Some Simple Reports in R
=======================================================
We will look at some of the summary methods in R.  This document will be available as a markdown doc, so you can use this to create MSoffice, pdf or html report files on your own data.


# Define datasets



```r
data(mtcars)
df <- mtcars
dim(df)
```

```
## [1] 32 11
```







```r
library(gmodels)
library(Hmisc)
library(ade4)
library(markdown)
```

```
## Error: there is no package called 'markdown'
```

```r
library(knitr)
```




View data


```r
View(df)
head(df)
```

```
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
```

```r
tail(df)
```

```
##                 mpg cyl  disp  hp drat    wt qsec vs am gear carb
## Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.7  0  1    5    2
## Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.9  1  1    5    2
## Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.5  0  1    5    4
## Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.5  0  1    5    6
## Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.6  0  1    5    8
## Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.6  1  1    4    2
```

```r
str(df)
```

```
## 'data.frame':	32 obs. of  11 variables:
##  $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
##  $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
##  $ disp: num  160 160 108 258 360 ...
##  $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
##  $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
##  $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
##  $ qsec: num  16.5 17 18.6 19.4 17 ...
##  $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
##  $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
##  $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
##  $ carb: num  4 4 1 1 2 1 4 2 2 4 ...
```




Basic Summary


```r
summary(df)
```

```
##       mpg            cyl            disp             hp       
##  Min.   :10.4   Min.   :4.00   Min.   : 71.1   Min.   : 52.0  
##  1st Qu.:15.4   1st Qu.:4.00   1st Qu.:120.8   1st Qu.: 96.5  
##  Median :19.2   Median :6.00   Median :196.3   Median :123.0  
##  Mean   :20.1   Mean   :6.19   Mean   :230.7   Mean   :146.7  
##  3rd Qu.:22.8   3rd Qu.:8.00   3rd Qu.:326.0   3rd Qu.:180.0  
##  Max.   :33.9   Max.   :8.00   Max.   :472.0   Max.   :335.0  
##       drat            wt            qsec            vs       
##  Min.   :2.76   Min.   :1.51   Min.   :14.5   Min.   :0.000  
##  1st Qu.:3.08   1st Qu.:2.58   1st Qu.:16.9   1st Qu.:0.000  
##  Median :3.69   Median :3.33   Median :17.7   Median :0.000  
##  Mean   :3.60   Mean   :3.22   Mean   :17.8   Mean   :0.438  
##  3rd Qu.:3.92   3rd Qu.:3.61   3rd Qu.:18.9   3rd Qu.:1.000  
##  Max.   :4.93   Max.   :5.42   Max.   :22.9   Max.   :1.000  
##        am             gear           carb     
##  Min.   :0.000   Min.   :3.00   Min.   :1.00  
##  1st Qu.:0.000   1st Qu.:3.00   1st Qu.:2.00  
##  Median :0.000   Median :4.00   Median :2.00  
##  Mean   :0.406   Mean   :3.69   Mean   :2.81  
##  3rd Qu.:1.000   3rd Qu.:4.00   3rd Qu.:4.00  
##  Max.   :1.000   Max.   :5.00   Max.   :8.00  
```




