# hR: Toolkit for Data Analytics in Human Resources

#### 2020-07-16

Transform and analyze workforce data in meaningful ways for human resources (HR) analytics. The use of three functions, hierarchyLong, hierarchyWide, and hierarchyStats are demonstrated below. These convert standard employee and supervisor relationship data to useful formats, summary statistics, and span of control metrics.

Install the package from CRAN by running the install.packages("hR") command.

## workforceHistory data

The examples in this vignette use the sample workforceHistory data set which reflects an artificial organization’s workforce history data. The sample is reduced to a data.table containing one row per active employee and contractor in order to properly use the subsequent functions.

data("workforceHistory")

# Reduce to DATE <= today to exclude future-dated records
dt = workforceHistory[DATE<=Sys.Date()]

# Reduce to max DATE and SEQ per person
dt = dt[dt[,.I[which.max(DATE)],by=.(EMPLID)]$V1] dt = dt[dt[,.I[which.max(SEQ)],by=.(EMPLID,DATE)]$V1]

# Only consider workers who are currently active
# This provides a reliable 'headcount' data set that reflects today's active workforce
dt = dt[STATUS=="Active"]

# Exclude the CEO because she does not have a supervisor
CEO = dt[TITLE=="CEO",EMPLID]
dt = dt[EMPLID!=CEO]

# Show the prepared table
# This represents an example, active workforce
print(dt[,.(EMPLID,NAME,TITLE,SUPVID)])
#>    EMPLID    NAME     TITLE SUPVID
#> 1: 131356  George   Analyst 199827
#> 2: 199827   Pablo  Director 111355
#> 3: 534441 Rebekah   Analyst 199827
#> 4: 199901 Enrique Associate 199827
#> 5: 268831 Hillary    Intern 131356

## hierarchyLong

The hierarchyLong function transforms a standard set of unique employee and supervisor identifiers (employee IDs, email addresses, etc.) into an elongated format that can be used to aggregate employee data by a particular line of leadership (i.e. include everyone who rolls up to Susan). The function returns a long data.table consisting of one row per employee for every supervisor above them, up to the top of the tree. The levels represent the number of supervisors from the employee (starting with “1” for an employee’s direct supervisor).

hLong = hierarchyLong(dt$EMPLID,dt$SUPVID)
print(hLong)
#>     Employee Level Supervisor
#>  1:   131356     1     199827
#>  2:   131356     2     111355
#>  3:   199827     1     111355
#>  4:   199901     1     199827
#>  5:   199901     2     111355
#>  6:   268831     1     131356
#>  7:   268831     2     199827
#>  8:   268831     3     111355
#>  9:   534441     1     199827
#> 10:   534441     2     111355

# Who reports up through Susan? (direct and indirect reports)
print(hLong[Supervisor==CEO])
#>    Employee Level Supervisor
#> 1:   131356     2     111355
#> 2:   199827     1     111355
#> 3:   199901     2     111355
#> 4:   268831     3     111355
#> 5:   534441     2     111355

## hierarchyWide

The hierarchyWide function transforms a standard set of unique employee and supervisor identifiers (employee IDs, email addresses, etc.) into a wide format that can be used to aggregate employee data by a particular line of leadership (i.e. include everyone who rolls up to Susan). The function returns a wide data.table with a column for every level in the hierarchy, starting from the top of the tree (i.e. “Supv1” is likely the CEO in your organization).

hWide = hierarchyWide(dt$EMPLID,dt$SUPVID)
print(hWide)
#>    Employee  Supv1  Supv2  Supv3
#> 1:   131356 111355 199827   <NA>
#> 2:   199827 111355   <NA>   <NA>
#> 3:   534441 111355 199827   <NA>
#> 4:   199901 111355 199827   <NA>
#> 5:   268831 111355 199827 131356

# Who reports up through Pablo? (direct and indirect reports)
print(hWide[Supv2==199827])
#>    Employee  Supv1  Supv2  Supv3
#> 1:   131356 111355 199827   <NA>
#> 2:   534441 111355 199827   <NA>
#> 3:   199901 111355 199827   <NA>
#> 4:   268831 111355 199827 131356

## hierarchyStats

The hierarchyStats function computes summary statistics and span of control metrics from a standard set of unique employee and supervisor identifiers (employee IDs, email addresses, etc.). The resulting metrics and table are accessible from a list object.

hStats = hierarchyStats(dt$EMPLID,dt$SUPVID)

# Total Levels:
print(hStats$levelsCount$value)
#> [1] 4

# Total Individual Contributors:
print(hStats$individualContributorsCount$value)
#> [1] 3

# Total People Managers:
print(hStats$peopleManagersCount$value)
#> [1] 3

# Median Direct Reports:
print(hStats$medianDirectReports$value)
#> [1] 0.5

# Median Span of Control (Direct and Indirect Reports):
print(hStats$medianSpanOfControl$value)
#> [1] 0.5

# Span of Control Table
print(hStats\$spanOfControlTable)
#>    Employee directReports spanOfControl
#> 1:   111355             1             5
#> 2:   131356             1             1
#> 3:   199827             3             4
#> 4:   199901             0             0
#> 5:   268831             0             0
#> 6:   534441             0             0