diff --git a/R_utils/model_eval.R b/R_utils/model_eval.R index a321ad58..5a919813 100644 --- a/R_utils/model_eval.R +++ b/R_utils/model_eval.R @@ -14,13 +14,13 @@ get_forecasts <- function(mable, newdata, ...) keyvars <- key_vars(fcast) keyvars <- keyvars[-length(keyvars)] indexvar <- index_var(fcast) - fcastvar <- as.character(attr(fcast, "response")[[1]]) + fcastvar <- names(fcast)[length(keyvars) + 3] fcast <- fcast %>% as_tibble() %>% pivot_wider( id_cols=all_of(c(keyvars, indexvar)), names_from=.model, - values_from=all_of(fcastvar)) + values_from=.mean) select(newdata, !!keyvars, !!indexvar, !!fcastvar) %>% rename(.response=!!fcastvar) %>% inner_join(fcast) diff --git a/examples/grocery_sales/R/01_dataprep.Rmd b/examples/grocery_sales/R/01_dataprep.Rmd index 05cd7d02..4e16e70c 100644 --- a/examples/grocery_sales/R/01_dataprep.Rmd +++ b/examples/grocery_sales/R/01_dataprep.Rmd @@ -69,6 +69,7 @@ library(ggplot2) oj_data %>% filter(store < 25, brand < 5) %>% + mutate(week=as.Date(week)) %>% ggplot(aes(x=week, y=logmove)) + geom_line() + scale_x_date(labels=NULL) + diff --git a/examples/grocery_sales/R/01_dataprep.nb.html b/examples/grocery_sales/R/01_dataprep.nb.html index bf37aa54..ee20fbda 100644 --- a/examples/grocery_sales/R/01_dataprep.nb.html +++ b/examples/grocery_sales/R/01_dataprep.nb.html @@ -332,11 +332,12 @@ oj_data <- orangeJuice$yx %>% - + 
library(ggplot2)
 
 oj_data %>%
     filter(store < 25, brand < 5) %>%
+    mutate(week=as.Date(week)) %>%
     ggplot(aes(x=week, y=logmove)) +
         geom_line() +
         scale_x_date(labels=NULL) +
@@ -380,7 +381,7 @@ head(oj_train[[1]])
-
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
+
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
diff --git a/examples/grocery_sales/R/02_basic_models.Rmd b/examples/grocery_sales/R/02_basic_models.Rmd index c12fc0e0..73702f20 100644 --- a/examples/grocery_sales/R/02_basic_models.Rmd +++ b/examples/grocery_sales/R/02_basic_models.Rmd @@ -53,7 +53,8 @@ oj_fcast_basic <- parallel::clusterMap(cl, get_forecasts, oj_modelset_basic, oj_ save_objects(oj_modelset_basic, oj_fcast_basic, example="grocery_sales", file="model_basic.Rdata") -do.call(rbind, oj_fcast_basic) %>% +oj_fcast_basic %>% + bind_rows() %>% mutate_at(-(1:3), exp) %>% eval_forecasts() ``` @@ -80,7 +81,8 @@ destroy_cluster(cl) save_objects(oj_modelset_ets, oj_fcast_ets, example="grocery_sales", file="model_ets.Rdata") -do.call(rbind, oj_fcast_ets) %>% +oj_fcast_ets %>% + bind_rows() %>% mutate_at(-(1:3), exp) %>% eval_forecasts() ``` diff --git a/examples/grocery_sales/R/02_basic_models.nb.html b/examples/grocery_sales/R/02_basic_models.nb.html index 6f91ee65..d0c9bfe8 100644 --- a/examples/grocery_sales/R/02_basic_models.nb.html +++ b/examples/grocery_sales/R/02_basic_models.nb.html @@ -247,7 +247,7 @@ summary {

This lets us speed up the training significantly. While the fable::model function can fit multiple models in parallel, we will run it sequentially here and instead parallelise by dataset. This avoids contention for cores, and also results in the simplest code. As a guard against returning invalid results, we also specify the argument .safely=FALSE; this forces model to throw an error if a model algorithm fails.

