Last week: Indexing and iteration

Three ways to index vectors, matrices, data frames, lists: integers, Booleans, names
Boolean on-the-fly indexing can be very useful
Named indexing will be especially useful for data frames
Indexing lists can be a bit tricky (beware of the difference between [ ] and [[ ]])
if(), elseif(), else: standard conditionals
ifelse(): shortcut for using if() and else in combination
switch(): shortcut for using if(), elseif(), and else in combination
for(), while(): standard loop constructs
Don’t overuse explicit for() loops, vectorization is your friend!
apply() and **ply(): can also be very useful (we’ll see them today)

Part I

Data frames

Data frames

The format for the “classic” data table in statistics: data frame. Lots of the “really-statistical” parts of the R programming language presume data frames

Think of each row as an observation/case
Think of each columns as a variable/feature
Not just a matrix because variables can have different types
Both rows and columns can be assigned names

Difference between data frames and lists? Each column in a data frame must have the same length (each element in the list can be of different lengths)

Creating a data frame

Use data.frame(), similar to how we create lists

my.df = data.frame(nums=seq(0.1,0.6,by=0.1), chars=letters[1:6], 
                   bools=sample(c(TRUE,FALSE), 6, replace=TRUE))
my.df

##   nums chars bools
## 1  0.1     a FALSE
## 2  0.2     b FALSE
## 3  0.3     c FALSE
## 4  0.4     d FALSE
## 5  0.5     e FALSE
## 6  0.6     f  TRUE

# Note, a list can have different lengths for different elements!
my.list = list(nums=seq(0.1,0.6,by=0.1), chars=letters[1:12], 
               bools=sample(c(TRUE,FALSE), 6, replace=TRUE))
my.list

## $nums
## [1] 0.1 0.2 0.3 0.4 0.5 0.6
## 
## $chars
##  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l"
## 
## $bools
## [1] TRUE TRUE TRUE TRUE TRUE TRUE

Indexing a data frame

By rows/columns: similar to how we index matrices
By columns only: similar to how we index lists

my.df[,1] # Also works for a matrix

## [1] 0.1 0.2 0.3 0.4 0.5 0.6

my.df[,"nums"] # Also works for a matrix

## [1] 0.1 0.2 0.3 0.4 0.5 0.6

my.df$nums # Doesn't work for a matrix, but works for a list

## [1] 0.1 0.2 0.3 0.4 0.5 0.6

my.df$chars # Note: this one has been converted into a factor data type

## [1] a b c d e f
## Levels: a b c d e f

as.character(my.df$chars) # Converting it back to a character data type

## [1] "a" "b" "c" "d" "e" "f"

Creating a data frame from a matrix

Often times it’s helpful to start with a matrix, and add columns (of different data types) to make it a data frame

class(state.x77) # Built-in matrix of states data, 50 states x 8 variables

## [1] "matrix"

head(state.x77)

##            Population Income Illiteracy Life Exp Murder HS Grad Frost
## Alabama          3615   3624        2.1    69.05   15.1    41.3    20
## Alaska            365   6315        1.5    69.31   11.3    66.7   152
## Arizona          2212   4530        1.8    70.55    7.8    58.1    15
## Arkansas         2110   3378        1.9    70.66   10.1    39.9    65
## California      21198   5114        1.1    71.71   10.3    62.6    20
## Colorado         2541   4884        0.7    72.06    6.8    63.9   166
##              Area
## Alabama     50708
## Alaska     566432
## Arizona    113417
## Arkansas    51945
## California 156361
## Colorado   103766

class(state.region) # Factor of regions for the 50 states

## [1] "factor"

head(state.region)

## [1] South West  West  South West  West 
## Levels: Northeast South North Central West

class(state.division) # Factor of divisions for the 50 states

## [1] "factor"

head(state.division)

## [1] East South Central Pacific            Mountain          
## [4] West South Central Pacific            Mountain          
## 9 Levels: New England Middle Atlantic ... Pacific

# Combine these into a data frame with 50 rows and 10 columns
state.df = data.frame(state.x77, Region=state.region, Division=state.division)
class(state.df)

