Todd enjoys his job as a Statistical Analyst for Sierra Trading Post. He has been working there for over 5 years. Todd completed his M.S. in Applied Statistics and a B.A. in Economics at the University of Northern Colorado. After spending the day sorting through data issues, writing SQL and fitting models in R, Todd comes home to his loving Wife and children. His research interests include Reproducible Research, Missing Data, Logistic Regression and Issues Related to Big Data.

## Job Market For Statisticians

 For White Males Median Salary SE 95 % Range Mathematics/Statistics \$100,000 1,500 (97,000 - 103,000) Economics \$126,000 5,500 (115,000 -137,000)

(Data taken from: Table 50 , the 95% range is mine based on the assumption of a normal distribution.)

There is also an article in the American Statistician recently about career paths, "Which Career Path Will You Follow?". Between these three events that occurred within a week, I thought that it merited a post. Have some other useful job advice or interesting statistics that current graduate students should know? Post a comment.

Filed under Uncategorized

## Helper Stored Procedures in SQL Server - Part 2

Continuing with last week's post about Helper Stored Procedures, I wanted to introduce two more that I use frequently. The first one is a built in stored procedure (  using SQL Server) called 'sp_helptext'. This stored procedure allows you to view the content of other stored procedures (SP) and more, here is a link to the Microsoft documentation. Here is an example of how you use it:

`sp_helptext @objname='sp_helptext'`

This will show you the contents of sp_helptext  by using it. This comes in very handy as a keyboard shortcut. I assigned mine to 'Ctrl+3'.  Often I have a SP that does data manipulation and puts the final data in a table that occurs as the last step of my SP. I may not remember which table the data goes into, but I can simply highlight the SP ( I typically double click) and then use 'Ctrl+3' and the contents of the SP appears in my results tab. This also works nicely for function and triggers that you might use.

Another piece of information that I am often curious about is how large is a particular table. If you like going through menus, then you can left click on a table name in the object explorer and choose properties. Then select 'storage' from the left navigation menu. You will see under the 'general' section: "Data Space","Row Count" and "Index Space". While this is okay if you only perform this task once a month, it takes way to long if you are running this 5 + times per day. I wrote a stored procedure called 'sp_tablesize' that is a wrapper for another Microsoft SP, that is sp_spaceused.

```USE [YourDatabase]
GO

SET ANSI_NULLS ON
GO

SET QUOTED_IDENTIFIER ON
GO

Alter Procedure [dbo].[TableSize_Sp]
@TableName varchar(500)
as

If object_id(@Tablename,'U') is not null
Begin
If Object_ID('tempdb..#TableSummary','U') Is Not Null
Drop Table #TableSummary

CREATE TABLE #TableSummary
(
[database] varchar(500) default db_name(),
table_name sysname ,
row_count BIGINT,
reserved_size VARCHAR(50),
data_size VARCHAR(50),
index_size VARCHAR(50),
unused_size VARCHAR(50),
[timestamp] datetime default getdate() not null,
Calendardate as convert(datetime,convert(varchar,[timestamp],101))
)

Declare @Sql varchar(max)
Set @Sql='
INSERT #TableSummary ([table_name],[row_count],[reserved_size],[data_size],[index_size],[unused_size])
EXEC sp_spaceused''' + @TableName + '''
'

Exec (@Sql)

If Object_ID('tempdb..#TableSummaryCleaned','U') is not null
Drop Table #TableSummaryCleaned
Select
a.[Database],
a.table_name [TableName],
round(sum(cast(a.row_count as bigint)),2)[Rows],
round(sum(cast(reserved_size_kb as float)/1024),2)ReservedSizeMB,
round(sum(cast(data_size_kb as float)/1024),2)DataSizeMB,
round(sum(cast(index_size_kb as float)/1024),2)IndexSizeMB,
round(sum(cast(unused_size_kb as float)/1024),2)UnusedSizeMB,
[Timestamp],
Calendardate
Into #TableSummaryCleaned
From
(
Select
[database],
table_name ,
row_count,
cast(replace(reserved_size, ' KB','') as bigint) reserved_size_kb,
cast(replace(data_size, ' KB','') as bigint) data_size_kb,
cast(replace(index_size, ' KB','') as bigint) index_size_kb,
cast(replace(unused_size, ' KB','') as bigint) unused_size_kb,
[timestamp],
[calendardate]
From #TableSummary
)a
Group By
a.[database],
a.table_name,
[Timestamp],
Calendardate

Alter Table #TableSummaryCleaned

Update #TableSummaryCleaned
Set TotalMB = ReservedSizeMB

Alter Table #TableSummaryCleaned

Update #TableSummaryCleaned
Set TotalGB =TotalMB/1024

Select *
From #TableSummaryCleaned
End
Else
Begin
Declare @ErrorText varchar(500)
Set @ErrorText = @TableName+' is not a valid table.'
RAISERROR(@ErrorText, 16, 1)
End

GO```

After you create the SP 'tablesize_SP' you can assign it to a shortcut in SSMS. I choose to use 'Ctrl+4' for myself. We can create a table and add data and an index to make sure that our new SP works.

