Command Line 101 For many non-technical people, the command line (also referred to as CLI, Terminal, bash, or shell) is a place of mystery. However, you only have to know a handful of basic commands to start feeling comfortable.

Opening Your Command Line Interface On a Mac, the most common application for command line gymnastics is 'Terminal.app'. It comes pre-installed with every Mac OS X system. You'll find it in the 'Applications' folder, inside the 'Utilities' subfolder. On Windows, following the installation guidelines earlier in this book will provide you with an application called 'Git Bash'. You'll find it in your Windows START menu, inside the 'Git' folder. Finding Your Way Around As the name already implies, the command line is used to execute commands: you type something and confirm the command by hitting ENTER. Most of these commands are dependent on your current location - where 'location' means a certain directory or path on your computer.

So, let's issue our first command to find out where we currently are: $ pwd You can easily remember this command when you know what it stands for: ' print working directory'. It will return the path to a local folder on your computer's disk.

To change this current working directory, you can use the 'cd' command (where 'cd' stands for ' change directory'). For example, to move one directory upwards (into the current folder's parent folder), you can just call: $ cd. To move into subfolders, you would call something like this: $ cd name-of-subfolder/sub-subfolder/ Often, you'll see a special kind of path notation: '. This sign stands for your user account's home folder. So, instead of typing something like 'cd /Users/your-username/projects/', you should use this shorthand form: $ cd /projects/ Also very important is the 'ls' command that lists the file contents of a directory.

I suggest you always use this command with two additional options: '-l' formats the output list a little more structured and '-a' also lists 'hidden' files (which is helpful when working with version control). Showing the contents of the current directory works as follows: $ ls -la Working with Files The most important file operations can be controlled with just a handful of commands. Let's start by removing a file: $ rm path/to/file.ext When trying to delete a folder, however, please note that you'll have to add the '-r' flag (which stand for 'recursive'): $ rm -r path/to/folder Moving a file is just as simple: $ mv path/to/file.ext different/path/file.ext The 'mv' command can also be used to rename a file: $ mv old-filename.ext new-filename.ext If, instead of moving the file, you want to copy it, simply use 'cp' instead of 'mv'. Finally, to create a new folder, you call the 'make directory' command: $ mkdir new-folder Generating Output The command line is quite an all-rounder: it can also display a file's contents - although it won't do this as elegantly as your favorite editor.

Nonetheless, there are cases where it's handy to use the command line for this. For example when you only want to take a quick look - or when GUI apps are simply not available because you're working on a remote server. The 'cat' command outputs the whole file in one go: $ cat file.ext In a similar way, the 'head' command displays the file's first 10 lines, while 'tail' shows the last 10 lines. You can simply scroll up and down in the output like you're used to from other applications. The 'less' command is a little different in this regard.

Execution Working Directory For Macvim

$ less file.ext Although it's also used to display output, it controls page flow itself. This means that it only displays one page full of content and then waits for your explicit instructions. You'll know you have 'less' in front of you if the last line of your screen either shows the file's name or just a colon (':') that waits to receive orders from you.

Hitting SPACE will scroll one page forward, 'b' will scroll one page backward, and 'q' will simply quit the 'less' program. Making Your Life Easier on the Command Line There's a handful of little tricks that make your life a lot easier while working with the command line. TAB Key Whenever you're entering file names (including paths to a file or directory), the TAB key comes in very handy. It autocompletes what you've written, which reduces typos very efficiently.

For example, when you want to switch to a different directory, you can either type every component of the path yourself: $ cd /projects/acmedesign/documentation/ Or you make use of the TAB key (try this yourself!): $ cd /prTABojects/acTABmedesign/docTABumentation/ In case your typed characters are ambiguous (because 'dev' could be the 'development' or the 'developers' folder.), the command line won't be able to autocomplete. In that case, you can hit TAB another time to get all the possible matches shown and can then type a few more characters. ARROW Keys The command line keeps a history of the most recent commands you executed. By pressing the ARROW UP key, you can step through the last commands you called (starting with the most recently used). ARROW DOWN will move forward in history towards the most recent call. CTRL Key When entering commands, pressing CTRL+A moves the caret to the beginning of the line, while CTRL+E moves it to the end of the line.

