_pkgdown/index.md
In rafzamb/sknifedatar: Swiss Knife of Data
sknifedatar 📦 “Swiss Knife of Data” 
Serves primarily as an extension to the
modeltime 📦
ecosystem. In addition to some functionalities of spatial data and
visualization.
Installation
CRAN version:
install.packages("sknifedatar")
Or install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("rafzamb/sknifedatar")
Features:
- multifit: multiple models into multiple time series (no panel).
- workflowsets: worklowset over a time
series.
- workflowset multifit: worklowset over multiple time series (no
panel).
- automagic tabs: automatic generation of Tabs in Distill/Rmarkdown
files.
- sliding windows: data partitioning in sliding
windows.
Multiple models on multiple series functions 📈
- libraries
library(modeltime)
library(rsample)
library(parsnip)
library(recipes)
library(workflows)
library(dplyr)
library(tidyr)
library(sknifedatar)
- Data
data("emae_series")
nested_serie = emae_series %>% filter(date < '2020-02-01') %>% nest(nested_column=-sector)
nested_serie
#> # A tibble: 16 x 2
#> sector nested_column
#> <chr> <list>
#> 1 Comercio <tibble [193 × 2]>
#> 2 Ensenanza <tibble [193 × 2]>
#> 3 Administracion publica <tibble [193 × 2]>
#> 4 Transporte y comunicaciones <tibble [193 × 2]>
#> 5 Servicios sociales/Salud <tibble [193 × 2]>
#> 6 Impuestos netos <tibble [193 × 2]>
#> 7 Sector financiero <tibble [193 × 2]>
#> 8 Mineria <tibble [193 × 2]>
#> 9 Agro/Ganaderia/Caza/Silvicultura <tibble [193 × 2]>
#> 10 Electricidad/Gas/Agua <tibble [193 × 2]>
#> 11 Hoteles/Restaurantes <tibble [193 × 2]>
#> 12 Inmobiliarias <tibble [193 × 2]>
#> 13 Otras actividades <tibble [193 × 2]>
#> 14 Pesca <tibble [193 × 2]>
#> 15 Industria manufacturera <tibble [193 × 2]>
#> 16 Construccion <tibble [193 × 2]>
- Recipes
recipe_1 = recipe(value ~ ., data = emae_series %>% select(-sector)) %>%
step_date(date, features = c("month", "quarter", "year"), ordinal = TRUE)
- Models
m_ets <- workflow() %>%
add_model(exp_smoothing() %>% set_engine('ets')) %>%
add_recipe(recipe_1)
m_nnetar <- workflow() %>%
add_recipe(recipe_1) %>%
add_model(nnetar_reg() %>% set_engine("nnetar"))
🔺 modeltime_multifit
model_table_emae = modeltime_multifit(serie = nested_serie %>% head(2),
.prop = 0.8,
m_ets,
m_nnetar)
model_table_emae
#> $table_time
#> # A tibble: 2 x 6
#> sector nested_column m_ets m_nnetar nested_model calibration
#> <chr> <list> <list> <list> <list> <list>
#> 1 Comercio <tibble [193 × … <workfl… <workflo… <model_time [2 … <model_time [2 …
#> 2 Ensenan… <tibble [193 × … <workfl… <workflo… <model_time [2 … <model_time [2 …
#>
#> $models_accuracy
#> # A tibble: 4 x 10
#> name_serie .model_id .model_desc .type mae mape mase smape rmse rsq
#> <chr> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Comercio 1 ETS(M,AD,M) Test 9.69 6.66 0.745 6.50 11.7 0.407
#> 2 Comercio 2 NNAR(1,1,10)[1… Test 10.2 6.63 0.784 6.87 12.4 0.495
#> 3 Ensenanza 1 ETS(A,A,A) Test 4.99 3.11 3.62 3.05 5.63 0.732
#> 4 Ensenanza 2 NNAR(1,1,10)[1… Test 3.56 2.22 2.58 2.19 3.96 0.834
🔺 modeltime_multiforecast
forecast_emae <- modeltime_multiforecast(
model_table_emae$table_time,
.prop = 0.8
)
forecast_emae %>%
select(sector, nested_forecast) %>%
unnest(nested_forecast) %>%
group_by(sector) %>%
plot_modeltime_forecast(
.legend_max_width = 12,
.facet_ncol = 2,
.line_size = 0.5,
.interactive = FALSE,
.facet_scales = 'free_y',
.title='Forecasting test')
