EXERCISE 1 [R-CODE]

Create a new column “quarter” in the dataframe firedata that describes the meteorological quarter of the observation based on the month. Distinguish between the following categories: “Q1”, “Q2”, “Q3”, “Q4”, referring to the four quarters of a calendar year. In particular, the year is divided into 4 quarters:

 Q1: January 1 to March 31

 Q2: April 1 to June 30

 Q3: July 1 to September 30

Q4: October 1 to December 31

Use ggplot() to create a density plot for the relative humidity across different quarters (using the newly created quarter variable) and directly export this plot as png-file titled “rh_quarters.png” with a resolution of 900x900.

EXERCISE 2 [R-CODE]

Use the ddply() function of the package “plyr” to create a data frame “summarystat” with the means of FFMC, DMC, DC, ISI, temp, RH, wind, rain, and area. Also include the number of observations N for each of the four quarters. The output should look like this:

Use the package “rtf” to create an rtf document called “output.rtf” and include the newly created data frame “summarystat” as a table in this document.

EXERCISE 3 [R-CODE]

Use the functions “tapply” and “cor” to create a Pearson correlation matrix for FFMC, DMC, DC, ISI, temp, RH, wind, and area, separately for each of the four quarters (Q1-4). Use the newly created correlation matrices to create a set of mixed correlation plots with the corrplot command. Save the result as a PDF file “corrplots.pdf” with a 9x9 resolution. Identify and interpret at least three differences across two or more quarters.

Optional: Try using “par(mfrow =c(2,2))” before creating the plots to display all four plots into one bigger plot.

EXERCISE 4 [R-CODE]

Use “plot” to draw a scatterplot showing RH and temp. Add a blue regression line to the plot.

EXERCISE 5 [R-CODE]

Use R to create a simple linear regression that regresses RH on temp. Interpret the coefficients. Retrieve and interpret the R².

EXERCISE 6 [R-CODE]

Use the function “skewness” of the package “moments” to investigate the distribution of the variable area. Interpret the result.

REFERENCES

Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

DATASET

Forestfires Forest Fires Dataset

Description

A meteorological dataset about forest fires in the northeast region of Portugal. Some of the data includes measures from the Fire Weather Index (FWI).

Usage

Forestfires

Format

A data frame with 517 observations on the following 13 variables.

X x-axis spatial coordinate within the Montesinho park map: 1 to 9

Y y-axis spatial coordinate within the Montesinho park map: 2 to 9

month month of the year: 'jan' to 'dec'

day day of the week: 'mon' to 'sun'

FFMC FFMC (Fine Fuel Moisture Code) index from the FWI system: 18.7 to 96.20. This is a numerical rating of the moisture content of surface litter and other cured fine fuels. It shows the relative ease of ignition and flammability of the fine fuels.

DMC DMC (Duff Moisture Code) index from the FWI system: 1.1 to 291.3. The DMC is a numerical rating of the average moisture content of loosely compacted organic layers of moderate depth.

DC DC (Drought Code) index from the FWI system: 7.9 to 860.6. is is a numerical rating of the moisture content of deep, compact, organic layers. It is a useful indicator of seasonal drought and shows the likelihood of fire involving the deep duff layers and large logs.

ISI ISI (Initial Spread Index) index from the FWI system: 0.0 to 56.10. This indicates the rate fire will spread in its early stages.

temp temperature in Celsius degrees: 2.2 to 33.30

RH relative humidity in %: 15.0 to 100

wind wind speed in km/h: 0.40 to 9.40

rain outside rain in mm/m2 : 0.0 to 6.4

area the burned area of the forest (in ha): 0.00 to 1090.84

Source

Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Cortez, P. & Morais, A. (2007). A Data Mining Approach to Predict Forest Fires using Meteorological Data. In J. Neves, M. F. Santos and J. Machado Eds., New Trends in Artificial Intelligence, Proceedings of the 13th EPIA 2007 - Portuguese Conference on Artificial Intelligence, December, Guimaraes, Portugal, pp. 512-523, 2007. APPIA, ISBN-13 978-989-95618-0-9.

FWI (Fire Weather Index) descriptions are taken from: http://www.malagaweather.com/fwi-txt.htm

Data Link