Finally, not all commands are finished by simply submitting them: some require further inputs from you after hitting return. In case you should ever be stuck in the middle of a command and want to abort it, hitting CTRL+C will cancel that command. While this is safe to do in most situations, please note that aborting a command can of course leave things in an unsteady state.

A Beginner’s Guide to Using R Posted on August 9, 2014 by steve in I prefer to teach applied statistical analysis to students using the R programming language as a guide. R is an open source programming language with origins in C and FORTRAN. Statisticians and quantitative researchers are moving to R because of its flexibility. It can handle advanced computational models better than some canned software packages like Stata and SPSS and can permit advanced users to write their own functions.

R is also useful because it is free, unlike Stata and SPSS. Any person can download and install R and all its capabilities without needing to shell out hundreds or thousands of dollars. R can be installed. Windows users will want to select to install the latest version of R. For first-time users, I strongly recommend getting a graphical user interface (GUI) front-end for R. The best option is. Install the base R programming language, then install Rstudio.

What Am I Looking At? The first thing first-time users of R may notice is that R is not a “push button, receive analyses” interface. In fact, R is less a “program” and more a programming language.

If you open Rstudio, you will see something like this. Rstudio interface (click to embiggen) The upper left panel is an R terminal, the base around which the Rstudio program operates.

The bottom left panel is an R script window, in which you can run commands into R. The upper right panel allow the user to see things like objects in the current R session (more on”objects” later). The bottom right panel includes various things like a tab for plots the user may have created and what packages are currently installed.

A file browser is also there if the user wants it. Know Where You Are in R After understanding what you are seeing when you open an R terminal (or Rstudio), the next step is to know where R‘s working directory is. You can do this with the getwd command. For example, my version of R in Linux has this as my default working directory. getwd 1 '/home/steve' Windows users will see something like this.

getwd 1 'C:/Users/Steve/Documents' Windows users should notice that R, like everything else in the world, sees forward slashes (/) and not backslashes. Windows continued use of backslashes in its file system. Linux and Mac are derivative of Unix.

Execution Working Directory For Mac Finder

Nonetheless, Windows users should note that specifying a working directory in R requires forward slashes and not backslashes. Specifying a new working directory can be done with the the setwd command. For example, here is my working directory for the analyses I am doing for one of my current manuscripts. setwd ( '/home/steve/Dropbox/projects/tolerance-corruption/analysis' ) getwd 1 '/home/steve/Dropbox/projects/tolerance-corruption/analysis' Doing everything in Dropbox is part of a workflow talk I intend to give soon.

You can specify your working directory to be whatever you want, as long as it refers to an actual directory in your file system. For example, this would give me an error.

setwd ( '/home/steve/Dropbox/daves-not-here-man' ) Error in setwd ( '/home/steve/Dropbox/daves-not-here-man' ): cannot change working directory R is also case-sensitive and requires working directories to be set in quotation marks. Observe the following output.

setwd ( / home / steve ) Error: unexpected '/' in 'setwd(/' setwd ( '/home/STEVE' ) Error in setwd ( '/home/STEVE' ): cannot change working directory setwd ( '/home/steve' ) getwd 1 '/home/steve' Specifying a new working directory for the sake of my quantitative methods class is optional. Knowing the current working directory is important since that is where R will look for files by default and where R will save any files. Starting to do Stuff in R An important part to understanding R is that R is an. A helpful way of thinking about this is that input is assigned to an output using various classes and methods. For example, if I were to type hello into an R terminal for a session that I just started, I would receive this output. hello Error: object 'hello' not found This is because hello is not a built-in object in R, like, say, pi. pi 1 3.141593 I could, however, assign an input to a new object called hello.

