Nothing
3. Manipulating multiple signals
In covidcast: Client for Delphi's 'COVIDcast Epidata' API
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"):
library(covidcast)
start_day <- "2020-06-01"
end_day <- "2020-10-01"
signals <- covidcast_signals(data_source = "jhu-csse",
signal = c("confirmed_7dav_incidence_prop",
"deaths_7dav_incidence_prop"),
start_day = start_day, end_day = end_day,
geo_type = "state", geo_values = "tx")
summary(signals[[1]])
A `covidcast_signal` dataframe with 123 rows and 15 columns.
data_source : jhu-csse
signal : confirmed_7dav_incidence_prop
geo_type : state
first date : 2020-06-01
last date : 2020-10-01
median number of geo_values per day : 1
summary(signals[[2]])
A `covidcast_signal` dataframe with 123 rows and 15 columns.
data_source : jhu-csse
signal : deaths_7dav_incidence_prop
geo_type : state
first date : 2020-06-01
last date : 2020-10-01
median number of geo_values per day : 1
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:
library(dplyr)
aggregate_signals(signals) %>% head()
geo_value time_value value+0:jhu-csse_confirmed_7dav_incidence_prop
1 tx 2020-06-01 3.393256
2 tx 2020-06-02 3.644320
3 tx 2020-06-03 3.723629
4 tx 2020-06-04 6.985028
5 tx 2020-06-05 7.920192
6 tx 2020-06-06 8.034533
value+0:jhu-csse_deaths_7dav_incidence_prop
1 0.0856342
2 0.0953654
3 0.0909864
4 0.0977982
5 0.1002310
6 0.0909864
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.
aggregate_signals(signals, dt = c(-1, 0)) %>%
head()
geo_value time_value value-1:jhu-csse_confirmed_7dav_incidence_prop
1 tx 2020-06-01 NA
2 tx 2020-06-02 3.393256
3 tx 2020-06-03 3.644320
4 tx 2020-06-04 3.723629
5 tx 2020-06-05 6.985028
6 tx 2020-06-06 7.920192
value+0:jhu-csse_confirmed_7dav_incidence_prop
1 3.393256
2 3.644320
3 3.723629
4 6.985028
5 7.920192
6 8.034533
value-1:jhu-csse_deaths_7dav_incidence_prop
1 NA
2 0.0856342
3 0.0953654
4 0.0909864
5 0.0977982
6 0.1002310
value+0:jhu-csse_deaths_7dav_incidence_prop
1 0.0856342
2 0.0953654
3 0.0909864
4 0.0977982
5 0.1002310
6 0.0909864
aggregate_signals(signals, dt = list(0, c(-1, 0, 1))) %>%
head()
geo_value time_value value+0:jhu-csse_confirmed_7dav_incidence_prop
1 tx 2020-06-01 3.393256
2 tx 2020-06-02 3.644320
3 tx 2020-06-03 3.723629
4 tx 2020-06-04 6.985028
5 tx 2020-06-05 7.920192
6 tx 2020-06-06 8.034533
value-1:jhu-csse_deaths_7dav_incidence_prop
1 NA
2 0.0856342
3 0.0953654
4 0.0909864
5 0.0977982
6 0.1002310
value+0:jhu-csse_deaths_7dav_incidence_prop
1 0.0856342
2 0.0953654
3 0.0909864
4 0.0977982
5 0.1002310
6 0.0909864
value+1:jhu-csse_deaths_7dav_incidence_prop
1 0.0953654
2 0.0909864
3 0.0977982
4 0.1002310
5 0.0909864
6 0.0885536
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:
aggregate_signals(signals[[1]], dt = c(-1, 0, 1)) %>%
head()
geo_value time_value value-1:jhu-csse_confirmed_7dav_incidence_prop
1 tx 2020-06-01 NA
2 tx 2020-06-02 3.393256
3 tx 2020-06-03 3.644320
4 tx 2020-06-04 3.723629
5 tx 2020-06-05 6.985028
6 tx 2020-06-06 7.920192
value+0:jhu-csse_confirmed_7dav_incidence_prop
1 3.393256
2 3.644320
3 3.723629
4 6.985028
5 7.920192
6 8.034533
value+1:jhu-csse_confirmed_7dav_incidence_prop
1 3.644320
2 3.723629
3 6.985028
4 7.920192
5 8.034533
6 7.957171
Aggregating signals, long format
We can also use aggregate_signals() in "long" format, with one observation
per row:
aggregate_signals(signals, format = "long") %>%
head()
data_source signal geo_value time_value source
1 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 jhu-csse
2 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 jhu-csse
3 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 jhu-csse
4 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-04 jhu-csse
5 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-05 jhu-csse
6 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-06 jhu-csse
geo_type time_type issue lag missing_value missing_stderr
1 state day 2023-03-03 1005 0 5
2 state day 2023-03-03 1004 0 5
3 state day 2023-03-03 1003 0 5
4 state day 2023-03-03 1002 0 5
5 state day 2023-03-03 1001 0 5
6 state day 2023-03-03 1000 0 5
missing_sample_size stderr sample_size dt value
