if (!require("pacman")) install.packages("pacman")#instala pacman si se requiereCargando paquete requerido: pacmanpacman::p_load(tidyverse,
readxl,
writexl,
haven,
sjlabelled,
janitor,
magrittr,
broom # para limpiar resultados de modelos
)concentradohogar <- read_dta("datos/concentradohogar.dta") %>%
mutate(across(c(sexo_jefe, clase_hog, educa_jefe), as.numeric)) %>% # ojo aquí
set_labels(sexo_jefe, labels=c("Hombre", "Mujer")) %>%
set_labels(clase_hog, labels=c("unipersonal","nuclear", "ampliado",
"compuesto","corresidente")) %>%
set_labels(educa_jefe,
labels=c("Sin instrucción",
"Preescolar",
"Primaria incompleta",
"Primaria completa",
"Secundaria incompleta",
"Secundaria completa",
"Preparatoria incompleta",
"Preparatoria completa",
"Profesional incompleta",
"Profesional completa",
"Posgrado")) %>%
mutate(ent=stringr::str_sub(ubica_geo, start = 1, end = 2)) %>%
mutate(mun=stringr::str_sub(ubica_geo, start = 3, end = 5))Unos de los elementos más poderosos de R es hacer nuestra propias funciones.
La lógica de las funciones es la siguiente:
nombre_de_funcion(argumento1, argumento2, ...) {
operaciones
salida
}Para ello haremos una función sencilla. Para sumarle un valor un 1
mi_funcion<-function(x) {
resultado<-x+1
return(resultado)
}
mi_funcion(5)[1] 6Vamos a agregar un argumento, podemos agregar un segundo número en lugar de 1
mi_funcion<-function(x, a) {
resultado<-x+a
return(resultado)
}
mi_funcion(x=5, a=6)[1] 11Los argumentos no necesariamente deben ser un sólo valor
mi_funcion(x=1:5, a=6:10)[1] 7 9 11 13 15E incluso podríamos llamar variables de nuestra base de concentrado
resultado_mi_funcion<-mi_funcion(x=concentradohogar$frutas, a=concentradohogar$azucar)Primero un poquito de pretty() {base}, es un comando que calcula una secuencia de aproximadamente n+1 valores ‘redondos’ igualmente espaciados que cubran el rango de los valores en x. Los valores se eligen para que sean 1, 2 o 5 veces una potencia de 10.
cortar <- function(x) {
cut(x,
breaks = pretty(x),
right = TRUE,
include.lowest = TRUE)
}#cortar(concentradohogar$edad_jefe)Podemos utilizarla junto con mutate
concentradohogar %>%
mutate(eda_cut=cortar(edad_jefe)) %>%
tabyl(eda_cut) eda_cut n percent
[0,20] 551 0.0061152916
(20,40] 24955 0.2769638854
(40,60] 38783 0.4304343966
(60,80] 22241 0.2468424674
(80,100] 3561 0.0395218752
(100,120] 11 0.0001220839forSupongamos que quisiéramos repetir una operación a lo largo de una secuencia, se puede realizar
for (i in secuencia) {
operación 1
operación 2
...
operación final
}
Por ejemplo si quisiéramos que por cada entidad federativa se imprimiera en pantalla el promedio de la edad de los jefes entrevistados
unique(concentradohogar$ent) # Nos dan los valores únicos de un vector [1] "01" "02" "03" "04" "05" "06" "07" "08" "09" "10" "11" "12" "13" "14" "15"
[16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30"
[31] "31" "32"estados<-unique(concentradohogar$ent)Hoy haremos nuestro bucle con “for”
for(i in estados){
x<-concentradohogar %>%
filter(ent==i) %>% # aquí ocupamos nuestro indice
summarise(media=mean(ing_cor))
assign(paste0("ingreso",i), x)
}Vamos a botar estos objetos
rm(list = ls(, pattern = "ingreso"))
rm(x)while()También tenemos while()
while (expresión a probar) {
Operaciones
}# variable que se cambia
numero = 1
# variable donde se calcula la meida
sum = 0
# Calcular la suma consecutiva hasta que llegue a 30
while(numero <= 30) {
# calculate sum
sum = sum + numero
# increment number by 1
numero = numero + 1
}Las operaciones están supeditadas a los elementos que cumplan una condición
if (condicion) {
operación 1
operación 2
...
operación final
}Supongamos tenemos dos valores
a<-45 # un vector entero
b<-5000 #numeros aleatorios que siguen una normalVeamos cómo podemos hacer un condicional muy simple
if(a>18){
print(b)
}[1] 5000También se puede combinar con else
if(a>18){
print("Mayor que 18")
} else {
print("No cumple")
}[1] "Mayor que 18"Estos elementos funcionan cuando se programan procesos. Son útiles para cuando computamos modelos y se busca cierto nivel de tolerancia o se hacen procesos sucesivos.
purrr::map()Dentro de tidyverse existe el paquete {purrr}, es un paquete que tiene muchas funcionalidades parecidas a los for.