- + 
srcdir <- here::here("R_utils")
 for(src in dir(srcdir, full.names=TRUE)) source(src)
 
@@ -270,7 +270,8 @@ oj_fcast_basic <- parallel::clusterMap(cl, get_forecasts, oj_modelset_basic,
 save_objects(oj_modelset_basic, oj_fcast_basic,
              example="grocery_sales", file="model_basic.Rdata")
 
-do.call(rbind, oj_fcast_basic) %>%
+oj_fcast_basic %>%
+    bind_rows() %>%
     mutate_at(-(1:3), exp) %>%
     eval_forecasts()
@@ -285,7 +286,7 @@ do.call(rbind, oj_fcast_basic) %>%

Having fit some basic models, we can also try an exponential smoothing model, fit using the ETS function. Unlike the others, ETS does not currently support time series with missing values; we therefore have to use one of the other models to impute missing values first via the interpolate function.

- + 
oj_modelset_ets <- parallel::clusterMap(cl, function(df, basicmod)
 {
     df %>%
@@ -303,7 +304,8 @@ destroy_cluster(cl)
 save_objects(oj_modelset_ets, oj_fcast_ets,
              example="grocery_sales", file="model_ets.Rdata")
 
-do.call(rbind, oj_fcast_ets) %>%
+oj_fcast_ets %>%
+    bind_rows() %>%
     mutate_at(-(1:3), exp) %>%
     eval_forecasts()
@@ -317,7 +319,7 @@ do.call(rbind, oj_fcast_ets) %>%

The ETS model does worse than the ARIMA model, something that should not be a surprise given the lack of strong seasonality and trend in this dataset. We conclude that any simple univariate approach is unlikely to do well.

