Remove manual class() calls in old blog posts

These can be replaced with as.covidcast_signal, which is much clearer.

Author: capnrefsmmat

content/blog/2020-08-26-fb-survey.Rmd

@@ -149,22 +149,23 @@ df_in = covidcast_signal("jhu-csse", "confirmed_7dav_incidence_prop",
 df_fb_avg = df_fb %>% group_by(geo_value) %>% summarize(value = mean(value))
 df_in_avg = df_in %>% group_by(geo_value) %>% summarize(value = mean(value))
 
-# Set a bunch of fields so that the data frames know how to plot themselves
-df_fb_avg$time_value = df_in_avg$time_value = start_day
-df_fb_avg$issue = df_in_avg$issue = start_day
-attributes(df_fb_avg)$metadata$geo_type = "state"
-attributes(df_in_avg)$metadata$geo_type = "state"
-class(df_fb_avg) = c("covidcast_signal", class(df_fb_avg))
-class(df_in_avg) = c("covidcast_signal", class(df_in_avg))
+# Turn these into covidcast_signal data frames that can be mapped
+df_fb_avg = df_fb_avg %>%
+  mutate(time_value = start_day) %>%
+  as.covidcast_signal(signal = "fb_avg", geo_type = "state")
+
+df_in_avg = df_in_avg %>%
+  mutate(time_value = start_day) %>%
+  as.covidcast_signal(signal = "in_avg", geo_type = "state")
 
 # Plot choropleth maps, using the covidcast plotting functionality
 subtitle = paste("Averaged over", start_day, "to", end_day)
 p1 = plot(df_fb_avg,
           title = "% of people with COVID symptoms, based on Facebook surveys",
-          range=c(0, 1), choro_params = list(subtitle = subtitle))
+          range = c(0, 1), choro_params = list(subtitle = subtitle))
 p2 = plot(df_in_avg,
           title = "Daily new confirmed COVID-19 cases per 100,000 people",
-          range=c(0, 30), choro_params = list(subtitle = subtitle))
+          range = c(0, 30), choro_params = list(subtitle = subtitle))
 grid.arrange(p1, p2, nrow = 1)
 ```
 
@@ -539,12 +540,13 @@ or we didn't have enough data from the surveys in
 order to estimate % CLI and % CLI-in-community signals there.
 
 ```{r, fig.width = 10, fig.height = 4, out.extra = 'class="wide-figure"'}
-# Set a bunch of fields so that the data frames know how to plot themselves
-df_cor1$time_value = df_cor2$time_value = start_day
-df_cor1$issue = df_cor2$issue = start_day
-attributes(df_cor1)$metadata$geo_type = "county"
-attributes(df_cor2)$metadata$geo_type = "county"
-class(df_cor1) = class(df_cor2) = c("covidcast_signal", "data.frame")
+# Use as.covidcast_signal to turn these into mappable signals
+df_cor1 = df_cor1 %>%
+  mutate(time_value = start_day) %>%
+  as.covidcast_signal(signal = "cor1")
+df_cor2 = df_cor2 %>%
+  mutate(time_value = start_day) %>%
+  as.covidcast_signal(signal = "cor1")
 
 # Plot choropleth maps, using the covidcast plotting functionality
 p1 = plot(df_cor1, title = "Correlation between % CLI and case rates",

content/blog/2020-10-14-dv-signal.Rmd

@@ -89,13 +89,13 @@ df_in = covidcast_signal("usa-facts", "confirmed_7dav_incidence_prop",
 df_dv_avg = df_dv %>% group_by(geo_value) %>% summarize(value = mean(value))
 df_in_avg = df_in %>% group_by(geo_value) %>% summarize(value = mean(value))
 
-# Set a bunch of fields so that the data frames know how to plot themselves
-df_dv_avg$time_value = df_in_avg$time_value = start_day
-df_dv_avg$issue = df_in_avg$issue = start_day
-attributes(df_dv_avg)$metadata$geo_type = "state"
-attributes(df_in_avg)$metadata$geo_type = "state"
-class(df_dv_avg) = c("covidcast_signal", class(df_dv_avg))
-class(df_in_avg) = c("covidcast_signal", class(df_in_avg))
+# Use as.covidcast_signal to turn these into covidcast data frames
+df_dv_avg <- df_dv_avg %>%
+  mutate(time_value = start_day) %>%
+  as.covidcast_signal(signal = "dv_avg", geo_type = "state")
+df_in_avg <- df_in_avg %>%
+  mutate(time_value = start_day) %>%
+  as.covidcast_signal(signal = "in_avg", geo_type = "state")
 
 # Plot choropleth maps, using the covidcast plotting functionality
 subtitle = paste("Averaged over", start_day, "to", end_day)
@@ -337,8 +337,12 @@ lm_dv_cases_df = dv_cases_df %>%
   ungroup()
 
 df_cor_by_time_adj = covidcast_cor(
-  dv_cases_df %>% mutate(value = dv*slope_6wk + int_6wk, issue = time_value),
-  dv_cases_df %>% mutate(value = cases, issue = time_value),
+  dv_cases_df %>%
+    mutate(value = dv*slope_6wk + int_6wk, issue = time_value) %>%
+    as.covidcast_signal(signal = "dv_cases"),
+  dv_cases_df %>%
+    mutate(value = cases, issue = time_value) %>%
+    as.covidcast_signal(signal = "dv_cases"),
   by = "time_value",
   method = "spearman")