Introduction

Making Sense of Data

Why R

Reproducibility, RMarkdown, Presentation, Webpages…., pdf, html, word, Latex and much more

What will you learn

Basic feature of this workshop will be to start exploring data using R along-with hands-on experience. All workshop and related data will be provided to all the participants once they will register for the workshop.

By the end of Data Analysis with R, you’ll understand:

How to use base R and tidyverse to carry out analysis on your own data
The core features of a data analysis workflow
Generating summary statistics and visualizing data

And you’ll be able to use:

Basic functions in the tidyverse to process raw data for typical data analysis questions
Logical expressions and indexing to ask specific questions of data
Functions to conduct common statistical tests

Why this is for you

You have your own data sets and you want to analyze it
You think R is important but intimidated by R and dont know where to start with
You want to do good in data analytics
You want to get data insight for your intelligent decision making
You want to be at par in academia with the rest of the world

Uploaded data and required R packages

Understanding datasets requires many hours/days or in some cases weeks.There are many commercially available softwares but open source community based softwares have now dominated and R is one of these. R makes data understanding process as easy as possible through the dplyr package. It is one of the easiest solution for code-based data analysis. We will learn in this training how to do it. I have already uploaded videos for downloading packages and relevant information. Installing R and RStudio,

R Packages, Library, Errors,

R Codes Basics

I have discussed the Gapminder dataset in my videos and we shall use it throughout this training. It’s available through CRAN, so make sure to install it. Here’s how to load in all required packages:

Ouline

Some Basic Commands

Reading csv files, excel, Stata , SPSS … data files
View
glimpse
structure
head, tail

Components

Column Selection
Data Filtering
Sort data with arrange
Creating Derived Columns
Calculating Summary Statistics
Summarising data groupwise
Creating Tables

Summarising data by groups

Average life expectancy is calculated by_grouping continents:

## # A tibble: 5 x 2
##   continent avglife
##   <fct>       <dbl>
## 1 Africa       54.8
## 2 Americas     73.6
## 3 Asia         70.7
## 4 Europe       77.6
## 5 Oceania      80.7

if_else command alongwith mutate

Percentiles

Understanding percentiles w.r.t lifeexpectancy and gdp per capita for 200 countries using gapminder data.

Advanced Analysis

Visualizing Data

Creating awesome graphs using themes of popular magzines like The Economist, Financial Times and many ohters.

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

## Warning: Ignoring unknown aesthetics: ids

## Warning: All elements of `...` must be named.
## Did you want `key = c(key)`?

## `geom_smooth()` using formula 'y ~ x'

## Setting the `off` event (i.e., 'plotly_doubleclick') to match the `on` event (i.e., 'plotly_hover'). You can change this default via the `highlight()` function.

Basic of Regression Analysis

After exploratory analysis, we shall discuss modeling of data in this section.

Pre-requisite for workshop preparation

install R and RStudio
install tidyverse package R Packages, Library, Errors,
install gapminder package R Packages, Library, Errors,

Two-days workshop on Data Analysis with R

Knowledge and Research Support Services

Zahid Asghar

2021-10-28