Data visualization
Gemma Bargalló Solé
2025-10-28
1. Area chart
Based on meteorological data from Barcelona for the year 2024, we create a dynamic area chart with the “date” variable on the X-axis and the “daily average temperature” variable on the Y-axis.
Data available on: https://opendata.aemet.es/centrodedescargas/productosAEMET
Code inspired by: https://r-graph-gallery.com/318-custom-dygraphs-time-series-example.html
# Library
library(dygraphs)
library(xts)
library(tidyverse)
library(lubridate)
library(readr)
library(data.table)
library(dplyr)
library(stringr)
# Data import
meteo_2024 <- readr::read_csv("meteo_2024.csv")
# I have to convert the date column to a date-time format
meteo_2024$fecha <- ymd(meteo_2024$fecha)
# Then you can create the xts necessary to use dygraph
don <- xts(x = meteo_2024$tmed, order.by = meteo_2024$fecha)
# Finally the plot
p <- dygraph(don, main = "Monthly evolution of the average temperature in Barcelona during 2024") %>%
dySeries(
name = "V1",
label = "Temperature",
color = "#D8AE5A"
) %>%
dyOptions(
fillGraph = TRUE,
fillAlpha = 0.3,
drawGrid = FALSE,
labelsUTC = TRUE,
strokeWidth = 2
) %>%
dyRangeSelector() %>%
dyCrosshair(direction = "vertical") %>%
dyHighlight(
highlightCircleSize = 6,
highlightSeriesBackgroundAlpha = 0.3,
hideOnMouseOut = FALSE
)
p2. Radar chart
Using a combined dataset with similar variables from 2023 and 2024, we took the numerical variables of daily average rainfall, daily average temperature, and wind direction. These variables were categorized to create individual radar charts for each.
Data available on: https://opendata.aemet.es/centrodedescargas/productosAEMET
Code inspired by: https://www.data-to-viz.com/caveat/spider.html
# Libraries
library(tidyverse)
library(fmsb)
# Data import
meteo_23_24 <- read.csv("meteo_23_24.csv")
# I have to convert the date column to a date-time format
meteo_23_24$fecha <- ymd(meteo_23_24$fecha)
# Define Category Labels
# Temperature Labels (4 Categories)
tmed_labels <- c("Cold", "Cool", "Mild", "Hot")
# Precipitation Labels (4 Categories)
prec_labels <- c("No Rain", "Light", "Moderate", "Heavy ")
# Wind Direction Labels (9 Categories - including 'Calm/No Data')
dir_labels <- c("Calm/No Data", "N", "NE", "E", "SE", "S", "SO", "O", "NO")
# Data preparation for both years
meteo_23_24 <- meteo_23_24 %>%
mutate(
# Convert variables to numeric
tmed = as.numeric(tmed),
prec = as.numeric(prec),
dir = as.numeric(dir)
)
# Calculate global max and min for temperature
max_tmed <- max(meteo_23_24$tmed, na.rm = TRUE)
min_tmed <- min(meteo_23_24$tmed, na.rm = TRUE)
# Calculate frequency for temperature categories
meteo_23_24 <- meteo_23_24 %>%
mutate(
tmed_cat = case_when(
tmed <= 13.80 ~ 0, # Cold (1st Qu)
tmed <= 17.60 ~ 1, # Cool (Median)
tmed <= 21.95 ~ 2, # Mild/Warm (3rd Qu)
TRUE ~ 3 # Hot (Max)
),
tmed_cat = factor(tmed_cat, levels = c(0, 1, 2, 3))
)
# Frequency count for temperature categories (2023)
tmed_freq_2023 <- meteo_23_24 %>%
filter(year(fecha) == 2023) %>%
count(tmed_cat) %>%
complete(tmed_cat = factor(c(0, 1, 2, 3), levels = c(0, 1, 2, 3)), fill = list(n = 0)) %>%
pull(n) %>%
t() %>%
as.data.frame()
# Assign labels to columns for vlabels
colnames(tmed_freq_2023) <- tmed_labels
# Frequency count for temperature categories (2024)
tmed_freq_2024 <- meteo_23_24 %>%
filter(year(fecha) == 2024) %>%
count(tmed_cat) %>%
complete(tmed_cat = factor(c(0, 1, 2, 3), levels = c(0, 1, 2, 3)), fill = list(n = 0)) %>%
pull(n) %>%
t() %>%
as.data.frame()
# Assign labels to columns for vlabels
colnames(tmed_freq_2024) <- tmed_labels
# Prepare data for temperature radar charts
tmed_final_2023 <- rbind(
