Hourly Temperature Heat-Map: New Delhi

Comparing temperature data from Historical Weather API of Open-Meteo.com to generate hourly temperature heat map of New Delhi for last 32 years

Inspiration from R-Graph-Gallery. Data from https://open-meteo.com/ historical weather API shared in D.I.P. by Jeremy S. Vine.

Figure 1: An hourly temperature heat map of the city of New Delhi from 1991-2023. X-axis denotes months and days, Y-axis denotes hours. Horizontal facets are the Months, and Vertical facets are the Years. Temperatures are coded in colour from green (cold) to yellow (medium) to red (hot).

How I made this graphic?

Loading libraries & data

# Data Import and Wrangling Tools
library(tidyverse)            # All things tidy

# Final plot tools
library(scales)               # Nice Scales for ggplot2
library(fontawesome)          # Icons display in ggplot2
library(ggtext)               # Markdown text support for ggplot2
library(showtext)             # Display fonts in ggplot2
library(colorspace)           # Lighten and Darken colours
library(seecolor)             # To print and view colours
library(patchwork)            # Combining plots

# Get data in .csv from https://open-meteo.com

# URL for this data:
# https://open-meteo.com/en/docs/historical-weather-api#start_date=1990-01-01&end_date=2024-09-01&location_mode=csv_coordinates&csv_coordinates=28.7041,+77.1025

rawdf <- read_csv(
  here::here("data/open-meteo-new-delhi-1950.csv"),
  skip = 2
)

Data Wrangling and EDA

df <- rawdf |> 
  janitor::clean_names() |> 
  mutate(
    time_local = with_tz(time, "Asia/Kolkata"),
    var_year = year(time_local),
    var_month = month(time_local, label = TRUE),
    var_hour = hour(time_local),
    var_day = day(time_local),
    .keep = "unused"
  )

# df_names <- rawdf1 |> 
#   as_tibble() |> 
#   slice_head(n = 5) |> 
#   select(1:4) |> 
#   mutate(
#     city = c(
#       "New Delhi",
#       "Cairo",
#       "Islamabad",
#       "Kuwait City",
#       "Baghdad"
#     )
#   )
# temp1 |> 
#   mutate(
#     local_time = case_when(
#       location_id == 0 ~ with_tz(time, "Asia/Kolkata"),
#       location_id == 1 ~ with_tz(time, "Africa/Cairo"),
#       location_id == 2 ~ with_tz(time, "Asia/Karachi"),
#       location_id == 3 ~ with_tz(time, "Asia/Kuwait"),
#       location_id == 4 ~ with_tz(time, "Asia/Baghdad"),
#       .default = NA
#     )
#   )

Visualization Parameters

# Font for titles
font_add_google("Oswald",
  family = "title_font"
) 

# Font for the caption
font_add_google("Barlow Condensed",
  family = "caption_font"
) 

# Font for plot text
font_add_google("Kanit",
  family = "body_font"
) 

showtext_auto()

bg_col <- "white"

text_col <- "grey15"
text_hil <- "grey15"

bts <- 90

# Caption stuff for the plot
sysfonts::font_add(
  family = "Font Awesome 6 Brands",
  regular = here::here("docs", "Font Awesome 6 Brands-Regular-400.otf")
)
github <- "&#xf09b"
github_username <- "aditya-dahiya"
xtwitter <- "&#xe61b"
xtwitter_username <- "@adityadahiyaias"
social_caption_1 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{github};</span> <span style='color: {text_hil}'>{github_username}  </span>")
social_caption_2 <- glue::glue("<span style='font-family:\"Font Awesome 6 Brands\";'>{xtwitter};</span> <span style='color: {text_hil}'>{xtwitter_username}</span>")

plot_title <- "New Delhi: Hourly Temperature Heat-Map (1991-2023)" 

plot_subtitle <- "The hourly temperature in India's Capital ........."

plot_caption <- paste0(
  "**Data:** open-meteo.com", 
  " |  **Code:** ", 
  social_caption_1, 
  " |  **Graphics:** ", 
  social_caption_2
  )

rm(github, github_username, xtwitter, 
   xtwitter_username, social_caption_1, 
   social_caption_2)

The static plot

g <- df |> 
  filter(var_year > 1990 & var_year < 2024) |> 
  ggplot(
    mapping = aes(
      x = var_day,
      y = var_hour,
      fill = temperature_2m_c
    )
  ) +
  geom_tile(
    linewidth = 0.01,
    colour = bg_col
  ) +
  