## [1] "data.frame"

head(state.df) # Note that the first 8 columns name carried over from state.x77

##            Population Income Illiteracy Life.Exp Murder HS.Grad Frost
## Alabama          3615   3624        2.1    69.05   15.1    41.3    20
## Alaska            365   6315        1.5    69.31   11.3    66.7   152
## Arizona          2212   4530        1.8    70.55    7.8    58.1    15
## Arkansas         2110   3378        1.9    70.66   10.1    39.9    65
## California      21198   5114        1.1    71.71   10.3    62.6    20
## Colorado         2541   4884        0.7    72.06    6.8    63.9   166
##              Area Region           Division
## Alabama     50708  South East South Central
## Alaska     566432   West            Pacific
## Arizona    113417   West           Mountain
## Arkansas    51945  South West South Central
## California 156361   West            Pacific
## Colorado   103766   West           Mountain

Adding columns to a data frame

To add columns: we can either use data.frame(), or directly define a new named column

# First way: use data.frame() to concatenate on a new column
state.df = data.frame(state.df, Cool=sample(c(T,F), nrow(state.df), rep=TRUE))
head(state.df, 4)

##          Population Income Illiteracy Life.Exp Murder HS.Grad Frost   Area
## Alabama        3615   3624        2.1    69.05   15.1    41.3    20  50708
## Alaska          365   6315        1.5    69.31   11.3    66.7   152 566432
## Arizona        2212   4530        1.8    70.55    7.8    58.1    15 113417
## Arkansas       2110   3378        1.9    70.66   10.1    39.9    65  51945
##          Region           Division  Cool
## Alabama   South East South Central  TRUE
## Alaska     West            Pacific  TRUE
## Arizona    West           Mountain FALSE
## Arkansas  South West South Central FALSE

# Second way: just directly define a new named column
state.df$Score = sample(1:100, nrow(state.df), replace=TRUE)
head(state.df, 4)

##          Population Income Illiteracy Life.Exp Murder HS.Grad Frost   Area
## Alabama        3615   3624        2.1    69.05   15.1    41.3    20  50708
## Alaska          365   6315        1.5    69.31   11.3    66.7   152 566432
## Arizona        2212   4530        1.8    70.55    7.8    58.1    15 113417
## Arkansas       2110   3378        1.9    70.66   10.1    39.9    65  51945
##          Region           Division  Cool Score
## Alabama   South East South Central  TRUE    86
## Alaska     West            Pacific  TRUE    22
## Arizona    West           Mountain FALSE    78
## Arkansas  South West South Central FALSE    28

Deleting columns from a data frame

To delete columns: we can either use negative integer indexing, or set a column to NULL

# First way: use negative integer indexing
state.df = state.df[,-ncol(state.df)]
head(state.df, 4)

##          Population Income Illiteracy Life.Exp Murder HS.Grad Frost   Area
## Alabama        3615   3624        2.1    69.05   15.1    41.3    20  50708
## Alaska          365   6315        1.5    69.31   11.3    66.7   152 566432
## Arizona        2212   4530        1.8    70.55    7.8    58.1    15 113417
## Arkansas       2110   3378        1.9    70.66   10.1    39.9    65  51945
##          Region           Division  Cool
## Alabama   South East South Central  TRUE
## Alaska     West            Pacific  TRUE
## Arizona    West           Mountain FALSE
## Arkansas  South West South Central FALSE

# Second way: just directly set a column to NULL
state.df$Cool = NULL
head(state.df, 4)

##          Population Income Illiteracy Life.Exp Murder HS.Grad Frost   Area
## Alabama        3615   3624        2.1    69.05   15.1    41.3    20  50708
## Alaska          365   6315        1.5    69.31   11.3    66.7   152 566432
## Arizona        2212   4530        1.8    70.55    7.8    58.1    15 113417
## Arkansas       2110   3378        1.9    70.66   10.1    39.9    65  51945
##          Region           Division
## Alabama   South East South Central
## Alaska     West            Pacific
## Arizona    West           Mountain
## Arkansas  South West South Central

Reminder: Boolean indexing

With matrices or data frames, we’ll often want to access a subset of the rows corresponding to some condition. You already know how to do this, with Boolean indexing

# Compare the averages of the Frost column between states in New England and
# Pacific divisions
mean(state.df[state.df$Division == "New England", "Frost"])

## [1] 145.3333

mean(state.df[state.df$Division == "Pacific", "Frost"]) # Those wimps!

