R/mapping_functions.R
In dmwelgus/MapChi: Tools for making maps of Chicago
# Maps!
# Create blank Chicago map with user-specified geographic boundaries.
#' Create a basic map of Chicago with specified regional boundaries.
#
#' @param background The background color.
#' @param lines The line color.
#' @param regions The type of region to plot. Options include: "CAs", "tracts,"
#' "districts," and "zips."
#' @param title Title.
#' @param title_size Title size.
#'
#' @examples
#' map_chi(background = "grey", lines = "blue", title = "Chicago Community Areas")
#' @return A map of Chicago. More specifically, an object of type "ggplot."
#' @export
map_chi <- function(background, lines, regions = "CAs", title = NULL, title_size = 15) {
spatial_df <- get(regions)
map <- ggplot2::ggplot(spatial_df) +
ggplot2::geom_polygon(ggplot2::aes(long, lat, group = group), fill = background) +
ggplot2::geom_path(ggplot2::aes(long, lat, group = group), color = lines) +
ggplot2::theme(axis.ticks = ggplot2::element_blank(), axis.text.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(), axis.title = ggplot2::element_blank(),
plot.title = ggplot2::element_text(size = title_size),
panel.background = ggplot2::element_rect(fill = "white"),
panel.grid.major = ggplot2::element_line(colour = "white"),
panel.grid.minor = ggplot2::element_line(colour = "white")) +
ggplot2::coord_equal() +
ggplot2::ggtitle(title)
map
}
# Continuous color palette
#' Create a heat map of Chicago with continuous shading.
#
#' @param regions The type of region to plot. Options include: "CAs", "tracts," "districts,"
#' and "zips."
#' @param summary_df A data frame with summary statistics by region. Should have one row for each
#' region.
#' @param regions_var The variable in summary_df that identifies the region. For example, if type = "CAs"
#' a common summary_var would be "Community.Areas." In general, regions_var should be numeric (e.g. use
#' community area numbers, not names.).
#' @param fill_var The variable that determines color. For example, if the map shows homicides by
#' police district, the fill_var would be the field that contains homicide totals.
#'
#' @param legend_name Self Explanatory. Should be a character vector of length one.
#' @param palette The color palette. Current options are: green, blue, red, organge, and purple.
#' @param low_color If palette is set to NULL, the low end of the color range you want to use.
#' @param high_color The high end of your color range
#' @param na_replace What value should replace NAs in the fill_var. This arises most commonly in
#' maps of homicides, where regions with zero homicides are sometimes missing from df.y.
#' @param lines Color of border lines. Default is black.
#' @param title Title.
#' @param title_size Title size.
#' @param region_labels Option to add labels for CAs or districts. Default is false
#'
#' @return A map of Chicago. More specifically, an object of type "ggplot."
#' @examples
#' data(hom_14)
#'
#' # Create summary table
#' hom_sum <- dplyr::summarise(group_by(hom_14, District), homicides = n())
#'
#' h_map <- heat_map_continuous(type = "districts", summary_df = hom_sum, regions_var = "District",
#' fill_var = "homicides", legend_name = "Homicides",
#' na_replace = 0)
#' @export
heat_map_continuous <- function(regions, summary_df, regions_var, fill_var,
legend_name, palette = NULL, low_color = "#fff5eb",
high_color = "#7f2704", na_replace = NA, lines = "black",
title = NULL, title_size = 15, region_labels = FALSE) {
if (region_labels == TRUE & !regions %in% c("CAs", "districts")) {
stop("region_labels only available for Community Areas and Police Districts")
}
if (!is.null(palette)) {
colors <- c("green", "blue", "red", "orange", "purple")
low <- c("#e5f5f9", "#deebf7", "#fee0d2", "#fee6ce", "#efedf5")
high <- c("#00441b", "#08306b", "#67000d", "#7f2704", "#3f007d")
min_color <- low[colors == palette]
max_color <- high[colors == palette]
} else {
min_color <- low_color
max_color <- high_color
}
step_1 <- get(regions)
step_1@data$id <- rownames(step_1@data)
step_2 <- ggplot2::fortify(step_1)
