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This function will produce two plots. Both of these are moving average plots. One of the plots is from xts::plot.xts() and the other a ggplot2 plot. This is done so that the user can choose which type is best for them. The plots are stacked so each graph is on top of the other.

Usage

ts_ma_plot(
  .data,
  .date_col,
  .value_col,
  .ts_frequency = "monthly",
  .main_title = NULL,
  .secondary_title = NULL,
  .tertiary_title = NULL
)

Arguments

.data

The data you want to visualize. This should be pre-processed and the aggregation should match the .frequency argument.

.date_col

The data column from the .data argument.

.value_col

The value column from the .data argument

.ts_frequency

The frequency of the aggregation, quoted, ie. "monthly", anything else will default to weekly, so it is very important that the data passed to this function be in either a weekly or monthly aggregation.

.main_title

The title of the main plot.

.secondary_title

The title of the second plot.

.tertiary_title

The title of the third plot.

Value

A few time series data sets and two plots.

Details

This function expects to take in a data.frame/tibble. It will return a list object so it is a good idea to save the output to a variable and extract from there.

Author

Steven P. Sanderson II, MPH

Examples

suppressPackageStartupMessages(library(dplyr))

data_tbl <- ts_to_tbl(AirPassengers) %>%
  select(-index)

output <- ts_ma_plot(
  .data = data_tbl,
  .date_col = date_col,
  .value_col = value
)
#> Warning: Non-numeric columns being dropped: date_col
#> Warning: Non-numeric columns being dropped: date_col
#> Warning: Non-numeric columns being dropped: date_col
#> Warning: `label_number_si()` was deprecated in scales 1.2.0.
#> Please use the `scale_cut` argument of `label_number()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
#> Warning: Removed 11 row(s) containing missing values (geom_path).

output$pgrid

output$xts_plt

output$data_summary_tbl %>% head()
#> # A tibble: 6 × 5
#>   date_col   value  ma12 diff_a diff_b
#>   <date>     <dbl> <dbl>  <dbl>  <dbl>
#> 1 1949-01-01   112    NA   0         0
#> 2 1949-02-01   118    NA   5.36      0
#> 3 1949-03-01   132    NA  11.9       0
#> 4 1949-04-01   129    NA  -2.27      0
#> 5 1949-05-01   121    NA  -6.20      0
#> 6 1949-06-01   135    NA  11.6       0

output <- ts_ma_plot(
  .data = data_tbl,
  .date_col = date_col,
  .value_col = value,
  .ts_frequency = "week"
)
#> Warning: Non-numeric columns being dropped: date_col
#> Warning: Non-numeric columns being dropped: date_col
#> Warning: Non-numeric columns being dropped: date_col
#> Warning: Removed 51 row(s) containing missing values (geom_path).

output$pgrid

output$xts_plt

output$data_summary_tbl %>% head()
#> # A tibble: 6 × 5
#>   date_col   value  ma12 diff_a diff_b
#>   <date>     <dbl> <dbl>  <dbl>  <dbl>
#> 1 1949-01-01   112    NA   0         0
#> 2 1949-02-01   118    NA   5.36      0
#> 3 1949-03-01   132    NA  11.9       0
#> 4 1949-04-01   129    NA  -2.27      0
#> 5 1949-05-01   121    NA  -6.20      0
#> 6 1949-06-01   135    NA  11.6       0