Making Sense of Stat Programs: Alexander C. Lopilato

Analyzing the complexity behind the names of statistical programs, this content dives into the unique perspective of Alexander C. Lopilato. Delve into the nuances of program nomenclature and the rationale behind the unconventional naming choices in the statistics realm. Gain insights into the world of statistical software and the distinctive approach of this thought-provoking piece.

• Statistics
• Programs
• Data Analysis
• Alexander Lopilato

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1. R: Because the names of other stat programs don t make sense so why should this one? ALEXANDER C. LOPILATO

2. Outline The three Ws of R: What, Where, and Why Commonly used operators Formatting your data for R Working with data in R Exporting data from R

3. What is R? R is a language and environment for statistical computing and graphics. (http://www.r-project.org) It s a programming language first and a statistical analysis tool second Entirely syntax based Similar to the SAS and SPSS syntax User can download packages which are similar to SPSS Modules

4. Where is R? Available for download at: http://www.r-project.org/ Works on PCs, Macs, and Linux OS Doesn t require a ton of computer memory (I find that it runs smoother than both SAS and SPSS)

5. Why use R? It s an open-source project aka FREE! It s gaining traction in both industry (Google, Facebook, & Kickstarter) and academia It s combination of programming flexibility and statistical analyses capabilities makes it one of the more powerful data analysis programs out there

6. Commonly used operators <- :Assignment operator # :Comment operator >, <, ==, | :Boolean operators +, -, *, ^ :Mathematical operators

7. Formatting your data for R: A Brief Intro R can read SAS, SPSS, STATA, txt files, and csv files I recommend that you store your data in a csv file R can easily read csv files Csv files can be imported to and exported from SAS and SPSS Other statistical programs can easily read csv files I write all of my code in notepad (more habit than anything else), but R has many different GUIs

8. Formatting your data for R: Three easy steps 1) Turn your data file into a csv file 2) Use the read.csv() function Dataset <- read.csv( Dataset location.csv ) 3) Dataset is now a user-defined object (in this particular case it s a dataframe in R) that contains all of your data

9. Formatting your data for R: Common Mistakes (That I ve made 100 times over) R cannot read \ (the backslash), thus when you write the location of your dataset you have to use either / or \\ R is case sensitive, so C:\\Dataset.csv and C:\\dataset.csv are not the same in R speak Always make sure you include the file extension (.csv, .txt, .whatever)!

10. Working with data in R: Things to check I always check the dimensions of my dataset dim(Dataset) this will return two numbers: row x column. Rows = number of cases and columns = number of variables Check the names of your dataset names(Dataset) Check the descriptive statistics for anything out of the ordinary: colMeans(Dataset[,1:10],na.rm=T) sapply(Dataset[,1:10],sd,na.rm=T) Notice the brackets?

11. Working with data in R: Subsetting your dataset First, begin thinking about your dataset as a matrix Rows = cases and columns = variables Dataset[5,1] means return the observation stored in row 5 column 1 Dataset[,1] means return all of the rows in column 1 Dataset[2,1:5] means return all of the observations in row 2 and columns 1 through 5

12. Working with data in R: Subsetting your dataset Alternatively, you can reference a column directly by using the $ operator: Dataset$Var1 will return the entire Var1 column from Dataset What if I want to filter by some variable? ds.Female <- Dataset[Dataset$Var11 == Female ,] The above creates a dataframe called ds.Female that filtered out any case where Var11 equaled Male

13. Working with data in R: Reverse Coding What do you do if you have some variables that need to be reverse coded? (1 + highest scale value Variable) is the general formula Dataset$Var12 <- 8 Dataset$Var10 This does two things. 1) Creates another column in Dataset labeled Var12 and 2) Sets Var12 equal to 8 Var10 Check with cor(Dataset$Var10, Dataset$Var12, use= complete.obs )

14. Working with data in R: Internal R Functions mean(Dataset$Var1,na.rm=T) = mean(Dataset[,1],na.rm=T) sd(Dataset$Var4, na.rm=T) min(Dataset$Var5,na.rm=T) and max(Dataset$Var5, na.rm=T) cor(Dataset, use= complete.obs )

15. Working with data in R: Internal R Functions modlm <- lm(Var2 ~ Var3, data=Dataset) Ordinary Least Squares Regression, regressing variable 2 onto variable 3 modanova <- lm(Var4 ~ as.factor(Var11), data=Dataset) OLS Regression, regressing variable 4 onto the categorical gender variable This is an ANOVA! modanova1 <- aov(Var4 ~ as.factor(Var11), data=Dataset) aov is R s built in ANOVA function dif <- TukeyHSD(modanova1) Tukey s Honestly Significant Difference Test

16. Exporting Data from R write.csv(Dataset, Location.csv ) BOOM goes the dynamite

17. Thank you!