chi.df <- merge(step_2, step_1@data, by = "id")
merge_vars <- c("AREA_NUMBE", "TRACTCE10", "DIST_NUM", "ZIP")
types <- c("CAs", "tracts", "districts", "zips")
merge_var.x <- merge_vars[types == regions]
df <- merge(chi.df, summary_df, by.x = merge_var.x, by.y = regions_var, all.x = TRUE)
df$fill_it <- df[, fill_var]
df$fill_it[is.na(df$fill_it)] <- na_replace
df <- df[order(df$order), ]
if (is.null(lines)) {
map_output <- ggplot2::ggplot(df) +
ggplot2::geom_polygon(ggplot2::aes(long, lat, group = group, fill = fill_it)) +
ggplot2::theme(axis.ticks = ggplot2::element_blank(),
axis.text.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(),
axis.title = ggplot2::element_blank(),
plot.title = ggplot2::element_text(size = title_size),
panel.background = ggplot2::element_rect(fill = "white"),
panel.grid.major = ggplot2::element_line(colour = "white"),
panel.grid.minor = ggplot2::element_line(colour = "white")) +
ggplot2::coord_equal() +
ggplot2::scale_fill_continuous(low = min_color, high = max_color,
guide = ggplot2::guide_colorbar(title = legend_name,
title.theme = ggplot2::element_text(size = 13, angle = 0),
label.theme = ggplot2::element_text(size = 11, angle = 0))) +
ggplot2::ggtitle(title)
} else {
map_output <- ggplot2::ggplot(df) +
ggplot2::geom_polygon(ggplot2::aes(long, lat, group = group, fill = fill_it)) +
ggplot2::geom_path(ggplot2::aes(long, lat, group = group), color = lines) +
ggplot2::theme(axis.ticks = ggplot2::element_blank(),
axis.text.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(),
axis.title = ggplot2::element_blank(),
plot.title = ggplot2::element_text(size = title_size),
panel.background = ggplot2::element_rect(fill = "white"),
panel.grid.major = ggplot2::element_line(colour = "white"),
panel.grid.minor = ggplot2::element_line(colour = "white")) +
ggplot2::coord_equal() +
ggplot2::scale_fill_continuous(low = min_color, high = max_color,
guide = ggplot2::guide_colorbar(title = legend_name,
title.theme = ggplot2::element_text(size = 13, angle = 0),
label.theme = ggplot2::element_text(size = 11, angle = 0))) +
ggplot2::ggtitle(title)
}
if(region_labels == FALSE){
map_output
} else {
centers <- sp::coordinates(step_1)
center_df <- data.frame(num = step_1@data[[merge_var.x]],
center_x = centers[, 1],
center_y = centers[, 2])
map_output + ggplot2::geom_text(data = center_df,
ggplot2::aes(x = center_x,
y = center_y,
label = num))
}
}
# Discrete color palette
#' Create a heat map of Chicago with discrete shading.
#
#' @param regions The type of region to plot. Options include: "CAs", "tracts," "districts,"
#' and "zips."
#' @param summary_df A data frame with summary statistics by region. Should have one row for each
#' region.
#' @param regions_var The variable in summary_df that identifies the region. For example, if type = "CAs"
#' a common merge_var.y would be "Community.Areas." In general, regions_var should be numeric (e.g. use
#' community area numbers, not names.).
#' @param fill_var The variable that determines color. For example, if the map shows homicides by
#' police district, the fill_var would be the field that contains homicide totals.
#'
#' @param legend_name Self Explanatory. Should be a character vector of length one.
#' @param palette The color palette. Current options are: green, blue, red, organge, and purple.
#' @param na_replace What value should replace NAs in the fill_var. This arises most commonly in
#' maps of homicides, where regions with zero homicides are sometimes missing from df.y. Add the
#' lowest discrete value for your scale.
#' @param lines Color of border lines. Default is black.
#' @param title Title.
#' @param title_size Title size.
#' @param region_labels Option to add labels for CAs or districts. Default is false
#'
#' @return A map of Chicago. More specifically, an object of type "ggplot."
#' @examples
#' data(hom_14)
#' library(dplyr)
#' hom_sum <- dplyr::summarise(group_by(hom_14, Community.Area), homicides = n())
#'
#' There is now an na_replace option so disregard the following:
#' # No na_replace option for discrete maps. Need to manually add zero homicide CAs.
