A continuación un análisis de BRFSS 2020 Survey Data Behavioral Risk Factor Surveillance System 2020 Survey Data
El presente documento desarrolla una exploración inicial de los datos correspondientes a la encuesta Behavioral Risk Factor Surveillance System (BRFSS) del año 2020. El objetivo de esta primera etapa es conocer la estructura de la base de datos, identificar algunas de sus principales variables y realizar una exploración visual de la información.
El análisis exploratorio de datos (EDA) permite obtener una primera aproximación al comportamiento de las variables y reconocer características generales de los registros antes de realizar análisis estadísticos posteriores.
Para este análisis se utilizaron herramientas del lenguaje R y R
Markdown, principalmente los paquetes tidyverse y
plotly, que permiten organizar, explorar y visualizar los
datos.
En esta primera etapa se realizó un reconocimiento general de la base de datos BRFSS 2020, con el propósito de conocer su tamaño, estructura y las variables disponibles para el análisis. Para ello, se revisó el número de registros y variables, una muestra inicial de los datos, la estructura de las variables y sus nombres.
## [1] 401958 279## Rows: 401,958
## Columns: 279
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## $ CNCRTYP1 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CSRVTRT3 <dbl> 2, NA, 2, NA, NA, NA, 5, NA, NA, NA, 5, NA, NA, NA, 2, NA, …
## $ CSRVDOC1 <dbl> 7, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 5, NA…
## $ CSRVSUM <dbl> 2, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA…
## $ CSRVRTRN <dbl> 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA…
## $ CSRVINST <dbl> 1, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA…
## $ CSRVINSR <dbl> 2, NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA…
## $ CSRVDEIN <dbl> 2, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA…
## $ CSRVCLIN <dbl> 1, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2, NA…
## $ CSRVPAIN <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CSRVCTL2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ PCPSADE1 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ PCDMDEC1 <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ HPVADVC4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ HPVADSHT <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ TETANUS1 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ IMFVPLA1 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ BIRTHSEX <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ SOMALE <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ SOFEMALE <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ TRNSGNDR <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ ACEDEPRS <dbl> 1, NA, 2, 2, 2, 2, NA, NA, 2, 1, 2, 2, NA, 2, 2, 2, 2, 2, 2…
## $ ACEDRINK <dbl> 1, NA, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, NA, 2, 2, 2, 2, 2, 2…
## $ ACEDRUGS <dbl> 1, NA, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, NA, 2, 2, 2, 2, 2, 2…
## $ ACEPRISN <dbl> 2, NA, 2, 2, 2, 2, NA, NA, 2, 2, 2, 2, NA, 2, 2, 2, 2, 2, 2…
## $ ACEDIVRC <dbl> 1, NA, 1, 2, 2, 2, NA, NA, 2, 2, 2, 2, NA, 2, 2, 1, 2, 2, 2…
## $ ACEPUNCH <dbl> 1, NA, 1, 1, 1, 2, NA, NA, 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1…
## $ ACEHURT1 <dbl> 1, NA, 1, 1, 1, 2, NA, NA, 1, 3, 1, 1, NA, 1, 1, 1, 1, 1, 1…
## $ ACESWEAR <dbl> 3, NA, 1, 1, 1, 3, NA, NA, 1, 1, 1, 1, NA, 3, 1, 1, 1, 1, 1…
## $ ACETOUCH <dbl> 3, NA, 