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Interval is a duration with specific start and end date. lubridate package comes with helper functions that work with interval.

- You can retrieve the amount of time interval covers, either as duration or period.

- You can test whether another date time is in the interval

- You can test whether two intervals overlap.
In this video, we will work with various helper functions that make working with time interval easy. We will use Indian Prime Minister's data set and use various time interval helper functions on it. We will try to answer some interesting questions such as, Who was the Prime Minister at office when some of the major events have unfolded in India's history post independence era.

The video link for parsing functions in lubridate package is:
https://youtu.be/LBh1QS-4Og0

The video link for mutate function in dply package is:
https://youtu.be/Kxt5H0LM-1A

Rmarkdown file and data set can be found at Git repo:
https://github.com/siddharth-sahasrabudhe/time-series-data

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In this video we will learn the basic arithmetic operations that can be performed on date time objects. We will also look at the time span concept - lubridate package has two time span classes :

- Duration class
- Period class

Duration - The Duration class will always supply mathematically precise results. A duration year will always equal 365 days. Duration class follows stop-watch concept of time-span. Thus, duration class won't take into account leap years or any other time changes.

Period - Periods, on the other hand, fluctuate the same way the timeline does to give intuitive results.
Period class follows human concept of time span. Periods will take care of leap year, day-light saving calculations and generate the result.

We will work on share_price and transactions data set and try to use arithmetic operators on its date-time variables.

Video link on Parsing Date time objects:
https://youtu.be/LBh1QS-4Og0

Video link on filter function from dplyr package
https://youtu.be/wAQGj5w4xy4

Video link on mutate function from dplyr package
https://youtu.be/Kxt5H0LM-1A

Video link on summarise function from dplyr package:
https://youtu.be/JMHJReB00ZA

The Rmarkdown file with data-sets is available on Github repo:
https://github.com/siddharth-sahasrabudhe/time-series-data

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We have a time series data, in this video we will extend our discussion further and look at various functions from lubridate package that are used to extract the information from date-time objects. For e.g. if I want to extract only year from date object, or only month from date object..I can able to do this using various extract functions.

In this video, we will carry out exploratory data analysis of flights data from nycflights13 package. We will filter out Delta Airways and try to answer below questions:
On average, Which month has the largest arrival delay?
On average, Which day of a week has the largest arrival delay?
On average, Which hour of a day has the largest arrival delay?

We will start with month and drill down to day and hour. We will use various functions from lubridate package to extract out the month, day and hour information from time-series data.

The Rmarkdown file can be found at Git Repo:
https://github.com/siddharth-sahasrabudhe/time-series-data/blob/main/extract_information.Rmd

The dplyr package video series can be found at:
https://youtu.be/Kxt5H0LM-1A - How to use mutate function
https://youtu.be/JMHJReB00ZA - Generate data summary using group_by ( ) and summarise ( ) functions

ggplot2 package video series can be found at:
https://youtu.be/wUWwL5ksIXM - How to create a bar chart

Link to previous video on lubridate package is:
https://youtu.be/LBh1QS-4Og0- Parsig time-series data.

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In R, there are special time series objects - called as Date-time objects which are designed to work best with many time series statistical models and visualisation libraries such as ggplot2.

These date time objects ensure that each observations time and value are "tied toether" so that they are not easily separated during analysis.

It is very common to have the time component (date,time etc.) of the time series data as Character object and not as date-time object. R has special classes called as Date and POSIXct class, that are designed to handle time series data. These classes have special methods that work very efficiently with time series data. R will only recognize the objects belonging to these clasees as date-time objects.

In this video, we will learn how to convert the character class objects into Date/POSIXct class objects using various parsing functions from lubridate package. We will use stock pricing data and IoT sensor data. These data-sets have time component (date variable) class as character class. We will convert this class into either Date or POSIXct class using parsing functions.

Datasets and Rmarkdown file can be found at Git repo:
https://github.com/siddharth-sahasrabudhe/time-series-data

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When constructing a data visualization, it is often necessary to make annotations to the data displayed. Annotations are nothing but the meta-data for the plot. They provide additional contextual information to the plot such as:
- Char title
- Axis title
- Text labels
- Legends
- Axis tick mark labels and their placements
- Colors in plots
In this video, we will use gapminder data and annotate its non-data components using various functions from ggplot2 package used for the annotation.

- geom_text ( ) function is used to annotate the chart
- annotate ( ) function is used to annotate only the outlier in the chart
- labs ( ) function is used for annotations of axis title, legends and chart title.
- scale_ functions are used to change the axis scales
- RcolorBrewer package is used to change the colors in the plot

The Rmarkdown file used in this video can be found at Github Repository:
https://github.com/siddharth-sahasrabudhe/ggplot2-package---data-visualisation-in-R/blob/main/axis_legend_version1.Rmd

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Faceting is data visualization technique, that partitions the plot into a matrix of panels. Each panel shows a different subset of the data. The subset of data is generated by dividing the main data set by some discrete or categorical variable.

There are two types of faceting:
- facet_wrap( )
- facet_grid( )

- facet_grid( ) produces 2D grid of panels, and uses all combinations between two categorical variables to create a sub-plots. Thus, if the data for some combinations is not available, it leads to empty sub-plot.

- facet_wrap( ) produces 1D chart but wraps it in 2D. Since it is essentially 1D chart, there are no empty sub-plots created using facet_wrap( ) function.

we will use gapminder data-set to create facets. We will also use some useful techniques that can be used to modify the facets in terms of Axis scales and plot layout.

the Rmarkdwon file used in this video can be found at Git repo:
https://github.com/siddharth-sahasrabudhe/ggplot2-package---data-visualisation-in-R/blob/main/facet_diagrma_version1.Rmd

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A heat map (or heatmap) is a 2-dimensional data visualization technique that represents the magnitude of individual values within a dataset as a color. By observing how cell colors change across each axis, you can observe if there are any patterns in value for one or both variables.
In this video, we will work upon a Flights data-set from Nycflights13 package. We will show the flight delays for Delta Airlines carriers across all its routes using Heat map.

Correlogram displays the correlation coefficient for all possible combinations of two variables in the data set. We will combine Flights and Weather data-set from NYCflights13 package, and create correlogram to check, whether any relationship exists between flight delays and various weather parameters.

The R-markdown file is uploaded on Github repository:
https://github.com/siddharth-sahasrabudhe/ggplot2-package---data-visualisation-in-R/blob/main/heat_map_youtube.Rmd

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lubridate package API:
https://lubridate.tidyverse.org/reference/ymd.html

Rmarkdown and Data-set files used in this video:
https://github.com/siddharth-sahasrabudhe/ggplot2-package---data-visualisation-in-R

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A box plot is a graphical representation of the distribution of a dataset. It provides a quick summary of key statistics such as the Median,
Quartiles, and potential outliers. The plot consists of a rectangular box (the Interquartile Range, IQR) and two “whiskers” that extend
from the box to the smallest and largest observations within a certain range.

Violin plots are a way of comparing multiple data distributions. The Violin plots is a combination of Box Plots and Kernel Density plots.
A Violin Plot is a Kernel Density Estimate, mirrored so that it forms a symmetrical shape.
Kernel Density estimate is nothing but a smooth version of histogram.

In this video, we will work on housing price data-set. We will check the distribution of house sale price for 3,4 and 5 bed-room house.
We will use Box-whisker plots and Violin plots to summarize the data and check its distribution.

The data-set, and Rmarkdown file is available in Git-hob repository:
https://github.com/siddharth-sahasrabudhe/ggplot2-package---data-visualisation-in-R

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