#' additions <- c(1:77)[which(!c(1:77) %in% hom_sum$Community.Area)]
#' df_additions <- data.frame(Community.Area = additions, homicides = 0)
#' hom_sum <- rbind(hom_sum, df_additions)
#' hom_sum$range <- cut(hom_sum$homicides, c(0, 5, 10, 15, 20, 30, 40),
#' labels = c("0-4", "5-9", "10-14", "15-19",
#' "20-29", "30-39"), right = FALSE)
#'
#' library(RColorBrewer)
#' colors <- brewer.pal(n = 6, name = "BuPu")
#' hm_dis <- heat_map_discrete(regions = "CAs", summary_df = hom_sum, regions_var = "Community.Area",
#' fill_var = "range", legend_name = "Total Homicides",
#' palette = colors, title = "Homicides by Community Area: 2014")
#' @export
heat_map_discrete <- function(regions, summary_df, regions_var,
fill_var, legend_name, palette, na_replace = NA,
lines = "black", title = NULL, title_size = 15, region_labels = FALSE) {
if (region_labels == TRUE & !regions %in% c("CAs", "districts")) {
stop("region_labels only available for Community Areas and Police Districts")
}
step_1 <- get(regions)
step_1@data$id <- rownames(step_1@data)
step_2 <- ggplot2::fortify(step_1)
chi.df <- merge(step_2, step_1@data, by = "id")
merge_vars <- c("AREA_NUMBE", "TRACTCE10", "DIST_NUM", "ZIP")
types <- c("CAs", "tracts", "districts", "zips")
merge_var.x <- merge_vars[types == regions]
df <- merge(chi.df, summary_df, by.x = merge_var.x, by.y = regions_var, all.x = TRUE)
df$fill_it <- df[, fill_var]
# add in na_replace
df$fill_it[is.na(df$fill_it)] <- na_replace
df <- df[order(df$order), ]
if (is.null(lines)) {
map_output <- ggplot2::ggplot(df) +
ggplot2::geom_polygon(ggplot2::aes(long, lat, group = group, fill = fill_it)) +
ggplot2::theme(axis.ticks = ggplot2::element_blank(),
axis.text.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(),
axis.title = ggplot2::element_blank(),
plot.title = ggplot2::element_text(size = title_size),
panel.background = ggplot2::element_rect(fill = "white"),
panel.grid.major = ggplot2::element_line(colour = "white"),
panel.grid.minor = ggplot2::element_line(colour = "white")) +
ggplot2::coord_equal() +
ggplot2::scale_fill_manual(values = palette,
guide = ggplot2::guide_legend(title = legend_name,
title.theme = ggplot2::element_text(size = 13, angle = 0),
label.theme = ggplot2::element_text(size = 11, angle = 0))) +
ggplot2::ggtitle(title)
} else {
map_output <- ggplot2::ggplot(df) +
ggplot2::geom_polygon(ggplot2::aes(long, lat, group = group, fill = fill_it)) +
ggplot2::geom_path(ggplot2::aes(long, lat, group = group), color = lines) +
ggplot2::theme(axis.ticks = ggplot2::element_blank(),
axis.text.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(),
axis.title = ggplot2::element_blank(),
plot.title = ggplot2::element_text(size = title_size),
panel.background = ggplot2::element_rect(fill = "white"),
panel.grid.major = ggplot2::element_line(colour = "white"),
panel.grid.minor = ggplot2::element_line(colour = "white")) +
ggplot2::coord_equal() +
ggplot2::scale_fill_manual(values = palette,
guide = ggplot2::guide_legend(title = legend_name,
title.theme = ggplot2::element_text(size = 13, angle = 0),
label.theme = ggplot2::element_text(size = 11, angle = 0))) +
ggplot2::ggtitle(title)
}
if(region_labels == FALSE){
map_output
} else {
centers <- sp::coordinates(step_1)
center_df <- data.frame(num = step_1@data[[merge_var.x]],
center_x = centers[, 1],
center_y = centers[, 2])
map_output + ggplot2::geom_text(data = center_df,
ggplot2::aes(x = center_x,
y = center_y,
label = num))
}
}
dmwelgus/MapChi documentation built on May 15, 2019, 9:38 a.m.
