• Quick tables with table() and ftable()
• summary()
• Aggregating data with aggregate()

• table()
• ftable()

• Useful for counting occurrences of factor levels, characters, or small ranges of integers.
• Typically used at the early stages of an analysis, but not necessarily.

let <- rep(LETTERS[1:4], times=4)
let

##  [1] "A" "B" "C" "D" "A" "B" "C" "D" "A" "B" "C" "D" "A" "B" "C" "D"

table(let)

## let
## A B C D 
## 4 4 4 4

Count factor levels on the esoph dataset

data(esoph)
str(esoph)

## 'data.frame':    88 obs. of  5 variables:
##  $ agegp    : Ord.factor w/ 6 levels "25-34"<"35-44"<..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcgp    : Ord.factor w/ 4 levels "0-39g/day"<"40-79"<..: 1 1 1 1 2 2 2 2 3 3 ...
##  $ tobgp    : Ord.factor w/ 4 levels "0-9g/day"<"10-19"<..: 1 2 3 4 1 2 3 4 1 2 ...
##  $ ncases   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ ncontrols: num  40 10 6 5 27 7 4 7 2 1 ...

Two-way table: agegp vs tobgp

etab <- table(esoph[, c("agegp", "tobgp")])
etab

##        tobgp
## agegp   0-9g/day 10-19 20-29 30+
##   25-34        4     4     3   4
##   35-44        4     4     4   3
##   45-54        4     4     4   4
##   55-64        4     4     4   4
##   65-74        4     4     4   3
##   75+          4     4     1   2

Three-way: agegp vs tobgp vs alcgp

etab3d <- table(esoph[, c("agegp", "tobgp", "alcgp")])
str(etab3d)

##  'table' int [1:6, 1:4, 1:4] 1 1 1 1 1 1 1 1 1 1 ...
##  - attr(*, "dimnames")=List of 3
##   ..$ agegp: chr [1:6] "25-34" "35-44" "45-54" "55-64" ...
##   ..$ tobgp: chr [1:4] "0-9g/day" "10-19" "20-29" "30+"
##   ..$ alcgp: chr [1:4] "0-39g/day" "40-79" "80-119" "120+"

• Result is a 3D array of counts
• Hard to read when printed raw

ft3d <- ftable(esoph[, c("agegp", "tobgp", "alcgp")])
ft3d

##                alcgp 0-39g/day 40-79 80-119 120+
## agegp tobgp                                     
## 25-34 0-9g/day               1     1      1    1
##       10-19                  1     1      1    1
##       20-29                  1     1      0    1
##       30+                    1     1      1    1
## 35-44 0-9g/day               1     1      1    1
##       10-19                  1     1      1    1
##       20-29                  1     1      1    1
##       30+                    1     1      1    0
## 45-54 0-9g/day               1     1      1    1
##       10-19                  1     1      1    1
##       20-29                  1     1      1    1
##       30+                    1     1      1    1
## 55-64 0-9g/day               1     1      1    1
##       10-19                  1     1      1    1
##       20-29                  1     1      1    1
##       30+                    1     1      1    1
## 65-74 0-9g/day               1     1      1    1
##       10-19                  1     1      1    1
##       20-29                  1     1      1    1
##       30+                    1     0      1    1
## 75+   0-9g/day               1     1      1    1
##       10-19                  1     1      1    1
##       20-29                  0     1      0    0
##       30+                    1     1      0    0

What groups are we missing?

ft.df <- as.data.frame(ft3d)
dim(ft.df)

## [1] 96  4

head(ft.df)

##   agegp    tobgp     alcgp Freq
## 1 25-34 0-9g/day 0-39g/day    1
## 2 35-44 0-9g/day 0-39g/day    1
## 3 45-54 0-9g/day 0-39g/day    1
## 4 55-64 0-9g/day 0-39g/day    1
## 5 65-74 0-9g/day 0-39g/day    1
## 6   75+ 0-9g/day 0-39g/day    1

What groups are we missing?

ft.df[ft.df$Freq == 0, ]

##    agegp tobgp     alcgp Freq
## 18   75+ 20-29 0-39g/day    0
## 47 65-74   30+     40-79    0
## 61 25-34 20-29    80-119    0
## 66   75+ 20-29    80-119    0
## 72   75+   30+    80-119    0
## 90   75+ 20-29      120+    0
## 92 35-44   30+      120+    0
## 96   75+   30+      120+    0