For example, I could create a vector of ten numbers drawn randomly from a normal distribution with a mean of 0 and a standard deviation of 1 as an object titled hello. hello hello 1 0.002107005 -0.024397830 -0.006861523 0.538063485 -0.912956921 -1.022940134 0.075493593 0.598293193 0.190683486 -1.007443455 Or, because this is my guide on my website and I can put what I want on it: hello hello 1 'annyong' Be careful with what you call your objects, though. pi pi 1 0.10700040 0.70122694 -0.96684009 -0.48112220 -0.57755359 1.02820036 -0.51794291 0.02571443 -0.17328885 -0.10942836 Generally, you can call your objects whatever you want. Avoid naming objects things like “pi”, “T”, “TRUE”, “F”, or “FALSE”. This is an incomplete list, but hopefully it gives a student a place to start. As a rule of thumb, I prefer shorthand in R where objects that begin with capital letters refer to data frames and objects with lower-case letters refer to variables or free-standing vectors.

This is completely optional, but it helps me keep track of what are my objects in an R session. You may also notice in my R scripts the appearance of dollar signs. In R, a dollar sign indicates a vector (i.e. A column) of a data frame. For example, looks at this line in my R script on American attitudes toward abortion in World Values Survey data.

Data $ z.age 58 + 1.96. ( 17.8 ) 1 92.888 58 - 1.96.

( 17.8 ) 1 23.112 Installing and Loading Packages R has hundreds upon hundreds of packages that can be installed for your use. My class will use the following packages. The install.packages command will install these packages and whatever additional dependencies are required. In Rstudio, you can do this in the “Packages” tab located in the bottom right panel. Or, you can enter it manually into an R terminal like this. Install.packages ( 'RCurl' ) install.packages ( 'WDI' ) install.packages ( 'countrycode' ) install.packages ( 'reshape' ) install.packages ( 'lattice' ) install.packages ( 'Zelig' ) install.packages ( 'lme4' ) RCurl will allow students to read data from my Github account.

WDI and countrycode are two quite useful packages created by at the University of Michigan. Respectively, they grab data from the World Bank’s data repository and convert a variety of country names into Correlates of War codes.

I use these for the first problem set I assign. Reshape does a variety of things, including allowing for easy renaming of columns in a data frame. Lattice creates a nicer histogram than what is default in R. Zelig and lme4 are packages that I use for the third problem set. Zelig does a variety of useful things, especially simulating of quantities of interest after estimating a statistical model. Lme4 allows for the estimation of advanced mixed effects models. It is worth reiterating that installing a package as a library and loading the package as a library for the current R session are two different things.

After a package is installed, it must be loaded into the current R session. This is done with the library command. For example, the first problem set requires the WDI, countrycode, lattice and reshape libraries. Library ( WDI ) library ( countrycode ) library ( reshape ) library ( lattice ) If you try to load a library without previously installing it, you’ll get an error message like this. library ( RCurl ) Error in library ( RCurl ): there is no package called ‘ RCurl ’ Running an R Script Students in my class can visit and find one of the three associated with a particular problem set I assign for homework. Each repository includes an R script that, for all intents and purposes, does the homework for the student.

The student simply runs the script, sees the output, and answers the questions based off what s/he sees. To that end, the student can save an R script from my Github by selecting on the appropriate repository, selecting the R script (with.R extension), right-clicking “Raw” and saving to the hard drive. The student can also see the input in Github and copy-paste it to an R script window in Rstudio.

From there, executing the R script from the R script window in Rstudio is straightforward. Cmd-Enter runs a line of script in Rstudio using Mac. For Linux and Windows users, this is a simple Ctrl-Enter. You can select several lines in your R script window and run them with this command as well.

You can also execute the entire script with Cmd-Shift-Enter in Mac and Ctrl-Shift-Enter in Linux and Windows. For example, I ran the first few lines of the R script associated with the first problem set. This is what the output looks like. Notice the histogram in the bottom right corner. Rstudio interface (click to embiggen) That’s really all there is to it for the sake of the classes that I teach that introduce the R programming languages. R is more daunting than it is in actuality because it is a command-line based interface. The student is encouraged to meet with me during office hours if there are still remaining questions.

Specifying a Regression Model in R To illustrate how to do an applied statistical analysis in R, this guide will conclude with specifying a basic logistic regression. The data for this exercise will come from the Zelig package, which has a built-in data set called voteincome. Let’s call it into R. library ( Zelig ) data ( voteincome ) summary ( voteincome ) I suppress the summary output for presentation. The on CRAN provides a summary of the data. The data come from the and are limited to two states (Arkansas and South Carolina) for illustration of the package. Vote is a dummy variable that assumes a 1 if the respondent says she or he voted in the November 7th election.

