title: "Mars Weather Data Dive"

author: "Rihab Badawi"

date: "2025-01-25"

output:html_decoment

Introduction:

This analysis investigates the Mars Weather dataset, focusing on temperature, pressure, and seasonal patterns. The goal is to explore trends, relationships, and anomalies in the data to better understand the Martian climate.

Load required libraries:

library(tidyverse)

Load the dataset:

marsdata <- readcsv("C:/Users/rbada/Downloads/mars-weather.csv")

Numeric and Categorical Summaries:

Numeric Summary

Numeric summary for min_temp and pressure

summaryfornumericcols <- marsdata %>% summarise( MinMinTemp = min(mintemp, na.rm = TRUE), MaxMinTemp = max(mintemp, na.rm = TRUE), MeanMinTemp = mean(mintemp, na.rm = TRUE), MedianMinTemp = median(mintemp, na.rm = TRUE), Q1MinTemp = quantile(mintemp, 0.25, na.rm = TRUE), Q3MinTemp = quantile(mintemp, 0.75, na.rm = TRUE),

Min_Pressure = min(pressure, na.rm = TRUE),
Max_Pressure = max(pressure, na.rm = TRUE),
Mean_Pressure = mean(pressure, na.rm = TRUE),
Median_Pressure = median(pressure, na.rm = TRUE),
Q1_Pressure = quantile(pressure, 0.25, na.rm = TRUE),
Q3_Pressure = quantile(pressure, 0.75, na.rm = TRUE)

) print(summaryfornumeric_cols)

Insight:

The numeric summary shows that minimum temperatures on Mars can drop as low as -90°C and reach a maximum of -62°C, with an average of -60°C. Atmospheric pressure ranges between 700 Pa and 925 Pa, with an average of 853 Pa. These values highlight the extreme cold and thin atmosphere on Mars, which are critical for planning future missions.

Categorical summary for unique values and counts:

categoricalsummary <- marsdata %>% summarise( UniqueMonths = ndistinct(month), UniqueAtmoOpacity = ndistinct(atmoopacity) ) print(categorical_summary)

Frequency counts for month and atmospheric opacity

monthcounts <- marsdata %>% count(month, sort = TRUE) opacitycounts <- marsdata %>% count(atmo_opacity, sort = TRUE)

print(monthcounts) print(opacitycounts)

Insight:

The dataset includes 12 unique Martian months. The atmospheric opacity is mostly reported as Sunny, with very few instances of unclear (--) values. This indicates relatively stable atmospheric conditions on Mars, which is promising for solar-powered systems. However, further investigation into the rare unclear values might provide insights into unusual weather events.

combinedsummary <- list( NumericSummary = numericsummary, CategoricalSummary = categoricalsummary, MonthFrequencies = monthcounts, OpacityFrequencies = opacitycounts, DistinctCategories = distinct_categories )

Print combined summary

print(combined_summary)

Novel Questions to Investigate:

1. How do minimum and maximum temperatures vary across different Martian months?

2, What is the relationship between atmospheric pressure and temperature on Mars?

3. Are there significant differences in atmospheric opacity (e.g., Sunny vs. --) during specific Martian months or seasons?

Column Summaries:

Numaric column

1. min_temp: Represents the minimum daily temperature on Mars (°C) and helps analyze the coldest weather conditions to understand extreme environments.

2. max_temp: Indicates the maximum daily temperature on Mars (°C), providing insights into the hottest conditions and daily temperature ranges.

3.pressure: Measures the atmospheric pressure on Mars (Pa), allowing for the study of stability and variability in Martian atmospheric conditions.

4.ls: Represents the solar longitude (0°–360°), which corresponds to Mars’s position in its orbit and helps analyze seasonal changes affecting weather patterns.

5.sol:Tracks the Martian solar day (count of days since the mission began) and enables the analysis of trends or changes in weather.

Categorical Columns

1.month:Specifies the Martian month (from Month 1 to Month 12), allowing seasonal trends and variations in Martian weather to be

studied throughout the year.

2.atmo_opacity:Describes the clarity of the Martian atmosphere (e.g.,Sunny), providing insights into atmospheric conditions such as clear skies or dusty weather.