“Generic” function — its behavior depends on the class of the object.

methods(summary)

##  [1] summary.aov                         summary.aovlist*                   
##  [3] summary.aspell*                     summary.check_packages_in_dir*     
##  [5] summary.connection                  summary.data.frame                 
##  [7] summary.Date                        summary.default                    
##  [9] summary.difftime                    summary.ecdf*                      
## [11] summary.factor                      summary.glm                        
## [13] summary.infl*                       summary.lm                         
## [15] summary.loess*                      summary.manova                     
## [17] summary.matrix                      summary.mlm*                       
## [19] summary.nls*                        summary.packageStatus*             
## [21] summary.POSIXct                     summary.POSIXlt                    
## [23] summary.ppr*                        summary.prcomp*                    
## [25] summary.princomp*                   summary.proc_time                  
## [27] summary.rlang_error*                summary.rlang_message*             
## [29] summary.rlang_trace*                summary.rlang_warning*             
## [31] summary.rlang:::list_of_conditions* summary.srcfile                    
## [33] summary.srcref                      summary.stepfun                    
## [35] summary.stl*                        summary.table                      
## [37] summary.tukeysmooth*                summary.warnings                   
## see '?methods' for accessing help and source code

summary(1:1000)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     1.0   250.8   500.5   500.5   750.2  1000.0

summary(esoph)

##    agegp          alcgp         tobgp        ncases         ncontrols     
##  25-34:15   0-39g/day:23   0-9g/day:24   Min.   : 0.000   Min.   : 0.000  
##  35-44:15   40-79    :23   10-19   :24   1st Qu.: 0.000   1st Qu.: 1.000  
##  45-54:16   80-119   :21   20-29   :20   Median : 1.000   Median : 4.000  
##  55-64:16   120+     :21   30+     :20   Mean   : 2.273   Mean   : 8.807  
##  65-74:15                                3rd Qu.: 4.000   3rd Qu.:10.000  
##  75+  :11                                Max.   :17.000   Max.   :60.000

• Numeric columns: min, quartiles, mean, max
• Factor columns: counts of levels

Also works on individual columns:

summary(esoph$agegp)

## 25-34 35-44 45-54 55-64 65-74   75+ 
##    15    15    16    16    15    11

summary(esoph$ncases)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   0.000   1.000   2.273   4.000  17.000

• Useful for finding means, sd, or custom calculations within levels of a factor
  • like pivot tables in Excel

?aggregate

• Has a few different variations: data.frame, formula, ts (time series)

methods(aggregate)

## [1] aggregate.data.frame aggregate.default*   aggregate.formula*  
## [4] aggregate.ts        
## see '?methods' for accessing help and source code

data(warpbreaks)

• Number of warp breaks per loom with different types of wool and tension levels

str(warpbreaks)

## 'data.frame':    54 obs. of  3 variables:
##  $ breaks : num  26 30 54 25 70 52 51 26 67 18 ...
##  $ wool   : Factor w/ 2 levels "A","B": 1 1 1 1 1 1 1 1 1 1 ...
##  $ tension: Factor w/ 3 levels "L","M","H": 1 1 1 1 1 1 1 1 1 2 ...

summary(warpbreaks)

##      breaks      wool   tension
##  Min.   :10.00   A:27   L:18   
##  1st Qu.:18.25   B:27   M:18   
##  Median :26.00          H:18   
##  Mean   :28.15                 
##  3rd Qu.:34.00                 
##  Max.   :70.00

?aggregate

## S3 method for class 'formula'
aggregate(formula, data, FUN, ...,
          subset, na.action = na.omit)

• formula = “aggregate y~x variables found in data”
• y is usually numeric and x is usually a factor
• FUN = function to apply to each group
• ... = other arguments to be passed to your FUN (e.g. na.rm)

What’s the mean number of breaks within wool type?

aggregate(breaks ~ wool, warpbreaks, FUN = mean)

##   wool   breaks
## 1    A 31.03704
## 2    B 25.25926

What’s the mean number of breaks within all possible pairings of wool type and tension?

aggregate(breaks ~ wool + tension, warpbreaks, FUN = mean)

