1. 1 An Introduction – UCF, Methods in Ecology, Fall 2008 An Introduction By Danny K. Hunt & Eric D. Stolen Working In R (with speaker notes)

2. 2 An Introduction – UCF, Methods in Ecology, Fall 2008 What We Will Learn More About Dataframes Slicing, Dicing and Sorting Data Manipulating Dataframes & Aggregating Data Simple Iterative Processing Basic Data Visualization

3. 3 An Introduction – UCF, Methods in Ecology, Fall 2008 More About Dataframes  Dataframes Reviewed –Are objects that consist of rows and columns –Closely related to MS Access or Excel tables –Specific requirements for dataframes  Observations are in rows  The response variable and explanatory variables are in columns  Same variable results go into the same column

4. 4 An Introduction – UCF, Methods in Ecology, Fall 2008 ObservationResponseTreatment Obs11.5A Obs21.6B Obs31.3C ………  Dataframes Reviewed –Specific requirements for dataframes (continued)  Observations are in rows  The response variable and explanatory variables are in columns More About Dataframes

5. 5 An Introduction – UCF, Methods in Ecology, Fall 2008 More About Dataframes ControlX1X2 1.31.41.3 1.11.31.6 1.41.51.8 IDResponseTreatment11.3Control 21.1Control 31.4Control 41.4X1 51.3X1 61.5X1 71.3X2 81.6X2 91.8X2  Dataframes Reviewed –Specific requirements for dataframes (continued)  Same variable results go into the same column  Good dataframe conventions

6. 6 An Introduction – UCF, Methods in Ecology, Fall 2008 More About Dataframes  Loading Dataframes with Data –Use the read.table() series of functions  Particularly simple and efficient is the read.delim function –Load Snake dataset and get acquainted  snake <- read.delim(“c:\\pract_i-t\\snake_data.txt", row.names="ID")  names(snake)  snake[1:5,]  summary(snake)

7. 7 An Introduction – UCF, Methods in Ecology, Fall 2008 More About Dataframes  Loading Dataframes with Data (continued) –Identifying columns as categorical variables  During read operations alpha-numeric fields are automatically encoded as factors  Numeric fields are automatically assumed to be continuous values  Identify numeric fields as factors using: –snake$landc <- factor(snake$landc) –snake$SEX <- factor(snake$SEX)  summary(snake)

8. 8 An Introduction – UCF, Methods in Ecology, Fall 2008 Slicing, Dicing and Sorting Data  Subscripts –Performing data extraction by indexing:  snake[1:5,]  snake[40,]  snake[,1]  snake[,c(7, 1, 2)]  snake[-c(10, 20, 30, 40), c("Name", "SEX", "ha")]

9. 9 An Introduction – UCF, Methods in Ecology, Fall 2008 Slicing, Dicing and Sorting Data  Subscripts (continued) –Performing data extraction by using filtering:  snake[snake$landc == 1,]  snake[snake$landc %in% c(1, 3),]  snake[snake$mcp > 200,]  snake[snake$mcp > 200 & snake$times 200 & snake$times < 60,]  snake[grep("^m", snake$Name ),]

10. 10 An Introduction – UCF, Methods in Ecology, Fall 2008 Slicing, Dicing and Sorting Data  Sorting Data –snake[order(snake$Name),] –snake[order(snake$landc, -snake$mcp),] –snake[order(-snake$times),][1:10,]

11. 11 An Introduction – UCF, Methods in Ecology, Fall 2008 Manipulating Dataframes & Aggregating Data  Adding Columns to a Dataframe –Close out and restart R –Load the rapid fish dataset  fish <- read.delim("c:\\pract_i-t\\rapid_fish.txt", row.names="ID")  rapid$impoundment <- factor(rapid$impoundment)  rapid$season <- factor(rapid$season)  rapid$open_veg <- factor(rapid$open_veg)  rapid$sea_code <- factor(rapid$sea_code)  rapid$imp_code <- factor(rapid$imp_code)  rapid$cov_code <- factor(rapid$cov_code)

12. 12 An Introduction – UCF, Methods in Ecology, Fall 2008 Manipulating Dataframes & Aggregating Data  Adding Columns to a Dataframe (cont.) –Add “unique” column  rapid$unique <- paste(rapid$Point, rapid$season, sep = "")  rapid$unique <- factor(rapid$unique) –Add log transformation of count column  rapid$lncount <- log(rapid$count + 1)

13. 13 An Introduction – UCF, Methods in Ecology, Fall 2008 Manipulating Dataframes & Aggregating Data  Overview the Rapid Fish Dataset –names(rapid) –rapid[1:5,] –summary(rapid)  Filtered Summaries –summary(rapid[rapid$open_veg == "open",]) –summary(rapid[rapid$open_veg == "vegetated",])

14. 14 An Introduction – UCF, Methods in Ecology, Fall 2008 Manipulating Dataframes & Aggregating Data  Cross Tabulation –Using the table() function  table(rapid$impoundment)  table(rapid[, c("open_veg", "impoundment")])  table(rapid[, c("open_veg", "impoundment", "season")])

15. 15 An Introduction – UCF, Methods in Ecology, Fall 2008 Manipulating Dataframes & Aggregating Data  Data Aggregation –Using the aggregation() function  aggregate(rapid$count, list(impoundment=rapid$impoundment), mean)  aggregate(rapid$count, list(open_veg=rapid$open_veg, impoundment=rapid$impoundment, season=rapid$season), mean)

16. 16 An Introduction – UCF, Methods in Ecology, Fall 2008 Manipulating Dataframes & Aggregating Data  Merging Results –Using the merge() function  n <- table(rapid[, c("open_veg", "impoundment", "season")])  m <- aggregate(rapid$count, list(open_veg=rapid$open_veg, impoundment=rapid$impoundment, season=rapid$season), mean)  names(m)[4] <- "mean"  s <- aggregate(rapid$count, list(open_veg=rapid$open_veg, impoundment=rapid$impoundment, season=rapid$season), sd)  names(s)[4] <- "sd"  nm <- merge(n, m)  names(nm)[4] <- "n"  habimpsea <- merge(nm, s)  habimpsea

17. 17 An Introduction – UCF, Methods in Ecology, Fall 2008 Simple Iterative Processing  Repeating a Process –Using the for() control-flow construct  site <- sort(unique(rapid$impoundment))  for (i in 1:length(site)) {  print (summary(rapid[rapid$impoundment == site[i],]))  }

18. 18 An Introduction – UCF, Methods in Ecology, Fall 2008 Basic Data Visualization  Visualizing Data –Using the plot() function  plot(as.numeric(rapid$sea_code), rapid$count) –Using the boxplot() function  boxplot(rapid$count~rapid$impoundment) –Using the hist() function  par(mfrow=c(1,2))  hist(rapid$count)  hist(rapid$lncount)

19. 19 An Introduction – UCF, Methods in Ecology, Fall 2008 The End