97 lines
2.8 KiB
R
97 lines
2.8 KiB
R
# dat <- data.frame(
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# x = c(15, 34, 59, 21, 33, 66),
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# y = c("Admin A", "Admin B", "Admin C", "Admin C", "Admin B", "Admin A"),
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# group = c("Displaced", "Non displaced", "Non displaced", "Displaced", "Displaced", "Non displaced")
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# )
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library(visualizeR)
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# dat |>
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# bar(
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# x = "y",
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# y = "x",
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# #group = "group",
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# group_title = "Displacement Status",
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# flip = T,
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# add_text = T,
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# title = "In Admin A and C, Non-Displaced Persons Face Greater WASH Challenges Than Their Displaced Counterparts",
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# subtitle = "% of households not accessing WASH services by admin 1 and displacement status",
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# caption = "Source: FAO 2022. No message is a real one. Fake data are used in this example. As a cautiom, no decision should be made based on this plot.",
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# ) +
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# theme_bar(flip = T, add_text = T) +
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# scale_color_visualizer_discrete() +
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# scale_fill_visualizer_discrete()
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library(rio)
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dat <- import("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/11_SevCatOneNumNestedOneObsPerGroup.csv")
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dumbbell(
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df,
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"stat",
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"setting",
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"admin1",
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title = "% of HHs that reported open defecation as sanitation facility",
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group_y_title = "Admin 1",
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group_x_title = "Setting"
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)
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library(dplyr)
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library(data.table)
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# dat as a data.table if it4s not
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if (!checkmate::test_data_table(dat)) {
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rlang::warn("Converting dat to data.table.")
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data.table::setDT(dat)
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}
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# in all character columns, tranform empty string to NA
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vars_chr <- colnames(dat)[sapply(dat, is.character)]
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dat[, (vars_chr) := lapply(.SD, function(x) fifelse(x == "", NA_character_, x)), .SDcols = vars_chr]
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# in value, if -1 replace with NA
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dat[, value := fifelse(value == -1, NA_real_, value)]
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# remove lines where value is NA (in place)
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dat <- dat[!is.na(value), ]
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dat
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# arrange(value) |>
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# group_by(region) |>
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# mutate(key = forcats::fct_reorder(key, value)) |>
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dumbbell(
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dat |> arrange(value) |> tail(50) |> mutate(
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value = value/1000000,
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key = ifelse(key == "Democratic Republic of the Congo", "DRC", key)) |>
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filter(region %in% c("Europe", "Americas")),
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"value",
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"region",
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"key",
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title = "% of HHs that reported open defecation as sanitation facility",
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group_y_title = "Admin 1",
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group_x_title = "Setting", point_size = 3, line_to_y_axis = T
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)
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bar(
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df = dat |> arrange(value) |> tail(20) |> mutate(
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value = value/1000000,
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key = ifelse(key == "Democratic Republic of the Congo", "DRC", key)),
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x = "key",
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y = "value",
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group = "region",
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group_title = "Region",
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order = "grouped",
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x_rm_na = T,
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y_rm_na = T,
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group_rm_na = T,
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flip = F,
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title = "Population of Global Regions in Million",
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) +
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theme_bar(flip = F, axis_text_x_angle = 45) +
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scale_color_visualizer_discrete() +
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scale_fill_visualizer_discrete()
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