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The 'get_ausplots' function extracts and compiles AusPlots data allowing substantial flexibility in the selection of the required data. Up to 8 different types of data can be retrieved into distinct data frames (i.e. data on sampling sites, vegetation structure, vegetation point intercept, vegetation vouchers, vegetation basal wedge, soil characterization, soil bulk density, and soil & soil metagenomics samples). In addition, data can be filtered for particular sets of plots and/or genus/species, as well as geographically using a rectangular bounding box.

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However, in some situations we are only interested in a subset of the data retrieved by 'get_ausplots'. To subset ausplot data we use the variables in the retrieved data frames corresponding to the concept by we would like to filter the data. In some occasions we would sub-setting a single data frame (i.e. type of variables) is all what we need. The retrieved data by the function ‘get_ausplots’ can be manipulated as any other R data. However, the ‘deep’ structure of the data (a list of multiple data frames) and interrelation of the data frames (via a common a common link variable) can make manipulating the data a bit more daunting (see below).

Variables in the 'site.info' data frame contain information that affect all other data frames; so typically after sub-setting the contents of the variable of interests in the 'site.info' data frame, we will also subset the remaining datasets using one of the common variables among all data frames. Common variables among datasets include 'site_location_name', 'site_location_visit_id', and 'site_unique'. Commonly 'site_unique' is the best option to ‘connect’ ausplots data frames, as it is the most specific variable representing a single visit to a particular site.

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Multiple examples includng various types of sub-settng are presented below. Exaples cover sub-setting a single data frame and all data frames, as well as not requiring variable class transformation and requiring it). All examples would start by loading the 'ausplotsR' library and extracting AusPlots data using the 'get_ausplots' function. In the examples we use the `AP.data' list of data frames that contains information for all the currently available AusPlots sites. This list was previously created in the 'Obtaining AusPlots data: 'get_ausplots' function' Step-by-Step Guide (we use the list created in Example 4).

Boxes with grey background contain code snippets, and boxes with white background containt code (text) outputs.

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# Site Slope
# ==========

# Explore Variable Type and Change to Numeric
# -------------------------------------------
class(AP.data$site.info$site_slope)

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# Subset to Plos with Steep Slopes (> 20 degrees) in 'site.info' data frame
# -------------------------------------------------------------------------
slope.AP.data = AP.data
dim(slope.AP.data$site.info)

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#summary(AP.data$site.info$site_slope.n)
slope.AP.data$site.info = slope.AP.data$site.info[slope.AP.data$site.info$site_slope.n >= 20,] 
dim(slope.AP.data$site.info)

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# Transect
# ========

# Explore Variable Type and Change to Factor
# ------------------------------------------
class(AP.data$veg.PI$transect)

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summary(AP.data$veg.PI$transect)

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## E1-W1 E2-W2 E3-W3 E4-W4 E5-W5 N1-S1 N2-S2 N3-S3 N4-S4 N5-S5 S1-N1 S2-N2 
## 17360 53886 18164 54046 18900 24573 44070 26962 44845 27579 49639 29566 
## S3-N3 S4-N4 S5-N5 W1-E1 W2-E2 W2-S2 W3-E3 W4-E4 W5-E5 
## 46476 28582 46296 55976 19447   102 55135 18983 53877

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# Subset an specific Transect (E1-W1) in 'veg.PI' data frame
# ----------------------------------------------------------
E1W1Tr.AP.data = AP.data
dim(E1W1Tr.AP.data$veg.PI)

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#summary(AP.data$veg.PI$transect)
E1W1Tr.AP.data$veg.PI = E1W1Tr.AP.data$veg.PI[E1W1Tr.AP.data$veg.PI$transect == "E1-W1",] 
levels(E1W1Tr.AP.data$veg.PI$transect)

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# Growth Form
# ===========

# Explore Variable Type and Change to Factor in 'veg.PI' data frame
# -----------------------------------------------------------------
class(AP.data$veg.PI$growth_form)

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# Subset to the Tree/Palm Growth Form
# -----------------------------------
TreePalm.AP.data = AP.data
dim(TreePalm.AP.data$veg.PI)

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#summary(AP.data$veg.PI$growth_form.f)
TreePalm.AP.data$veg.PI = TreePalm.AP.data$veg.PI[TreePalm.AP.data$veg.PI$growth_form.f == "Tree/Palm",] 
levels(TreePalm.AP.data$veg.PI$growth_form.f)

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# Site Slope
# ==========

# Explore Variable Type and Change to Numeric
# -------------------------------------------
class(AP.data$site.info$site_slope)

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# Subset to Plos with Steep Slopes (> 20 degrees) in 'site.info' data frame
# -------------------------------------------------------------------------
slope.AP.data = AP.data
dim(slope.AP.data$site.info)

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#summary(AP.data$site.info$site_slope.n)
slope.AP.data$site.info = slope.AP.data$site.info[slope.AP.data$site.info$site_slope.n >= 20,] 
dim(slope.AP.data$site.info)

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# Subset to Plots with Steep Slopes in other Data Frames
# ------------------------------------------------------
# To do so we use the common variable 'site_unique'

# Subset in 'veg.PI' Data Frame
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dim(slope.AP.data$veg.PI)

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slope.AP.data$veg.PI = slope.AP.data$veg.PI[slope.AP.data$veg.PI$site_unique %in% slope.AP.data$site.info$site_unique, ]
dim(slope.AP.data$veg.PI)

