soilDB (version 2.3)

fetchSDA_component: Extract component tables from Soil Data Access

Description

Get, format, impute, and return component tables.

Usage

fetchSDA_component(WHERE = NULL, duplicates = FALSE, childs = TRUE, rmHzErrors = FALSE, 
                   stringsAsFactors = default.stringsAsFactors()
                   )
                   
get_mapunit_from_SDA(WHERE = NULL, stringsAsFactors = default.stringsAsFactors())
get_component_from_SDA(WHERE = NULL, duplicates = FALSE, childs = TRUE,
                       stringsAsFactors = default.stringsAsFactors()
                       )
                       
get_chorizon_from_SDA(WHERE = NULL, duplicates = FALSE, 
                      stringsAsFactors = default.stringsAsFactors()
                      )
                      
get_cosoilmoist_from_SDA(WHERE = NULL, duplicates = FALSE, impute = TRUE, 
                         stringsAsFactors = default.stringsAsFactors()
                         )
                         
get_cosoilmoist_from_NASIS(impute = TRUE, stringsAsFactors = default.stringsAsFactors())

Arguments

WHERE

text string formated as an SQL WHERE clause (default: FALSE)

duplicates

logical; if TRUE duplicate nationalmusym are returned

childs

logical; if FALSE parent material and geomorphic child tables are not flattened and appended

impute

replace missing (i.e. NULL) values with "Not_Populated" for categorical data, or the "RV" for numeric data or 201 cm if the "RV" is also NULL (default: TRUE)

rmHzErrors

should pedons with horizonation errors be removed from the results? (default: FALSE)

stringsAsFactors

logical: should character vectors be converted to factors? This argument is passed to the uncode() function. It does not convert those vectors that have set outside of uncode() (i.e. hard coded). The 'factory-fresh' default is TRUE, but this can be changed by setting options(stringsAsFactors = FALSE)

Value

A dataframe or list with the results.

Details

The SDA functions can get and fetch data with an internet connection and a WHERE clause.

If the duplicates argument is set to TRUE, duplicate components are returned. This is not necessary with data returned from NASIS, which has one unique national map unit. SDA has duplicate map national map units, one for each legend it exists in.

The function get_cosoilmoist_from_NASIS_db() only works only on Windows , and requires a 'nasis_local' ODBC connection. See the NASIS ODBC Setup tutorial for instructions.

Examples

# NOT RUN {
library(soilDB)
library(ggplot2)
library(gridExtra)

# query soil components by areasymbol and musym
test = fetchSDA_component(WHERE = "areasymbol = 'IN005' AND musym = 'MnpB2'")

# profile plot
plot(test)

# convert the data for depth plot
clay_slice = horizons(slice(test, 0:200 ~ claytotal_l + claytotal_r + claytotal_h))
names(clay_slice) <- gsub("claytotal_", "", names(clay_slice))

om_slice = horizons(slice(test, 0:200 ~ om_l + om_r + om_h))
names(om_slice) = gsub("om_", "", names(om_slice))

test2 = rbind(data.frame(clay_slice, var = "clay"),
              data.frame(om_slice, var = "om")
              )

h = merge(test2, site(test)[c("nationalmusym", "cokey", "compname", "comppct_r")],
          by = "cokey", 
          all.x = TRUE
          )

# depth plot of clay content by soil component
gg_comp <- function(x) {
  ggplot(x) +
  geom_line(aes(y = r, x = hzdept_r)) +
  geom_line(aes(y = r, x = hzdept_r)) +
  geom_ribbon(aes(ymin = l, ymax = h, x = hzdept_r), alpha = 0.2) +
  xlim(200, 0) +
  xlab("depth (cm)") +
  facet_grid(var ~ nationalmusym + paste(compname, comppct_r)) +
  coord_flip()
  }
g1 <- gg_comp(subset(h, var == "clay"))
g2 <- gg_comp(subset(h, var == "om"))

grid.arrange(g1, g2)


# query cosoilmoist (e.g. water table data) by mukey
# NA depths are interpreted as (???) with impute=TRUE argument
x <- get_cosoilmoist_from_SDA(WHERE = "mukey = '1395352'", impute = TRUE)

ggplot(x, aes(x = as.integer(month), y = dept_r, lty = status)) +
  geom_rect(aes(xmin = as.integer(month), xmax = as.integer(month) + 1,
                ymin = 0, ymax = max(x$depb_r),
                fill = flodfreqcl)) +
  geom_line(cex = 1) +
  geom_point() +
  geom_ribbon(aes(ymin = dept_l, ymax = dept_h), alpha = 0.2) +
  ylim(max(x$depb_r), 0) +
  xlab("month") + ylab("depth (cm)") +
  scale_x_continuous(breaks = 1:12, labels = month.abb, name="Month") +
  facet_wrap(~ paste0(compname, ' (', comppct_r , ')')) +
  ggtitle(paste0(x$nationalmusym[1], 
  ': Water Table Levels from Component Soil Moisture Month Data'))



# query all Miami major components
s <- get_component_from_SDA(WHERE = "compname = 'Miami' AND majcompflag = 'Yes'")

# landform vs 3-D morphometry
test <- {
  subset(s, ! is.na(landform) | ! is.na(geompos)) ->.;
  split(., .$drainagecl, drop = TRUE) ->.;
  lapply(., function(x) {
    test = data.frame()
    test = as.data.frame(table(x$landform, x$geompos))
    test$compname   = x$compname[1]
    test$drainagecl = x$drainagecl[1]
    names(test)[1:2] <- c("landform", "geompos")
    return(test)
    }) ->.;
  do.call("rbind", .) ->.;
  .[.$Freq > 0, ] ->.;
  within(., {
    landform = reorder(factor(landform), Freq, max)
    geompos  = reorder(factor(geompos),  Freq, max)
    geompos  = factor(geompos, levels = rev(levels(geompos)))
  }) ->.;
  }
test$Freq2 <- cut(test$Freq, 
                  breaks = c(0, 5, 10, 25, 50, 100, 150),
                  labels = c("<5", "5-10", "10-25", "25-50", "50-100", "100-150")
                  )
ggplot(test, aes(x = geompos, y = landform, fill = Freq2)) + 
  geom_tile(alpha = 0.5) + facet_wrap(~ paste0(compname, "\n", drainagecl)) +
  viridis::scale_fill_viridis(discrete = T) +
  theme(aspect.ratio = 1, axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
  ggtitle("Landform vs 3-D Morphometry for Miami Major Components on SDA")

  
# }