1 5 NA NA 0 3.393256
2 5 NA NA 0 3.644320
3 5 NA NA 0 3.723629
4 5 NA NA 0 6.985028
5 5 NA NA 0 7.920192
6 5 NA NA 0 8.034533
aggregate_signals(signals, dt = c(-1, 0), format = "long") %>%
head()
data_source signal geo_value time_value source
1 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 jhu-csse
2 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 jhu-csse
3 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 jhu-csse
4 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 jhu-csse
5 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 jhu-csse
6 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 jhu-csse
geo_type time_type issue lag missing_value missing_stderr
1 state day 2023-03-03 1005 0 5
2 state day 2023-03-03 1005 0 5
3 state day 2023-03-03 1004 0 5
4 state day 2023-03-03 1004 0 5
5 state day 2023-03-03 1003 0 5
6 state day 2023-03-03 1003 0 5
missing_sample_size stderr sample_size dt value
1 5 NA NA -1 NA
2 5 NA NA 0 3.393256
3 5 NA NA -1 3.393256
4 5 NA NA 0 3.644320
5 5 NA NA -1 3.644320
6 5 NA NA 0 3.723629
aggregate_signals(signals, dt = list(-1, 0), format = "long") %>%
head()
data_source signal geo_value time_value source
1 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 jhu-csse
2 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 jhu-csse
3 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 jhu-csse
4 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-04 jhu-csse
5 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-05 jhu-csse
6 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-06 jhu-csse
geo_type time_type issue lag missing_value missing_stderr
1 state day 2023-03-03 1005 0 5
2 state day 2023-03-03 1004 0 5
3 state day 2023-03-03 1003 0 5
4 state day 2023-03-03 1002 0 5
5 state day 2023-03-03 1001 0 5
6 state day 2023-03-03 1000 0 5
missing_sample_size stderr sample_size dt value
1 5 NA NA -1 NA
2 5 NA NA -1 3.393256
3 5 NA NA -1 3.644320
4 5 NA NA -1 3.723629
5 5 NA NA -1 6.985028
6 5 NA NA -1 7.920192
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:
aggregate_signals(signals[[1]], dt = c(-1, 0), format = "long") %>%
head()
data_source signal geo_value time_value source
1 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 jhu-csse
2 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 jhu-csse
3 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 jhu-csse
4 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 jhu-csse
5 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 jhu-csse
6 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 jhu-csse
geo_type time_type issue lag missing_value missing_stderr
1 state day 2023-03-03 1005 0 5
2 state day 2023-03-03 1005 0 5
3 state day 2023-03-03 1004 0 5
4 state day 2023-03-03 1004 0 5
5 state day 2023-03-03 1003 0 5
6 state day 2023-03-03 1003 0 5
missing_sample_size stderr sample_size dt value
1 5 NA NA -1 NA
2 5 NA NA 0 3.393256
3 5 NA NA -1 3.393256
4 5 NA NA 0 3.644320
5 5 NA NA -1 3.644320
6 5 NA NA 0 3.723629
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:
aggregate_signals(signals, dt = list(-1, 0)) %>%
covidcast_longer() %>%
head()
data_source signal geo_value time_value dt value
1 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 -1 NA
2 jhu-csse deaths_7dav_incidence_prop tx 2020-06-01 0 0.0856342
3 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 -1 3.3932560
4 jhu-csse deaths_7dav_incidence_prop tx 2020-06-02 0 0.0953654
5 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 -1 3.6443200
6 jhu-csse deaths_7dav_incidence_prop tx 2020-06-03 0 0.0909864
And to pivot wider:
aggregate_signals(signals, dt = list(-1, 0), format = "long") %>%
covidcast_wider() %>%
head()
geo_value time_value value-1:jhu-csse_confirmed_7dav_incidence_prop
1 tx 2020-06-01 NA
2 tx 2020-06-02 3.393256
3 tx 2020-06-03 3.644320
4 tx 2020-06-04 3.723629
5 tx 2020-06-05 6.985028
6 tx 2020-06-06 7.920192
value+0:jhu-csse_deaths_7dav_incidence_prop
1 0.0856342
2 0.0953654
3 0.0909864
4 0.0977982
5 0.1002310
6 0.0909864
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:
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)
[1] TRUE