1, 1, 1, 3, NA, NA, 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1…
## $ ACETTHEM <dbl> 3, NA, 1, 1, 1, 3, NA, NA, 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1…
## $ ACEHVSEX <dbl> 2, NA, 1, 1, 1, 3, NA, NA, 1, 1, 1, 1, NA, 1, 1, 1, 1, 1, 1…
## $ RCSGENDR <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ RCSRLTN2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CASTHDX2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ CASTHNO2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ QSTVER <dbl> 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,…
## $ QSTLANG <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ `_METSTAT` <dbl> 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 1, 2, 2,…
## $ `_URBSTAT` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1,…
## $ MSCODE <dbl> 1, 1, 1, 1, 1, 5, 5, 1, 2, 1, 5, 1, 5, 5, 5, 2, 1, 1, 5, 5,…
## $ `_STSTR` <dbl> 11011, 11011, 11012, 11011, 11011, 11011, 11011, 11011, 110…
## $ `_STRWT` <dbl> 69.64021, 69.64021, 279.74890, 69.64021, 69.64021, 69.64021…
## $ `_RAWRAKE` <dbl> 1, 1, 1, 1, 1, 2, 1, 1, 3, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 2,…
## $ `_WT2RAKE` <dbl> 69.64021, 69.64021, 279.74890, 69.64021, 69.64021, 139.2804…
## $ `_IMPRACE` <dbl> 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ `_CHISPNC` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `_CRACE1` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `_CPRACE` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `_CLLCPWT` <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `_DUALUSE` <dbl> 1, 1, 1, 9, 9, 1, 1, 1, 1, 9, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1,…
## $ `_DUALCOR` <dbl> 0.465411, 0.465411, 0.465411, NA, NA, 0.465411, 0.465411, 0…
## $ `_LLCPWT2` <dbl> 460.0028, 460.0028, 1847.8591, 1472.4173, 1472.4173, 920.00…
## $ `_LLCPWT` <dbl> 284.3357, 171.2833, 1334.3689, 1297.4866, 454.8151, 616.146…
## $ `_RFHLTH` <dbl> 1, 1, 1, 1, 1, 2, 1, 2, 1, 2, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1,…
## $ `_PHYS14D` <dbl> 2, 1, 1, 1, 1, 3, 1, 3, 3, 2, 3, 2, 1, 1, 1, 3, 1, 1, 2, 1,…
## $ `_MENT14D` <dbl> 3, 1, 1, 1, 1, 3, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3,…
## $ `_HCVU651` <dbl> 2, 9, 9, 9, 9, 9, 9, 9, 1, 9, 9, 9, 9, 9, 9, 1, 1, 9, 9, 1,…
## $ `_TOTINDA` <dbl> 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2,…
## $ `_MICHD` <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, NA, 2, 2, 1, 2, 2, 2, 2…
## $ `_LTASTH1` <dbl> 2, 2, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1,…
## $ `_CASTHM1` <dbl> 2, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 1, 1, 1,…
## $ `_ASTHMS1` <dbl> 1, 2, 3, 3, 3, 1, 3, 3, 3, 3, 1, 1, 3, 3, 3, 1, 3, 3, 3, 3,…
## $ `_DRDXAR2` <dbl> 1, 1, 1, 2, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ `_EXTETH3` <dbl> 2, 2, 9, 2, 1, 2, 2, 9, 1, 9, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2,…
## $ `_ALTETH3` <dbl> NA, 1, 9, 1, 1, 1, 1, 9, NA, 9, 1, 1, 1, 1, 1, NA, NA, 1, 1…
## $ `_DENVST3` <dbl> 1, 1, 1, 9, 9, 2, 1, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2,…
## $ `_PRACE1` <dbl> 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ `_MRACE1` <dbl> 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ `_HISPANC` <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
## $ `_RACE` <dbl> 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ `_RACEG21` <dbl> 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ `_RACEGR3` <dbl> 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ `_RACEPRV` <dbl> 1, 2, 2, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ `_SEX` <dbl> 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2,…
## $ `_AGEG5YR` <dbl> 8, 10, 10, 13, 13, 10, 12, 10, 5, 12, 11, 13, 12, 13, 10, 9…
## $ `_AGE65YR` <dbl> 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 1,…
## $ `_AGE80` <dbl> 56, 65, 65, 80, 80, 66, 75, 69, 41, 78, 73, 80, 78, 80, 68,…
## $ `_AGE_G` <dbl> 5, 6, 6, 6, 6, 6, 6, 6, 3, 6, 6, 6, 6, 6, 6, 5, 5, 6, 6, 5,…
## $ HTIN4 <dbl> 67, 64, 68, NA, 66, 69, 66, 63, 71, 63, 66, 60, NA, 61, 72,…
## $ HTM4 <dbl> 170, 163, 173, NA, 168, 175, 168, 160, 180, 160, 168, 152, …
## $ WTKG3 <dbl> 4808, 7711, NA, NA, 5715, 8165, 6804, 6804, 7711, 7394, 607…
## $ `_BMI5` <dbl> 1660, 2918, NA, NA, 2034, 2658, 2421, 2657, 2371, 2887, 216…
## $ `_BMI5CAT` <dbl> 1, 3, NA, NA, 2, 3, 2, 3, 2, 3, 2, 4, NA, 3, 4, 4, 3, 3, 3,…
## $ `_RFBMI5` <dbl> 1, 2, 9, 9, 1, 2, 1, 2, 1, 2, 1, 2, 9, 2, 2, 2, 2, 2, 2, 2,…
## $ `_CHLDCNT` <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ `_EDUCAG` <dbl> 4, 4, 3, 2, 4, 2, 2, 2, 4, 1, 4, 3, 2, 1, 2, 2, 2, 4, 4, 2,…
## $ `_INCOMG` <dbl> 1, 9, 5, 9, 9, 3, 4, 3, 4, 9, 5, 2, 9, 2, 1, 9, 3, 5, 5, 5,…
## $ `_SMOKER3` <dbl> 1, 9, 4, 4, 4, 3, 4, 1, 4, 3, 4, 3, 9, 4, 4, 1, 3, 3, 4, 1,…
## $ `_RFSMOK3` <dbl> 2, 9, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 9, 1, 1, 2, 1, 1, 1, 2,…
## $ DRNKANY5 <dbl> 2, 9, 2, 2, 2, 2, 2, 9, 2, 2, 2, 2, 9, 2, 2, 2, 2, 1, 2, 2,…
## $ DROCDY3_ <dbl> 0, 900, 0, 0, 0, 0, 0, 900, 0, 0, 0, 0, 900, 0, 0, 0, 0, 10…
## $ `_RFBING5` <dbl> 1, 9, 1, 1, 1, 1, 1, 9, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 1,…
## $ `_DRNKWK1` <dbl> 0, 99900, 0, 0, 0, 0, 0, 99900, 0, 0, 0, 0, 99900, 0, 0, 0,…
## $ `_RFDRHV7` <dbl> 1, 9, 1, 1, 1, 1, 1, 9, 1, 1, 1, 1, 9, 1, 1, 1, 1, 1, 1, 1,…
## $ `_FLSHOT7` <dbl> NA, 9, 1, 2, 1, 1, 1, 9, NA, 1, 2, 1, 9, 1, 1, NA, NA, 1, 1…
## $ `_PNEUMO3` <dbl> NA, 9, 2, 2, 1, 2, 1, 9, NA, 1, 1, 2, 9, 1, 1, NA, NA, 1, 1…
## $ `_RFSEAT2` <dbl> 1, 9, 1, 1, 1, 1, 1, 9, 1, 1, 1, 1, 9, 1, 1, 2, 1, 1, 1, 1,…
## $ `_RFSEAT3` <dbl> 1, 9, 1, 1, 1, 2, 1, 9, 1, 1, 1, 1, 9, 1, 1, 2, 1, 1, 1, 1,…
## $ `_DRNKDRV` <dbl> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 1, 9, 9,…
## $ `_RFMAM22` <dbl> 2, 9, 1, 2, 1, NA, 1, 9, 2, 2, 1, 2, 9, 2, NA, NA, 1, NA, 1…
## $ `_MAM5023` <dbl> 2, NA, 1, NA, NA, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA…
## $ `_RFPAP35` <dbl> NA, NA, 1, NA, NA, NA, NA, NA, 1, NA, NA, NA, NA, NA, NA, N…
## $ `_RFPSA23` <dbl> NA, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, NA, NA, NA, 1, 2…
## $ `_CLNSCPY` <dbl> 1, NA, 1, NA, NA, 1, 1, NA, NA, NA, 1, NA, NA, NA, 1, 1, 2,…
## $ `_SGMSCPY` <dbl> 3, NA, 3, NA, NA, 3, NA, NA, NA, NA, 2, NA, NA, NA, 3, 3, 3…
## $ `_SGMS10Y` <dbl> 3, NA, 3, NA, NA, 3, NA, NA, NA, NA, 2, NA, NA, NA, 3, 3, 3…
## $ `_RFBLDS4` <dbl> 3, NA, 3, NA, NA, 3, 2, NA, NA, NA, 2, NA, NA, NA, 3, 3, 3,…
## $ `_STOLDNA` <dbl> 3, NA, 3, NA, NA, 3, 1, NA, NA, NA, 3, NA, NA, NA, 3, 3, 3,…
## $ `_VIRCOLN` <dbl> 3, NA, 3, NA, NA, 3, 3, NA, NA, NA, 3, NA, NA, NA, 3, 3, 3,…
## $ `_SBONTIM` <dbl> 3, 2, 3, NA, NA, 3, 2, 2, NA, NA, 2, NA, NA, NA, 3, 3, 3, N…
## $ `_CRCREC1` <dbl> 1, NA, 1, NA, NA, 1, 1, NA, NA, NA, 1, NA, NA, NA, 1, 1, 2,…
## $ `_AIDTST4` <dbl> 1, NA, 2, 2, 9, 1, 2, NA, 2, 2, 1, 2, NA, 2, 2, 2, 2, 2, 2,…## [1] "_STATE" "FMONTH" "IDATE" "IMONTH" "IDAY" "IYEAR"
## [7] "DISPCODE" "SEQNO" "_PSU" "CTELENM1" "PVTRESD1" "COLGHOUS"