Por ejemplo y siguiendo los ejemplos del for()
1:10 %>%
map(~.x+1)[[1]]
[1] 2
[[2]]
[1] 3
[[3]]
[1] 4
[[4]]
[1] 5
[[5]]
[1] 6
[[6]]
[1] 7
[[7]]
[1] 8
[[8]]
[1] 9
[[9]]
[1] 10
[[10]]
[1] 11Si guardamos esto en un objeto, vemos que nos da como resultado una lista
map1<-1:10 %>%
map(~.x+1)
class(map1)[1] "list"Compliquemos esto un poquito más…
names(concentradohogar) %>%
map_chr(~str_detect(.x,"ing")) # ojo a veces tenemos que hacer explícito el tipo de mapeoWarning: Automatic coercion from logical to character was deprecated in purrr 1.0.0.
ℹ Please use an explicit call to `as.character()` within `map_chr()` instead. [1] "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE"
[10] "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE"
[19] "FALSE" "FALSE" "TRUE" "FALSE" "TRUE" "TRUE" "FALSE" "FALSE" "FALSE"
[28] "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE"
[37] "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE"
[46] "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE"
[55] "FALSE" "TRUE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE"
[64] "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE"
[73] "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE"
[82] "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE"
[91] "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE"
[100] "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE"
[109] "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE"
[118] "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE" "FALSE"
[127] "FALSE" "FALSE"Por ejemplo, quizás queremos identificar esta base de datos con 2020
names(concentradohogar) %>%
map_chr(~paste0(.x,"2020")) [1] "folioviv2020" "foliohog2020" "ubica_geo2020" "tam_loc2020"
[5] "est_socio2020" "est_dis2020" "upm2020" "factor2020"
[9] "clase_hog2020" "sexo_jefe2020" "edad_jefe2020" "educa_jefe2020"
[13] "tot_integ2020" "hombres2020" "mujeres2020" "mayores2020"
[17] "menores2020" "p12_642020" "p65mas2020" "ocupados2020"
[21] "percep_ing2020" "perc_ocupa2020" "ing_cor2020" "ingtrab2020"
[25] "trabajo2020" "sueldos2020" "horas_extr2020" "comisiones2020"
[29] "aguinaldo2020" "indemtrab2020" "otra_rem2020" "remu_espec2020"
[33] "negocio2020" "noagrop2020" "industria2020" "comercio2020"
[37] "servicios2020" "agrope2020" "agricolas2020" "pecuarios2020"
[41] "reproducc2020" "pesca2020" "otros_trab2020" "rentas2020"
[45] "utilidad2020" "arrenda2020" "transfer2020" "jubilacion2020"
[49] "becas2020" "donativos2020" "remesas2020" "bene_gob2020"
[53] "transf_hog2020" "trans_inst2020" "estim_alqu2020" "otros_ing2020"
[57] "gasto_mon2020" "alimentos2020" "ali_dentro2020" "cereales2020"
[61] "carnes2020" "pescado2020" "leche2020" "huevo2020"
[65] "aceites2020" "tuberculo2020" "verduras2020" "frutas2020"
[69] "azucar2020" "cafe2020" "especias2020" "otros_alim2020"
[73] "bebidas2020" "ali_fuera2020" "tabaco2020" "vesti_calz2020"
[77] "vestido2020" "calzado2020" "vivienda2020" "alquiler2020"
[81] "pred_cons2020" "agua2020" "energia2020" "limpieza2020"
[85] "cuidados2020" "utensilios2020" "enseres2020" "salud2020"
[89] "atenc_ambu2020" "hospital2020" "medicinas2020" "transporte2020"
[93] "publico2020" "foraneo2020" "adqui_vehi2020" "mantenim2020"
[97] "refaccion2020" "combus2020" "comunica2020" "educa_espa2020"
[101] "educacion2020" "esparci2020" "paq_turist2020" "personales2020"
[105] "cuida_pers2020" "acces_pers2020" "otros_gas2020" "transf_gas2020"
[109] "percep_tot2020" "retiro_inv2020" "prestamos2020" "otras_perc2020"
[113] "ero_nm_viv2020" "ero_nm_hog2020" "erogac_tot2020" "cuota_viv2020"
[117] "mater_serv2020" "material2020" "servicio2020" "deposito2020"
[121] "prest_terc2020" "pago_tarje2020" "deudas2020" "balance2020"
[125] "otras_erog2020" "smg2020" "ent2020" "mun2020" Con estos nuevos nombres podríamos rápidamente volver a declarar nuestros nombres de la base y todos tienen sufijos.