R Package Documentation
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# Maps!
# Create blank Chicago map with user-specified geographic boundaries.
#' Create a basic map of Chicago with specified regional boundaries.
#
#' @param background The background color.
#' @param lines The line color.
#' @param regions The type of region to plot. Options include: "CAs", "tracts,"
#' "districts," and "zips."
#' @param title Title.
#' @param title_size Title size.
#'
#' @examples
#' map_chi(background = "grey", lines = "blue", title = "Chicago Community Areas")
#' @return A map of Chicago. More specifically, an object of type "ggplot."
#' @export
map_chi <- function(background, lines, regions = "CAs", title = NULL, title_size = 15) {
spatial_df <- get(regions)
map <- ggplot2::ggplot(spatial_df) +
ggplot2::geom_polygon(ggplot2::aes(long, lat, group = group), fill = background) +
ggplot2::geom_path(ggplot2::aes(long, lat, group = group), color = lines) +
ggplot2::theme(axis.ticks = ggplot2::element_blank(), axis.text.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(), axis.title = ggplot2::element_blank(),
plot.title = ggplot2::element_text(size = title_size),
panel.background = ggplot2::element_rect(fill = "white"),
panel.grid.major = ggplot2::element_line(colour = "white"),
panel.grid.minor = ggplot2::element_line(colour = "white")) +
ggplot2::coord_equal() +
ggplot2::ggtitle(title)
map
}
# Continuous color palette
#' Create a heat map of Chicago with continuous shading.
#
#' @param regions The type of region to plot. Options include: "CAs", "tracts," "districts,"
#' and "zips."
#' @param summary_df A data frame with summary statistics by region. Should have one row for each
#' region.
#' @param regions_var The variable in summary_df that identifies the region. For example, if type = "CAs"
#' a common summary_var would be "Community.Areas." In general, regions_var should be numeric (e.g. use
#' community area numbers, not names.).
#' @param fill_var The variable that determines color. For example, if the map shows homicides by
#' police district, the fill_var would be the field that contains homicide totals.
#'
#' @param legend_name Self Explanatory. Should be a character vector of length one.
#' @param palette The color palette. Current options are: green, blue, red, organge, and purple.
#' @param low_color If palette is set to NULL, the low end of the color range you want to use.
#' @param high_color The high end of your color range
#' @param na_replace What value should replace NAs in the fill_var. This arises most commonly in
#' maps of homicides, where regions with zero homicides are sometimes missing from df.y.
#' @param lines Color of border lines. Default is black.
#' @param title Title.
#' @param title_size Title size.
#' @param region_labels Option to add labels for CAs or districts. Default is false
#'
#' @return A map of Chicago. More specifically, an object of type "ggplot."
#' @examples
#' data(hom_14)
#'
#' # Create summary table
#' hom_sum <- dplyr::summarise(group_by(hom_14, District), homicides = n())
#'
#' h_map <- heat_map_continuous(type = "districts", summary_df = hom_sum, regions_var = "District",
#' fill_var = "homicides", legend_name = "Homicides",
#' na_replace = 0)
#' @export
heat_map_continuous <- function(regions, summary_df, regions_var, fill_var,
legend_name, palette = NULL, low_color = "#fff5eb",
high_color = "#7f2704", na_replace = NA, lines = "black",
title = NULL, title_size = 15, region_labels = FALSE) {
if (region_labels == TRUE & !regions %in% c("CAs", "districts")) {
stop("region_labels only available for Community Areas and Police Districts")
}
if (!is.null(palette)) {
colors <- c("green", "blue", "red", "orange", "purple")
low <- c("#e5f5f9", "#deebf7", "#fee0d2", "#fee6ce", "#efedf5")
high <- c("#00441b", "#08306b", "#67000d", "#7f2704", "#3f007d")
min_color <- low[colors == palette]
max_color <- high[colors == palette]
} else {
min_color <- low_color
max_color <- high_color
}
step_1 <- get(regions)
step_1@data$id <- rownames(step_1@data)
step_2 <- ggplot2::fortify(step_1)
