Apply 10
# for Core packages
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors# for financial analysis
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## 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 4.5.3##
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## FANGGoal: Apply Matt Dancho’s tutorial to state unemployment initial claims of New England states.
The following is the replication of Matt Dancho’s tutorial on this page
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## # A tibble: 11,670 × 3
## 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 11,660 more rowsclaims_tbl %>%
plot_time_series(.date_var = date, .value = claims)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the timetk package.
## Please report the issue at
## <https://github.com/business-science/timetk/issues>.
## This warning is displayed once per session.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Ignoring unknown labels:
## • colour : "Legend"claims_tbl %>% count(symbol)## # A tibble: 6 × 2
## symbol n
## <fct> <int>
## 1 Connecticut 1945
## 2 Massachusetts 1945
## 3 Maine 1945
## 4 New Hampshire 1945
## 5 Rhode Island 1945
## 6 Vermont 1945claims_tbl %>%
group_by(symbol) %>%
plot_time_series(
.date_var = date,
.value = claims,
.facet_ncol = 2,
.facet_scales = "free",
.interactive = FALSE
)## Ignoring unknown labels:
## • colour : "Legend"
Box plots
claims_tbl %>% count(symbol)## # A tibble: 6 × 2
## symbol n
## <fct> <int>
## 1 Connecticut 1945
## 2 Massachusetts 1945
## 3 Maine 1945
## 4 New Hampshire 1945
## 5 Rhode Island 1945
## 6 Vermont 1945claims_tbl %>%
filter_by_time(.date_var = date,
.end_date = "2000") %>%
group_by(symbol) %>%
plot_time_series_boxplot(.date_var = date, .value = claims, .period = "1 year", .facet_ncol = 2)## Ignoring unknown labels:
## • colour : "Legend"Regression plots
claims_tbl %>%
group_by(symbol) %>%
plot_time_series_regression(
.date_var = date,
.facet_ncol = 2,
.formula = log(claims) ~ as.numeric(date) + month(date,
label = TRUE),
.show_summary = FALSE)Plotting Seasonality and Correlation
Correlation Plots
claims_tbl %>%
group_by(symbol) %>%
plot_acf_diagnostics(
date, claims, .lags = "1 year")Seasonality
claims_tbl %>% count(symbol)## # A tibble: 6 × 2
## symbol n
## <fct> <int>
## 1 Connecticut 1945
## 2 Massachusetts 1945
## 3 Maine 1945
## 4 New Hampshire 1945
## 5 Rhode Island 1945
## 6 Vermont 1945claims_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", "remainder","trend"))## 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 yearTime Series Data Wrangling
Summarize by Time
claims_tbl %>%
group_by(symbol) %>%
summarise_by_time(.date_var = date, claims =sum(claims), .by = "year") %>%
plot_time_series(date, claims, .facet_ncol = 2, .interactive = FALSE)## Ignoring unknown labels:
## • colour : "Legend"
Filter By Time
claims_tbl %>%
group_by(symbol) %>%
filter_by_time(.date_var = date,
.start_date = "1990-01",
.end_date = "1991-01") %>%
plot_time_series(date, claims, .facet_ncol = 2)## Ignoring unknown labels:
## • colour : "Legend"Padding Data
claims_tbl %>%
group_by(symbol) %>%
pad_by_time(date, .by ="week", .pad_value = 0)## # A tibble: 11,670 × 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 11,660 more rowsSliding (Rolling) Calculations
claims_tbl %>%
head(10) %>%
mutate(rolling_avg2 = slidify_vec(claims, mean, .period = 2, .align = "right", .partial = TRUE))## # A tibble: 10 × 4
## symbol date claims rolling_avg2
## <fct> <date> <int> <dbl>
## 1 Connecticut 1989-01-07 8345 8345
## 2 Connecticut 1989-01-14 6503 7424
## 3 Connecticut 1989-01-21 3821 5162
## 4 Connecticut 1989-01-28 4663 4242
## 5 Connecticut 1989-02-04 4162 4412.
## 6 Connecticut 1989-02-11 4337 4250.
## 7 Connecticut 1989-02-18 4079 4208
## 8 Connecticut 1989-02-25 3556 3818.
## 9 Connecticut 1989-03-04 3826 3691
## 10 Connecticut 1989-03-11 3515 3670.