-
LS0tCnRpdGxlOiBCYXNpYyBtb2RlbHMKb3V0cHV0OiBodG1sX25vdGVib29rCi0tLQoKX0NvcHlyaWdodCAoYykgTWljcm9zb2Z0IENvcnBvcmF0aW9uLl88YnIvPgpfTGljZW5zZWQgdW5kZXIgdGhlIE1JVCBMaWNlbnNlLl8KCmBgYHtyLCBlY2hvPUZBTFNFLCByZXN1bHRzPSJoaWRlIiwgbWVzc2FnZT1GQUxTRX0KbGlicmFyeSh0aWR5cikKbGlicmFyeShkcGx5cikKbGlicmFyeSh0c2liYmxlKQpsaWJyYXJ5KGZlYXN0cykKbGlicmFyeShmYWJsZSkKYGBgCgpXZSBmaXQgc29tZSBzaW1wbGUgbW9kZWxzIHRvIHRoZSBvcmFuZ2UganVpY2UgZGF0YSBmb3IgaWxsdXN0cmF0aXZlIHB1cnBvc2VzLiBIZXJlLCBlYWNoIG1vZGVsIGlzIGFjdHVhbGx5IGEgX2dyb3VwXyBvZiBtb2RlbHMsIG9uZSBmb3IgZWFjaCBjb21iaW5hdGlvbiBvZiBzdG9yZSBhbmQgYnJhbmQuIFRoaXMgaXMgdGhlIHN0YW5kYXJkIGFwcHJvYWNoIHRha2VuIGluIHN0YXRpc3RpY2FsIGZvcmVjYXN0aW5nLCBhbmQgaXMgc3VwcG9ydGVkIG91dC1vZi10aGUtYm94IGJ5IHRoZSB0aWR5dmVydHMgZnJhbWV3b3JrLgoKLSBgbWVhbmA6IFRoaXMgaXMganVzdCBhIHNpbXBsZSBtZWFuLgotIGBuYWl2ZWA6IEEgcmFuZG9tIHdhbGsgbW9kZWwgd2l0aG91dCBhbnkgb3RoZXIgY29tcG9uZW50cy4gVGhpcyBhbW91bnRzIHRvIHNldHRpbmcgYWxsIGZvcmVjYXN0IHZhbHVlcyB0byB0aGUgbGFzdCBvYnNlcnZlZCB2YWx1ZS4KLSBgZHJpZnRgOiBUaGlzIGFkanVzdHMgdGhlIGBuYWl2ZWAgbW9kZWwgdG8gaW5jb3Jwb3JhdGUgYSBzdHJhaWdodC1saW5lIHRyZW5kLgotIGBhcmltYWA6IEFuIEFSSU1BIG1vZGVsIHdpdGggdGhlIHBhcmFtZXRlciB2YWx1ZXMgZXN0aW1hdGVkIGZyb20gdGhlIGRhdGEuCgpOb3RlIHRoYXQgdGhlIG1vZGVsIHRyYWluaW5nIHByb2Nlc3MgaXMgZW1iYXJyYXNzaW5nbHkgcGFyYWxsZWwgb24gMyBsZXZlbHM6CgotIFdlIGhhdmUgbXVsdGlwbGUgaW5kZXBlbmRlbnQgdHJhaW5pbmcgZGF0YXNldHM7Ci0gRm9yIHdoaWNoIHdlIGZpdCBtdWx0aXBsZSBpbmRlcGVuZGVudCBtb2RlbHM7Ci0gV2l0aGluIHdoaWNoIHdlIGhhdmUgaW5kZXBlbmRlbnQgc3ViLW1vZGVscyBmb3IgZWFjaCBzdG9yZSBhbmQgYnJhbmQuCgpUaGlzIGxldHMgdXMgc3BlZWQgdXAgdGhlIHRyYWluaW5nIHNpZ25pZmljYW50bHkuIFdoaWxlIHRoZSBgZmFibGU6Om1vZGVsYCBmdW5jdGlvbiBjYW4gZml0IG11bHRpcGxlIG1vZGVscyBpbiBwYXJhbGxlbCwgd2Ugd2lsbCBydW4gaXQgc2VxdWVudGlhbGx5IGhlcmUgYW5kIGluc3RlYWQgcGFyYWxsZWxpc2UgYnkgZGF0YXNldC4gVGhpcyBhdm9pZHMgY29udGVudGlvbiBmb3IgY29yZXMsIGFuZCBhbHNvIHJlc3VsdHMgaW4gdGhlIHNpbXBsZXN0IGNvZGUuIEFzIGEgZ3VhcmQgYWdhaW5zdCByZXR1cm5pbmcgaW52YWxpZCByZXN1bHRzLCB3ZSBhbHNvIHNwZWNpZnkgdGhlIGFyZ3VtZW50IGAuc2FmZWx5PUZBTFNFYDsgdGhpcyBmb3JjZXMgYG1vZGVsYCB0byB0aHJvdyBhbiBlcnJvciBpZiBhIG1vZGVsIGFsZ29yaXRobSBmYWlscy4KCmBgYHtyfQpzcmNkaXIgPC0gaGVyZTo6aGVyZSgiUl91dGlscyIpCmZvcihzcmMgaW4gZGlyKHNyY2RpciwgZnVsbC5uYW1lcz1UUlVFKSkgc291cmNlKHNyYykKCmxvYWRfb2JqZWN0cygiZ3JvY2VyeV9zYWxlcyIsICJkYXRhLlJkYXRhIikKCmNsIDwtIG1ha2VfY2x1c3RlcihsaWJzPWMoInRpZHlyIiwgImRwbHlyIiwgImZhYmxlIiwgInRzaWJibGUiLCAiZmVhc3RzIikpCgpval9tb2RlbHNldF9iYXNpYyA8LSBwYXJhbGxlbDo6cGFyTGFwcGx5KGNsLCBval90cmFpbiwgZnVuY3Rpb24oZGYpCnsKICAgIG1vZGVsKGRmLAogICAgICAgIG1lYW49TUVBTihsb2dtb3ZlKSwKICAgICAgICBuYWl2ZT1OQUlWRShsb2dtb3ZlKSwKICAgICAgICBkcmlmdD1SVyhsb2dtb3ZlIH4gZHJpZnQoKSksCiAgICAgICAgYXJpbWE9QVJJTUEobG9nbW92ZSB+IHBkcSgpICsgUERRKDAsIDAsIDApKSwKICAgICAgICAuc2FmZWx5PUZBTFNFCiAgICApCn0pCm9qX2ZjYXN0X2Jhc2ljIDwtIHBhcmFsbGVsOjpjbHVzdGVyTWFwKGNsLCBnZXRfZm9yZWNhc3RzLCBval9tb2RlbHNldF9iYXNpYywgb2pfdGVzdCkKCnNhdmVfb2JqZWN0cyhval9tb2RlbHNldF9iYXNpYywgb2pfZmNhc3RfYmFzaWMsCiAgICAgICAgICAgICBleGFtcGxlPSJncm9jZXJ5X3NhbGVzIiwgZmlsZT0ibW9kZWxfYmFzaWMuUmRhdGEiKQoKZG8uY2FsbChyYmluZCwgb2pfZmNhc3RfYmFzaWMpICU+JQogICAgbXV0YXRlX2F0KC0oMTozKSwgZXhwKSAlPiUKICAgIGV2YWxfZm9yZWNhc3RzKCkKYGBgCgpUaGUgQVJJTUEgbW9kZWwgZG9lcyB0aGUgYmVzdCBvZiB0aGUgc2ltcGxlIG1vZGVscywgYnV0IG5vdCBhbnkgYmV0dGVyIHRoYW4gYSBzaW1wbGUgbWVhbi4KCkhhdmluZyBmaXQgc29tZSBiYXNpYyBtb2RlbHMsIHdlIGNhbiBhbHNvIHRyeSBhbiBleHBvbmVudGlhbCBzbW9vdGhpbmcgbW9kZWwsIGZpdCB1c2luZyB0aGUgYEVUU2AgZnVuY3Rpb24uIFVubGlrZSB0aGUgb3RoZXJzLCBgRVRTYCBkb2VzIG5vdCBjdXJyZW50bHkgc3VwcG9ydCB0aW1lIHNlcmllcyB3aXRoIG1pc3NpbmcgdmFsdWVzOyB3ZSB0aGVyZWZvcmUgaGF2ZSB0byB1c2Ugb25lIG9mIHRoZSBvdGhlciBtb2RlbHMgdG8gaW1wdXRlIG1pc3NpbmcgdmFsdWVzIGZpcnN0IHZpYSB0aGUgYGludGVycG9sYXRlYCBmdW5jdGlvbi4KCmBgYHtyfQpval9tb2RlbHNldF9ldHMgPC0gcGFyYWxsZWw6OmNsdXN0ZXJNYXAoY2wsIGZ1bmN0aW9uKGRmLCBiYXNpY21vZCkKewogICAgZGYgJT4lCiAgICAgICAgaW50ZXJwb2xhdGUob2JqZWN0PXNlbGVjdChiYXNpY21vZCwgLWMobWVhbiwgbmFpdmUsIGRyaWZ0KSkpICU+JQogICAgICAgIG1vZGVsKAogICAgICAgICAgICBldHM9RVRTKGxvZ21vdmUgfiBlcnJvcigiQSIpICsgdHJlbmQoIkEiKSArIHNlYXNvbigiTiIpKSwKICAgICAgICAgICAgLnNhZmVseT1GQUxTRQogICAgICAgICkKfSwgb2pfdHJhaW4sIG9qX21vZGVsc2V0X2Jhc2ljKQoKb2pfZmNhc3RfZXRzIDwtIHBhcmFsbGVsOjpjbHVzdGVyTWFwKGNsLCBnZXRfZm9yZWNhc3RzLCBval9tb2RlbHNldF9ldHMsIG9qX3Rlc3QpCgpkZXN0cm95X2NsdXN0ZXIoY2wpCgpzYXZlX29iamVjdHMob2pfbW9kZWxzZXRfZXRzLCBval9mY2FzdF9ldHMsCiAgICAgICAgICAgICBleGFtcGxlPSJncm9jZXJ5X3NhbGVzIiwgZmlsZT0ibW9kZWxfZXRzLlJkYXRhIikKCmRvLmNhbGwocmJpbmQsIG9qX2ZjYXN0X2V0cykgJT4lCiAgICBtdXRhdGVfYXQoLSgxOjMpLCBleHApICU+JQogICAgZXZhbF9mb3JlY2FzdHMoKQpgYGAKClRoZSBFVFMgbW9kZWwgZG9lcyBfd29yc2VfIHRoYW4gdGhlIEFSSU1BIG1vZGVsLCBzb21ldGhpbmcgdGhhdCBzaG91bGQgbm90IGJlIGEgc3VycHJpc2UgZ2l2ZW4gdGhlIGxhY2sgb2Ygc3Ryb25nIHNlYXNvbmFsaXR5IGFuZCB0cmVuZCBpbiB0aGlzIGRhdGFzZXQuIFdlIGNvbmNsdWRlIHRoYXQgYW55IHNpbXBsZSB1bml2YXJpYXRlIGFwcHJvYWNoIGlzIHVubGlrZWx5IHRvIGRvIHdlbGwuCg==
+
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
diff --git a/examples/grocery_sales/R/02a_reg_models.Rmd b/examples/grocery_sales/R/02a_reg_models.Rmd index a2d790a3..19fbfa42 100644 --- a/examples/grocery_sales/R/02a_reg_models.Rmd +++ b/examples/grocery_sales/R/02a_reg_models.Rmd @@ -79,7 +79,8 @@ destroy_cluster(cl) save_objects(oj_modelset_reg, oj_fcast_reg, example="grocery_sales", file="model_reg.Rdata") -do.call(rbind, oj_fcast_reg) %>% +oj_fcast_reg %>% + bind_rows() %>% mutate_at(-(1:3), exp) %>% eval_forecasts() ``` diff --git a/examples/grocery_sales/R/02a_reg_models.nb.html b/examples/grocery_sales/R/02a_reg_models.nb.html index dea3ef98..c50c4b31 100644 --- a/examples/grocery_sales/R/02a_reg_models.nb.html +++ b/examples/grocery_sales/R/02a_reg_models.nb.html @@ -4,228 +4,194 @@ - - - - - - - ARIMA-Regression models - - - - - - - - - - - - - - - - - - - - - - + + + - - - + + + - - - .tabset-dropdown>.nav-tabs>li.active:before { - content: ""; - font-family: 'Glyphicons Halflings'; - display: inline-block; - padding: 10px; - border-right: 1px solid #ddd; - } - .tabset-dropdown>.nav-tabs.nav-tabs-open>li.active:before { - content: ""; - border: none; - } - .tabset-dropdown>.nav-tabs.nav-tabs-open:before { - content: ""; - font-family: 'Glyphicons Halflings'; - display: inline-block; - padding: 10px; - border-right: 1px solid #ddd; - } - .tabset-dropdown>.nav-tabs>li.active { - display: block; - } + - .tabset-dropdown>.nav-tabs>li>a, - .tabset-dropdown>.nav-tabs>li>a:focus, - .tabset-dropdown>.nav-tabs>li>a:hover { - border: none; - display: inline-block; - border-radius: 4px; - background-color: transparent; - } + +.kable-table table>thead>tr>th { + border: none; + border-bottom: 2px solid #dddddd; +} - - +.kable-table table>thead { + background-color: #fff; +} + + + + + + + + + @@ -235,55 +201,48 @@ -
+
- - -