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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"):
library(covidcast) start_day <- "2020-06-01" end_day <- "2020-10-01" signals <- covidcast_signals(data_source = "jhu-csse", signal = c("confirmed_7dav_incidence_prop", "deaths_7dav_incidence_prop"), start_day = start_day, end_day = end_day, geo_type = "state", geo_values = "tx") summary(signals[[1]])
A `covidcast_signal` dataframe with 123 rows and 15 columns. data_source : jhu-csse signal : confirmed_7dav_incidence_prop geo_type : state first date : 2020-06-01 last date : 2020-10-01 median number of geo_values per day : 1
summary(signals[[2]])
A `covidcast_signal` dataframe with 123 rows and 15 columns. data_source : jhu-csse signal : deaths_7dav_incidence_prop geo_type : state first date : 2020-06-01 last date : 2020-10-01 median number of geo_values per day : 1
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:
library(dplyr) aggregate_signals(signals) %>% head()
geo_value time_value value+0:jhu-csse_confirmed_7dav_incidence_prop 1 tx 2020-06-01 3.393256 2 tx 2020-06-02 3.644320 3 tx 2020-06-03 3.723629 4 tx 2020-06-04 6.985028 5 tx 2020-06-05 7.920192 6 tx 2020-06-06 8.034533 value+0:jhu-csse_deaths_7dav_incidence_prop 1 0.0856342 2 0.0953654 3 0.0909864 4 0.0977982 5 0.1002310 6 0.0909864
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.
aggregate_signals(signals, dt = c(-1, 0)) %>% head()
geo_value time_value value-1:jhu-csse_confirmed_7dav_incidence_prop 1 tx 2020-06-01 NA 2 tx 2020-06-02 3.393256 3 tx 2020-06-03 3.644320 4 tx 2020-06-04 3.723629 5 tx 2020-06-05 6.985028 6 tx 2020-06-06 7.920192 value+0:jhu-csse_confirmed_7dav_incidence_prop 1 3.393256 2 3.644320 3 3.723629 4 6.985028 5 7.920192 6 8.034533 value-1:jhu-csse_deaths_7dav_incidence_prop 1 NA 2 0.0856342 3 0.0953654 4 0.0909864 5 0.0977982 6 0.1002310 value+0:jhu-csse_deaths_7dav_incidence_prop 1 0.0856342 2 0.0953654 3 0.0909864 4 0.0977982 5 0.1002310 6 0.0909864
aggregate_signals(signals, dt = list(0, c(-1, 0, 1))) %>% head()
geo_value time_value value+0:jhu-csse_confirmed_7dav_incidence_prop 1 tx 2020-06-01 3.393256 2 tx 2020-06-02 3.644320 3 tx 2020-06-03 3.723629 4 tx 2020-06-04 6.985028 5 tx 2020-06-05 7.920192 6 tx 2020-06-06 8.034533 value-1:jhu-csse_deaths_7dav_incidence_prop 1 NA 2 0.0856342 3 0.0953654 4 0.0909864 5 0.0977982 6 0.1002310 value+0:jhu-csse_deaths_7dav_incidence_prop 1 0.0856342 2 0.0953654 3 0.0909864 4 0.0977982 5 0.1002310 6 0.0909864 value+1:jhu-csse_deaths_7dav_incidence_prop 1 0.0953654 2 0.0909864 3 0.0977982 4 0.1002310 5 0.0909864 6 0.0885536
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:
aggregate_signals(signals[[1]], dt = c(-1, 0, 1)) %>% head()
geo_value time_value value-1:jhu-csse_confirmed_7dav_incidence_prop 1 tx 2020-06-01 NA 2 tx 2020-06-02 3.393256 3 tx 2020-06-03 3.644320 4 tx 2020-06-04 3.723629 5 tx 2020-06-05 6.985028 6 tx 2020-06-06 7.920192 value+0:jhu-csse_confirmed_7dav_incidence_prop 1 3.393256 2 3.644320 3 3.723629 4 6.985028 5 7.920192 6 8.034533 value+1:jhu-csse_confirmed_7dav_incidence_prop 1 3.644320 2 3.723629 3 6.985028 4 7.920192 5 8.034533 6 7.957171
Aggregating signals, long format
We can also use aggregate_signals() in "long" format, with one observation
per row:
aggregate_signals(signals, format = "long") %>% head()
data_source signal geo_value time_value source 1 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 jhu-csse 2 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 jhu-csse 3 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 jhu-csse 4 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-04 jhu-csse 5 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-05 jhu-csse 6 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-06 jhu-csse geo_type time_type issue lag missing_value missing_stderr 1 state day 2023-03-03 1005 0 5 2 state day 2023-03-03 1004 0 5 3 state day 2023-03-03 1003 0 5 4 state day 2023-03-03 1002 0 5 5 state day 2023-03-03 1001 0 5 6 state day 2023-03-03 1000 0 5 missing_sample_size stderr sample_size dt value 1 5 NA NA 0 3.393256 2 5 NA NA 0 3.644320 3 5 NA NA 0 3.723629 4 5 NA NA 0 6.985028 5 5 NA NA 0 7.920192 6 5 NA NA 0 8.034533
