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When a project is opened within RStudio the following actions are taken: 1. A new R session process is started 2. Rprofile file in the project's main directory if any is sourced by R 3. RData file in the project's main directory is loaded if project options indicate that it should be loaded. The current working directory is set to the project directory.
Previously edited source documents are restored into editor tabs 7. Other RStudio settings e. Quitting a Project When you are within a project and choose to either Quit, close the project, or open another project the following actions are taken: 1. Rhistory are written to the project directory if current options indicate they should be 2. The list of open source documents is saved so it can be restored next time the project is opened 3.
Other RStudio settings as described above are saved. The R session is terminated. Working with Multiple Projects at Once You can work with more than one RStudio project at a time by simply opening each project in its own instance of RStudio. There are two ways to accomplish this: 1. Opening multiple project files via the system shell i. Project Options There are several options that can be set on a per-project basis to customize the behavior of RStudio.
You can edit these options using the Project Options command on the Projectmenu: General Note that the General project options are all overrides of existing global options. To inherit the default global behavior for a project you can specify Default as the option value. RData into workspace at startup — Load the. RData file if any found in the initial working directory into the R workspace global environment at startup. If you have a very large. RData file then unchecking this option will improve startup time considerably.
Save workspace to. RData on exit — Ask whether to save. RData on exit, always save it, or never save it. Note that if the workspace is not dirty no changes made at the end of a session then no prompt to save occurs even if Ask is specified. Always save history even when not saving. RData — Make sure that the. Rhistory file is always saved with the commands from your session even if you choose not to save the. RData file when exiting. Editing 1. Index R source files — Determines whether R source files within the project directory are indexed for code navigation i.
Normally this should remain enabled, however if you have a project directory with thousands of files and are concerned about the overhead of monitoring and indexing them you can disable indexing here. Insert spaces for tab — Determine whether the tab key inserts multiple spaces rather than a tab character soft tabs. Configure the number of spaces per soft-tab. Text encoding — Specify the default text encoding for source files.
Note that source files which don't match the default encoding can still be opened correctly using the File : Reopen with Encoding menu command. Version Control 1. Version control system — Specify the version control system to use with this project. Note that RStudio automatically detects the presence of version control for projects by scanning for a.
Therefore it isn't normally necessary to change this setting. Origin — Read-only display of the remote origin if any for the project version control repository. The character indicates a comment. Anything to the right of the including the itself is ignored. This is the only comment character in R. Unlike some other languages, R does not support multi-line comments or comment blocks.
The result may be auto-printed. So entering 1 in R gives you a numeric object; entering 1L explicitly gives you an integer object. This way, Inf can be used in ordinary calculations; e. These metadata can be very useful in that they help to describe the object. For example, column names on a data frame help to tell us what data are contained in each of the columns.
You can also use the vector function to initialize vectors. Sometimes this happens by accident but it can also happen on purpose. So what happens with the following code? In each case above, we are mixing objects of two different classes in a vector. But remember that the only rule about vectors says this is not allowed.
When different objects are mixed in a vector, coercion occurs so that every element in the vector is of the same class. In the example above, we see the effect of implicit coercion. What R tries to do is find a way to represent all of the objects in the vector in a reasonable fashion. Sometimes this does exactly what you want and For example, combining a numeric object with a character object will create a character vector, because numbers can usually be easily represented as strings.
When nonsensical coercion takes place, you will usually get a warning from R. Matrices can be created by column-binding or row- binding with the cbind and rbind functions. Lists are a special type of vector that can contain elements of different classes. Lists are a very important data type in R and you should get to know them well.
Lists can be explicitly created using the list function, which takes an arbitrary number of arguments. We can also create an empty list of a prespecified length with the vector function One can think of a factor as an integer vector where each integer has a label.