Combinación con el comando split()
concentradohogar %>%
split(.$ent) %>%
map(~ mean(.$ing_cor)) $`01`
[1] 73161.58
$`02`
[1] 80340.94
$`03`
[1] 87776.71
$`04`
[1] 57920.87
$`05`
[1] 69760.74
$`06`
[1] 63840.8
$`07`
[1] 40764.97
$`08`
[1] 73980.63
$`09`
[1] 77909.25
$`10`
[1] 53608.34
$`11`
[1] 55513.69
$`12`
[1] 40568.36
$`13`
[1] 52075.46
$`14`
[1] 68482.6
$`15`
[1] 51906.31
$`16`
[1] 56305.69
$`17`
[1] 55489.63
$`18`
[1] 66293.86
$`19`
[1] 70860.65
$`20`
[1] 41136.28
$`21`
[1] 46926.68
$`22`
[1] 68109.38
$`23`
[1] 67529.32
$`24`
[1] 54751.66
$`25`
[1] 73983.52
$`26`
[1] 70544.93
$`27`
[1] 52634.36
$`28`
[1] 60026.75
$`29`
[1] 46319.45
$`30`
[1] 40760.94
$`31`
[1] 55796.84
$`32`
[1] 49802.73Nos da una lista de valores… si hacemos algo más complejo
concentradohogar %>%
split(.$ent) %>%
map(~ mean(.$ing_cor)) %>%
map_dfr(~as.data.frame(.x)) .x
1 73161.58
2 80340.94
3 87776.71
4 57920.87
5 69760.74
6 63840.80
7 40764.97
8 73980.63
9 77909.25
10 53608.34
11 55513.69
12 40568.36
13 52075.46
14 68482.60
15 51906.31
16 56305.69
17 55489.63
18 66293.86
19 70860.65
20 41136.28
21 46926.68
22 68109.38
23 67529.32
24 54751.66
25 73983.52
26 70544.93
27 52634.36
28 60026.75
29 46319.45
30 40760.94
31 55796.84
32 49802.73concentradohogar %>%
split(.$ent) %>%
map(~ mean(.$ing_cor)) %>%
map_dfc(~as.data.frame(.x)) %>%
clean_names()New names:
• `.x` -> `.x...1`
• `.x` -> `.x...2`
• `.x` -> `.x...3`
• `.x` -> `.x...4`
• `.x` -> `.x...5`
• `.x` -> `.x...6`
• `.x` -> `.x...7`
• `.x` -> `.x...8`
• `.x` -> `.x...9`
• `.x` -> `.x...10`
• `.x` -> `.x...11`
• `.x` -> `.x...12`
• `.x` -> `.x...13`
• `.x` -> `.x...14`
• `.x` -> `.x...15`
• `.x` -> `.x...16`
• `.x` -> `.x...17`
• `.x` -> `.x...18`
• `.x` -> `.x...19`
• `.x` -> `.x...20`
• `.x` -> `.x...21`
• `.x` -> `.x...22`
• `.x` -> `.x...23`
• `.x` -> `.x...24`
• `.x` -> `.x...25`
• `.x` -> `.x...26`
• `.x` -> `.x...27`
• `.x` -> `.x...28`
• `.x` -> `.x...29`
• `.x` -> `.x...30`
• `.x` -> `.x...31`
• `.x` -> `.x...32` x_1 x_2 x_3 x_4 x_5 x_6 x_7 x_8
1 73161.58 80340.94 87776.71 57920.87 69760.74 63840.8 40764.97 73980.63
x_9 x_10 x_11 x_12 x_13 x_14 x_15 x_16
1 77909.25 53608.34 55513.69 40568.36 52075.46 68482.6 51906.31 56305.69
x_17 x_18 x_19 x_20 x_21 x_22 x_23 x_24
1 55489.63 66293.86 70860.65 41136.28 46926.68 68109.38 67529.32 54751.66
x_25 x_26 x_27 x_28 x_29 x_30 x_31 x_32
1 73983.52 70544.93 52634.36 60026.75 46319.45 40760.94 55796.84 49802.73mi_funcion_summ<-function(x){
mu<-mean(x, na.rm=T)
me<-median(x,na.rm=T)
sd<-sd(x,na.rm=T)
total<-as.data.frame(cbind(mu,me,sd))
return(total)
}
mi_funcion_summ(concentradohogar$ing_cor) mu me sd
1 61489.96 46073.68 78324.84concentradohogar %>%
split(.$ent) %>%
map(~ mi_funcion_summ(.$ing_cor)) $`01`
mu me sd
1 73161.58 54286 78307.01
$`02`
mu me sd
1 80340.94 62690.15 130742.9
$`03`
mu me sd
1 87776.71 66968.51 91908.2
$`04`
mu me sd
1 57920.87 41853.95 70497.39
$`05`
mu me sd
1 69760.74 54137.06 67692.08
$`06`
mu me sd
1 63840.8 50362.42 54881.68
$`07`
mu me sd
1 40764.97 28469.6 52185.97
$`08`
mu me sd
1 73980.63 51688.5 152446.2
$`09`
mu me sd
1 77909.25 57196.71 83006.61
$`10`
mu me sd
1 53608.34 42352.22 44166.63
$`11`
mu me sd
1 55513.69 44170.5 54047.34
$`12`
mu me sd
1 40568.36 29920.98 46900.77
$`13`
mu me sd
1 52075.46 40035.77 49509.11
$`14`
mu me sd
1 68482.6 54661.66 56214.05
$`15`
mu me sd
1 51906.31 40305.14 47406.46
$`16`
mu me sd
1 56305.69 42317.16 85617.26
$`17`
mu me sd
1 55489.63 41499.77 53868.24
$`18`
mu me sd
1 66293.86 49164.88 147920.8
$`19`
mu me sd
1 70860.65 54175.63 66062.9
$`20`
mu me sd
1 41136.28 29356.59 56642.89
$`21`
mu me sd
1 46926.68 36328.16 41569.52
$`22`
mu me sd
1 68109.38 53348.3 57981.94
$`23`
mu me sd
1 67529.32 53748.4 54798.19
$`24`
mu me sd
1 54751.66 40984.1 50266.83
$`25`
mu me sd
1 73983.52 58731.8 113364.5
$`26`
mu me sd
1 70544.93 53728.18 62656.42
$`27`
mu me sd
1 52634.36 38458.52 46653
$`28`
mu me sd
1 60026.75 46112.5 54881
$`29`
mu me sd
1 46319.45 37420.04 35319.69
$`30`
mu me sd
1 40760.94 30588.12 49582.39
$`31`
mu me sd
1 55796.84 42938.52 64106.4
$`32`
mu me sd
1 49802.73 37918.64 46036.56Lo interesante es que podemos hacer elementos más complejos
concentradohogar %>%
split(.$ent) %>%
map(~ lm(ing_cor ~ edad_jefe, data = .))$`01`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
65927.5 144.3
$`02`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
88995.9 -175.8
$`03`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
88059.947 -5.778
$`04`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
48492.6 190.2
$`05`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
78874.5 -178.8
$`06`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
65117.79 -24.77
$`07`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
35432.0 105.8
$`08`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
94472.5 -401.1
$`09`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
60466.8 328.7
$`10`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
