si, no, si, no, no, si, no, si, no, si, no, …
## $ married <fct> nunca, alguna_vez, nunca, nunca, alguna_vez, nun…
## $ familysize <int> 1, 3, 1, 3, 2, 3, 3, 3, 2, 2, 4, 2, 3, 4, 2, 5, …
## $ uniqueNetworkLocation <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ country <fct> US, US, US, US, US, US, US, US, US, US, US, US, …
## $ source <dbl> 2, 1, 1, 0, 0, 1, 0, 2, 1, 2, 1, 0, 1, 1, 1, 1, …
## $ educ_4_niveles <fct> 2_secundario, 2_secundario, 2_secundario, 2_secu…
## $ educ_binario <fct> basico, basico, basico, basico, basico, basico, …
## $ R_f <dbl> 2.500, 1.750, 1.125, 1.000, 4.000, 1.375, 3.000,…
## $ I_f <dbl> 4.125, 4.375, 1.375, 1.375, 4.625, 2.375, 1.500,…
## $ A_f <dbl> 3.375, 2.375, 1.375, 1.000, 4.000, 3.875, 1.500,…
## $ S_f <dbl> 4.625, 2.750, 3.750, 2.500, 4.375, 3.625, 2.500,…
## $ E_f <dbl> 2.000, 1.250, 3.000, 1.000, 4.250, 3.250, 1.000,…
## $ C_f <dbl> 1.500, 1.250, 2.000, 2.250, 4.000, 3.750, 1.000,…
## $ extraversion <dbl> 3.0, 3.5, 6.5, 4.0, 4.5, 4.0, 7.0, 4.5, 6.0, 3.5…
## $ amabilidad <dbl> 4.5, 4.5, 5.5, 5.5, 5.0, 5.0, 7.0, 4.5, 5.5, 4.5…
## $ responsab <dbl> 2.0, 5.5, 5.0, 5.0, 7.0, 4.5, 7.0, 6.5, 6.5, 7.0…
## $ estabilidad <dbl> 5.5, 5.0, 3.0, 4.0, 5.0, 2.5, 7.0, 6.5, 5.5, 4.5…
## $ apertura <dbl> 2.5, 7.0, 5.0, 4.0, 5.0, 6.5, 7.0, 7.0, 5.0, 5.0…
## $ major_clean <chr> "no_declara", "nursing", "no_declara", "family r…
## $ major_group <fct> No declara, Salud (S/I), No declara, Convenciona…