```Create Table dbo.TestTable
( numbers float )

-- Create Table with numbers 1 -100
Declare @Counter int
Set @Counter=1
While @Counter <=1000
Begin
Insert Into dbo.TestTable Select @counter*1000
Set @Counter=@Counter+1
End

-- Check Table Size
exec tablesize_sp'TestTable'

-- Create Index
Create NonClustered Index [NC_Numbers] on dbo.TestTable
(numbers)

-- Check size after creating index
exec tablesize_sp'TestTable'```

You will notice that our index size increased after adding one, which is to be expected. Hopefully this will allow you to quickly check the size of a table. Combine that with 'sp_helptext' and 'sp_top10' you can find out a lot of information about a table quite quickly. Let me leave you with a quick reference guide of which SP to use depending on what you want to know.

 SP Name Use Shortcut sp_helptext View the contents of a stored procedure or function Ctrl+3 tablesize_SP Check the size of a table including indicies Ctrl+4 sp_top10 View the top 10 records of a table Ctrl+1 sp_help View Information about an object for instance table schema Alt+F1

Note: SSMS 2012 was used for this example.

Filed under Sql Server

## Helper Stored Procedures in SQL Server - Part 1

Often I have days where a large amount of my time is spent writing SQL code in SQL Server Management Studio (SSMS). Building tables, creating indices and verifying data are all common tasks. However, I often myself needing to checking which columns are in a table or to take a quick look at the data. I got tired of typing Select Top 10 * From [MyTable] more than five times per day.   In order to get around typing that repeatedly I made a function called 'sp_top10'.

```CREATE Procedure [dbo].[sp_top10]
@Table as varchar(100)

as
Begin
set nocount on

Declare  @dbname as varchar(100)
Set @dbname = db_name()

Declare @MyQuery Varchar(max)
Set @MyQuery='
Select Top 10 *
From ' + @dbname + ' ..' + @Table + ' with(nolock)'

Exec (@MyQuery)
End```

This stored procedure can be invoked by typing

exec sp_top10'YourTableHere'  where you substitute in your own table name. The real power comes when you assign your new stored procedure to a shortcut. In SSMS you can do this by going to Tools > Options > Keyboard > Query Shortcuts .  Simply add  'sp_top10' to an open key combination and then restart SSMS. You should now be able to highlight ( or doubleclick) a tablename and then use your new shortcut to view the top 10 records in that table. I set my shortcut to 'Ctrl+F1'.

An example:

```-- Create Table To Hold Our Dataset
If Object_ID('tempdb..#TestData','U') is not null
Drop Table #TestData

Create Table #TestData
(ID bigint not null identity(1,1) ,
Letter varchar(1)
)

-- Quick Loop to get all lower case letters into our table
Declare @Counter Int;Set @Counter=97
While @Counter <=122
Begin
Declare @Sql Varchar(Max)
Set @SQL ='
Insert into #TestData (letter)
Select char(' + cast(@Counter as varchar)  + ')
'
--print @SQL
Exec (@Sql)
Set @Counter = @Counter+1
End

-- View all 26 records
Select *
From #TestData

-- Just view the top 10
exec sp_top10'#testdata'
```

You output should look similar to figure 1.

Figure 1.

Feel free to edit the SP to suit your particular situation. I use this in conjunction with 'sp_help' which has a default shortcut of 'Alt+F1'  in SSMS almost everyday. So I can select a tablename and 'Alt+F1' if I need to see what type of columns I have and then 'Ctrl+1' to view the first ten records of that table. Hopefully this will help save you time in your daily work.

Note: SSMS 2012 was used for this example.

1 Comment

Filed under Sql Server

## Making a Code Book in Sql Server

While working through a coursera course recently (https://www.coursera.org/course/getdata) I started to think about how a code book could be implemented in sql server. There are 3 points that Jeff Leek makes regarding a code book and they are as follows:

1. Information about the variables ...
3. Information about the study design you used

My focus for this article will be able point number one, information about the variables. Often times it would be nice to know more about your data than simply what data structure it is defined by. For a simple working example, load the Iris datasets into Sql Server and then add some metadata about the columns to provide further information for the users.

