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G Andreasen submission #19

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71 changes: 71 additions & 0 deletions Exercise09.R
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# BIOS 30318 Biocomputing -- Plotting data
# G Andreasen
# 11 18 2022

library(ggplot2)
library(cowplot)

setwd("~/Documents/Courses/Biocomputing_BIOS_60318/Tutorials/Exercise09")

# Data from Bartel et al. 2005 "Mammal abundance indices in the northern
# portion of the Great Basin"
# <https://www.esapubs.org/archive/ecol/E086/172/default.htm#data>
# Indices of abundance of selected mammals were obtained for two study areas
# within the Great Basin: the Idaho National Engineering and Environmental
# Laboratory, Idaho, (INEEL) and Curlew Valley, (CV) Utah, USA. Data collection
# occurred biannually 1962–1993, with varying durations among species and sites.
mammals <- read.table(file = "Mammal_abundance_indices.txt",
sep = '\t', header = TRUE)

# "-999 denotes no data collected given appropriate season"
# Let's replace -999 with 'NA'
mammals[mammals == -999] <- NA

# Problem 1: scatter plot w/ trend line
# Let's assume a correlation between the number of coyotes (Canis) in the CV
# region and the number of rabbits (Lepus) in the CV region.
ggplot(data = mammals,
aes(x = CV.Canis, y = CV.Lepus, color = Season)) +
geom_point() +
xlab("No. of coyotes in CV") +
ylab("No. of rabbits in CV") +
scale_color_manual(values = c("orange", "yellowgreen")) +
stat_smooth(method="lm") +
theme_classic()


# Problem 2: data.txt plots -- barplot and scatter
data <- read.delim(file = "data.txt", sep = ',', header = TRUE)

# Calculate the means
data_means <- aggregate(data[, 2], list(data$region), mean)
# Rename the columns
colnames(data_means) <- c("region", "observation_mean")

# Barplot time
ggplot(data = data_means,
aes(x = region, y = observation_mean, fill = region)) +
geom_bar(stat = "identity") +
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@qtran4 qtran4 Nov 21, 2022

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you can also use stat_summary as shown in the lecture

xlab("Region") +
ylab("Mean observation")

# Scatterplot time
ggplot(data = data,
aes(x = region, y = observations, color = region)) +
geom_point() +
geom_jitter(width = 0.25) +
xlab("Region") +
ylab("Observations")

# Do the bar and scatter plots tell you different stories? Why?
# They do. In it's nature, the bar plot can only show you one value - in our
# case, the mean - unless you add error bars. While this is informative, it
# does not depict the breadth and variability in the data -- unlike a
# scatterplot. The bar plot minimizes the data to one value, while the
# scatterplot shows the variability in the data.
# When looking at the barplot, there seems to be very little difference in the
# data. However, looking at the scatterplot, we can see the huge distribution
# of data points in east compared to north, as well as the weird split in
# observations in the south region. It's also easier to see the lack of
# natural distribution in the data points in the west region.

