Analyzing and visualizing Data - Overview with 12 slides

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ITS530 Analyzing and Visualizing Data Chapter 4 Working with Data

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ITS530 Chapter 04

Data Assets and Tabulation Types

Two main categories

Data that exist in tables; Datasets

Data that exist as isolated values

Data Types

Levels of data or scales of measurement

Type of exploratory data analysis you can undertake

Editorial thinking you establish

Specific chart types you might use

Color choices and layout decisions around composition

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Normalized vs Tabulated Data

Normalized

Data presented in a table with different fields

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Tabulated

The data is aggregated

Data Assets and Tabulation Types cont.

Textual (Qualitative)

Unstructured streams of words

Descriptive details of a weather forecast for a given city

The full title of an academic research project

The description of a product on Amazon

Nominal (Qualitative)

Ordinal data is still categorical and qualitative in nature

Characteristics of order

The response to a survey question: based on a scale of 1 (unhappy) to 5 (very happy)

The general weather forecast: expressed as Very Hot, Hot, Mild, Cold, Freezing

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Graphic Language: The Curse of the CEO

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https://www.bloomberg.com/graphics/infographics/graphic-language-the-curse-of-the-ceo.html

Data Assets and Tabulation Types cont.

Interval (Quantitative)

Interval data is the less common form of quantitative data

Quantitative and numeric measurement

Measure for temperature

Ratio (Quantitative)

Most common quantitative variable

Age of a survey participant in years

Forecasted amount of rainfall in millimetres

Unlike interval data, for ratio data variables zero means something

Temporal Data

Time-based data

Textual: ‘Four o’clock in the afternoon on Monday, 12 March 2016’ Ordinal: ‘PM’, ‘Afternoon’, ‘March’, ‘Q1’

Interval: ‘12’, ‘12/03/2016’, ‘2016’

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Data Assets and Tabulation Types cont.

Discrete

No ‘in-between’ state

Days of the week

Heads or tails for a coin toss

1,2,3,4,5,6,etc.

Continuous

Has in-between state

Height and weight

Temperature

Time

1.1,1.2,1.3,1.4,1.5,etc.

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Data Acquisition

What data do you need and why?

From where, how, and by whom will the data be acquired?

When can you obtain it?

Curated by You

Primary data collection

Manual collection and data foraging

Extracted from pdf files

Web scraping (also known as web harvesting)

Curated by Others

Issued to you

Download from the Web

System report or export

Third-party services

API

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https://www.computerworld.com/article/2921176/business-intelligence/great-r-packages-for-data-import-wrangling-visualization.html

Great R packages for data import, wrangling & visualization

Data Examination

Data Properties

Data types

Size

Condition

Missing values

Erroneous values

Inconsistencies

Duplicate records

Out of date

Uncommon system characters or line breaks

Leading or trailing spaces

How to Approach This?

Inspect and scan

Data operations

Statistical methods

Frequency counts

Frequency distribution

Measurements of central tendency

Measurements of spread

Maximum, minimum and range

Percentiles

Standard deviation

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Influence on Process

Moving forward

Purpose map ‘tone’

Editorial angles

Physical properties influence scale

Potential Activities

Transform to clean

Transform to convert

Transform to create

Transform to consolidate

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Data Exploration

Exploratory Data Analysis

Instinct of the analyst

Reasoning

Deductive

Inductive

Chart types

Research

Statistical methods

Nothings

Not always needed

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Data Exploration

What Good Marathons and Bad Investments Have in Common By Justin Wolfers April 22, 2014 NYTImes

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Case Study 1

Background:

Globally every year 529,000 maternal deaths occur, 99% of this in developing countries

ANC: (Antenatal Care) is to monitor health of pregnant women and reduce the material deaths

Intermittent preventive therapy or intermittent preventive treatment (IPT) is a public health intervention aimed at treating and preventing malaria episodes in infants (IPTi), children (IPTc), schoolchildren (IPTsc) and pregnant women (IPTp).

Question:

Are ANC clinics in country X reaching their coverage targets for IPTp?

Data Source:

Routine health information

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Speaker notes

Now we are going to consider how we could answer the following question:

? We will answer this question using routine health information.

Code	Variables
1.	New ANC clients
2.	Group pre-test counseled
3.	Individual pre-test counseled
4.	Accepted HIV test
5A.	HIV test result - Positive
5B.	HIV test result – Negative
5C.	HIV test result - Indeterminate
6 A.	Post-test counseled - Positive
6 B.	Post-test counseled – Negative
8A.	ARV therapy received – Current NVP
9.	IPTp-1
10.	IPTp-2

Data Source

General ANC Registers

Which of these variables are relevant to answer your question?

