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<!DOCTYPE html> | |
<html> | |
<head> | |
<meta charset="utf-8" /> | |
<meta name="generator" content="pandoc" /> | |
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" /> | |
<meta name="author" content="Ziyi Bao" /> | |
<meta name="date" content="2024-09-10" /> | |
<title>baoz3-assignment2-f24:</title> | |
<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to | |
// be compatible with the behavior of Pandoc < 2.8). | |
document.addEventListener('DOMContentLoaded', function(e) { | |
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); | |
var i, h, a; | |
for (i = 0; i < hs.length; i++) { | |
h = hs[i]; | |
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 | |
a = h.attributes; | |
while (a.length > 0) h.removeAttribute(a[0].name); | |
} | |
}); | |
</script> | |
<script>/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */ | |
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) 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sel.removeAllRanges(); | |
} | |
}; | |
var columnNavigationWidthPX = 5; | |
var renderColumnNavigation = function(increment, backwards) { | |
var arrow = document.createElement("div"); | |
arrow.setAttribute("style", | |
"border-top: " + columnNavigationWidthPX + "px solid transparent;" + | |
"border-bottom: " + columnNavigationWidthPX + "px solid transparent;" + | |
"border-" + (backwards ? "right" : "left") + ": " + columnNavigationWidthPX + "px solid;"); | |
var header = document.createElement("th"); | |
header.appendChild(arrow); | |
header.setAttribute("style", | |
"cursor: pointer;" + | |
"vertical-align: middle;" + | |
"min-width: " + columnNavigationWidthPX + "px;" + | |
"width: " + columnNavigationWidthPX + "px;"); | |
header.onclick = function() { | |
columns.incColumnNumber(backwards ? -1 : increment); | |
me.animateColumns(backwards); | |
renderFooter(); | |
clearSelection(); | |
triggerOnChange(); | |
}; | |
return header; | |
}; | |
var maxColumnWidth = function(width) { | |
var padding = 80; | |
var columnMax = Math.max(cachedPagedTableClientWidth - padding, 0); | |
return parseInt(width) > 0 ? | |
Math.min(columnMax, parseInt(width)) + "px" : | |
columnMax + "px"; | |
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var clearHeader = function() { | |
var thead = pagedTable.querySelectorAll("thead")[0]; | |
thead.innerHTML = ""; | |
}; | |
var renderHeader = function(clear) { | |
cachedPagedTableClientWidth = pagedTable.clientWidth; | |
var fragment = document.createDocumentFragment(); | |
header = document.createElement("tr"); | |
fragment.appendChild(header); | |
if (columns.number > 0) | |
header.appendChild(renderColumnNavigation(-columns.visible, true)); | |
columns.subset = columns.subset.map(function(columnData) { | |
var column = document.createElement("th"); | |
column.setAttribute("align", columnData.align); | |
column.style.textAlign = columnData.align; | |
column.style.maxWidth = maxColumnWidth(null); | |
if (columnData.width) { | |
column.style.minWidth = | |
column.style.maxWidth = maxColumnWidth(columnData.width); | |
} | |
var columnName = document.createElement("div"); | |
columnName.setAttribute("class", "pagedtable-header-name"); | |
if (columnData.label === "") { | |
columnName.innerHTML = " "; | |
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else { | |
columnName.appendChild(document.createTextNode(columnData.label)); | |
} | |
column.appendChild(columnName); | |
var columnType = document.createElement("div"); | |
columnType.setAttribute("class", "pagedtable-header-type"); | |
if (columnData.type === "") { | |
columnType.innerHTML = " "; | |
} | |
else { | |
columnType.appendChild(document.createTextNode("<" + columnData.type + ">")); | |
} | |
column.appendChild(columnType); | |
header.appendChild(column); | |
columnData.element = column; | |
return columnData; | |
}); | |
for (var idx = 0; idx < columns.getPaddingCount(); idx++) { | |
var paddingCol = document.createElement("th"); | |
paddingCol.setAttribute("class", "pagedtable-padding-col"); | |
header.appendChild(paddingCol); | |
} | |
if (columns.number + columns.visible < columns.total) | |
header.appendChild(renderColumnNavigation(columns.visible, false)); | |
if (typeof(clear) == "undefined" || clear) clearHeader(); | |
var thead = pagedTable.querySelectorAll("thead")[0]; | |