  # Scales and Coordinates
  scale_x_continuous(
    breaks = c(0, 10, 20, 30),
    expand = expansion(0)
  ) +
  scale_y_continuous(
    expand = expansion(0),
    breaks = seq(0, 24, 6)
  ) +
  paletteer::scale_fill_paletteer_c(
    "grDevices::Temps",
    # "ggthemes::Red-Green-Gold Diverging",
    direction = 1,
    breaks = seq(0, 50, 10)
  ) +
  facet_grid(
    var_year ~ var_month,
    scales = "free_x"
  ) +
  coord_cartesian(
    clip = "off"
  ) +
  
  labs(
    x = "Day of the month (1 - 31)",
    y = "Hour of the day (24 hrs)",
    fill = "Temperature (in degrees Celsius)",
    title = plot_title,
    subtitle = NULL,
    caption = plot_caption
  ) +
  theme_minimal(
    base_family = "title_font",
    base_size = bts
  ) +
  theme(
    # Overall Plot
    panel.grid = element_blank(),
    legend.position = "bottom",
    text = element_text(
      margin = margin(0,0,0,0, "mm"),
      colour = text_col,
      lineheight = 0.3
    ),
    plot.title.position = "plot",
    plot.margin = margin(5,10,5,5, "mm"),
    
    # Strips and Axes
    axis.ticks = element_blank(),
    axis.ticks.length = unit(0, "mm"),
    axis.text.x = element_text(
      margin = margin(1,0,0,0, "mm"),
      size = 0.3 * bts
    ),
    axis.text.y = element_text(
      margin = margin(0,1,0,0, "mm"),
      size = 0.2 * bts
    ),
    axis.title = element_text(
      margin = margin(-2,0,0,0, "mm")
    ),
    panel.spacing = unit(0.9, "mm"),
    strip.text.x = element_text(
      margin = margin(0,0,3,0, "mm"),
      size = 1.2 * bts
    ),
    strip.text.y = element_text(
      margin = margin(0,0,0,3, "mm"),
      angle = 0,
      size = 0.8 * bts
    ),
    
    # Labels
    plot.title = element_text(
      family = "title_font",
      margin = margin(10,0,5,0, "mm"),
      size = 1.75 * bts,
      hjust = 0.5
    ),
    plot.subtitle = element_text(
      margin = margin(0,0,5,0, "mm"),
      hjust = 0.5
    ),
    plot.caption = element_textbox(
      family = "caption_font",
      hjust = 0.5,
      margin = margin(-10,0,0,0, "mm"),
      size = 0.6 * bts
    ),
    
    # Legend
    legend.title.position = "top",
    legend.key.height = unit(5, "mm"),
    legend.key.width = unit(70, "mm"),
    legend.title = element_text(
      margin = margin(-30,0,5,0, "mm"),
      hjust = 0.5
    ),
    legend.text = element_text(
      margin = margin(2,0,0,0, "mm")
    )
  )


ggsave(
  filename = here::here("data_vizs", "dip_temp_heatmap.png"),
  plot = g,
  width = 400,    # Best Twitter Aspect Ratio = 5:4
  height = 500,   
  units = "mm",
  bg = "white"
)

Savings the graphics

ggsave(
  filename = here::here("data_vizs", "a4_dip_temp_heatmap.png"),
  plot = g,
  width = 210 * 2,    # Best Twitter Aspect Ratio = 5:4
  height = 297 * 2,   
  units = "mm",
  bg = "white"
)


library(magick)
# Saving a thumbnail for the webpage
image_read(here::here("data_vizs", "dip_temp_heatmap.png")) |> 
  image_resize(geometry = "400") |> 
  image_write(
    here::here(
      "data_vizs", 
      "thumbnails", 
      "dip_temp_heatmap.png"
    )
  )

To bring out a pattern, lets try a line graph or boxplots

# https://open-meteo.com/en/docs/historical-weather-api#start_date=1950-01-01&end_date=2023-12-31&timezone=GMT

rawdf <- read_csv(
  here::here("data/open-meteo-new-delhi-1950.csv"),
  skip = 2
)

df <- rawdf |> 
  janitor::clean_names() |> 
  mutate(
    time_local = with_tz(time, "Asia/Kolkata"),
    var_year = year(time_local),
    var_month = month(time_local, label = TRUE),
    var_hour = hour(time_local),
    var_day = day(time_local),
    .keep = "unused"
  ) |> 
  filter(var_year < 2024)

vline_df <- df |> 
  group_by(var_month) |> 
  summarise(
    med_temp = median(temperature_2m_c, na.rm = TRUE)
  )

g <- df |> 
  ggplot(
    mapping = aes(
      x = temperature_2m_c
    )
  ) +
  geom_boxplot(
    outliers = F,
    staplewidth = 0.25,
    colour = "grey10",
    linewidth = 0.25
  ) +
  geom_vline(
    data = vline_df,
    mapping = aes(
      xintercept = med_temp
    ),
    colour = "purple",
    linetype = 1,
    linewidth = 1.5,
    alpha = 0.3
  ) +
  facet_grid(
    var_year~var_month,
    scales = "free_x"
  ) +
  coord_cartesian(
    clip = "off"
  ) +
  
  labs(
    x = "Temperature (in degrees Celsius)",
    y = NULL,
    title = "New Delhi: Daily Temperatures (1950 - 2023)",
    subtitle = NULL,
    caption = plot_caption
  ) +
  theme_minimal(
    base_family = "title_font",
    base_size = bts
  ) +
  theme(
    # Overall Plot
    panel.grid = element_blank(),
    text = element_text(
      margin = margin(0,0,0,0, "mm"),
      colour = text_col,
      lineheight = 0.3
    ),
    plot.title.position = "plot",
    plot.margin = margin(5,10,5,5, "mm"),
    
