How to handle date in R
Date data type is very important for all programming languages. To handle the date in a proper way, we need to apply some formatting logic.
This is the case for R programming language as well. This post explains you, how we can handle the Date data in R. There are different functions available in R to handle date.
To get the today’s date, use Sys.Date()
Sys.Date()
> Sys.Date()
[1] “2018-05-22”
To get date and time, use date()
> date()[1] “Tue May 22 15:29:18 2018”
Check the below list of symbols to play with date and time format.
| Symbol | Meaning | Example |
| %d | day as a number (0-31) | 01-31 |
| %a %A |
abbreviated weekday unabbreviated weekday |
Mon Monday |
| %m | month (00-12) | 00-12 |
| %b %B |
abbreviated month unabbreviated month |
Jan January |
| %y %Y |
2-digit year 4-digit year |
07 2007 |
> today <- Sys.Date()> format(today, format=”%B %d %Y”)[1] “May 22 2018”
Date Conversion
In a real-time scenario, we will not get date as date data type. Always, we need to convert to proper date data type. To convert to date data type, we need to use as.Date() function.
Syntax
as.Date(x, “format”)
x – Field or column
format – use the above symbols to frame the proper format.
DateVec<- c(“01/05/2018”, “08/16/2018”)
DateVec
[1] “01/05/2018” “08/16/2018”
summary(DateVec)
Length Class Mode
2 character character
dates <- as.Date(DateVec, “%m/%d/%Y”)
dates
[1] “2018-01-05” “2018-08-16”
summary(dates)
Min. 1st Qu. Median Mean 3rd Qu. Max.
“2018-01-05” “2018-03-01” “2018-04-26” “2018-04-26” “2018-06-21” “2018-08-16”
Run Your Python Script
Use the below console to run your python scripts
# Assign value to the variable
# a = 5
#Print
R Package dplyr Function vs T-SQL – Part 2
Following my previous post, we have another few functions on dplyr package which will be covered in this post.
Arrange Function
As part of data modelling, we need to sort the data to analyze further.
In T-SQL we can easily perform the sort on the data as like below. It uses “Order by” clause to sort the data.
In R, we need to use arrange function.
#Arrange
DF_Arrange<- DF_select %>%
group_by(State) %>%
summarise(Total = sum(Price)) %>%
arrange(desc(Total))
DF_Arrange
Mutate Function
The next, very common task is to build the calculated column to satisfy the business logic. This can be easily done with the help of “mutate” function.
#mutate
DF_mutate <- DF_select %>%
group_by(State) %>%
summarise(Total = sum(Price)) %>%
mutate(“10%of Total” = Total/10) %>%
arrange(desc(Total))
DF_mutate
R Package dplyr Function vs T-SQL
R is one of the very famous tools to handle the data science projects because it has all the capabilities right from the extracting the data from different sources, data modelling and transformation, data visualization and finally building machine learning models using the data.
This post explains how the data modelling can be done with R using “dplyr” package.
To make it easy, let me compare this dplyr function with T-SQL.
First, let us load the data into R studio.
fulldata<-read.csv(“D:\\Projects 2018\\DSKA\\DataWranglingDemo.csv”)
fulldata
Select Function
When we have a large dataset with more than 1000 columns, if we need only certain columns then we can use “SELECT” option to choose the specific columns. Check the below example,
The same option is available in R with dplyr package.
#install.packages(“dplyr”)
require(“dplyr”)
#select
DF_select <- fulldata %>% select (State,Price)
DF_select
Filter Function
In T-SQL, we have where condition to filter any data with different conditions.
The same filter option is available with dplyr functions.
#filter
DF_select %>% filter(State==”Alabama”)
Same as T-SQL, we have group by, summarise functions are available in R
Group by Function
#Group by
DF_Group <- DF_select %>% group_by(State)
DF_Group
Summarise Function
#Summarise
DF_Sum<- DF_select %>% group_by(State) %>%
summarise(Total = sum(Price))
DF_Sum
Power BI Report using SSRS Shared Datasets
Creating a Power BI report is very easy, and it supports loads of data connectors. When we start creating a report against any relational database, we must choose any of the below query modes,
1. Import
2. Direct Query or Connect Live
For example, when you try to create a report against SQL Server database, then you need to choose any of the above methods based on the requirement.
If you take SSRS report server, we have below elements available
1. Paginated Reports
2. Mobile Reports
3. KPIs
4. Data Sources
5. Datasets
If you are SSRS administrator, then you can easily manage all these stuff in a centralized place.
Is it possible to use the SSRS dataset to create a Power BI Report?
Yes, it is possible. You must have the latest version of Power BI Report Server.
We have REST APIs and is supported in Power BI Report Server, which is an extended version of SSRS 2017. Check REST API on SSRS 2017.
We need to use the OData Feed data connector to connect with SSRS datasets.
Below is the information required to create a report.