## [1] 49.6

`subset()`

The subset() function provides a convenient alternative way of accessing rows for data frames

# Using subset(), we can just use the column names directly (i.e., no need for
# using $)
state.df.ne.1 = subset(state.df, Division == "New England")
# Get same thing by extracting the appropriate rows manually
state.df.ne.2 = state.df[state.df$Division == "New England", ]
all(state.df.ne.1 == state.df.ne.2)

## [1] TRUE

# Same calculation as in the last slide, using subset()
mean(subset(state.df, Division == "New England")$Frost)

## [1] 145.3333

mean(subset(state.df, Division == "Pacific")$Frost) # Wimps

## [1] 49.6

Part II

apply()

The apply family

R offers a family of apply functions, which allow you to apply a function across different chunks of data. Offers an alternative to explicit iteration using for() loop; can be simpler and faster, though not always. Summary of functions:

apply(): apply a function to rows or columns of a matrix or data frame
lapply(): apply a function to elements of a list or vector
sapply(): same as the above, but simplify the output (if possible)
tapply(): apply a function to levels of a factor vector

`apply()`, rows or columns of a matrix or data frame

The apply() function takes inputs of the following form:

apply(x, MARGIN=1, FUN=my.fun), to apply my.fun() across rows of a matrix or data frame x
apply(x, MARGIN=2, FUN=my.fun), to apply my.fun() across columns of a matrix or data frame x

apply(state.x77, MARGIN=2, FUN=min) # Minimum entry in each column

## Population     Income Illiteracy   Life Exp     Murder    HS Grad 
##     365.00    3098.00       0.50      67.96       1.40      37.80 
##      Frost       Area 
##       0.00    1049.00

apply(state.x77, MARGIN=2, FUN=max) # Maximum entry in each column

## Population     Income Illiteracy   Life Exp     Murder    HS Grad 
##    21198.0     6315.0        2.8       73.6       15.1       67.3 
##      Frost       Area 
##      188.0   566432.0

apply(state.x77, MARGIN=2, FUN=which.max) # Index of the max in each column

## Population     Income Illiteracy   Life Exp     Murder    HS Grad 
##          5          2         18         11          1         44 
##      Frost       Area 
##         28          2

apply(state.x77, MARGIN=2, FUN=summary) # Summary of each col, get back matrix!

##         Population Income Illiteracy Life Exp Murder HS Grad  Frost   Area
## Min.           365   3098      0.500    67.96  1.400   37.80   0.00   1049
## 1st Qu.       1080   3993      0.625    70.12  4.350   48.05  66.25  36990
## Median        2838   4519      0.950    70.68  6.850   53.25 114.50  54280
## Mean          4246   4436      1.170    70.88  7.378   53.11 104.50  70740
## 3rd Qu.       4968   4814      1.575    71.89 10.680   59.15 139.80  81160
## Max.         21200   6315      2.800    73.60 15.100   67.30 188.00 566400

Applying a custom function

For a custom function, we can just define it before hand, and the use apply() as usual

# Our custom function: trimmed mean
trimmed.mean = function(v) {  
  q1 = quantile(v, prob=0.1)
  q2 = quantile(v, prob=0.9)
  return(mean(v[q1 <= v & v <= q2]))
}

apply(state.x77, MARGIN=2, FUN=trimmed.mean)

##  Population      Income  Illiteracy    Life Exp      Murder     HS Grad 
##  3384.27500  4430.07500     1.07381    70.91775     7.29750    53.33750 
##       Frost        Area 
##   104.68293 56575.72500

We’ll learn more about functions later (don’t worry too much at this point about the details of the function definition)

Applying a custom function “on-the-fly”

Instead of defining a custom function before hand, we can just define it “on-the-fly”. Sometimes this is more convenient

# Compute trimmed means, defining this on-the-fly
apply(state.x77, MARGIN=2, FUN=function(v) { 
  q1 = quantile(v, prob=0.1)
  q2 = quantile(v, prob=0.9)
  return(mean(v[q1 <= v & v <= q2]))
})