🔺 modeltime_multibestmodel
best_model_emae <- modeltime_multibestmodel(
.table = model_table_emae$table_time,
.metric = "rmse",
.minimize = TRUE,
.forecast = FALSE
)
best_model_emae
#> # A tibble: 2 x 7
#> sector nested_column m_ets m_nnetar nested_model calibration best_model
#> <chr> <list> <list> <list> <list> <list> <list>
#> 1 Comerc… <tibble [193 ×… <workf… <workfl… <model_time … <model_time… <int [1]>
#> 2 Ensena… <tibble [193 ×… <workf… <workfl… <model_time … <model_time… <int [1]>
🔺 modeltime_multirefit
model_refit_emae <- modeltime_multirefit(models_table = best_model_emae)
model_refit_emae
#> # A tibble: 2 x 7
#> sector nested_column m_ets m_nnetar nested_model calibration best_model
#> <chr> <list> <list> <list> <list> <list> <list>
#> 1 Comerc… <tibble [193 ×… <workf… <workfl… <model_time … <model_time… <int [1]>
#> 2 Ensena… <tibble [193 ×… <workf… <workfl… <model_time … <model_time… <int [1]>
forecast_emae <- modeltime_multiforecast(
model_refit_emae,
.prop = 0.8,
.h = "1 years"
)
forecast_emae %>%
select(sector, nested_forecast) %>%
unnest(nested_forecast) %>%
group_by(sector) %>%
plot_modeltime_forecast(
.legend_max_width = 12,
.facet_ncol = 2,
.line_size = 0.5,
.interactive = FALSE,
.facet_scales = 'free_y',
.title='Forecasting'
)
Others functions 🌀
🔹 Function multieval
For a set of predictions from different models, it allows you to
evaluate multiple metrics and return the results in a tabular format
that makes it easy to compare the predictions.
library(yardstick)
library(erer)
set.seed(123)
predictions =
data.frame(truth = runif(100),
predict_model_1 = rnorm(100, mean = 1,sd =2),
predict_model_2 = rnorm(100, mean = 0,sd =2))
tibble(predictions)
#> # A tibble: 100 x 3
#> truth predict_model_1 predict_model_2
#> <dbl> <dbl> <dbl>
#> 1 0.288 1.51 1.58
#> 2 0.788 0.943 1.54
#> 3 0.409 0.914 0.664
#> 4 0.883 3.74 -2.02
#> 5 0.940 0.548 -0.239
#> 6 0.0456 4.03 -0.561
#> 7 0.528 -2.10 1.13
#> 8 0.892 2.17 -0.745
#> 9 0.551 1.25 1.95
#> 10 0.457 1.43 -0.749
#> # … with 90 more rows
multieval(.dataset = predictions,
.observed = "truth",
.predictions = c("predict_model_1","predict_model_2"),
.metrics = listn(rmse, rsq, mae))
#> $summary_table
#> # A tibble: 2 x 4
#> modelo rmse rsq mae
#> <chr> <dbl> <dbl> <dbl>
#> 1 predict_model_1 1.99 0.000704 1.59
#> 2 predict_model_2 1.95 0.00115 1.61
🔹 Function insert_na
This function allows adding NA values to a data frame, being able to
select the columns and the proportion of NAs desired.
insert_na(.dataset = iris, columns = c("Sepal.Length","Petal.Length"), .p = 0.25)
#> # A tibble: 150 x 5
#> Sepal.Width Petal.Width Species Sepal.Length Petal.Length
#> <dbl> <dbl> <fct> <dbl> <dbl>
#> 1 3.5 0.2 setosa 5.1 NA
#> 2 3 0.2 setosa NA 1.4
#> 3 3.2 0.2 setosa 4.7 1.3
#> 4 3.1 0.2 setosa NA 1.5
#> 5 3.6 0.2 setosa NA 1.4
#> 6 3.9 0.4 setosa 5.4 1.7
#> 7 3.4 0.3 setosa 4.6 1.4
#> 8 3.4 0.2 setosa NA 1.5
#> 9 2.9 0.2 setosa 4.4 1.4
#> 10 3.1 0.1 setosa 4.9 1.5
#> # … with 140 more rows
Use cases
To consult projects where this package was used, visit:
the logo icon was developed by
OpenClipart-Vectors
rafzamb/sknifedatar documentation built on July 22, 2021, 4:10 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
sknifedatar 📦 “Swiss Knife of Data”
Serves primarily as an extension to the modeltime 📦 ecosystem. In addition to some functionalities of spatial data and visualization.
