Final_Project_ANT191

Loading Packages

As always, I make a chunk at the top of the markdown document that I use as a list of the packages I will use for the project. As I come across packages I need to construct a figure, or a function I find usefiul, I add the inclusive package to this chunk

library(igraph)

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum

## The following object is masked from 'package:base':
## 
##     union

library(plotly)

## Loading required package: ggplot2

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:igraph':
## 
##     groups

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:igraph':
## 
##     as_data_frame, groups, union

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Organizing Data

Here I set the working drive so that the program will look there by default. A working drive is a good strategy to keep all of your data sources and outputs grouped into the same folder

setwd("~/Desktop/ANT191WD")

The main data set I will be working with is the Frantz et al. data on ancient and modern dog haplotypes by country. Looking at the haplogroup frequency by country is too fine-grained and approach, so prior to loading the data into the environment, I opened the table using Microsoft Excel and added a column titled “Region.” Additionally, I have trimmed the data to remove any individuals not classifiable by region, or outside of the haplogroups A,B,C,D,E, and F.

dna <- read.csv("~/Desktop/ANT191WD/mtDNA_info.csv")

Subsetting the Data

The data can be broken into any number of subsets to facilitate interpretation and visualization. I have broken the data into modern and archaeological subsets below

Arch <- dna[dna$Age > 0,]
Modern <- dna[dna$Age == 0,]

Here I have broken them into subsets by region. These data subsets are not necessary to produce the following plots, but were useful for determining the frequency of haplogroup by region

Africa <- dna[dna$Region == "Africa",]
Caribbean <- dna[dna$Region == "Caribbean",]
C.America <- dna[dna$Region == "Central America",]
E.Asia <- dna[dna$Region == "East Asia",]
Europe <- dna[dna$Region == "Europe",]
Middle.East <- dna[dna$Region == "Middle East",]
N.America <- dna[dna$Region == "North America",]
Oceania <- dna[dna$Region == "Oceania",]
SCA <- dna[dna$Region == "SCA",]
SEA <- dna[dna$Region == "SEA",]
Siberia <- dna[dna$Region == "Siberia",]
S.America <- dna[dna$Region == "South America",]
S.Asia <- dna[dna$Region == "South Asia",]

dna_freq <- read.csv("~/Desktop/ANT191WD/dna_freq.csv", row.names = 1)

Creating the Dot Charts

In the chunk below, I create the dot charts. These dot charts show the frequency of each haplogroup by region. I used the dotchart function and assigned each haplogroup (row on the chart) a color using and object I created from a concatenation of six color values (mycolors)

mycolors <- c("royalblue", "orange2", "gold1", "darkorchid4", "cyan", "darkgoldenrod4")

AfrDot <- dotchart(dna_freq$Africa, color = mycolors, labels = row.names(dna_freq), pch=16, xlim=c(0,1.1), main="Africa")

CarDot <- dotchart(dna_freq$Caribbean, color = mycolors, labels = row.names(dna_freq), pch=16, xlim = c(0,1.1), main = "Caribbean")

CamDot <- dotchart(dna_freq$CentAmerica, color = mycolors, labels = row.names(dna_freq), pch=16, xlim = c(0,1.1), main = "Central America")

EasDot <- dotchart(dna_freq$EAsia, color = mycolors, labels = row.names(dna_freq), pch=16, xlim=c(0,1.1), main = "East Asia")

EurDot <- dotchart(dna_freq$Europe, color = mycolors, labels = row.names(dna_freq), pch=16, xlim = c(0,1.1), main = "Europe")

MeDot <- dotchart(dna_freq$MiddleEast, color = mycolors, labels = row.names(dna_freq), pch=16, xlim = c(0,1.1), main = "Middle East")

NamDot <- dotchart(dna_freq$Namerica, color = mycolors, labels = row.names(dna_freq), pch=16, xlim = c(0,1.1), main = "North America")

