**R for Public Health**, and kindly contributed to R-bloggers)

In this post, I’ll go over four functions that you can use to nicely summarize your data. Before any regression analysis, a descriptive analysis is key to understanding your variables and the relationships between them. Next week, I’ll have a post on plotting, so this post is limited to the *summary()*, *table()*, and *aggregate()* functions.

Here is my dataset for this example:

The first thing I want to do is look at my data overall – get the range of values for each variable, and see what missing values I have. I can do this simply by doing:

summary(mydata)

This produces the output below, and shows me that both Weight and Height have missing values. The Migrantstatus variable is a factor (categorical), so it lists the number in each category.

If I want to just summarize one variable, I can do summary(mydata$Weight) for example. And remember from last week, that if I just want to summarize some portion of my data, I can subset using indexing like so: summary(mydata[,c(2:5)])

Next, I want to tabulate my data. I can do univariate and bivariate tables (I can even do more dimensions than that!) by using the *table()* function. *Table()* gives me the totals in each group. If I want proportions instead of totals, I can use* prop.table()* around my *table()* function. The code looks like this with the output below:

table1<-table(mydata$Sex)

table1

prop.table(table1)

Next, I do the bivariate tables. The first variable is the row and the second is the column. I can do proportions here as well, but I must be careful about the margin. Margin=1 means that R calculates the proportions across rows, while margin=2 is down columns. I show a table of Sex vs Marital status below with two types of proportion tables.

table2<-table(mydata$Sex, mydata$Married)

table2

prop.table(table2, margin=1)

prop.table(table2, margin=2)

And if I want to do three dimensions, I put all three variables in my *table()* function. R will give me the 2×2 table of sex and marital status, stratified by the third variable, migrant status.

The great part about R is that I can take any component of this table that I want. For example, if I just want the table for migrants, I can do:

*aggregate()*function. Aggregate does exactly that: it takes one variable (the first argument) and calculates some kind of function on it (the FUN= argument), by another variable (the by=list() argument). So here I am going to do the mean weight for each sex. Here the syntax is a little funny because R wants a list for the by variable. I will go over lists at another post in the future, or you can look it up on another R site.

aggtable<-aggregate(mydata$Weight, by=list(mydata$Sex), FUN=mean)

aggtable

However, something is wrong. The NA in the weight column is messing up my mean calculation. To get around this, I use the na.rm=TRUE parameter which removes any NAs from consideration.

aggtable.narm<-aggregate(mydata$Weight, by=list(mydata$Sex), FUN=mean, na.rm=TRUE)

aggtable.narm

Victory! If I want to name my columns of this table, I can do:

names(aggtable.narm)<-c(“Sex”,”Meanweight”)

And of course if you want to do mean tables by more than one variable, you can put the all in the list argument, like so: by=list(mydata$Sex, mydata$Married). The code and output would look like this:

aggtable.3<-aggregate(mydata$Weight, by=list(mydata$Sex, mydata$Married), FUN=mean, na.rm=TRUE)

names(aggtable.3)<-c(“Sex”,”Married”,”Meanweight”)

aggtable.3

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