Which elements will be included in your numerator and which in your denominator?

Answers:

1) New ANC clients, IPTp-1

2) New ANC clients =Denominator,

IPTp-1 and IPTp-2= Numerator

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Speaker notes

Which of these variables are relevant to answer your question? We’re going to focus on elements 1, 9 and 10. Which elements will be included in your numerator and which in your denominator?

IPTp Coverage-Facility Performance

Code	Variables	Facility 1	Facility 2	Facility 3	Facility 4	Facility 5
9.	IPTp-1	536	1435	39	969	862
10.	IPTp-2	372	542	38	452	780

Number of ANC clients receiving IPTp

Question:

Among the five facilities, which one performed better?

Answer:

Cannot tell because we don’t know the denominators

Speaker notes

Here we have the data on IPTp-1 and 2 to assess facility performance. Among the five facilities, which one performed better?

IPTp Coverage-Facility Performance

Code	Variables	Facility 1	Facility 2	Facility 3	Facility 4	Facility 5
1	New ANC Clients	744	2708	105	1077	908
9.	IPTp-1	536	1435	39	969	862
10.	IPTp-2	372	542	38	452	780

Number of ANC clients receiving IPTp

Question: Now, you have the denominators, which of these facility performed better?

Indicator	Facility 1	Facility 2	Facility 3	Facility 4	Facility 5
% of new ANC clients who receive IPTp-1 in the past year	72%	53%	37%	90%	95%
% of new ANC clients who receive IPTp-2 in the past year	50%	20%	36%	42%	86%

Response: Facility 5

Speaker notes

Now, you have the denominators, which of these facility performed better? We can see that it was actually facility 5.

Are facilities reaching coverage targets?

Target-80%

* National coverage target for pregnant women receiving IPTp-2 is 80%.

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Speaker notes

Here is the same information presented as a chart. We need to use this information to determine, or interpret, whether or not facilites are reaching their coverage targets. Let’s assume that the national coverage target for pregnant women receiving IPTp is 80%. Are the facilities reaching the coverage target? What else can we interpret from this information?

Possible answers

Facility 1 needs to do a better job following up and increase IPTp coverage a bit.

Facility 2 does a better job with IPTp-1 coverage than IPTp-2, but needs to increase coverage of both.

Facility 3 does a good job administering IPTp-2 to patients that receive the first round, but they need to increase initial coverage and maintain follow-up.

Facility 4 does a good job with IPTp-1 coverage, but this falls of with IPTp-2. Is this loss to follow-up, or are they not administering IPTp-2 when patients return?

Facility 5 can be seen as a model and we could investigate their best practices for use in other programs

This information does not tell you why coverage is at these levels. You would have to investigate further, but you can see which facilities you need to work with.

Additional Questions

Which facility is performing better/worse than expected?

What is the trend over time for these facilities?

How would you assess each facility’s performance based on the data?

What other data or information should you consider in providing recommendations or guidance to the facilities?

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Speaker notes

Here are some other questions that we might want to ask to help interpret this information and identify how to improve performance.

Case Study 2

Olympic Games Analysis 1896 - 2012

Olympic Games have been the world's biggest sporting event for over a 100 years.

Here we have a dataset for the Olympic Games. We have information about all the athletes who have won medals for every Olympic Games since the inaugural games of 1896.

Here are the columns in the dataset

Year

Host Venue

Sport

Discipline

Athlete

Country

Gender

Event

Medal

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https://www.kaggle.com/arpitsolanki14/olympic-games-data-analysis

Top Overall Countries

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The countries which win a lot of medals at the Olympic games are the developed countries in North America, Asia and Europe.

Poorer countries in Africa and South America do not win lot of medals.

To enable effective visualization of data, we will filter out the data for the top 5 countries overall.

We will identify the top 10 countries with the highest number of medals and then plot various charts to better understand their performance over the years.

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Performance of Top Countries over the years

Highest number of medals overall - by Country

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USA has the highest overall number of medals, its total medals go above 4000.

Even though Soviet Union broke down in the 1980s, it is still the country with the second highest number of overall medals

Total number of Medals per Sport

As can be seen in the Top athletes charts, sports of Aquatics, Athletics, Rowing and Gymnastics offer the highest number of medals. Therefore it is no surprise that these sports provide the most successful athletes in terms of number of medals won.

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Questions?