Installation
CRAN version:
install.packages("sknifedatar")
Or install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("rafzamb/sknifedatar")
Features:
- multifit: multiple models into multiple time series (no panel).
- workflowsets: worklowset over a time series.
- workflowset multifit: worklowset over multiple time series (no panel).
- automagic tabs: automatic generation of Tabs in Distill/Rmarkdown files.
- sliding windows: data partitioning in sliding windows.
Multiple models on multiple series functions 📈
- libraries
library(modeltime)
library(rsample)
library(parsnip)
library(recipes)
library(workflows)
library(dplyr)
library(tidyr)
library(sknifedatar)
- Data
data("emae_series")
nested_serie = emae_series %>% filter(date < '2020-02-01') %>% nest(nested_column=-sector)
nested_serie
#> # A tibble: 16 x 2
#> sector nested_column
#> <chr> <list>
#> 1 Comercio <tibble [193 × 2]>
#> 2 Ensenanza <tibble [193 × 2]>
#> 3 Administracion publica <tibble [193 × 2]>
#> 4 Transporte y comunicaciones <tibble [193 × 2]>
#> 5 Servicios sociales/Salud <tibble [193 × 2]>
#> 6 Impuestos netos <tibble [193 × 2]>
#> 7 Sector financiero <tibble [193 × 2]>
#> 8 Mineria <tibble [193 × 2]>
#> 9 Agro/Ganaderia/Caza/Silvicultura <tibble [193 × 2]>
#> 10 Electricidad/Gas/Agua <tibble [193 × 2]>
#> 11 Hoteles/Restaurantes <tibble [193 × 2]>
#> 12 Inmobiliarias <tibble [193 × 2]>
#> 13 Otras actividades <tibble [193 × 2]>
#> 14 Pesca <tibble [193 × 2]>
#> 15 Industria manufacturera <tibble [193 × 2]>
#> 16 Construccion <tibble [193 × 2]>
- Recipes
recipe_1 = recipe(value ~ ., data = emae_series %>% select(-sector)) %>%
step_date(date, features = c("month", "quarter", "year"), ordinal = TRUE)
- Models
m_ets <- workflow() %>%
add_model(exp_smoothing() %>% set_engine('ets')) %>%
add_recipe(recipe_1)
m_nnetar <- workflow() %>%
add_recipe(recipe_1) %>%
add_model(nnetar_reg() %>% set_engine("nnetar"))
🔺 modeltime_multifit
model_table_emae = modeltime_multifit(serie = nested_serie %>% head(2),
.prop = 0.8,
m_ets,
m_nnetar)
model_table_emae
#> $table_time
#> # A tibble: 2 x 6
#> sector nested_column m_ets m_nnetar nested_model calibration
#> <chr> <list> <list> <list> <list> <list>
#> 1 Comercio <tibble [193 × … <workfl… <workflo… <model_time [2 … <model_time [2 …
#> 2 Ensenan… <tibble [193 × … <workfl… <workflo… <model_time [2 … <model_time [2 …
#>
#> $models_accuracy
#> # A tibble: 4 x 10
#> name_serie .model_id .model_desc .type mae mape mase smape rmse rsq
#> <chr> <int> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Comercio 1 ETS(M,AD,M) Test 9.69 6.66 0.745 6.50 11.7 0.407
#> 2 Comercio 2 NNAR(1,1,10)[1… Test 10.2 6.63 0.784 6.87 12.4 0.495
#> 3 Ensenanza 1 ETS(A,A,A) Test 4.99 3.11 3.62 3.05 5.63 0.732
#> 4 Ensenanza 2 NNAR(1,1,10)[1… Test 3.56 2.22 2.58 2.19 3.96 0.834
🔺 modeltime_multiforecast
forecast_emae <- modeltime_multiforecast(
model_table_emae$table_time,
.prop = 0.8
)
forecast_emae %>%
select(sector, nested_forecast) %>%
unnest(nested_forecast) %>%
group_by(sector) %>%
plot_modeltime_forecast(
.legend_max_width = 12,
.facet_ncol = 2,
.line_size = 0.5,
.interactive = FALSE,
.facet_scales = 'free_y',
.title='Forecasting test')
🔺 modeltime_multibestmodel