## [13] "STATERE1" "CELPHONE" "LADULT1" "COLGSEX" "NUMADULT" "LANDSEX"
## [19] "NUMMEN" "NUMWOMEN" "RESPSLCT" "SAFETIME" "CTELNUM1" "CELLFON5"
## [25] "CADULT1" "CELLSEX" "PVTRESD3" "CCLGHOUS" "CSTATE1" "LANDLINE"
## [31] "HHADULT" "SEXVAR" "GENHLTH" "PHYSHLTH" "MENTHLTH" "POORHLTH"
## [37] "HLTHPLN1" "PERSDOC2" "MEDCOST" "CHECKUP1" "EXERANY2" "SLEPTIM1"
## [43] "CVDINFR4" "CVDCRHD4" "CVDSTRK3" "ASTHMA3" "ASTHNOW" "CHCSCNCR"
## [49] "CHCOCNCR" "CHCCOPD2" "HAVARTH4" "ADDEPEV3" "CHCKDNY2" "DIABETE4"
## [55] "DIABAGE3" "LASTDEN4" "RMVTETH4" "MARITAL" "EDUCA" "RENTHOM1"
## [61] "NUMHHOL3" "NUMPHON3" "CPDEMO1B" "VETERAN3" "EMPLOY1" "CHILDREN"
## [67] "INCOME2" "PREGNANT" "WEIGHT2" "HEIGHT3" "DEAF" "BLIND"
## [73] "DECIDE" "DIFFWALK" "DIFFDRES" "DIFFALON" "SMOKE100" "SMOKDAY2"
## [79] "STOPSMK2" "LASTSMK2" "USENOW3" "ALCDAY5" "AVEDRNK3" "DRNK3GE5"
## [85] "MAXDRNKS" "FLUSHOT7" "FLSHTMY3" "SHINGLE2" "PNEUVAC4" "FALL12MN"
## [91] "FALLINJ4" "SEATBELT" "DRNKDRI2" "HADMAM" "HOWLONG" "HADPAP2"
## [97] "LASTPAP2" "HPVTEST" "HPLSTTST" "HADHYST2" "PCPSAAD3" "PCPSADI1"
## [103] "PCPSARE1" "PSATEST1" "PSATIME" "PCPSARS1" "COLNSCPY" "COLNTEST"
## [109] "SIGMSCPY" "SIGMTEST" "BLDSTOL1" "LSTBLDS4" "STOOLDNA" "SDNATEST"
## [115] "VIRCOLON" "VCLNTEST" "HIVTST7" "HIVTSTD3" "HIVRISK5" "PDIABTST"
## [121] "PREDIAB1" "INSULIN1" "BLDSUGAR" "FEETCHK3" "DOCTDIAB" "CHKHEMO3"
## [127] "FEETCHK" "EYEEXAM1" "DIABEYE" "DIABEDU" "TOLDCFS" "HAVECFS"
## [133] "WORKCFS" "TOLDHEPC" "TRETHEPC" "PRIRHEPC" "HAVEHEPC" "HAVEHEPB"
## [139] "MEDSHEPB" "HLTHCVR1" "CIMEMLOS" "CDHOUSE" "CDASSIST" "CDHELP"
## [145] "CDSOCIAL" "CDDISCUS" "CAREGIV1" "CRGVREL4" "CRGVLNG1" "CRGVHRS1"
## [151] "CRGVPRB3" "CRGVALZD" "CRGVPER1" "CRGVHOU1" "CRGVEXPT" "ECIGARET"
## [157] "ECIGNOW" "MARIJAN1" "USEMRJN2" "RSNMRJN1" "LCSFIRST" "LCSLAST"
## [163] "LCSNUMCG" "LCSCTSCN" "CNCRDIFF" "CNCRAGE" "CNCRTYP1" "CSRVTRT3"
## [169] "CSRVDOC1" "CSRVSUM" "CSRVRTRN" "CSRVINST" "CSRVINSR" "CSRVDEIN"
## [175] "CSRVCLIN" "CSRVPAIN" "CSRVCTL2" "PCPSADE1" "PCDMDEC1" "HPVADVC4"
## [181] "HPVADSHT" "TETANUS1" "IMFVPLA1" "BIRTHSEX" "SOMALE" "SOFEMALE"
## [187] "TRNSGNDR" "ACEDEPRS" "ACEDRINK" "ACEDRUGS" "ACEPRISN" "ACEDIVRC"
## [193] "ACEPUNCH" "ACEHURT1" "ACESWEAR" "ACETOUCH" "ACETTHEM" "ACEHVSEX"
## [199] "RCSGENDR" "RCSRLTN2" "CASTHDX2" "CASTHNO2" "QSTVER" "QSTLANG"
## [205] "_METSTAT" "_URBSTAT" "MSCODE" "_STSTR" "_STRWT" "_RAWRAKE"
## [211] "_WT2RAKE" "_IMPRACE" "_CHISPNC" "_CRACE1" "_CPRACE" "_CLLCPWT"
## [217] "_DUALUSE" "_DUALCOR" "_LLCPWT2" "_LLCPWT" "_RFHLTH" "_PHYS14D"
## [223] "_MENT14D" "_HCVU651" "_TOTINDA" "_MICHD" "_LTASTH1" "_CASTHM1"
## [229] "_ASTHMS1" "_DRDXAR2" "_EXTETH3" "_ALTETH3" "_DENVST3" "_PRACE1"
## [235] "_MRACE1" "_HISPANC" "_RACE" "_RACEG21" "_RACEGR3" "_RACEPRV"
## [241] "_SEX" "_AGEG5YR" "_AGE65YR" "_AGE80" "_AGE_G" "HTIN4"
## [247] "HTM4" "WTKG3" "_BMI5" "_BMI5CAT" "_RFBMI5" "_CHLDCNT"
## [253] "_EDUCAG" "_INCOMG" "_SMOKER3" "_RFSMOK3" "DRNKANY5" "DROCDY3_"
## [259] "_RFBING5" "_DRNKWK1" "_RFDRHV7" "_FLSHOT7" "_PNEUMO3" "_RFSEAT2"
## [265] "_RFSEAT3" "_DRNKDRV" "_RFMAM22" "_MAM5023" "_RFPAP35" "_RFPSA23"
## [271] "_CLNSCPY" "_SGMSCPY" "_SGMS10Y" "_RFBLDS4" "_STOLDNA" "_VIRCOLN"
## [277] "_SBONTIM" "_CRCREC1" "_AIDTST4"En esta etapa se seleccionaron algunas variables de interés para realizar una revisión inicial de la información disponible en la base de datos. Las variables fueron seleccionadas considerando aspectos sociodemográficos, estado de salud, hábitos y acceso a servicios de salud.
