This analysis investigates weather trends in Indianapolis over a five-year period (2019–2023), focusing on temperature fluctuations, precipitation patterns, and seasonal variations. Weather data were acquired from the Open-Meteo API, encompassing daily maximum/minimum temperatures and total precipitation. The analysis highlighted distinct seasonal patterns, including consistent summer peaks in maximum temperatures and winter lows, and identified frequent precipitation events and occasional extreme weather conditions such as heat waves (>90°F) and significant rainfall events (>25 mm/day). An interactive dashboard was developed using R, Quarto, and Shiny, enabling dynamic exploration of weather data through user-defined date ranges, which supports deeper insights into Indianapolis’s climate trends.
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(lubridate)
Attaching package: 'lubridate'The following objects are masked from 'package:base':
date, intersect, setdiff, unionlibrary(openmeteo)
library(tidyr)
library(purrr)
library(ggplot2)
library(shiny)Indianapolis coordinates (latitude: 39.7684°N, longitude: 86.1581°W) are set to retrieve localized weather data. Data is fetched dynamically for the past five years up to the current date, ensuring the analysis is always up-to-date.
indy_lat <- 39.7684
indy_lon <- -86.1581end_date <- Sys.Date()
start_date <- end_date - years(5)indy_weather_raw <- weather_history(
location = c(indy_lat, indy_lon),
start = start_date,
end = end_date,
hourly = c("temperature_2m", "relative_humidity_2m", "precipitation", "wind_speed_10m"),
daily = c("temperature_2m_max", "temperature_2m_min", "precipitation_sum")
)str(indy_weather_raw)tibble [45,675 × 9] (S3: tbl_df/tbl/data.frame)
$ date : Date[1:45675], format: "2020-11-26" "2020-11-27" ...
$ time : chr [1:45675] NA NA NA NA ...
$ daily_temperature_2m_max : num [1:45675] 9.9 10.1 7.9 10.2 4.7 0.6 5.6 7.3 8.2 4.9 ...
$ daily_temperature_2m_min : num [1:45675] 7.1 3.7 -2 -1.8 -1.5 -3.4 -5 -3.6 -1.5 -1.8 ...
$ daily_precipitation_sum : num [1:45675] 0.1 0 0 0 5.1 0 0 0 0 0 ...
$ hourly_temperature_2m : num [1:45675] NA NA NA NA NA NA NA NA NA NA ...
$ hourly_relative_humidity_2m: int [1:45675] NA NA NA NA NA NA NA NA NA NA ...
$ hourly_precipitation : num [1:45675] NA NA NA NA NA NA NA NA NA NA ...
$ hourly_wind_speed_10m : num [1:45675] NA NA NA NA NA NA NA NA NA NA ...Extract relevant columns (daily maximum/minimum temperatures, precipitation). Convert Celsius temperatures to Fahrenheit. Generate new informative variables: average temperature, seasonal labels, extreme temperature flags, rain events, and temperature range. Limit dataset to precisely five years (1825 days).
indy_weather <- indy_weather_raw %>%
select(date,
daily_temperature_2m_max,
daily_temperature_2m_min,
daily_precipitation_sum) %>%
rename(
temp_max = daily_temperature_2m_max,
temp_min = daily_temperature_2m_min,
precipitation = daily_precipitation_sum
) %>%
mutate(
date = ymd(date),
temp_max = as.numeric(temp_max),
temp_min = as.numeric(temp_min),
year = year(date),
month = month(date),
day = day(date),
temp_avg = (temp_max + temp_min) / 2,
temp_avg_f = (temp_avg * 9/5) + 32,
temp_max_f = (temp_max * 9/5) + 32,
temp_min_f = (temp_min * 9/5) + 32,
month_name = month(date, label = TRUE, abbr = TRUE),
season = case_when(
month %in% c(12, 1, 2) ~ "Winter",
month %in% c(3, 4, 5) ~ "Spring",
month %in% c(6, 7, 8) ~ "Summer",
month %in% c(9, 10, 11) ~ "Fall"
),
extreme_heat = temp_max_f >= 90,
extreme_cold = temp_min_f <= 20,
rain_day = precipitation > 0,
heavy_rain = precipitation > 25,
daily_temp_range = temp_max_f - temp_min_f
) %>%
slice(1:1825) # Keep only the correct rowsSummarize data by season, including temperature averages and extreme weather events counts.
