Es la presentación más simple de un número real (R^1$)
escalar1 <- 3
escalar1 ## [1] 3escalar2 <- 4El motor de progrmación R puede servir como calculadora.
escalar3<-escalar1+escalar2
escalar3 <- escalar1 + escalar2
escalar3<-escalar1+escalar2
escalar4 <- escalar3 * escalar2
escalar5 <- escalar4/escalar1
escalar5## [1] 9.333333summary(escalar5)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 9.333 9.333 9.333 9.333 9.333 9.333table(escalar5)## escalar5
## 9.33333333333333
## 1vect1 <- c(2,1.5,4) #vector numerico
vect2 <- c("Juan","Pedro","Sara")
vect3 <- c(5,6,2)
prd1 <- vect1 * escalar4vect1[2]## [1] 1.5vect2[3]## [1] "Sara"objeto1 <- vect3[1]
objeto2 <- vect2[2:3]
objeto3 <- vect2[1:2]
objeto4 <- vect2[c(1,3)]
objeto4_f2 <- vect2[-2]vector1 <- c(1,2,3)
vector2 <- c(4,5,6)
vector3 <- c(7,8,9)
matrizA <- cbind(vector1,vector2,vector3)
matrizA## vector1 vector2 vector3
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9matrizB <- rbind(vector1,vector2,vector3)
matrizB## [,1] [,2] [,3]
## vector1 1 2 3
## vector2 4 5 6
## vector3 7 8 9base1 <- as.data.frame(matrizA)
base2 <- as.data.frame(matrizB)base1$vector1## [1] 1 2 3nombre_vectores <- c("variable1","variable2","variable3")
names(base1) <- nombre_vectoresrm(list = ls())
base <- mtcarsbase1 <- subset(base,base$cyl > 4)base1_f2 <- base[base$cyl>4,]library(dplyr)## Warning: package 'dplyr' was built under R version 4.4.3##
## Adjuntando el paquete: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionbase1_f3 <- base %>%
filter(cyl>4)rm(list = ls())
getwd()## [1] "C:/Users/59398/Downloads/Proyecto Prueba/Insumos"setwd("C:/Users/59398/Downloads/Proyecto Prueba/Insumos")
base <- read.csv('datos_ecuador - datos_ecuador.csv')options(scipen = 999)
names(base)## [1] "anio" "trim"
## [3] "RiesgoPais" "PIB_MillonesUSD"
## [5] "TasaDesempleo_Porcentaje" "IED_MillonesUSD"modelo_desempleo <- lm(TasaDesempleo_Porcentaje ~ PIB_MillonesUSD, data=base)
modelo_desempleo2 <- lm(base$TasaDesempleo_Porcentaje ~ base$PIB_MillonesUSD)
summary(modelo_desempleo)##
## Call:
## lm(formula = TasaDesempleo_Porcentaje ~ PIB_MillonesUSD, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6369 -0.8933 -0.1452 0.4323 7.6118
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.37282462 0.92082867 9.093 0.000000000000595 ***
## PIB_MillonesUSD -0.00010715 0.00003556 -3.014 0.00376 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 61 degrees of freedom
## Multiple R-squared: 0.1296, Adjusted R-squared: 0.1153
## F-statistic: 9.081 on 1 and 61 DF, p-value: 0.003758summary(modelo_desempleo2)##
## Call:
## lm(formula = base$TasaDesempleo_Porcentaje ~ base$PIB_MillonesUSD)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6369 -0.8933 -0.1452 0.4323 7.6118
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.37282462 0.92082867 9.093 0.000000000000595 ***
## base$PIB_MillonesUSD -0.00010715 0.00003556 -3.014 0.00376 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.377 on 61 degrees of freedom
## Multiple R-squared: 0.1296, Adjusted R-squared: 0.1153
## F-statistic: 9.081 on 1 and 61 DF, p-value: 0.003758options(scipen = 999)
names(base)## [1] "anio" "trim"
## [3] "RiesgoPais" "PIB_MillonesUSD"
## [5] "TasaDesempleo_Porcentaje" "IED_MillonesUSD"model_ied <- lm(IED_MillonesUSD ~ RiesgoPais, data=base)
summary(model_ied)##
## Call:
## lm(formula = IED_MillonesUSD ~ RiesgoPais, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -317.37 -55.76 -16.82 54.14 279.09
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 229.89653 29.74552 7.729 0.000000000129 ***