datos %>%
select(GENHLTH, SEXVAR, PHYSHLTH, MENTHLTH,
HLTHPLN1, CHECKUP1, EXERANY2, SLEPTIM1,
DIABETE4, SMOKE100, `_AGE_G`, `_BMI5CAT`) %>%
glimpse()## Rows: 401,958
## Columns: 12
## $ GENHLTH <dbl> 2, 3, 3, 1, 2, 4, 3, 4, 2, 4, 4, 3, 2, 4, 3, 5, 2, 2, 3, 3,…
## $ SEXVAR <dbl> 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2,…
## $ PHYSHLTH <dbl> 3, 88, 88, 88, 88, 20, 88, 15, 28, 6, 15, 5, 88, 88, 88, 30…
## $ MENTHLTH <dbl> 30, 88, 88, 88, 88, 30, 88, 10, 88, 88, 88, 88, 88, 88, 88,…
## $ HLTHPLN1 <dbl> 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ CHECKUP1 <dbl> 4, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ EXERANY2 <dbl> 1, 1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2,…
## $ SLEPTIM1 <dbl> 5, 7, 7, 6, 7, 8, 6, 6, 8, 12, 4, 9, 6, 5, 10, 15, 5, 8, 7,…
## $ DIABETE4 <dbl> 1, 3, 3, 3, 3, 1, 3, 1, 3, 3, 3, 1, 3, 4, 3, 1, 3, 1, 3, 3,…
## $ SMOKE100 <dbl> 1, NA, 2, 2, 2, 1, 2, 1, 2, 1, 2, 1, NA, 2, 2, 1, 1, 1, 2, …
## $ `_AGE_G` <dbl> 5, 6, 6, 6, 6, 6, 6, 6, 3, 6, 6, 6, 6, 6, 6, 5, 5, 6, 6, 5,…
## $ `_BMI5CAT` <dbl> 1, 3, NA, NA, 2, 3, 2, 3, 2, 3, 2, 4, NA, 3, 4, 4, 3, 3, 3,…La siguiente tabla presenta una descripción general de las principales variables seleccionadas para la exploración inicial de la base de datos.
| Variable | Descripción |
|---|---|
GENHLTH |
Estado general de salud |
SEXVAR |
Sexo |
PHYSHLTH |
Salud física |
MENTHLTH |
Salud mental |
HLTHPLN1 |
Cobertura de salud |
CHECKUP1 |
Consulta médica de rutina |
EXERANY2 |
Actividad física |
SLEPTIM1 |
Horas de sueño |
DIABETE4 |
Diabetes |
SMOKE100 |
Antecedente de consumo de cigarrillos |
_AGE_G |
Grupo de edad |
_BMI5CAT |
Categoría de índice de masa corporal |
## [1] 2 3 1 4 5 7 9 NA## [1] 2 1## [1] 5 7 6 8 12 4 9 10 15 1 77 99 3 2 16 11 18 14 13 NA 20 24 17 19 21
## [26] 22 23En esta etapa se realizó una revisión general de la calidad de los datos, con el propósito de identificar la presencia de valores ausentes en la base de datos. Esta revisión permite reconocer qué variables presentan información incompleta y tenerlo en cuenta antes de realizar análisis posteriores.
## [1] 51295182## _STATE FMONTH IDATE IMONTH IDAY IYEAR DISPCODE SEQNO
## 0 0 0 0 0 0 0 0
## _PSU CTELENM1 PVTRESD1 COLGHOUS STATERE1 CELPHONE LADULT1 COLGSEX
## 0 277207 277207 401958 277207 277207 277207 401958
## NUMADULT LANDSEX NUMMEN NUMWOMEN RESPSLCT SAFETIME CTELNUM1 CELLFON5
## 277216 350509 291536 291536 354532 124751 124751 124751
## CADULT1 CELLSEX PVTRESD3 CCLGHOUS CSTATE1 LANDLINE HHADULT SEXVAR
## 124755 124771 124752 400979 124758 124752 124753 0
## GENHLTH PHYSHLTH MENTHLTH POORHLTH HLTHPLN1 PERSDOC2 MEDCOST CHECKUP1
## 8 5 5 200343 3 3 3 5
## EXERANY2 SLEPTIM1 CVDINFR4 CVDCRHD4 CVDSTRK3 ASTHMA3 ASTHNOW CHCSCNCR
## 3 3 6 3 3 3 347817 3
## CHCOCNCR CHCCOPD2 HAVARTH4 ADDEPEV3 CHCKDNY2 DIABETE4 DIABAGE3 LASTDEN4
## 3 5 5 6 6 6 349864 7
## RMVTETH4 MARITAL EDUCA RENTHOM1 NUMHHOL3 NUMPHON3 CPDEMO1B VETERAN3
## 7 12 12 15 277212 371708 116 1307
## EMPLOY1 CHILDREN INCOME2 PREGNANT WEIGHT2 HEIGHT3 DEAF BLIND
## 2925 4767 7358 326368 9852 10824 12415 13324
## DECIDE DIFFWALK DIFFDRES DIFFALON SMOKE100 SMOKDAY2 STOPSMK2 LASTSMK2
## 14351 15280 15892 16769 17860 245324 349535 298233
## USENOW3 ALCDAY5 AVEDRNK3 DRNK3GE5 MAXDRNKS FLUSHOT7 FLSHTMY3 SHINGLE2
## 18493 20927 211099 211626 212112 23525 205030 165874
## PNEUVAC4 FALL12MN FALLINJ4 SEATBELT