MLRA 104 - Aquolls, Aquents, Intermittent Water, Wetlands Recorrelation
Stephen Roecker and Ryan Dermody
2017-05-22
1 Introduction
Most of these series from this analysis, are poorly drained alluvial soils. What we are trying to determine is what the particle size is by watershed. The pattern we see form this edge analysis and our field data is the alluvial soils in our northern watersheds (MLRA104) are more fine loamy with a mix of fine silty poorly drained ponded soils on the flood plain and low stream terraces. As we look at the Alluvial soils in 108, they become more fine silty and fine poorly drained alluvial soils. The most common edge soils such as Nodaway, Lawson, Vesser, Spillville are the better drained frequently flooded aluvial soils that should be next to these Aquol depressions.
2 Data Preparation
# load packages
library(plyr)
library(ggplot2)
# import data
aquolls <- read.csv("C:/Users/stephen/ownCloud/projects/Aquolls_edges.csv")
# modify column headers
names(aquolls) <- tolower(names(aquolls))
names(aquolls)[names(aquolls) %in% c("shape_length_meters", "boundry_miles")] <- c("meters", "miles")
# extract serie names from muname
aquolls$series <- sapply(aquolls$muname, function(x)
strsplit(as.character(x), " ")[[1]][1]
)
# extract the dominant component from the muname
aquolls$series <- sapply(aquolls$series, function(x)
strsplit(as.character(x), "-")[[1]][1]
)
# remove water
aquolls <- subset(aquolls, ! series %in% c("Water", "Alluvial"))3 Group by Series
aquolls_s <- ddply(aquolls, .(series), summarize, meters = sum(meters))
aquolls_s <- arrange(aquolls_s, desc(meters), series)
aquolls_s_sub <- aquolls_s[1:10, ]
print(aquolls_s_sub)## series meters
## 1 Colo 188990.50
## 2 Ambraw 188656.56
## 3 Spillville 159696.89
## 4 Perks 154154.63
## 5 Coland 125341.33
## 6 Nodaway 102076.80
## 7 Chelsea 98117.96
## 8 Sparta 95273.57
## 9 Marshan 84306.74
## 10 Zook 81273.99ggplot(aquolls_s_sub, aes(x = meters, y = reorder(series, meters))) +
geom_point() +
ggtitle("Series for the Project")
4 Group by Series and Subbasin
aquolls_sb <- ddply(aquolls, .(subbasin, series), summarize, meters = sum(meters))
aquolls_sb <- arrange(aquolls_sb, subbasin, desc(meters), series)
# Top 1 Series by Subbasin
aquolls_sb_top1 <- ddply(aquolls_sb, .(subbasin), function(x) x[1, ])
aquolls_sb_top1 <- arrange(aquolls_sb_top1, series, subbasin, desc(meters))
aquolls_sb_top1 <- with(aquolls_sb_top1, data.frame(series, subbasin, meters))
print(aquolls_sb_top1)## series subbasin meters
## 1 Ambraw Lower Iowa 25593.4680
## 2 Ambraw Lower Wapsipinicon 58445.6653
## 3 Bixby Cannon 6928.6310
## 4 Chaseburg Apple-Plum 894.7418
## 5 Clyde Root 768.4564
## 6 Coland Copperas-Duck 32300.5854
## 7 Coland Upper Cedar 8496.4160
## 8 Coland West Fork Cedar 9684.5873
## 9 Colo Middle Iowa 43168.4659
## 10 Colo Upper Iowa 8069.3346
## 11 Hawick Shell Rock 35338.6098
## 12 Lawson Upper Chariton 19172.2362
## 13 Lester Le Sueur 4761.3632
## 14 Marshan Upper Wapsipinicon 18230.0188
## 15 Nodaway Lake Red Rock 5836.4780
## 16 Nodaway Lower Des Moines 13796.9662
## 17 Nodaway North Skunk 11553.1011
## 18 Nodaway Skunk 31037.4880
## 19 Nodaway South Skunk 8875.7434
## 20 Perks Lower Cedar 140251.4787
## 21 Readlyn Zumbro 1166.3206
## 22 Spillville Middle Cedar 88593.6139
## 23 Spillville Middle Des Moines 11807.4349
## 24 Spillville Turkey 4270.3396
## 25 Titus Flint-Henderson 19376.5540
## 26 Udorthents Blue Earth 595.4621
## 27 Vesser Maquoketa 3436.7064
## 28 Webster Winnebago 15631.9355# Top 5 Series by Subbasin
aquolls_sb_top5 <- ddply(aquolls_sb, .(subbasin), function(x) x[1:5, ])
aquolls_sb_top5$subbasin <- factor(aquolls_sb_top5$subbasin, levels = aquolls_sb_top1$subbasin)
sub <- with(aquolls_sb_top1, data.frame(subbasin, "top_series" = series))
aquolls_sb_top5 <- join(aquolls_sb_top5, sub, by = "subbasin")
ggplot(aquolls_sb_top5, aes(x = meters, y = series)) +
geom_point() +
facet_wrap(~ paste0(top_series, "-", subbasin), scales = "free", ncol = 4) +
ggtitle("Top 5 Series by Top 1 Series and Subbasin")