55315.12 -33.27
$`11`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
51365.80 82.63
$`12`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
37221.10 63.97
$`13`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
49322.23 53.19
$`14`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
67643.73 16.16
$`15`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
40766.1 216.2
$`16`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
60335.14 -77.47
$`17`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
50174.7 101.2
$`18`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
61100.8 100.9
$`19`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
83568.2 -240.4
$`20`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
40764.627 7.032
$`21`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
42818.94 80.48
$`22`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
58982.1 183.6
$`23`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
61408 128
$`24`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
53596.18 21.96
$`25`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
69928.11 76.93
$`26`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
79309.4 -169.7
$`27`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
43586.6 179.2
$`28`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
74320 -277
$`29`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
41664.60 91.35
$`30`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
43034 -43
$`31`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
48798.9 137.6
$`32`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Coefficients:
(Intercept) edad_jefe
53702.89 -74.91 Y el mapeo se puede ir agregando…
concentradohogar %>%
split(.$ent) %>%
map(~ lm(ing_cor ~ edad_jefe, data = .)) %>% # da solo los coeficientes
map(summary) # da la parte de inferencia$`01`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-73203 -39462 -18286 12345 1170612
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 65927.49 5110.36 12.901 <2e-16 ***
edad_jefe 144.31 97.36 1.482 0.138
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 78290 on 2667 degrees of freedom
Multiple R-squared: 0.0008231, Adjusted R-squared: 0.0004485
F-statistic: 2.197 on 1 and 2667 DF, p-value: 0.1384
$`02`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-83723 -39926 -17474 15884 7073035
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 88995.9 6780.1 13.126 <2e-16 ***
edad_jefe -175.8 131.4 -1.338 0.181
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 130700 on 4139 degrees of freedom
Multiple R-squared: 0.0004324, Adjusted R-squared: 0.0001909
F-statistic: 1.79 on 1 and 4139 DF, p-value: 0.181
$`03`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-85780 -45240 -20766 18752 2579515
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 88059.947 5702.582 15.442 <2e-16 ***
edad_jefe -5.778 110.757 -0.052 0.958
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 91920 on 2774 degrees of freedom
Multiple R-squared: 9.811e-07, Adjusted R-squared: -0.0003595
F-statistic: 0.002722 on 1 and 2774 DF, p-value: 0.9584
$`04`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-58401 -30373 -15152 11968 1701405
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48492.64 5071.47 9.562 <2e-16 ***
edad_jefe 190.24 97.75 1.946 0.0518 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 70450 on 2202 degrees of freedom
Multiple R-squared: 0.001717, Adjusted R-squared: 0.001264
F-statistic: 3.788 on 1 and 2202 DF, p-value: 0.05175
$`05`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-67856 -35772 -15696 15540 2061447
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 78874.50 3618.51 21.797 <2e-16 ***
edad_jefe -178.82 67.93 -2.633 0.0085 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 67640 on 4123 degrees of freedom
Multiple R-squared: 0.001678, Adjusted R-squared: 0.001436
F-statistic: 6.931 on 1 and 4123 DF, p-value: 0.008504
$`06`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-60222 -32325 -13442 14920 867728
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 65117.79 3325.50 19.581 <2e-16 ***
edad_jefe -24.77 61.53 -0.403 0.687
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 54890 on 3029 degrees of freedom
Multiple R-squared: 5.349e-05, Adjusted R-squared: -0.0002766
F-statistic: 0.162 on 1 and 3029 DF, p-value: 0.6873
$`07`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-40632 -22529 -11893 8243 1593452
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 35431.96 3820.03 9.275 <2e-16 ***
edad_jefe 105.84 72.39 1.462 0.144
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 52170 on 2110 degrees of freedom
Multiple R-squared: 0.001012, Adjusted R-squared: 0.0005387
F-statistic: 2.138 on 1 and 2110 DF, p-value: 0.1439
$`08`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-80591 -42136 -22702 8901 6056953
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 94472.5 7616.8 12.403 < 2e-16 ***
