---
title: 3. Manipulating multiple signals
description: Download multiple signals at once, and aggregate and manipulate them in various ways.
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{3. Manipulating multiple signals}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

Various analyses involve working with multiple signals at once. The covidcast
package provides some helper functions for fetching multiple signals from the
API, and aggregating them into one data frame for various downstream uses.

## Fetching multiple signals

To load confirmed cases and deaths at the state level, in a single function
call, we can use `covidcast_signals()` (note the plural form of "signals"):

```{r, message = FALSE}
library(covidcast)

start_day <- "2020-06-01"
end_day <- "2020-10-01"

signals <- covidcast_signals(data_source = "usa-facts",
                             signal = c("confirmed_incidence_num",
                                        "deaths_incidence_num"),
                             start_day = start_day, end_day = end_day,
                             geo_type = "state")

summary(signals[[1]])
summary(signals[[2]])
```

This returns a list of `covidcast_signal` objects. The argument structure for
`covidcast_signals()` matches that of `covidcast_signal()`, except the first
four arguments (`data_source`, `signal`, `start_day`, `end_day`) are allowed to
be vectors. See the `covidcast_signals()` documentation for details.

## Aggregating signals, wide format

To aggregate multiple signals together, we can use the `aggregate_signals()`
function, which accepts a list of `covidcast_signal` objects, as returned by
`covidcast_signals()`. With all arguments set to their default values,
`aggregate_signals()` returns a data frame in "wide" format:

```{r, message=FALSE}
library(dplyr)

aggregate_signals(signals) %>% head()
```

In "wide" format, only the latest issue of data is retained, and the columns
`data_source`, `signal`, `issue`, `lag`, `stderr`, `sample_size` are all dropped
from the returned data frame. Each unique signal---defined by a combination of
data source name, signal name, and time-shift---is given its own column, whose
name indicates its defining quantities.

As hinted above, `aggregate_signals()` can also apply time-shifts to the given
signals, through the optional `dt` argument. This can be either be a single
vector of shifts or a list of vectors of shifts, this list having the same
length as the list of `covidcast_signal` objects (to apply, respectively, the
same shifts or a different set of shifts to each `covidcast_signal` object).
Negative shifts translate into in a *lag* value and positive shifts into a
*lead* value; for example, if `dt = -1`, then the value on June 2 that gets
reported is the original value on June 1; if `dt = 0`, then the values are left
as is.

```{r}
aggregate_signals(signals, dt = c(-1, 0)) %>%
  filter(geo_value == "tx") %>% head()
aggregate_signals(signals, dt = list(0, c(-1, 0, 1))) %>%
  filter(geo_value == "tx") %>% head()
```

Finally, `aggregate_signals()` also accepts a single data frame (instead of a
list of data frames), intended to be convenient when applying shifts to a single
`covidcast_signal` object:

```{r}
aggregate_signals(signals[[1]], dt = c(-1, 0, 1)) %>%
  filter(geo_value == "tx") %>% head()
```

## Aggregating signals, long format

We can also use `aggregate_signals()` in "long" format, with one observation
per row:

```{r}
aggregate_signals(signals, format = "long") %>%
  filter(geo_value == "tx") %>% head()

aggregate_signals(signals, dt = c(-1, 0), format = "long") %>%
  filter(geo_value == "tx") %>% head()

aggregate_signals(signals, dt = list(-1, 0), format = "long") %>%
  filter(geo_value == "tx") %>% head()
```

As we can see, time-shifts work just as before, in "wide" format. However, in
"long" format, all columns are retained, and an additional `dt` column is added
to record the time-shift being used.

Just as before, `covidcast_signals()` can also operate on a single data frame,
to conveniently apply shifts, in "long" format:

```{r}
aggregate_signals(signals[[1]], dt = c(-1, 0), format = "long") %>%
  filter(geo_value == "tx") %>% head()
```

## Pivoting longer or wider

The package also provides functions for pivoting an aggregated signal data frame
longer or wider. These are essentially wrappers around `pivot_longer()` and
`pivot_wider()` from the `tidyr` package, that set the column structure and
column names appropriately. For example, to pivot longer:

```{r}
aggregate_signals(signals, dt = list(-1, 0)) %>%
  covidcast_longer() %>%
  filter(geo_value == "tx") %>% head()
```

And to pivot wider:

```{r}
aggregate_signals(signals, dt = list(-1, 0), format = "long") %>%
  covidcast_wider() %>%
  filter(geo_value == "tx") %>% head()
```

## A sanity check

Lastly, here's a small sanity check, that lagging cases by 7 days using
`aggregate_signals()` and correlating this with deaths using `covidcast_cor()`
yields the same result as telling `covidcast_cor()` to do the time-shifting
itself:

```{r}
df_cor1 <- covidcast_cor(x = aggregate_signals(signals[[1]], dt = -7,
                                              format = "long"),
                        y = signals[[2]])

df_cor2 <- covidcast_cor(x = signals[[1]], y = signals[[2]], dt_x = -7)
identical(df_cor1, df_cor2)
```