chi.df <- merge(step_2, step_1@data, by = "id")
merge_vars <- c("AREA_NUMBE", "TRACTCE10", "DIST_NUM", "ZIP")
types <- c("CAs", "tracts", "districts", "zips")
merge_var.x <- merge_vars[types == regions]
df <- merge(chi.df, summary_df, by.x = merge_var.x, by.y = regions_var, all.x = TRUE)
df$fill_it <- df[, fill_var]
df$fill_it[is.na(df$fill_it)] <- na_replace
df <- df[order(df$order), ]
if (is.null(lines)) {
map_output <- ggplot2::ggplot(df) +
ggplot2::geom_polygon(ggplot2::aes(long, lat, group = group, fill = fill_it)) +
ggplot2::theme(axis.ticks = ggplot2::element_blank(),
axis.text.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(),
axis.title = ggplot2::element_blank(),
plot.title = ggplot2::element_text(size = title_size),
panel.background = ggplot2::element_rect(fill = "white"),
panel.grid.major = ggplot2::element_line(colour = "white"),
panel.grid.minor = ggplot2::element_line(colour = "white")) +
ggplot2::coord_equal() +
ggplot2::scale_fill_continuous(low = min_color, high = max_color,
guide = ggplot2::guide_colorbar(title = legend_name,
title.theme = ggplot2::element_text(size = 13, angle = 0),
label.theme = ggplot2::element_text(size = 11, angle = 0))) +
ggplot2::ggtitle(title)
} else {
map_output <- ggplot2::ggplot(df) +
ggplot2::geom_polygon(ggplot2::aes(long, lat, group = group, fill = fill_it)) +
ggplot2::geom_path(ggplot2::aes(long, lat, group = group), color = lines) +
ggplot2::theme(axis.ticks = ggplot2::element_blank(),
axis.text.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(),
axis.title = ggplot2::element_blank(),
plot.title = ggplot2::element_text(size = title_size),
panel.background = ggplot2::element_rect(fill = "white"),
panel.grid.major = ggplot2::element_line(colour = "white"),
panel.grid.minor = ggplot2::element_line(colour = "white")) +
ggplot2::coord_equal() +
ggplot2::scale_fill_continuous(low = min_color, high = max_color,
guide = ggplot2::guide_colorbar(title = legend_name,
title.theme = ggplot2::element_text(size = 13, angle = 0),
label.theme = ggplot2::element_text(size = 11, angle = 0))) +
ggplot2::ggtitle(title)
}
if(region_labels == FALSE){
map_output
} else {
centers <- sp::coordinates(step_1)
center_df <- data.frame(num = step_1@data[[merge_var.x]],
center_x = centers[, 1],
center_y = centers[, 2])
map_output + ggplot2::geom_text(data = center_df,
ggplot2::aes(x = center_x,
y = center_y,
label = num))
}
}
# Discrete color palette
#' Create a heat map of Chicago with discrete shading.
#
#' @param regions The type of region to plot. Options include: "CAs", "tracts," "districts,"
#' and "zips."
#' @param summary_df A data frame with summary statistics by region. Should have one row for each
#' region.
#' @param regions_var The variable in summary_df that identifies the region. For example, if type = "CAs"
#' a common merge_var.y would be "Community.Areas." In general, regions_var should be numeric (e.g. use
#' community area numbers, not names.).
#' @param fill_var The variable that determines color. For example, if the map shows homicides by
#' police district, the fill_var would be the field that contains homicide totals.
#'
#' @param legend_name Self Explanatory. Should be a character vector of length one.
#' @param palette The color palette. Current options are: green, blue, red, organge, and purple.
#' @param na_replace What value should replace NAs in the fill_var. This arises most commonly in
#' maps of homicides, where regions with zero homicides are sometimes missing from df.y. Add the
#' lowest discrete value for your scale.
#' @param lines Color of border lines. Default is black.
#' @param title Title.
#' @param title_size Title size.
#' @param region_labels Option to add labels for CAs or districts. Default is false
#'
#' @return A map of Chicago. More specifically, an object of type "ggplot."
#' @examples
#' data(hom_14)
#' library(dplyr)
#' hom_sum <- dplyr::summarise(group_by(hom_14, Community.Area), homicides = n())
#'
#' There is now an na_replace option so disregard the following:
#' # No na_replace option for discrete maps. Need to manually add zero homicide CAs.
