1. Research methodology R Statistics – Introduction
Dr. Sanna Härkönen, R&D Manager, Bitcomp Oy

2. Contents Topic Contents 15.11. Introduction to R Basic use of Rstudio
Basic commands (reading and writing data, using data frames)
17.11.
Modeling examples
Example studies with R
21.11.
Introduction to group work
Model fitting
Aggregating, plotting, linear regression and its interpretation
(1)
Model validation
RMSE, bias, t-test
(2)
GIS analysis with R
Rasters, shapefiles -> mapping
25.11.
Group presentations
Best practices: using R for data interpretation in scientific reports and studies

3. R Statistics Script language
Great for data analysis and statistical computing
Efficient vector and matrix calculations
Advantages:
Any programming tasks, modeling etc
Versatile packages for environmental analysis! For example data clustering, decision trees, kNN imputation, GIS data analysis, …
Links:
Just Google, there is huge amount of tutorials and example codes available!

4. R Studio
Code window
Console

5. Tips R code a <- b + c is same as a = b + c
Variable names are case sensitive (a is not same as A)
Running code: mark the desired line(s) in code window and press CTRL + ENTER
Clean the console: CTRL + L
Show the previous code lines in console: up-array
sheet-3.pdf

6. 1 Basic commands Reading data in read.csv() Cheking first lines head()
Checking summary statistics summary()
Data frames data.frame()
Creating new column to data frame & calculating its value my_dataframe$my_new_variable <- my_dataframe$var1 + my_dataframe$var2
Removing column my_dataframe$my_new_variable <- NULL
Conditionals: Ifelse()
Taking subset subset()
Plotting data plot()
Writing data out write.csv()

7. Exercise 1
Download ”Modeling_data_all.csv” from Wiki
Read modeling data set in RStudio to object called A A <- read.csv(”C:/temp/Modeling_data_all.csv”)
Check first lines of your data set: head(A)
Check summary statistics on data set A summary(A)

8. Calculate new variable N to data frame A (number of stems / ha, based on mean diameter D, cm, and total basal area BA, m2/ha) A$N <- A$BA / (pi * (0.5 * A$D / 100)^2)
Calculate new variable ”mean_stem_volume1” to data frame A, based on total volume and N
A$mean_stem_volume1 <- A$TOTAL_VOLUME / A$N

9. Calculate new variable mean_stem_volume2: using Laasasenaho volume function [note! Ln in R is log() ]
Laasasenaho volume (V, liters) function (based on D, diameter (cm)):
Scots pine: ln(V) = * ln( * D) * D
A$mean_stem_volume2 <- exp( * log( * A$D) * A$D) / 1000
(converted from liters to m3)

10. Print summary statistics on your data set: summary(A)
Check visually how well the two different mean stem volumes correlate together : plot(x, y)
Print boxplots showing 1) mean stem volume, 2) total volume and 3) difference on mean stem volume1 and mean stem volume2 by different tree species classes and site types boxplot(x~y)

11. Aggregate the data based on species and site type
A_agg <- aggregate(A, list(A$SP_GROUP, A$FOREST_TYPE), mean)
Consider, how could you utilize R for interpreting your modeling data in ”Material” chapter of scientific report