R Packages - Revision history

Rb: /* Updating Packages */

2015-09-23T14:40:39Z

‎Updating Packages

Rb at 14:39, 23 September 2015

2015-09-23T14:39:50Z

Rb: /* Installing packages */

2015-07-13T15:17:17Z

‎Installing packages

Rb at 22:36, 16 January 2015

2015-01-16T22:36:02Z

Rb at 22:33, 16 January 2015

2015-01-16T22:33:43Z

Rb at 22:32, 16 January 2015

2015-01-16T22:32:33Z

Rb at 22:23, 16 January 2015

2015-01-16T22:23:04Z

Rb at 22:20, 16 January 2015

2015-01-16T22:20:59Z

Rb at 22:17, 16 January 2015

2015-01-16T22:17:43Z

Rb at 22:12, 16 January 2015

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@@ Line 46: / Line 46: @@
 == Updating Packages ==
-Every once in a while you will want to update your packages (as they get maintained and updated by their authors). This is done pretty easily using the function <code>update.packages()</code> (part of the "utils" package). All you need to do is call <code>update.packages()</code> from the command line and R will iterate through the packages you have installed and ask you whether you want them updated.
+Every once in a while you will want to update your packages (as they get maintained and updated by their authors). This is done pretty easily using the function <code>update.packages()</code> (part of the "utils" package). All you need to do is call <code>update.packages()</code> from the command line and R will iterate through the packages you have installed and ask you whether you want them updated. Depending on how many packages you have this can take a while.
 == Additional Resources for Packages ==

← Older revision		Revision as of 14:39, 23 September 2015
Line 43:		Line 43:

	from which we can see that 80% of women in the dataset have no children under the age of 6 and only 3% have two.		from which we can see that 80% of women in the dataset have no children under the age of 6 and only 3% have two.
		+
		+	== Updating Packages ==
		+
		+	Every once in a while you will want to update your packages (as they get maintained and updated by their authors). This is done pretty easily using the function <code>update.packages()</code> (part of the "utils" package). All you need to do is call <code>update.packages()</code> from the command line and R will iterate through the packages you have installed and ask you whether you want them updated.

	== Additional Resources for Packages ==		== Additional Resources for Packages ==

@@ Line 21: / Line 21: @@
 Which produces an object ip which contains the list of all the packges that are already installed. The process you need to go through to use a new package is the following:
-# Install the package: <source enclose=none>install.packages("NAME_OF_PACKAGE")</source>. The name comes in inverted commas. This only needs to be done once on any computer. The very nice thing is that you will not need to download anything yourself. R will do all the work for you! <ref>The first time you do that on your computer, R will ask you from which Mirri you want to download this and will offer a list. Choose the one that is geographically closest to you.</ref>
+# Install the package: <source enclose=none>install.packages("NAME_OF_PACKAGE")</source>. The name comes in inverted commas. This only needs to be done once on any computer. The very nice thing is that you will not need to download anything yourself. R will do all the work for you! <ref>The first time you do that on your computer, R will ask you from which Mirror you want to download this and will offer a list. Choose the one that is geographically closest to you.</ref>
 # Load the package into your particular code. You need to do this every time you load R. So, if you are working in a script you would have the following command at the beginning: <source enclose=none>library(NAME_OF_PACKAGE)</source>. Now you use the package name without inverted commas.

@@ Line 37: / Line 37: @@
         kidslt6       probs
-       0 0.804780876
+       0   0.804780876
-       1 0.156706507
+       1   0.156706507
-       2 0.034528552
+       2   0.034528552
-       3 0.003984064
+       3   0.003984064
-from which we can see that 80% of women in the dataset have no children under the age of 6.
+from which we can see that 80% of women in the dataset have no children under the age of 6 and only 3% have two.
 == Additional Resources for Packages ==

@@ Line 41: / Line 41: @@
        2 0.034528552
        3 0.003984064
 == Additional Resources for Packages ==

@@ Line 30: / Line 30: @@
 [[File:R_empirical.JPG|frameless|500px]]
-This is a typical describtion of a R function. As it turns out, this is a rather easy function which only accepts one input, the variable for which you want to calculate the empirical distribution.
+This is a typical describtion of a R function. As it turns out, this is a rather easy function which only accepts one input, the variable for which you want to calculate the empirical distribution. The following applies this function to the <source enclose=none>kidslt6</source> variable:
 == Additional Resources for Packages ==