Using the describe function


```r
library(Hmisc)
describe(df)
```

```
## df 
## 
##  11  Variables      32  Observations
## ---------------------------------------------------------------------------
## mpg 
##       n missing  unique    Mean     .05     .10     .25     .50     .75 
##      32       0      25   20.09   12.00   14.34   15.43   19.20   22.80 
##     .90     .95 
##   30.09   31.30 
## 
## lowest : 10.4 13.3 14.3 14.7 15.0, highest: 26.0 27.3 30.4 32.4 33.9 
## ---------------------------------------------------------------------------
## cyl 
##       n missing  unique    Mean 
##      32       0       3   6.188 
## 
## 4 (11, 34%), 6 (7, 22%), 8 (14, 44%) 
## ---------------------------------------------------------------------------
## disp 
##       n missing  unique    Mean     .05     .10     .25     .50     .75 
##      32       0      27   230.7   77.35   80.61  120.83  196.30  326.00 
##     .90     .95 
##  396.00  449.00 
## 
## lowest :  71.1  75.7  78.7  79.0  95.1
## highest: 360.0 400.0 440.0 460.0 472.0 
## ---------------------------------------------------------------------------
## hp 
##       n missing  unique    Mean     .05     .10     .25     .50     .75 
##      32       0      22   146.7   63.65   66.00   96.50  123.00  180.00 
##     .90     .95 
##  243.50  253.55 
## 
## lowest :  52  62  65  66  91, highest: 215 230 245 264 335 
## ---------------------------------------------------------------------------
## drat 
##       n missing  unique    Mean     .05     .10     .25     .50     .75 
##      32       0      22   3.597   2.853   3.007   3.080   3.695   3.920 
##     .90     .95 
##   4.209   4.314 
## 
## lowest : 2.76 2.93 3.00 3.07 3.08, highest: 4.08 4.11 4.22 4.43 4.93 
## ---------------------------------------------------------------------------
## wt 
##       n missing  unique    Mean     .05     .10     .25     .50     .75 
##      32       0      29   3.217   1.736   1.956   2.581   3.325   3.610 
##     .90     .95 
##   4.048   5.293 
## 
## lowest : 1.513 1.615 1.835 1.935 2.140
## highest: 3.845 4.070 5.250 5.345 5.424 
## ---------------------------------------------------------------------------
## qsec 
##       n missing  unique    Mean     .05     .10     .25     .50     .75 
##      32       0      30   17.85   15.05   15.53   16.89   17.71   18.90 
##     .90     .95 
##   19.99   20.10 
## 
## lowest : 14.50 14.60 15.41 15.50 15.84
## highest: 19.90 20.00 20.01 20.22 22.90 
## ---------------------------------------------------------------------------
## vs 
##       n missing  unique     Sum    Mean 
##      32       0       2      14  0.4375 
## ---------------------------------------------------------------------------
## am 
##       n missing  unique     Sum    Mean 
##      32       0       2      13  0.4062 
## ---------------------------------------------------------------------------
## gear 
##       n missing  unique    Mean 
##      32       0       3   3.688 
## 
## 3 (15, 47%), 4 (12, 38%), 5 (5, 16%) 
## ---------------------------------------------------------------------------
## carb 
##       n missing  unique    Mean 
##      32       0       6   2.812 
## 
##            1  2 3  4 6 8
## Frequency  7 10 3 10 1 1
## %         22 31 9 31 3 3
## ---------------------------------------------------------------------------
```




1,2 and 3-way Cross Tabulations
==================


```r
table(df$cyl)
```

```
## 
##  4  6  8 
## 11  7 14 
```

```r
table(df$cyl, df$gear)
```

```
##    
##      3  4  5
##   4  1  8  2
##   6  2  4  1
##   8 12  0  2
```

```r
# Number of cyclinders, numbers of gear, transmission type
table(df$cyl, df$gear, df$am)
```

```
## , ,  = 0
## 
##    
##      3  4  5
##   4  1  2  0
##   6  2  2  0
##   8 12  0  0
## 
## , ,  = 1
## 
##    
##      3  4  5
##   4  0  6  2
##   6  0  2  1
##   8  0  0  2
## 
```




Crosstabulation using formula format


```r
xtabs(cyl ~ gear, df)
```

```
## gear
##   3   4   5 
## 112  56  30 
```

```r
xtabs(cyl ~ gear + am + vs, df)
```

```
## , , vs = 0
## 
##     am
## gear  0  1
##    3 96  0
##    4  0 12
##    5  0 26
## 
## , , vs = 1
## 
##     am
## gear  0  1
##    3 16  0
##    4 20 24
##    5  0  4
## 
```




