#Fontlab 5.1 change x height how to#

# 1st Qu.: 11 Class :character Class :character # Mode :character Mode :character Median : 58.0 Median : 6.60 # Class :character Class :character 1st Qu.: 45.0 1st Qu.: 5.60 # Mode :character Mode :character Mode :character # Class :character Class :character Class :character # 6 NA NA NA NA summary(movies) # Title Year Appropriate # Rotten rtomUserMeter rtomUserRating rtomUserVotes BoxOffice # imdbVotes Language Country Awards rtomRating rtomMeter rtomVotes Fresh

# Released Director Writer Metacritic imdbRating # 5 Blacksmith Scene 1893 UNRATED 1 min Short # 3 Pauvre Pierrot 1892 4 min Animation, Comedy, Short # 2 Le clown et ses chiens 1892 Animation, Short # 1 Carmencita 1894 NOT RATED 1 min Documentary, Short We can do that with head (first 6 rows), tail (last 6 rows), summary (variable names and quantiles, max, and NAs of numeric variables), and str (type of variables and examples of the first few rows from each variable) head(movies) # Title Year Appropriate Runtime Genre The first step is to see how the data looks and to see how R has imported the various variables.

#Fontlab 5.1 change x height how to#

See lesson 1 for some examples of loading and how to get data from common statistical programs. We will look at a few different types of loading formats over the rest of the course including csv and fixed widths. Once we have saved our file we can load it with a simple load statement (for RData file). I tend to partition a drive for doing analyses that automatically backs up every couple of days. Note: Making directories close to a root or in a home directory (documents in Windows, home in Linux, and whatever the equivalent is on an Apple machine) will make things a little less messy. We can start off by saving the file to the directory we set in the setwd step. You can find and create your own dataset with these variables or use the one linked here. The dataset we will be using is a from IMDB and Rotten Tomatoes. Then set as working directory setwd('E:/Rcourse/е…ѓ') We can also have R create directories for us Show warnings = FALSE ignores warnings telling us the directory already exists (if it does already exist) dir.create('E:/Rcourse/е…ѓ', showWarnings = FALSE) First, we set a working directory to save files, graphs, etc. We are going to begin working with an actual dataset that will hopefully be a little entertaining as well as informative. In this module we will begin to talk about how we can manipulate data in R to be the form we want for the analyses we want to conduct. This starts with reading in a file and turns into transforming variables, correcting mistakes, and sometimes redesigning the entire dataset (e.g., long to tall changes). Most of the time someone spends working on a data analysis problem will be getting the data into the program.