So far, we've covered how to read in data, and select specific rows and columns.

All of these steps help you set up your analysis or data exploration.

Now we are going to cover manipulating your data and summarizing it using basic statistics and visualizations.

sort(x, decreasing=FALSE): 'sort (or order) a vector or factor (partially) into ascending or descending order.' Note that this returns an object that has been sorted/ordered

order(...,decreasing=FALSE): 'returns a permutation which rearranges its first argument into ascending or descending order, breaking ties by further arguments.' Note that this returns the indices corresponding to the sorted data.

x = c(1,4,7,6,4,12,9,3)
sort(x)

## [1]  1  3  4  4  6  7  9 12

order(x)

## [1] 1 8 2 5 4 3 7 6

Note you would have to assign the sorted variable to a new variable to retain it

the rep() ["repeat"] function is useful for creating new variables

bg = rep(c("boy","girl"),each=50)
head(bg)

## [1] "boy" "boy" "boy" "boy" "boy" "boy"

bg2 = rep(c("boy","girl"),times=50)
head(bg2)

## [1] "boy"  "girl" "boy"  "girl" "boy"  "girl"

length(bg)==length(bg2)

## [1] TRUE

One frequently-used tool is creating categorical variables out of continuous variables, like generating quantiles of a specific continuously measured variable.

A general function for creating new variables based on existing variables is the ifelse() function, which "returns a value with the same shape as test which is filled with elements selected from either yes or no depending on whether the element of test is TRUE or FALSE."

ifelse(test, yes, no)

# test: an object which can be coerced
    to logical mode.
# yes: return values for true elements of test.
# no: return values for false elements of test.

For example, we can create a new variable that records whether daily ridership on the Circulator was above 10,000.

hi_rider = ifelse(circ$daily > 10000, 1, 0)
head(hi_rider)

## [1] 0 0 0 0 0 0

table(hi_rider)

## hi_rider
##   0   1 
## 740 282

You can also nest ifelse() within itself to create 3 levels of a variable.

riderLevels = ifelse(circ$daily < 10000, "low", 
                  ifelse(circ$daily > 20000,
                  "high", "med"))
head(riderLevels)

## [1] "low" "low" "low" "low" "low" "low"

table(riderLevels)

## riderLevels
## high  low  med 
##    2  740  280

However, it's much easier to use cut() to create categorical variables from continuous variables.

'cut divides the range of x into intervals and codes the values in x according to which interval they fall. The leftmost interval corresponds to level one, the next leftmost to level two and so on.'

cut(x, breaks, labels = NULL, include.lowest = FALSE, 
  right = TRUE, dig.lab = 3,
  ordered_result = FALSE, ...)

x: a numeric vector which is to be converted to a factor by cutting.

breaks: either a numeric vector of two or more unique cut points or a single number (greater than or equal to 2) giving the number of intervals into which x is to be cut.

labels: labels for the levels of the resulting category. By default, labels are constructed using "(a,b]" interval notation. If labels = FALSE, simple integer codes are returned instead of a factor.

Factors are used to represent categorical data, and can also be used for ordinal data (ie categories have an intrinsic ordering)

Note that R reads in character strings as factors by default in functions like read.table()

'The function factor is used to encode a vector as a factor (the terms 'category' and 'enumerated type' are also used for factors). If argument ordered is TRUE, the factor levels are assumed to be ordered.'

factor(x = character(), levels, labels = levels,
       exclude = NA, ordered = is.ordered(x))

Suppose we have a vector of case-control status

cc = factor(c("case","case","case",
        "control","control","control"))
cc

## [1] case    case    case    control control control
## Levels: case control

levels(cc) = c("control","case")
cc

## [1] control control control case    case    case   
## Levels: control case

Note that the levels are alphabetically ordered by default. We can also specify the levels within the factor call

factor(c("case","case","case","control",
          "control","control"), 
        levels =c("control","case") )

## [1] case    case    case    control control control
## Levels: control case

factor(c("case","case","case","control",
            "control","control"), 
        levels =c("control","case"), ordered=TRUE)

## [1] case    case    case    control control control
## Levels: control < case

Factors can be converted to numeric or character very easily

x = factor(c("case","case","case","control",
      "control","control"),
        levels =c("control","case") )
as.character(x)

## [1] "case"    "case"    "case"    "control" "control" "control"

as.numeric(x)

## [1] 2 2 2 1 1 1

Now that we know more about factors, cut() will make more sense:

x = 1:100
cx = cut(x, breaks=c(0,10,25,50,100))
head(cx)  

## [1] (0,10] (0,10] (0,10] (0,10] (0,10] (0,10]
## Levels: (0,10] (10,25] (25,50] (50,100]

table(cx)

## cx
##   (0,10]  (10,25]  (25,50] (50,100] 
##       10       15       25       50

We can also leave off the labels

cx = cut(x, breaks=c(0,10,25,50,100), labels=FALSE)
head(cx)  

## [1] 1 1 1 1 1 1

table(cx)

## cx
##  1  2  3  4 
## 10 15 25 50

Note that you have to specify the endpoints of the data, otherwise some of the categories will not be created

cx = cut(x, breaks=c(10,25,50), labels=FALSE)
head(cx)  

## [1] NA NA NA NA NA NA

table(cx)

## cx
##  1  2 
## 15 25

table(cx,useNA="ifany")

## cx
##    1    2 <NA> 
##   15   25   60

m1 = matrix(1:9, nrow = 3, 
      ncol = 3, byrow = FALSE)
m1

##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9

m2 = matrix(1:9, nrow = 3, 
      ncol = 3, byrow = TRUE)
m2

##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6
## [3,]    7    8    9

• abs(x): absolute value
• sqrt(x): square root
• ceiling(x): ceiling(3.475) is 4
• floor(x): floor(3.475) is 3
• trunc(x): trunc(5.99) is 5
• round(x, digits=n): round(3.475, digits=2) is 3.48
• signif(x, digits=n): signif(3.475, digits=2) is 3.5
• log(x): natural logarithm
• log10(x): common logarithm
• exp(x): e^x

(via: http://statmethods.net/management/functions.html)