INTRODUCCION AL SOFWARE ESTADISTICO

INTRODUCCION

library(readxl) # read_excel

library(writexl)

## Warning: package 'writexl' was built under R version 4.2.3

# write_xlsx

library(knitr)
library(tidyverse) #%>%

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.2     ✔ purrr   1.0.1
## ✔ tibble  3.2.1     ✔ dplyr   1.1.1
## ✔ tidyr   1.3.0     ✔ stringr 1.5.0
## ✔ readr   2.1.4     ✔ forcats 1.0.0

## Warning: package 'ggplot2' was built under R version 4.2.3

## Warning: package 'tibble' was built under R version 4.2.3

## Warning: package 'dplyr' was built under R version 4.2.3

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(dplyr)  # selec, mutate,...
library(psych) # describe()

## 
## Attaching package: 'psych'
## 
## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

library(FSA) # Summarize()

## Warning: package 'FSA' was built under R version 4.2.3

## ## FSA v0.9.5. See citation('FSA') if used in publication.
## ## Run fishR() for related website and fishR('IFAR') for related book.
## 
## Attaching package: 'FSA'
## 
## The following object is masked from 'package:psych':
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##     headtail

library(readxl)
fallecidos_covid_Peru <- read_excel("C:/Users/octa1/Downloads/INTRODUCCION SDE SOTWARE/fallecidos_covid_Peru.xlsx")
View(fallecidos_covid_Peru)

attach(fallecidos_covid_Peru)#se carga base de datos
class(fallecidos_covid_Peru)#tipos de datos

## [1] "tbl_df"     "tbl"        "data.frame"

res<-table(SEXO,EDAD_DECLARADA)#solo para acalar
 View(res)
 class(res)

## [1] "table"

 res<-as.data.frame(res)
 class(res)

## [1] "data.frame"

 ruta<-"C:/Users/octa1/Downloads/INTRODUCCION SDE SOTWARE/Resultados.xlsx"
  


##########################

#########################

dat<-fallecidos_covid_Peru%>%
  select(SEXO,EDAD_DECLARADA)%>%
  filter(SEXO=="FEMENINO",EDAD_DECLARADA==18)%>%
  count(SEXO)
dat

## # A tibble: 1 × 2
##   SEXO         n
##   <chr>    <int>
## 1 FEMENINO     3

View(dat)

dat1<-fallecidos_covid_Peru%>%
  select(SEXO,EDAD_DECLARADA)%>%
  filter(SEXO=="FEMENINO",EDAD_DECLARADA==(18:30))%>%
  count(SEXO)
dat1

## # A tibble: 1 × 2
##   SEXO         n
##   <chr>    <int>
## 1 FEMENINO     7

View(dat1)