Create Contingency Table 


```r
`?`(ftable)
ftable(df$cyl, df$vs, df$am, df$gear, row.vars = c(2, 4), dnn = c("Cylinders", 
    "V/S", "Transmission", "Gears"))
```

```
##           Cylinders     4     6     8   
##           Transmission  0  1  0  1  0  1
## V/S Gears                               
## 0   3                   0  0  0  0 12  0
##     4                   0  0  0  2  0  0
##     5                   0  1  0  1  0  2
## 1   3                   1  0  2  0  0  0
##     4                   2  6  2  0  0  0
##     5                   0  1  0  0  0  0
```

```r

ftable(df$cyl, df$vs, df$am, df$gear, row.vars = c(2, 3), dnn = c("Cylinders", 
    "V/S", "Transmission", "Gears"))
```

```
##                  Cylinders  4        6        8      
##                  Gears      3  4  5  3  4  5  3  4  5
## V/S Transmission                                     
## 0   0                       0  0  0  0  0  0 12  0  0
##     1                       0  0  1  0  2  1  0  0  2
## 1   0                       1  2  0  2  2  0  0  0  0
##     1                       0  6  1  0  0  0  0  0  0
```




2 way cross tabulation in SAS  format


```r
library(gmodels)
CrossTable(df$cyl, df$gear, format = "SAS")
```

```
## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## | Chi-square contribution |
## |           N / Row Total |
## |           N / Col Total |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  32 
## 
##  
##              | df$gear 
##       df$cyl |         3 |         4 |         5 | Row Total | 
## -------------|-----------|-----------|-----------|-----------|
##            4 |         1 |         8 |         2 |        11 | 
##              |     3.350 |     3.640 |     0.046 |           | 
##              |     0.091 |     0.727 |     0.182 |     0.344 | 
##              |     0.067 |     0.667 |     0.400 |           | 
##              |     0.031 |     0.250 |     0.062 |           | 
## -------------|-----------|-----------|-----------|-----------|
##            6 |         2 |         4 |         1 |         7 | 
##              |     0.500 |     0.720 |     0.008 |           | 
##              |     0.286 |     0.571 |     0.143 |     0.219 | 
##              |     0.133 |     0.333 |     0.200 |           | 
##              |     0.062 |     0.125 |     0.031 |           | 
## -------------|-----------|-----------|-----------|-----------|
##            8 |        12 |         0 |         2 |        14 | 
##              |     4.505 |     5.250 |     0.016 |           | 
##              |     0.857 |     0.000 |     0.143 |     0.438 | 
##              |     0.800 |     0.000 |     0.400 |           | 
##              |     0.375 |     0.000 |     0.062 |           | 
## -------------|-----------|-----------|-----------|-----------|
## Column Total |        15 |        12 |         5 |        32 | 
##              |     0.469 |     0.375 |     0.156 |           | 
## -------------|-----------|-----------|-----------|-----------|
## 
##  
```

```r
CrossTable(df$cyl, df$gear, expected = TRUE, format = "SAS")
```

```
## Warning: Chi-squared approximation may be incorrect
```

```
## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## |              Expected N |
## | Chi-square contribution |
## |           N / Row Total |
## |           N / Col Total |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  32 
## 
##  
##              | df$gear 
##       df$cyl |         3 |         4 |         5 | Row Total | 
## -------------|-----------|-----------|-----------|-----------|
##            4 |         1 |         8 |         2 |        11 | 
##              |     5.156 |     4.125 |     1.719 |           | 
##              |     3.350 |     3.640 |     0.046 |           | 
##              |     0.091 |     0.727 |     0.182 |     0.344 | 
##              |     0.067 |     0.667 |     0.400 |           | 
##              |     0.031 |     0.250 |     0.062 |           | 
## -------------|-----------|-----------|-----------|-----------|
##            6 |         2 |         4 |         1 |         7 | 
##              |     3.281 |     2.625 |     1.094 |           | 
##              |     0.500 |     0.720 |     0.008 |           | 
##              |     0.286 |     0.571 |     0.143 |     0.219 | 
##              |     0.133 |     0.333 |     0.200 |           | 
##              |     0.062 |     0.125 |     0.031 |           | 
## -------------|-----------|-----------|-----------|-----------|
##            8 |        12 |         0 |         2 |        14 | 
##              |     6.562 |     5.250 |     2.188 |           | 
##              |     4.505 |     5.250 |     0.016 |           | 
##              |     0.857 |     0.000 |     0.143 |     0.438 | 
##              |     0.800 |     0.000 |     0.400 |           | 
##              |     0.375 |     0.000 |     0.062 |           | 
## -------------|-----------|-----------|-----------|-----------|
## Column Total |        15 |        12 |         5 |        32 | 
##              |     0.469 |     0.375 |     0.156 |           | 
## -------------|-----------|-----------|-----------|-----------|
## 
##  
## Statistics for All Table Factors
## 
## 
## Pearson's Chi-squared test 
## ------------------------------------------------------------
## Chi^2 =  18.04     d.f. =  4     p =  0.001214 
## 
## 
##  
```