```Use YourDataBaseName
Go

-- Create Table
If Object_Id('YourDataBaseName.dbo.Iris','U') is not null
Drop Table dbo.Iris

Create Table dbo.Iris
(
IrisID bigint not null identity(1,1) ,
SepalLength numeric,
SepalWidth numeric,
PetalLength numeric,
PetalWidth numeric,
Species nvarchar(100)
)
-- Insert Values
Insert Into dbo.Iris Values('5.1','3.5','1.4','0.2','setosa')
Insert Into dbo.Iris Values('4.9','3','1.4','0.2','setosa')
Insert Into dbo.Iris Values('4.7','3.2','1.3','0.2','setosa')
Insert Into dbo.Iris Values('4.6','3.1','1.5','0.2','setosa')
Insert Into dbo.Iris Values('5','3.6','1.4','0.2','setosa')
Insert Into dbo.Iris Values('5.4','3.9','1.7','0.4','setosa')
Insert Into dbo.Iris Values('4.6','3.4','1.4','0.3','setosa')
Insert Into dbo.Iris Values('5','3.4','1.5','0.2','setosa')
Insert Into dbo.Iris Values('4.4','2.9','1.4','0.2','setosa')
Insert Into dbo.Iris Values('4.9','3.1','1.5','0.1','setosa')
Insert Into dbo.Iris Values('5.4','3.7','1.5','0.2','setosa')
Insert Into dbo.Iris Values('4.8','3.4','1.6','0.2','setosa')
Insert Into dbo.Iris Values('4.8','3','1.4','0.1','setosa')
Insert Into dbo.Iris Values('4.3','3','1.1','0.1','setosa')
Insert Into dbo.Iris Values('5.8','4','1.2','0.2','setosa')
Insert Into dbo.Iris Values('5.7','4.4','1.5','0.4','setosa')
Insert Into dbo.Iris Values('5.4','3.9','1.3','0.4','setosa')
Insert Into dbo.Iris Values('5.1','3.5','1.4','0.3','setosa')
Insert Into dbo.Iris Values('5.7','3.8','1.7','0.3','setosa')
Insert Into dbo.Iris Values('5.1','3.8','1.5','0.3','setosa')
Insert Into dbo.Iris Values('5.4','3.4','1.7','0.2','setosa')
Insert Into dbo.Iris Values('5.1','3.7','1.5','0.4','setosa')
Insert Into dbo.Iris Values('4.6','3.6','1','0.2','setosa')
Insert Into dbo.Iris Values('5.1','3.3','1.7','0.5','setosa')
Insert Into dbo.Iris Values('4.8','3.4','1.9','0.2','setosa')
Insert Into dbo.Iris Values('5','3','1.6','0.2','setosa')
Insert Into dbo.Iris Values('5','3.4','1.6','0.4','setosa')
Insert Into dbo.Iris Values('5.2','3.5','1.5','0.2','setosa')
Insert Into dbo.Iris Values('5.2','3.4','1.4','0.2','setosa')
Insert Into dbo.Iris Values('4.7','3.2','1.6','0.2','setosa')
Insert Into dbo.Iris Values('4.8','3.1','1.6','0.2','setosa')
Insert Into dbo.Iris Values('5.4','3.4','1.5','0.4','setosa')
Insert Into dbo.Iris Values('5.2','4.1','1.5','0.1','setosa')
Insert Into dbo.Iris Values('5.5','4.2','1.4','0.2','setosa')
Insert Into dbo.Iris Values('4.9','3.1','1.5','0.2','setosa')
Insert Into dbo.Iris Values('5','3.2','1.2','0.2','setosa')
Insert Into dbo.Iris Values('5.5','3.5','1.3','0.2','setosa')
Insert Into dbo.Iris Values('4.9','3.6','1.4','0.1','setosa')
Insert Into dbo.Iris Values('4.4','3','1.3','0.2','setosa')
Insert Into dbo.Iris Values('5.1','3.4','1.5','0.2','setosa')
Insert Into dbo.Iris Values('5','3.5','1.3','0.3','setosa')
Insert Into dbo.Iris Values('4.5','2.3','1.3','0.3','setosa')
Insert Into dbo.Iris Values('4.4','3.2','1.3','0.2','setosa')
Insert Into dbo.Iris Values('5','3.5','1.6','0.6','setosa')
Insert Into dbo.Iris Values('5.1','3.8','1.9','0.4','setosa')
Insert Into dbo.Iris Values('4.8','3','1.4','0.3','setosa')
Insert Into dbo.Iris Values('5.1','3.8','1.6','0.2','setosa')
Insert Into dbo.Iris Values('4.6','3.2','1.4','0.2','setosa')