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# Subset in 'veg.basal' Data Frame
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dim(slope.AP.data$veg.basal)

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slope.AP.data$veg.basal = slope.AP.data$veg.basal[slope.AP.data$veg.basal$site_unique %in% slope.AP.data$site.info$site_unique, ]
dim(slope.AP.data$veg.basal)

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# Site Aspect
# ===========

# Explore Variable Type and Change to Numeric
# -------------------------------------------
class(AP.data$site.info$site_aspect)

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# Subset to Plots with a South (SE to SW; i.e. 135 to 225) Aspect in 'site.info' data frame
# -----------------------------------------------------------------------------------------
aspect.AP.data = AP.data
dim(aspect.AP.data$site.info)

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#summary(AP.data$site.info$site_aspect.n)
aspect.AP.data$site.info = aspect.AP.data$site.info[(aspect.AP.data$site.info$site_aspect.n > 135 &
aspect.AP.data$site.info$site_aspect.n <= 225),] 
dim(aspect.AP.data$site.info)

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# Subset to Plots with a South (SE to SW) Aspect in other Data Frames
# -------------------------------------------------------------------
# To do so we use the common variable 'site_unique'

# Subset in 'veg.PI' Data Frame
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dim(aspect.AP.data$veg.PI)

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aspect.AP.data$veg.PI = aspect.AP.data$veg.PI[aspect.AP.data$veg.PI$site_unique %in% aspect.AP.data$site.info$site_unique, ]
dim(aspect.AP.data$veg.PI)

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# Subset in 'veg.basal' Data Frame
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dim(aspect.AP.data$veg.basal)

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aspect.AP.data$veg.basal = aspect.AP.data$veg.basal[aspect.AP.data$veg.basal$site_unique %in% aspect.AP.data$site.info$site_unique, ]
dim(aspect.AP.data$veg.basal)

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# State
# =====

# Explore Variable Type and Change to Factor
# ------------------------------------------
class(AP.data$site.info$state)

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# Subset to Plots in the State of Queensland in 'site.info' data frame
# --------------------------------------------------------------------
# Subset to "QLD"
QLD.AP.data = AP.data
dim(QLD.AP.data$site.info)

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#summary(AP.data$site.info$state.f)
QLD.AP.data$site.info = QLD.AP.data$site.info[QLD.AP.data$site.info$state.f == "QLD",] 
levels(QLD.AP.data$site.info$state.f)

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# Subset to Plots iw the State of Queensland in other Data Frames
# ---------------------------------------------------------------
# To do so we use the common variable 'site_unique'

# Subset in 'veg.PI' Data Frame
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dim(QLD.AP.data$veg.PI)

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## [1] 734464     14

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QLD.AP.data$veg.PI = QLD.AP.data$veg.PI[QLD.AP.data$veg.PI$site_unique %in% QLD.AP.data$site.info$site_unique, ]
dim(QLD.AP.data$veg.PI)

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# Subset in 'veg.basal' Data Frame
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dim(QLD.AP.data$veg.basal)

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QLD.AP.data$veg.basal = QLD.AP.data$veg.basal[QLD.AP.data$veg.basal$site_unique %in% QLD.AP.data$site.info$site_unique, ]
dim(QLD.AP.data$veg.basal)

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# Bioregion name
# ==============

# Explore Variable Type and Change to Factor
# ------------------------------------------
class(AP.data$site.info$bioregion_name)

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# Subset to Plots in Bioregions in the Eastern (~ Qld) Gulf of Carpentaria in 'site.info' data frame
# --------------------------------------------------------------------------------------------------
# Subset to "CYP" (Cape York Peninsula) and "GUP" (Gulf Plains) 
EGCBioregs.AP.data = AP.data
dim(EGCBioregs.AP.data$site.info)

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#summary(AP.data$site.info$bioregion_name.f)
EastCarpGulf.Bioreg = c("CYP", "GUP")
EGCBioregs.AP.data$site.info = EGCBioregs.AP.data$site.info[EGCBioregs.AP.data$site.info$bioregion_name.f %in% EastCarpGulf.Bioreg,] 
levels(EGCBioregs.AP.data$site.info$bioregion_name.f)

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# Subset to Plots in Bioregions in the Eastern (~ Qld) Gulf of Carpentaria in other Data Frames
# ---------------------------------------------------------------------------------------------
# To do so we use the common variable 'site_unique'

# Subset in 'veg.PI' Data Frame
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dim(EGCBioregs.AP.data$veg.PI)

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EGCBioregs.AP.data$veg.PI = EGCBioregs.AP.data$veg.PI[EGCBioregs.AP.data$veg.PI$site_unique %in% EGCBioregs.AP.data$site.info$site_unique, ]
dim(EGCBioregs.AP.data$veg.PI)

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# Subset in 'veg.basal' Data Frame
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
dim(EGCBioregs.AP.data$veg.basal)

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EGCBioregs.AP.data$veg.basal = EGCBioregs.AP.data$veg.basal[EGCBioregs.AP.data$veg.basal$site_unique %in% EGCBioregs.AP.data$site.info$site_unique, ]
dim(EGCBioregs.AP.data$veg.basal)

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