##   wool tension   breaks
## 1    A       L 44.55556
## 2    B       L 28.22222
## 3    A       M 24.00000
## 4    B       M 28.77778
## 5    A       H 24.55556
## 6    B       H 18.77778

Only one aggregation function allowed.

aggregate(breaks ~ wool, warpbreaks, FUN = c(mean, sd))

## Error in `get()`:
## ! object 'FUN' of mode 'function' was not found

• We’ll see a workaround for this later with ddply()

Using aggregate(), find the mean support for the statusquo (statusquo) of surveyed individuals within each region in the Chile data set in package carData.

library(carData)
data(Chile)
str(Chile)

## 'data.frame':    2700 obs. of  8 variables:
##  $ region    : Factor w/ 5 levels "C","M","N","S",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ population: int  175000 175000 175000 175000 175000 175000 175000 175000 175000 175000 ...
##  $ sex       : Factor w/ 2 levels "F","M": 2 2 1 1 1 1 2 1 1 2 ...
##  $ age       : int  65 29 38 49 23 28 26 24 41 41 ...
##  $ education : Factor w/ 3 levels "P","PS","S": 1 2 1 1 3 1 2 3 1 1 ...
##  $ income    : int  35000 7500 15000 35000 35000 7500 35000 15000 15000 15000 ...
##  $ statusquo : num  1.01 -1.3 1.23 -1.03 -1.1 ...
##  $ vote      : Factor w/ 4 levels "A","N","U","Y": 4 2 4 2 2 2 2 2 3 2 ...

head(Chile)

##   region population sex age education income statusquo vote
## 1      N     175000   M  65         P  35000   1.00820    Y
## 2      N     175000   M  29        PS   7500  -1.29617    N
## 3      N     175000   F  38         P  15000   1.23072    Y
## 4      N     175000   F  49         P  35000  -1.03163    N
## 5      N     175000   F  23         S  35000  -1.10496    N
## 6      N     175000   F  28         P   7500  -1.04685    N

Using aggregate(), find the mean support for the statusquo (statusquo) of surveyed individuals within each region in the Chile data set in package carData.

aggregate(statusquo ~ region, Chile, mean)

##   region   statusquo
## 1      C -0.02983546
## 2      M  0.28677120
## 3      N  0.13556488
## 4      S  0.16496487
## 5     SA -0.17955745

You can aggregate more than one numeric variable at a time using cbind() on the left side of the formula:

aggregate(cbind(statusquo, income) ~ region, Chile, mean, na.rm = TRUE)

##   region   statusquo   income
## 1      C -0.04521872 31349.48
## 2      M  0.31627763 26505.38
## 3      N  0.13912207 30764.33
## 4      S  0.16666916 26971.01
## 5     SA -0.20265865 42445.41

Using the ggplot2::diamonds dataset, plot the median price as a function of median carat size of diamonds both grouped by cut.

Tip: you should end up with a scatter plot with only five points.

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.5.2

data("diamonds")
head(diamonds)

## # A tibble: 6 × 10
##   carat cut       color clarity depth table price     x     y     z
##   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
## 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
## 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
## 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
## 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
## 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48

d_price <- aggregate(price ~ cut, diamonds, median)
d_price

##         cut  price
## 1      Fair 3282.0
## 2      Good 3050.5
## 3 Very Good 2648.0
## 4   Premium 3185.0
## 5     Ideal 1810.0

d_carat <- aggregate(carat ~ cut, diamonds, median)
d_carat

##         cut carat
## 1      Fair  1.00
## 2      Good  0.82
## 3 Very Good  0.71
## 4   Premium  0.86
## 5     Ideal  0.54

d_pc <- cbind(d_price, carat = d_carat[, 2])
d_pc

##         cut  price carat
## 1      Fair 3282.0  1.00
## 2      Good 3050.5  0.82
## 3 Very Good 2648.0  0.71
## 4   Premium 3185.0  0.86
## 5     Ideal 1810.0  0.54

plot(price ~ carat, d_pc, pch = 19, 
     main = "Median price vs. median carat by cut")
text(d_pc$carat, d_pc$price, labels = d_pc$cut, pos = 3, cex = 0.8)

• Merging data with merge()
• Binding data with cbind() and rbind()