edad_jefe -401.1 142.4 -2.817 0.00487 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 152300 on 4553 degrees of freedom
Multiple R-squared: 0.00174, Adjusted R-squared: 0.00152
F-statistic: 7.935 on 1 and 4553 DF, p-value: 0.00487
$`09`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-80544 -39928 -20627 10483 1468301
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 60466.8 5709.0 10.591 < 2e-16 ***
edad_jefe 328.7 103.1 3.188 0.00145 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 82860 on 2583 degrees of freedom
Multiple R-squared: 0.003919, Adjusted R-squared: 0.003533
F-statistic: 10.16 on 1 and 2583 DF, p-value: 0.00145
$`10`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-51665 -28015 -11133 13420 645526
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 55315.12 2848.29 19.420 <2e-16 ***
edad_jefe -33.27 53.01 -0.628 0.53
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 44170 on 2711 degrees of freedom
Multiple R-squared: 0.0001453, Adjusted R-squared: -0.0002235
F-statistic: 0.394 on 1 and 2711 DF, p-value: 0.5303
$`11`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-53931 -26401 -10984 10051 1371951
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 51365.80 3199.97 16.052 <2e-16 ***
edad_jefe 82.63 60.72 1.361 0.174
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 54040 on 3074 degrees of freedom
Multiple R-squared: 0.0006021, Adjusted R-squared: 0.000277
F-statistic: 1.852 on 1 and 3074 DF, p-value: 0.1737
$`12`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-39935 -22992 -10499 8475 1318806
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 37221.10 3050.32 12.202 <2e-16 ***
edad_jefe 63.97 55.51 1.152 0.249
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 46900 on 2536 degrees of freedom
Multiple R-squared: 0.0005233, Adjusted R-squared: 0.0001292
F-statistic: 1.328 on 1 and 2536 DF, p-value: 0.2493
$`13`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-49533 -26559 -11846 11260 852224
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49322.23 3744.63 13.171 <2e-16 ***
edad_jefe 53.19 69.31 0.767 0.443
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 49510 on 2133 degrees of freedom
Multiple R-squared: 0.000276, Adjusted R-squared: -0.0001927
F-statistic: 0.5888 on 1 and 2133 DF, p-value: 0.443
$`14`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-68274 -33136 -13859 15782 786367
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 67643.73 3628.72 18.641 <2e-16 ***
edad_jefe 16.16 66.61 0.243 0.808
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 56220 on 2623 degrees of freedom
Multiple R-squared: 2.242e-05, Adjusted R-squared: -0.0003588
F-statistic: 0.05882 on 1 and 2623 DF, p-value: 0.8084
$`15`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-54284 -24233 -10838 10716 1090714
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40766.05 2802.10 14.548 < 2e-16 ***
edad_jefe 216.18 52.13 4.147 3.45e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 47300 on 3525 degrees of freedom
Multiple R-squared: 0.004854, Adjusted R-squared: 0.004572
F-statistic: 17.2 on 1 and 3525 DF, p-value: 3.453e-05
$`16`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-55841 -29613 -14160 9676 3040566
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 60335.14 5923.62 10.186 <2e-16 ***
edad_jefe -77.47 108.38 -0.715 0.475
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 85630 on 2213 degrees of freedom
Multiple R-squared: 0.0002308, Adjusted R-squared: -0.000221
F-statistic: 0.5109 on 1 and 2213 DF, p-value: 0.4748
$`17`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-53396 -28449 -13553 10174 815912
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 50174.74 3807.78 13.177 <2e-16 ***
edad_jefe 101.17 69.48 1.456 0.145
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 53860 on 2459 degrees of freedom
Multiple R-squared: 0.0008617, Adjusted R-squared: 0.0004553
F-statistic: 2.121 on 1 and 2459 DF, p-value: 0.1455
$`18`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-64796 -34974 -16857 10725 6365071
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 61100.8 10475.8 5.833 6.29e-09 ***
edad_jefe 100.9 193.8 0.521 0.603
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 147900 on 2128 degrees of freedom
Multiple R-squared: 0.0001274, Adjusted R-squared: -0.0003425
F-statistic: 0.2711 on 1 and 2128 DF, p-value: 0.6026
$`19`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-72697 -37580 -16507 15217 1161379
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 83568.16 3760.86 22.220 < 2e-16 ***
edad_jefe -240.37 68.04 -3.533 0.000417 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 65960 on 3610 degrees of freedom
Multiple R-squared: 0.003445, Adjusted R-squared: 0.003169
F-statistic: 12.48 on 1 and 3610 DF, p-value: 0.0004166
$`20`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-39720 -23464 -11759 8069 1539910
