PROCESAMIENTO ESTADISTICO CLASICO
DIEGO CAMILO CELADA LOZADA
10/3/2021
PROCESAMIENTO ESTADISTICO DE UNA BASE DE DATOS
En este Rpubs se desarrolla el procesamiento de una base de datos con herramientas estadistica clasicas
1. Cargar libreria y la base de datos
Cargamos la libreria readxl, para acceder a la base de datos “datos1” y visualizamos su contenido
library(readxl)
datos1 <- read_excel("datos1.xlsx")
View(datos1)Observamos la descripción, clase y dimension de la base de datos “datos1”
class(datos1) # ofrece la clase de objeto que es?## [1] "tbl_df" "tbl" "data.frame"dim(datos1) # Tama?o de la matriz o base de datos Filas x Columnas## [1] 519 64str(datos1) # Peque?a descripci?n de la base de datos ## tibble [519 x 64] (S3: tbl_df/tbl/data.frame)
## $ CONSECUTIVE : num [1:519] 5590429 5594244 5592856 5598456 5598555 ...
## $ COD_EVE : num [1:519] 220 220 220 220 220 220 220 220 220 220 ...
## $ FEC_NOT : POSIXct[1:519], format: "2018-09-06" "2018-10-18" ...
## $ SEMANA : num [1:519] 36 42 46 27 36 48 28 24 3 40 ...
## $ ANO : num [1:519] 2018 2018 2018 2018 2018 ...
## $ COD_PRE : num [1:519] 1.34e+09 7.03e+09 2.00e+09 4.13e+09 4.10e+09 ...
## $ COD_SUB : num [1:519] 1 1 1 1 1 1 3 3 1 1 ...
## $ EDAD : num [1:519] 1 7 1 14 19 5 57 10 10 24 ...
## $ UNI_MED : num [1:519] 1 1 1 1 1 1 1 1 2 1 ...
## $ SEXO : chr [1:519] "F" "F" "M" "M" ...
## $ COD_PAIS_O : num [1:519] 170 170 170 170 170 170 170 170 170 170 ...
## $ COD_DPTO_O : num [1:519] 13 13 20 41 41 81 85 85 68 68 ...
## $ COD_MUN_O : num [1:519] 442 655 750 13 298 794 139 139 79 79 ...
## $ AREA : num [1:519] 1 3 2 1 1 3 1 1 1 3 ...
## $ LOCALIDAD : chr [1:519] "SIN DATO" "SIN DATO" "SIN DATO" "SIN DATO" ...
## $ CEN_POBLA : chr [1:519] "SIN DATO" "SIN DATO" "SIN DATO" "SIN DATO" ...
## $ VEREDA : logi [1:519] NA NA NA NA NA NA ...
## $ BAR_VER : logi [1:519] NA NA NA NA NA NA ...
## $ OCUPACION : num [1:519] 9999 9997 9999 9997 9994 ...
## $ TIP_SS : chr [1:519] "S" "S" "S" "S" ...
## $ COD_ASE : chr [1:519] "ESS207" "ESS076" "ESS062" "CCF024" ...
## $ PER_ETN : num [1:519] 5 6 6 6 6 1 6 6 6 6 ...
## $ GRU_POB : logi [1:519] NA NA NA NA NA NA ...
## $ GP_DISCAPA : num [1:519] 2 2 2 2 2 2 2 2 2 2 ...