## RiesgoPais -0.04321 0.02458 -1.758 0.0838 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 107.9 on 61 degrees of freedom
## Multiple R-squared: 0.04821, Adjusted R-squared: 0.0326
## F-statistic: 3.089 on 1 and 61 DF, p-value: 0.08382library(dplyr)
base<-base %>%
mutate(ied_rezagado1=lag(IED_MillonesUSD, n=1),
rp_rezagado1=lag(RiesgoPais, n=2),
rp_rezagado2=lag(RiesgoPais, n=2))names(base)## [1] "anio" "trim"
## [3] "RiesgoPais" "PIB_MillonesUSD"
## [5] "TasaDesempleo_Porcentaje" "IED_MillonesUSD"
## [7] "ied_rezagado1" "rp_rezagado1"
## [9] "rp_rezagado2"model_ied_lag1<-lm(IED_MillonesUSD~rp_rezagado1, data = base)
summary(model_ied_lag1)##
## Call:
## lm(formula = IED_MillonesUSD ~ rp_rezagado1, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -320.59 -53.15 -14.34 49.29 266.92
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 214.06880 30.81112 6.948 0.00000000332 ***
## rp_rezagado1 -0.02707 0.02533 -1.069 0.29
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 111 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.01899, Adjusted R-squared: 0.002358
## F-statistic: 1.142 on 1 and 59 DF, p-value: 0.2896model_ied_lag2<-lm(IED_MillonesUSD~rp_rezagado2, data = base)
summary(model_ied_lag2)##
## Call:
## lm(formula = IED_MillonesUSD ~ rp_rezagado2, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -320.59 -53.15 -14.34 49.29 266.92
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 214.06880 30.81112 6.948 0.00000000332 ***
## rp_rezagado2 -0.02707 0.02533 -1.069 0.29
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 111 on 59 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.01899, Adjusted R-squared: 0.002358
## F-statistic: 1.142 on 1 and 59 DF, p-value: 0.2896##desde 2015 hasta 2023
base <- base%>%
filter(anio>=2015 & anio <=2023)
modelo_desempleo <- lm(TasaDesempleo_Porcentaje ~ PIB_MillonesUSD, data=base)
summary(modelo_desempleo)##
## Call:
## lm(formula = TasaDesempleo_Porcentaje ~ PIB_MillonesUSD, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8356 -0.8271 -0.1033 0.1037 6.8451
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.6811501 2.7428198 4.623 0.0000526 ***
## PIB_MillonesUSD -0.0002485 0.0001002 -2.481 0.0182 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.451 on 34 degrees of freedom
## Multiple R-squared: 0.1533, Adjusted R-squared: 0.1284
## F-statistic: 6.155 on 1 and 34 DF, p-value: 0.01821modelo_ied <- lm(TasaDesempleo_Porcentaje ~ log(PIB_MillonesUSD), data = base)
summary(modelo_desempleo)##
## Call:
## lm(formula = TasaDesempleo_Porcentaje ~ PIB_MillonesUSD, data = base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8356 -0.8271 -0.1033 0.1037 6.8451
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.6811501 2.7428198 4.623 0.0000526 ***
## PIB_MillonesUSD -0.0002485 0.0001002 -2.481 0.0182 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.451 on 34 degrees of freedom
## Multiple R-squared: 0.1533, Adjusted R-squared: 0.1284
## F-statistic: 6.155 on 1 and 34 DF, p-value: 0.01821coef(modelo_ied)[2]## log(PIB_MillonesUSD)
## -6.875101rm(list = ls())
base <- read.csv('datos_ecuador - datos_ecuador.csv')base1<- base %>%
filter(anio>2015 & anio<2023)
names(base1)## [1] "anio" "trim"
## [3] "RiesgoPais" "PIB_MillonesUSD"
## [5] "TasaDesempleo_Porcentaje" "IED_MillonesUSD"modelo1<-lm(TasaDesempleo_Porcentaje~log(PIB_MillonesUSD), data=base1)
summary(modelo1)##