#' additions <- c(1:77)[which(!c(1:77) %in% hom_sum$Community.Area)]
#' df_additions <- data.frame(Community.Area = additions, homicides = 0)
#' hom_sum <- rbind(hom_sum, df_additions)
#' hom_sum$range <- cut(hom_sum$homicides, c(0, 5, 10, 15, 20, 30, 40),
#' labels = c("0-4", "5-9", "10-14", "15-19",
#' "20-29", "30-39"), right = FALSE)
#'
#' library(RColorBrewer)
#' colors <- brewer.pal(n = 6, name = "BuPu")
#' hm_dis <- heat_map_discrete(regions = "CAs", summary_df = hom_sum, regions_var = "Community.Area",
#' fill_var = "range", legend_name = "Total Homicides",
#' palette = colors, title = "Homicides by Community Area: 2014")
#' @export
heat_map_discrete <- function(regions, summary_df, regions_var,
fill_var, legend_name, palette, na_replace = NA,
lines = "black", title = NULL, title_size = 15, region_labels = FALSE) {
if (region_labels == TRUE & !regions %in% c("CAs", "districts")) {
stop("region_labels only available for Community Areas and Police Districts")
}
step_1 <- get(regions)
step_1@data$id <- rownames(step_1@data)
step_2 <- ggplot2::fortify(step_1)
chi.df <- merge(step_2, step_1@data, by = "id")
merge_vars <- c("AREA_NUMBE", "TRACTCE10", "DIST_NUM", "ZIP")
types <- c("CAs", "tracts", "districts", "zips")
merge_var.x <- merge_vars[types == regions]
df <- merge(chi.df, summary_df, by.x = merge_var.x, by.y = regions_var, all.x = TRUE)
df$fill_it <- df[, fill_var]
# add in na_replace
df$fill_it[is.na(df$fill_it)] <- na_replace
df <- df[order(df$order), ]
if (is.null(lines)) {
map_output <- ggplot2::ggplot(df) +
ggplot2::geom_polygon(ggplot2::aes(long, lat, group = group, fill = fill_it)) +
ggplot2::theme(axis.ticks = ggplot2::element_blank(),
axis.text.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(),
axis.title = ggplot2::element_blank(),
plot.title = ggplot2::element_text(size = title_size),
panel.background = ggplot2::element_rect(fill = "white"),
panel.grid.major = ggplot2::element_line(colour = "white"),
panel.grid.minor = ggplot2::element_line(colour = "white")) +
ggplot2::coord_equal() +
ggplot2::scale_fill_manual(values = palette,
guide = ggplot2::guide_legend(title = legend_name,
title.theme = ggplot2::element_text(size = 13, angle = 0),
label.theme = ggplot2::element_text(size = 11, angle = 0))) +
ggplot2::ggtitle(title)
} else {
map_output <- ggplot2::ggplot(df) +
ggplot2::geom_polygon(ggplot2::aes(long, lat, group = group, fill = fill_it)) +
ggplot2::geom_path(ggplot2::aes(long, lat, group = group), color = lines) +
ggplot2::theme(axis.ticks = ggplot2::element_blank(),
axis.text.x = ggplot2::element_blank(),
axis.text.y = ggplot2::element_blank(),
axis.title = ggplot2::element_blank(),
plot.title = ggplot2::element_text(size = title_size),
panel.background = ggplot2::element_rect(fill = "white"),
panel.grid.major = ggplot2::element_line(colour = "white"),
panel.grid.minor = ggplot2::element_line(colour = "white")) +
ggplot2::coord_equal() +
ggplot2::scale_fill_manual(values = palette,
guide = ggplot2::guide_legend(title = legend_name,
title.theme = ggplot2::element_text(size = 13, angle = 0),
label.theme = ggplot2::element_text(size = 11, angle = 0))) +
ggplot2::ggtitle(title)
}
if(region_labels == FALSE){
map_output
} else {
centers <- sp::coordinates(step_1)
center_df <- data.frame(num = step_1@data[[merge_var.x]],
center_x = centers[, 1],
center_y = centers[, 2])
map_output + ggplot2::geom_text(data = center_df,
ggplot2::aes(x = center_x,
y = center_y,
label = num))
}
}
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