Manipulating data

This recipe will discuss how to use the built-in R functions to manipulate data. As data manipulation is the most time-consuming part of most analysis procedures, you should gain knowledge of how to apply these functions on data.

Getting ready

Ensure you have completed the previous recipes by installing R on your operating system.

How to do it...

Perform the following steps to manipulate the data with R.

Subset the data using the bracelet notation:

1. Load the dataset iris into the R session:

        > data(iris)  

2. To select values, you may use a bracket notation that designates the indices of the dataset. The first index is for the rows and the second for the columns:

        > iris[1,"Sepal.Length"]
        Output:
    
        [1] 5.1  

3. You can also select multiple columns using c():

        > Sepal.iris = iris[, c("Sepal.Length", "Sepal.Width")]  

4. You can then use str() to summarize and display the internal structure of Sepal.iris:

        > str(Sepal.iris)
        Output:
       'data.frame':  150 obs. of  2 variables:
        $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
        $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ..  

5. To subset data with the rows of given indices, you can specify the indices at the first index with the bracket notation. In this example, we show you how to subset data with the top five records with the Sepal.Length column and the Sepal.Width selected:

        > Five.Sepal.iris = iris[1:5, c("Sepal.Length", "Sepal.Width")]
        > str(Five.Sepal.iris)
        Output:
        'data.frame':   5 obs. of  2 variables:
        $ Sepal.Length: num  5.1 4.9 4.7 4.6 5
        $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 

6. It is also possible to set conditions to filter the data. For example, to filter returned records containing the setosa data with all five variables. In the following example, the first index specifies the returning criteria, and the second index specifies the range of indices of the variable returned:

        > setosa.data = iris[iris$Species=="setosa",1:5]
        > str(setosa.data)
        Output:
        'data.frame':   50 obs. of  5 variables:
        $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
        $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
        $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
        $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
        $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1
        1 1 1 1 1 1 1 ...

7. Alternatively, the which function returns the indexes of satisfied data. The following example returns the indices of the iris data containing species equal to setosa:

        > which(iris$Species=="setosa")
        Output:
        [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
        [19] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
        [37] 37 38 39 40 41 42 43 44 45 46 47 48 49 50

8. The indices returned by the operation can then be applied as the index to select the iris containing the setosa species. The following example returns the setosa with all five variables:

        > setosa.data = iris[which(iris$Species=="setosa"),1:5]
        > str(setosa.data)
        Output:
        'data.frame':   50 obs. of  5 variables:
         $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
         $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
         $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
         $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
         $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 
        1 1 1 1 1 1 1 ...

Subset data using the subset function:

1. Besides using the bracket notation, R provides a subset function that enables users to subset the DataFrame by observations with a logical statement.
2. First, subset species, sepal length, and sepal width out of the iris data. To select the sepal length and width out of the iris data, one should specify the column to be subset in the select argument:

        > Sepal.data = subset(iris, select=c("Sepal.Length", "Se-
        pal.Width"))
        > str(Sepal.data)
        Output:
       'data.frame': 150 obs. of  2 variables:
       $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
       $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
    
  

This reveals that Sepal.data contains 150 objects with the Sepal.Length variable and Sepal.Width.

1. On the other hand, you can use a subset argument to get subset data containing setosa only. In the second argument of the subset function, you can specify the subset criteria:

        > setosa.data = subset(iris, Species =="setosa")
        > str(setosa.data)
        Output:
       'data.frame': 50 obs. of  5 variables:
        $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
        $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
        $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
        $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
        $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1
        1 1 1 1 1 1 1 ... 

2. Most of the time, you may want to apply a union or intersect a condition while subsetting data. The OR and AND operations can be further employed for this purpose. For example, if you would like to retrieve data with Petal.Width >=0.2 and Petal.Length < = 1.4:

        > example.data= subset(iris, Petal.Length <=1.4 & Petal.Width >=
        0.2, select=Species )
        > str(example.data)
        Output:
       'data.frame': 21 obs. of  1 variable:
       $ Species: Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 
       1 1 1 1 ...

• Merging data: Merging data involves joining two DataFrames into a merged DataFrame by a common column or row name. The following example shows how to merge the flower.type DataFrame and the first three rows of the iris with a common row name within the Species column:

        > flower.type = data.frame(Species = "setosa", Flower = "iris")
        > merge(flower.type, iris[1:3,], by ="Species")
        Output:
        Species Flower Sepal.Length Sepal.Width Petal.Length Petal.Width
      1  setosa   iris          5.1         3.5          1.4         0.2
      2  setosa   iris          4.9         3.0          1.4         0.2
      3  setosa   iris          4.7         3.2          1.3         0.2

• Ordering data: The order function will return the index of a sorted DataFrame with a specified column. The following example shows the results from the first six records with the sepal length ordered (from big to small) iris data:

        > head(iris[order(iris$Sepal.Length, decreasing = TRUE),])
        Output:
          Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
        132          7.9         3.8          6.4         2.0 virginica
        118          7.7         3.8          6.7         2.2 virginica
        119          7.7         2.6          6.9         2.3 virginica
        123          7.7         2.8          6.7         2.0 virginica
        136          7.7         3.0          6.1         2.3 virginica
        106          7.6         3.0          6.6         2.1 virginica
    
  

How it works...

Before conducting data analysis, it is important to organize collected data into a structured format. Therefore, we can simply use the R DataFrame to subset, merge, and order a dataset. This recipe first introduces two methods to subset data: one uses the bracket notation, while the other uses the subset function. You can use both methods to generate the subset data by selecting columns and filtering data with the given criteria. The recipe then introduces the merge function to merge DataFrames. Last, the recipe introduces how to use order to sort the data.

There's more...

The sub and gsub functions allow using regular expression to substitute a string. The sub and gsub functions perform the replacement of the first and all the other matches, respectively:

> sub("e", "q", names(iris))
Output:
[1] "Sqpal.Length" "Sqpal.Width"  "Pqtal.Length" "Pqtal.Width"  "Spqcies"     
> gsub("e", "q", names(iris))
Output:
[1] "Sqpal.Lqngth" "Sqpal.Width"  "Pqtal.Lqngth" "Pqtal.Width"  "Spqciqs"