You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Epipredict is a framework for building transformation and forecasting pipelines
81
-
for epidemiological and other panel time-series datasets.
82
-
In addition to tools for building forecasting pipelines, it contains a number of
83
-
"canned" forecasters meant to run with little modification as an easy way to get
84
-
started forecasting.
80
+
`{epipredict}` is a framework for building transformation and forecasting pipelines for epidemiological and other panel time-series datasets.
81
+
In addition to tools for building forecasting pipelines, it contains a number of “canned” forecasters meant to run with little modification as an easy way to get started forecasting.
85
82
86
83
It is designed to work well with
87
-
[`epiprocess`](https://cmu-delphi.github.io/epiprocess/), a utility for handling
88
-
various time series and geographic processing tools in an epidemiological
89
-
context.
84
+
[`{epiprocess}`](https://cmu-delphi.github.io/epiprocess/), a utility for time series handling and geographic processing in an epidemiological context.
90
85
Both of the packages are meant to work well with the panel data provided by
To install (unless you're planning on contributing to package development, we
99
-
suggest using the stable version):
96
+
Unless you’re planning on contributing to package development, we suggest using the stable version.
97
+
To install, run:
100
98
101
99
```r
102
100
# Stable version
@@ -113,23 +111,43 @@ The documentation for the stable version is at
113
111
114
112
## Motivating example
115
113
116
-
To demonstrate the kind of forecast epipredict can make, say we're predicting
117
-
COVID deaths per 100k for each state on
114
+
To demonstrate using `{epipredict}` for forecasting, say we want to
115
+
predict COVID-19 deaths per 100k people for each of a subset of states
116
+
117
+
```{r subset_geos}
118
+
used_locations <- c("ca", "ma", "ny", "tx")
119
+
```
120
+
121
+
on
118
122
119
123
```{r fc_date}
120
124
forecast_date <- as.Date("2021-08-01")
121
125
```
122
126
123
-
Below the fold, we construct this dataset as an `epiprocess::epi_df` from JHU
124
-
data.
127
+
<details>
128
+
<summary> Required packages </summary>
129
+
130
+
```{r install, run = FALSE}
131
+
library(epipredict)
132
+
library(epidatr)
133
+
library(epiprocess)
134
+
library(dplyr)
135
+
library(ggplot2)
136
+
```
137
+
</details>
138
+
139
+
140
+
Below the fold, we construct this dataset as an `epiprocess::epi_df` from
141
+
[Johns Hopkins Center for Systems Science and Engineering deaths data](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html).
125
142
126
143
<details>
127
144
<summary> Creating the dataset using `{epidatr}` and `{epiprocess}` </summary>
128
145
129
-
This dataset can be found in the package as `covid_case_death_rates`; we
130
-
demonstrate some of the typically ubiquitous cleaning operations needed to be
131
-
able to forecast.
132
-
First we pull both jhu-csse cases and deaths from
146
+
This section is intended to demonstrate some of the ubiquitous cleaning operations needed to be able to forecast.
147
+
The dataset prepared here is also included ready-to-go in `{epipredict}` as `covid_case_death_rates`.
148
+
149
+
First we pull both `jhu-csse` cases and deaths data from the
150
+
[Delphi API](https://cmu-delphi.github.io/delphi-epidata/api/covidcast.html) using the
0 commit comments