##  Population      Income  Illiteracy    Life Exp      Murder     HS Grad 
##  3384.27500  4430.07500     1.07381    70.91775     7.29750    53.33750 
##       Frost        Area 
##   104.68293 56575.72500

Applying a function that takes extra arguments

Can tell apply() to pass extra arguments to the function in question. E.g., can use: apply(x, MARGIN=1, FUN=my.fun, extra.arg.1, extra.arg.2), for two extra arguments extra.arg.1, extra.arg.2 to be passed to my.fun()

# Our custom function: trimmed mean, with user-specified percentiles
trimmed.mean = function(v, p1, p2) {
  q1 = quantile(v, prob=p1)
  q2 = quantile(v, prob=p2)
  return(mean(v[q1 <= v & v <= q2]))
}

apply(state.x77, MARGIN=2, FUN=trimmed.mean, p1=0.01, p2=0.99)

##   Population       Income   Illiteracy     Life Exp       Murder 
##  3974.125000  4424.520833     1.136735    70.882708     7.341667 
##      HS Grad        Frost         Area 
##    53.131250   104.895833 61860.687500

What’s the return argument?

What kind of data type will apply() give us? Depends on what function we pass. Summary, say, with FUN=my.fun():

If my.fun() returns a single value, then apply() will return a vector
If my.fun() returns k values, then apply() will return a matrix with k rows (note: this is true regardless of whether MARGIN=1 or MARGIN=2)
If my.fun() returns different length outputs for different inputs, then apply() will return a list
If my.fun() returns a list, then apply() will return a list

We’ll grapple with this on the lab. One advantage of **ply() functions in the plyr package: there is a much more transparent return object type

Optimized functions for special tasks

Don’t overuse the apply paradigm! There’s lots of special functions that optimized are will be both simpler and faster than using apply(). E.g.,

rowSums(), colSums(): for computing row, column sums of a matrix
rowMeans(), colMeans(): for computing row, column means of a matrix
max.col(): for finding the maximum position in each row of a matrix

Combining these functions with logical indexing and vectorized operations will enable you to do quite a lot. E.g., how to count the number of positives in each row of a matrix?

x = matrix(rnorm(9), 3, 3)
# Don't do this (much slower for big matrices)
apply(x, MARGIN=1, function(v) { return(sum(v > 0)) })

## [1] 2 2 3

# Do this insted (much faster, simpler)
rowSums(x > 0)

## [1] 2 2 3

Part III

lapply(), sapply(), tapply()

`lapply()`, elements of a list or vector

The lapply() function takes inputs as in: lapply(x, FUN=my.fun), to apply my.fun() across elements of a list or vector x. The output is always a list

my.list

## $nums
## [1] 0.1 0.2 0.3 0.4 0.5 0.6
## 
## $chars
##  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l"
## 
## $bools
## [1] TRUE TRUE TRUE TRUE TRUE TRUE

lapply(my.list, FUN=mean) # Get a warning: mean() can't be applied to chars

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

## $nums
## [1] 0.35
## 
## $chars
## [1] NA
## 
## $bools
## [1] 1

lapply(my.list, FUN=summary)

## $nums
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.100   0.225   0.350   0.350   0.475   0.600 
## 
## $chars
##    Length     Class      Mode 
##        12 character character 
## 
## $bools
##    Mode    TRUE    NA's 
## logical       6       0

`sapply()`, elements of a list or vector

The sapply() function works just like lapply(), but tries to simplify the return value whenever possible. E.g., most common is the conversion from a list to a vector

sapply(my.list, FUN=mean) # Simplifies the result, now a vector

## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA

##  nums chars bools 
##  0.35    NA  1.00

sapply(my.list, FUN=summary) # Can't simplify, so still a list

## $nums
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.100   0.225   0.350   0.350   0.475   0.600 
## 
## $chars
##    Length     Class      Mode 
##        12 character character 
## 
## $bools
##    Mode    TRUE    NA's 
## logical       6       0

`tapply()`, levels of a factor vector

The function tapply() takes inputs as in: tapply(x, INDEX=my.index, FUN=my.fun), to apply my.fun() to subsets of entries in x that share a common level in my.index

# Compute the mean and sd of the Frost variable, within each region
tapply(state.x77[,"Frost"], INDEX=state.region, FUN=mean)