## Call:
## lm(formula = TasaDesempleo_Porcentaje ~ log(PIB_MillonesUSD),
## data = base1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4957 -0.9472 -0.3826 0.3515 6.4448
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 105.698 42.899 2.464 0.0207 *
## log(PIB_MillonesUSD) -9.759 4.205 -2.321 0.0284 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.559 on 26 degrees of freedom
## Multiple R-squared: 0.1716, Adjusted R-squared: 0.1398
## F-statistic: 5.387 on 1 and 26 DF, p-value: 0.0284base2<- base %>%
filter(anio>=2015& anio<=2023)
modelo2<-lm(TasaDesempleo_Porcentaje~PIB_MillonesUSD, data = base2)
summary(modelo2)##
## Call:
## lm(formula = TasaDesempleo_Porcentaje ~ PIB_MillonesUSD, data = base2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8356 -0.8271 -0.1033 0.1037 6.8451
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12.6811501 2.7428198 4.623 0.0000526 ***
## PIB_MillonesUSD -0.0002485 0.0001002 -2.481 0.0182 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.451 on 34 degrees of freedom
## Multiple R-squared: 0.1533, Adjusted R-squared: 0.1284
## F-statistic: 6.155 on 1 and 34 DF, p-value: 0.01821rm(list = ls())
##Librerias
library(dplyr)
library(srvyr)## Warning: package 'srvyr' was built under R version 4.4.3##
## Adjuntando el paquete: 'srvyr'## The following object is masked from 'package:stats':
##
## filterlibrary(rio)## Warning: package 'rio' was built under R version 4.4.3library(import)## Warning: package 'import' was built under R version 4.4.3## The import package should not be attached.
## Use "colon syntax" instead, e.g. import::from, or import:::from.personas <- import('1_BDD_ENS2018_f1_personas.dta')
table(personas$dcronica_2)##
## 0 1
## 5591 2210dci<-2210/(5591+2210)
dci*100## [1] 28.3297dm <- personas %>%
as_survey_design(ids=upm, #unidad primaria de muestreo
strata=estrato, #estrato
weights = fexp) #factor de expansión
options(survey.lonely.psu = 'certainty') #forzando a que las obsevaciones sean unicas e individuales
prev_nac_dci2 <- dm %>%
summarise(survey_mean(dcronica_2, vartype =c('se','cv'),na.rm=T),
n_muestra=sum(!is.na(dcronica_2))) %>%
mutate(dominio="Nacional") %>%
select(dominio, porcentaje=coef, se=`_se`, cv=`_cv`, n_muestra) %>%
mutate(porcentaje=round((porcentaje*100),digits = 1))rm(list = ls())
# Librerias
library(dplyr)
library(srvyr)
library(rio)df <- read.csv2('enemdu_persona_2025_II_trimestre.csv')
df2 <- read.csv('enemdu_persona_2025_II_trimestre.csv',sep = ';')dm <- df %>%
as_survey_design(ids=upm, #unidad primaria de muestreo
strata=estrato, #estrato
weights = fexp) #flibrary(haven)## Warning: package 'haven' was built under R version 4.4.3library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.4.3## Warning: package 'ggplot2' was built under R version 4.4.3## Warning: package 'tibble' was built under R version 4.4.3## Warning: package 'tidyr' was built under R version 4.4.3## Warning: package 'readr' was built under R version 4.4.3## Warning: package 'purrr' was built under R version 4.4.3## Warning: package 'stringr' was built under R version 4.4.3## Warning: package 'forcats' was built under R version 4.4.3## Warning: package 'lubridate' was built under R version 4.4.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ readr 2.1.5
## ✔ ggplot2 4.0.0 ✔ stringr 1.5.2
## ✔ lubridate 1.9.4 ✔ tibble 3.3.0
## ✔ purrr 1.1.0 ✔ tidyr 1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ srvyr::filter() masks dplyr::filter(), stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(srvyr)tasa_desempleo_nac <- dm %>%
filter(p03>=15) %>%
summarise(
