coursera data visualization assignment

Data Visualization Week 3 Programming Assignment 2

An information flow model for conflict and fission in small group.

This file implements Programming Assignment 2: Visualize Network Data for the Coursera Data Visualization Class.

This assignment uses R Programming Language to visualize data.

Q1. What is the data that you chose? Why?

Data from a voluntary association are used to construct a new formal model for a traditional anthropological problem, fission in small groups. The process leading to fission is viewed as an unequal flow of sentiments and information across the ties in a social network. This flow is unequal because it is uniquely constrained by the contextual range and sensitivity of each relationship in the network. The subsequent differential sharing of sentiments leads to the formation of subgroups with more internal stability than the group as a whole and results in fission.

Q2. Did you use a subset of the data? If so, what was it?

Ans: No I used the full data set as available from this link.

Q3. Are there any particular aspects of your visualization to which you would like to bring attention?

To improve the visualization and make it more clear:

• Colors (Hue,): I’ve used colors to indicate which conferences the nodes belong to. The colors corespondent to the colors as seen in the legend on the left.
• Clustering: All nodes are clustered.

The chart is made interactive:

• Hovering a node will highlight and color the links to it’s linked nodes.
• Hovering a node will temporary enlarge a node.

Q4. What do you think the data and your visualization show?

An edge is drawn if two individuals consistently were observed to interact outside the normal activities of the club (karate classes and club meetings). That is, an edge is drawn if the individuals could be said to be friends outside the club activities. All the edges in Figure 1 are non-directional (they represent interaction in both directions), and the graph is said to be symmetrical.

Loading the Data

The Data is obtained from University of Michigan Network Data: Zachary’s karate club social network of friendships between 34 members of a karate club at a US university in the 1970s. Please cite W. W. Zachary, An information flow model for conflict and fission in small groups, Journal of Anthropological Research 33, 452-473 (1977).

Loading the packages which will be used in analyzing the data

Convert the igraph data into something more suitable for networkD3

networkD3 requires edge references to nodes start from 0.

Create the interactive D3 plot.

Figure 1: This is the graphic representation of the social relationships among the 34 individuals in the karate club. A line is drawn between two points when the two individuals being represented consistently interacted in contexts outside those of karate classes, workouts, and club meetings. Each such line drawn is referred to as an edge.

Note: You can Zoom with the scroll wheel.

The Increasing Global Temperature

Coursera data visualization project 1, created by jean pan.

This is a simple data visualization exercise from Coursera Data Visualization course.

The data used in this assignment is GISTEMP data from NASA.

The visualization tool I'm using is D3.js .

Explanation

This graph visualizes the GISTEMP data for the Globe and the North and South Hemispheres through all the given years ( 1880 - 2014 ). The Blue line is for the Globe, the Orange line describes the data for the Northern Hemisphere and the Green for the South Hemisphere.

From the resulting graph, although there is a little decreasing during 19th century, we can see that the overall trend of global temperature is increasing. Both north and south follow the same trend as the global, but we can find the south increases smoother than the north.

Data Visualization with Tableau Specialization on Coursera

In 2020 the world will generate 50 times the amount of data as in 2011. And 75 times the number of information sources (IDC, 2011). Being able to use this data provides huge opportunities and to turn these opportunities into reality, people need to use data to solve problems.

Created by UC Davis Continuing and Professional Education and hosted on the Coursera platform, this is an online course consisting of pre-recorded video lectures, auto-graded and peer-reviewed assignments and community discussion forums. This specialization program is self-paced and designed to help you master a specific career skill in as little as 4-6 months. This specialization, offered  in collaboration with Tableau, is intended for newcomers to data visualization with no prior experience using Tableau. We leverage Tableau's library of resources to demonstrate best practices for data visualization and data storytelling. You will view examples from real world business cases and journalistic examples from leading media companies.

By the end of this specialization, you will be able to generate powerful reports and dashboards that will help people make decisions and take action based on their business data. You will use Tableau to create high-impact visualizations of common data analyses to help you see and understand your data. You will apply predicative analytics to improve business decision making. The Specialization culminates in a Capstone Project in which you will use sample data to create visualizations, dashboards and data models to prepare a presentation to the executive leadership of a fictional company.

Instruction Method Online class

Section Notes

Enrollments are accepted on a continuous basis. Complete the course at your own pace. This Specialization consists of four courses on the Coursera platform:

• Fundamentals of Visualization with Tableau
• Essential Design Principles for Tableau
• Visual Analytics with Tableau
• Creating Dashboards and Storytelling with Tableau
• Data Visualization with Tableau Project

For more information on Coursera online courses, including enrollment policies and technical requirements, please visit https://coursera.org/about/terms .