    # Strips and Axes
    axis.ticks = element_blank(),
    axis.ticks.length = unit(0, "mm"),
    axis.text.x = element_text(
      margin = margin(1,0,0,0, "mm"),
      size = 0.3 * bts
    ),
    axis.text.y = element_blank(),
    axis.title = element_text(
      margin = margin(-2,0,0,0, "mm")
    ),
    panel.spacing = unit(0.2, "mm"),
    strip.text.x = element_text(
      margin = margin(0,0,3,0, "mm"),
      size = 1.2 * bts
    ),
    strip.text.y = element_text(
      margin = margin(0,0,0,3, "mm"),
      angle = 0,
      size = 0.4 * bts
    ),
    
    # Labels
    plot.title = element_text(
      family = "title_font",
      margin = margin(10,0,5,0, "mm"),
      size = 1.75 * bts,
      hjust = 0.5
    ),
    plot.subtitle = element_text(
      margin = margin(0,0,5,0, "mm"),
      hjust = 0.5
    ),
    plot.caption = element_textbox(
      family = "caption_font",
      hjust = 0.5,
      margin = margin(5,0,0,0, "mm"),
      size = 0.6 * bts
    )
  )


ggsave(
  filename = here::here("data_vizs", "dip_temp_heatmap_boxplot.png"),
  plot = g,
  width = 400,    # Best Twitter Aspect Ratio = 5:4
  height = 500,   
  units = "mm",
  bg = "white"
)

Figure 2: The same data with a boxplot. So indeed, we dont see any noticeable increase in temperatures - no discernable movement of boxplots to the right.

Another attempt with a boxplot overlaid with a smoother curve to see pattern

g <- df |> 
  ggplot(
    mapping = aes(
      x = var_year,
      y = temperature_2m_c
    )
  ) +
  geom_boxplot(
    mapping = aes(
      group = var_year
    ),
    outliers = FALSE,
    linewidth = 0.2,
    colour = "grey20"
  ) +
  geom_smooth(
    colour = "red",
    se = TRUE,
    span = 0.9
  ) +
  facet_wrap(
    ~var_month,
    scales = "free_y",
    ncol = 2
  ) +
  
  labs(
    y = "Temperature (in degrees Celsius)",
    x = "Year",
    title = "New Delhi: Monthly Temperatures (1950 - 2023)",
    subtitle = NULL,
    caption = plot_caption
  ) +
  theme_minimal(
    base_family = "title_font",
    base_size = bts
  ) +
  theme(
    # Overall Plot
    panel.grid = element_blank(),
    panel.grid.major.y = element_line(
      colour = "grey40",
      linetype = "longdash",
      linewidth = 0.25
    ),
    text = element_text(
      margin = margin(0,0,0,0, "mm"),
      colour = text_col,
      lineheight = 0.3
    ),
    plot.title.position = "plot",
    plot.margin = margin(5,10,5,5, "mm"),
    
    # Strips and Axes
    axis.ticks = element_blank(),
    axis.ticks.length = unit(0, "mm"),
    axis.text = element_text(
      margin = margin(1,0,0,0, "mm"),
      size = 0.5 * bts
    ),
    axis.title = element_text(
      margin = margin(-2,0,0,0, "mm")
    ),
    panel.spacing = unit(0.2, "mm"),
    strip.text = element_text(
      margin = margin(3,0,-1,0, "mm"),
      size = 1.2 * bts
    ),
    
    # Labels
    plot.title = element_text(
      family = "title_font",
      margin = margin(10,0,5,0, "mm"),
      size = 1.75 * bts,
      hjust = 0.5
    ),
    plot.subtitle = element_text(
      margin = margin(0,0,5,0, "mm"),
      hjust = 0.5
    ),
    plot.caption = element_textbox(
      family = "caption_font",
      hjust = 0.5,
      margin = margin(5,0,0,0, "mm"),
      size = 0.6 * bts
    )
  )


ggsave(
  filename = here::here("data_vizs", "dip_temp_heatmap_boxplot2.png"),
  plot = g,
  width = 400,    # Best Twitter Aspect Ratio = 5:4
  height = 500,   
  units = "mm",
  bg = "white"
)

Figure 3: Boxplot of monthly temperature ranges, with a overlaid smoother line, from 1950 to 2023.