aggregate_signals(signals, dt = c(-1, 0), format = "long") %>% head()
data_source signal geo_value time_value source 1 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 jhu-csse 2 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 jhu-csse 3 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 jhu-csse 4 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 jhu-csse 5 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 jhu-csse 6 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 jhu-csse geo_type time_type issue lag missing_value missing_stderr 1 state day 2023-03-03 1005 0 5 2 state day 2023-03-03 1005 0 5 3 state day 2023-03-03 1004 0 5 4 state day 2023-03-03 1004 0 5 5 state day 2023-03-03 1003 0 5 6 state day 2023-03-03 1003 0 5 missing_sample_size stderr sample_size dt value 1 5 NA NA -1 NA 2 5 NA NA 0 3.393256 3 5 NA NA -1 3.393256 4 5 NA NA 0 3.644320 5 5 NA NA -1 3.644320 6 5 NA NA 0 3.723629
aggregate_signals(signals, dt = list(-1, 0), format = "long") %>% head()
data_source signal geo_value time_value source 1 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 jhu-csse 2 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 jhu-csse 3 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 jhu-csse 4 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-04 jhu-csse 5 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-05 jhu-csse 6 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-06 jhu-csse geo_type time_type issue lag missing_value missing_stderr 1 state day 2023-03-03 1005 0 5 2 state day 2023-03-03 1004 0 5 3 state day 2023-03-03 1003 0 5 4 state day 2023-03-03 1002 0 5 5 state day 2023-03-03 1001 0 5 6 state day 2023-03-03 1000 0 5 missing_sample_size stderr sample_size dt value 1 5 NA NA -1 NA 2 5 NA NA -1 3.393256 3 5 NA NA -1 3.644320 4 5 NA NA -1 3.723629 5 5 NA NA -1 6.985028 6 5 NA NA -1 7.920192
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:
aggregate_signals(signals[[1]], dt = c(-1, 0), format = "long") %>% head()
data_source signal geo_value time_value source 1 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 jhu-csse 2 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 jhu-csse 3 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 jhu-csse 4 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 jhu-csse 5 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 jhu-csse 6 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 jhu-csse geo_type time_type issue lag missing_value missing_stderr 1 state day 2023-03-03 1005 0 5 2 state day 2023-03-03 1005 0 5 3 state day 2023-03-03 1004 0 5 4 state day 2023-03-03 1004 0 5 5 state day 2023-03-03 1003 0 5 6 state day 2023-03-03 1003 0 5 missing_sample_size stderr sample_size dt value 1 5 NA NA -1 NA 2 5 NA NA 0 3.393256 3 5 NA NA -1 3.393256 4 5 NA NA 0 3.644320 5 5 NA NA -1 3.644320 6 5 NA NA 0 3.723629
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:
aggregate_signals(signals, dt = list(-1, 0)) %>% covidcast_longer() %>% head()
data_source signal geo_value time_value dt value 1 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-01 -1 NA 2 jhu-csse deaths_7dav_incidence_prop tx 2020-06-01 0 0.0856342 3 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-02 -1 3.3932560 4 jhu-csse deaths_7dav_incidence_prop tx 2020-06-02 0 0.0953654 5 jhu-csse confirmed_7dav_incidence_prop tx 2020-06-03 -1 3.6443200 6 jhu-csse deaths_7dav_incidence_prop tx 2020-06-03 0 0.0909864
And to pivot wider:
aggregate_signals(signals, dt = list(-1, 0), format = "long") %>% covidcast_wider() %>% head()
geo_value time_value value-1:jhu-csse_confirmed_7dav_incidence_prop 1 tx 2020-06-01 NA 2 tx 2020-06-02 3.393256 3 tx 2020-06-03 3.644320 4 tx 2020-06-04 3.723629 5 tx 2020-06-05 6.985028 6 tx 2020-06-06 7.920192 value+0:jhu-csse_deaths_7dav_incidence_prop 1 0.0856342 2 0.0953654 3 0.0909864 4 0.0977982 5 0.1002310 6 0.0909864
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:
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)
[1] TRUE
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