2 way cross tabulation in SPSS format


```r
library(gmodels)
CrossTable(df$cyl, df$gear, format = "SPSS")
```

```
## 
##    Cell Contents
## |-------------------------|
## |                   Count |
## | Chi-square contribution |
## |             Row Percent |
## |          Column Percent |
## |           Total Percent |
## |-------------------------|
## 
## Total Observations in Table:  32 
## 
##              | df$gear 
##       df$cyl |        3  |        4  |        5  | Row Total | 
## -------------|-----------|-----------|-----------|-----------|
##            4 |        1  |        8  |        2  |       11  | 
##              |    3.350  |    3.640  |    0.046  |           | 
##              |    9.091% |   72.727% |   18.182% |   34.375% | 
##              |    6.667% |   66.667% |   40.000% |           | 
##              |    3.125% |   25.000% |    6.250% |           | 
## -------------|-----------|-----------|-----------|-----------|
##            6 |        2  |        4  |        1  |        7  | 
##              |    0.500  |    0.720  |    0.008  |           | 
##              |   28.571% |   57.143% |   14.286% |   21.875% | 
##              |   13.333% |   33.333% |   20.000% |           | 
##              |    6.250% |   12.500% |    3.125% |           | 
## -------------|-----------|-----------|-----------|-----------|
##            8 |       12  |        0  |        2  |       14  | 
##              |    4.505  |    5.250  |    0.016  |           | 
##              |   85.714% |    0.000% |   14.286% |   43.750% | 
##              |   80.000% |    0.000% |   40.000% |           | 
##              |   37.500% |    0.000% |    6.250% |           | 
## -------------|-----------|-----------|-----------|-----------|
## Column Total |       15  |       12  |        5  |       32  | 
##              |   46.875% |   37.500% |   15.625% |           | 
## -------------|-----------|-----------|-----------|-----------|
## 
##  
```

```r
CrossTable(df$cyl, df$gear, expected = TRUE, format = "SPSS")
```

```
## Warning: Chi-squared approximation may be incorrect
```

```
## 
##    Cell Contents
## |-------------------------|
## |                   Count |
## |         Expected Values |
## | Chi-square contribution |
## |             Row Percent |
## |          Column Percent |
## |           Total Percent |
## |-------------------------|
## 
## Total Observations in Table:  32 
## 
##              | df$gear 
##       df$cyl |        3  |        4  |        5  | Row Total | 
## -------------|-----------|-----------|-----------|-----------|
##            4 |        1  |        8  |        2  |       11  | 
##              |    5.156  |    4.125  |    1.719  |           | 
##              |    3.350  |    3.640  |    0.046  |           | 
##              |    9.091% |   72.727% |   18.182% |   34.375% | 
##              |    6.667% |   66.667% |   40.000% |           | 
##              |    3.125% |   25.000% |    6.250% |           | 
## -------------|-----------|-----------|-----------|-----------|
##            6 |        2  |        4  |        1  |        7  | 
##              |    3.281  |    2.625  |    1.094  |           | 
##              |    0.500  |    0.720  |    0.008  |           | 
##              |   28.571% |   57.143% |   14.286% |   21.875% | 
##              |   13.333% |   33.333% |   20.000% |           | 
##              |    6.250% |   12.500% |    3.125% |           | 
## -------------|-----------|-----------|-----------|-----------|
##            8 |       12  |        0  |        2  |       14  | 
##              |    6.562  |    5.250  |    2.188  |           | 
##              |    4.505  |    5.250  |    0.016  |           | 
##              |   85.714% |    0.000% |   14.286% |   43.750% | 
##              |   80.000% |    0.000% |   40.000% |           | 
##              |   37.500% |    0.000% |    6.250% |           | 
## -------------|-----------|-----------|-----------|-----------|
## Column Total |       15  |       12  |        5  |       32  | 
##              |   46.875% |   37.500% |   15.625% |           | 
## -------------|-----------|-----------|-----------|-----------|
## 
##  
## Statistics for All Table Factors
## 
## 
## Pearson's Chi-squared test 
## ------------------------------------------------------------
## Chi^2 =  18.04     d.f. =  4     p =  0.001214 
## 
## 
##  
##        Minimum expected frequency: 1.094 
## Cells with Expected Frequency < 5: 6 of 9 (66.67%)
## 
```