DRNKDRI2 HADMAM HOWLONG HADPAP2
## 24437 137808 333322 25511 208430 198438 245446 198942
## LASTPAP2 HPVTEST HPLSTTST HADHYST2 PCPSAAD3 PCPSADI1 PCPSARE1 PSATEST1
## 215438 199388 336493 201690 275393 275622 275882 276043
## PSATIME PCPSARS1 COLNSCPY COLNTEST SIGMSCPY SIGMTEST BLDSTOL1 LSTBLDS4
## 343595 343707 143403 220603 143927 370070 144393 318862
## STOOLDNA SDNATEST VIRCOLON VCLNTEST HIVTST7 HIVTSTD3 HIVRISK5 PDIABTST
## 144914 383572 145453 390737 34037 285136 35932 183016
## PREDIAB1 INSULIN1 BLDSUGAR FEETCHK3 DOCTDIAB CHKHEMO3 FEETCHK EYEEXAM1
## 182965 388849 388850 388850 388850 388850 388925 388850
## DIABEYE DIABEDU TOLDCFS HAVECFS WORKCFS TOLDHEPC TRETHEPC PRIRHEPC
## 388850 388850 401958 401958 401958 383151 401958 401958
## HAVEHEPC HAVEHEPB MEDSHEPB HLTHCVR1 CIMEMLOS CDHOUSE CDASSIST CDHELP
## 401958 383186 401807 362563 334120 395834 395847 400293
## CDSOCIAL CDDISCUS CAREGIV1 CRGVREL4 CRGVLNG1 CRGVHRS1 CRGVPRB3 CRGVALZD
## 395855 395865 371480 396104 396112 396130 396148 396612
## CRGVPER1 CRGVHOU1 CRGVEXPT ECIGARET ECIGNOW MARIJAN1 USEMRJN2 RSNMRJN1
## 396163 396172 372320 137397 351208 271339 390513 390525
## LCSFIRST LCSLAST LCSNUMCG LCSCTSCN CNCRDIFF CNCRAGE CNCRTYP1 CSRVTRT3
## 387914 388332 388351 370711 378792 379265 379282 377950
## CSRVDOC1 CSRVSUM CSRVRTRN CSRVINST CSRVINSR CSRVDEIN CSRVCLIN CSRVPAIN
## 388388 388401 388422 391979 388442 388451 388459 379939
## CSRVCTL2 PCPSADE1 PCDMDEC1 HPVADVC4 HPVADSHT TETANUS1 IMFVPLA1 BIRTHSEX
## 400105 400117 401958 386846 399179 394003 394375 300719
## SOMALE SOFEMALE TRNSGNDR ACEDEPRS ACEDRINK ACEDRUGS ACEPRISN ACEDIVRC
## 291919 271880 161936 272572 272753 272819 272878 272915
## ACEPUNCH ACEHURT1 ACESWEAR ACETOUCH ACETTHEM ACEHVSEX RCSGENDR RCSRLTN2
## 272990 273103 273191 273271 273360 273459 346998 347440
## CASTHDX2 CASTHNO2 QSTVER QSTLANG _METSTAT _URBSTAT MSCODE _STSTR
## 352527 396463 0 0 7127 7127 279833 0
## _STRWT _RAWRAKE _WT2RAKE _IMPRACE _CHISPNC _CRACE1 _CPRACE _CLLCPWT
## 0 0 0 0 125339 345583 345583 347004
## _DUALUSE _DUALCOR _LLCPWT2 _LLCPWT _RFHLTH _PHYS14D _MENT14D _HCVU651
## 0 224237 0 0 0 0 0 0
## _TOTINDA _MICHD _LTASTH1 _CASTHM1 _ASTHMS1 _DRDXAR2 _EXTETH3 _ALTETH3
## 0 3571 0 0 0 2303 0 256096
## _DENVST3 _PRACE1 _MRACE1 _HISPANC _RACE _RACEG21 _RACEGR3 _RACEPRV
## 0 9 9 0 1 1 1 1
## _SEX _AGEG5YR _AGE65YR _AGE80 _AGE_G HTIN4 HTM4 WTKG3
## 0 0 0 0 0 23808 21731 34948
## _BMI5 _BMI5CAT _RFBMI5 _CHLDCNT _EDUCAG _INCOMG _SMOKER3 _RFSMOK3
## 41357 41357 0 0 0 0 0 0
## DRNKANY5 DROCDY3_ _RFBING5 _DRNKWK1 _RFDRHV7 _FLSHOT7 _PNEUMO3 _RFSEAT2
## 0 0 0 0 0 256096 256096 0
## _RFSEAT3 _DRNKDRV _RFMAM22 _MAM5023 _RFPAP35 _RFPSA23 _CLNSCPY _SGMSCPY
## 0 0 235983 306666 304555 275354 224036 230472
## _SGMS10Y _RFBLDS4 _STOLDNA _VIRCOLN _SBONTIM _CRCREC1 _AIDTST4
## 230472 226105 225601 227684 207741 226260 34037## COLGHOUS COLGSEX TOLDCFS HAVECFS WORKCFS TRETHEPC PRIRHEPC HAVEHEPC
## 401958 401958 401958 401958 401958 401958 401958 401958
## PCDMDEC1 MEDSHEPB CCLGHOUS CDHELP PCPSADE1 CSRVCTL2 HPVADSHT CRGVALZD
## 401958 401807 400979 400293 400117 400105 399179 396612
## CASTHNO2 CRGVHOU1 CRGVPER1 CRGVPRB3 CRGVHRS1 CRGVLNG1 CRGVREL4 CDDISCUS
## 396463 396172 396163 396148 396130 396112 396104 395865
## CDSOCIAL CDASSIST CDHOUSE IMFVPLA1 TETANUS1 CSRVINST VCLNTEST RSNMRJN1
## 395855 395847 395834 394375 394003 391979 390737 390525
## USEMRJN2 