Factors are important in statistical modeling and are treated specially by modelling functions like lm and glm. Using factors with labels is better than using integers because factors are self- describing. Factor objects can be created with the factor function. Often factors will be automatically created for you when you read a dataset in using a function like read. Those functions often default to creating factors when they encounter data that look like characters or strings.
The order of the levels of a factor can be set using the levels argument to factor. This can be important in linear modelling because the first level is used as the baseline level. They are an important type of object in R and are used in a variety of statistical modeling applications. Each element of the list can be thought of as a column and the length of each element of the list is the number of rows.
Matrices must have every element be the same class e. However, data frames can also be created explicitly with the data. While it might seem that the as. NAMES R objects can have names, which is very useful for writing readable code and self-describing objects. Here is an example of assigning names to an integer vector. Matrices can have both column and row names. Column names and row names can be set separately using the colnames and rownames functions. Also, data frames do not have column names, they just have names like lists.
So to set the column names of a data frame just use the names function. Yes, I know its confusing. There are of course, many R packages that have been developed to read in all kinds of other datasets, and you may need to resort to one of these packages if you are working in a specific area. TABLE 1. The read. The help file for read. I know, I know, everyone always says to read the help file, but this one is actually worth reading.
By default read. This defalts to " ". This defaults to TRUE because back in the old days, if you had data that were stored as strings, it was because those strings represented levels of a categorical variable. For small to moderately sized datasets, you can usually call read. If the dataset is larger than the amount of RAM on your computer, you can probably stop right here. In order to use this option, you have to know the class of each column in your data frame.
A quick an dirty way to figure out the classes of each column is the following: Setnrows. Can you close any of them? Some operating system scan limit the amount of memory a single process can access Roughly, how much memory is required to store this data frame? Well, on most modern computers double precision floating point numbers38 are stored using 64 bits of memory, or 8 bytes. Given that information, you can do the following calculation So the dataset would require about 1.
Most computers these days have at least that much RAM. This is usually an unpleasant experience that usually requires you to kill the R process, in the best case scenario, or reboot your computer, in the worst case. So make sure to do a rough calculation of memeory requirements before reading in a large dataset.
Overview The RStudio Connections Pane makes it possible to easily connect to a variety of data sources, and explore the objects and data inside the connection. It extends, and is designed to work with, a variety of other tools for working with databases in R.
You can read more about these other tools on the Databases with RStudio site. The Connection Pane helps you to connect to existing data sources. Like the Data Import feature, it helps you craft an R statement that you can run to help work with your data in R.
It also remembers the R statement so that you can reconnect easily, and provides a means of exploring the data source once you're connected. Opening a Data Connection Data connections are typically ephemeral and are closed when your R session ends or is restarted. To re-establish a data connection, click the Connections tab. This shows a list of all the connections RStudio knows about see Connections History below for details.
R Console will create the connection immediately by executing the code at the R console. New R Script will put your connection into a new R script, and then immediately run the script. New R Notebook will create a new R Notebook with a setup chunk that connects to the data, and then immediately run the setup chunk.
Copy to Clipboard will place the connection code onto the clipboard, to make it easy to insert into an existing script or document. Exploring Connections When you select a connection that is currently connected, you can explore the objects and data in the connection.
The package provides replacements for functions like read. This functions are oven much faster than their base R analogues and provide a few other nice features such as progress meters. Notice that the dput output is in the form of R code and that it preserves metadata like the class of the object, the row names, and the column names. The output of dput can also be saved directly to a file. Multiple objects can be deparsed at once using the dump function and read back in using source.
We can dump R objects to a file by passing a character vector of their names. Connections must be opened, then the are read from or written to, and then they are closed. This function is useful for reading text files that may be unstructured or contain non-standard data. This approach is useful because it allows you to read from a file without having to uncompress the file first, which would be a waste of space and time.