Copyright (c) Microsoft Corporation.
Licensed under the MIT License.

- - - - -

This notebook builds on the output from “Basic models” by including regressor variables in the ARIMA model(s). We - fit the following model types:

- -

As part of the modelling, we also compute a new independent variable maxpricediff, the log-ratio of - the price of this brand compared to the best competing price. A positive maxpricediff means this - brand is cheaper than all the other brands, and a negative maxpricediff means it is more expensive. -

- - - - 
srcdir <- here::here("R_utils")
+
+
Copyright (c) Microsoft Corporation.
 Licensed under the MIT License.

+
+
+
+
+
This notebook builds on the output from “Basic models” by including regressor variables in the ARIMA model(s). We fit the following model types:

+
    
+
  • ar_trend includes only a linear trend over time.
    • 
+
  • ar_reg allows stepwise selection of independent regressors.
    • 
+
  • ar_reg_price: rather than allowing the algorithm to select from the 11 price variables, we use only the price relevant to each brand. This is to guard against possible overfitting, something that classical stepwise procedures are wont to do.
    • 
+
  • ar_reg_price_trend is the same as ar_reg_price, but including a linear trend.
    • 
+

+
As part of the modelling, we also compute a new independent variable maxpricediff, the log-ratio of the price of this brand compared to the best competing price. A positive maxpricediff means this brand is cheaper than all the other brands, and a negative maxpricediff means it is more expensive.

+
+
+
+
srcdir <- here::here("R_utils")
 for(src in dir(srcdir, full.names=TRUE)) source(src)
 
 load_objects("grocery_sales", "data.Rdata")
@@ -337,87 +296,83 @@ destroy_cluster(cl)
 save_objects(oj_modelset_reg, oj_fcast_reg,
              example="grocery_sales", file="model_reg.Rdata")
 
-do.call(rbind, oj_fcast_reg) %>%
+oj_fcast_reg %>%
+    bind_rows() %>%
     mutate_at(-(1:3), exp) %>%
     eval_forecasts()

-    
-    

-      
-    

-    
-    
-    
This shows that the models incorporating price are a significant improvement over the previous naive models. The
-      model that uses stepwise selection to choose the best price variable does worse than the one where we choose the
-      price beforehand, confirming the suspicion that stepwise leads to overfitting in this case.

-    
+
+ + +

This shows that the models incorporating price are a significant improvement over the previous naive models. The model that uses stepwise selection to choose the best price variable does worse than the one where we choose the price beforehand, confirming the suspicion that stepwise leads to overfitting in this case.

+ -
- 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 -
+
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
-
+ - + - + - +$(document).ready(function () { + $('.tabset-dropdown > .nav-tabs > li').click(function () { + $(this).parent().toggleClass('nav-tabs-open') + }); +}); + - - + + - - + + - - \ No newline at end of file + diff --git a/examples/grocery_sales/R/02b_prophet_models.Rmd b/examples/grocery_sales/R/02b_prophet_models.Rmd index 13d33f94..35754ecc 100644 --- a/examples/grocery_sales/R/02b_prophet_models.Rmd +++ b/examples/grocery_sales/R/02b_prophet_models.Rmd @@ -64,7 +64,8 @@ destroy_cluster(cl) save_objects(oj_modelset_pr, oj_fcast_pr, example="grocery_sales", file="model_pr.Rdata") -do.call(rbind, oj_fcast_pr) %>% +oj_fcast_pr %>% + bind_rows() %>% mutate_at(-(1:3), exp) %>% eval_forecasts() ``` diff --git a/examples/grocery_sales/R/02b_prophet_models.nb.html b/examples/grocery_sales/R/02b_prophet_models.nb.html index 4bf9ae60..7ec4e08c 100644 --- a/examples/grocery_sales/R/02b_prophet_models.nb.html +++ b/examples/grocery_sales/R/02b_prophet_models.nb.html @@ -238,7 +238,7 @@ summary {

Here, we will use the fable.prophet package which provides a tidyverts frontend to the prophet package itself. As with ETS, prophet does not support time series with missing values, so we again impute them using the ARIMA model forecasts.

