Getting the most out of

your data: a pivot table

workshop

Julia Hill, Jon Ma, William Elliott

Student Data Management and Analysis

Tuesday 27 September 2016

Welcome

• Pivot tables:

• Data summarization tool

• Used with “flat” datasets with defined columns of

data

• Can automatically sort, count, total and give

averages – among other functions

• Exists in many different data processing and

visualization software, most commonly used in

Microsoft Excel

Outline of session

• Cleaning and shaping data

• Data Analysis I - getting started with Pivots

• Joining data

• Data Analysis II - more Pivot functions

• Data workshop

CLEANING AND SHAPING

YOUR DATA

Extracting data

• Sign in to eVision using your SSO

• Navigate to your chosen report

• Filter using parameters as required

• Watch out for required fields

• Always export the data file

Reviewing your data

• Familiarise yourself with

the data

• Watch out for data

issues:

• Issues with column

headers

• Mismatched data types

• Numbers stored as text

• Error values

• Obvious outliers

• Active formulas

• “Blank” cells

• Duplicate values

(depends on the context)

Cleaning and shaping data

• Use filters + +

• Use formulae:

• =COUNT for numerical values

• =COUNTIF for duplicates

• Error checking

• Find and replace +

• Pivot tables themselves can be useful to identify

data integrity issues

DATA ANALYSIS I

Data Analysis – Basic Methods

Autosum +

• Useful for summarising whole data ranges

• Can change aggregation type

Selection + + or

• Useful for summarising on the fly

• Can choose summary types

Filter + +

• Allows review of specific data

PivotTables I – Why use them?

• A powerful feature for summarising larger datasets

• Much easier and more flexible than summarising

separate subsets

• Can help answer the following questions

• How many students are in each college?

• Which college has the most undergraduate

students?

PivotTables I – Things you can do

• Choose the relevant aggregation

eg Sum / Count / Average

• Filter to show the exact data you want to analyse

• Create crosstabs to analyse multiple dimensions

• Drill down to see the data

JOINING DATASETS

Data Joins

• How can I join data from different spreadsheets?

• Common field between both data sources:

• Student Number

• UCAS Number … ?

• Is it truly unique?

• May need to create a unique ID if there are multiple

instances (e.g. student applied more than once)

• VLOOKUP and INDEX/MATCH

Absolute Cell References

Not absolute A2 Row only absolute A$2

Will change rows and columnsWill always reference row 2 but

columns will change

Column only absolute $A2 All absolute $A$2

Will always reference column A but

rows will changeWill always reference cell A2

If you copy or drag a formula, what happens?

Consider this carefully when using lookup formulas!

F4

VLOOKUP

1. The unique lookup value

2. The range (array) of cells your additional data is found inCan be specified as:• Columns – tab!B:D• Specified cells – tab!A2:E300• Named Range - NewDataV

3. The column reference where your new data is found.The ID column is always 1 -count along to the right

4. Approximate match: TRUE or 1orExact match: FALSE or 099% always Exact

INDEX/MATCH

1. The column where your new data is

2. The unique lookup ID value in your original dataset

3. The matching ID column in your new dataset

5. Column index number (1)

4. Match type0 = exact

Demo

Matching Data

• VLOOKUP

• Simpler

• All new data must be to the RIGHT of the unique ID

column in the new dataset

• INDEX/MATCH

• Slightly more complex

• New data can be anywhere in the new dataset

DATA ANALYSIS II

PivotTables II

• Refresh a Dataset

• Use other summaries

• Average / Maximum / Minimum

• Add a calculation eg to show % of Total

• Add multiple summaries eg count and percentage

of students

• Group Data

DATA WORKSHOP

Data Workshop

• Use your own data to explore the techniques we

have covered

• Or – use the model data provided!

• Play with pivots and data joins

• Think about how these techniques can help you in

your job

• Ask for help – don’t be shy!

Conclusion

• A Pivot Table is a powerful tool for providing

summaries of large datasets

• Underlying data must be cleaned and shaped

before pivoting

• Underlying data can be enriched with data from

other spreadsheets using VLOOKUP or

INDEX/MATCH

QUESTIONS?