Insert Into dbo.Iris Values('5.3','3.7','1.5','0.2','setosa')
Insert Into dbo.Iris Values('5','3.3','1.4','0.2','setosa')
Insert Into dbo.Iris Values('7','3.2','4.7','1.4','versicolor')
Insert Into dbo.Iris Values('6.4','3.2','4.5','1.5','versicolor')
Insert Into dbo.Iris Values('6.9','3.1','4.9','1.5','versicolor')
Insert Into dbo.Iris Values('5.5','2.3','4','1.3','versicolor')
Insert Into dbo.Iris Values('6.5','2.8','4.6','1.5','versicolor')
Insert Into dbo.Iris Values('5.7','2.8','4.5','1.3','versicolor')
Insert Into dbo.Iris Values('6.3','3.3','4.7','1.6','versicolor')
Insert Into dbo.Iris Values('4.9','2.4','3.3','1','versicolor')
Insert Into dbo.Iris Values('6.6','2.9','4.6','1.3','versicolor')
Insert Into dbo.Iris Values('5.2','2.7','3.9','1.4','versicolor')
Insert Into dbo.Iris Values('5','2','3.5','1','versicolor')
Insert Into dbo.Iris Values('5.9','3','4.2','1.5','versicolor')
Insert Into dbo.Iris Values('6','2.2','4','1','versicolor')
Insert Into dbo.Iris Values('6.1','2.9','4.7','1.4','versicolor')
Insert Into dbo.Iris Values('5.6','2.9','3.6','1.3','versicolor')
Insert Into dbo.Iris Values('6.7','3.1','4.4','1.4','versicolor')
Insert Into dbo.Iris Values('5.6','3','4.5','1.5','versicolor')
Insert Into dbo.Iris Values('5.8','2.7','4.1','1','versicolor')
Insert Into dbo.Iris Values('6.2','2.2','4.5','1.5','versicolor')
Insert Into dbo.Iris Values('5.6','2.5','3.9','1.1','versicolor')
Insert Into dbo.Iris Values('5.9','3.2','4.8','1.8','versicolor')
Insert Into dbo.Iris Values('6.1','2.8','4','1.3','versicolor')
Insert Into dbo.Iris Values('6.3','2.5','4.9','1.5','versicolor')
Insert Into dbo.Iris Values('6.1','2.8','4.7','1.2','versicolor')
Insert Into dbo.Iris Values('6.4','2.9','4.3','1.3','versicolor')
Insert Into dbo.Iris Values('6.6','3','4.4','1.4','versicolor')
Insert Into dbo.Iris Values('6.8','2.8','4.8','1.4','versicolor')
Insert Into dbo.Iris Values('6.7','3','5','1.7','versicolor')
Insert Into dbo.Iris Values('6','2.9','4.5','1.5','versicolor')
Insert Into dbo.Iris Values('5.7','2.6','3.5','1','versicolor')
Insert Into dbo.Iris Values('5.5','2.4','3.8','1.1','versicolor')
Insert Into dbo.Iris Values('5.5','2.4','3.7','1','versicolor')
Insert Into dbo.Iris Values('5.8','2.7','3.9','1.2','versicolor')
Insert Into dbo.Iris Values('6','2.7','5.1','1.6','versicolor')
Insert Into dbo.Iris Values('5.4','3','4.5','1.5','versicolor')
Insert Into dbo.Iris Values('6','3.4','4.5','1.6','versicolor')
Insert Into dbo.Iris Values('6.7','3.1','4.7','1.5','versicolor')
Insert Into dbo.Iris Values('6.3','2.3','4.4','1.3','versicolor')
Insert Into dbo.Iris Values('5.6','3','4.1','1.3','versicolor')
Insert Into dbo.Iris Values('5.5','2.5','4','1.3','versicolor')
Insert Into dbo.Iris Values('5.5','2.6','4.4','1.2','versicolor')
Insert Into dbo.Iris Values('6.1','3','4.6','1.4','versicolor')
Insert Into dbo.Iris Values('5.8','2.6','4','1.2','versicolor')
Insert Into dbo.Iris Values('5','2.3','3.3','1','versicolor')
Insert Into dbo.Iris Values('5.6','2.7','4.2','1.3','versicolor')
Insert Into dbo.Iris Values('5.7','3','4.2','1.2','versicolor')
Insert Into dbo.Iris Values('5.7','2.9','4.2','1.3','versicolor')
Insert Into dbo.Iris Values('6.2','2.9','4.3','1.3','versicolor')
Insert Into dbo.Iris Values('5.1','2.5','3','1.1','versicolor')
