As a data consumer, the focus is always on searching for right information to form a true analysis from the information gathered over time. Data Exploration is a term used for finding insightful information from data. To find insights from data various steps such as data munging, data analysis, data modeling, and model evaluation are taken.

In any real data exploration project, commonly six steps are involved in the exploration process. They are as follows:

• Asking the right questions: Asking the right questions will help in understanding the objective and target information sought from the data. Questions can be asked such as What are my expected findings after the exploration is finished?, or What kind of information can I extract through the exploration?.
• Data collection: Once the right questions have been asked the target of exploration is cleared. Data collected...

Air quality datasets come bundled with R. They contain data about the New York Air Quality Measurements of 1973 for five months from May to September recorded daily. To view all the available datasets use the data() function, it will display all the datasets available with R installation.

In this recipe, we will use air quality datasets, which are available with R.

Perform the following step to see all the datasets in R and using airquality:

> data()
> str(airquality)
Output
'data.frame': 153 obs. of...

In R, since nominal, ordinal, interval, and ratio variable are treated differently in statistical modeling, we have to convert a nominal variable from a character or int into a factor.

You need to have completed the previous recipe, that is, viewing the airquality dataset using the str and head commands, to get an idea about what it contains.

Perform the following steps to convert the types of character variables:

1. Assign the airquality dataset to mydata:

        > mydata <- airquality  

2. To transform...

Missing values reduce the representativeness of the sample, and furthermore, might distort inferences about the population. This recipe will focus on detecting missing values within the airquality dataset.

You need to have completed the previous recipes, which involve converting Month into a factor type.

Perform the following steps to detect the missing value:

1. The is.na function is used to denote which...

After detecting the number of missing values within each attribute, we have to impute them since they might have a significant effect on the conclusions that can be drawn from the data.

This recipe will require mydata to be loaded in the R session and have the previous recipe completed.

Perform the following steps to impute the missing values:

1. List the Ozone attribute:

        > mydata$Ozone
        Output
       [1] 41 36 12 18 NA 28 23 19 8 NA 7 16 11 14 18 14 34 6 30 11
      [21] 1 11 4 32 NA NA NA 23 45 115 37...

After imputing the missing values, one should perform an exploratory analysis, which involves using a visualization plot and an aggregation method to summarize the data characteristics. The result helps the user gain a better understanding of the data in use. The following recipe will introduce how to use basic plotting techniques with a view to help the user with exploratory analysis.

This recipe needs the previous recipe to be completed by imputing the missing value in the Ozone and Solar.R attribute.

Perform...

The exploratory analysis helps users gain insights into single or multiple variables. However, it does not determine what combinations may generate a prediction model, so as to predict the temperature. On the other hand, machine learning can generate a prediction model from a training dataset, so that the user can apply the model to predict the possible labels from the given attributes. In this recipe, we will introduce how to predict the temperature and find the correlation between attributes.

We will use the data, mydata, which we have already used in previous recipes.