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dshemetov committed Oct 4, 2024
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<!-- README.md is generated from README.Rmd. Please edit that file -->

# epiprocess

## TODO: Condense these paragraphs

The [`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/) package
works with epidemiological time series data to provide situational
awareness, processing, and transformations in preparation for modeling,
and version-faithful model backtesting. It contains:

- `epi_df`, a class for working with epidemiological time series data
which behaves like a tibble (and can be manipulated with
[`{dplyr}`](https://dplyr.tidyverse.org/)-esque “verbs”) but with
some additional structure;
- `epi_archive`, a class for working with the version history of such
time series data;
- sample epidemiological data in these formats;
- `epi_df`, a class for working with epidemiological time series data
which behaves like a tibble (and can be manipulated with
[`{dplyr}`](https://dplyr.tidyverse.org/)-esque “verbs”) but with
some additional structure;
- `epi_archive`, a class for working with the version history of such
time series data;
- sample epidemiological data in these formats;

This package is provided by the Delphi group at Carnegie Mellon
University. The Delphi group provides many tools also hosts the Delphi
Expand Down Expand Up @@ -48,7 +51,7 @@ many common tasks instead.

To install:

```r
``` r
# Stable version
pak::pkg_install("cmu-delphi/epiprocess@main")

Expand All @@ -63,7 +66,7 @@ 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)
Expand All @@ -74,7 +77,7 @@ 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",
Expand All @@ -101,11 +104,11 @@ df
#> # ℹ 2,798 more rows
```

Convert the data to an epi_df object and sort by geo_value and
time_value. You can work with the epi_df object like a tibble using
Convert the data to an epi\_df object and sort by geo\_value and
time\_value. You can work with the epi\_df object like a tibble using
dplyr

```r
``` r
edf <- df %>%
as_epi_df() %>%
arrange_canonical() %>%
Expand All @@ -115,8 +118,8 @@ edf
#> An `epi_df` object, 2,808 x 4 with metadata:
#> * geo_type = state
#> * time_type = day
#> * as_of = 2024-10-04 13:32:23.730165
#>
#> * as_of = 2024-10-04 22:31:35.502626
#>
#> # A tibble: 2,808 × 4
#> # Groups: geo_value [4]
#> geo_value time_value cases_cumulative cases_daily
Expand All @@ -134,56 +137,56 @@ edf
#> # ℹ 2,798 more rows
```

Autoplot the confirmed daily cases for each geo_value
Autoplot the confirmed daily cases for each geo\_value

```r
``` r
edf %>%
autoplot(cases_cumulative)
```

<img src="man/figures/README-unnamed-chunk-6-1.png" width="100%" />

Compute the 7 day moving average of the confirmed daily cases for each
geo_value
geo\_value

```r
``` r
edf %>%
group_by(geo_value) %>%
epi_slide_mean(cases_daily, .window_size = 7, na.rm = TRUE)
#> An `epi_df` object, 2,808 x 5 with metadata:
#> * geo_type = state
#> * time_type = day
#> * as_of = 2024-10-04 13:32:23.730165
#>
#> * as_of = 2024-10-04 22:31:35.502626
#>
#> # A tibble: 2,808 × 5
#> # Groups: geo_value [4]
#> geo_value time_value cases_cumulative cases_daily slide_value_cases_daily
#> * <chr> <date> <dbl> <dbl> <dbl>
#> 1 ca 2020-03-01 19 19 19
#> 2 ca 2020-03-02 23 4 11.5
#> 1 ca 2020-03-01 19 19 19
#> 2 ca 2020-03-02 23 4 11.5
#> 3 ca 2020-03-03 29 6 9.67
#> 4 ca 2020-03-04 40 11 10
#> 5 ca 2020-03-05 50 10 10
#> 6 ca 2020-03-06 68 18 11.3
#> 7 ca 2020-03-07 94 26 13.4
#> 8 ca 2020-03-08 113 19 13.4
#> 9 ca 2020-03-09 136 23 16.1
#> 10 ca 2020-03-10 158 22 18.4
#> 4 ca 2020-03-04 40 11 10
#> 5 ca 2020-03-05 50 10 10
#> 6 ca 2020-03-06 68 18 11.3
#> 7 ca 2020-03-07 94 26 13.4
#> 8 ca 2020-03-08 113 19 13.4
#> 9 ca 2020-03-09 136 23 16.1
#> 10 ca 2020-03-10 158 22 18.4
#> # ℹ 2,798 more rows
```

Compute the growth rate of the confirmed cumulative cases for each
geo_value
geo\_value

```r
``` r
edf %>%
group_by(geo_value) %>%
mutate(cases_growth = growth_rate(x = time_value, y = cases_cumulative, method = "rel_change", h = 7))
#> An `epi_df` object, 2,808 x 5 with metadata:
#> * geo_type = state
#> * time_type = day
#> * as_of = 2024-10-04 13:32:23.730165
#>
#> * as_of = 2024-10-04 22:31:35.502626
#>
#> # A tibble: 2,808 × 5
#> # Groups: geo_value [4]
#> geo_value time_value cases_cumulative cases_daily cases_growth
Expand All @@ -204,7 +207,7 @@ edf %>%
Detect outliers in the growth rate of the confirmed cumulative cases for
each

```r
``` r
edf %>%
group_by(geo_value) %>%
mutate(outlier_info = detect_outlr(x = time_value, y = cases_daily)) %>%
Expand All @@ -228,8 +231,8 @@ edf %>%
#> An `epi_df` object, 2,808 x 5 with metadata:
#> * geo_type = state
#> * time_type = day
#> * as_of = 2024-10-04 13:32:23.730165
#>
#> * as_of = 2024-10-04 22:31:35.502626
#>
#> # A tibble: 2,808 × 5
#> geo_value time_value cases_cumulative cases_daily outlier_info$rm_geo_value
#> * <chr> <date> <dbl> <dbl> <dbl>
Expand All @@ -249,11 +252,11 @@ edf %>%
#> # $combined_replacement <dbl>
```

Add a column to the epi_df object with the daily deaths for each
geo_value and compute the correlations between cases and deaths for
each geo_value
Add a column to the epi\_df object with the daily deaths for each
geo\_value and compute the correlations between cases and deaths for
each geo\_value

```r
``` r
df <- pub_covidcast(
source = "jhu-csse",
signals = "deaths_incidence_num",
Expand Down

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