Aula-RMarkDown

R Markdown

Este é um documento R Markdown. Markdown é uma sintaxe de formatação simples para criação de documentos HTML, PDF e MS Word. Para obter mais detalhes sobre o uso do R Markdown, consulte http://rmarkdown.rstudio.com.

Quando você clica no botão Knit, será gerado um documento que inclui tanto o conteúdo quanto a saída de quaisquer pedaços de código R incorporados no documento. Você pode incorporar um pedaço de código R como este:

summary(cars)

##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

Incluindo Parcelas

Você também pode incorporar gráficos, por exemplo:

Observe que o parâmetro echo = FALSE foi adicionado ao bloco de código para evitar a impressão do código R que gerou o gráfico.

fazendo teste

#Rio de janeiro 15 de novembro de 2024 #teste em casa

#imagem

#

Tutorial para introdução

Foto cachorro com oculos

#teste ok

Projeto RMarkdown

Centro Universitário Celso Lisboa

Aula de Algoritmos e Estrutura de Dados - Engenharia Civil 4° Período

Professor Adriano

Sergio Correia Jose, Vitor Façanha de Araújo Serrão

Rio de Janeiro, em 28 de novembro de 2024

library (ggplot2)
library(zoo)

## 
## Attaching package: 'zoo'

## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric

library(data.table)

## 
## Attaching package: 'data.table'

## The following objects are masked from 'package:zoo':
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##     yearmon, yearqtr

library(dplyr)

## 
## Attaching package: 'dplyr'

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## The following objects are masked from 'package:stats':
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##     filter, lag

## The following objects are masked from 'package:base':
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##     intersect, setdiff, setequal, union

library(naivebayes) 

## naivebayes 1.0.0 loaded

## For more information please visit:

## https://majkamichal.github.io/naivebayes/

## 
## Attaching package: 'naivebayes'

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library(neuralnet)

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library(quantmod)   

## Loading required package: xts

## 
## ######################### Warning from 'xts' package ##########################
## #                                                                             #
## # The dplyr lag() function breaks how base R's lag() function is supposed to  #
## # work, which breaks lag(my_xts). Calls to lag(my_xts) that you type or       #
## # source() into this session won't work correctly.                            #
## #                                                                             #
## # Use stats::lag() to make sure you're not using dplyr::lag(), or you can add #
## # conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop           #
## # dplyr from breaking base R's lag() function.                                #
## #                                                                             #
## # Code in packages is not affected. It's protected by R's namespace mechanism #
## # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning.  #
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## ###############################################################################

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## Attaching package: 'xts'

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## Loading required package: TTR

## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

library(fpp) 

## Loading required package: forecast

## Loading required package: fma

## Loading required package: expsmooth

## Loading required package: lmtest

## Loading required package: tseries

library(fpp2)

## 
## Attaching package: 'fpp2'

## The following objects are masked from 'package:fpp':
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##     ausair, ausbeer, austa, austourists, debitcards, departures,
##     elecequip, euretail, guinearice, oil, sunspotarea, usmelec

library(forecast)
library(DMwR2)

## 
## Attaching package: 'DMwR2'

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##     sales

library(stats)  
library(psych)

## 
## Attaching package: 'psych'

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##     %+%, alpha

#Ler os arquivos das planilhas Excel.

library(readxl)
Salario_base2024 <- read_excel("Salario_base2024.xlsx")



library(readxl)
Vendas2024 <- read_excel("Vendas2024.xlsx")

vendas <- read_excel("Vendas2024.xlsx")

salario_base <- read_excel("Salario_base2024.xlsx")

venda_mes <- vendas  %>%
  group_by(ano ,mes) %>%
  summarise(venda_mensal = sum(venda_diaria)) %>%
  arrange(ano, mes)

## `summarise()` has grouped output by 'ano'. You can override using the `.groups`
## argument.

plot(venda_mes$venda_mensal)

vendas_mes_ts <- ts(venda_mes$venda_mensal, start = c(2018,1), frequency = 12)
plot(vendas_mes_ts)

decomp_vendas_mes <- decompose(vendas_mes_ts, type = 'additive')
plot(decomp_vendas_mes)

forecast(vendas_mes_ts , 6, 90)

##          Point Forecast    Lo 90    Hi 90
## Apr 2024       592128.1 508364.8 675891.4
## May 2024       677756.0 563531.7 791980.3
## Jun 2024       626941.9 488818.4 765065.3
## Jul 2024       546903.3 388445.2 705361.4
## Aug 2024       546792.1 370327.1 723257.0
## Sep 2024       543842.9 351045.7 736640.1