## $ GP_DESPLAZ : num [1:519] 2 2 2 2 2 2 2 2 2 2 ...
## $ GP_MIGRANT : num [1:519] 2 2 2 2 2 2 2 2 2 2 ...
## $ GP_CARCELA : num [1:519] 2 2 2 2 2 2 2 2 2 2 ...
## $ GP_GESTAN : num [1:519] 2 2 2 2 2 2 2 2 2 2 ...
## $ GP_INDIGEN : num [1:519] 2 2 2 2 2 1 2 2 2 2 ...
## $ GP_POBICFB : num [1:519] 2 2 2 2 2 2 2 2 2 2 ...
## $ GP_MAD_COM : num [1:519] 2 2 2 2 2 2 2 2 2 2 ...
## $ GP_DESMOVI : num [1:519] 2 2 2 2 2 2 2 2 2 2 ...
## $ GP_PSIQUIA : num [1:519] 2 2 2 2 2 2 2 2 2 2 ...
## $ GP_VIC_VIO : num [1:519] 2 2 2 2 2 2 2 2 2 2 ...
## $ GP_OTROS : num [1:519] 1 1 1 1 1 2 1 1 1 1 ...
## $ COD_DPTO_R : num [1:519] 13 13 20 41 41 81 85 85 68 68 ...
## $ COD_MUN_R : num [1:519] 442 654 750 13 298 794 139 139 79 79 ...
## $ COD_DPTO_N : num [1:519] 13 70 20 41 41 73 85 85 68 68 ...
## $ COD_MUN_N : num [1:519] 13442 70265 20001 41298 41001 ...
## $ FEC_CON : POSIXct[1:519], format: "2018-09-06" "2018-10-18" ...
## $ INI_SIN : POSIXct[1:519], format: "2018-09-05" "2018-10-16" ...
## $ TIP_CAS : num [1:519] 3 3 3 2 2 2 2 2 3 2 ...
## $ PAC_HOS : num [1:519] 2 1 1 1 1 1 1 1 2 1 ...
## $ FEC_HOS : POSIXct[1:519], format: NA "2018-10-18" ...
## $ CON_FIN : num [1:519] 1 1 1 1 2 1 1 1 1 1 ...
## $ FEC_DEF : POSIXct[1:519], format: NA NA ...
## $ AJUSTE : num [1:519] 0 0 3 3 0 0 0 0 0 3 ...
## $ FECHA_NTO : POSIXct[1:519], format: "2016-12-05" "2011-01-28" ...
## $ CER_DEF : num [1:519] NA NA NA NA 7.19e+08 ...
## $ CBMTE : chr [1:519] NA NA NA NA ...
## $ FEC_ARC_XL : POSIXct[1:519], format: "2019-05-08" "2019-05-08" ...
## $ FEC_AJU : POSIXct[1:519], format: "2018-09-06" "2018-10-22" ...
## $ FM_FUERZA : logi [1:519] NA NA NA NA NA NA ...
## $ FM_UNIDAD : logi [1:519] NA NA NA NA NA NA ...
## $ FM_GRADO : logi [1:519] NA NA NA NA NA NA ...
## $ VERSION : logi [1:519] NA NA NA NA NA NA ...
## $ confirmados : num [1:519] 1 1 1 1 0 0 0 0 1 1 ...
## $ est_f_caso : num [1:519] 3 3 3 3 2 2 2 2 3 3 ...
## $ Evento : chr [1:519] "DENGUE GRAVE" "DENGUE GRAVE" "DENGUE GRAVE" "DENGUE GRAVE" ...
## $ estado_final_de_caso : chr [1:519] "Confirmado por laboratorio" "Confirmado por laboratorio" "Confirmado por laboratorio" "Confirmado por laboratorio" ...