64 changes: 64 additions & 0 deletions Mammal_abundance_indices.txt
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Year Season INEEL Canis INEEL Lepus INEEL Rabbits INEEL Peromyscus INEEL Perognathus INEEL Dipodomys ordii INEEL Tamias INEEL Spermophilus CV Canis CV Lepus CV Peromyscus CV Reithrodontomys CV Onychomys CV Perognathus CV Dipodomys ordii CV Dipodomys microps CV Tamias CV Ammospermophilus
1962 Fall -999 -999 -999 -999 -999 -999 -999 -999 -999 9.7 -999 -999 -999 -999 -999 -999 -999 -999
1963 Spring -999 -999 -999 -999 -999 -999 -999 -999 -999 7.0 -999 -999 -999 -999 -999 -999 -999 -999
1963 Fall -999 -999 -999 -999 -999 -999 -999 -999 7.5 23.4 -999 -999 -999 -999 -999 -999 -999 -999
1964 Spring -999 -999 -999 -999 -999 -999 -999 -999 -999 9.0 -999 -999 -999 -999 -999 -999 -999 -999
1964 Fall -999 -999 -999 -999 -999 -999 -999 -999 6.4 16.8 -999 -999 -999 -999 -999 -999 -999 -999
1965 Spring -999 -999 -999 -999 -999 -999 -999 -999 -999 9.3 -999 -999 -999 -999 -999 -999 -999 -999
1965 Fall -999 -999 -999 -999 -999 -999 -999 -999 6.3 12.0 -999 -999 -999 -999 -999 -999 -999 -999
1966 Spring -999 -999 -999 -999 -999 -999 -999 -999 -999 6.4 -999 -999 -999 -999 -999 -999 -999 -999
1966 Fall -999 -999 -999 -999 -999 -999 -999 -999 4.3 9.8 -999 -999 -999 -999 -999 -999 -999 -999
1967 Spring -999 -999 -999 -999 -999 -999 -999 -999 -999 3.4 -999 -999 -999 -999 -999 -999 -999 -999
1967 Fall -999 -999 -999 -999 -999 -999 -999 -999 2.1 4.3 -999 -999 -999 -999 -999 -999 -999 -999
1968 Spring -999 -999 -999 -999 -999 -999 -999 -999 -999 4.8 -999 -999 -999 -999 -999 -999 -999 -999
1968 Fall -999 -999 -999 -999 -999 -999 -999 -999 1.4 25.9 -999 -999 -999 -999 -999 -999 -999 -999
1969 Spring -999 -999 -999 -999 -999 -999 -999 -999 -999 40.1 -999 -999 -999 -999 -999 -999 -999 -999
1969 Fall -999 -999 -999 -999 -999 -999 -999 -999 2.4 55.0 -999 -999 -999 -999 -999 -999 -999 -999
1970 Spring -999 -999 -999 -999 -999 -999 -999 -999 -999 36.1 -999 -999 -999 -999 -999 -999 -999 -999
1970 Fall -999 -999 -999 -999 -999 -999 -999 -999 5.2 85.0 -999 -999 -999 -999 -999 -999 -999 -999
1971 Spring -999 -999 -999 -999 -999 -999 -999 -999 -999 53.2 -999 -999 -999 -999 -999 -999 -999 -999
1971 Fall -999 -999 -999 -999 -999 -999 -999 -999 8.2 79.4 -999 -999 -999 -999 -999 -999 -999 -999
1972 Spring -999 -999 -999 -999 -999 -999 -999 -999 -999 35.9 -999 -999 -999 -999 -999 -999 -999 -999
1972 Fall -999 -999 -999 -999 -999 -999 -999 -999 15.1 31.6 -999 -999 -999 -999 -999 -999 -999 -999
1973 Spring -999 -999 -999 -999 -999 -999 -999 -999 -999 6.6 -999 -999 -999 -999 -999 -999 -999 -999
1973 Fall -999 -999 -999 -999 -999 -999 -999 -999 6.0 3.7 9.0 0.1 0.0 4.7 1.3 0.8 1.3 0.2
1974 Spring -999 -999 -999 -999 -999 -999 -999 -999 2.7 1.9 12.5 0.0 0.2 4.1 2.1 0.2 0.2 0.3
1974 Fall -999 -999 -999 -999 -999 -999 -999 -999 5.5 12.9 15.4 0.0 0.0 2.8 5.0 1.4 2.2 0.6
1975 Spring 7.5 0.1 -999 4.0 0.3 1.8 2.2 1.4 4.7 0.5 6.4 0.1 0.1 4.8 0.6 0.9 0.6 0.0
1975 Fall 26.2 0.1 -999 4.5 0.5 1.5 1.9 0.0 6.5 3.4 2.7 0.0 0.1 1.6 1.0 0.4 0.7 0.0
1976 Spring 13.2 0.0 -999 12.3 0.7 1.1 2.3 1.7 4.6 0.8 23.5 0.0 0.1 2.9 0.6 0.6 0.8 0.1