seasonal_summary <- indy_weather %>%
group_by(year, season) %>%
summarize(
avg_temp = mean(temp_avg_f, na.rm = TRUE),
avg_max_temp = mean(temp_max_f, na.rm = TRUE),
avg_min_temp = mean(temp_min_f, na.rm = TRUE),
total_precip = sum(precipitation, na.rm = TRUE),
extreme_heat_days = sum(extreme_heat, na.rm = TRUE),
extreme_cold_days = sum(extreme_cold, na.rm = TRUE),
rainy_days = sum(rain_day, na.rm = TRUE),
heavy_rain_days = sum(heavy_rain, na.rm = TRUE),
.groups = "drop"
)Monthly summaries to highlight detailed monthly patterns and trends.
monthly_summary <- indy_weather %>%
group_by(year, month, month_name) %>%
summarize(
avg_temp = mean(temp_avg_f, na.rm = TRUE),
avg_max_temp = mean(temp_max_f, na.rm = TRUE),
avg_min_temp = mean(temp_min_f, na.rm = TRUE),
total_precip = sum(precipitation, na.rm = TRUE),
rainy_days = sum(rain_day, na.rm = TRUE),
.groups = "drop"
)Yearly summaries provide an overview for each year for easy comparisons.
yearly_summary <- indy_weather %>%
group_by(year) %>%
summarize(
avg_temp = mean(temp_avg_f, na.rm = TRUE),
avg_max_temp = mean(temp_max_f, na.rm = TRUE),
avg_min_temp = mean(temp_min_f, na.rm = TRUE),
total_precip = sum(precipitation, na.rm = TRUE),
extreme_heat_days = sum(extreme_heat, na.rm = TRUE),
extreme_cold_days = sum(extreme_cold, na.rm = TRUE),
rainy_days = sum(rain_day, na.rm = TRUE),
heavy_rain_days = sum(heavy_rain, na.rm = TRUE),
.groups = "drop"
)Generate visual insights into temperature and precipitation patterns.
p1 <- ggplot(indy_weather, aes(x = date, y = temp_avg_f)) +
geom_line(color = "blue") +
labs(title = "Daily Average Temperature (°F)",
x = "Date", y = "Avg Temperature (°F)") +
theme_minimal()p2 <- ggplot(indy_weather, aes(x = date, y = precipitation)) +
geom_bar(stat = "identity", fill = "skyblue") +
labs(title = "Daily Precipitation (mm)",
x = "Date", y = "Precipitation (mm)") +
theme_minimal()p3 <- ggplot(seasonal_summary, aes(x = interaction(year, season, sep = " - "), y = avg_max_temp)) +
geom_col(fill = "coral") +
labs(title = "Seasonal Average Maximum Temperature (°F)",
x = "Year - Season", y = "Avg Max Temperature (°F)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))print(p1)
print(p2)
print(p3)
Create an interactive Shiny-powered dashboard to explore weather data dynamically by adjusting date ranges.
shiny::sliderInput(
inputId = "daterange",
label = "Select Date Range:",
min = min(indy_weather$date),
max = max(indy_weather$date),
value = c(min(indy_weather$date), max(indy_weather$date)),
timeFormat = "%Y-%m-%d"
)renderPlot({
# Inline filtering inside the reactive context
filtered_data <- indy_weather %>%
filter(date >= input$daterange[1] & date <= input$daterange[2])
ggplot(filtered_data, aes(x = date, y = temp_avg_f)) +
geom_line(color = "blue") +
labs(title = "Daily Average Temperature (°F)",
x = "Date", y = "Avg Temperature (°F)") +
theme_minimal()
})renderPlot({
filtered_data <- indy_weather %>%
filter(date >= input$daterange[1] & date <= input$daterange[2])
ggplot(filtered_data, aes(x = date, y = precipitation)) +
geom_bar(stat = "identity", fill = "skyblue") +
labs(title = "Daily Precipitation (mm)",
x = "Date", y = "Precipitation (mm)") +
theme_minimal()
})renderPlot({
seasonal_summary <- indy_weather %>%
filter(date >= input$daterange[1] & date <= input$daterange[2]) %>%
group_by(year, season) %>%
summarize(avg_max_temp = mean(temp_max_f, na.rm = TRUE), .groups = "drop")
ggplot(seasonal_summary, aes(x = interaction(year, season, sep = " - "), y = avg_max_temp)) +
geom_col(fill = "coral") +
labs(title = "Seasonal Average Maximum Temperature (°F)",
x = "Year - Season", y = "Avg Max Temperature (°F)") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
})