## $ Departanento_ocurrencia: chr [1:519] "BOLIVAR" "BOLIVAR" "CESAR" "HUILA" ...
## $ Municipio_ocurrencia : chr [1:519] "MARIA LA BAJA" "SAN JACINTO DEL CAUCA" "SAN DIEGO" "AGRADO" ...
## $ Departamento_residencia: chr [1:519] "BOLIVAR" "BOLIVAR" "CESAR" "HUILA" ...
## $ Municipio_residencia : chr [1:519] "MARIA LA BAJA" "SAN JACINTO" "SAN DIEGO" "AGRADO" ...2. Procesamiento de la base de datos
Convertimos una variable numerica en facto y realizamos la independización de la variables
datos1$periodo = factor(datos1$SEMANA) #convertir una variable de numerica a factor
attach(datos1) #Permite trabajar con las variables independientes de una base de datos, solo se ejecuta una vezPara examinar una variable, solo se debe ejecutar su nombre o invocarla de la siguiente forma:
EDAD## [1] 1 7 1 14 19 5 57 10 10 24 5 6 22 71 9 3 18 12 4 5 26 33 48 23 7
## [26] 43 10 5 8 18 10 50 3 57 25 12 4 18 3 23 18 44 67 8 16 3 10 7 18 7
## [51] 8 40 10 47 18 66 10 69 41 26 57 66 6 21 10 4 27 42 56 56 52 47 17 53 5
## [76] 17 24 21 31 1 46 5 24 14 3 38 5 18 13 74 7 13 23 16 4 3 88 14 50 16
## [101] 74 74 20 19 18 4 6 19 13 7 53 11 75 5 11 10 17 39 19 10 32 2 4 70 7
## [126] 7 28 52 63 7 12 23 12 3 6 68 21 16 3 21 3 11 14 19 13 5 3 3 5 7
## [151] 83 4 26 6 7 74 58 81 81 12 3 61 18 44 8 24 13 9 3 12 3 45 29 56 5
## [176] 16 55 6 2 15 3 2 5 4 57 29 10 92 27 9 44 64 20 16 28 12 5 10 37 4
## [201] 69 11 46 4 5 26 9 31 4 16 12 9 10 16 66 30 3 81 48 6 28 13 24 5 2
## [226] 45 25 9 3 10 4 5 21 18 9 27 80 35 11 13 3 28 56 49 8 12 70 19 2 40
## [251] 2 58 9 5 8 14 61 1 61 7 81 30 63 26 72 5 3 4 46 8 28 39 6 4 34
## [276] 29 10 9 20 5 25 6 9 16 59 2 7 19 28 10 3 65 54 5 23 13 4 23 9 2
## [301] 9 7 8 7 9 7 6 27 4 10 4 7 11 6 2 19 9 16 40 3 28 7 26 7 7
## [326] 44 16 4 5 5 7 10 2 18 2 19 30 25 92 61 5 8 4 29 9 54 59 8 2 75
## [351] 11 62 6 16 17 3 3 69 14 6 67 16 1 6 11 75 12 3 5 9 8 27 9 6 4
## [376] 4 2 16 3 51 46 11 14 12 56 40 13 3 4 3 4 1 1 3 19 24 18 9 6 11
## [401] 7 11 9 17 10 6 15 11 24 6 2 1 17 13 10 10 17 26 27 16 18 24 10 5 9
## [426] 5 50 52 2 14 4 7 4 2 52 39 25 7 4 8 17 25 3 4 8 16 4 11 5 5
## [451] 3 10 24 7 3 11 11 19 3 2 8 5 44 3 6 5 4 5 5 11 5 5 6 6 10
## [476] 9 4 6 10 11 4 15 16 7 6 11 3 6 7 4 50 16 9 2 1 47 26 11 6 8
## [501] 51 4 2 23 4 4 10 24 13 15 4 79 64 9 8 19 9 11 6datos1$EDAD # permite trabajar con la variable independiente## [1] 1 7 1 14 19 5 57 10 10 24 5 6 22 71 9 3 18 12 4 5 26 33 48 23 7
## [26] 43 10 5 8 18 10 50 3 57 25 12 4 18 3 23 18 44 67 8 16 3 10 7 18 7
## [51] 8 40 10 47 18 66 10 69 41 26 57 66 6 21 10 4 27 42 56 56 52 47 17 53 5
## [76] 17 24 21 31 1 46 5 24 14 3 38 5 18 13 74 7 13 23 16 4 3 88 14 50 16
## [101] 74 74 20 19 18 4 6 19 13 7 53 11 75 5 11 10 17 39 19 10 32 2 4 70 7
## [126] 7 28 52 63 7 12 23 12 3 6 68 21 16 3 21 3 11 14 19 13 5 3 3 5 7
## [151] 83 4 26 6 7 74 58 81 81 12 3 61 18 44 8 24 13 9 3 12 3 45 29 56 5
## [176] 16 55 6 2 15 3 2 5 4 57 29 10 92 27 9 44 64 20 16 28 12 5 10 37 4
## [201] 69 11 46 4 5 26 9 31 4 16 12 9 10 16 66 30 3 81 48 6 28 13 24 5 2
## [226] 45 25 9 3 10 4 5 21 18 9 27 80 