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40764.627 3730.838 10.926 <2e-16 ***
edad_jefe 7.032 67.416 0.104 0.917
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 56650 on 2618 degrees of freedom
Multiple R-squared: 4.156e-06, Adjusted R-squared: -0.0003778
F-statistic: 0.01088 on 1 and 2618 DF, p-value: 0.9169
$`21`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-45156 -23218 -10412 9697 518980
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 42818.94 3066.07 13.96 <2e-16 ***
edad_jefe 80.48 57.48 1.40 0.162
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 41560 on 2173 degrees of freedom
Multiple R-squared: 0.0009014, Adjusted R-squared: 0.0004416
F-statistic: 1.961 on 1 and 2173 DF, p-value: 0.1616
$`22`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-70697 -33759 -14084 16440 799248
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 58982.13 3115.67 18.931 < 2e-16 ***
edad_jefe 183.63 59.78 3.072 0.00214 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 57920 on 3820 degrees of freedom
Multiple R-squared: 0.002464, Adjusted R-squared: 0.002203
F-statistic: 9.435 on 1 and 3820 DF, p-value: 0.002144
$`23`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-66977 -32763 -13415 14356 506693
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 61407.62 3848.96 15.954 <2e-16 ***
edad_jefe 128.03 77.05 1.662 0.0967 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 54780 on 2416 degrees of freedom
Multiple R-squared: 0.001141, Adjusted R-squared: 0.000728
F-statistic: 2.761 on 1 and 2416 DF, p-value: 0.09672
$`24`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-52125 -30259 -13752 14325 851575
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53596.18 3274.77 16.37 <2e-16 ***
edad_jefe 21.96 59.39 0.37 0.712
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 50280 on 2628 degrees of freedom
Multiple R-squared: 5.203e-05, Adjusted R-squared: -0.0003285
F-statistic: 0.1368 on 1 and 2628 DF, p-value: 0.7116
$`25`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-73092 -36176 -14881 15749 5840057
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 69928.11 6703.54 10.432 <2e-16 ***
edad_jefe 76.93 121.82 0.631 0.528
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 113400 on 3477 degrees of freedom
Multiple R-squared: 0.0001147, Adjusted R-squared: -0.0001729
F-statistic: 0.3988 on 1 and 3477 DF, p-value: 0.5278
$`26`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-68407 -36006 -17159 14276 823084
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 79309.43 4250.08 18.661 <2e-16 ***
edad_jefe -169.73 78.72 -2.156 0.0312 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 62610 on 2546 degrees of freedom
Multiple R-squared: 0.001823, Adjusted R-squared: 0.00143
F-statistic: 4.649 on 1 and 2546 DF, p-value: 0.03117
$`27`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-52984 -27520 -13455 11636 683119
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 43586.63 3481.98 12.518 < 2e-16 ***
edad_jefe 179.24 66.07 2.713 0.00672 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 46580 on 2165 degrees of freedom
Multiple R-squared: 0.003388, Adjusted R-squared: 0.002928
F-statistic: 7.36 on 1 and 2165 DF, p-value: 0.006723
$`28`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-66009 -30806 -14422 13803 831461
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 74319.9 3800.6 19.555 < 2e-16 ***
edad_jefe -277.0 70.3 -3.941 8.37e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 54710 on 2320 degrees of freedom
Multiple R-squared: 0.006649, Adjusted R-squared: 0.006221
F-statistic: 15.53 on 1 and 2320 DF, p-value: 8.366e-05
$`29`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-44897 -20585 -8870 9399 466561
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 41664.60 2544.79 16.373 <2e-16 ***
edad_jefe 91.35 47.81 1.911 0.0562 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 35300 on 2305 degrees of freedom
Multiple R-squared: 0.001581, Adjusted R-squared: 0.001148
F-statistic: 3.65 on 1 and 2305 DF, p-value: 0.05618
$`30`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-41486 -21007 -10278 7260 1676516
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 43034.14 3133.45 13.734 <2e-16 ***
edad_jefe -43.00 56.67 -0.759 0.448
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 49590 on 2913 degrees of freedom
Multiple R-squared: 0.0001976, Adjusted R-squared: -0.0001456
F-statistic: 0.5758 on 1 and 2913 DF, p-value: 0.448
$`31`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-57435 -28492 -12505 9745 1538090
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48798.89 3946.60 12.365 <2e-16 ***
edad_jefe 137.56 74.03 1.858 0.0632 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 64080 on 2945 degrees of freedom
Multiple R-squared: 0.001171, Adjusted R-squared: 0.000832
F-statistic: 3.453 on 1 and 2945 DF, p-value: 0.06324
$`32`
Call:
lm(formula = ing_cor ~ edad_jefe, data = .)