thead.appendChild(fragment); | |
}; | |
me.animateColumns = function(backwards) { | |
var thead = pagedTable.querySelectorAll("thead")[0]; | |
var headerOld = thead.querySelectorAll("tr")[0]; | |
var tbodyOld = table.querySelectorAll("tbody")[0]; | |
me.fitColumns(backwards); | |
renderHeader(false); | |
header.style.opacity = "0"; | |
header.style.transform = backwards ? "translateX(-30px)" : "translateX(30px)"; | |
header.style.transition = "transform 200ms linear, opacity 200ms"; | |
header.style.transitionDelay = "0"; | |
renderBody(false); | |
if (headerOld) { | |
headerOld.style.position = "absolute"; | |
headerOld.style.transform = "translateX(0px)"; | |
headerOld.style.opacity = "1"; | |
headerOld.style.transition = "transform 100ms linear, opacity 100ms"; | |
headerOld.setAttribute("class", "pagedtable-remove-head"); | |
if (headerOld.style.transitionEnd) { | |
headerOld.addEventListener("transitionend", function() { | |
var headerOldByClass = thead.querySelector(".pagedtable-remove-head"); | |
if (headerOldByClass) thead.removeChild(headerOldByClass); | |
}); | |
} | |
else { | |
thead.removeChild(headerOld); | |
} | |
} | |
if (tbodyOld) table.removeChild(tbodyOld); | |
tbody.style.opacity = "0"; | |
tbody.style.transition = "transform 200ms linear, opacity 200ms"; | |
tbody.style.transitionDelay = "0ms"; | |
// force relayout | |
window.getComputedStyle(header).opacity; | |
window.getComputedStyle(tbody).opacity; | |
if (headerOld) { | |
headerOld.style.transform = backwards ? "translateX(20px)" : "translateX(-30px)"; | |
headerOld.style.opacity = "0"; | |
} | |
header.style.transform = "translateX(0px)"; | |
header.style.opacity = "1"; | |
tbody.style.opacity = "1"; | |
} | |
me.onChange = function(callback) { | |
onChangeCallbacks.push(callback); | |
}; | |
var triggerOnChange = function() { | |
onChangeCallbacks.forEach(function(onChange) { | |
onChange(); | |
}); | |
}; | |
var clearBody = function() { | |
if (tbody) { | |
table.removeChild(tbody); | |
tbody = null; | |
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var renderBody = function(clear) { | |
cachedPagedTableClientWidth = pagedTable.clientWidth | |
var fragment = document.createDocumentFragment(); | |
var pageData = data.slice(page.getRowStart(), page.getRowEnd()); | |
pageData.forEach(function(dataRow, idxRow) { | |
var htmlRow = document.createElement("tr"); | |
htmlRow.setAttribute("class", (idxRow % 2 !==0) ? "even" : "odd"); | |
if (columns.hasMoreLeftColumns()) | |
htmlRow.appendChild(document.createElement("td")); | |
columns.subset.forEach(function(columnData) { | |
var cellName = columnData.name; | |
var dataCell = dataRow[cellName]; | |
var htmlCell = document.createElement("td"); | |
if (dataCell === "NA") htmlCell.setAttribute("class", "pagedtable-na-cell"); | |
if (dataCell === "__NA__") dataCell = "NA"; | |
var cellText = document.createTextNode(dataCell); | |
htmlCell.appendChild(cellText); | |
if (dataCell.length > 50) { | |
htmlCell.setAttribute("title", dataCell); | |
} | |
htmlCell.setAttribute("align", columnData.align); | |
htmlCell.style.textAlign = columnData.align; | |
htmlCell.style.maxWidth = maxColumnWidth(null); | |
if (columnData.width) { | |
htmlCell.style.minWidth = htmlCell.style.maxWidth = maxColumnWidth(columnData.width); | |
} | |
htmlRow.appendChild(htmlCell); | |
}); | |
for (var idx = 0; idx < columns.getPaddingCount(); idx++) { | |
var paddingCol = document.createElement("td"); | |
paddingCol.setAttribute("class", "pagedtable-padding-col"); | |
htmlRow.appendChild(paddingCol); | |
} | |
if (columns.hasMoreRightColumns()) | |
htmlRow.appendChild(document.createElement("td")); | |
fragment.appendChild(htmlRow); | |
}); | |
for (var idxPadding = 0; idxPadding < page.getPaddingRows(); idxPadding++) { | |
var paddingRow = document.createElement("tr"); | |
var paddingCellRow = document.createElement("td"); | |
paddingCellRow.innerHTML = " "; | |
paddingCellRow.setAttribute("colspan", "100%"); | |
paddingRow.appendChild(paddingCellRow); | |
fragment.appendChild(paddingRow); | |
} | |
if (typeof(clear) == "undefined" || clear) clearBody(); | |
tbody = document.createElement("tbody"); | |
tbody.appendChild(fragment); | |
table.appendChild(tbody); | |
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var pageEnd = page.getRowEnd(); | |
var totalRows = data.length; | |
