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(ggplot2)
library(lubridate)
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
library(timetk)

Plotting time series

taylor_30_min
## # A tibble: 4,032 × 2
##    date                value
##    <dttm>              <dbl>
##  1 2000-06-05 00:00:00 22262
##  2 2000-06-05 00:30:00 21756
##  3 2000-06-05 01:00:00 22247
##  4 2000-06-05 01:30:00 22759
##  5 2000-06-05 02:00:00 22549
##  6 2000-06-05 02:30:00 22313
##  7 2000-06-05 03:00:00 22128
##  8 2000-06-05 03:30:00 21860
##  9 2000-06-05 04:00:00 21751
## 10 2000-06-05 04:30:00 21336
## # ℹ 4,022 more rows
taylor_30_min %>%
    plot_time_series(.date_var = date, .value = value)
m4_daily %>% count(id)
## # A tibble: 4 × 2
##   id        n
##   <fct> <int>
## 1 D10     674
## 2 D160   4197
## 3 D410    676
## 4 D500   4196
m4_daily %>%
    group_by(id) %>%
    plot_time_series(
        .date_var = date, 
        .value = value, 
        .facet_ncol = 2, 
        .facet_scales = "free", 
        .interactive = FALSE)

See visualizing Transformations and Sub-groups

m4_hourly %>% count(id)
## # A tibble: 4 × 2
##   id        n
##   <fct> <int>
## 1 H10     700
## 2 H50     700
## 3 H150    700
## 4 H410    960
m4_hourly %>%
    group_by(id) %>%
    plot_time_series(
        .date_var = date, 
        .value = value, 
        .facet_ncol = 2, 
        .facet_scales = "free", 
        .color_var = week(date))

Static ggplot2 Visualizations and Customizations

taylor_30_min %>%
    plot_time_series(date, value,
                     .color_var = month(date, label = TRUE),
                     # Returns static ggplot
                     .interactive = FALSE,
                     
                     # Customize
                     .title = "Taylor's MegaWatt Data",
                     .x_lab = "Date (30-min intervals",
                     .y_lab = "Energy Demand (MW)",
                     .color_lab = "Month")

Box Plots

m4_monthly %>% count(id)
## # A tibble: 4 × 2
##   id        n
##   <fct> <int>
## 1 M1      469
## 2 M2      469
## 3 M750    306
## 4 M1000   330
m4_monthly %>% 
    filter_by_time(.date_var = date, .end_date = "1976") %>%
    group_by(id) %>%
    plot_time_series_boxplot(
        .date_var = date, 
        .value = value, 
        .period = "1 year", 
        .facet_ncol = 2)

Regression Plots

m4_monthly %>%
    group_by(id) %>%
    plot_time_series_regression(
        .date_var = date, 
        .facet_ncol = 2,  
        .formula = log(value) ~ as.numeric(date) + month(date, label = TRUE),
        .show_summary = FALSE)

Plotting Seasonality and Correlation

Correlation Plots

m4_hourly %>%
    group_by(id) %>%
    plot_acf_diagnostics(
        date, value, .lags = "7 days")
walmart_sales_weekly %>%
    group_by(id) %>%
    plot_acf_diagnostics(
        Date, Weekly_Sales,
        .ccf_vars = c(Temperature, Fuel_Price),
        .lags = "3 months")

Seasonality

taylor_30_min %>%
    plot_seasonal_diagnostics(date, value)
m4_hourly %>% count(id)
## # A tibble: 4 × 2
##   id        n
##   <fct> <int>
## 1 H10     700
## 2 H50     700
## 3 H150    700
## 4 H410    960
m4_hourly %>%
    group_by(id) %>%
    plot_seasonal_diagnostics(date, value)

STL Diagnostics

m4_hourly %>%
    group_by(id) %>%
    plot_stl_diagnostics(
        date, value, 
        .feature_set = c("observed", "season", "trend", "remainder"))
## frequency = 24 observations per 1 day
## trend = 336 observations per 14 days
## frequency = 24 observations per 1 day
## trend = 336 observations per 14 days
## frequency = 24 observations per 1 day
## trend = 336 observations per 14 days
## frequency = 24 observations per 1 day
## trend = 336 observations per 14 days

Time Series Data Wrangling

Summarize by time

daily data

FANG %>%
    group_by(symbol) %>%
    summarize_by_time(.date_var = date, volume = sum(volume), .by = "quarter") %>%
    plot_time_series(date, volume, .facet_ncol = 2, .interactive = FALSE)

