Data Science: Visualization!)

Section 1: Introduction to Data Visualization and Distributions

You will get started with data visualization and distributions in R.

• understand the importance of data visualization for communicating data-driven findings.
• be able to use distributions to summarize data.
• be able to use the average and the standard deviation to understand the normal distribution.
• be able to assess how well a normal distribution fits the data using a quantile-quantile plot.
• be able to interpret data from a boxplot.

Section 2: Introduction to ggplot2

You will learn how to use the ggplot2 package to create plots.

Section 3: Summarizing with dplyr

You will learn how to summarize data using the dplyr package.

Section 4: Gapminder

You will see examples of ggplot2 and dplyr in action with the Gapminder dataset.

Section 5: Data Visualization Principles

You will learn general principles to guide you in developing effective data visualizations.

Section 1)

Data Types

Functions Overview:

        numeric

Code From Video:

numeric

Key Points:

• Categorical data are variables that are defined by a small number of groups.

• Ordinal categorical data have an inherent order to the categories (mild/medium/hot, for example).
• Non-ordinal categorical data have no order to the categories.

• Numerical data take a variety of numeric values.

• Continuous variables can take any value.
• Discrete variables are limited to sets of specific values.

        DataCamp Data Types)

                Code:

table()                #counts frequency

1.2 Intro to Distributions

DataCamp Assessment: Normal distribution)

        Code:

library(dslabs)

data(heights)

x <- heights$height[heights$sex == "Male"]

mean(x>69 & x<=72)         #What proportion of the data is between 69 and 72 inches (taller than 69 but shorter or equal to 72)? A proportion is between 0 and 1.