########################### ## 03 DATA VISUALIZATION ## ########################### # Load general packages library(tidyverse) library(lubridate) # for dates and times library(scales) # for scale layouts (breaks and labels) ############## ## PENGUINS ## ############## # Load and assign data data <- penguins # Take brief look head(data) # Calculate median and standard deviation for bill length and depth data_summary <- data |> group_by(species) |> summarise(across(c(bill_len, bill_dep), list(median = ~median(., na.rm = TRUE), sd = ~sd(., na.rm = TRUE)))) # Scatter plot with error bars by species # Idea by Cedric Scherer: https://www.behance.net/gallery/101517403/Bill-Dimensions-of-Penguins data |> ggplot(aes(x = bill_len, y = bill_dep, color = species)) + # Error bars at the median with the standard deviations # Attention: we take other data here with new aesthetics, so inherit.aes = F geom_errorbar( data = data_summary, aes(x = bill_len_median, ymin = bill_dep_median - bill_dep_sd, ymax = bill_dep_median + bill_dep_sd, color = species, color = after_scale(colorspace::darken(color, .2, space = "combined")) ), inherit.aes = F, width = .8, linewidth = .8 ) + geom_errorbar( data = data_summary, aes(y = bill_dep_median, xmin = bill_len_median - bill_len_sd, xmax = bill_len_median + bill_len_sd, color = species, color = after_scale(colorspace::darken(color, .2, space = "combined")) ), inherit.aes = F, width = .8, linewidth = .8 ) + geom_point(size = 1.5, alpha = 0.5) + scale_color_manual(name = NULL, values = MetBrewer::met.brewer("Lakota")) + scale_x_continuous(labels = scales::number_format(suffix="mm")) + scale_y_continuous(labels = scales::number_format(suffix="mm", accuracy = 1)) + # Add labels in the plot rather than in legend annotate("text", x = c(34.7, 55.7, 50.7), y = c(20.7, 19, 13.6), color = MetBrewer::met.brewer("Lakota")[1:3], label = c("Adélie","Chinstrap","Gentoo"), fontface = "bold", size = 4) + labs(x = "Bill length", y = "Bill depth", title = "Penguins are awesome", subtitle = "Depth and length of bills") + theme_minimal() + theme(legend.position = "none", plot.title.position = "plot", plot.title = element_text(size = 15), plot.subtitle = element_text(size = 13), panel.grid.minor = element_blank()) ############## ## EUROSTAT ## ############## library(eurostat) library(geomtextpath) library(MetBrewer) # Search datasets for median income search_eurostat("median income") |> View() # Get data for ilc_di03 rawinc <- get_eurostat("ilc_di03", time_format = "num", type = "label", filters = list(geo = c("AT","FR","IT","DE","ES"))) # Alternatively, load local RData file # load("03_geometries.RData") View(rawinc) inc <- rawinc |> filter(age == "Total", sex == "Total", unit == "Purchasing power standard (PPS)") |> filter(time %in% 2005:2023) ## Let's try different geometries # 1. Line plot with evolution of median income inc |> filter(str_starts(indic_il, "Median")) |> ggplot(aes(x = time, y = values, group = geo, color = geo)) + geom_line(linewidth = 1) + scale_color_manual(name = NULL, values = met.brewer("Juarez")) + scale_y_continuous(labels = scales::number_format(prefix = "€", big.mark = ",")) + labs(x = NULL, y = NULL, title = "Evolution of median household income 2005-2023", subtitle = "Median income in € (PPS)") + theme_minimal() + theme(panel.grid.minor = element_blank(), legend.position = "bottom") ggsave("plots/inc_evolution.png", width = 6, height = 4, dpi = 320) # Annotation within the plot; for casual style, try "stat = 'smooth'" library(geomtextpath) inc |> filter(str_starts(indic_il, "Median")) |> ggplot(aes(x = time, y = values, group = geo, color = geo)) + geomtextpath::geom_textline(aes(label = geo), hjust = 0.7, vjust = 0.5, size = 3, fontface = "bold", linewidth = 0.8) + scale_color_manual(values = met.brewer("Juarez")) + scale_y_continuous(labels = scales::number_format(prefix = "€", big.mark = ",")) + labs(x = NULL, y = NULL, title = "Evolution of median household income 2005-2023", subtitle = "Median income in € (PPS)") + theme_minimal() + theme(legend.position = "none") ggsave("plots/inc_evolution_label.png", width = 6, height = 4, dpi = 320) # 2. Barplot with 2023 mean values inc |> filter(str_starts(indic_il, "Mean")) |> slice_max(time, by = geo) |> ggplot(aes(x = geo, y = values)) + geom_bar(stat = "identity") + labs(x = NULL, y = NULL, title = "Mean household income 2023", subtitle = "Mean income in € (PPS)") + theme_minimal() # 3. Facets of these barplots inc |> filter(str_starts(indic_il, "Mean"), time > 2018) |> ggplot(aes(x = time, y = values, fill = geo)) + geom_bar(stat = "identity") + facet_wrap(~geo) + scale_fill_manual(values = met.brewer("Juarez")) + labs(x = NULL, y = NULL, title = "Mean household income 2018-2023", subtitle = "Mean income in € (PPS)") + theme_minimal() + theme(legend.position = "none", panel.grid.minor = element_blank(), plot.title.position = "plot") # 4. Lollipop chart inc |> filter(str_starts(indic_il, "Mean"), time > 2018) |> ggplot(aes(x = time, y = values)) + geom_segment(aes(xend = time, yend = 0), color = "gray80", linewidth = 2.5) + geom_hline(yintercept = 0, color = "black", size = 0.3) + geom_point(aes(color = geo), size = 2.5) + facet_wrap(~geo, nrow = 1) + scale_color_manual(values = met.brewer("Juarez")) + scale_y_continuous(labels = scales::number_format(scale = 1/1000, prefix = "€", suffix ="K")) + labs(x = NULL, y = NULL, title = "Mean household income 2018-2023") + theme_minimal(base_family = "Roboto Condensed") + theme(legend.position = "none", panel.spacing.x = unit(1, unit = "lines"), panel.grid.minor = element_blank(), panel.grid.major.x = element_blank(), plot.title = element_text(hjust = .5))