FEETCHK BLDSUGAR FEETCHK3 DOCTDIAB CHKHEMO3 EYEEXAM1 DIABEYE
## 390513 388925 388850 388850 388850 388850 388850 388850
## DIABEDU INSULIN1 CSRVCLIN CSRVDEIN CSRVINSR CSRVRTRN CSRVSUM CSRVDOC1
## 388850 388849 388459 388451 388442 388422 388401 388388
## LCSNUMCG LCSLAST LCSFIRST HPVADVC4 SDNATEST HAVEHEPB TOLDHEPC CSRVPAIN
## 388351 388332 387914 386846 383572 383186 383151 379939
## CNCRTYP1 CNCRAGE CNCRDIFF CSRVTRT3 CRGVEXPT NUMPHON3 CAREGIV1 LCSCTSCN
## 379282 379265 378792 377950 372320 371708 371480 370711
## SIGMTEST HLTHCVR1 RESPSLCT CASTHDX2 ECIGNOW LANDSEX DIABAGE3 STOPSMK2
## 370070 362563 354532 352527 351208 350509 349864 349535
## ASTHNOW RCSRLTN2 _CLLCPWT RCSGENDR _CRACE1 _CPRACE PCPSARS1 PSATIME
## 347817 347440 347004 346998 345583 345583 343707 343595
## HPLSTTST CIMEMLOS FALLINJ4 PREGNANT LSTBLDS4 _MAM5023 _RFPAP35 BIRTHSEX
## 336493 334120 333322 326368 318862 306666 304555 300719
## LASTSMK2 SOMALE NUMMEN NUMWOMEN HIVTSTD3 MSCODE NUMADULT NUMHHOL3
## 298233 291919 291536 291536 285136 279833 277216 277212
## CTELENM1 PVTRESD1 STATERE1 CELPHONE LADULT1 PSATEST1 PCPSARE1 PCPSADI1
## 277207 277207 277207 277207 277207 276043 275882 275622
## PCPSAAD3 _RFPSA23 ACEHVSEX ACETTHEM ACETOUCH ACESWEAR ACEHURT1 ACEPUNCH
## 275393 275354 273459 273360 273271 273191 273103 272990
## ACEDIVRC ACEPRISN ACEDRUGS ACEDRINK ACEDEPRS SOFEMALE MARIJAN1 _ALTETH3
## 272915 272878 272819 272753 272572 271880 271339 256096
## _FLSHOT7 _PNEUMO3 HOWLONG SMOKDAY2 _RFMAM22 _SGMSCPY _SGMS10Y _VIRCOLN
## 256096 256096 245446 245324 235983 230472 230472 227684
## _CRCREC1 _RFBLDS4 _STOLDNA _DUALCOR _CLNSCPY COLNTEST LASTPAP2 MAXDRNKS
## 226260 226105 225601 224237 224036 220603 215438 212112
## DRNK3GE5 AVEDRNK3 DRNKDRI2 _SBONTIM FLSHTMY3 HADHYST2 POORHLTH HPVTEST
## 211626 211099 208430 207741 205030 201690 200343 199388
## HADPAP2 HADMAM PDIABTST PREDIAB1 SHINGLE2 TRNSGNDR VIRCOLON STOOLDNA
## 198942 198438 183016 182965 165874 161936 145453 144914
## BLDSTOL1 SIGMSCPY COLNSCPY FALL12MN ECIGARET _CHISPNC CELLSEX CSTATE1
## 144393 143927 143403 137808 137397 125339 124771 124758
## CADULT1 HHADULT PVTRESD3 LANDLINE SAFETIME CTELNUM1 CELLFON5 _BMI5
## 124755 124753 124752 124752 124751 124751 124751 41357
## _BMI5CAT HIVRISK5 WTKG3 HIVTST7 _AIDTST4 SEATBELT PNEUVAC4 HTIN4
## 41357 35932 34948 34037 34037 25511 24437 23808
## FLUSHOT7 HTM4 ALCDAY5 USENOW3 SMOKE100 DIFFALON DIFFDRES DIFFWALK
## 23525 21731 20927 18493 17860 16769 15892 15280
## DECIDE BLIND DEAF HEIGHT3 WEIGHT2 INCOME2 _METSTAT _URBSTAT
## 14351 13324 12415 10824 9852 7358 7127 7127
## CHILDREN _MICHD EMPLOY1 _DRDXAR2 VETERAN3 CPDEMO1B RENTHOM1 MARITAL
## 4767 3571 2925 2303 1307 116 15 12
## EDUCA _PRACE1 _MRACE1 GENHLTH LASTDEN4 RMVTETH4 CVDINFR4 ADDEPEV3
## 12 9 9 8 7 7 6 6
## CHCKDNY2 DIABETE4 PHYSHLTH MENTHLTH CHECKUP1 CHCCOPD2 HAVARTH4 HLTHPLN1
## 6 6 5 5 5 5 5 3
## PERSDOC2 MEDCOST EXERANY2 SLEPTIM1 CVDCRHD4 CVDSTRK3 ASTHMA3 CHCSCNCR
## 3 3 3 3 3 3 3 3
## CHCOCNCR _RACE _RACEG21 _RACEGR3 _RACEPRV _STATE FMONTH IDATE
## 3 1 1 1 1 0 0 0
## IMONTH IDAY IYEAR DISPCODE SEQNO _PSU SEXVAR QSTVER
## 0 0 0 0 0 0 0 0
## QSTLANG _STSTR _STRWT _RAWRAKE _WT2RAKE _IMPRACE _DUALUSE _LLCPWT2
## 0 0 0 0 0 0 0 0
## _LLCPWT _RFHLTH _PHYS14D _MENT14D _HCVU651 _TOTINDA _LTASTH1 _CASTHM1
## 0 0 0 0 0 0 0 0
## _ASTHMS1 _EXTETH3 _DENVST3 _HISPANC _SEX _AGEG5YR _AGE65YR _AGE80
## 0 0 0 0 0 0 0 0
## _AGE_G _RFBMI5 _CHLDCNT _EDUCAG _INCOMG _SMOKER3 _RFSMOK3 DRNKANY5
## 0 0 0 0 0 0 0 0
## DROCDY3_ _RFBING5 _DRNKWK1 _RFDRHV7 _RFSEAT2 _RFSEAT3 _DRNKDRV
## 0 0 0 0 0 0 0faltantes <- data.frame(
variable = names(datos),
