Are they the seeds to be nurtured to bring in automation, innovation and transformation. There is a saying, necessity is the mother of invention. I would say, innovation is amalgamation of creativity and necessity. We need to understand the ecosystem, to apply creativity and identify the ideas to bring in change. We need to be competent with changing ecosystem and think beyond the possible. What is the biggest challenge in doing this? "Unlearning and Learning", we think the current ecosystem is the best. Be it health, finserve, agriculture or mechanical domain, we need to emphasize with the stakeholders, to come up with the strategy to drive. The very evident example here is the quality of life is changing every millisecond. Few decades back the phone connection was limited to few, but today all the millennials are having a mobile phone. Now phone is not just a medium to talk, but are so powerful devices that an innovative solution can be developed on it....
Hello Data Experts,
Let me continue from my last blog http://outstandingoutlier.blogspot.in/2017/08/basic-r-programing.html “Basic R Programming” where we discussed Assignment and data Operators.
Let us move forward and understand how to load Datasets. First let me define .csv with data in it and we will use it for this session. Copy the below data and paste it in a notepad, save it as “Plasma.csv” file.
**************
Number of times Pregnant, Plasma glucose concentration, Diastolic blood pressure, Triceps Skin fold thickness
6,148,72,35
1,85,66,29
8,183,64,0
1,89,66,23
0,137,40,35
5,116,74,0
3,78,50,32
10,115,0,0
2,197,70,45
8,125,96,0
4,110,92,0
10,168,74,0
10,139,80,0
1,189,60,23
**************
As a best practice one should set the working directory using setwd() command. First let us find out the working directory which h is by default set, by executing below command.
getwd()
If the result of the above command does not match your source directory location, one can set working directory path by executing below commands
setwd("C:/ABC/XYZ")
setwd("C:\\ABC\\XYZ")
Kindly note by default windows file path has "\" whereas for R programming it is "/". Another way to perform the same operation is by using “\\” as path definition.
To load the data from CSV file, we should “read.csv()” command. Let us load plasma.csv data into an object. Create an object name “Plasma” and assign read.csv with path to it.
Plasma <- read.csv(“Plasma.csv”)
In Global environment window, we can notice a new row added with below details
Observation are same as # of Rows
Variables are same as # of columns
Now to understand what is the data that got loaded in the Plasma object, we can execute View command
View(Plasma)
Another quick way to achieve the same is by clicking on the row, data will be loaded in data viewer window on another tab. Once data got loaded in the object, let us get the list of columns by executing one of the below commands:
names(Plasma)
colnames(Plasma)
Output will be as below
"Number.of.times.Pregnant" "Plasma.glucose.concentration" "Diastolic.blood.pressure"
"Triceps.Skin.fold.thickness"
Similarly, to understand the name of rows execute below command
rownames(Plasma)
Output will be as below:
"1" "2" "3" "4" "5" "6" "7" "8” “9" "10" "11" "12" "13" "14"
Let us now understand the structure of loaded data by executing below command. It will help us understand number of observations and variables in the dataset, along with the datatype for each variable.
str(Plasma)
Output will be as shown below:
'data.frame': 14 obs. of 4 variables:
$ Number.of.times.Pregnant : int 6 1 8 1 0 5 3 10 2 8 ...
$ Plasma.glucose.concentration: int 148 85 183 89 137 116 78 115 197 125 ...
$ Diastolic.blood.pressure : int 72 66 64 66 40 74 50 0 70 96 ...
$ Triceps.Skin.fold.thickness : int 35 29 0 23 35 0 32 0 45 0 ...
Once the dataset gets loaded, how can we retrieve the values from it.
To get data for second row with header execute below command
Plasma[2,]
To get data for second column execute below command
Plasma[,2]
To get data we can also execute below command since it was not explicitly state row or a column, it will pick up column by default and all rows
Plasma[2]
To retrieve a specific value from dataset and if we are aware of coordinates, specify Row number and the column number.
Plasma[2,3]
To retrieve data for first 2 rows but value only from third column execute as below
Plasma[1:2,3]
We can also club Data operators with retrieved data as listed below
sum(Plasma[1:2,3])
It is not always easy to retrieve data based on coordinate, in that case we should be able to get value based on column and row name. this is how we can reference data in the dataset to retrieve all value for a column
Plasma$Number.of.times.Pregnant
As we will do some advance statistical programming we will feel the need to adding a new calculated column. This is how we can add a new calculated column
Plasma$NewCol <- 1 + Plasma$Number.of.times.Pregnant
We can always retrieve subset of the dataset by specifying rows and column as below, Outcome will be all rows with only first 4 columns
Plasma[,1:4]
To retrieve the number of rows in a dataset we can execute following commands. length(Plasma$Number.of.times.Pregnant)
Filtered dataset can be created based on certain criteria as shown below
Plasma[Plasma$Number.of.times.Pregnant == 10 & Plasma$Diastolic.blood.pressure > 0,]
I hope this blog helped you understand how to load and execute commands to work on dataset. Now we understand various Data Operations and Dataset related commands, we should be able to move forward with some advance aspects of R programming. In my next blog, I will cover “Basic statistical programming using R Studio”.
Thank you for continuing with me reading through this blog I hope it was insightful. Kindly share your valuable and kind opinion. Please do not forget to suggest what you would like to understand and hear from me in my future blogs.
Thank you...
Outstanding Outliers:: "AG".
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