library(echarts4r)
library(lubridate)
library(dplyr)
library(tidyr)
library(ggiraph)
library(ggplot2)
library(ggconsort)
df <- airquality
df <- df |> mutate(Month = sprintf("%02d", Month),
Day = sprintf("%02d", Day)) |>
mutate(Date = as.Date(paste("1973", Month, Day, sep = "-"),
format = "%Y-%m-%d"))
df_long <- df |> pivot_longer(c(Temp, Wind))#option 1: long form requires a group_by and a single e_line call
df_long |> group_by(name) |> e_charts(x = Date) |> e_line(serie = value)#option 2: wide form can skip the grouping but needs two separate e_line calls
df |> e_charts(x = Date) |>
e_line(serie = Temp) |>
e_line(serie = Wind) |>
e_tooltip(trigger = "item") |> #mouse over item for its value
e_title(text = "Example Plot", #add title
subtext = "generated from wide form") |> #add subtitle
e_axis_labels(x = "Month in 1973", #add axis labels
y = "Whatever, I don't really care") |>
e_x_axis(nameLocation = 'center') |> #axis label placement (default: right)
e_y_axis(nameLocation = "center") #axis label placement (default: top left)df_long |>
mutate(dv = paste0(Date, ": ", value)) |>
ggplot(aes(x = Date, y = value, color = name)) +
geom_line() +
geom_point_interactive(aes(tooltip = dv, data_id = Date),
hover_nearest = TRUE) + #create interactive points
scale_y_continuous(limits = c(0,100)) +
scale_color_manual(values = c("#5070dd", "#b6d635")) +
labs(color = "Measurement") +
theme_bw() +
theme(legend.position = "bottom") -> plot_obj
girafe(ggobj = plot_obj) #generate interactive plotThank you to Travis Gerke for developing this example.
study_cohorts <- trial_data |>
cohort_start("Assessed for eligibility") |>
# Define cohorts using named expressions --------------------
# Notice that you can use previously defined cohorts in subsequent steps
cohort_define(
consented = .full |> filter(declined != 1),
consented_chemonaive = consented |> filter(prior_chemo != 1),
randomized = consented_chemonaive |> filter(bone_mets != 1),
treatment_a = randomized |> filter(treatment == "Drug A"),
treatment_b = randomized |> filter(treatment == "Drug B"),
# anti_join is useful for counting exclusions -------------
excluded = anti_join(.full, randomized, by = "id"),
excluded_declined = anti_join(.full, consented, by = "id"),
excluded_chemo = anti_join(consented, consented_chemonaive, by = "id"),
excluded_mets = anti_join(consented_chemonaive, randomized, by = "id")
) |>
# Provide text labels for cohorts ---------------------------
cohort_label(
consented = "Consented",
consented_chemonaive = "Chemotherapy naive",
randomized = "Randomized",
treatment_a = "Allocated to arm A",
treatment_b = "Allocated to arm B",
excluded = "Excluded",
excluded_declined = "Declined to participate",
excluded_chemo = "Prior chemotherapy",
excluded_mets = "Bone metastasis"
)study_consort <- study_cohorts |>
consort_box_add(
"full", 0, 50, cohort_count_adorn(study_cohorts, .full)
) |>
consort_box_add(
"exclusions", 20, 40, glue::glue(
'{cohort_count_adorn(study_cohorts, excluded)}<br>
• {cohort_count_adorn(study_cohorts, excluded_declined)}<br>
• {cohort_count_adorn(study_cohorts, excluded_chemo)}<br>
• {cohort_count_adorn(study_cohorts, excluded_mets)}
')
) |>
consort_box_add(
"randomized", 0, 30, cohort_count_adorn(study_cohorts, randomized)
) |>
consort_box_add(
"arm_a", -30, 10, cohort_count_adorn(study_cohorts, treatment_a)
) |>
consort_box_add(
"arm_b", 30, 10, cohort_count_adorn(study_cohorts, treatment_b)
) |>
consort_arrow_add(
end = "exclusions", end_side = "left", start_x = 0, start_y = 40
) |>
consort_arrow_add(
"full", "bottom", "randomized", "top"
) |>
consort_arrow_add(
start_x = 0, start_y = 30, end_x = 0, end_y = 20,
) |>
consort_line_add(
start_x = -30, start_y = 20, end_x = 30, end_y = 20,
) |>
consort_arrow_add(
end = "arm_a", end_side = "top", start_x = -30, start_y = 20
) |>
consort_arrow_add(
end = "arm_b", end_side = "top", start_x = 30, start_y = 20
)
study_consort |>
ggplot() +
geom_consort() +
theme_consort(margin_h = 8, margin_v = 1) 