Categorical Data
=================
The library *vcd* is very useful


Some Plots for Exploring Data
=================================

- scatterplot



```r
attach(df)
```

```
## The following object(s) are masked from 'df (position 3)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'df (position 4)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'df (position 5)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'df (position 6)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'df (position 7)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'df (position 8)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'df (position 9)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'df (position 10)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'df (position 11)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'df (position 12)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'df (position 13)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'df (position 14)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'df (position 15)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'df (position 16)':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
## The following object(s) are masked from 'mtcars':
## 
##     am, carb, cyl, disp, drat, gear, hp, mpg, qsec, vs, wt
```

```r
plot(qsec, mpg, col = cyl, pch = 19, main = "Miles per gallon by 1/4 mile time (by cylinder)")
legend("topleft", legend = unique(cyl), fill = unique(cyl))
```

![plot of chunk scatterplot](figure/scatterplot.png) 


- boxplot


```r
plot(qsec ~ factor(cyl), col = unique(cyl))
```

![plot of chunk boxplot](figure/boxplot.png) 


- boxplot all of the columns


```r
boxplot(df)
```

![plot of chunk boxplotALL](figure/boxplotALL.png) 


- Correlation across 



```r
plot(df)
```

![plot of chunk pairs](figure/pairs.png) 


Or calculate correlation and view on heatmap


```r
heatmap(cor(df))
```

![plot of chunk heatmap](figure/heatmap.png) 


Basic principcal component analysis


```r
res <- prcomp(df)
screeplot(res)
```

![plot of chunk prcomp](figure/prcomp1.png) 

```r
biplot(res)
```

![plot of chunk prcomp](figure/prcomp2.png) 


Or using fast.prcomp (optimized for big wide datasets)


```r
res <- fast.prcomp(df)
s.class(res$li, factor(cyl), col = unique(cyl))
```

```
## Error: undefined columns selected
```

```r
s.arrow(res$li, cpoint = cyl)
```

```
## Error: Non convenient selection for xax
```






```r
library(ade4)
res <- dudi.pca(df, scan = FALSE)
par(mfrow = c(2, 2))
barplot(res$eig)
```

![plot of chunk dudi.pca](figure/dudi.pca1.png) 

```r
s.class(res$li, factor(cyl))
```

![plot of chunk dudi.pca](figure/dudi.pca2.png) 

```r
s.label(res$co)
```

![plot of chunk dudi.pca](figure/dudi.pca3.png) 

```r
s.label(res$li, clabel = 0.5)
```

