inst/doc/plotting-signals.R

In covidcast: Client for Delphi's 'COVIDcast Epidata' API

## ---- message=FALSE-----------------------------------------------------------
library(covidcast)

dv <- covidcast_signal(data_source = "doctor-visits",
                       signal = "smoothed_adj_cli",
                       start_day = "2020-07-01", end_day = "2020-07-14")
summary(dv)

inum <- covidcast_signal(data_source = "jhu-csse",
                         signal = "confirmed_7dav_incidence_prop",
                         start_day = "2020-07-01", end_day = "2020-07-14")
summary(inum)

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(fig.width = 6, fig.height = 4)

## -----------------------------------------------------------------------------
plot(dv)

## -----------------------------------------------------------------------------
plot(dv, time_value = "2020-07-04", choro_col = cm.colors(10), alpha = 0.4,
     title = "COVID doctor visits on 2020-07-04")

## -----------------------------------------------------------------------------
breaks <- c(0, 1, 2, 5, 10, 20, 50, 100, 200)
colors <- c("#D3D3D3", "#FFFFCC", "#FEDDA2", "#FDBB79", "#FD9950", "#EB7538",
            "#C74E32", "#A3272C", "#800026")

# Note that length(breaks) == length(colors) by design. This works as follows:
# we assign colors[i] iff the value satisfies breaks[i] <= value < breaks[i+1],
# where we take breaks[0] = -Inf and breaks[N+1] = Inf, for N = length(breaks)

plot(inum, choro_col = colors, choro_params = list(breaks = breaks),
     title = "New COVID cases (7-day trailing average) on 2020-07-14")

## ---- message=FALSE-----------------------------------------------------------
cprop <- covidcast_signal(data_source = "jhu-csse",
                          signal = "confirmed_cumulative_prop",
                          start_day = "2020-07-01", end_day = "2020-07-14")

breaks <- c(0, 1000)
colors <- c("#D3D3D3", "#FFC0CB")

plot(cprop, choro_col = colors,
     choro_params = list(breaks = breaks, legend_width = 3),
     title = "Cumulative COVID cases per 100k people on 2020-07-14")

## -----------------------------------------------------------------------------
plot(inum, plot_type = "bubble")

## -----------------------------------------------------------------------------
plot(inum, plot_type = "bubble",
     bubble_params = list(breaks = seq(20, 200, len = 6)))

## ---- message=FALSE-----------------------------------------------------------
iprop <- covidcast_signal(data_source = "jhu-csse",
                          signal = "confirmed_7dav_incidence_prop",
                          start_day = "2020-07-01", end_day = "2020-07-14")

## ---- fig.width=8, fig.height=4, message=FALSE--------------------------------
library(gridExtra)

breaks1 <- c(1, 10, 100, 1000)
breaks2 <- c(10, 50, 100, 500)

p1 <- plot(inum, plot_type = "bubble",
           bubble_params = list(breaks = breaks1, max_size = 6),
           include = "TX", bubble_col = "red",
           title = paste("Incidence number on", max(inum$time_value)))
p2 <- plot(iprop, plot_type = "bubble",
           bubble_params = list(breaks = breaks2, max_size = 6),
           include = "TX", bubble_col = "red",
           title = paste("Incidence rate on", max(iprop$time_value)))

grid.arrange(p1, p2, nrow = 1)

## ---- message=FALSE-----------------------------------------------------------
dv_st <- covidcast_signal(data_source = "doctor-visits",
                          signal = "smoothed_adj_cli",
                          start_day = "2020-04-15", end_day = "2020-07-01",
                          geo_type = "state")
inum_st <- covidcast_signal(data_source = "jhu-csse",
                            signal = "confirmed_7dav_incidence_prop",
                            start_day = "2020-04-15", end_day = "2020-07-01",
                            geo_type = "state")

## ---- message = FALSE---------------------------------------------------------
library(dplyr)

states <- c("ca", "pa", "tx", "ny")
plot(dv_st %>% filter(geo_value %in% states), plot_type = "line")
plot(inum_st %>% filter(geo_value %in% states), plot_type = "line")

## ---- warning = FALSE---------------------------------------------------------
library(ggplot2)

dv_md <- covidcast_signal(data_source = "doctor-visits",
                          signal = "smoothed_adj_cli",
                          start_day = "2020-06-01", end_day = "2020-07-15",
                          geo_values = name_to_fips("Miami-Dade"))
inum_md <- covidcast_signal(data_source = "jhu-csse",
                            signal = "confirmed_7dav_incidence_prop",
                            start_day = "2020-06-01", end_day = "2020-07-15",
                            geo_values = name_to_fips("Miami-Dade"))

# Compute the ranges of the two signals
range1 <- inum_md %>% select("value") %>% range
range2 <- dv_md %>% select("value") %>% range

# Function to transform from one range to another
trans <- function(x, from_range, to_range) {
  (x - from_range[1]) / (from_range[2] - from_range[1]) *
    (to_range[2] - to_range[1]) + to_range[1]
}

# Convenience functions for our two signal ranges
trans12 <- function(x) trans(x, range1, range2)
trans21 <- function(x) trans(x, range2, range1)

# Transform the doctor visits signal to the incidence range, then stack
# these rowwise into one data frame
df <- select(rbind(dv_md %>% mutate_at("value", trans21),
                   inum_md), c("time_value", "value"))
df$signal <- c(rep("Doctor visits", nrow(dv_md)),
               rep("New COVID-19 cases", nrow(inum_md)))

# Finally, plot both signals
ggplot(df, aes(x = time_value, y = value)) +
  labs(x = "Date", title = "Miami-Dade County") +
  geom_line(aes(color = signal)) +
  scale_y_continuous(
    name = "New COVID-19 cases (7-day trailing average)",
    sec.axis = sec_axis(trans12, name = "Doctor visits")
  ) +
  theme(legend.position = "bottom",
        legend.title = ggplot2::element_blank())