Since web pages are basically text files that are stored on a remote server, there is conceptually not much difference between a web page and a local text file. However, we need R to negotiate the communication between your computer and the web server. This is what the url function can do for you, by creating a url connection to a web server. This code might take time depending on your connection speed. However, more commonly we can use URL connection to read in specific data files that are stored on web servers.
This is approach is preferable to opening a web browser and downloading a dataset by hand. Of course, the code you write with connections may not be executable at a later date if things on the server side are changed or reorganized. It can only be used to extract a single element and the class of the returned object will not necessarily be a list or data frame. Itssemantics are similar to that of [[. Here we extract the first four elements of the vector.
Vectors are basic objects in R and they can be subsetted using the [ operator. The sequence does not have to be in order; you can specify any arbitrary integer vector We can also pass a logical sequence to the [ operator to extract elements of a vector that satisfy a given condition. Another, more compact, way to do this would be to skip the creation of a logical vector and just subset the vector directly with the logical expression.
Indices can also be missing. This behavior is used to access entire rows or columns of a matrix. This is a feature that is often quite useful during interactive work, but can later come back to bite you when you are writing longer programs or functions. The [[ operator can be used to extract single elements from a list. Here we extract the first element of the list.
For example, if you wanted to extract the first and third elements of a list, you would do the following Note that x[c 1, 3 ] is NOT the same as x[[c 1, 3 ]]. Remember that the [ operator always returns an object of the same class as the original. Since the original object was a list, the [ operator returns a list.
In the above code, we returned a list with two elements the first and the third. In those cases, you should refer to the full element name if possible. What if there are multiple R objects and you want to take the subset with no missing values in any of those objects? Many operations in R are vectorized, meaning that operations occur in parallel in certain R objects.
This allows you to write code that is efficient, concise, and easier to read than in non-vectorized languages. The simplest example is when adding two vectors together. Another operation you can do in a vectorized manner is logical comparisons.
So suppose you wanted to know which elements of a vector were greater than 2. You could do he following. Here are other vectorized logical operations. Of course, subtraction, multiplication and division are also vectorized This way, we can do element-by- element operations on matrices without having to loop over every element. Dates are stored internally as the number of days since while times are stored internally as the number of seconds since I just thought those were fun facts.
Date function. This is a common way to end up with a Date object in R. You can see the internal representation of a Date object by using the unclass function. POSIXct is just a very large integer under the hood. It use a useful class when you want to store times in something like a data frame. POSIXlt is a list underneath and it stores a bunch of other useful information like the day of the week, day of the year, month, day of the month.
This is useful when you need that kind of information. POSIXlt or as. POSIXct function. You can do comparisons too i. Date, as. POSIXlt, or as. POSIXct The basic structure of a data frame is that there is one observation per row and each column represents a variable, a measure, feature, or characteristic of that observation. R has an internal implementation of data frames that is likely the one you will use most often. However, other operations, like filtering, re-ordering, and collapsing, can often be tedious operations in R whose syntax is not very intuitive.
The dplyr package is designed to mitigate a lot of these problems and to provide a highly optimized set of routines specifically for dealing with data frames. With this grammar, you can sensibly communicate what it is that you are doing to a data frame that other people can understand assuming they also know the grammar. For example, there is a handy print method that prevents you from printing a lot of data to the console.
Most of the time, these additional data types are transparent to the user and do not need to be worried about. The GitHub repository will usually contain the latest updates to the package and the development version. To install from CRAN, just run After installing the package it is important that you load it into your R session with the library function. The dataset is available from my web site. After unzipping the archive, you can load the data into R using the readRDS function.
You can see some basic characteristics of the dataset with the dim and str functions The select function can be used to select columns of a data frame that you want to focus on. The select function allows you to get the few columns you might need. Suppose we wanted to take the first 3 columns only.
There are a few ways to do this. We could for example use numerical indices. But we can also use the names directly. Note that the : normally cannot be used with names or strings, but inside the select function you can use it to specify a range of variable names.
