LA-01 PRESENATION

BATCH 21

Air Quality Dataset

Loading data and library

library(ggplot2)
library(tidyverse)

## Warning: package 'tidyverse' was built under R version 4.5.3

## Warning: package 'readr' was built under R version 4.5.3

## Warning: package 'forcats' was built under R version 4.5.3

## Warning: package 'lubridate' was built under R version 4.5.3

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## ✔ purrr     1.2.1     ✔ tidyr     1.3.2
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library(lubridate)
library(corrplot)

## Warning: package 'corrplot' was built under R version 4.5.3

## corrplot 0.95 loaded

data <- read.csv("C:/Users/Vigne/Downloads/air+quality/AirQualityUCI.csv", sep=";", dec=",")
data <- data[,1:13]

data$Datetime <- lubridate::dmy_hms(paste(data$Date, data$Time))

## Warning: 114 failed to parse.

data <- na.omit(data)


data$Month <- lubridate::month(data$Datetime, label = TRUE)
pollutants <- data %>%
  dplyr::select(Datetime, CO.GT., NO2.GT., NOx.GT.) %>%
  tidyr::pivot_longer(-Datetime)
corr_data <- data %>%
  select(CO.GT., NO2.GT., NOx.GT., PT08.S1.CO., PT08.S2.NMHC.)

corr_matrix <- cor(corr_data, use = "complete.obs")

Overview

• Dataset: UCI Air Quality
• Goal: Analyze pollution trends

Time Series

ggplot(data, aes(Datetime, CO.GT.)) + geom_line()

Multi Pollutants

ggplot2::ggplot(pollutants, aes(Datetime, value, color = name))

Seasonal Trends

ggplot(data, aes(Month, CO.GT.)) + geom_boxplot()

Correlation

corrplot(corr_matrix, method="color")

Insights

• Pollution varies over time
• Seasonal peaks observed
• Strong pollutant correlation