Data Visualization with R | Week 1

Course link :  https://www.coursera.org/learn/data-visualization-r

These are answers for Data Visualization with R Week 1

Practice Quiz – 1

1. Which chart is a type of correlation chart?

Scatterplot

2. Which R statement creates a chart object based on the data frame “salesdata”, but allows you to vary the aesthetics from one layer to another?

ggplot(salesdata)

ggplot(salesdata, aes(x = feature1, y = feature2))

3. True or False: The qplot() function has no defaults so you have more control over the output.

Graded Quiz – 1

1. Which R packages will this course use to create data visualizations? Select two answers.

None, you will use base R

2. Which chart is a type of part to the whole chart?

Stacked bar chart

Horizontal bar chart

Grouped bar chart

3. Which ggplot2 function can create a complete plot given the data, mappings, and geom as parameters?

Practice Quiz – 2

1. True or False: Numeric data can be qualitative or quantitative.

2. Which of the following statements about histograms is true? Select two answers.

A histogram divides data into bins and then counts the number of times a data point falls into each bin.

A histogram counts the frequency of each individual number in the data set.

A histogram displays quantitative data, while a bar chart displays qualitative data.

A histogram displays qualitative data, while a bar chart displays quantitative data.

3. Complete the sentence: A pie chart is the same as a ___________________________ in polar coordinates.

Graded Quiz – 2

1. Which parameter of the qplot() function changes the border color of the bars in a bar chart to blue?

border = I(“blue”)

colour = I(“blue”)

outline = I(“blue”)

fill = I(“blue”)

2. How can you improve the smoothness of a histogram?

Changing the number of bins has no impact of the smoothness of the histogram.

Reduce the number of bins to increase the bin width.

Always go with the default number of bins.

Increase the number of bins to reduce the bin width.

3. What step must you take before you can add the coord_polar() function to ggplot() to create a pie chart?

Add the geom_bar(position = “stack”) command to the ggplot() function.

Add the geom_bar(position = “dodge”) command to the ggplot() function.

Set the x argument of the aes() function used in the ggplot() function to the factor.

Add the geom_circle() command to the ggplot() function.

More in this line: https://progies.in/answers/summer-training-2022/data-visualization-with-r

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Data Visualization with Python - Final Assignment

NatashadT/Final-Assignment

Folders and files, repository files navigation, final-assignment, import required libraries.

import pandas as pd import dash import dash_html_components as html import dash_core_components as dcc from dash.dependencies import Input, Output, State import plotly.graph_objects as go import plotly.express as px from dash import no_update

Create a dash application

app = dash.Dash( name )

REVIEW1: Clear the layout and do not display exception till callback gets executed

app.config.suppress_callback_exceptions = True

Read the airline data into pandas dataframe

airline_data = pd.read_csv(' https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DV0101EN-SkillsNetwork/Data%20Files/airline_data.csv ', encoding = "ISO-8859-1", dtype={'Div1Airport': str, 'Div1TailNum': str, 'Div2Airport': str, 'Div2TailNum': str})

List of years

year_list = [i for i in range(2005, 2021, 1)]

"""Compute graph data for creating yearly airline performance report

Function that takes airline data as input and create 5 dataframes based on the grouping condition to be used for plottling charts and grphs.

Returns: Dataframes to create graph. """ def compute_data_choice_1(df): # Cancellation Category Count bar_data = df.groupby(['Month','CancellationCode'])['Flights'].sum().reset_index() # Average flight time by reporting airline line_data = df.groupby(['Month','Reporting_Airline'])['AirTime'].mean().reset_index() # Diverted Airport Landings div_data = df[df['DivAirportLandings'] != 0.0] # Source state count map_data = df.groupby(['OriginState'])['Flights'].sum().reset_index() # Destination state count tree_data = df.groupby(['DestState', 'Reporting_Airline'])['Flights'].sum().reset_index() return bar_data, line_data, div_data, map_data, tree_data

"""Compute graph data for creating yearly airline delay report

This function takes in airline data and selected year as an input and performs computation for creating charts and plots.

Arguments: df: Input airline data.

Returns: Computed average dataframes for carrier delay, weather delay, NAS delay, security delay, and late aircraft delay. """ def compute_data_choice_2(df): # Compute delay averages avg_car = df.groupby(['Month','Reporting_Airline'])['CarrierDelay'].mean().reset_index() avg_weather = df.groupby(['Month','Reporting_Airline'])['WeatherDelay'].mean().reset_index() avg_NAS = df.groupby(['Month','Reporting_Airline'])['NASDelay'].mean().reset_index() avg_sec = df.groupby(['Month','Reporting_Airline'])['SecurityDelay'].mean().reset_index() avg_late = df.groupby(['Month','Reporting_Airline'])['LateAircraftDelay'].mean().reset_index() return avg_car, avg_weather, avg_NAS, avg_sec, avg_late

Application layout

Task1: add title to the dashboard, enter your code below. make sure you have correct formatting..

app.layout = html.Div(children=[html.H1('US Domestic Airline Flights Performance', style={'textAlign': 'center', 'color': '#503D36', 'font-size': 24}),

Callback function definition

Task4: add 5 ouput components.

@app.callback([Output(component_id='plot1',component_property='children'), Output(component_id='plot2',component_property='children'), Output(component_id='plot3',component_property='children'), Output(component_id='plot4',component_property='children'), Output(component_id='plot5',component_property='children')],

REVIEW4: Holding output state till user enters all the form information. In this case, it will be chart type and year

Add computation to callback function and return graph.

def get_graph(chart, year, children1, children2, c3, c4, c5):

Run the app

if name == ' main ': app.run_server()

• Python 100.0%