OceDot <- dotchart(dna_freq$Oceania, color = mycolors, labels = row.names(dna_freq), pch=16, xlim = c(0,1.1), main = "Oceania")

SibDot <- dotchart(dna_freq$Siberia, color = mycolors, labels = row.names(dna_freq), pch=16, xlim = c(0,1.1), main = "Siberia")

SamDot <- dotchart(dna_freq$Samerica, color = mycolors, labels = row.names(dna_freq), pch=16, xlim = c(0,1.1), main = "South America")

SasDot <- dotchart(dna_freq$SAsia, color = mycolors, labels = row.names(dna_freq), pch=16, xlim = c(0,1.1), main = "South Asia")

SeaDot <- dotchart(dna_freq$SEA, color = mycolors, labels = row.names(dna_freq), pch=16, xlim = c(0,1.1), main = "South East Asia")

ScaDot <- dotchart(dna_freq$SCA, color = mycolors, labels = row.names(dna_freq), pch=16, xlim = c(0,1.1), main = "South Central Asia")

par(mfrow= c(4,4))
NamDot

## NULL

CamDot

## NULL

CarDot

## NULL

SamDot

## NULL

AfrDot

## NULL

MeDot

## NULL

EurDot

## NULL

ScaDot

## NULL

SasDot

## NULL

SeaDot

## NULL

EasDot

## NULL

SibDot

## NULL

OceDot

## NULL

I tried to combine the plots into a single plot using the par(mfrow =) function, but the objects I created showed up as “NULL” in the environment. Sources online said I should reset my graphics by using the dev.off function, but this did not remedy the problem

Pie charts

If you look at the help file for pie charts, there is a note that says any information a pie chart can convey can be better communicated by a dot chart. However, I would like to take the pie charts I produce by region and put them into my map. First, I have to make another data farme. Whereas to exclude haplogroups not represented in each sample, “NA”s were left in the data frame, the pie function will not work with them. They must be converted to zeros, and that is what I have done in this data frame

dnaf <- read.csv("~/Desktop/ANT191WD/dnaf.csv", row.names=1)

Below is the code used for producing each pie chart. Notice, I use the same object mycolors for the colorscheme in this function as well

AfrPie <- pie(dnaf$Africa, col = mycolors, labels = c("A","B","C",NULL,NULL,NULL))

CarPie <- pie(dnaf$Caribbean, col = mycolors, labels = c("A",NULL,NULL,NULL,NULL,NULL))

CamPie <- pie(dnaf$Africa, col = mycolors, labels = c("A","B","C",NULL,NULL,NULL))

EasPie <- pie(dnaf$EAsia, col = mycolors, labels = c("A","B","C",NULL,"E","F"))

EurPie <- pie(dnaf$Europe, col = mycolors, labels = c("A","B","C","D",NULL,NULL))

MePie <- pie(dnaf$MiddleEast, col = mycolors, labels = c("A","B","C","D",NULL,NULL))

NamPie <- pie(dnaf$Namerica, col = mycolors, labels = c("A","B","C",NULL,NULL,NULL))

OcePie <- pie(dnaf$Oceania, col = mycolors, labels = c("A","B","C",NULL,NULL,NULL))

ScaPie <- pie(dnaf$SCA, col = mycolors, labels = c("A","B",NULL,NULL,NULL,NULL))

SeaPie <- pie(dnaf$SEA, col = mycolors, labels = c("A","B","C",NULL,"E",NULL))

SibPie <- pie(dnaf$Siberia, col = mycolors, labels = c("A","B","C","D",NULL,NULL))

SamPie <- pie(dnaf$Samerica, col = mycolors, labels = c("A","B","C","D",NULL,NULL))

SasPie <- pie(dnaf$SAsia, col = mycolors, labels = c("A","B","C",NULL,NULL,NULL))

The pie charts were saved by manually clicking on them, as the export function would not work. As with the dot charts, the objects I created registered as “NULL” in my environment. I saved the pie charts and put them in Adobe Illustrator to crop them, and now they are ready for inclusion in my map