Merge pull request #339 from cmu-delphi/missing-rlang-imports

Missing rlang imports

NAMESPACE

@@ -235,6 +235,7 @@ importFrom(magrittr,"%>%")
 importFrom(quantreg,rq)
 importFrom(recipes,bake)
 importFrom(recipes,prep)
+importFrom(rlang,"!!!")
 importFrom(rlang,"!!")
 importFrom(rlang,"%@%")
 importFrom(rlang,"%||%")
@@ -243,7 +244,10 @@ importFrom(rlang,abort)
 importFrom(rlang,as_function)
 importFrom(rlang,caller_env)
 importFrom(rlang,global_env)
+importFrom(rlang,inject)
+importFrom(rlang,is_logical)
 importFrom(rlang,is_null)
+importFrom(rlang,is_true)
 importFrom(rlang,set_names)
 importFrom(smoothqr,smooth_qr)
 importFrom(stats,as.formula)

R/epipredict-package.R

@@ -1,6 +1,7 @@
 ## usethis namespace: start
 #' @importFrom tibble tibble
-#' @importFrom rlang := !! %||% as_function global_env set_names
+#' @importFrom rlang := !! %||% as_function global_env set_names !!!
+#' @importFrom rlang is_logical is_true inject
 #' @importFrom stats poly predict lm residuals quantile
 #' @importFrom cli cli_abort
 #' @importFrom checkmate assert assert_character assert_int assert_scalar

vignettes/articles/sliding.Rmd

@@ -27,12 +27,12 @@ library(purrr)
 A key function from the epiprocess package is `epi_slide()`, which allows the
 user to apply a function or formula-based computation over variables in an
 `epi_df` over a running window of `n` time steps (see the following `epiprocess`
-vignette to go over the basics of the function: ["Slide a computation over
-signal values"](https://cmu-delphi.github.io/epiprocess/articles/slide.html)).
+vignette to go over the basics of the function: 
+["Slide a computation over signal values"](https://cmu-delphi.github.io/epiprocess/articles/slide.html)).
 The equivalent sliding method for an `epi_archive` object can be called by using
 the wrapper function `epix_slide()` (refer to the following vignette for the
-basics of the function: ["Work with archive objects and data
-revisions"](https://cmu-delphi.github.io/epiprocess/articles/archive.html)). The
+basics of the function: 
+["Work with archive objects and data revisions"](https://cmu-delphi.github.io/epiprocess/articles/archive.html)). The
 key difference from `epi_slide()` is that it performs version-aware
 computations. That is, the function only uses data that would have been
 available as of time t for that reference time.
@@ -171,7 +171,9 @@ in such forecasting. Including such factors as well as making enhancements such
 as correcting for outliers are some improvements one could make to this simple
 model.[^1]
 
-[^1]: Note that, despite the above caveats, simple models like this tend to out-perform many far more complicated models in the online Covid forecasting due to those models high variance predictions.
+[^1]: Note that, despite the above caveats, simple models like this tend to
+out-perform many far more complicated models in the online Covid forecasting due
+to those models high variance predictions.
 
 ### Example using case data from Canada
 
@@ -327,7 +329,8 @@ fc <- bind_rows(
 ) %>% pivot_quantiles_wider(fc_.pred_distn)
 ```
 
-Now we can plot the results on top of the latest case rates. As before, we will only display and focus on the results for FL and CA for simplicity.
+Now we can plot the results on top of the latest case rates. As before, we will
+only display and focus on the results for FL and CA for simplicity.
 
 ```{r plot-ar-asof, message = FALSE, warning = FALSE, fig.width = 9, fig.height = 6}
 fc_cafl <- fc %>% filter(geo_value %in% c("ca", "fl"))