FANG %>%
    group_by(symbol) %>%
    summarize_by_time(.date_var = date, adjusted = mean(adjusted), .by = "month") %>%
    plot_time_series(date, adjusted, .facet_ncol = 2, .interactive = FALSE)

Filter by time

FANG %>%
    group_by(symbol) %>%
    filter_by_time(.date_var = date, 
                   .start_date = "2013-09", 
                   .end_date = "2013") %>%
    plot_time_series(date, adjusted, .facet_ncol = 2)

Padding Data

FANG %>%
    group_by(symbol) %>%
    pad_by_time(date, .by = "day", .pad_value = 0)
## # A tibble: 5,836 × 8
## # Groups:   symbol [4]
##    symbol date        open  high   low close  volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>   <dbl>    <dbl>
##  1 AMZN   2013-01-02  256.  258.  253.  257. 3271000     257.
##  2 AMZN   2013-01-03  257.  261.  256.  258. 2750900     258.
##  3 AMZN   2013-01-04  258.  260.  257.  259. 1874200     259.
##  4 AMZN   2013-01-05    0     0     0     0        0       0 
##  5 AMZN   2013-01-06    0     0     0     0        0       0 
##  6 AMZN   2013-01-07  263.  270.  263.  268. 4910000     268.
##  7 AMZN   2013-01-08  267.  269.  264.  266. 3010700     266.
##  8 AMZN   2013-01-09  268.  270.  265.  266. 2265600     266.
##  9 AMZN   2013-01-10  269.  269.  262.  265. 2863400     265.
## 10 AMZN   2013-01-11  265.  268.  264.  268. 2413300     268.
## # ℹ 5,826 more rows

Sliding (Rolling) Calculations

FANG %>%
    head(10) %>%
    mutate(rolling_avg_2 = slidify_vec(adjusted, mean, 
                                       .period = 2, 
                                       .align = "right", 
                                       .partial = TRUE))
## # A tibble: 10 × 9
##    symbol date        open  high   low close    volume adjusted rolling_avg_2
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>         <dbl>
##  1 FB     2013-01-02  27.4  28.2  27.4  28    69846400     28            28  
##  2 FB     2013-01-03  27.9  28.5  27.6  27.8  63140600     27.8          27.9
##  3 FB     2013-01-04  28.0  28.9  27.8  28.8  72715400     28.8          28.3
##  4 FB     2013-01-07  28.7  29.8  28.6  29.4  83781800     29.4          29.1
##  5 FB     2013-01-08  29.5  29.6  28.9  29.1  45871300     29.1          29.2
##  6 FB     2013-01-09  29.7  30.6  29.5  30.6 104787700     30.6          29.8
##  7 FB     2013-01-10  30.6  31.5  30.3  31.3  95316400     31.3          30.9
##  8 FB     2013-01-11  31.3  32.0  31.1  31.7  89598000     31.7          31.5
##  9 FB     2013-01-14  32.1  32.2  30.6  31.0  98892800     31.0          31.3
## 10 FB     2013-01-15  30.6  31.7  29.9  30.1 173242600     30.1          30.5
# Rolling regressions are easy to implement using `.unlist = FALSE`
lm_roll <- slidify(~ lm(..1 ~ ..2 + ..3), .period = 90,
                   .unlist = FALSE, .align = "right")

FANG %>%
    select(symbol, date, adjusted, volume) %>%
    group_by(symbol) %>%
    mutate(numeric_date = as.numeric(date)) %>%
    # Apply rolling regression
    mutate(rolling_lm = lm_roll(adjusted, volume, numeric_date)) %>%
    filter(!is.na(rolling_lm))
## # A tibble: 3,676 × 6
## # Groups:   symbol [4]
##    symbol date       adjusted   volume numeric_date rolling_lm
##    <chr>  <date>        <dbl>    <dbl>        <dbl> <list>    
##  1 FB     2013-05-10     26.7 30847100        15835 <lm>      
##  2 FB     2013-05-13     26.8 29068800        15838 <lm>      
##  3 FB     2013-05-14     27.1 24930300        15839 <lm>      
##  4 FB     2013-05-15     26.6 30299800        15840 <lm>      
##  5 FB     2013-05-16     26.1 35499100        15841 <lm>      
##  6 FB     2013-05-17     26.2 29462700        15842 <lm>      
##  7 FB     2013-05-20     25.8 42402900        15845 <lm>      
##  8 FB     2013-05-21     25.7 26261300        15846 <lm>      
##  9 FB     2013-05-22     25.2 45314500        15847 <lm>      
## 10 FB     2013-05-23     25.1 37663100        15848 <lm>      
## # ℹ 3,666 more rows