Insert Into dbo.Iris Values('5.7','2.8','4.1','1.3','versicolor')
Insert Into dbo.Iris Values('6.3','3.3','6','2.5','virginica')
Insert Into dbo.Iris Values('5.8','2.7','5.1','1.9','virginica')
Insert Into dbo.Iris Values('7.1','3','5.9','2.1','virginica')
Insert Into dbo.Iris Values('6.3','2.9','5.6','1.8','virginica')
Insert Into dbo.Iris Values('6.5','3','5.8','2.2','virginica')
Insert Into dbo.Iris Values('7.6','3','6.6','2.1','virginica')
Insert Into dbo.Iris Values('4.9','2.5','4.5','1.7','virginica')
Insert Into dbo.Iris Values('7.3','2.9','6.3','1.8','virginica')
Insert Into dbo.Iris Values('6.7','2.5','5.8','1.8','virginica')
Insert Into dbo.Iris Values('7.2','3.6','6.1','2.5','virginica')
Insert Into dbo.Iris Values('6.5','3.2','5.1','2','virginica')
Insert Into dbo.Iris Values('6.4','2.7','5.3','1.9','virginica')
Insert Into dbo.Iris Values('6.8','3','5.5','2.1','virginica')
Insert Into dbo.Iris Values('5.7','2.5','5','2','virginica')
Insert Into dbo.Iris Values('5.8','2.8','5.1','2.4','virginica')
Insert Into dbo.Iris Values('6.4','3.2','5.3','2.3','virginica')
Insert Into dbo.Iris Values('6.5','3','5.5','1.8','virginica')
Insert Into dbo.Iris Values('7.7','3.8','6.7','2.2','virginica')
Insert Into dbo.Iris Values('7.7','2.6','6.9','2.3','virginica')
Insert Into dbo.Iris Values('6','2.2','5','1.5','virginica')
Insert Into dbo.Iris Values('6.9','3.2','5.7','2.3','virginica')
Insert Into dbo.Iris Values('5.6','2.8','4.9','2','virginica')
Insert Into dbo.Iris Values('7.7','2.8','6.7','2','virginica')
Insert Into dbo.Iris Values('6.3','2.7','4.9','1.8','virginica')
Insert Into dbo.Iris Values('6.7','3.3','5.7','2.1','virginica')
Insert Into dbo.Iris Values('7.2','3.2','6','1.8','virginica')
Insert Into dbo.Iris Values('6.2','2.8','4.8','1.8','virginica')
Insert Into dbo.Iris Values('6.1','3','4.9','1.8','virginica')
Insert Into dbo.Iris Values('6.4','2.8','5.6','2.1','virginica')
Insert Into dbo.Iris Values('7.2','3','5.8','1.6','virginica')
Insert Into dbo.Iris Values('7.4','2.8','6.1','1.9','virginica')
Insert Into dbo.Iris Values('7.9','3.8','6.4','2','virginica')
Insert Into dbo.Iris Values('6.4','2.8','5.6','2.2','virginica')
Insert Into dbo.Iris Values('6.3','2.8','5.1','1.5','virginica')
Insert Into dbo.Iris Values('6.1','2.6','5.6','1.4','virginica')
Insert Into dbo.Iris Values('7.7','3','6.1','2.3','virginica')
Insert Into dbo.Iris Values('6.3','3.4','5.6','2.4','virginica')
Insert Into dbo.Iris Values('6.4','3.1','5.5','1.8','virginica')
Insert Into dbo.Iris Values('6','3','4.8','1.8','virginica')
Insert Into dbo.Iris Values('6.9','3.1','5.4','2.1','virginica')
Insert Into dbo.Iris Values('6.7','3.1','5.6','2.4','virginica')
Insert Into dbo.Iris Values('6.9','3.1','5.1','2.3','virginica')
Insert Into dbo.Iris Values('5.8','2.7','5.1','1.9','virginica')
Insert Into dbo.Iris Values('6.8','3.2','5.9','2.3','virginica')
Insert Into dbo.Iris Values('6.7','3.3','5.7','2.5','virginica')
Insert Into dbo.Iris Values('6.7','3','5.2','2.3','virginica')
Insert Into dbo.Iris Values('6.3','2.5','5','1.9','virginica')
Insert Into dbo.Iris Values('6.5','3','5.2','2','virginica')
Insert Into dbo.Iris Values('6.2','3.4','5.4','2.3','virginica')
Insert Into dbo.Iris Values('5.9','3','5.1','1.8','virginica')