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if (totalRows < page.rows) { | |
infoText = totalRowsLabel + " row" + (totalRows != 1 ? "s" : ""); | |
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if (columns.total > columns.visible) { | |
var totalColumnsLabel = options.columns.total ? options.columns.total : columns.total; | |
infoText = infoText + " | " + (columns.number + 1) + "-" + | |
(Math.min(columns.number + columns.visible, columns.total)) + | |
" of " + totalColumnsLabel + " columns"; | |
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return infoText; | |
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var clearFooter = function() { | |
footer = pagedTable.querySelectorAll("div.pagedtable-footer")[0]; | |
footer.innerHTML = ""; | |
return footer; | |
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var pageLink = document.createElement("a"); | |
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pageLink.setAttribute("class", pageLinkClass); | |
pageLink.setAttribute("data-page-index", idxPage); | |
pageLink.onclick = function() { | |
page.setPageNumber(parseInt(this.getAttribute("data-page-index"))); | |
renderBody(); | |
renderFooter(); | |
triggerOnChange(); | |
}; | |
pageLink.appendChild(document.createTextNode(idxPage + 1)); | |
return pageLink; | |
} | |
var renderFooter = function() { | |
footer = clearFooter(); | |
var next = document.createElement("a"); | |
next.appendChild(document.createTextNode("Next")); | |
next.onclick = function() { | |
page.setPageNumber(page.number + 1); | |
renderBody(); | |
renderFooter(); | |
triggerOnChange(); | |
}; | |
if (data.length > page.rows) footer.appendChild(next); | |
var pageNumbers = document.createElement("div"); | |
pageNumbers.setAttribute("class", "pagedtable-indexes"); | |
var pageRange = page.getVisiblePageRange(); | |
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var pageLink = createPageLink(0); | |
pageNumbers.appendChild(pageLink); | |
var pageSeparator = document.createElement("div"); | |
pageSeparator.setAttribute("class", "pagedtable-index-separator-left"); | |
pageSeparator.appendChild(document.createTextNode("...")) | |
pageNumbers.appendChild(pageSeparator); | |
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for (var idxPage = pageRange.start; idxPage < pageRange.end; idxPage++) { | |
var pageLink = createPageLink(idxPage); | |
pageNumbers.appendChild(pageLink); | |
} | |
if (pageRange.last) { | |
var pageSeparator = document.createElement("div"); | |
pageSeparator.setAttribute("class", "pagedtable-index-separator-right"); | |
pageSeparator.appendChild(document.createTextNode("...")) | |
pageNumbers.appendChild(pageSeparator); | |
var pageLink = createPageLink(page.total - 1); | |
pageNumbers.appendChild(pageLink); | |
} | |
if (data.length > page.rows) footer.appendChild(pageNumbers); | |
var previous = document.createElement("a"); | |
previous.appendChild(document.createTextNode("Previous")); | |
previous.onclick = function() { | |
page.setPageNumber(page.number - 1); | |
renderBody(); | |
renderFooter(); | |
triggerOnChange(); | |
}; | |
if (data.length > page.rows) footer.appendChild(previous); | |
var infoLabel = document.createElement("div"); | |
infoLabel.setAttribute("class", "pagedtable-info"); | |
infoLabel.setAttribute("title", getLabelInfo()); | |
infoLabel.appendChild(document.createTextNode(getLabelInfo())); | |
footer.appendChild(infoLabel); | |
var enabledClass = "pagedtable-index-nav"; | |
var disabledClass = "pagedtable-index-nav pagedtable-index-nav-disabled"; | |
previous.setAttribute("class", page.number <= 0 ? disabledClass : enabledClass); | |
next.setAttribute("class", (page.number + 1) * page.rows >= data.length ? disabledClass : enabledClass); | |
}; | |
var measuresCell = null; | |
var renderMeasures = function() { | |
var measuresTable = document.createElement("table"); | |
measuresTable.style.visibility = "hidden"; | |
measuresTable.style.position = "absolute"; | |
measuresTable.style.whiteSpace = "nowrap"; | |
measuresTable.style.height = "auto"; | |
measuresTable.style.width = "auto"; | |
var measuresRow = document.createElement("tr"); | |
measuresTable.appendChild(measuresRow); | |
measuresCell = document.createElement("td"); | |
var sampleString = "ABCDEFGHIJ0123456789"; | |
measuresCell.appendChild(document.createTextNode(sampleString)); | |
