library(tidyr)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(knitr)
library(utils)
library(ggplot2)
library(forecast)
library(readxl)
library(fpp2)
## Loading required package: fma
## Loading required package: expsmooth

2.1

Use the help function to explore what the series gold, woolyrnq and gas represent.

Use autoplot() to plot each of these in separate plots. What is the frequency of each series? Hint: apply the frequency() function. Use which.max() to spot the outlier in the gold series. Which observation was it?

autoplot(gold)+ggtitle("Autoplot of Gold")
## Warning in is.na(main): is.na() applied to non-(list or vector) of type
## 'NULL'

autoplot(woolyrnq)+ggtitle("Autoplot of Woolyrnq")
## Warning in is.na(main): is.na() applied to non-(list or vector) of type
## 'NULL'

autoplot(gas)+ggtitle("Autoplot of Gas")
## Warning in is.na(main): is.na() applied to non-(list or vector) of type
## 'NULL'

paste("Gold Frequency =",frequency(gold))
## [1] "Gold Frequency = 1"
paste("Woolyrnq Frequency =",frequency(woolyrnq))
## [1] "Woolyrnq Frequency = 4"
paste("Gas Frequency =",frequency(gas))
## [1] "Gas Frequency = 12"
paste("Gold Max =",which.max(gold))
## [1] "Gold Max = 770"
paste("Woolyrnq Max =",which.max(woolyrnq))
## [1] "Woolyrnq Max = 21"
paste("Gas Max =",which.max(gas))
## [1] "Gas Max = 475"

2.2

tute1 <- read.csv("tute1.xls", header=TRUE)
head(tute1)
##        X  Sales AdBudget   GDP
## 1 Mar-81 1020.2    659.2 251.8
## 2 Jun-81  889.2    589.0 290.9
## 3 Sep-81  795.0    512.5 290.8
## 4 Dec-81 1003.9    614.1 292.4
## 5 Mar-82 1057.7    647.2 279.1
## 6 Jun-82  944.4    602.0 254.0
mytimeseries <- ts(tute1[,-1], start=1981, frequency=4)
autoplot(mytimeseries, facets=TRUE)
## Warning in is.na(main): is.na() applied to non-(list or vector) of type
## 'NULL'

3.3

Read Retail Data

retaildata <- readxl::read_excel("retail.xlsx", skip=1)
head(retaildata)
## # A tibble: 6 x 190
##   `Series ID`         A3349335T A3349627V A3349338X A3349398A A3349468W
##   <dttm>                  <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
## 1 1982-04-01 00:00:00      303.      41.7      63.9      409.      65.8
## 2 1982-05-01 00:00:00      298.      43.1      64        405.      65.8
## 3 1982-06-01 00:00:00      298       40.3      62.7      401       62.3
## 4 1982-07-01 00:00:00      308.      40.9      65.6      414.      68.2
## 5 1982-08-01 00:00:00      299.      42.1      62.6      404.      66  
## 6 1982-09-01 00:00:00      305.      42        64.4      412.      62.3
## # ... with 184 more variables: A3349336V <dbl>, A3349337W <dbl>,
## #   A3349397X <dbl>, A3349399C <dbl>, A3349874C <dbl>, A3349871W <dbl>,
## #   A3349790V <dbl>, A3349556W <dbl>, A3349791W <dbl>, A3349401C <dbl>,
## #   A3349873A <dbl>, A3349872X <dbl>, A3349709X <dbl>, A3349792X <dbl>,
## #   A3349789K <dbl>, A3349555V <dbl>, A3349565X <dbl>, A3349414R <dbl>,
## #   A3349799R <dbl>, A3349642T <dbl>, A3349413L <dbl>, A3349564W <dbl>,
## #   A3349416V <dbl>, A3349643V <dbl>, A3349483V <dbl>, A3349722T <dbl>,
## #   A3349727C <dbl>, A3349641R <dbl>, A3349639C <dbl>, A3349415T <dbl>,
## #   A3349349F <dbl>, A3349563V <dbl>, A3349350R <dbl>, A3349640L <dbl>,
## #   A3349566A <dbl>, A3349417W <dbl>, A3349352V <dbl>, A3349882C <dbl>,
## #   A3349561R <dbl>, A3349883F <dbl>, A3349721R <dbl>, A3349478A <dbl>,
## #   A3349637X <dbl>, A3349479C <dbl>, A3349797K <dbl>, A3349477X <dbl>,
## #   A3349719C <dbl>, A3349884J <dbl>, A3349562T <dbl>, A3349348C <dbl>,
## #   A3349480L <dbl>, A3349476W <dbl>, A3349881A <dbl>, A3349410F <dbl>,
## #   A3349481R <dbl>, A3349718A <dbl>, A3349411J <dbl>, A3349638A <dbl>,
## #   A3349654A <dbl>, A3349499L <dbl>, A3349902A <dbl>, A3349432V <dbl>,
## #   A3349656F <dbl>, A3349361W <dbl>, A3349501L <dbl>, A3349503T <dbl>,
## #   A3349360V <dbl>, A3349903C <dbl>, A3349905J <dbl>, A3349658K <dbl>,
## #   A3349575C <dbl>, A3349428C <dbl>, A3349500K <dbl>, A3349577J <dbl>,
## #   A3349433W <dbl>, A3349576F <dbl>, A3349574A <dbl>, A3349816F <dbl>,
## #   A3349815C <dbl>, A3349744F <dbl>, A3349823C <dbl>, A3349508C <dbl>,
## #   A3349742A <dbl>, A3349661X <dbl>, A3349660W <dbl>, A3349909T <dbl>,
## #   A3349824F <dbl>, A3349507A <dbl>, A3349580W <dbl>, A3349825J <dbl>,
## #   A3349434X <dbl>, A3349822A <dbl>, A3349821X <dbl>, A3349581X <dbl>,
## #   A3349908R <dbl>, A3349743C <dbl>, A3349910A <dbl>, A3349435A <dbl>,
## #   A3349365F <dbl>, A3349746K <dbl>, ...

Set as Time Series

myts <- ts(retaildata[,"A3349338X"],
  frequency=12, start=c(1982,4))

Autoplot

autoplot(myts)+ggtitle("Autoplot of A3349338X Sales")
## Warning in is.na(main): is.na() applied to non-(list or vector) of type
## 'NULL'

ggseasonplot(myts)+ggtitle("Seasonal Plot of A3349338X Sales")
## Warning in is.na(ylab): is.na() applied to non-(list or vector) of type
## 'NULL'

ggsubseriesplot(myts)+ggtitle("Subseries of A3349338X Sales")

gglagplot(myts)+ggtitle("Lag Plot of A3349338X Sales")
## Warning in is.na(xlab): is.na() applied to non-(list or vector) of type
## 'NULL'
## Warning in is.na(ylab): is.na() applied to non-(list or vector) of type
## 'NULL'

ggAcf(myts)+ggtitle("Autocorrelation Function of A3349338X Sales")