1.terrestrial_date:Refers to the Earth-based date corresponding to the Martian weather data, enabling chronological analysis and linking Mars observations to Earth’s timeline.

1.id:unique identifier for each observation, ensuring each row can be tracked and distinguished for analysis purposes.

2.wind_speed:Records the wind speed on Mars,which provides valuable information about wind dynamics and weather patterns.

Dataset Documentation:

This dataset contains weather observations from Mars, collected over a specific period. It includes both numerical and categorical data, such as temperature, atmospheric pressure, and seasonal attributes, offering insights into the Martian climate. The data was likely obtained from Mars weather missions, such as NASA's Curiosity rover or similar projects, and serves as a valuable resource for studying Martian environmental conditions.

project's goals/purpose:

The goal of this project is to analyze the Mars weather dataset to better understand the Martian climate. This includes studying temperature patterns, atmospheric pressure, and seasonal changes across Martian months. The project aims to identify trends, explore relationships between variables like temperature and pressure, and gain insights into how Martian weather behaves over time. These findings can provide valuable context for future Mars missions and research.

Addressing a Question Using Aggregation:

Aggregation: How do minimum and maximum temperatures vary across Martian months?

Aggregate mean mintemp and maxtemp by Martian month

tempbymonth <- marsdata %>% groupby(month) %>% summarise( MeanMinTemp = mean(mintemp, na.rm = TRUE), MeanMaxTemp = mean(maxtemp, na.rm = TRUE) ) print(tempbymonth)

Aggregation: What is the relationship between atmospheric pressure and temperature?

Aggregate mean pressure and temperature by Martian month

pressuretempbymonth <- marsdata %>% groupby(month) %>% summarise( MeanPressure = mean(pressure, na.rm = TRUE), MeanMinTemp = mean(mintemp, na.rm = TRUE) ) print(pressuretempby_month)

Insight:

Aggregating minimum and maximum temperatures by Martian month reveals seasonal trends. Month 1 shows the coldest temperatures, while Month 8 is relatively warmer. These findings help us understand how temperatures change with Martian seasons, which can inform planning for seasonal activities or missions on Mars.

Visual Summaries:

Filter and Clean the Data

cleaneddata <- marsdata %>% filter(!is.na(mintemp) & !is.na(pressure) & !is.na(atmoopacity))

Check the cleaned dataset

summary(cleaned_data)

Boxplot

Boxplot: Minimum Temperatures by Martian Month

cleaneddata |> ggplot() + geomboxplot(mapping = aes(x = month, y = mintemp, fill = month)) + ggtitle("Minimum Temperatures by Martian Month") + thememinimal() + theme(axis.text.x = element_text(angle = 45, hjust = 1))

Insight:

The boxplot shows that minimum temperatures vary significantly across months. For example, Month 3 experiences the coldest temperatures, while Month 4 has greater variability. This variability could indicate transitional weather patterns, which are crucial to consider for long-term exploration missions.

Line Plot

Line Plot: Atmospheric Pressure Over Time

cleaneddata |> ggplot(aes(x = terrestrialdate, y = pressure)) + geomline(color = "blue") + labs( title = "Atmospheric Pressure on Mars Over Time", x = "Earth Date", y = "Atmospheric Pressure (Pa)" ) + thememinimal()

Insight:

The line plot shows how atmospheric pressure changes over time on Mars. It highlights periods of high and low pressure, which could be linked to Martian seasons or other patterns. This helps in understanding how pressure varies and its impact on Mars missions.

Scatter plot

Scatter Plot: Pressure vs Minimum Temperature

cleaneddata |> ggplot(mapping = aes(x = pressure, y = mintemp, color = month)) + geompoint() + labs( title = "Pressure vs Minimum Temperature", x = "Atmospheric Pressure (Pa)", y = "Minimum Temperature (°C)" ) + thememinimal()

Insight

The scatter plot highlights a positive correlation between atmospheric pressure and minimum temperature. This suggests that higher pressures are generally associated with warmer temperatures. Further analysis of outliers in this relationship could reveal extreme weather events or anomalies