35 11 13 3 28 56 49 8 12 70 19 2 40
## [251] 2 58 9 5 8 14 61 1 61 7 81 30 63 26 72 5 3 4 46 8 28 39 6 4 34
## [276] 29 10 9 20 5 25 6 9 16 59 2 7 19 28 10 3 65 54 5 23 13 4 23 9 2
## [301] 9 7 8 7 9 7 6 27 4 10 4 7 11 6 2 19 9 16 40 3 28 7 26 7 7
## [326] 44 16 4 5 5 7 10 2 18 2 19 30 25 92 61 5 8 4 29 9 54 59 8 2 75
## [351] 11 62 6 16 17 3 3 69 14 6 67 16 1 6 11 75 12 3 5 9 8 27 9 6 4
## [376] 4 2 16 3 51 46 11 14 12 56 40 13 3 4 3 4 1 1 3 19 24 18 9 6 11
## [401] 7 11 9 17 10 6 15 11 24 6 2 1 17 13 10 10 17 26 27 16 18 24 10 5 9
## [426] 5 50 52 2 14 4 7 4 2 52 39 25 7 4 8 17 25 3 4 8 16 4 11 5 5
## [451] 3 10 24 7 3 11 11 19 3 2 8 5 44 3 6 5 4 5 5 11 5 5 6 6 10
## [476] 9 4 6 10 11 4 15 16 7 6 11 3 6 7 4 50 16 9 2 1 47 26 11 6 8
## [501] 51 4 2 23 4 4 10 24 13 15 4 79 64 9 8 19 9 11 6Para examinar las variables de la base de datos u observar las primeras seis o las ultimas seis, ejecutamos:
names(datos1) # Da el nombre de las variables de la base de datos## [1] "CONSECUTIVE" "COD_EVE"
## [3] "FEC_NOT" "SEMANA"
## [5] "ANO" "COD_PRE"
## [7] "COD_SUB" "EDAD"
## [9] "UNI_MED" "SEXO"
## [11] "COD_PAIS_O" "COD_DPTO_O"
## [13] "COD_MUN_O" "AREA"
## [15] "LOCALIDAD" "CEN_POBLA"
## [17] "VEREDA" "BAR_VER"
## [19] "OCUPACION" "TIP_SS"
## [21] "COD_ASE" "PER_ETN"
## [23] "GRU_POB" "GP_DISCAPA"
## [25] "GP_DESPLAZ" "GP_MIGRANT"
## [27] "GP_CARCELA" "GP_GESTAN"
## [29] "GP_INDIGEN" "GP_POBICFB"
## [31] "GP_MAD_COM" "GP_DESMOVI"
## [33] "GP_PSIQUIA" "GP_VIC_VIO"
## [35] "GP_OTROS" "COD_DPTO_R"
## [37] "COD_MUN_R" "COD_DPTO_N"
## [39] "COD_MUN_N" "FEC_CON"
## [41] "INI_SIN" "TIP_CAS"
## [43] "PAC_HOS" "FEC_HOS"
## [45] "CON_FIN" "FEC_DEF"
## [47] "AJUSTE" "FECHA_NTO"
## [49] "CER_DEF" "CBMTE"
## [51] "FEC_ARC_XL" "FEC_AJU"
## [53] "FM_FUERZA" "FM_UNIDAD"
## [55] "FM_GRADO" "VERSION"
## [57] "confirmados" "est_f_caso"
## [59] "Evento" "estado_final_de_caso"
## [61] "Departanento_ocurrencia" "Municipio_ocurrencia"
## [63] "Departamento_residencia" "Municipio_residencia"
## [65] "periodo"head(datos1) # Permite observar las seis primeras filas de la base de datostail(datos1) # Permite observar las seis ultimas filas de la base de datosPara observar los datos estadisticos mas comunes todas la variables de la base de datos, ejecutamos
summary(datos1)## CONSECUTIVE COD_EVE FEC_NOT SEMANA
## Min. :5580674 Min. :220 Min. :2018-01-03 00:00:00 Min. : 1.00
## 1st Qu.:5592144 1st Qu.:220 1st Qu.:2018-06-03 00:00:00 1st Qu.:21.00
## Median :5604314 Median :220 Median :2018-09-09 00:00:00 Median :35.00
## Mean :5604495 Mean :220 Mean :2018-08-17 13:38:29 Mean :32.26
## 3rd Qu.:5615391 3rd Qu.:220 3rd Qu.:2018-11-19 12:00:00 3rd Qu.:46.00
## Max. :5628716 Max. :220 Max. :2019-03-27 00:00:00 Max. :52.00
##
## ANO COD_PRE COD_SUB EDAD
## Min. :2018 Min. :5.001e+08 Min. : 0.000 Min. : 1.00
## 1st Qu.:2018 1st Qu.:1.300e+09 1st Qu.: 1.000 1st Qu.: 5.00
## Median :2018 Median :4.400e+09 Median : 1.000 Median :11.00
## Mean :2018 Mean :3.950e+09 Mean : 2.721 Mean :19.89
## 3rd Qu.:2018 3rd Qu.:6.800e+09 3rd Qu.: 1.000 3rd Qu.:26.00
## Max. :2018 