faltantes = colSums(is.na(datos))
)
head(
faltantes[order(-faltantes$faltantes), ],
20
)La revisión de los valores ausentes permitió identificar que algunas variables de la base de datos presentan información incompleta. La cantidad de valores ausentes puede variar entre las diferentes variables, por lo que es importante considerar esta característica antes de realizar análisis posteriores.
grafico_salud <- ggplot(
salud,
aes(x = factor(GENHLTH), y = n)
) +
geom_col(fill = "steelblue") +
labs(
title = "Estado general de salud",
x = "Código de estado de salud",
y = "Número de registros"
)
ggplotly(grafico_salud)El gráfico muestra la distribución de los participantes según las categorías de estado general de salud reportadas en la encuesta. La visualización permite identificar cómo se concentran los registros entre las diferentes categorías de respuesta y reconocer cuáles presentan una mayor o menor cantidad de participantes.
grafico_edad <- ggplot(
edad,
aes(x = factor(`_AGE_G`), y = n)
) +
geom_col(fill = "darkseagreen3") +
labs(
title = "Distribución de los registros por grupo de edad",
x = "Grupo de edad",
y = "Número de registros"
)
ggplotly(grafico_edad)El gráfico muestra la distribución de los participantes entre los diferentes grupos de edad considerados en la encuesta. La visualización permite identificar cómo se concentran los registros en las distintas categorías de edad y reconocer cuáles tienen una mayor o menor presencia dentro de la base de datos.
grafico_sueno <- ggplot(
sueno,
aes(x = SLEPTIM1)
) +
geom_histogram(
binwidth = 1,
fill = "mediumpurple",
color = "white"
) +
labs(
title = "Distribución de las horas de sueño",
x = "Horas de sueño",
y = "Número de registros"
)
ggplotly(grafico_sueno)El gráfico permite observar la distribución de las horas de sueño reportadas por los participantes. La visualización facilita identificar las cantidades de horas que presentan una mayor concentración de registros y observar la distribución general de esta variable.
La exploración inicial de la base de datos BRFSS 2020 permitió reconocer su estructura, identificando 401.958 registros y 279 variables relacionadas con características sociodemográficas, estado de salud, factores de riesgo, acceso a servicios de salud y utilización de servicios preventivos.
La revisión de las variables permitió identificar diferentes tipos de información y reconocer la existencia de valores ausentes en algunas variables. También se observó que varias variables de la base utilizan códigos numéricos, por lo que su interpretación debe realizarse teniendo en cuenta la documentación oficial de la encuesta.
Las visualizaciones exploratorias permitieron obtener una primera aproximación a la distribución de los registros según estado general de salud, grupos de edad y horas de sueño. Estos gráficos facilitan el reconocimiento de las características generales de la información antes de realizar análisis posteriores.
En conclusión, esta etapa de EDA permitió familiarizarse con la base de datos, identificar sus principales variables y reconocer aspectos que deberán ser considerados en etapas posteriores del análisis.