Income is an ordinal variable that ranges from 4 (makes less than $5,000) to 17 (makes more than $75,000). Education is an ordinal variable that ranges from 1 (did not complete high school) to 4 (more than a college education). Age and female should be self-explanatory. Let’s look at just South Carolina for this exercise since that is where I live now. We’ll subset voteincome to just South Carolina and call it a new object Data. Recall, my preferred shorthand for objects in R is objects that begin with capital letters refer to data frames and objects with all-lower case letters refer to variables or free-floating vectors.

Data summary ( Data ) Let’s specify a regression model explaining an individual South Carolinian’s decision to vote in the 2000 general election. We will use all relevant predictor variables. M 1 summary ( M 1 ) Call: glm ( formula = vote age + female + income + education, family = binomial ( link = 'logit' ), data = Data ) Deviance Residuals: Min 1 Q Median 3 Q Max -2.4178 0.3763 0.4540 0.5430 0.9196 Coefficients: Estimate Std. Error z value Pr ( z ) ( Intercept ) -0.331963 0.498973 -0.665 0.505864 age 0.006639 0.005690 1.167 0.243328 female 0.373736 0.196091 1.906 0.056659.

Income 0.099773 0.028194 3.539 0.000402. education 0.204404 0.115985 1.762 0.078013. Codes: 0 ‘. ’ 0.001 ‘.

’ 0.01 ‘. ’ 0.05 ‘. ’ 0.1 ‘ ’ 1 What we did was simple. We used the glm function to regress vote on (i.e. ) a linear combination of age, female, income, and education. Afterward (i.e.,) we specified that the data to be used for the regression should come from the object we created and called Data from just the South Carolinian respondents.

Then (i.e., again) we specified the family of generalized linear model to use. We wanted logistic regression ( family=binomial(link='logit')) in lieu of. Briefly, the results suggest that gender, income, and education all have a statistically significant effect on a South Carolinian’s decision to vote in the 2000 general election. Women are more likely to vote and the more educated are more likely to vote.

Wealthier South Carolinians are more likely to say they vote than poorer South Carolinians. Age has no statistically significant effect on voting, which is curious. What if a simple measure for age is masking a curvilinear effect? I respecified the model to include a square term for age with the I function, which can force through a variable. This is a useful way of adding quadratic terms to a regression formula without having to create new variables that are clearly not independent of the constituent term (here: age, the most common variable in survey research that appears as a square term). My suggestion of a curvilinear effect is supported in the results that follow.

M 2 summary ( M 2 ) Call: glm ( formula = vote age + I ( age ^ 2 ) + female + income + education, family = binomial ( link = 'logit' ), data = Data ) Deviance Residuals: Min 1 Q Median 3 Q Max -2.5326 0.3413 0.4231 0.5471 1.0125 Coefficients: Estimate Std. Error z value Pr ( z ) ( Intercept ) -2.7010553 0.7218704 -3.742 0.000183. age 0.1320099 0.0288877 4.570 4.88e-06. I ( age ^ 2 ) -0.0012592 0.0002849 -4.420 9.87e-06. female 0.3306201 0.1978679 1.671 0.094739. Income 0.0786490 0.0289024 2.721 0.006505. education 0.1940867 0.1167682 1.662 0.096482.

Codes: 0 ‘. ’ 0.001 ‘. ’ 0.01 ‘.

’ 0.05 ‘. ’ 0.1 ‘ ’ 1 Here is what this curvilinear effect looks like when graphed. The relationship between age and voting in South Carolina (2000).

Ubuntu or Fedora users (i.e. Linux users) may want to consider a Debian or Red Hat build and install it as a package, rather than compiling from the source. That 85% respondents said they voted, especially in these two states, suggests either a social desirability bias or maybe that vote also includes those who are registered to vote. p.1, which would lead many political scientists to say that this relationship is not statistically significant. But, whatever. “Statistical significance” is also a.