# for Core packages
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# for financial analysis
library(tidyquant)
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo 
## ── Attaching core tidyquant packages ──────────────────────── tidyquant 1.0.9 ──
## ✔ PerformanceAnalytics 2.0.4      ✔ TTR                  0.24.4
## ✔ quantmod             0.4.26     ✔ xts                  0.14.0── Conflicts ────────────────────────────────────────── tidyquant_conflicts() ──
## ✖ zoo::as.Date()                 masks base::as.Date()
## ✖ zoo::as.Date.numeric()         masks base::as.Date.numeric()
## ✖ dplyr::filter()                masks stats::filter()
## ✖ xts::first()                   masks dplyr::first()
## ✖ dplyr::lag()                   masks stats::lag()
## ✖ xts::last()                    masks dplyr::last()
## ✖ PerformanceAnalytics::legend() masks graphics::legend()
## ✖ quantmod::summary()            masks base::summary()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# for times series
library(timetk)
## Warning: package 'timetk' was built under R version 4.4.3
## 
## Attaching package: 'timetk'
## 
## The following object is masked from 'package:tidyquant':
## 
##     FANG

Goal: Apply Matt Dancho’s tutorial to state unemployment initial claims of New England states.

start_date <- "1989-01-01"

symbols_txt <- c("CTICLAIMS", # Connecticut
                 "MEICLAIMS", # Maine
                 "MAICLAIMS", # Massachusetts
                 "NHICLAIMS", # New Hampshire
                 "RIICLAIMS", # Rhode Island
                 "VTICLAIMS") # Vermont

claims_tbl <- tq_get(symbols_txt, get = "economic.data", from = start_date) %>%
    mutate(symbol = fct_recode(symbol,
                               "Connecticut"   = "CTICLAIMS",
                               "Maine"         = "MEICLAIMS",
                               "Massachusetts" = "MAICLAIMS",
                               "New Hampshire" = "NHICLAIMS",
                               "Rhode Island"  = "RIICLAIMS",
                               "Vermont"       = "VTICLAIMS")) %>%
    rename(claims = price)

Plotting time series

claims_tbl %>%
  group_by(symbol) %>%
  plot_time_series(date, claims, .facet_ncol = 2, .interactive = FALSE)

Box plots

claims_tbl %>%
  filter_by_time(.date_var = date, .end_date = "2000-01-01") %>%
  group_by(symbol) %>%
  plot_time_series_boxplot(.date_var = date, .value = claims, .period = "1 year", .facet_ncol = 2)

Plotting Seasonality and Correlation

Correlation Plots

claims_tbl %>%
  group_by(symbol) %>%
  plot_acf_diagnostics(date, claims, .lags = "3 years")

Seasonality

claims_tbl %>%
  group_by(symbol) %>%
  plot_seasonal_diagnostics(date, claims)

STL Diagnostics

claims_tbl %>%
  group_by(symbol) %>%
  plot_stl_diagnostics(date, claims, .feature_set = c("observed", "season", "trend", "remainder"))
## frequency = 13 observations per 1 quarter
## trend = 53 observations per 1 year
## frequency = 13 observations per 1 quarter
## trend = 53 observations per 1 year
## frequency = 13 observations per 1 quarter
## trend = 53 observations per 1 year
## frequency = 13 observations per 1 quarter
## trend = 53 observations per 1 year
## frequency = 13 observations per 1 quarter
## trend = 53 observations per 1 year
## frequency = 13 observations per 1 quarter
## trend = 53 observations per 1 year

Time Series Data Wrangling

Summarize by Time

claims_tbl %>%
  group_by(symbol) %>%
  summarise_by_time(.date_var = date, claims = mean(claims), .by = "month") %>%
  plot_time_series(date, claims, .facet_ncol = 2, .interactive = FALSE)

Filter By Time

claims_tbl %>%
  group_by(symbol) %>%
  filter_by_time(.date_var = date, .start_date = "2008", .end_date = "2010") %>%
  plot_time_series(date, claims, .facet_ncol = 2)

Padding Data

claims_tbl %>%
  group_by(symbol) %>%
  pad_by_time(date, .by = "week", .pad_value = 0)
## # A tibble: 11,352 × 3
## # Groups:   symbol [6]
##    symbol      date       claims
##    <fct>       <date>      <int>
##  1 Connecticut 1989-01-07   8345
##  2 Connecticut 1989-01-14   6503
##  3 Connecticut 1989-01-21   3821
##  4 Connecticut 1989-01-28   4663
##  5 Connecticut 1989-02-04   4162
##  6 Connecticut 1989-02-11   4337
##  7 Connecticut 1989-02-18   4079
##  8 Connecticut 1989-02-25   3556
##  9 Connecticut 1989-03-04   3826
## 10 Connecticut 1989-03-11   3515
## # ℹ 11,342 more rows

Sliding (Rolling) Calculations

lm_roll <- slidify(~ lm(..1 ~ ..2 + ..3), .period = 52, .unlist = FALSE, .align = "right")

claims_tbl %>%
  select(symbol, date, claims) %>%
  group_by(symbol) %>%
  mutate(numeric_date = as.numeric(date)) %>%
  mutate(rolling_lm = lm_roll(claims, numeric_date, numeric_date)) %>%
  filter(!is.na(rolling_lm))
## # A tibble: 11,046 × 5
## # Groups:   symbol [6]
##    symbol      date       claims numeric_date rolling_lm
##    <fct>       <date>      <int>        <dbl> <list>    
##  1 Connecticut 1989-12-30   7225         7303 <lm>      
##  2 Connecticut 1990-01-06   9184         7310 <lm>      
##  3 Connecticut 1990-01-13  12992         7317 <lm>      
##  4 Connecticut 1990-01-20   6886         7324 <lm>      
##  5 Connecticut 1990-01-27   7951         7331 <lm>      
##  6 Connecticut 1990-02-03   5583         7338 <lm>      
##  7 Connecticut 1990-02-10   5376         7345 <lm>      
##  8 Connecticut 1990-02-17   5526         7352 <lm>      
##  9 Connecticut 1990-02-24   4360         7359 <lm>      
## 10 Connecticut 1990-03-03   5460         7366 <lm>      
## # ℹ 11,036 more rows