- + 
srcdir <- here::here("R_utils")
 for(src in dir(srcdir, full.names=TRUE)) source(src)
 
@@ -275,13 +275,14 @@ destroy_cluster(cl)
 save_objects(oj_modelset_pr, oj_fcast_pr,
              example="grocery_sales", file="model_pr.Rdata")
 
-do.call(rbind, oj_fcast_pr) %>%
+oj_fcast_pr %>%
+    bind_rows() %>%
     mutate_at(-(1:3), exp) %>%
     eval_forecasts()
@@ -289,7 +290,7 @@ do.call(rbind, oj_fcast_pr) %>%

It appears that Prophet does not do better than the simple ARIMA model with regression variables. This is possibly because the dataset does not have a strong time series nature: there is no seasonality, and only weak or nonexistent trends. These are features which the Prophet algorithm is designed to detect, and their absence means that there would be little advantage in using it.

-
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
+
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
diff --git a/examples/retail_turnover/01_explore.nb.html b/examples/retail_turnover/01_explore.nb.html index 862fde50..0fa8e493 100644 --- a/examples/retail_turnover/01_explore.nb.html +++ b/examples/retail_turnover/01_explore.nb.html @@ -236,12 +236,33 @@ summary {

Let’s look at how to do some simple summaries. We’ll use the aus_retail dataset, which contains retail turnover statistics by (Australian) state and industry, going back to 1982. This is part of the tsibbledata package, which contains several example time series datasets.

- -
library(dplyr)
-library(tsibbledata)
+
+
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(tsibbledata)
 library(tsibble)
-library(feasts)
-library(fable)
+library(feasts)

+
+
+
Loading required package: fabletools

+
+
+
library(fable)
 
 slice(aus_retail, 1:6)

 
@@ -303,7 +324,7 @@ Industry
@@ -317,7 +338,7 @@ Industry
diff --git a/examples/retail_turnover/02_model.nb.html b/examples/retail_turnover/02_model.nb.html index 6cc10ce8..da7163fc 100644 --- a/examples/retail_turnover/02_model.nb.html +++ b/examples/retail_turnover/02_model.nb.html @@ -290,7 +290,7 @@ fcasts %>% theme(legend.position="bottom") -

+

diff --git a/examples/retail_turnover/README.md b/examples/retail_turnover/README.md index c9a24a93..d3f5a53b 100644 --- a/examples/retail_turnover/README.md +++ b/examples/retail_turnover/README.md @@ -21,24 +21,13 @@ The following packages and their dependencies are needed to run the notebooks in | Tidyverse | dplyr, tidyr, ggplot2 | | Tidyverts | tsibble, tsibbledata, fabletools, fable, feasts | | Future | future, future.apply | -| Other | urca, rmarkdown, distributional, devtools (see below) | - -It's likely that you will already have many of these (particularly the Tidyverse packages) installed. However, currently (June 2020) the notebooks do require the _development_ versions of the Tidyverts packages; these can be installed from GitHub using the devtools package. +| Other | urca, rmarkdown | ```r -install.packages("tidyverse") +install.packages("tidyverse") # installs all Tidyverse packages install.packages(c("future", "future.apply")) install.packages(c("rmarkdown", "urca")) - -# install Tidyverts packages from GitHub -install.packages("devtools") - -devtools::install_github("tidyverts/tsibble@a19cda281c3f1e0061b5b0de93b059c52052ebda") -devtools::install_github("tidyverts/tsibbledata@b06a965b788722157a149296c47f821c99cc41f0") -devtools::install_github("mitchelloharawild/distributional@e668b520b415f417f71eacd7e1e940561eecffd6") -devtools::install_github("tidyverts/fabletools@864b2daa983446017f2ed757a3b8889b935cc2cb") -devtools::install_github("tidyverts/fable@d2600c151fde1609cc491d8f94bd136c71f87523") -devtools::install_github("tidyverts/feasts@f006746effa10bc223479441ebede136ca016b11") +install.packages(c("tsibble", "tsibbledata", "fabletools", "fable", "feasts")) ``` ## Acknowledgements