@@ Line 1: / Line 1: @@
 We introduce how packages are used in R. Being able to use packages is extremely important in R and will save you a lot of time as you will use solutions that have been programmed by other people.
-== Packages ==
+== What are they ==
 The R software has some basic functionality, but the power of R comes from the ability to use code written to perform statistical and econometric techniques that has been written by other people. These additional pieces of software are called packages. Packages usually include a host of functions that can perform tasks that are all related to a particular problem type (say, using probability distributions, estimating GARCH models, performing Bayesian inference etc.). The next step will be to learn how to find them, install them and use them.
 The most difficult task is often to find the right package you want to use. Usually Dr Google or Prof Bing will be the right people to ask.
@@ Line 10: / Line 12: @@
 Scanning the results there appears a link to a package called "prob" and if you open the pdf file (to which my serach engine linked) you will find a list of function that are contained in that package and you will soon see an "empirical" function which appears to be designed to do the job. Keep this file open as we will need to consult it to understand how to use the function. But first we need to make this code available to our script file.
 Such packages do not come pre-installed into R, but luckily, they are easily installed and used. If you first want to check which packages are already installed on your computer, you can use the following command:
@@ Line 19: / Line 23: @@
 # Install the package: <source enclose=none>install.packages("NAME_OF_PACKAGE")</source>. The name comes in inverted commas. This only needs to be done once on any computer. The very nice thing is that you will not need to download anything yourself. R will do all the work for you! <ref>The first time you do that on your computer, R will ask you from which Mirri you want to download this and will offer a list. Choose the one that is geographically closest to you.</ref>
 # Load the package into your particular code. You need to do this every time you load R. So, if you are working in a script you would have the following command at the beginning: <source enclose=none>library(NAME_OF_PACKAGE)</source>. Now you use the package name without inverted commas.
 Once you installed a package and then included the <source enclose=none>library(NAME_OF_PACKAGE)</source> statement, you can access all the functions contained in a package. Go pack to the pdf file that described the "prob" package and find the section on the "empirical" function
@@ Line 26: / Line 32: @@
 This is a typical describtion of a R function. As it turns out, this is a rather easy function which only accepts one input, the variable for which you want to calculate the empirical distribution.
-=== Additional Resources for Packages ===
+== Additional Resources for Packages ==
 * A [http://cran.r-project.org/web/packages/available_packages_by_name.html list of available packages] can be found on the CRAN webpage.

← Older revision		Revision as of 22:20, 16 January 2015
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		+	We introduce how packages are used in R. Being able to use packages is extremely important in R and will save you a lot of time as you will use solutions that have been programmed by other people.

