How To Make A Table In Rstudio – Basically a collection of vectors containing columns, all of the same length but possibly of different types. The convention in R and elsewhere is:
, which we will use later (Section 4.4), contains a table with information about weather stations in Israel (Figure 4.1). The rows in the table correspond to 169 weather stations. The columns of the table refer to different variables:
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Note that the data type is the same for each individual column, but can differ from column to column. For instance.,
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Function if one or more vectors are columns14. For example, this expression creates a table with four attributes for three train stations in Israel. Features are:
With RStudio, the view also has sort and filter buttons (Figure 4.2). Note that sorting or filtering the view does not affect the actual objects.
Alternatively, we can get both the number of rows and the number of columns (in that order!) as a vector of length 2, where
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Change the names of the rows or columns by assigning new values to these properties or to a subset of them. For example, here’s how we can change the name of the first column
Based on the appearance of the printout, you may have noticed that the first two expressions evaluate to (
Characteristic! ). Section 4.1.5.3 describes how to determine the type of the return subset and how to use it
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Parameter determines whether we want to reduce a subset of the results to simpler data structures and “remove” more complex classes if possible (
, default), or we want to always keep a subset in its original class (
Why do you think the simplification works using a single column subset instead of a single row subset?
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Let’s go back to the Kinneret example from chapter 3. For this we again define two vectors
This is a longer form than the previous one, so it is inconvenient to print them all because they would take up a lot of space on the console. Instead we can use
Using a logical index, we can get the subset of dates that have a Kinneret level less than
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.Note that the following expressions are the same ones we used when working with single vectors
The default execution mode has the computer execute all expressions in the same order as they are specified in the code. The flow control command is a method
) is omitted because the code section contains only an expression equal to the function definition (§3.3.4).
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How can the above conditional expression be extended to print one of three messages indicating whether x is positive, negative, or zero?
Conditional statements are often used when our code branches into two (or more) scenarios based on the value of a particular object. For example, we can use conditionals to define (Section 3.3) our version
What if the length of the abs2 argument is > 1, where the first element is negative and some others are positive? Based on the warning message, can you guess what is the reason for this behavior?
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For loops, the number of times the code is executed is predetermined based on the length of the vector passed during initialization of the loop. The code runs once for each element of the vector. In each “round”, the current item is assigned to a variable that we can use in the loop code.
How can we use a for loop to print ten vectors of length 10 including the multiplication table from 1 to 10?
The last part of the code, the code is very long, not very generic: it depends on the length
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In the function, we want to “collect” the results into a vector that will be returned to the user, not just printed. There are at least two ways to do this. way to start
Circular vectors we discuss in two versions. In the first version, we revised
Follow the function definition above and then follow the specific example shown above to show that the x_in_y function works correctly. What changes need to be made to the x_in_y function to create a new function called times_x_in_y that returns the number of times each element of x appears in y as shown below?
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This is very common in data processing where you need to iterate over subsets of a dataset, process those subsets, and then combine the results into a single object. A kind of
Loops are the default method for such tasks, unless there are “shortcuts” we might prefer, e.g.
(Section 4.1.2), we can read an existing table from a file, such as a comma-separated values (CSV)15 file. This action produces a
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(Figure 4.1). The file contains a table of monthly average rainfall data (September-May) based on 169 weather stations in Israel for the period 1980-2010. The table also contains the station name, number, altitude and X-Y coordinates.
The read should succeed if the correct path separator is used and the path to an existing file, in which case a new one
There is another important concept we need to be aware of when reading files in the R: working directory. The R environment always points to a specific directory on our computer, the working directory. we can
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When reading a file from the working directory, we can only specify the filename, not the full path:
Setting the working directory at the start of our script is useful when reading and/or writing multiple files from the same directory. This way, in the rest of the script, we can refer to the various files with just the file name instead of the full path.
However, the best practice is to avoid specifying the working directory or other machine-specific file locations. Instead, we want to assume the working directory is where the code files reside. This way the code is as reproducible as possible.
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, as shown above (Section 4.1.4.2), to display the dimensions, column names, column data types, and the top values in each column:
From the precipitation table (Figure 4.4) the rainfall for January (Jan) is plotted as a function of altitude.
We can extract specific information from a table by doing subsets and aggregations. For example, what are the altitudes of the lowest and highest stations?
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Loop in this case, more generally: in cases where we are interested in using the same functionality
As another example, we can calculate the average monthly precipitation for each month for all 169 stations. this time,
For most purposes, such as when using arithmetic or logical operators, named vectors work just like regular (unnamed) vectors. However, there are situations where vector names are used. For example, we can use
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A function that displays the names of the vectors on the x-axis and the values of the vectors on the y-axis (Figure 4.6):
Find the name of the station with the highest monthly rainfall. This is a more complicated example that requires two steps. First, let’s apply
It makes sense to combine the final result with the month names in a table, for example:
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What would have to be changed in the above piece of code to get a table of stations with the lowest monthly rainfall?
, which is a time series of monthly NDVI images (Fig. 5.13). We will begin working with this grid in Chapter 5.
Which season does the table (and therefore each painting) belong to? Our approach will consist of two parts. First (see below), we extract the month part of each date. Second (Section 4.6.2), we will use table joins to classify months into season names.
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First, we need to find out which month each date belongs to, which we know from 3.1.2. sections. First, we transform
The simplest way to classify months into seasons is to assign subset operations (Section 2.3.9). That is, we can name each season on the right
Values (in the first expression), then substitute them (in the last four expressions) as in the last
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This sorting method can be inconvenient if we have many complex categories or criteria. A more general approach is to use
A table join combines rows from two tables into one table based on the identity of the values in the shared columns. There are different types of connections, e.g.
4.7. Figure: Schematic representation of connection types. The circles represent the two connected tables, the left and the right. The shaded areas represent the records returned, such as a
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We will use only one type of join in this book, called a left join, and it is the most useful type of join. In the left join, given two tables
16. Matching rows are determined based on identical values in one or more columns
Functions can perform many types of table joins in R. the first two
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