1. Basics in R part 2

2. Variable types in R Common variable types: Numeric - numeric value: 3, 5.9, 0.0001 Logical - logical value: TRUE or FALSE (1 or 0) Factor - categorical value: “male”, “female” Strings - sequence of letters: “cat” Lists – set of variables Typically variables can be given as scalars, vectors or matrixes

3. What is a factor? A categorical variable. Instead of numerical values a factor has levels. Levels are indicated with a ‘tag’, i.e. the name of the level. Examples –Sex: “male” or “female” –Disease: “0” or “1” –Age class: “young”, “middle age”, “old”, “ancient” –Temperature treatment: “cold”, “intermediate”,”hot” Numeric variables can be converted into a factor by splitting the values into categories –[0,3), [3,8), [8,14), [14,…)

4. What is a logical variable? It always gets a value TRUE or FALSE (can be indicated with T or F or sometimes coded as “0” and “1”) A logical variable is a response to a logical proposition > a = 3 > a > 0 [1] TRUE Proposition can be a combination of several propositions > (a>1)&(a<4) > (a 2)

5. What is a string? a = “Species: Pinus sylvestris” Always given in quotation marks Typically used in graphics and parameterizations: for instance an axis label for a graph has to be given as a string

6. What to do with variables: basic statistics Basic statistics for numeric variables –mean: mean value –var/sd: variance/standard deviation –sum: sum of elements, –min: minimum value –max: maximum value, –range: range within which values are –quantile: proportion/probability quantiles For factors and logical variables: table() returns category frequencies for factors, and frequencies of T and F for logical variables -> DEMO 1

7. Missing values Missing values are coded with NA When calculating statistics etc one has to declare how to deal with NAs xx=c(2,6,9,NA,11) mean(xx,na.rm=T)

8. which() function Structure of the function call: aa = which (“a logical proposition”) Returns indices (locations) for those elements of a vector for which the proposition is TRUE. This function is useful when choosing particular elements from a vector Later on, this function will be used for subsetting datasets

9. ifelse() function Function call aa = ifelse(“a logical test for a vector”,yes,no) Returns a vector of similar length that the original vector: each element contains the value indicated above by ‘yes’ or ‘no’ Value to be given to the corresponding element of aa if test obtains value TRUE -> DEMO 2