Max. :9.500e+09 Max. :83.000 Max. :92.00
##
## UNI_MED SEXO COD_PAIS_O COD_DPTO_O
## Min. :1.000 Length:519 Min. :170.0 Min. : 1.00
## 1st Qu.:1.000 Class :character 1st Qu.:170.0 1st Qu.:13.00
## Median :1.000 Mode :character Median :170.0 Median :41.00
## Mean :1.071 Mean :187.3 Mean :38.19
## 3rd Qu.:1.000 3rd Qu.:170.0 3rd Qu.:54.00
## Max. :3.000 Max. :862.0 Max. :95.00
##
## COD_MUN_O AREA LOCALIDAD CEN_POBLA
## Min. : 0.0 Min. :1.000 Length:519 Length:519
## 1st Qu.: 1.0 1st Qu.:1.000 Class :character Class :character
## Median :168.0 Median :1.000 Mode :character Mode :character
## Mean :287.9 Mean :1.355
## 3rd Qu.:565.5 3rd Qu.:1.000
## Max. :980.0 Max. :3.000
##
## VEREDA BAR_VER OCUPACION TIP_SS
## Mode:logical Mode:logical Min. :1311 Length:519
## NA's:519 NA's:519 1st Qu.:9997 Class :character
## Median :9997 Mode :character
## Mean :9454
## 3rd Qu.:9999
## Max. :9999
##
## COD_ASE PER_ETN GRU_POB GP_DISCAPA
## Length:519 Min. :1.000 Mode:logical Min. :1.000
## Class :character 1st Qu.:6.000 NA's:519 1st Qu.:2.000
## Mode :character Median :6.000 Median :2.000
## Mean :5.888 Mean :1.996
## 3rd Qu.:6.000 3rd Qu.:2.000
## Max. :6.000 Max. :2.000
##
## GP_DESPLAZ GP_MIGRANT GP_CARCELA GP_GESTAN
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :1.992 Mean :1.971 Mean :1.998 Mean :1.987
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. :2.000 Max. :2.000
##
## GP_INDIGEN GP_POBICFB GP_MAD_COM GP_DESMOVI GP_PSIQUIA
## Min. :1.000 Min. :2 Min. :2 Min. :2 Min. :2
## 1st Qu.:2.000 1st Qu.:2 1st Qu.:2 1st Qu.:2 1st Qu.:2
## Median :2.000 Median :2 Median :2 Median :2 Median :2
## Mean :1.996 Mean :2 Mean :2 Mean :2 Mean :2
## 3rd Qu.:2.000 3rd Qu.:2 3rd Qu.:2 3rd Qu.:2 3rd Qu.:2
## Max. :2.000 Max. :2 Max. :2 Max. :2 Max. :2
##
## GP_VIC_VIO GP_OTROS COD_DPTO_R COD_MUN_R COD_DPTO_N
## Min. :2 Min. :1.00 Min. : 0.00 Min. : 0.0 Min. : 5.0
## 1st Qu.:2 1st Qu.:1.00 1st Qu.:13.00 1st Qu.: 1.0 1st Qu.:13.0
## Median :2 Median :1.00 Median :41.00 Median :124.0 Median :44.0
## Mean :2 Mean :1.04 Mean :37.78 Mean :268.0 Mean :39.4
## 3rd Qu.:2 3rd Qu.:1.00 3rd Qu.:54.00 3rd Qu.:518.5 3rd Qu.:68.0
## Max. :2 Max. :2.00 Max. :95.00 Max. :980.0 Max. :95.0
##
## COD_MUN_N FEC_CON INI_SIN
## Min. : 5001 Min. :2018-01-03 00:00:00 Min. :2017-12-31 00:00:00
## 1st Qu.:13001 1st Qu.:2018-05-29 12:00:00 1st Qu.:2018-05-23 12:00:00
## Median :44001 Median :2018-08-30 00:00:00 Median :2018-08-27 00:00:00
## Mean :39501 Mean :2018-08-12 19:19:46 Mean :2018-08-08 18:43:41
## 3rd Qu.:68001 3rd Qu.:2018-11-16 12:00:00 3rd Qu.:2018-11-12 00:00:00
## Max. :95001 Max. :2019-01-04 00:00:00 Max. :2018-12-29 00:00:00
##
## TIP_CAS PAC_HOS FEC_HOS CON_FIN
## Min. :2.000 Min. :1.000 Min. :2018-01-03 00:00:00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:2018-05-31 12:00:00 1st Qu.:1.000
## Median :2.000 Median :1.000 Median :2018-09-03 00:00:00 Median :1.000
## Mean :2.347 Mean :1.054 Mean :2018-08-12 19:38:58 Mean :1.139
## 3rd Qu.:3.000 3rd Qu.:1.000 3rd Qu.:2018-11-16 00:00:00 3rd Qu.:1.000
## Max. :3.000 Max. :2.000 Max. :2019-01-04 