James Young

How To Make A Standalone Desktop Application With Shiny and Electron On Windows

2021-02-04T00:00:00+00:00

Refer to my article on How To Make A Standalone Desktop Application With Shiny and Electron On Windows

Forecasting: Building a Base Model

2020-01-19T00:00:00+00:00

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actuals <- NULL for (i in 45:60) { long_data2 <- long_data long_data2$month <- long_data2$month*30 long_data2$month <- as.Date(long_data2$month) ts <- long_data2 %>% group_by(dept_id, state_id) %>% nest() actuals <- rbind(actuals, ts) } train$train <- train$data train$data <- NULL train$actuals <- actuals$data

#This function sums the mean, or point forecast, of the forecasts from 3 months into one value representing the whole 3 month forecasted period. forecasts_sum <- function(x) { sum(x[["mean"]]) }

#This function will strip out the last date in the train data so we can keep track of performance at different points in time. str_date <- function(x) { max(x$month) }

#I use map and map2 functions below to apply a through a column. train_tbl_ts2 <- train_tbl_ts %>% mutate(fcast.ets = map(fit.ets, forecast, h = 3), fcast.tbats = map(fit.tbats, forecast, h = 3), fcst.ets_sum = map_dbl(fcast.ets, forecasts_sum), fcst.tbats_sum = map_dbl(fcast.tbats, forecasts_sum), fcst.mean_sum = map_dbl(fit.mean, forecasts_sum), fcst.snaive_sum = map_dbl(fit.snaive, forecasts_sum), fcst.naive_sum = map_dbl(fit.naive, forecasts_sum), fcst.meanf_sum = map(fcast.tbats, forecasts_sum), actuals_sum = map2_dbl(actuals, train, actuals_sum), date = map_dbl(train, str_date))

colnames(results)[6] <- "Level"

kable(results, digits = 3)

plot1 <- PLOT_level(train_tbl_ts2, dept_id ) %>% filter(str_detect(dept_id ,'HO')) %>% ggplot(aes(x=date)) + geom_line(aes(y = fcst.tbats_sum), color = "darkred") + geom_line(aes(y = fcst.ets_sum), color = "red") + geom_line(aes(y = actuals_sum), color = "green") + geom_line(aes(y = fcst.naive_sum), color = "blue") +facet_grid(dept_id~.)+ labs(y = "Actual Sales and Forecasts")

plot2 <- PLOT_level(train_tbl_ts2, dept_id ) %>% filter(str_detect(dept_id ,'HO')) %>% ggplot(aes(x=date)) + geom_line(aes(y = tbats_AE), color = "darkred") + geom_line(aes(y = ets_AE), color = "red") + geom_line(aes(y = naive_AE), color = "blue") +facet_grid(dept_id~.) + labs(y = "Absolute Error")

plot3 <- PLOT_level(train_tbl_ts2, dept_id ) %>% filter(str_detect(dept_id ,'HO')) %>% ggplot(aes(x=date)) + geom_line(aes(y = tbats_MAPE), color = "darkred") + geom_line(aes(y = ets_MAPE), color = "red") + geom_line(aes(y = naive_MAPE), color = "blue") +facet_grid(dept_id~.) + labs(y = "Mean Absolute Percent Error")

plot1+plot2+plot3

plot2 <- PLOT_level(train_tbl_ts2, dept_id ) %>% filter(str_detect(dept_id ,'FO')) %>% ggplot(aes(x=date)) + geom_line(aes(y = tbats_AE), color = "darkred") + geom_line(aes(y = ets_AE), color = "red") + geom_line(aes(y = naive_AE), color = "blue") +facet_grid(dept_id~.) + labs(y = "Absolute Error")

plot3 <- PLOT_level(train_tbl_ts2, dept_id ) %>% filter(str_detect(dept_id ,'FO')) %>% ggplot(aes(x=date)) + geom_line(aes(y = tbats_MAPE), color = "darkred") + geom_line(aes(y = ets_MAPE), color = "red") + geom_line(aes(y = naive_MAPE), color = "blue") +facet_grid(dept_id~.) + labs(y = "Mean Absolute Percent Error")

plot1+plot2+plot3

plot2 <- PLOT_level(train_tbl_ts2, state_id ) %>% #filter(str_detect(dept_id ,'FO')) %>% ggplot(aes(x=date)) + geom_line(aes(y = tbats_AE), color = "darkred") + geom_line(aes(y = ets_AE), color = "red") + geom_line(aes(y = naive_AE), color = "blue") +facet_grid(state_id~.) + labs(y = "Absolute Error")

plot3 <- PLOT_level(train_tbl_ts2, state_id ) %>% #filter(str_detect(dept_id ,'HO')) %>% ggplot(aes(x=date)) + geom_line(aes(y = tbats_MAPE), color = "darkred") + geom_line(aes(y = ets_MAPE), color = "red") + geom_line(aes(y = naive_MAPE), color = "blue") +facet_grid(state_id~.) + labs(y = "Mean Absolute Percent Error")

plot1+plot2+plot3

plot2 <- PLOT_level(train_tbl_ts2, dept_id, state_id ) %>% filter(str_detect(dept_id ,'FO')) %>% filter(str_detect(state_id ,'CA')) %>% ggplot(aes(x=date)) + geom_line(aes(y = tbats_AE), color = "darkred") + geom_line(aes(y = ets_AE), color = "red") + geom_line(aes(y = naive_AE), color = "blue") +facet_grid(state_id+dept_id~.) + labs(y = "Absolute Error")

plot3 <- PLOT_level(train_tbl_ts2, dept_id, state_id ) %>% filter(str_detect(dept_id ,'FO')) %>% filter(str_detect(state_id ,'CA')) %>% ggplot(aes(x=date)) + geom_line(aes(y = tbats_MAPE), color = "darkred") + geom_line(aes(y = ets_MAPE), color = "red") + geom_line(aes(y = naive_MAPE), color = "blue") +facet_grid(state_id+dept_id~.) + labs(y = "Mean Absolute Percent Error")

plot1+plot2+plot3

Reproducible Workflows with Drake

2020-01-18T00:00:00+00:00

Refer to my article on reproducible workflows using {drake} in R

Forecasting: Exploratory Data Analysis

2020-01-18T00:00:00+00:00

};

time_series <- function(group1, x, var) { long %>% group_by(get(group1), state_id, gr=cut(day, breaks= seq(1, 1941, by = x), labels = F) ) %>% summarise(sales= sum(sales)) %>% arrange(as.numeric(gr)) %>% filter(!is.na(gr)) %>% ungroup() %>% mutate( group1 = get(group1)) %>% ggplot(aes(x=gr, y=sales, group=group1, colour= group1 )) + geom_line() + facet_grid(state_id~.) + labs(x = var) + scale_colour_discrete(group1) }

    labs(x = var)

}

time_series2('item_id', 7, "weekly")