1. REST API Url
2. REST API Url with specific dataset id
The base URL of the REST API will be like below,
http:///Reports/api/v2.0
Python scatter plot in SQL Server 2017
As you know, Microsoft released the latest version of SQL Server which is SQL Server 2017. If you worked with SQL Server 2016 then you could realize that Microsoft SQL Server is not just a relational database anymore because it started to support big data and so many options to handle the non-relational data.
R Language is very popular to work with data science-related projects. It was integrated with SQL Server 2016 and we can run the R Scripts in SQL Server Management Studio itself.
It helps us to avoid the data movement between the relational database to R server and process. The same way, Microsoft now introduced Python integration with SQL Server. These 2 languages are coming from machine learning services in SQL Server.
To run Python scripts in SQL Server management studio, you need to enable the external script stored procedure.
Run the below command in your SSMS and see whether you are getting the “hello world” as an output.
In case, you are getting an error message then the configuration part was not properly done.
execute sp_execute_external_script
@language = N’Python’,
@script = N’
print(“hello world”)’
The below script is to generate the scatter plot.
DECLARE @Query nvarchar(max) = N’SELECT Year, Sales from [dbo].[SalesByYear]’
execute sp_execute_external_script
@language = N’Python’,
@script = N’
import matplotlib.pyplot as myplot
X = myplot.figure()
myplot.scatter(InputDataSet.Year,InputDataSet.Sales)
myplot.xlabel (“Year”)
myplot.ylabel (“Sales”)
myplot.title (“Sales by Year”)
myplot.savefig (“D:\Win – 8\myfig.png”)
‘,
@input_data_1 = @Query
Check the below step by step procedure to create a scatter plot using python.
Graph database in SQL Server 2017
As you know Graph Database is one of the latest features from SQL Server 2017.
Let us first understand the purpose of Graph database. We have a relational database which handles most of the scenarios but as we are started to handle big data and complex scenario our database also should be capable enough to handle those scenarios.
Yes. Graph database handles those complex scenarios easily which I have explained. As part of graph database, Microsoft team introduced two different tables.
1. Node
2. Edge
Check out the below explanation of those tables and how to work with graph database.
Use the below scripts
Use SQL2017
—Create Main table
CREATE TABLE People (
ID INT PRIMARY KEY,
Name NVARCHAR(25)
) AS NODE;
–Create Edge Table for relationships
CREATE TABLE RELATIONSHIP (
TYPE NVARCHAR(25)
) AS EDGE;
—Insert values to People
INSERT INTO People VALUES (1, ‘David’)
INSERT INTO People VALUES (2, ‘John’)
SELECT * FROM People;
SELECT * FROM RELATIONSHIP;
–Create relationships
INSERT INTO RELATIONSHIP VALUES (
(SELECT $node_id from People WHERE Name = ‘David’),
(SELECT $NODE_ID FROM People where Name = ‘John’), ‘Father’);
INSERT INTO RELATIONSHIP VALUES (
(SELECT $node_id from People WHERE Name = ‘John’),
(SELECT $NODE_ID FROM People where Name = ‘David’), ‘Son’);
—Cartesian Product result
–No need to use joins since nodes and edges are interconnected in structure
SELECT FromName.Name, RELATIONSHIP.TYPE, ToName.Name
FROM People AS FromName, People As ToName , RELATIONSHIP
–Proper Result
SELECT FromName.Name, RELATIONSHIP.TYPE, ToName.Name
FROM People AS FromName, People As ToName , RELATIONSHIP
WHERE MATCH (FromName-(Relationship)->ToName)
–more Records
INSERT INTO People VALUES (3, ‘Nancy’)
INSERT INTO RELATIONSHIP VALUES (
(SELECT $node_id from People WHERE Name = ‘Nancy’),
(SELECT $NODE_ID FROM People where Name = ‘David’), ‘Daughter’);
INSERT INTO RELATIONSHIP VALUES (
(SELECT $node_id from People WHERE Name = ‘David’),
(SELECT $NODE_ID FROM People where Name = ‘Nancy’), ‘Father’);
INSERT INTO RELATIONSHIP VALUES (
(SELECT $node_id from People WHERE Name = ‘John’),
(SELECT $NODE_ID FROM People where Name = ‘Nancy’), ‘Brother’);
INSERT INTO RELATIONSHIP VALUES (
(SELECT $node_id from People WHERE Name = ‘Nancy’),
(SELECT $NODE_ID FROM People where Name = ‘John’), ‘Sister’);
SELECT FromName.Name, RELATIONSHIP.TYPE, ToName.Name
FROM People AS FromName, People As ToName , RELATIONSHIP
WHERE MATCH (FromName-(Relationship)->ToName)
SELECT FromName.Name, RELATIONSHIP.TYPE, ToName.Name
FROM People AS FromName, People As ToName , RELATIONSHIP
WHERE MATCH (FromName-(Relationship)->ToName)
AND FromName.Name = ‘Nancy’
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