00:00:00 Max. :2.000
## NA's :28
## FEC_DEF AJUSTE FECHA_NTO
## Min. :2018-01-07 00:00:00 Min. :0.000 Min. :1925-04-18 00:00:00
## 1st Qu.:2018-05-18 06:00:00 1st Qu.:0.000 1st Qu.:1992-08-02 12:00:00
## Median :2018-08-23 00:00:00 Median :3.000 Median :2007-09-05 00:00:00
## Mean :2018-08-04 12:40:00 Mean :1.973 Mean :1998-09-28 09:27:06
## 3rd Qu.:2018-11-02 00:00:00 3rd Qu.:3.000 3rd Qu.:2013-08-08 00:00:00
## Max. :2018-12-28 00:00:00 Max. :7.000 Max. :2018-07-22 00:00:00
## NA's :447 NA's :1
## CER_DEF CBMTE FEC_ARC_XL
## Min. : 999999 Length:519 Min. :2018-08-08 00:00:00
## 1st Qu.:716768001 Class :character 1st Qu.:2019-05-08 00:00:00
## Median :718040048 Mode :character Median :2019-05-08 00:00:00
## Mean :657978785 Mean :2019-04-08 09:12:08
## 3rd Qu.:719028216 3rd Qu.:2019-05-08 00:00:00
## Max. :815973520 Max. :2019-05-08 00:00:00
## NA's :458
## FEC_AJU FM_FUERZA FM_UNIDAD FM_GRADO
## Min. :2018-01-05 00:00:00 Mode:logical Mode:logical Mode:logical
## 1st Qu.:2018-07-04 12:00:00 NA's:519 NA's:519 NA's:519
## Median :2018-10-19 00:00:00
## Mean :2018-09-24 13:24:37
## 3rd Qu.:2018-12-19 00:00:00
## Max. :2019-03-27 00:00:00
##
## VERSION confirmados est_f_caso Evento
## Mode:logical Min. :0.0000 Min. :2.000 Length:519
## NA's:519 1st Qu.:0.0000 1st Qu.:2.000 Class :character
## Median :1.0000 Median :3.000 Mode :character
## Mean :0.7225 Mean :2.723
## 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :1.0000 Max. :3.000
##
## estado_final_de_caso Departanento_ocurrencia Municipio_ocurrencia
## Length:519 Length:519 Length:519
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Departamento_residencia Municipio_residencia periodo
## Length:519 Length:519 49 : 28
## Class :character Class :character 48 : 25
## Mode :character Mode :character 52 : 22
## 47 : 21
## 44 : 18
## 28 : 16
## (Other):389Para observar los datos estadisticos mas comunes de una de nuestras variables, ejecutamos
summary(EDAD) # Resumen de datos estadisticos## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 5.00 11.00 19.89 26.00 92.003. Graficos estadisticos
Realizamos el digrama de bigotes vertical y horizontal de una variable y el histograma de esta.
boxplot(EDAD) # Caja de bigotes
boxplot(EDAD, horizontal = T, col="yellow", xlab = "Peso", main = "Diagrama de Caja y Bigotes")
hist(EDAD) # Histograma
Convertirmos a factor las siguientes variables de la base de datos
datos1$AREA = factor(datos1$AREA)
datos1$COD_MUN_o = factor(datos1$COD_MUN_O)Realizamos un diagrama de torta con sus respectivas etiquetas, colores asignados y tabla de contenido
pie(table(AREA)) # diagrama de torta
pct <- round(table(AREA)/sum(table(AREA))*100)
etiquetas <- c("UNO", "DOS", "TRES") # vector con etiquetas
etiquetas <- paste(etiquetas, pct) # A?adimos porcentajes a etiquetas
etiquetas <- paste(etiquetas,"%",sep="") # A?adimos el s?mbolo de %
pie(table(AREA),labels = etiquetas,
col=rainbow(length(etiquetas)),
main="Diagrama de torta")
# A?adimos un cuadro con leyendas
legend("topright", c("UNO", "DOS", "TRES"), cex = 0.8,
fill = rainbow(table(AREA)))