![plot of chunk dudi.pca](figure/dudi.pca4.png) 



Missing Data
===============


```r
df[sample(1:nrow(df), 2), sample(1:ncol(df), 2)] <- NA
summary(df)
```

```
##       mpg            cyl            disp             hp       
##  Min.   :10.4   Min.   :4.00   Min.   : 71.1   Min.   : 52.0  
##  1st Qu.:15.3   1st Qu.:4.00   1st Qu.:125.4   1st Qu.: 96.5  
##  Median :18.9   Median :6.00   Median :196.3   Median :123.0  
##  Mean   :20.1   Mean   :6.19   Mean   :228.7   Mean   :146.7  
##  3rd Qu.:22.8   3rd Qu.:8.00   3rd Qu.:314.5   3rd Qu.:180.0  
##  Max.   :33.9   Max.   :8.00   Max.   :472.0   Max.   :335.0  
##  NA's   :2                     NA's   :2                      
##       drat            wt            qsec            vs       
##  Min.   :2.76   Min.   :1.51   Min.   :14.5   Min.   :0.000  
##  1st Qu.:3.08   1st Qu.:2.58   1st Qu.:16.9   1st Qu.:0.000  
##  Median :3.69   Median :3.33   Median :17.7   Median :0.000  
##  Mean   :3.60   Mean   :3.22   Mean   :17.8   Mean   :0.438  
##  3rd Qu.:3.92   3rd Qu.:3.61   3rd Qu.:18.9   3rd Qu.:1.000  
##  Max.   :4.93   Max.   :5.42   Max.   :22.9   Max.   :1.000  
##                                                              
##        am             gear           carb     
##  Min.   :0.000   Min.   :3.00   Min.   :1.00  
##  1st Qu.:0.000   1st Qu.:3.00   1st Qu.:2.00  
##  Median :0.000   Median :4.00   Median :2.00  
##  Mean   :0.406   Mean   :3.69   Mean   :2.81  
##  3rd Qu.:1.000   3rd Qu.:4.00   3rd Qu.:4.00  
##  Max.   :1.000   Max.   :5.00   Max.   :8.00  
##                                               
```




Analyzing >1 Dataset
=========================
Often we have 2 or more tables either reflecting different time points of the same sample population or different measuments on the same population.

*Merge Data*
There are several function for manipulating data,  see the plyr library for functions. Also see the function reshape and stack which make it easier to convert a "wide" table into a narrow one.



```r
x1 <- data.frame(Case = sample(letters, 10), A1 = rnorm(10), B1 = 1:10, 
    C1 = rep(1:5, 2))
x1
```

```
##    Case      A1 B1 C1
## 1     f -0.4227  1  1
## 2     w  1.1173  2  2
## 3     c  0.2895  3  3
## 4     u  0.2005  4  4
## 5     l -0.2262  5  5
## 6     x  1.1932  6  1
## 7     g -0.4561  7  2
## 8     e -0.6621  8  3
## 9     o  0.2095  9  4
## 10    h  0.2013 10  5
```

```r
x2 <- data.frame(A1 = seq(1, 10, 2), Case = sample(letters, 10), 
    D1 = rnorm(10, 4), E1 = rep(1:5, 2), B1 = c(rep(c("Non-Smoker", "Smoker"), 
        each = 4), NA, NA))
x2
```

```
##    A1 Case    D1 E1         B1
## 1   1    z 4.567  1 Non-Smoker
## 2   3    f 4.649  2 Non-Smoker
## 3   5    y 4.286  3 Non-Smoker
## 4   7    d 3.085  4 Non-Smoker
## 5   9    r 3.391  5     Smoker
## 6   1    c 4.558  1     Smoker
## 7   3    j 2.966  2     Smoker
## 8   5    b 5.230  3     Smoker
## 9   7    q 2.708  4       <NA>
## 10  9    w 2.815  5       <NA>
```

```r
merge(x1, x2, "Case")
```

```
##   Case    A1.x B1.x C1 A1.y    D1 E1       B1.y
## 1    c  0.2895    3  3    1 4.558  1     Smoker
## 2    f -0.4227    1  1    3 4.649  2 Non-Smoker
## 3    w  1.1173    2  2    9 2.815  5       <NA>
```




