Tutorial: Managing Ocean Data with R

Transcription

1 Tutorial: Managing Ocean Data with R Using data from the Ocean Networks Canada observatory at Cambridge Bay, Nunavut By: Allan Roberts, Ocean Networks Canada Version: August 15, 2013 Purpose. This tutorial has two main goals: (i) To introduce the user to some R functions that are useful for data management. (ii) To introduce some of the oceanographic data types collected by the Ocean Networks Canada ocean bottom platform at Cambridge Bay, Nunavut. (See Table 1.) Note: This tutorial assumes that the user has R installed on a computer, and is familiar with entering basic instructions onto the R command line. R is a free software application for statistics, graphics, and programming. Users requiring more background are referred to the manual An Introduction to R that is available on the R Project website. For an example of customizing a data plot, refer to the tutorial Plot Ocean Data with R. To search for Ocean Networks Canada Data, go to Data & Tools. Instructions. The statements in the R script provided below can be typed, or cut and paste, onto the R command line one at a time, in sequence. Alternatively, multiple statements can be cut and paste as a block. A semi-colon indicates the end of each statement. Section 2 of the script will load one month of data into R. It is recommended that all of Section 2 be cut and paste onto the R command line as a unit, and that the other instructions be typed, or cut and paste, onto the command line one at a time. R Script ############################################################### #Section 1: Creating Vectors and Sequences ############################################################### #The following commands allow the user to create vectors. c(3,4); #A vector with two elements. "c" is the concatenation operator. rep(2,5); #A 5-element vector with "2" repeated 5 times. 1:100; #A sequence from 1 to 100, by steps of 1. seq(1, 100); #An alternative notation for a sequence from 1 to 100 by steps of 1. seq(0, 100, 2); #A sequence from 0 to 100, by steps of 2. #Sample 10 pseudorandom numbers from 1 to 100, without replacement: sample(1:100, 5, replace = FALSE); 1

2 ##################################################################################### #Section 2: Building a data frame. Cut and paste as a block onto the R command line. ##################################################################################### year = rep(2012,31); #A vector with 31 entries representing the year. month = rep(12,31); #A vector representing the month ("12" for Dec.). day.of.month = 1:31; #A sequence from 1 to 31 for the day of month. day.of.year = 336:366; #A sequence from 336 to 366 for the day of year. #A vector of daily mean ice draft values (in metres): ice.draft = c(0.620,0.626,0.629,0.634,0.642,0.652,0.662,0.670,0.682,0.696,0.704, 0.709,0.718,0.723,0.727,0.735,0.740,0.747,0.751,0.758,0.758,0.764,0.762,0.765,0.768, 0.773,0.778,0.784,0.793,0.801,0.806); #The following creates a vector of daily mean water temperature values: water.temperature = c(-0.975,-0.946,-0.925,-0.899,-0.902,-0.883,-0.857,-0.929,-1.230, ,-1.360,-1.341,-1.313,-1.285,-1.289,-1.311,-1.302,-1.311,-1.327,-1.331,-1.345, ,-1.354,-1.375,-1.386,-1.374,-1.406,-1.420,-1.429,-1.441,-1.449); #The following creates a vector of daily mean salinity values: salinity = c(27.781,27.813,27.871,27.931,27.961,27.989,28.021,28.035,28.045,28.066, ,28.116,28.138,28.155,28.174,28.184,28.194,28.212,28.217,28.227,28.242,28.250, ,28.269,28.286,28.295,28.316,28.335,28.350,28.368,28.378); #The following creates a vector of daily mean chlorophyll: chlorophyll.concentration = c(0.0845, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ); #The following creates a vector of daily mean oxygen concentration values: oxygen.concentration = c(7.776,7.745,7.737,7.724,7.732,7.715,7.631,7.664,7.819,7.926, 7.925,7.901,7.885,7.858,7.836,7.817,7.805,7.785,7.770,7.754,7.743,7.765,7.758,7.741, 7.721,7.706,7.694,7.684,7.673,7.655,7.665); #The function "data.frame" creates a data frame out of the above defined vectors: data = data.frame(year, month, day.of.month, day.of.year, ice.draft, water.temperature, salinity, chlorophyll.concentration, oxygen.concentration); #################################################### #Section 3: Getting information about a data frame. #################################################### class(data); #Tells us that "data" is a data frame. is.data.frame(data); #Ask if "data" is a data frame. names(data); #The column names. head(data); #Prints the first few lines of "data", with column names. tail(data); #Prints the last few lines of "data", with column names. nrow(data); #Tell us the number of rows. ncol(data); #Tell us the number of columns; dim(data); #The number of rows and columns in data. data[4,5]; #Gives us the entry in the row number 5 and column number 4. 2