tasa_desempleo = survey_ratio(
numerator= condact %in% c(7,8),
denominator=condact %in% 1:8,
vartype = c('se','ci')
)
) %>%
mutate('tasa_desempleo = mean_desempleo * 100')dm <- df %>%
as_survey_design(ids=upm,
strata=estrato,
weights = fexp)
tasa_empleo_adec <- dm %>%
filter(p03>=15) %>%
summarise(
tasa_empleo_adecuado = survey_ratio(
numerator=( condact == 1),
denominator=(condact %in% 1:8),
vartype = c('se','ci')
)
) %>%
mutate(tasa_empleo_adecuado = round((tasa_empleo_adecuado*100),digits = 1))
print(tasa_empleo_adec)## # A tibble: 1 × 4
## tasa_empleo_adecuado tasa_empleo_adecuado_se tasa_empleo_adecuado_low
## <dbl> <dbl> <dbl>
## 1 36.2 0.0113 0.340
## # ℹ 1 more variable: tasa_empleo_adecuado_upp <dbl>tasa_empleo_norem <- dm %>%
filter(p03>=15) %>%
summarise(
tasa_empleo_norem = survey_ratio(
numerator=( condact == 5),
denominator=(condact %in% 1:8),
vartype = c('se','ci')
)
) %>%
mutate(tasa_empleo_norem = round((tasa_empleo_norem*100),digits = 1))library(readxl)## Warning: package 'readxl' was built under R version 4.4.3library(dplyr)base <- read_excel('pib_can_anual.xlsx')## New names:
## • `` -> `...20`names(base)[6] <- 'agricultura'base[is.na(base)]<- 0table(base$prov)##
## AZUAY BOLÍVAR CAÑAR CARCHI
## 150 70 70 60
## CHIMBORAZO COTOPAXI EL ORO ESMERALDAS
## 100 70 140 71
## GALÁPAGOS GUAYAS IMBABURA LOJA
## 30 250 60 160
## LOS RÍOS MANABÍ MORONA SANTIAGO NAPO
## 130 220 120 50
## ORELLANA PASTAZA PICHINCHA SANTA ELENA
## 40 40 80 30
## SANTO DOMINGO SUCUMBÍOS TUNGURAHUA ZAMORA CHINCHIPE
## 19 70 90 90base[is.na(base)]<- 0table(base$prov)##
## AZUAY BOLÍVAR CAÑAR CARCHI
## 150 70 70 60
## CHIMBORAZO COTOPAXI EL ORO ESMERALDAS
## 100 70 140 71
## GALÁPAGOS GUAYAS IMBABURA LOJA
## 30 250 60 160
## LOS RÍOS MANABÍ MORONA SANTIAGO NAPO
## 130 220 120 50
## ORELLANA PASTAZA PICHINCHA SANTA ELENA
## 40 40 80 30
## SANTO DOMINGO SUCUMBÍOS TUNGURAHUA ZAMORA CHINCHIPE
## 19 70 90 90base_santo <- base%>%
filter(dpa_prov=='23')
base_manabi <- base %>%
filter(prov == "manabi")names(base_manabi)## [1] "year"
## [2] "prov"
## [3] "dpa_prov"
## [4] "canton"
## [5] "dpa_can"
## [6] "agricultura"
## [7] "Explotacion de minas y canteras"
## [8] "Manufactura\n\n"
## [9] "Suministro de electricidad y de agua"
## [10] "Construccion\n\n"
## [11] "comercio\n\n"
## [12] "Alojamiento y servicios de comida"
## [13] "Transporte, informacion y comunicaciones"
## [14] "Actividades financieras\n"
## [15] "Actividades profesionales e inmobiliarias"
## [16] "Administracion publica \n"
## [17] "Enseñanza\n\n"
## [18] "Salud\n\n"
## [19] "Otros servicios\n\n"
## [20] "...20"base_manabi <- base_manabi %>%
select(year, prov, dpa_prov, canton, dpa_can, agricultura, `Explotacion de minas y canteras`)#Vista resumen de una variables
summary(base_manabi$agricultura)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## #Valor limite 29351.06 ## Eliminar ultima variable
base_manabi <- base_manabi[-7]base_manabi <- base_manabi %>%
mutate(tipo_pib = ifelse(agricultura > 29351, "pib_alto", "pib_bajo"))
base_manabi <- base_manabi %>%
mutate(tipo=ifelse(agricultura > 29351,1,0))
base_manabi <- base_manabi %>%
mutate(periodo=ifelse(year<2016,"pre","post"))base_manabi_pre <- base_manabi %>%
filter(periodo=="pre")
base_manabi_post <- base_manabi %>%
filter(periodo=="post")b_m_pre_agg <- base_manabi_pre %>%
group_by(canton) %>%
summarise(total_agricultura_pre=sum(agricultura))
b_m_post_agg <- base_manabi_post %>%
group_by(canton) %>%
summarise(total_agricultura_post=sum(agricultura))