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CHAPTER 7 Basic statistics |
type in the describe() function and R searches for it, R comes to the psych package first and executes it. If you want the Hmisc version instead, you can type Hmisc::describe(mt). The function is still there. You have to give R more information to find it.
Now that you know how to generate descriptive statistics for the data as a whole, let’s review how to obtain statistics for subgroups of the data.
7.1.3Descriptive statistics by group
When comparing groups of individuals or observations, the focus is usually on the descriptive statistics of each group, rather than the total sample. Again, there are several ways to accomplish this in R. We’ll start by getting descriptive statistics for each level of transmission type. In chapter 5, we discussed methods of aggregating data. You can use the aggregate() function (section 5.6.2) to obtain descriptive statistics by group, as shown in the following listing.
Listing 7.6 Descriptive statistics by group using aggregate()
>myvars <- c("mpg", "hp", "wt")
>aggregate(mtcars[myvars], by=list(am=mtcars$am), mean)
|
am |
mpg |
hp |
wt |
1 |
0 |
17.1 |
160 |
3.77 |
2 |
1 |
24.4 |
127 |
2.41 |
> aggregate(mtcars[myvars], by=list(am=mtcars$am), sd)
|
am |
mpg |
hp |
wt |
1 |
0 |
3.83 |
53.9 |
0.777 |
2 |
1 |
6.17 |
84.1 |
0.617 |
Note the use of list(am=mtcars$am). If you used list(mtcars$am), the am column would be labeled Group.1 rather than am. You use the assignment to provide a more useful column label. If you have more than one grouping variable, you can use code like by=list(name1=groupvar1, name2=groupvar2, ... , nameN=groupvarN).
Unfortunately, aggregate() only allows you to use single-value functions such as mean, standard deviation, and the like in each call. It won’t return several statistics at once. For that task, you can use the by() function. The format is
by(data, INDICES, FUN)
where data is a data frame or matrix, INDICES is a factor or list of factors that defines the groups, and FUN is an arbitrary function that operates on all the columns of a data frame. The next listing provides an example.
Listing 7.7 Descriptive statistics by group using by()
>dstats <- function(x)sapply(x, mystats)
>myvars <- c("mpg", "hp", "wt")
>by(mtcars[myvars], mtcars$am, dstats)
|
|
Descriptive statistics |
143 |
|
mtcars$am: |
0 |
|
|
|
|
mpg |
hp |
wt |
|
n |
19.000 |
19.0000 |
19.000 |
|
mean |
17.147 |
160.2632 |
3.769 |
|
stdev |
3.834 |
53.9082 |
0.777 |
|
skew |
0.014 |
-0.0142 |
0.976 |
|
kurtosis |
-0.803 |
-1.2097 |
0.142 |
|
---------------------------------------- |
|
|||
mtcars$am: |
1 |
|
|
|
|
mpg |
hp |
wt |
|
n |
13.0000 |
13.000 |
13.000 |
|
mean |
24.3923 |
126.846 |
2.411 |
|
stdev |
6.1665 |
84.062 |
0.617 |
|
skew |
0.0526 |
1.360 |
0.210 |
|
kurtosis |
-1.4554 |
0.563 |
-1.174 |
|
In this case, dstats() applies the mystats() function from listing 7.2 to each column of the data frame. Placing it in the by() function gives you summary statistics for each level of am.
7.1.4Additional methods by group
The doBy package and the psych package also provide functions for descriptive statistics by group. Again, they aren’t distributed in the base installation and must be installed before first use. The summaryBy() function in the doBy package has the format
summaryBy(formula, data=dataframe, FUN=function)
where the formula takes the form
var1 + var2 + var3 + ... + varN ~ groupvar1 + groupvar2 + ... + groupvarN
Variables on the left of the ~ are the numeric variables to be analyzed, and variables on the right are categorical grouping variables. The function can be any built-in or user-created R function. An example using the mystats() function created in section 7.2.1 is shown in the following listing.
Listing 7.8 Summary statistics by group using summaryBy() in the doBy package
>library(doBy)
>summaryBy(mpg+hp+wt~am, data=mtcars, FUN=mystats)
|
am |
mpg.n |
mpg.mean |
mpg.stdev |
mpg.skew |
mpg.kurtosis |
hp.n |
hp.mean hp.stdev |
|
1 |
0 |
19 |
17.1 |
3.83 |
0.0140 |
-0.803 |
19 |
160 |
53.9 |
2 |
1 |
13 |
24.4 |
6.17 |
0.0526 |
-1.455 |
13 |
127 |
84.1 |
|
hp.skew |
hp.kurtosis |
wt.n |
wt.mean |
wt.stdev |
wt.skew |
wt.kurtosis |
1 |
-0.0142 |
-1.210 |
19 |
3.77 |
0.777 |
0.976 |
0.142 |
2 |
1.3599 |
0.563 |
13 |
2.41 |
0.617 |
0.210 |
-1.174 |
The describeBy() function contained in the psych package provides the same descriptive statistics as describe(), stratified by one or more grouping variables, as you can see in the following listing.