Residuals:
Min 1Q Median 3Q Max
-50022 -25950 -12157 9973 622731
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53702.89 3029.76 17.725 <2e-16 ***
edad_jefe -74.91 55.47 -1.351 0.177
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 46030 on 2520 degrees of freedom
Multiple R-squared: 0.0007233, Adjusted R-squared: 0.0003268
F-statistic: 1.824 on 1 and 2520 DF, p-value: 0.177Si queremos esto en una sola base de datos
concentradohogar %>%
split(.$ent) %>%
map(~ lm(ing_cor ~ edad_jefe, data = .)) %>% # da solo los coeficientes
map(~ broom::tidy(.x))$`01`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 65927. 5110. 12.9 5.56e-37
2 edad_jefe 144. 97.4 1.48 1.38e- 1
$`02`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 88996. 6780. 13.1 1.36e-38
2 edad_jefe -176. 131. -1.34 1.81e- 1
$`03`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 88060. 5703. 15.4 1.13e-51
2 edad_jefe -5.78 111. -0.0522 9.58e- 1
$`04`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 48493. 5071. 9.56 2.97e-21
2 edad_jefe 190. 97.7 1.95 5.18e- 2
$`05`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 78874. 3619. 21.8 8.65e-100
2 edad_jefe -179. 67.9 -2.63 8.50e- 3
$`06`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 65118. 3325. 19.6 1.73e-80
2 edad_jefe -24.8 61.5 -0.403 6.87e- 1
$`07`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 35432. 3820. 9.28 4.24e-20
2 edad_jefe 106. 72.4 1.46 1.44e- 1
$`08`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 94473. 7617. 12.4 9.09e-35
2 edad_jefe -401. 142. -2.82 4.87e- 3
$`09`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 60467. 5709. 10.6 1.09e-25
2 edad_jefe 329. 103. 3.19 1.45e- 3
$`10`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 55315. 2848. 19.4 9.13e-79
2 edad_jefe -33.3 53.0 -0.628 5.30e- 1
$`11`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 51366. 3200. 16.1 9.61e-56
2 edad_jefe 82.6 60.7 1.36 1.74e- 1
$`12`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 37221. 3050. 12.2 2.55e-33
2 edad_jefe 64.0 55.5 1.15 2.49e- 1
$`13`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 49322. 3745. 13.2 3.79e-38
2 edad_jefe 53.2 69.3 0.767 4.43e- 1
$`14`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 67644. 3629. 18.6 6.25e-73
2 edad_jefe 16.2 66.6 0.243 8.08e- 1
$`15`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 40766. 2802. 14.5 1.31e-46
2 edad_jefe 216. 52.1 4.15 3.45e- 5
$`16`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 60335. 5924. 10.2 7.66e-24
2 edad_jefe -77.5 108. -0.715 4.75e- 1
$`17`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 50175. 3808. 13.2 2.30e-38
2 edad_jefe 101. 69.5 1.46 1.45e- 1
$`18`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 61101. 10476. 5.83 0.00000000629
2 edad_jefe 101. 194. 0.521 0.603
$`19`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 83568. 3761. 22.2 1.23e-102
2 edad_jefe -240. 68.0 -3.53 4.17e- 4
$`20`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 40765. 3731. 10.9 3.30e-27
2 edad_jefe 7.03 67.4 0.104 9.17e- 1
$`21`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 42819. 3066. 14.0 1.65e-42
2 edad_jefe 80.5 57.5 1.40 1.62e- 1
$`22`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 58982. 3116. 18.9 1.82e-76
2 edad_jefe 184. 59.8 3.07 2.14e- 3
$`23`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 61408. 3849. 16.0 1.47e-54
2 edad_jefe 128. 77.1 1.66 9.67e- 2
$`24`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 53596. 3275. 16.4 2.09e-57
2 edad_jefe 22.0 59.4 0.370 7.12e- 1
$`25`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 69928. 6704. 10.4 4.16e-25
2 edad_jefe 76.9 122. 0.631 5.28e- 1
$`26`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 79309. 4250. 18.7 6.10e-73
2 edad_jefe -170. 78.7 -2.16 3.12e- 2
$`27`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 43587. 3482. 12.5 9.25e-35
2 edad_jefe 179. 66.1 2.71 6.72e- 3
$`28`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 74320. 3801. 19.6 6.06e-79
2 edad_jefe -277. 70.3 -3.94 8.37e- 5
$`29`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 41665. 2545. 16.4 4.41e-57
2 edad_jefe 91.4 47.8 1.91 5.62e- 2
$`30`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 43034. 3133. 13.7 1.23e-41
2 edad_jefe -43.0 56.7 -0.759 4.48e- 1