3 ############################################## #Section 4: Plotting data. ############################################## #This section provides three different ways for plotting the temperature data #versus the day of month; the difference lies in how we access the variable names #for the data frame. with(data, plot(water.temperature ~ day.of.month, col="red", type="l") ); #The columns of a data frame can be referenced using the "$" symbol. Example: plot(data$water.temperature ~ data$day.of.month, type="l", col="red"); #The function attach makes the variable names in the data frame accessible. #Using the function attach can be convenient; however, it can also cause confusion, #especially if other data frames are using the same variable names. It is a good idea #to use detach after you are done using the variable names for a data frame. Example: attach(data); plot(water.temperature~day.of.month, type="l", col="red"); detach(data); ############################# #Section 5: Indexing columns. ############################# names(data)[8]; data[,8]; data$salinity; data$water.temperature; #The name of the 8th column. #The data values in the 8th column. #The salinity data values. #The water temperature data values. head(data[,4:7]); #The top of columns 4 through 7. data[,c(2,5)]; #View columns 2 and 5. ############################ #Section 6: Indexing rows. ############################ data[3, ]; #View third first row. data[5:10, ]; #View the 5th to the 10th row. data[seq(1,31,2), ]; #View every second row. data[sample(31,31), ]; #View the rows in random order. data[c(2,5), ]; #View rows 2 and 5. #The row with the highest temperature: data[which(data$water.temperature == max(data$water.temperature)), ] #The rows ordered by temperature: data[order(data$water.temperature),] data[rev(order(data$water.temperature)),] #Highest to lowest. #Lowest to highest. 3

4 ###################################### #Section 7: Modifying the data frame. ###################################### data <- data[,-c(1,2)]; #Delete columns 1, 2 & 3. names(data); #Check the variable names. #Change the column names: names(data)[1] = c("column A"); names(data)[2] = c("column B"); ############################################################## #Section 8: Apply a function to the columns of the data frame. ############################################################## # MARGIN=2 indicates that the function is applied to the columns. apply(data, MARGIN=2, FUN=mean); #The function "mean" is applied to the columns. apply(data, MARGIN=2, FUN=max); #The function "max" is applied to the columns. apply(data, MARGIN=2, FUN=min); #The function "min" is applied to the columns. ########################### #END of R Script. ########################### Data Source and Documentation Data Source: Ocean Networks Canada Arctic Observatory at Cambridge Bay, Nunavut. Data accessed April 2013 at: Measurements (units): ice thickness (m); water temperature (C); salinity, (psu); chlorophyll concentration, (µg/l); oxygen concentration (ml/l). Station: Cambridge Bay Dock, Latitude: N; Longitude W; Depth 6.0 m. For this tutorial, measurements have each been rounded to a reduced number of decimal places. For details about instrumentation and data collection, see the NEPTUNE Canada website: References R Core Team, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: R Core Team. An Introduction to R. Available at: 4

5 Arctic Observatory, Cambridge Bay, Nunavut Daily Averages (UTC) for December 2012 Ice Thickness [m] Water Temperature [C] Chlorophyll Concentration [µg/l] Oxygen Concentration [ml/l] Year Month Day of Month Day of Year Salinity [psu] Table 1. Daily averages for December 2012, from the Arctic Observatory, Cambridge Bay, Nunavut. Data Source: NEPTUNE Canada, (Accessed January 2013.) Daily averages are relative to time in UTC. For this tutorial, measurements have each been rounded to a reduced number of decimal places. These data are intended for instructional purposes only; for research purposes, raw data are available from the Ocean Networks Canada website. The daily averages in this table can be loaded into the application R by cutting and pasting the instructions in Section 1 onto the R command line. 5