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January 21, 2022 13:46
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example of spatial data which may be desirable to # overlay on a map (for Twitter thread)
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# example_geo_data.R: example of spatial data which may be desirable to | |
# overlay on a map. | |
# Cameron Patrick <cameron.patrick@unimelb.edu.au>, 22 Jan 2022. | |
library(tidyverse) | |
library(rnaturalearth) | |
library(ragg) | |
# You will also need the natural earth data: | |
# remotes::install_github("ropensci/rnaturalearthhires") | |
n_rows <- 6 | |
n_cols <- 8 | |
n_times <- 5 | |
### example geographic region | |
aus_polys <- fortify(ne_countries(country = "Australia", scale = 10)) | |
grid_centres <- | |
expand_grid(grid_row = seq_len(n_rows), | |
grid_col = seq_len(n_cols)) %>% | |
mutate(grid = paste0(LETTERS[grid_col], grid_row), | |
lat = -37.5 - 0.25 * grid_row + 0.125, | |
long = 144 + 0.25 * grid_col - 0.125) | |
ggplot(aus_polys, aes(x = long, y = lat, group = group)) + | |
geom_polygon(colour = "black", fill = "cornsilk") + | |
geom_text(data = grid_centres, | |
aes(x = long, y = lat, label = grid), | |
size = 9 / .pt, | |
inherit.aes = FALSE) + | |
geom_hline(data = tibble(lat = seq(-37.5, -39, by = -0.25)), | |
aes(yintercept = lat), | |
colour = "grey60", | |
linetype = "dashed", | |
inherit.aes = FALSE) + | |
geom_vline(data = tibble(long = seq(144, 146, by = 0.25)), | |
aes(xintercept = long), | |
colour = "grey60", | |
linetype = "dashed", | |
inherit.aes = FALSE) + | |
annotate("point", x = 144.96, y = -37.81) + | |
annotate("text", x = 144.96, y = -37.79, label = "Melbourne", | |
hjust = 0.5, vjust = 0, size = 11 / .pt) + | |
coord_quickmap(xlim = c(144, 146), | |
ylim = c(-39, -37.5)) + | |
labs(x = "Longitude", y = "Latitude") + | |
theme_minimal() + | |
theme(panel.background = element_rect(fill = "lightskyblue", colour = NA), | |
panel.grid = element_blank()) | |
ggsave("fig1_map.png", width = 8, height = 6, dpi = 300, device = agg_png) | |
ggplot(aus_polys, aes(x = long, y = lat, group = group)) + | |
geom_polygon(colour = "black", fill = "cornsilk") + | |
coord_cartesian(xlim = c(144, 146), | |
ylim = c(-39, -37.5)) + | |
labs(x = "Longitude", y = "Latitude") + | |
theme_minimal() + | |
theme(panel.background = element_rect(fill = "lightskyblue", colour = NA), | |
panel.grid = element_blank()) | |
ggsave("fig1b_map_stretched.png", | |
width = 8, height = 6, dpi = 300, device = agg_png) | |
### simulate some example data | |
set.seed(1234) | |
grid_data <- | |
expand_grid(grid_row = seq_len(n_rows), | |
grid_col = LETTERS[seq_len(n_cols)]) %>% | |
mutate(grid = paste0(grid_col, grid_row), | |
grid_intercept = rnorm(n(), 3, 0.5), | |
grid_slope_1 = rnorm(n(), 0, 0.2), | |
grid_slope_2 = rnorm(n(), -0.5, 0.2)) %>% | |
filter(!(grid %in% c("A1", "H3", | |
"D3", "C4", "A5", "B5", "C5", "D5", "E5", "F5", | |
"A6", "B6", "C6", "D6", "E6", "F6", "G6"))) | |
grid_time_data <- grid_data %>% | |
expand_grid(time = letters[seq_len(n_times)]) %>% | |
mutate(time_num = as.numeric(factor(time)) - 1, | |
group_1 = rnorm(n(), grid_intercept + grid_slope_1 * time_num, 0.2), | |
group_2 = rnorm(n(), grid_intercept + grid_slope_2 * time_num, 0.2)) | |
full_data <- grid_time_data %>% | |
pivot_longer(group_1:group_2, | |
names_to = "intervention", | |
values_to = "log_estimate") %>% | |
mutate(estimate = exp(log_estimate), | |
conf_low = exp(log_estimate - 0.4), | |
conf_high = exp(log_estimate + 0.4)) | |
### plot simulated data | |
full_data %>% | |
ggplot(aes(x = time, y = estimate, ymin = conf_low, ymax = conf_high, | |
colour = intervention, group = intervention)) + | |
geom_line() + | |
geom_pointrange(fatten = 1.25) + | |
facet_grid(rows = vars(grid_row), | |
cols = vars(grid_col)) + | |
scale_colour_manual(values = c("dodgerblue3", "firebrick3")) + | |
labs(x = "Time", | |
y = "Mean outcome (95% CI)", | |
colour = "Intervention") + | |
theme_bw() + | |
theme(panel.grid.minor = element_blank(), | |
legend.position = c(0.01, 0.01), | |
legend.justification = c(0, 0)) | |
ggsave("fig2_data.png", width = 8, height = 6, dpi = 300, device = agg_png) | |
### current best approach | |
empty_grids <- tribble( | |
~grid, ~fill, | |
"A1", "cornsilk", | |
"H3", "cornsilk", | |
"D3", "lightskyblue", | |
"C4", "lightskyblue", | |
"A5", "lightskyblue", | |
"B5", "lightskyblue", | |
"C5", "lightskyblue", | |
"D5", "lightskyblue", | |
"E5", "lightskyblue", | |
"F5", "lightskyblue", | |
"A6", "lightskyblue", | |
"B6", "lightskyblue", | |
"C6", "lightskyblue", | |
"D6", "lightskyblue", | |
"E6", "lightskyblue", | |
"F6", "lightskyblue", | |
"G6", "lightskyblue", | |
) %>% | |
mutate(grid_col = str_sub(grid, 1, 1), | |
grid_row = str_sub(grid, 2, 2)) | |
full_data %>% | |
ggplot(aes(x = time, y = estimate, ymin = conf_low, ymax = conf_high, | |
colour = intervention, group = intervention)) + | |
geom_line() + | |
geom_pointrange(fatten = 1.25) + | |
geom_rect(data = empty_grids, | |
xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf, | |
aes(fill = fill), | |
inherit.aes = FALSE) + | |
facet_grid(rows = vars(grid_row), | |
cols = vars(grid_col)) + | |
scale_fill_identity() + | |
scale_colour_manual(values = c("dodgerblue3", "firebrick3")) + | |
labs(x = "Time", | |
y = "Mean outcome (95% CI)", | |
colour = "Intervention") + | |
theme_bw() + | |
theme(panel.grid.minor = element_blank(), | |
legend.position = c(0.01, 0.01), | |
legend.justification = c(0, 0)) | |
ggsave("fig2b_data_panel_bg.png", width = 8, height = 6, dpi = 300, device = agg_png) | |
ggsave("fig2c_data_trans_bg.png", | |
plot = last_plot() + | |
theme(plot.background = element_rect(fill = "transparent", colour = NA)), | |
width = 8, height = 6, dpi = 300, device = agg_png) |
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