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bor is an R package designed to transform
focal observations’ data, referring to the occurrence of social
interactions, into asymmetric data matrices. Each matrix cell provides
counts on the number of times a specific type of social interaction was
initiated by the row subject and directed to the column subject.
Currently, the package includes three objects:
dtable() a function that transforms raw focal
observations’ data into a data frame more suitable for data
analysis
countb() a function that computes asymmetric data
matrices, separately for each observer and type of social interaction,
from a data frame with a structure similar to that of the output of
dtable() function
ex_field_data a data frame containing an example of
raw focal observations’ data; data frames passed to
dtable() function should have this structure.
Read bor help file (?bor) to learn more
about this package.
# to install R packages from github you need the devtools package
# to install devtools from CRAN type:
install.packages("devtools")
# to install bor from github type:
devtools::install_github("davidnsousa/bor")ex_field_data details the required structure of raw
focal observations’ data that can be passed to the dtable()
function. Below we present the first lines of this data frame:
head(ex_field_data)## id1 act obs
## 1 s2 x 1
## 2 s3 0 1
## 3 s5 x 1
## 4 s1 +.s4 1
## 5 s1 x 1
## 6 s5 s4.-;s2.+ 1id1 - focal subject’s identification code.
This example includes 9 different subjects (coded s1 to s9).
act - recorded social interactions (SIs) in each
focal sample.
In this example two different SIs were recorded (coded + and -); SIs codes, subjects’ id codes, and separation characters should not overlap.
The . character is used to separate subjects’ id codes from the SIs’ codes, and the ; character is used to separate different SIs occurring within the same focal sample.
Whenever a subject’s id appears before a SI’s code it means that,
that subject initiated a SI with the focal subject (see row 6 of
ex_field_data); whenever a subject’s id appears after the
SI’s it means that the focal subject (id1 column) initiated a SI with
that subject (see row 4).
For example, in ex_field_data, the 4th row indicates
that the focal subject s1 initiated a
+ social interaction with subject s4,
while the 6th row indicates that the focal subject s5
was involved in two social interactions- the first initiated by
s4 (-), the second
(+) initiated by s2.
0s in this column refer to focal observations where no SIs were recorded, and xs refer to focal observations where the focal subject was unavailable for observation.
obs - observer’s identification code.
This example includes 2 different observers (coded 1 and 2).
See ?ex_field_data for further details.
dtable() function does not require that the input data
frame has matching column names to that of ex_field_data,
but input data frame should include three columns, with the type of data
as described above and in the same column order.
The following uses dtable() function to convert
ex_field_data data frame (see details above) in a new data
frame (e.g., data) that can be passed to
countb() function. dtable() function requires
that recorded social interactions’ codes are provided
(bset argument). In ex_field_data these
codes are + and -. Below we leave
bsep, asep, missing
and noc dtable()’s arguments at their
default values (“.”, “;”, “x”, “0” respectively). See
?dtable for further details.
b <- c("+","-")
data <- dtable(ex_field_data, bset = b)
head(data)## id1 id2 sender_id1 behavior no_occurrence missing observer
## 1 s2 <NA> NA <NA> NA 1 1
## 2 s3 <NA> NA <NA> 1 NA 1
## 3 s5 <NA> NA <NA> NA 1 1
## 4 s1 s4 1 + NA NA 1
## 5 s1 <NA> NA <NA> NA 1 1
## 6 s5 s4 0 - NA NA 1data object has 7 columns: * id1- focal
subject’s identification code. * id2- identification code
of the social interactions partner. * sender_id1 -
indicates whether the focal subject was the initiator/sender (coded 1)
or the target of the social interaction (coded 0). *
behavior - indicates the code of the social interaction
recorded. * no_occurrence - indicates whether no social
interaction were recorded (coded 1; NA otherwise). *
missing - indicates whether the focal subject was
unavailable for observation (coded 1; NA otherwise). *
observer - observer’s identification code.
countb() function can now be used on data
to compute asymmetric data matrices, containing the number of times a
specific type of social interaction was initiated by the row subject and
directed to the column subject (target), separately for each social
interaction and for each observer. Data matrices are stored inside a
list (e.g., observations).
data2 <- countb(data)
data2## $`1`
## $`1`$`-`
## s1 s2 s3 s4 s5
## s1 0 1 0 0 2
## s2 0 0 0 0 1
## s3 0 0 0 1 0
## s4 0 0 1 0 1
## s5 0 1 1 1 0
##
## $`1`$`+`
## s1 s2 s3 s4 s5
## s1 0 1 1 1 0
## s2 0 0 2 0 2
## s3 2 0 0 0 0
## s4 0 1 0 0 0
## s5 0 0 0 1 0
##
##
## $`2`
## $`2`$`-`
## s1 s2 s3 s4 s5
## s1 0 0 1 2 0
## s2 0 0 0 3 0
## s3 0 1 0 1 0
## s4 0 2 1 0 1
## s5 1 1 1 0 0
##
## $`2`$`+`
## s1 s2 s3 s4 s5
## s1 0 0 1 0 0
## s2 0 0 0 2 0
## s3 0 1 0 2 1
## s4 0 0 1 0 0
## s5 1 0 1 0 0data2 is a list of lists: one for each observer (in this
example data2$`1` and data2$`2`). Inside each
list there is one asymmetric interaction matrix per social interaction
recorded (e.g., data2$`1`$`-`). Cells in these matrices
provide counts on the number of times a specific type of social
interaction was initiated by the row subject and directed to the column
subject.
See ?countb for further details on the
countb() function.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.