rep(max(tmed_freq_2023), 4), # Maximum value for each axis
rep(0, 4), # Minimum value for each axis
tmed_freq_2023 # Data to be plotted (frequencies)
)
tmed_final_2024 <- rbind(
rep(max(tmed_freq_2024), 4), # Maximum value for each axis
rep(0, 4), # Minimum value for each axis
tmed_freq_2024 # Data to be plotted (frequencies)
)
# Frequency count for precipitation categories (2023)
prec_freq_2023 <- meteo_23_24 %>%
filter(year(fecha) == 2023) %>%
mutate(
prec_cat = case_when(
prec == 0 ~ 0, # No rain
prec > 0 & prec <= 5 ~ 1, # Light rain
prec > 5 & prec <= 20 ~ 2, # Moderate rain
prec > 20 ~ 3 # Heavy rain
),
prec_cat = factor(prec_cat, levels = c(0, 1, 2, 3))
) %>%
count(prec_cat) %>%
complete(prec_cat = factor(c(0, 1, 2, 3), levels = c(0, 1, 2, 3)), fill = list(n = 0)) %>%
pull(n) %>%
t() %>%
as.data.frame()
# Assign labels to columns for vlabels
colnames(prec_freq_2023) <- prec_labels
# Frequency count for precipitation categories (2024)
prec_freq_2024 <- meteo_23_24 %>%
filter(year(fecha) == 2024) %>%
mutate(
prec_cat = case_when(
prec == 0 ~ 0, # No rain
prec > 0 & prec <= 5 ~ 1, # Light rain
prec > 5 & prec <= 20 ~ 2, # Moderate rain
prec > 20 ~ 3 # Heavy rain
),
prec_cat = factor(prec_cat, levels = c(0, 1, 2, 3))
) %>%
count(prec_cat) %>%
complete(prec_cat = factor(c(0, 1, 2, 3), levels = c(0, 1, 2, 3)), fill = list(n = 0)) %>%
pull(n) %>%
t() %>%
as.data.frame()
# Assign labels to columns for vlabels
colnames(prec_freq_2024) <- prec_labels
# Prepare data for precipitation radar charts
prec_final_2023 <- rbind(
rep(max(prec_freq_2023), 4), # Maximum value for each axis
rep(0, 4), # Minimum value for each axis
prec_freq_2023 # Data to be plotted (frequencies)
)
prec_final_2024 <- rbind(
rep(max(prec_freq_2024), 4), # Maximum value for each axis
rep(0, 4), # Minimum value for each axis
prec_freq_2024 # Data to be plotted (frequencies)
)
# Frequency count for wind direction categories (2023)
dir_freq_2023 <- meteo_23_24 %>%
filter(year(fecha) == 2023) %>%
mutate(
dir_cat = case_when(
dir == 99 ~ "Calm/No Data", # Special code for calm or no data
dir >= 1 & dir <= 3 ~ "N",
dir > 3 & dir <= 8 ~ "NE",
dir > 8 & dir <= 13 ~ "E",
dir > 13 & dir <= 18 ~ "SE",
dir > 18 & dir <= 23 ~ "S",
dir > 23 & dir <= 28 ~ "SO",
dir > 28 & dir <= 33 ~ "O",
dir > 33 & dir <= 36 ~ "NO",
TRUE ~ "Other Code"
)
) %>%
count(dir_cat) %>%
complete(dir_cat = factor(c("Calm/No Data", "N", "NE", "E", "SE", "S", "SO", "O", "NO"),
levels = c("Calm/No Data", "N", "NE", "E", "SE", "S", "SO", "O", "NO")),
fill = list(n = 0)) %>%
pull(n) %>%
t() %>%
as.data.frame()
# Assign labels to columns for vlabels
colnames(dir_freq_2023) <- dir_labels
# Frequency count for wind direction categories (2024)
dir_freq_2024 <- meteo_23_24 %>%
filter(year(fecha) == 2024) %>%
mutate(
dir_cat = case_when(
dir == 99 ~ "Calm/No Data", # Special code for calm or no data
dir >= 1 & dir <= 3 ~ "N",
dir > 3 & dir <= 8 ~ "NE",
dir > 8 & dir <= 13 ~ "E",
dir > 13 & dir <= 18 ~ "SE",
dir > 18 & dir <= 23 ~ "S",
dir > 23 & dir <= 28 ~ "SO",
dir > 28 & dir <= 33 ~ "O",
dir > 33 & dir <= 36 ~ "NO",
TRUE ~ "Other Code"
)
) %>%
count(dir_cat) %>%
complete(dir_cat = factor(c("Calm/No Data", "N", "NE", "E", "SE", "S", "SO", "O", "NO"),
levels = c("Calm/No Data", "N", "NE", "E", "SE", "S", "SO", "O", "NO")),
fill = list(n = 0)) %>%
pull(n) %>%
t() %>%
as.data.frame()
# Assign labels to columns for vlabels
colnames(dir_freq_2024) <- dir_labels
# Prepare data for wind direction radar charts
dir_final_2023 <- rbind(
rep(max(dir_freq_2023), 9), # Maximum value for each axis
rep(0, 9), # Minimum value for each axis
dir_freq_2023 # Data to be plotted (frequencies)
)
dir_final_2024 <- rbind(