Sepal Length of Iris plant in centimetres.
' , @level0type=N'SCHEMA',
@level0name=N'dbo',
@level1type=N'TABLE',
@level1name=N'Iris',
@level2type=N'COLUMN',
@level2name=N'SepalLength'
GO

Sepal width of Iris plant in centimetres.
' , @level0type=N'SCHEMA',
@level0name=N'dbo',
@level1type=N'TABLE',
@level1name=N'Iris',
@level2type=N'COLUMN',
@level2name=N'SepalWidth'
GO

Petal Length of Iris plant in centimetres.
' , @level0type=N'SCHEMA',
@level0name=N'dbo',
@level1type=N'TABLE',
@level1name=N'Iris',
@level2type=N'COLUMN',
@level2name=N'PetalLength'
GO

Petal Width  of Iris plant in centimetres.
' , @level0type=N'SCHEMA',
@level0name=N'dbo',
@level1type=N'TABLE',
@level1name=N'Iris',
@level2type=N'COLUMN',
@level2name=N'PetalWidth'
GO

Species of Iris plant.
' , @level0type=N'SCHEMA',
@level0name=N'dbo',
@level1type=N'TABLE',
@level1name=N'Iris',
@level2type=N'COLUMN',
@level2name=N'Species'
GO

```

Now, you should have a table called Iris with some basic metadata about the columns. While you can navigate to the extended properties through the object explorer, it would be nice to access this information through a query.

```-- View the Code book for the Iris Table and some information about the data structure
SELECT
[major_id],
[minor_id],
[t.name] AS [Table Name],
[c.name] AS [Column Name],
[value] AS [Extended Property],
infos.[Data_Type],
infos.[is_nullable],
infos.[Numeric_Precision],
infos.[Numeric_Scale]
FROM
sys.extended_properties AS ep
inner join
sys.tables AS t
ON ep.major_id = t.object_id
inner join
sys.columns AS c
ON ep.major_id = c.object_id
and ep.minor_id = c.column_id
inner join
INFORMATION_SCHEMA.COLUMNS infos
on infos.table_name = t.name
and infos.column_name = c.name
Where
class = 1
and t.name ='Iris' /* This is our Table name */

-- Drop Iris Table to avoid cluttering up your database
Drop Table dbo.Iris```

Now you have the framework for creating a data code book that can be self contained within the table itself. This will prove most useful when you can can share a sql server table with someone else.