vignettes/panel-data.Rmd

@@ -239,7 +239,9 @@ summary(extract_fit_engine(wf_linreg))
 ```
 
 This output tells us the coefficients of the fitted model; for instance, 
-the estimated intercept is $\widehat{\alpha}_0 =$ `r round(coef(extract_fit_engine(wf_linreg))[1], 3)` and the coefficient for $y_{tijk}$ is 
+the estimated intercept is $\widehat{\alpha}_0 =$ 
+`r round(coef(extract_fit_engine(wf_linreg))[1], 3)` and the coefficient for 
+$y_{tijk}$ is 
 $\widehat\alpha_1 =$ `r round(coef(extract_fit_engine(wf_linreg))[2], 3)`.
 The summary also tells us that all estimated coefficients are significantly 
 different from zero. Extracting the 95% confidence intervals for the 
@@ -315,7 +317,8 @@ defined as follows:
 \end{aligned}
 \]
 
-where $y_{tijk}$ is the 5-year median income (proportion) at time $t$ (in location $i$, age group $j$ with education quality $k$), 
+where $y_{tijk}$ is the 5-year median income (proportion) at time $t$ (in
+location $i$, age group $j$ with education quality $k$), 
 $x_{tijk}$ is the 2-year median income (proportion) at time $t$, and 
 $z_{tijk}$ is the number of graduates (proportion) at time $t$. 
 

vignettes/preprocessing-and-models.Rmd

@@ -58,7 +58,8 @@ and forecasts to characterize the state of an outbreak and its course. They use
 it to inform public health decision makers on potential consequences of
 deploying control measures.
 
-One of the outcomes that the CDC forecasts is [death counts from COVID-19](https://www.cdc.gov/coronavirus/2019-ncov/science/forecasting/forecasting-us.html).
+One of the outcomes that the CDC forecasts is 
+[death counts from COVID-19](https://www.cdc.gov/coronavirus/2019-ncov/science/forecasting/forecasting-us.html).
 Although there are many state-of-the-art models, we choose to use Poisson
 regression, the textbook example for modeling count data, as an illustration
 for using the `epipredict` package with other existing tidymodels packages.
@@ -109,7 +110,8 @@ Poisson distribution with mean $\mu_{t+7}$; $s_{\text{state}}$ are dummy
 variables for each state and take values of either 0 or 1.
 
 Preprocessing steps will be performed to prepare the
-data for model fitting. But before diving into them, it will be helpful to understand what `roles` are in the `recipes` framework.
+data for model fitting. But before diving into them, it will be helpful to 
+understand what `roles` are in the `recipes` framework.
 
 ---
 
@@ -235,9 +237,10 @@ using
 `layer_residual_quantiles()` should be used before population scaling or else
 the transformation will make the results uninterpretable.
 
-We wish, now, to predict the 7-day ahead death counts with lagged case rates and death
-rates, along with some extra behaviourial predictors. Namely, we will use survey data
-from [COVID-19 Trends and Impact Survey](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/fb-survey.html#behavior-indicators).
+We wish, now, to predict the 7-day ahead death counts with lagged case rates and
+death rates, along with some extra behaviourial predictors. Namely, we will use
+survey data from 
+[COVID-19 Trends and Impact Survey](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/fb-survey.html#behavior-indicators).
 
 The survey data provides the estimated percentage of people who wore a mask for
 most or all of the time while in public in the past 7 days and the estimated
@@ -413,7 +416,8 @@ We say location $\ell$ is a hotspot at time $t$ when $Z_{\ell,t}$ is
 `up`, meaning the number of newly reported cases over the past 7 days has
 increased by at least 25% compared to the preceding week. When $Z_{\ell,t}$
 is categorized as `down`, it suggests that there has been at least a 20%
-decrease in newly reported cases over the past 7 days (a 20% decrease is the inverse of a 25% increase). Otherwise, we will
+decrease in newly reported cases over the past 7 days (a 20% decrease is the 
+inverse of a 25% increase). Otherwise, we will
 consider the trend to be `flat`.
 
 The expression of the multinomial regression we will use is as follows:
@@ -439,7 +443,8 @@ g_{\text{up}}(x) &= \log\left(\frac{Pr(Z_{\ell,t}=\text{up}\mid x)}{Pr(Z_{\ell,t
 \end{aligned}
 
 Preprocessing steps are similar to the previous models with an additional step
-of categorizing the response variables. Again, we will use a subset of death rate and case rate data from our built-in dataset
+of categorizing the response variables. Again, we will use a subset of death
+rate and case rate data from our built-in dataset
 `case_death_rate_subset`.
 
 ```{r}
@@ -585,7 +590,12 @@ Sciences 118.51 (2021): e2111453118. [doi:10.1073/pnas.2111453118](https://doi.o
 
 ## Attribution
 
-This object contains a modified part of the [COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University](https://github.com/CSSEGISandData/COVID-19) as [republished in the COVIDcast Epidata API.](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html)
+This object contains a modified part of the 
+[COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University](https://github.com/CSSEGISandData/COVID-19) 
+as [republished in the COVIDcast Epidata API.](https://cmu-delphi.github.io/delphi-epidata/api/covidcast-signals/jhu-csse.html)
 
-This data set is licensed under the terms of the [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/) by the Johns Hopkins University
-on behalf of its Center for Systems Science in Engineering. Copyright Johns Hopkins University 2020.
+This data set is licensed under the terms of the 
+[Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/) 
+by the Johns Hopkins
+University on behalf of its Center for Systems Science in Engineering. Copyright
+Johns Hopkins University 2020.