rep(max(dir_freq_2024), 9), # Maximum value for each axis
rep(0, 9), # Minimum value for each axis
dir_freq_2024 # Data to be plotted (frequencies)
)
# Set up the plotting area to have 3 rows and 2 columns
par(mfrow = c(3, 2), mar = c(1, 1, 1, 1), oma = c(3, 3, 6, 3))
# Common axis labels based on global maximum for temperature
common_max_tmed <- max(max(tmed_freq_2023), max(tmed_freq_2024))
# Radar chart for temperature (2023)
radarchart(
tmed_final_2023,
axistype = 1,
vlabels = tmed_labels,
pcol = rgb(1, 0.65, 0, 0.9), # Color of the polygon (orange)
pfcol = rgb(1, 0.65, 0, 0.5), # Fill color of the polygon (orange)
plwd = 4, # Line width
cglcol = "grey", # Color of the grid
cglty = 1, # Type of grid lines
axislabcol = "grey", # Color of axis labels
caxislabels = seq(0, common_max_tmed, by = common_max_tmed / 4), # Shared Axis Labels
cglwd = 0.8, # Width of grid lines
vlcex = 0.8 # Size of variable labels
)
title(main = "Temperature (2023)") # Title for temperature chart (2023)
# Radar chart for temperature (2024)
radarchart(
tmed_final_2024,
axistype = 1,
vlabels = tmed_labels,
pcol = rgb(1, 0.65, 0, 0.9), # Color of the polygon (orange)
pfcol = rgb(1, 0.65, 0, 0.5), # Fill color of the polygon (orange)
plwd = 4, # Line width
cglcol = "grey", # Color of the grid
cglty = 1, # Type of grid lines
axislabcol = "grey", # Color of axis labels
caxislabels = seq(0, common_max_tmed, by = common_max_tmed / 4), # Shared Axis Labels
cglwd = 0.8, # Width of grid lines
vlcex = 0.8 # Size of variable labels
)
title(main = "Temperature (2024)") # Title for temperature chart (2024)
# Common axis labels based on global maximum for precipitation
common_max_prec <- max(max(prec_freq_2023), max(prec_freq_2024))
# Radar chart for precipitation (2023)
radarchart(
prec_final_2023,
axistype = 1,
vlabels = prec_labels,
pcol = rgb(0.2, 0.5, 0.5, 0.9), # Color of the polygon (teal)
pfcol = rgb(0.2, 0.5, 0.5, 0.5), # Fill color of the polygon (teal)
plwd = 4, # Line width
cglcol = "grey", # Color of the grid
cglty = 1, # Type of grid lines
axislabcol = "grey", # Color of axis labels
caxislabels = seq(0, common_max_prec, by = common_max_prec / 4), # Shared Axis Labels
cglwd = 0.8, # Width of grid lines
vlcex = 0.8 # Size of variable labels
)
title(main = "Precipitation (2023)") # Title for precipitation chart (2023)
# Radar chart for precipitation (2024)
radarchart(
prec_final_2024,
axistype = 1,
vlabels = prec_labels,
pcol = rgb(0.2, 0.5, 0.5, 0.9), # Color of the polygon (teal)
pfcol = rgb(0.2, 0.5, 0.5, 0.5), # Fill color of the polygon (teal)
plwd = 4, # Line width
cglcol = "grey", # Color of the grid
cglty = 1, # Type of grid lines
axislabcol = "grey", # Color of axis labels
caxislabels = seq(0, common_max_prec, by = common_max_prec / 4), # Shared Axis Labels
cglwd = 0.8, # Width of grid lines
vlcex = 0.8 # Size of variable labels
)
title(main = "Precipitation (2024)") # Title for precipitation chart (2024)
# Common axis labels based on global maximum for wind direction
common_max_dir <- max(max(dir_freq_2023), max(dir_freq_2024))
# Radar chart for wind direction (2023)
radarchart(
dir_final_2023,
axistype = 1,
vlabels = dir_labels,
pcol = rgb(0.1, 0.4, 0.9, 0.9), # Color of the polygon (blue)
pfcol = rgb(0.1, 0.4, 0.9, 0.5), # Fill color of the polygon (blue)
plwd = 4, # Line width
cglcol = "grey", # Color of the grid
cglty = 1, # Type of grid lines
axislabcol = "grey", # Color of axis labels
caxislabels = seq(0, common_max_dir, by = common_max_dir / 4), # Shared Axis Labels
cglwd = 0.8, # Width of grid lines
vlcex = 0.8 # Size of variable labels
)
title(main = "Wind Direction (2023)") # Title for wind direction chart (2023)
