title: “Time series R Notebook” output: html_notebook
Creating a time series The ts() function will convert a numeric vector into an R time series object. The format is ts(vector, start=, end=, frequency=) where start and end are the times of the first and last observation and frequency is the number of observations per unit time (1=annual, 4=quartly, 12=monthly, etc.).
the file http://robjhyndman.com/tsdldata/data/fancy.dat contains monthly sales for a souvenir shop at a beach resort town in Queensland, Australia, for January 1987-December 1993 (original data from Wheelwright and Hyndman, 1998). We can read the data into R by typing:
#install.packages("readr)
library(readr)## Warning: package 'readr' was built under R version 4.2.3fancy_dat <- read_csv("fancy.dat.txt")## Rows: 84 Columns: 1
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (1): monthly_sales
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(fancy_dat)## # A tibble: 6 × 1
## monthly_sales
## <dbl>
## 1 1665.
## 2 2398.
## 3 2841.
## 4 3547.
## 5 3753.
## 6 3715.souvenirtimeseries <- ts(fancy_dat, frequency=12, start=c(1987,1))
souvenirtimeseries## Jan Feb Mar Apr May Jun Jul
## 1987 1664.81 2397.53 2840.71 3547.29 3752.96 3714.74 4349.61
## 1988 2499.81 5198.24 7225.14 4806.03 5900.88 4951.34 6179.12
## 1989 4717.02 5702.63 9957.58 5304.78 6492.43 6630.80 7349.62
## 1990 5921.10 5814.58 12421.25 6369.77 7609.12 7224.75 8121.22
## 1991 4826.64 6470.23 9638.77 8821.17 8722.37 10209.48 11276.55
## 1992 7615.03 9849.69 14558.40 11587.33 9332.56 13082.09 16732.78
## 1993 10243.24 11266.88 21826.84 17357.33 15997.79 18601.53 26155.15
## Aug Sep Oct Nov Dec
## 1987 3566.34 5021.82 6423.48 7600.60 19756.21
## 1988 4752.15 5496.43 5835.10 12600.08 28541.72
## 1989 8176.62 8573.17 9690.50 15151.84 34061.01
## 1990 7979.25 8093.06 8476.70 17914.66 30114.41
## 1991 12552.22 11637.39 13606.89 21822.11 45060.69
## 1992 19888.61 23933.38 25391.35 36024.80 80721.71
## 1993 28586.52 30505.41 30821.33 46634.38 104660.67 plot.ts(souvenirtimeseries)
Add a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.
kings_dat <- read_csv("kings.dat.txt")## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)## Rows: 44 Columns: 1
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Age of Death of Successive Kings of England
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(kings_dat)## # A tibble: 6 × 1
## `Age of Death of Successive Kings of England`
## <chr>
## 1 "#starting with William the Conqueror"
## 2 "#Source: McNeill, \"Interactive Data Analysis\""
## 3 "60"
## 4 "43"
## 5 "67"
## 6 "50"kingstimeseries <- ts(kings_dat)
kingstimeseries## Time Series:
## Start = 1
## End = 44
## Frequency = 1
## Age of Death of Successive Kings of England
## [1,] 2
## [2,] 1
## [3,] 20
## [4,] 10
## [5,] 22
## [6,] 14
## [7,] 18
## [8,] 9
## [9,] 14
## [10,] 21
## [11,] 23
## [12,] 10
## [13,] 21
## [14,] 6
## [15,] 11
## [16,] 6
## [17,] 13
## [18,] 8
## [19,] 3
## [20,] 7
## [21,] 16
## [22,] 18
## [23,] 4
## [24,] 10
## [25,] 24
## [26,] 19
## [27,] 12
## [28,] 19
## [29,] 29
## [30,] 17
## [31,] 23
## [32,] 15
## [33,] 5
## [34,] 13
## [35,] 22
## [36,] 27
## [37,] 28
## [38,] 22
## [39,] 26
## [40,] 28
## [41,] 23
## [42,] 25
## [43,] 27
## [44,] 18 plot(kingstimeseries)