best_model_emae <- modeltime_multibestmodel(
.table = model_table_emae$table_time,
.metric = "rmse",
.minimize = TRUE,
.forecast = FALSE
)
best_model_emae
#> # A tibble: 2 x 7
#> sector nested_column m_ets m_nnetar nested_model calibration best_model
#> <chr> <list> <list> <list> <list> <list> <list>
#> 1 Comerc… <tibble [193 ×… <workf… <workfl… <model_time … <model_time… <int [1]>
#> 2 Ensena… <tibble [193 ×… <workf… <workfl… <model_time … <model_time… <int [1]>
🔺 modeltime_multirefit
model_refit_emae <- modeltime_multirefit(models_table = best_model_emae)
model_refit_emae
#> # A tibble: 2 x 7
#> sector nested_column m_ets m_nnetar nested_model calibration best_model
#> <chr> <list> <list> <list> <list> <list> <list>
#> 1 Comerc… <tibble [193 ×… <workf… <workfl… <model_time … <model_time… <int [1]>
#> 2 Ensena… <tibble [193 ×… <workf… <workfl… <model_time … <model_time… <int [1]>
forecast_emae <- modeltime_multiforecast(
model_refit_emae,
.prop = 0.8,
.h = "1 years"
)
forecast_emae %>%
select(sector, nested_forecast) %>%
unnest(nested_forecast) %>%
group_by(sector) %>%
plot_modeltime_forecast(
.legend_max_width = 12,
.facet_ncol = 2,
.line_size = 0.5,
.interactive = FALSE,
.facet_scales = 'free_y',
.title='Forecasting'
)
Others functions 🌀
🔹 Function multieval
For a set of predictions from different models, it allows you to evaluate multiple metrics and return the results in a tabular format that makes it easy to compare the predictions.
library(yardstick)
library(erer)
set.seed(123)
predictions =
data.frame(truth = runif(100),
predict_model_1 = rnorm(100, mean = 1,sd =2),
predict_model_2 = rnorm(100, mean = 0,sd =2))
tibble(predictions)
#> # A tibble: 100 x 3
#> truth predict_model_1 predict_model_2
#> <dbl> <dbl> <dbl>
#> 1 0.288 1.51 1.58
#> 2 0.788 0.943 1.54
#> 3 0.409 0.914 0.664
#> 4 0.883 3.74 -2.02
#> 5 0.940 0.548 -0.239
#> 6 0.0456 4.03 -0.561
#> 7 0.528 -2.10 1.13
#> 8 0.892 2.17 -0.745
#> 9 0.551 1.25 1.95
#> 10 0.457 1.43 -0.749
#> # … with 90 more rows
multieval(.dataset = predictions,
.observed = "truth",
.predictions = c("predict_model_1","predict_model_2"),
.metrics = listn(rmse, rsq, mae))
#> $summary_table
#> # A tibble: 2 x 4
#> modelo rmse rsq mae
#> <chr> <dbl> <dbl> <dbl>
#> 1 predict_model_1 1.99 0.000704 1.59
#> 2 predict_model_2 1.95 0.00115 1.61
🔹 Function insert_na
This function allows adding NA values to a data frame, being able to select the columns and the proportion of NAs desired.
insert_na(.dataset = iris, columns = c("Sepal.Length","Petal.Length"), .p = 0.25)
#> # A tibble: 150 x 5
#> Sepal.Width Petal.Width Species Sepal.Length Petal.Length
#> <dbl> <dbl> <fct> <dbl> <dbl>
#> 1 3.5 0.2 setosa 5.1 NA
#> 2 3 0.2 setosa NA 1.4
#> 3 3.2 0.2 setosa 4.7 1.3
#> 4 3.1 0.2 setosa NA 1.5
#> 5 3.6 0.2 setosa NA 1.4
#> 6 3.9 0.4 setosa 5.4 1.7
#> 7 3.4 0.3 setosa 4.6 1.4
#> 8 3.4 0.2 setosa NA 1.5
#> 9 2.9 0.2 setosa 4.4 1.4
#> 10 3.1 0.1 setosa 4.9 1.5
#> # … with 140 more rows
Use cases
To consult projects where this package was used, visit:
the logo icon was developed by OpenClipart-Vectors
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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