AULA RMARKDOWN
HUGO,ARTHUR
2024-11-14
knitr::opts_chunk$set(echo = TRUE)
library(dplyr)##
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## intersect, setdiff, setequal, union library(readr)
library(readxl)
library(writexl)
library(openxlsx)
library(lubridate)##
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## date, intersect, setdiff, union library(quantmod)## Loading required package: xts## Loading required package: zoo##
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library(AutoPlots)
library(fpp)## Loading required package: fma## Loading required package: expsmooth## Loading required package: lmtest## Loading required package: tseries##
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## insurance library(mFilter)
library(xts)
library(zoo)
library(plogr)
library(patchwork)
library(tseries)
library(quantmod)
library(fpp3)
library(forecast)
library(fpp)
library(xts)
library(zoo)
library(plogr)
Vendas2024 <- read_excel("Vendas2024.xlsx")
vendas_mes <- Vendas2024 %>%
group_by(ano, mes) %>%
summarise(venda_mensal = sum(venda_diaria)) %>%
arrange(ano, mes)## `summarise()` has grouped output by 'ano'. You can override using the `.groups`
## argument. plot(vendas_mes$venda_mensal)
vendas_mes_ts <- ts(vendas_mes$venda_mensal, start = c(2018,1), frequency = 12)
plot(vendas_mes_ts)
decomp_vendas_mes <- decompose(vendas_mes_ts, type = 'additive')
plot(decomp_vendas_mes)
forecast(vendas_mes_ts, 6, 90)## Point Forecast Lo 90 Hi 90
## Apr 2024 592128.1 508364.8 675891.4
## May 2024 677756.0 563531.7 791980.3
## Jun 2024 626941.9 488818.4 765065.3
## Jul 2024 546903.3 388445.2 705361.4
## Aug 2024 546792.1 370327.1 723257.0
## Sep 2024 543842.9 351045.7 736640.1 plot(forecast(vendas_mes_ts, 6, 90))
PROJETO FINAL
Esta aula é introdutória para o Rmarkdown.Este é o projeto final, que exemplifica uma serie temporal feita atraves de graficos.
summary(vendas_mes)## ano mes venda_mensal
## Min. :2018 Min. : 1.00 Min. : 83419
## 1st Qu.:2019 1st Qu.: 3.00 1st Qu.:163259
## Median :2021 Median : 6.00 Median :400116
## Mean :2021 Mean : 6.32 Mean :393003
## 3rd Qu.:2022 3rd Qu.: 9.00 3rd Qu.:551466
## Max. :2024 Max. :12.00 Max. :898481Including Plots
vendas_mes <- Vendas2024 %>% group_by(ano, mes) %>% summarise(venda_mensal = sum(venda_diaria)) %>% arrange(ano, mes)
plot(vendas_mes$venda_mensal)
vendas_mes_ts <- ts(vendas_mes$venda_mensal, start = c(2018,1), frequency = 12) plot(vendas_mes_ts)
decomp_vendas_mes <- decompose(vendas_mes_ts, type = ‘additive’)
plot(decomp_vendas_mes) forecast(vendas_mes_ts, 6, 90)
plot(forecast(vendas_mes_ts, 6, 90))
You can also embed plots, for example:
plot(vendas_mes)
vendas_mes <- Vendas2024 %>%
group_by(ano, mes) %>%
summarise(venda_mensal = sum(venda_diaria)) %>%
arrange(ano, mes)## `summarise()` has grouped output by 'ano'. You can override using the `.groups`
## argument. plot(vendas_mes$venda_mensal)
vendas_mes_ts <- ts(vendas_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 plot(forecast(vendas_mes_ts, 6, 90))
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.