Data

You can find a overview of the data that is included in the textbook package fpp3 by running the following code:

data(package = "fpp3")

# A tibble: 13 × 2
   Item              Title                                                 
   <chr>             <chr>                                                 
 1 aus_accommodation Australian accommodation data                         
 2 aus_airpassengers Air Transport Passengers Australia                    
 3 aus_arrivals      International Arrivals to Australia                   
 4 bank_calls        Call volume for a large North American commercial bank
 5 boston_marathon   Boston marathon winning times since 1897              
 6 canadian_gas      Monthly Canadian gas production                       
 7 guinea_rice       Rice production (Guinea)                              
 8 insurance         Insurance quotations and advertising expenditure      
 9 prices            Price series for various commodities                  
10 souvenirs         Sales for a souvenir shop                             
11 us_change         Percentage changes in economic variables in the USA.  
12 us_employment     US monthly employment data                            
13 us_gasoline       US finished motor gasoline product supplied.

These are avaiable when the fpp3 package is loaded, i.e.

library(fpp3)
bank_calls

# A tsibble: 27,716 x 2 [5m] <UTC>
   DateTime            Calls
   <dttm>              <dbl>
 1 2003-03-03 07:00:00   111
 2 2003-03-03 07:05:00   113
 3 2003-03-03 07:10:00    76
 4 2003-03-03 07:15:00    82
 5 2003-03-03 07:20:00    91
 6 2003-03-03 07:25:00    87
 7 2003-03-03 07:30:00    75
 8 2003-03-03 07:35:00    89
 9 2003-03-03 07:40:00    99
10 2003-03-03 07:45:00   125
# ℹ 27,706 more rows

To load a specific data set explicitly in your R environment:

data("bank_calls")

Other examples used in the videos and content on this website is available for download at github.com/holleland/BAN430/tree/master/data. You should also be able to load them directly into R using the raw link:

# CPI Norway
read.csv(
  "https://raw.githubusercontent.com/holleland/BAN430/master/data/CPI_norway.csv", sep = ";") %>% head()

     X Y.avg2   Jan   Feb   Mar   Apr   May   Jun   Jul   Aug   Sep   Oct   Nov
1 2022      . 117.8 119.1 119.8 121.2 121.5 122.6 124.2 123.9 125.6     .     .
2 2021  116.1 114.1 114.9 114.6 115.0 114.9 115.3 116.3 116.3 117.5 117.2 118.1
3 2020  112.2 111.3 111.2 111.2 111.7 111.9 112.1 112.9 112.5 112.9 113.2 112.4
4 2019  110.8 109.3 110.2 110.4 110.8 110.5 110.6 111.4 110.6 111.1 111.3 111.6
5 2018  108.4 106.0 107.0 107.3 107.7 107.8 108.5 109.3 108.9 109.5 109.3 109.8
6 2017  105.5 104.3 104.7 105.0 105.2 105.4 105.8 106.1 105.3 105.9 106.0 106.1
    Dec
1     .
2 118.9
3 112.9
4 111.3
5 109.8
6 106.1

For excel files it is a bit less convenient, because you will need to download the file. But you can let R do that for you (if you insist).

loadExcel_url <- function(url) {
    temp_file <- tempfile(fileext = ".xlsx")
    download.file(url = url, destfile = temp_file, mode = "wb", quiet = TRUE)
    readxl::read_excel(temp_file)
}
loadExcel_url("https://github.com/holleland/BAN430/blob/master/data/NorwayEmployment_15-74years_bySex.xlsx?raw=true")

# A tibble: 214 × 3
   Sex   Quarter `Employed persons (1 000 persons)`
   <chr> <chr>                                <dbl>
 1 Male  1996K1                                1133
 2 Male  1996K2                                1152
 3 Male  1996K3                                1171
 4 Male  1996K4                                1161
 5 Male  1997K1                                1164
 6 Male  1997K2                                1189
 7 Male  1997K3                                1200
 8 Male  1997K4                                1189
 9 Male  1998K1                                1195
10 Male  1998K2                                1214
# ℹ 204 more rows

The code above will save the file temporary in your computers temporary folder and load it into R from there. You could also adjust the code so that it stores the file in your working directory by adjusting the function.

temp_file <- paste0(getwd(), "/NorwayEmployment.xlsx")

But the easiest will maybe be to just download the files manually from github and save them in a data folder of your own.