Filed under Sql Server

## Unexpected behavior with summary function in R

I often find myself working with data that includes dates and times. Sometimes I am interested in looking at what happened on on a particular calendar day. I usually avoid dealing with actual datetime formats when working at the day level and prefer to use an integer for representing a day. For instance the first day of each quarter can be represented as integers (numeric also works for this example). For instance if I wanted to know the oldest date in my dataset I can just take the minimum since I am using a numerical data structure.``` ```

```mydates  <- c(20140101L,20140401L,20140701L,20141001L)
`min(mydates)````

[1] 20140101

For some reason which I don't recall, I tried using the summary() function on my dates. The only values that would be valid are the minimum and the maximum, or so I thought.

`summary(mydates)`

The output:
# Min.            1st Qu.       Median      Mean          3rd Qu.       Max.
# 20140000 20140000 20140000 20140000 20140000 20140000

This behavior which seemed odd to me, is caused by the way summary() deals with numerical data. So I decided to look at what summary is actually doing. To view the code behind the summary function type:

`summary.default`

The portion of code that we are interested in is this:``` ```

```else if (is.numeric(object)) {
nas <- is.na(object)
object <- object[!nas]
qq <- stats::quantile(object)
qq <- signif(c(qq[1L:3L], mean(object), qq[4L:5L]), digits)
names(qq) <- c("Min.", "1st Qu.", "Median", "Mean", "3rd Qu.",
"Max.")
if (any(nas))
c(qq, `NA's` = sum(nas))
else qq
}```

This code chunk gets executed when the object that we pass to summary is numeric.  If we substitute in our object 'mydates' we get the following code.``` ```

```digits = max(3L, getOption("digits") -     3L)
nas <- is.na(mydates)
mydates<- mydates[!nas]
qq <- stats::quantile(mydates)
qq <- signif(c(qq[1L:3L], mean(mydates), qq[4L:5L]), digits)```

If you step through the code line by line, you will notice that after line 4, summary produces what we would expect to see for a min and max value. However, after you execute line 5, the numbers are changed because they are not the actual numbers but they are changed to be significant figures. For example try:

`signif(20140101,digits)`

[1] 20140000

So be careful when using generic functions if you don't know what they are doing. I would encourage to take a look at the code behind some of the R functions you use the most. For instance using the fivenum() function does not change my min and max values the same way summary did.

Filed under R

## Assumption Checking - Part I

Often when working, we are under deadlines to produce results in a reasonable timeframe. Sometimes an analyst may not check his assumptions if he is under a tight deadline. A simple example to illustrate this would be a one sample t-test. You might need to test your sample to see if the mean is different from a specific number. One assumption of a t-test that is often overlooked, is that the sample needs be drawn randomly from the population and the population is suppose to follow a Gaussian distribution. When is the last time in the workplace that you heard of someone performing a normality test before running a t-test? It is considered an extra step that is not usually taken. It should really not be considered a burden and can easily be accomplished with a wrapper function in R.
``` ```

```mytest <- function(x, value=0) {
xx <- as.character(substitute(x))
if(!is.numeric(x)) stop(sprintf('%s is not numeric', xx))
if(shapiro.test(x)\$p.value>.10){
print(t.test(x, mu=value))
}else{
print(wilcox.test(x, mu=value))
}}```

We can combine that with another function to produce a density plot.``` ```

```myplot <- function(x,color="blue"){
xx <- as.character(substitute(x))
if(!is.numeric(x)) stop(sprintf('%s is not numeric', xx))
title <- paste("Density Plot","\n","Dataset = ",deparse(substitute(x)))
mydens <- density(x)
plot(mydens,main=title,las=1)
polygon(mydens,col=color)
}```

Now, let's see how our functions work.  If we generate some random values from a Gaussian distribution, we would expect it to "normally" pass a normality test and a t-test to be performed. However, if we had data that was generated from another distribution that is not 'normal', than typically we would expect to see the results from the Wilcox test.

```set.seed(123)
n <- 1000
normal <- rnorm(n,0,1)
chisq <- rchisq(n,df=5)

mytest(normal)
myplot(normal)

#Test for difference from 5 for chi-square data
mytest(chisq,value=5)
myplot(chisq ,color="orange")```

Results from 'mytest(normal)':

One Sample t-test
data: x
t = 0.5143, df = 999, p-value = 0.6072
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
-0.04541145 0.07766719
sample estimates:
mean of x
0.01612787

Results from 'mytest(chisq,value=5)':

Wilcoxon signed rank test with continuity correction

data: x
V = 214385, p-value = 8.644e-05
alternative hypothesis: true location is not equal to 5

Conclusion
The benefit of working ahead can be seen. Once you have these functions written you can add them to your personal R package that you host on github. Then you will be able to use them whenever you have an internet connection and the whole R community has the chance to benefit. Also, it is easy to combine these two functions into one.

` `

```#Combine the functions
PlotAndTest <- function(x){
mytest(x)
myplot(x)
}```

``` ```