# Radar chart for wind direction (2024)
radarchart(
dir_final_2024,
axistype = 1,
vlabels = dir_labels,
pcol = rgb(0.1, 0.4, 0.9, 0.9), # Color of the polygon (blue)
pfcol = rgb(0.1, 0.4, 0.9, 0.5), # Fill color of the polygon (blue)
plwd = 4, # Line width
cglcol = "grey", # Color of the grid
cglty = 1, # Type of grid lines
axislabcol = "grey", # Color of axis labels
caxislabels = seq(0, common_max_dir, by = common_max_dir / 4), # Shared Axis Labels
cglwd = 0.8, # Width of grid lines
vlcex = 0.8 # Size of variable labels
)
title(main = "Wind Direction (2024)") # Title for wind direction chart (2024)
# Title for all radar charts (global title)
mtext(
"Comparison of daily weather patterns between 2023 and 2024 in the city of Barcelona",
outer = TRUE, # Title outside the plot area
cex = 1.3, # Title size
line = 1, # Adjust vertical position
font = 2 # Bold
)
3. Rose chart
Using the BCN traffic accident data for 2024, we aim to create a rose chart to visualize how accidents are distributed throughout the days of the week.
Data available on: https://opendata-ajuntament.barcelona.cat/data/ca/dataset/accidents-gu-bcn
Code inspired by: https://rpubs.com/chidungkt/819554
# Libraries
library(tidyverse)
library(stringr)
library(lubridate)
library(showtext)
# Data Import
accidents_bcn24 <- read_csv("2024_accidents_gu_bcn.csv")
accidents_bcn24$Descripcio_dia_setmana <- as.factor(accidents_bcn24$Descripcio_dia_setmana)
# Data Preparation: Calculate frequency per day of the week
# Define the order of the days of the week for the polar axis (in English)
day_order <- c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday")
# Assuming the 'Descripcio_dia_setmana' column contains the Catalan names,
# we need to map them to the English names for the plot labels.
# Mapping Catalan to English
day_mapping <- c(
"Dilluns" = "Monday",
"Dimarts" = "Tuesday",
"Dimecres" = "Wednesday",
"Dijous" = "Thursday",
"Divendres" = "Friday",
"Dissabte" = "Saturday",
"Diumenge" = "Sunday"
)
# Calculate frequency and prepare the data
freq_data <- accidents_bcn24 %>%
filter(!is.na(Descripcio_dia_setmana)) %>%
mutate(
# Map the raw Catalan names to the English names
Day_of_Week = recode(Descripcio_dia_setmana, !!!day_mapping),
# Convert to factor with the correct order for the plot
Day_of_Week = factor(Day_of_Week, levels = day_order)
) %>%
count(Day_of_Week, name = "Accidents_Count") %>% # Count the frequency
drop_na(Day_of_Week)
# Rose Chart Visualization
# Recreate Rose Diagram with frequency data and the standard ggplot2 theme
freq_data %>%
ggplot(aes(x = Day_of_Week, y = Accidents_Count, fill = Day_of_Week)) +
# The geom_col layer creates the bar chart
geom_col(color = "grey20", show.legend = FALSE) +
# Use theme_minimal
theme_minimal() +
# Manual color scale
scale_fill_manual(values = hcl.colors(n = length(day_order), palette = "Dynamic")) +
# Apply the polar transformation
coord_polar() +
# Adjust titles and labels
labs(
title = "Frequency of traffic accidents in BCN\nby day of the week",
subtitle = "Distribution of incidents throughout the week",
caption = "Data source: Barcelona city council (Traffic accidents)"
) +
# Aesthetics customization
theme(
legend.position = "none",
text = element_text(size = 14),
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
plot.margin = unit(rep(0.7, 4), "cm"),
plot.title = element_text(color = "black", size = 20, face = "bold", hjust = 0.5),
plot.caption = element_text(color = "grey50", size = 11),
plot.subtitle = element_text(color = "grey50", size = 13),
# Adjust the color of the X-axis labels (day names)
axis.text.x = element_text(size = 12, color = "black", face = "bold")
)