Multivariate methods for exploring covariance across studies
=============================================================
Lets look at the doubs data in the ade4 package. This data set gives environmental variables, fish species and spatial coordinates for 30 sites



```r
require(ade4)
data(doubs)
lapply(doubs, head)
```

```
## $env
##   dfs alt   slo flo pH har pho nit amm oxy bdo
## 1   3 934 6.176  84 79  45   1  20   0 122  27
## 2  22 932 3.434 100 80  40   2  20  10 103  19
## 3 102 914 3.638 180 83  52   5  22   5 105  35
## 4 185 854 3.497 253 80  72  10  21   0 110  13
## 5 215 849 3.178 264 81  84  38  52  20  80  62
## 6 324 846 3.497 286 79  60  20  15   0 102  53
## 
## $fish
##   Cogo Satr Phph Neba Thth Teso Chna Chto Lele Lece Baba Spbi Gogo Eslu
## 1    0    3    0    0    0    0    0    0    0    0    0    0    0    0
## 2    0    5    4    3    0    0    0    0    0    0    0    0    0    0
## 3    0    5    5    5    0    0    0    0    0    0    0    0    0    1
## 4    0    4    5    5    0    0    0    0    0    1    0    0    1    2
## 5    0    2    3    2    0    0    0    0    5    2    0    0    2    4
## 6    0    3    4    5    0    0    0    0    1    2    0    0    1    1
##   Pefl Rham Legi Scer Cyca Titi Abbr Icme Acce Ruru Blbj Alal Anan
## 1    0    0    0    0    0    0    0    0    0    0    0    0    0
## 2    0    0    0    0    0    0    0    0    0    0    0    0    0
## 3    0    0    0    0    0    0    0    0    0    0    0    0    0
## 4    2    0    0    0    0    1    0    0    0    0    0    0    0
## 5    4    0    0    2    0    3    0    0    0    5    0    0    0
## 6    1    0    0    0    0    2    0    0    0    1    0    0    0
## 
## $xy
##     x  y
## 1  88  7
## 2  94 14
## 3 102 18
## 4 100 28
## 5 106 39
## 6 112 51
## 
## $species
##                 Scientific        French           English code
## 1             Cottus gobio        chabot european bullhead Cogo
## 2       Salmo trutta fario  truite fario       brown trout Satr
## 3        Phoxinus phoxinus        vairon            minnow Phph
## 4   Nemacheilus barbatulus loche franche       stone loach Neba
## 5      Thymallus thymallus         ombre          grayling Thth
## 6 Telestes soufia agassizi       blageon           blageon Teso
## 
```






```r
dudi1 <- dudi.pca(doubs$env, scale = TRUE, scannf = FALSE, nf = 3)
dudi2 <- dudi.pca(doubs$fish, scale = FALSE, scannf = FALSE, nf = 2)
coin1 <- coinertia(dudi1, dudi2, scan = FALSE, nf = 2)
plot(coin1)
```

![plot of chunk coinertia](figure/coinertia.png) 

```r
# s.arrow(coin1$l1, clab = 0.7)
```




How to Process this document
=================================
```{}
require(knitr)
dir(pattern="Rmd")
knit("Reports.Rmd")
knit2html("Reports.Rmd")
knit2pdf("Reports.Rmd")
purl("Reports.Rmd")
```

Or use pandoc to convert markdown file
```{}
system("pandoc -s Reports.md  -o Reports.pdf")
system("pandoc -s Reports.md  -o Reports.docx")
system("pandoc -s Reports.md  -o Reports.html")
dir()
```