1976 Fall 50.1 0.1 -999 14.3 0.4 2.3 2.9 0.0 13.1 13.5 17.1 0.2 0.2 0.8 1.2 0.9 1.5 0.7
1977 Spring 10.1 0.0 -999 11.6 0.8 1.6 2.5 1.7 0.9 3.5 11.2 1.1 0.3 3.6 2.8 1.0 1.1 0.6
1977 Fall 44.6 7.0 -999 7.5 0.1 0.7 1.1 0.0 6.5 19.8 3.0 0.1 0.2 0.1 0.1 0.2 0.2 0.4
1978 Spring 20.6 12.0 -999 8.1 0.3 0.8 0.9 0.6 2.7 11.9 24.1 1.0 0.4 0.7 1.0 0.4 0.3 0.2
1978 Fall 33.9 11.2 -999 11.9 0.1 0.8 1.9 0.0 9.7 59.9 13.4 0.3 0.8 0.2 2.0 1.1 1.2 1.4
1979 Spring 15.3 43.0 19.0 -999 -999 -999 -999 -999 6.4 41.0 10.8 0.0 0.2 0.8 1.8 0.7 0.7 0.8
1979 Fall 61.5 37.6 28.0 -999 -999 -999 -999 -999 15.9 93.2 3.2 0.0 0.3 0.0 0.8 0.5 0.5 0.4
1980 Spring 34.7 151.0 25.0 -999 -999 -999 -999 -999 4.4 60.4 9.5 0.2 0.1 0.3 1.1 0.4 0.2 0.1
1980 Fall 59.8 119.5 38.0 30.1 0.1 1.4 1.8 0.0 29.0 163.8 8.7 0.1 0.2 0.1 0.4 0.4 0.2 0.8
1981 Spring 55.6 422.0 18.0 43.0 0.5 1.9 3.9 3.3 16.3 124.4 21.4 1.9 0.1 0.6 0.7 0.2 0.7 0.2
1981 Fall 101.3 102.4 58.0 32.1 0.2 7.3 5.7 0.0 27.7 160.0 20.7 1.6 0.5 0.2 1.5 0.6 0.6 0.4
1982 Spring 62.9 242.0 6.0 6.6 0.5 0.4 3.3 3.1 4.7 85.7 3.0 0.0 0.0 0.2 0.0 0.1 0.7 0.0
1982 Fall 128.9 102.9 23.0 12.7 0.2 0.7 2.2 0.0 20.3 56.2 6.0 0.1 0.1 0.5 0.0 0.2 0.4 0.0
1983 Spring 68.8 73.0 5.0 -999 -999 -999 -999 -999 3.3 44.1 9.0 0.2 0.0 0.5 0.1 0.1 0.5 0.0
1983 Fall 137.0 35.0 3.0 24.1 0.5 2.4 2.1 0.0 28.3 37.0 36.7 0.3 0.1 0.4 0.1 0.1 1.1 0.0
1984 Spring 65.3 0.0 0.0 -999 -999 -999 -999 -999 8.9 5.0 5.9 0.0 0.0 0.2 0.1 0.0 0.6 0.0
1984 Fall 54.9 0.1 1.0 11.5 0.1 2.0 1.4 0.0 19.3 0.4 12.3 0.0 0.0 0.8 0.0 0.0 0.6 0.0
1985 Spring 47.0 0.0 0.0 9.3 0.4 1.8 1.3 1.4 8.5 1.8 14.1 0.5 0.0 1.8 0.1 0.0 0.7 0.0
1985 Fall 37.9 0.0 0.0 18.8 0.1 2.2 1.7 0.0 22.7 2.2 12.3 0.1 0.0 0.2 0.5 0.0 0.6 0.0
1986 Spring 31.3 0.0 0.0 20.4 0.1 1.7 2.6 4.4 13.0 2.2 10.4 0.0 0.0 2.1 0.3 0.0 0.4 0.0
1986 Fall 81.1 0.0 0.0 19.4 0.0 1.8 1.6 0.0 15.1 1.4 12.2 0.0 0.0 0.6 0.2 0.0 1.3 0.0
1987 Spring -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999
1987 Fall -999 -999 -999 -999 -999 -999 -999 -999 17.5 -999 -999 -999 -999 -999 -999 -999 -999 -999
1988 Spring -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999
1988 Fall -999 -999 -999 -999 -999 -999 -999 -999 26.3 -999 -999 -999 -999 -999 -999 -999 -999 -999
1989 Spring -999 -999 -999 -999 -999 -999 -999 -999 10.9 14.1 -999 -999 -999 -999 -999 -999 -999 -999
1989 Fall -999 -999 -999 -999 -999 -999 -999 -999 20.1 31.7 -999 -999 -999 -999 -999 -999 -999 -999
1990 Spring -999 -999 -999 -999 -999 -999 -999 -999 5.3 36.5 -999 -999 -999 -999 -999 -999 -999 -999
1990 Fall -999 -999 -999 -999 -999 -999 -999 -999 17.3 92.3 -999 -999 -999 -999 -999 -999 -999 -999
1991 Spring -999 -999 -999 -999 -999 -999 -999 -999 7.9 60.3 -999 -999 -999 -999 -999 -999 -999 -999
1991 Fall -999 -999 -999 -999 -999 -999 -999 -999 22.3 103.6 -999 -999 -999 -999 -999 -999 -999 -999
1992 Spring -999 -999 -999 -999 -999 -999 -999 -999 6.7 75.7 -999 -999 -999 -999 -999 -999 -999 -999
1992 Fall -999 -999 -999 -999 -999 -999 -999 -999 16.3 112.7 -999 -999 -999 -999 -999 -999 -999 -999
1993 Spring -999 -999 -999 -999 -999 -999 -999 -999 23.1 29.4 -999 -999 -999 -999 -999 -999 -999 -999
1993 Fall -999 -999 -999 -999 -999 -999 -999 -999 8.6 28.5 -999 -999 -999 -999 -999 -999 -999 -999