You can also omit variables using the select function by using the negative sign. With select you can do The select function also allows a special syntax that allows you to specify variable names based on patterns. This function is similar to the existing subset function in R but is quite a bit faster in my experience. You can see that there are now only rows in the data frame and the distribution of the pm25tmean2 values is. We can place an arbitrarily complex logical sequence inside of filter , so we could for example extract the rows where PM2.
Reordering rows of a data frame while preserving corresponding order of other columns is normally a pain to do in R. The arrange function simplifies the process quite a bit. We can now check the first few rows We can now check the first few rows The rename function is designed to make this process easier.
Here you can see the names of the first five variables in the chicago data frame. The dptp column is supposed to represent the dew point temperature and the pm25tmean2 column provides the PM2. However, these names are pretty obscure or awkward and probably be renamed to something more sensible. Often, you want to create new variables that are derived from existing variables and mutate provides a clean interface for doing that.
There is also the related transmute function, which does the same thing as mutate but then drops all non- transformed variables. Here we detrend the PM10 and ozone O3 variables. First, we can create a year varible using as. Now we can create a separate data frame that splits the original data frame by year. Finally, we compute summary statistics for each year in the data frame with the summarize function. More sophisticated statistical modeling can help to provide precise answers to these questions, but a simple application of dplyr functions can often get you most of the way there.
Notice above that every time we wanted to apply more than one function, the sequence gets buried in a sequence of nested function calls that is difficult to read, i. Take the example that we just did in the last section where we computed the mean of o3 and no2 within quintiles of pm There we had to 1. With these functions we can do a number of complex operations in just a few lines of code.
Mungkin saja dua variabel mempunyai keeratan hubungan yang kuat namun nilai koefisien korelasinya mendekati nol, misalnya pada kasus hubungan non linier. Harus diingat pula bahwa adanya hubungan linier yang kuat di antara variabel tidak selalu berarti ada hubungan kausalitas, sebab-akibat. Artinya variabel X mempunyai hubungan sangat kuat dengan variabel Y. Korelasi Pearson, misalnya, menunjukkan adanya kekuatan hubungan linier dalam dua variabel.
Linieritas artinya asumsi adanya hubungan dalam bentuk garis lurus antara variabel. Linearitas antara dua variabel dapat dinilai melalui observasi scatterplots bivariat. Jika kedua variabel berdistribusi normal dan behubungan secara linier, maka scatterplot berbentuk oval; jika tidak berdistribusi normal scatterplot tidak berbentuk oval. Dalam praktinya kadang data yang digunakan akan menghasilkan korelasi tinggi tetapi hubungan tidak linier; atau sebaliknya korelasi rendah tetapi hubungan linier.
Dengan demikian agar linieritas hubungan dipenuhi, maka data yang digunakan harus mempunyai distribusi normal. Konsep Linieritasdan Korelasi Since the Sigmoid function represents the probability that a student passes , the likelihood that a student fails is 1 the total probability minus the y value at that point along the line.
The product of two numbers inside of a log is equivalent to the addition of their logs. We end up with the following likelihood. We then repeat the entire process for a different line and compare the likelihoods. We choose the line with the maximum likelihood highest positive number. To begin, import the following libraries. As we mentioned previously, Logistic Regression is only applicable to binary classification problems.
Thus, the data points are composed of two classes. We plot the relationship between the feature and classes. We instantiate an instance of the LogisticRegression class and call the fit function with the features and the labels since Logistic Regression is a supervised machine learning algorithm as arguments. We can access the following properties to actually view the coefficient for the slope and y-intercept of the best fitting line. Given that this consists of a classification problem, we use a confusion matrix to measure the accuracy of our model.
From our confusion matrix we conclude that:. The first column corresponds to the probability that the sample belongs to the first class and the second column corresponds to the probability that the sample belongs to the second class. Before attempting to plot the Sigmoid function, we create and sort a DataFrame containing our test data. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday.
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