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08.R
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08.R
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# LIBRARIES ----
library(tidyverse)
library(timetk)
library(lubridate)
# DATA ----
google_analytics_tbl <- read_csv("https://raw.githubusercontent.com/Aranaur/datasets/main/time_series/google_analytics_summary_hourly.csv")
google_analytics_tbl
mailchimp_users_tbl <- read_csv("https://raw.githubusercontent.com/Aranaur/datasets/main/time_series/mailchimp_users.csv")
mailchimp_users_tbl
# DATA PREPARATION ----
google_analytics_long_hour_tbl <- google_analytics_tbl %>%
mutate(date = ymd_h(dateHour)) %>%
select(-dateHour) %>%
pivot_longer(
cols = pageViews:sessions
)
subscribers_day_tbl <- mailchimp_users_tbl %>%
summarise_by_time(
.date_var = optin_time,
.by = "day",
optins = n()
) %>%
pad_by_time(
.date_var = optin_time,
.by = "day",
.pad_value = 0
)
# 1.0 TIME SERIES PLOT ----
# - Primary Visualization Tool
# - Spot issues and understand one or more time series
?plot_time_series
# * Basics ----
subscribers_day_tbl %>%
plot_time_series(
.date_var = optin_time,
.value = optins
)
# * Facets/Groups ----
google_analytics_long_hour_tbl %>%
group_by(name) %>%
plot_time_series(
.date_var = date,
.value = value,
.facet_ncol = 2,
.color_var = name
)
google_analytics_long_hour_tbl %>%
plot_time_series(
.date_var = date,
.value = value,
.facet_vars = name,
.facet_ncol = 2,
.color_var = name
)
# * Mutations/Transformations ----
subscribers_day_tbl %>%
plot_time_series(
.date_var = optin_time,
.value = log(optins + 1)
)
google_analytics_long_hour_tbl %>%
group_by(name) %>%
plot_time_series(
.date_var = date,
.value = value,
.color_var = name
)
google_analytics_long_hour_tbl %>%
group_by(name) %>%
plot_time_series(
.date_var = date,
.value = log(value + 1),
.color_var = name
)
# * Smoother Adjustment
subscribers_day_tbl %>%
plot_time_series(
.date_var = optin_time,
.value = log(optins + 1),
.smooth = FALSE
)
subscribers_day_tbl %>%
plot_time_series(
.date_var = optin_time,
.value = log(optins + 1),
.smooth_period = "30 days",
.smooth_degree = 0
)
google_analytics_long_hour_tbl %>%
plot_time_series(
.date_var = date,
.value = log(value + 1),
.facet_vars = name,
.smooth_period = "7 days"
)
# * Static ggplot ----
subscribers_day_tbl %>%
plot_time_series(
.date_var = optin_time,
.value = log(optins + 1),
.interactive = FALSE
)
# 2.0 ACF Diagnostics ----
# - Detecting Lagged Features
?plot_acf_diagnostics
# * ACF / PACF -----
# - Date Features & Fourier Series
subscribers_day_tbl %>%
plot_acf_diagnostics(.date_var = optin_time, .value = log(optins + 1))
subscribers_day_tbl %>%
plot_acf_diagnostics(.date_var = optin_time, .value = log(optins + 1), .lags = 100)
# * CCF ----
# - Lagged External Regressors
google_analytics_day_tbl <- google_analytics_long_hour_tbl %>%
pivot_wider(names_from = name, values_from = value) %>%
summarise_by_time(.date_var = date, .by = "day", across(pageViews:sessions, .fns = sum))
subscribers_ga_day_tbl <- subscribers_day_tbl %>%
left_join(google_analytics_day_tbl, by = c("optin_time" = "date"))
subscribers_ga_day_tbl %>%
drop_na() %>%
plot_acf_diagnostics(
.date_var = optin_time,
.value = optins,
.ccf_vars = pageViews:sessions,
.show_ccf_vars_only = TRUE,
.facet_ncol = 3
)
# 3.0 SEASONALITY ----
# - Detecting Time-Based Features
?plot_seasonal_diagnostics
google_analytics_long_hour_tbl %>%
group_by(name) %>%
plot_seasonal_diagnostics(.date_var = date, .value = log(value + 1))
google_analytics_long_hour_tbl %>%
group_by(name) %>%
plot_seasonal_diagnostics(
.date_var = date,
.value = log(value + 1),
.feature_set = "hour"
)
# 4.0 ANOMALIES ----
# - Detecting Events & Possible Data Issues
?plot_anomaly_diagnostics
subscribers_day_tbl %>%
plot_anomaly_diagnostics(
.date_var = optin_time,
.value = optins,
.alpha = 0.01,
.max_anomalies = 0.01
)
subscribers_day_tbl %>%
tk_anomaly_diagnostics(
.date_var = optin_time,
.value = optins
)
# Grouped Anomalies
google_analytics_long_hour_tbl %>%
group_by(name) %>%
plot_anomaly_diagnostics(
.date_var = date,
.value = value
)
# 5.0 SEASONAL DECOMPOSITION ----
# - Detecting Trend and Seasonal Cycles
?plot_stl_diagnostics
subscribers_day_tbl %>%
plot_stl_diagnostics(
.date_var = optin_time,
.value = optins
)
google_analytics_long_hour_tbl %>%
group_by(name) %>%
plot_stl_diagnostics(
.date_var = date,
.value = log(value + 1)
)
# 6.0 TIME SERIES REGRESSION PLOT ----
# - Finding features
?plot_time_series_regression
subscribers_day_tbl %>%
plot_time_series_regression(
.date_var = optin_time,
.formula = log(optins + 1) ~ as.numeric(optin_time) +
wday(optin_time, label = TRUE) +
month(optin_time, label = TRUE),
.show_summary = TRUE
)
google_analytics_long_hour_tbl %>%
group_by(name) %>%
plot_time_series_regression(
.date_var = date,
.formula = log(value + 1) ~ as.numeric(date) +
as.factor(hour(date)) +
wday(date, label = TRUE) +
month(date, label = TRUE)
)