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02040608010012345PercentFacility

Percent of ANC Clients Receiving IPTp in Select Facilities

IPTp-1IPTp-2

Chart1

1	1
2	2
3	3
4	4
5	5

IPTp-1

IPTp-2

Facility

Percent

Percent of ANC Clients Receiving IPTp in Select Facilities

Sheet1 (2)

Figure 2. Household Ownership of at Least 1 Net or ITN, 2008
	Country 1	Country 2	Country 3	Country 4	Country 5	CI
Any net	56	63	77	66	70	64	75
	THMIS	NIMR	PSI	NMCP	IHI/LS
LLIN	38	29	45	57	46	40	52
	80	80	80	80	80
Total	46	65	56
Use of Nets or ITN by Children <5 yrs of Age, 2008
	Country 1	Country 2	Country 3	Country 4	Country 5	CI
Any net	35	54	74	48	48	41	56
	THMIS	NIMR	PSI	NMCP	IHI/LS
ITN	25	32	48	29	29	22	36
Total	46	65	56
Use of Nets or ITNs by Pregnant Women, 2008
	THMIS	NMCP	IHI/LS	CI
Any net	36	52	39	31	47
ITN	26	30	19	13	27
Total	46
Use of IPTp by Pregnant Women, 2008							Use of IPTp by Pregnant Women, 2008
	THMIS	IHI/LS							1	2	3	4	5
IPTp-1	57	50	47	54			IPTp-1		72	53	37	90	95
IPTp-2	30	26	23	29			IPTp-2		50	20	36	42	86
Total	46	56
% Children <5 with Fever who Took Specific Antimalarial, 2008
	2008	2007
Sulfadoxine-Pyrimethamine	2	2
Chloroquine	0.5	0.5
Amodiaquine	11	20
Quinine	9	9
ACT	36	26
Other	3	0.5
% Children <5 with Fever Who Took Specific Antimalarial within Same or Next Day, 2008
	THMIS	NMCP
Sulfadoxine-Pyrimethamine	0.5	1
Chloroquine	0	0
Amodiaquine	12	4
Quinine	6	5
ACT	13	13
Other	3	0.5
Percent Overall malaria prevalence and overall anemia prevalence
	THMIS	NMCP	IHI/LS	CI			THMIS CI
Parasitemia	18	14	11	8	14		16	20
Anemia (HB <8 g/dL)	8	6	3	3	4		7	9
Months	Parasitaemia		HB <8 g/dl
6-11	9	0	11
12-23	14	0	12
24-35	20	0	8
36-47	20	0	5
48-59	22	0	3
Mainland	18	0	8
Zanzibar	1	0	5
	2001	2003	2005	2008
Artemisinin Mono				0
ACT			3	57
Quinine	16	19	16	18
Chloroquine	54	3	1	0
Amodiaquine	2	22	32	20
Sulfadoxine-Pyrimethamine	28	57	48	5
Net was sold	1
Net was given away to relatives	68
Net was given away to others	9
Material used for other purpose	1

Sheet1 (2)

Parasitemia

Percent

Figure 10. Percent Overall Malaria Prevalence and

ACT

Quinine

Amodiaquine

Sulfadoxine-Pyrimethamine

Chloroquine

Other

Percent

ACT

Quinine

Amodiaquine

Sulfadoxine-Pyrimethamine

Chloroquine

Other

Percent

Target >80%

IPTp-1

IPTp-2

Percent

Figure 6. Use of IPTp by Pregnant Women, 2008

Target >80%

Any net

ITN

Percent

Figure 5. Use of Nets or ITNs by Pregnant Women, 2008

Target >80%

Any net

ITN

Use of Nets or ITN by Children <5 yrs of Age, 2008

Target >80%

Any net

LLIN

Percent

Household Ownership of at Least 1 Net or ITN, 2008

Target >80%

Any net

Percent

Figure 2. Household Ownership of at Least 1 Net, 2008

Target >80%

LLIN

Percent

Figure 3. Household Ownership of at Least 1 ITN, 2008

Target >80%

Any net

Percent

Use of Nets by Children <5 yrs of Age, 2008

Target >80%

ITN

Percent

Use of ITNs by Children <5 yrs of Age, 2008

Target >80%

ITN

Percent

Figure 4. Use of ITNs by Pregnant Women, 2008

Target >80%

Any net

Percent

Figure 4. Use of Nets by Pregnant Women, 2008

Target>80%

Any net

Percent

Figure 2. Household Ownership of at Least 1 Net or ITN, 2008

Anemia (HB <8 g/dL)

Percent

Figure 11. Percent Overall Anemia Prevalence

Parasitaemia

HB <8 g/dl

Age in Months

Percent

Parasitemia and Anemia among Children under Five in Tanzania, 2008

Net was sold

Net was given away to relatives

Net was given away to others

Material used for other purpose

Status of Lost Nets among Households that Lost Any Nets

IPTp-1

IPTp-2

Facility

Percent

Percent of Pregnant Women Receiving IPTp-2 in Facility Catchment Area