##     Northeast         South North Central          West 
##      132.7778       64.6250      138.8333      102.1538

tapply(state.x77[,"Frost"], INDEX=state.region, FUN=sd)

##     Northeast         South North Central          West 
##      30.89408      31.30682      23.89307      68.87652

`split()`, split by levels of a factor

The function split() split up the rows of a data frame by levels of a factor, as in: split(x, f=my.index) to split a data frame x according to levels of my.index

# Split up the state.x77 matrix according to region
state.by.reg = split(data.frame(state.x77), f=state.region)
class(state.by.reg) # The result is a list

## [1] "list"

names(state.by.reg) # This has 4 elements for the 4 regions

## [1] "Northeast"     "South"         "North Central" "West"

class(state.by.reg[[1]]) # Each element is a data frame

## [1] "data.frame"

# For each region, display the first 3 rows of the data frame
lapply(state.by.reg, FUN=head, 3)

## $Northeast
##               Population Income Illiteracy Life.Exp Murder HS.Grad Frost
## Connecticut         3100   5348        1.1    72.48    3.1    56.0   139
## Maine               1058   3694        0.7    70.39    2.7    54.7   161
## Massachusetts       5814   4755        1.1    71.83    3.3    58.5   103
##                Area
## Connecticut    4862
## Maine         30920
## Massachusetts  7826
## 
## $South
##          Population Income Illiteracy Life.Exp Murder HS.Grad Frost  Area
## Alabama        3615   3624        2.1    69.05   15.1    41.3    20 50708
## Arkansas       2110   3378        1.9    70.66   10.1    39.9    65 51945
## Delaware        579   4809        0.9    70.06    6.2    54.6   103  1982
## 
## $`North Central`
##          Population Income Illiteracy Life.Exp Murder HS.Grad Frost  Area
## Illinois      11197   5107        0.9    70.14   10.3    52.6   127 55748
## Indiana        5313   4458        0.7    70.88    7.1    52.9   122 36097
## Iowa           2861   4628        0.5    72.56    2.3    59.0   140 55941
## 
## $West
##            Population Income Illiteracy Life.Exp Murder HS.Grad Frost
## Alaska            365   6315        1.5    69.31   11.3    66.7   152
## Arizona          2212   4530        1.8    70.55    7.8    58.1    15
## California      21198   5114        1.1    71.71   10.3    62.6    20
##              Area
## Alaska     566432
## Arizona    113417
## California 156361

# For each region, average each of the 8 numeric variables
lapply(state.by.reg, FUN=function(df) { 
  return(apply(df, MARGIN=2, mean)) 
})

## $Northeast
##   Population       Income   Illiteracy     Life.Exp       Murder 
##  5495.111111  4570.222222     1.000000    71.264444     4.722222 
##      HS.Grad        Frost         Area 
##    53.966667   132.777778 18141.000000 
## 
## $South
##  Population      Income  Illiteracy    Life.Exp      Murder     HS.Grad 
##  4208.12500  4011.93750     1.73750    69.70625    10.58125    44.34375 
##       Frost        Area 
##    64.62500 54605.12500 
## 
## $`North Central`
##  Population      Income  Illiteracy    Life.Exp      Murder     HS.Grad 
##  4803.00000  4611.08333     0.70000    71.76667     5.27500    54.51667 
##       Frost        Area 
##   138.83333 62652.00000 
## 
## $West
##   Population       Income   Illiteracy     Life.Exp       Murder 
## 2.915308e+03 4.702615e+03 1.023077e+00 7.123462e+01 7.215385e+00 
##      HS.Grad        Frost         Area 
## 6.200000e+01 1.021538e+02 1.344630e+05

Summary

Data frames are a representation of the “classic” data table in R: rows are observations/cases, columns are variables/features
Each column can be a different data type (but must be the same length)
subset(): function for extracting rows of a data frame meeting a condition
split(): function for splitting up rows of a data frame, according to a factor variable
apply(): function for applying a given routine to rows or columns of a matrix or data frame
lapply(): similar, but used for applying a routine to elements of a vector or list
sapply(): similar, but will try to simplify the return type, in comparison to lapply()
tapply(): function for applying a given routine to groups of elements in a vector or list, according to a factor variable

Data Frames and Apply