Introduction

This is the third in a 6-part DataSHIELD tutorial series.

The other parts in this DataSHIELD tutorial series are:

1: Introduction and logging in
2: Basic statistics and data manipulations
3: Assign functions and tables
4: Plotting graphs
5: Subsetting
6: Modelling

Quick reminder for logging in:

Descriptive statistics: assigning variables

So far all the functions in the tutorial have returned something to the screen. Some functions (assign functions) create new objects in the server-side R session that are required for analysis but do not return an anything to the client screen. For example, in analysis the log values of a variable may be required.

By default the function ds.log computes the natural logarithm. It is possible to compute a different logarithm by setting the argument base to a different value. There is no output to screen:

In the above example the name of the new object was not specified. By default the name of the new variable is set to the input vector followed by the suffix '_log' (i.e. 'LAB_HDL_log')

It is possible to customise the name of the new object by using the newobj argument:

The new object is not attached to assigned variables data frame (default name "D"). We can check the size of the new LAB_HDL_log vector we generated above; the command should return the same figure as the number of rows in the data frame 'D'.

Using ds.assignwe subtract the pooled mean calculated earlier from LAB_HDL (mean centring) and assign the output to a new variable called LAB_HDL.c. The function returns no output to the client screen, the newly created variable is stored server-side.

Further DataSHIELD functions can now be run on this new mean-centred variable LAB_HDL.c. The example below calculates the mean of the new variable LAB_HDL.c which should be approximately 0.

Contingency tables

The function ds.table creates contingency tables of a categorical variables. The default is set to run on pooled data from all studies, to obtain an output of each study set the argument type='split'.

The example below calculates a one-dimensional table for the variable GENDER. The function returns the counts and the column and row percent per category, as well as information about the validity of the variable in each study dataset:

The function ds.table also creates two-dimensional contingency tables of a categorical variable. The example below constructs a two-dimensional table comprising cross-tabulation of the variables DIS_DIAB (diabetes status) and GENDER.

The function can additionally compute a chi-squared test for homogeneity on (nc-1)*(nr-1) degrees of freedom (where nc is the number of columns and nr is the number of rows):

Below code omits the first section of output which is an exact duplicate of above, only chisquare reports shown:

Conclusion