$`31`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 48799. 3947. 12.4 2.83e-34
2 edad_jefe 138. 74.0 1.86 6.32e- 2
$`32`
# A tibble: 2 × 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 53703. 3030. 17.7 2.42e-66
2 edad_jefe -74.9 55.5 -1.35 1.77e- 1Como broom::tidy los volvio tibble, lo podemos guardar en un excelito
concentradohogar %>%
split(.$ent) %>%
map(~ lm(ing_cor ~ edad_jefe, data = .)) %>% # da solo los coeficientes
map(~ broom::tidy(.x)) %>%
write_xlsx(path="modelos.xlsx")Veamos…
names(concentradohogar) [1] "folioviv" "foliohog" "ubica_geo" "tam_loc" "est_socio"
[6] "est_dis" "upm" "factor" "clase_hog" "sexo_jefe"
[11] "edad_jefe" "educa_jefe" "tot_integ" "hombres" "mujeres"
[16] "mayores" "menores" "p12_64" "p65mas" "ocupados"
[21] "percep_ing" "perc_ocupa" "ing_cor" "ingtrab" "trabajo"
[26] "sueldos" "horas_extr" "comisiones" "aguinaldo" "indemtrab"
[31] "otra_rem" "remu_espec" "negocio" "noagrop" "industria"
[36] "comercio" "servicios" "agrope" "agricolas" "pecuarios"
[41] "reproducc" "pesca" "otros_trab" "rentas" "utilidad"
[46] "arrenda" "transfer" "jubilacion" "becas" "donativos"
[51] "remesas" "bene_gob" "transf_hog" "trans_inst" "estim_alqu"
[56] "otros_ing" "gasto_mon" "alimentos" "ali_dentro" "cereales"
[61] "carnes" "pescado" "leche" "huevo" "aceites"
[66] "tuberculo" "verduras" "frutas" "azucar" "cafe"
[71] "especias" "otros_alim" "bebidas" "ali_fuera" "tabaco"
[76] "vesti_calz" "vestido" "calzado" "vivienda" "alquiler"
[81] "pred_cons" "agua" "energia" "limpieza" "cuidados"
[86] "utensilios" "enseres" "salud" "atenc_ambu" "hospital"
[91] "medicinas" "transporte" "publico" "foraneo" "adqui_vehi"
[96] "mantenim" "refaccion" "combus" "comunica" "educa_espa"
[101] "educacion" "esparci" "paq_turist" "personales" "cuida_pers"
[106] "acces_pers" "otros_gas" "transf_gas" "percep_tot" "retiro_inv"
[111] "prestamos" "otras_perc" "ero_nm_viv" "ero_nm_hog" "erogac_tot"
[116] "cuota_viv" "mater_serv" "material" "servicio" "deposito"
[121] "prest_terc" "pago_tarje" "deudas" "balance" "otras_erog"
[126] "smg" "ent" "mun" vars<-c("clase_hog", "sexo_jefe", "edad_jefe" , "educa_jefe")
tabs<- vars %>%
map(~ count(x=concentradohogar,
!!as.name(.x), # para que ponga la variable a tabular
wt=factor) %>% # para que use el pes
mutate(pct = round((n / sum(n) * 100), 2)) %>% # Para que ponga el %
adorn_totals()
)
tabs # tabulados expandidos para mandar a un excel[[1]]
clase_hog n pct
1 4877089 12.98
2 23072585 61.43
3 9257028 24.65
4 229438 0.61
5 123983 0.33
Total 37560123 100.00
[[2]]
sexo_jefe n pct
1 25397559 67.62
2 12162564 32.38
Total 37560123 100.00
[[3]]
edad_jefe n pct
13 935 0.00
14 964 0.00
15 4457 0.01
16 3794 0.01
17 8976 0.02
18 42690 0.11
19 56798 0.15
20 84568 0.23
21 125587 0.33
22 172548 0.46
23 214177 0.57
24 263898 0.70
25 305519 0.81
26 323739 0.86
27 406520 1.08
28 487736 1.30
29 460839 1.23
30 603298 1.61
31 528902 1.41
32 658202 1.75
33 629727 1.68
34 597334 1.59
35 685949 1.83
36 664793 1.77
37 683702 1.82
38 813973 2.17
39 705804 1.88
40 887214 2.36
41 661093 1.76
42 946094 2.52
43 768157 2.05
44 738765 1.97
45 851881 2.27
46 772802 2.06
47 873677 2.33
48 896786 2.39
49 832883 2.22
50 1066993 2.84
51 715468 1.90
52 954582 2.54
53 805504 2.14
54 808037 2.15
55 807356 2.15
56 807281 2.15
57 747464 1.99
58 761389 2.03
59 695903 1.85
60 803140 2.14
61 571932 1.52
62 792777 2.11
63 699460 1.86
64 599027 1.59
65 735991 1.96
66 531815 1.42
67 618153 1.65
68 559199 1.49
69 433164 1.15
70 494162 1.32
71 325245 0.87
72 474634 1.26
73 401630 1.07
74 359345 0.96
75 373163 0.99
76 313712 0.84
77 275198 0.73
78 338111 0.90
79 206371 0.55
80 264252 0.70
81 154863 0.41
82 204772 0.55
83 147867 0.39
84 150853 0.40
85 143122 0.38
86 132169 0.35
87 105581 0.28
88 82715 0.22
89 63628 0.17
90 68203 0.18
91 35632 0.09
92 42520 0.11
93 45118 0.12
94 17606 0.05
95 14526 0.04
96 14364 0.04
97 8395 0.02
98 7867 0.02
99 9742 0.03
100 1987 0.01
101 195 0.00
102 1513 0.00
103 568 0.00
104 607 0.00
106 426 0.00
109 45 0.00
Total 37560123 99.99
[[4]]
educa_jefe n pct
1 2165354 5.77
2 9118 0.02
3 4964721 13.22
4 5963630 15.88
5 1072192 2.85
6 9886027 26.32
7 1300623 3.46
8 5237713 13.94
9 1195033 3.18
10 4835658 12.87
11 930054 2.48
Total 37560123 99.99names(tabs)<-vars # para que las hojas del excel se llamen como la variables
write_xlsx(tabs, path="tabs.xlsx")