	== Packages ==		== Packages ==

← Older revision		Revision as of 22:17, 16 January 2015
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−	In this section we shall demonstrate how to do some basic data analysis on data in a dataframe. Eventually we will use this task to also introduce how packages are used in R. being able to use packages is extremely important in R and will save you a lot of time as you will use solutions that have been programmed by other people.
−
−	~~== Basic Data Analysis ==~~
−
−	~~The easiest way to find basic summary statistics on your variables contained in a dataframe is the following command:~~
−
−	~~summary(mydata)~~
−
−	You will find that this will provide a range of summary statistics for each variable (Minimum and Maximum, Quartiles, Mean and Median). If the dataframe contains a lot of variables, as the dataframe based on [[media:mroz.xls\|mroz.xls]], this output can be somewhat lengthy. Say you are only interested in the summary statistics for two of the variables <source enclose=none>hours and husage</source>, then you would want to select these two variables only. The way to do that is the following:
−
−	~~summary(mydata[c("hours","husage")])~~
−
−	~~This will produce the following output:~~
−
−	~~hours husage~~
−	~~Min. : 0.0 Min. :30.00~~
−	~~1st Qu.: 0.0 1st Qu.:38.00~~
−	~~Median : 288.0 Median :46.00~~
−	~~Mean : 740.6 Mean :45.12~~
−	~~3rd Qu.:1516.0 3rd Qu.:52.00~~
−	~~Max. :4950.0 Max. :60.00~~
−
−	Another extremely useful statistic is the correlation between different variables. This is achieved with the <source enclose=none>cor( )</source> function. Let's say we want the correlation between <source enclose=none>educ, motheduc, fatheduc</source>, then we use in the same manner:
−
−	~~cor(mydata[c("educ","motheduc","fatheduc")])~~
−
−	~~resulting in the following correlation matrix~~
−
−	~~educ motheduc fatheduc~~
−	~~educ 1.0000000 0.4353365 0.4424582~~
−	~~motheduc 0.4353365 1.0000000 0.5730717~~
−	~~fatheduc 0.4424582 0.5730717 1.0000000~~
−
−	~~=== Dealing with missing observations ===~~
−
−	~~So far all is honky dory. Let's show some difficulties/issues. Consider we want to calculate the correlation between <source enclose=none>educ, wage</source>~~
−
−	~~cor(mydata[c("educ","wage")])~~
−
−	~~The output we get is:~~
−
−	~~educ wage~~
−	~~educ 1 NA~~
−	~~wage NA 1~~
−
−	~~The reason for R's inability to calculate a correlation between these two variables can be seen here:~~
−
−	~~> summary(mydata[c("hours","wage")])~~
−	~~hours wage~~
−	~~Min. : 0.0 Min. : 0.1282~~
−	~~1st Qu.: 0.0 1st Qu.: 2.2626~~
−	~~Median : 288.0 Median : 3.4819~~
−	~~Mean : 740.6 Mean : 4.1777~~
−	~~3rd Qu.:1516.0 3rd Qu.: 4.9707~~
−	~~Max. :4950.0 Max. :25.0000~~
−	~~NA's :325~~
−
−	The important information is that the variable <source enclose=none>wage</source> has 325 missing observations (NA). It is not immediately obvious how to tackle this issue. We need to consult either Dr. Google or the R help function. The latter is done by typing <source enclose=none>?con</source>. The help will pop up in the "Help" tab on the right hand side. You will need to read through it to find a solution to the issue. Frankly, the clever people who write the R software are not always the most skilful in writing clearly and it is often most useful to go to the bottom of the help where you can usually find some examples. If you do that you will find that the solution to our problem is the following:
−
−	~~> cor(mydata[c("educ","wage")],use = "complete")~~
−	~~educ wage~~
−	~~educ 1.0000000 0.3419544~~
−	~~wage 0.3419544 1.0000000~~
−
−	It is perhaps worth adding a word of explanation here. <source enclose=none>cor( )</source> is what is called a function. It needs some inputs to work. The first input is the data for which to calculate correlations, <source enclose=none>mydata[c("educ","wage")]</source>. Most functions also have what are called parameters. These are like little dials and levers to the function which change how the function works. And one of these levers can be used to tell the function to only use observations that are complete, i.e. don't have missing observations, <source enclose=none>use = "complete"</source>. Read the help function to see what over levers are at your disposal.
−
−	~~=== Using Subsets of Data ===~~
−
−	Often you will want to perform some analysis on a subset of data. The way to do this in R is to use the subset function, together with a logical (boolean) statement. I will first write down the statement and then explain what it does:
−
−	~~mydata.sub1 <- subset(mydata, hours > 0)~~
−
−	On the left hand side of <source enclose=none><-</source> we have a new object named <source enclose=none>mydata.sub1</source>. On the right hand side of <source enclose=none><-</source> we can see how that new object is defined. We are using the function <source enclose=none>subset()</source> which has been designed to select observations and/or columns from a dataframe such as <source enclose=none>mydata</source>. This function needs at least two inputs. The first input is the dataframe from which we are selecting observations and variables. Here we are selecting from <source enclose=none>mydata</source>. The second element indicates which observations/rows we want to select. <source enclose=none>hours > 0</source> tells R to select all those observations for which the variable <source enclose=none>hours</source> is larger than 0.
−
−	Often (if not always) you will not remember how exactly a function works. The internet is then usually a good source, but in your console you could also type <source enclose=none>?subset</source> which would open a help function. There you could see that you could add a third input to the subset function which would indicate which variables you want to include (e.g. <source enclose=none>select = c(hours, wage)</source> which would only select these two variables). By not using this third input we indicate to R that it should select all variables in <source enclose=none>mydata</source>.
−
−	~~==== Logical/Boolean Statements ====~~
−
−	The way in which we selected the observations, i.e. by using the logical statement <source enclose=none>hours > 0</source> is worth dwelling on for a moment. These type of logical statements create variables in R that are given the <source enclose=none>logical</source> data type. Sometimes these are also called boolean variables.
−
−	To see what is special about these go to your console and just type something like <source enclose=none>5>9</source> and then press ENTER. You will realise that R is a clever little thing and will tell you that in fact 5 is not larger than 9 by returning the answer <source enclose=none>FALSE</source>. When provided R with <source enclose=none>hours > 0</source>, the software, for all our 753 observations, checks whether the value of the hour variable is larger than 0 or not. It will create a variable (vector) with 753 entries and in each entry there will be either a <source enclose=none>hours >TRUE</source> or <source enclose=none>hours >FALSE</source>, depending on whether the respective value is larger than 0 or not.
−
−	You can create logical variables on the basis of more complicated logical statements as well. You can combine statements by noting that <source enclose=none>&</source> represents AND, <source enclose=none>\|</source> represents OR and <source enclose=none>==</source> checks whether two things are equal. To figure out how these work, try the following statements in your console and see whether you can guess the right answers:
−
−	~~(3 > 2) & (3 > 1)~~
−	~~(3 > 2) & (3 > 6)~~
−	~~(3 > 5) & (3 > 6)~~
−	~~(3 > 2) \| (3 > 1)~~
−	~~(3 > 2) \| (3 > 6)~~
−	~~(3 > 5) \| (3 > 6)~~
−	~~((3 == 5) & (3 > 2)) \| (3 > 1)~~
−
−	~~Being comfortable with these logical statements will make the life of every programmer much easier.~~

	== Packages ==		== Packages ==