docs: document (GHA)

Author: dshemetov

README.md

@@ -1,3 +1,4 @@
+
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
 # epiprocess
@@ -12,26 +13,26 @@ time series analysis and forecasting.
 
 `{epiprocess}` contains:
 
-- `epi_df()` and `epi_archive()`, two data frame classes (that work
-  like a `{tibble}` with `{dplyr}` verbs) for working with
-  epidemiological time series data;
-- signal processing tools building on these data structures such as
-  - `epi_slide()` for sliding window operations;
-  - `epix_slide()` for sliding window operations on archives;
-  - `growth_rate()` for computing growth rates;
-  - `detect_outlr()` for outlier detection;
-  - `epi_cor()` for computing correlations;
+  - `epi_df()` and `epi_archive()`, two data frame classes (that work
+    like a `{tibble}` with `{dplyr}` verbs) for working with
+    epidemiological time series data;
+  - signal processing tools building on these data structures such as
+      - `epi_slide()` for sliding window operations;
+      - `epix_slide()` for sliding window operations on archives;
+      - `growth_rate()` for computing growth rates;
+      - `detect_outlr()` for outlier detection;
+      - `epi_cor()` for computing correlations;
 
 If you are new to this set of tools, you may be interested learning
 through a book format: [Introduction to Epidemiological
 Forecasting](https://cmu-delphi.github.io/delphi-tooling-book/).
 
 You may also be interested in:
 
-- `{epidatr}`, for accessing wide range of epidemiological data sets,
-  including COVID-19 data, flu data, and more.
-- `{rtestim}`, a package for estimating the time-varying reproduction
-  number of an epidemic.
+  - `{epidatr}`, for accessing wide range of epidemiological data sets,
+    including COVID-19 data, flu data, and more.
+  - `{rtestim}`, a package for estimating the time-varying reproduction
+    number of an epidemic.
 
 This package is provided by the [Delphi group](https://delphi.cmu.edu/)
 at Carnegie Mellon University.
@@ -40,7 +41,7 @@ at Carnegie Mellon University.
 
 To install:
 
-```r
+``` r
 # Stable version
 pak::pkg_install("cmu-delphi/epiprocess@main")
 
@@ -55,7 +56,7 @@ The package is not yet on CRAN.
 Once `epiprocess` and `epidatr` are installed, you can use the following
 code to get started:
 
-```r
+``` r
 library(epiprocess)
 library(epidatr)
 library(dplyr)
@@ -66,7 +67,7 @@ Get COVID-19 confirmed cumulative case data from JHU CSSE for
 California, Florida, New York, and Texas, from March 1, 2020 to January
 31, 2022
 
-```r
+``` r
 df <- pub_covidcast(
   source = "jhu-csse",
   signals = "confirmed_cumulative_num",
@@ -89,11 +90,11 @@ df
 #> # ℹ 2,802 more rows
 ```
 
-Convert the data to an epi_df object and sort by geo_value and
-time_value. You can work with an `epi_df` like you can with a
+Convert the data to an epi\_df object and sort by geo\_value and
+time\_value. You can work with an `epi_df` like you can with a
 `{tibble}` by using `{dplyr}` verbs
 
-```r
+``` r
 edf <- df %>%
   as_epi_df() %>%
   arrange_canonical() %>%
@@ -103,8 +104,8 @@ edf
 #> An `epi_df` object, 2,808 x 4 with metadata:
 #> * geo_type  = state
 #> * time_type = day
-#> * as_of     = 2024-10-14 16:29:54.397851
-#>
+#> * as_of     = 2024-10-15 02:26:30.787809
+#> 
 #> # A tibble: 2,808 × 4
 #> # Groups:   geo_value [4]
 #>   geo_value time_value cases_cumulative cases_daily
@@ -119,16 +120,20 @@ edf
 ```
 
 Compute the 7 day moving average of the confirmed daily cases for each
-geo_value
+geo\_value
 
-````r
+``` r
 edf <- edf %>%
   group_by(geo_value) %>%
+  epi_slide_mean(cases_daily, .window_size = 7, na.rm = TRUE) %>%
+  rename(smoothed_cases_daily = slide_value_cases_daily)
+```
+
 Autoplot the confirmed daily cases for each geo\_value
 
 ``` r
 edf %>%
   autoplot(smoothed_cases_daily)
-````
+```
 
 <img src="man/figures/README-unnamed-chunk-7-1.svg" width="90%" style="display: block; margin: auto;" />