Exploratory Data Analysis (EDA)

Building regression models requires an in-depth analysis of the patterns and relationship between target and input variables. The Beijing dataset provides a magnitude of different environmental factors that may affect the PM2.5 levels in the atmosphere.

Exercise 42: Exploring the Time Series Views of PM2.5, DEWP, TEMP, and PRES variables of the Beijing PM2.5 Dataset

In this exercise, we will visualize the pm2.5, DEWP, TEMP, and PRES variables in a time series plot and observe any patterns that may emerge over the years in these variables.

Perform the following steps to complete the exercise:

1. Import all the required libraries in the system:

   library(dplyr)
   library(lubridate)
   library(tidyr)
   library(grid)
   library(ggplot2)

2. Next, transform year, month, and hour into datetime using the lubridate package function named ymd_h:

   PM25$datetime <- with(PM25, ymd_h(sprintf('%04d%02d%02d%02d', year, month, day,hour)))

3. Plot the PM2.5, TEMP, DEWP, and PRES for all the years using...