measuresRow.appendChild(measuresCell); | |
tableDiv.appendChild(measuresTable); | |
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me.init = function() { | |
tableDiv = document.createElement("div"); | |
pagedTable.appendChild(tableDiv); | |
var pagedTableClass = data.length > 0 ? | |
"pagedtable pagedtable-not-empty" : | |
"pagedtable pagedtable-empty"; | |
if (columns.total == 0 || (columns.emptyNames() && data.length == 0)) { | |
pagedTableClass = pagedTableClass + " pagedtable-empty-columns"; | |
} | |
tableDiv.setAttribute("class", pagedTableClass); | |
renderMeasures(); | |
measurer.calculate(measuresCell); | |
columns.calculateWidths(measurer.measures); | |
table = document.createElement("table"); | |
table.setAttribute("cellspacing", "0"); | |
table.setAttribute("class", "table table-condensed"); | |
tableDiv.appendChild(table); | |
table.appendChild(document.createElement("thead")); | |
var footerDiv = document.createElement("div"); | |
footerDiv.setAttribute("class", "pagedtable-footer"); | |
tableDiv.appendChild(footerDiv); | |
// if the host has not yet provided horizontal space, render hidden | |
if (tableDiv.clientWidth <= 0) { | |
tableDiv.style.opacity = "0"; | |
} | |
me.render(); | |
// retry seizing columns later if the host has not provided space | |
function retryFit() { | |
if (tableDiv.clientWidth <= 0) { | |
setTimeout(retryFit, 100); | |
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me.render(); | |
triggerOnChange(); | |
} | |
} | |
if (tableDiv.clientWidth <= 0) { | |
retryFit(); | |
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}; | |
var registerWidths = function() { | |
columns.subset = columns.subset.map(function(column) { | |
column.width = columns.widths[column.name].inner; | |
return column; | |
}); | |
}; | |
var parsePadding = function(value) { | |
return parseInt(value) >= 0 ? parseInt(value) : 0; | |
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me.fixedHeight = function() { | |
return options.rows.max != null; | |
} | |
me.fitRows = function() { | |
if (me.fixedHeight()) | |
return; | |
measurer.calculate(measuresCell); | |
var rows = options.rows.min !== null ? options.rows.min : 0; | |
var headerHeight = header !== null && header.offsetHeight > 0 ? header.offsetHeight : 0; | |
var footerHeight = footer !== null && footer.offsetHeight > 0 ? footer.offsetHeight : 0; | |
if (pagedTable.offsetHeight > 0) { | |
var availableHeight = pagedTable.offsetHeight - headerHeight - footerHeight; | |
rows = Math.floor((availableHeight) / measurer.measures.height); | |
} | |
rows = options.rows.min !== null ? Math.max(options.rows.min, rows) : rows; | |
page.setRows(rows); | |
} | |
// The goal of this function is to add as many columns as possible | |
// starting from left-to-right, when the right most limit is reached | |
// it tries to add columns from the left as well. | |
// | |
// When startBackwards is true columns are added from right-to-left | |
me.fitColumns = function(startBackwards) { | |
measurer.calculate(measuresCell); | |
columns.calculateWidths(measurer.measures); | |
if (tableDiv.clientWidth > 0) { | |
tableDiv.style.opacity = 1; | |
} | |
var visibleColumns = tableDiv.clientWidth <= 0 ? Math.max(columns.min, 1) : 1; | |
var columnNumber = columns.number; | |
var paddingCount = 0; | |
// track a list of added columns as we build the visible ones to allow us | |
// to remove columns when they don't fit anymore. | |
var columnHistory = []; | |
var lastTableHeight = 0; | |
var backwards = startBackwards; | |
var tableDivStyle = window.getComputedStyle(tableDiv, null); | |
var tableDivPadding = parsePadding(tableDivStyle.paddingLeft) + | |
parsePadding(tableDivStyle.paddingRight); | |
var addPaddingCol = false; | |
var currentWidth = 0; | |
while (true) { | |
columns.setVisibleColumns(columnNumber, visibleColumns, paddingCount); | |
currentWidth = columns.getWidth(); | |
if (tableDiv.clientWidth - tableDivPadding < currentWidth) { | |
break; | |
} | |
columnHistory.push({ | |
columnNumber: columnNumber, | |
visibleColumns: visibleColumns, | |
paddingCount: paddingCount | |
}); | |
if (columnHistory.length > 100) { | |
console.error("More than 100 tries to fit columns, aborting"); | |
break; | |
} | |
if (columns.max !== null && | |
columns.visible + columns.getPaddingCount() >= columns.max) { | |
break; | |
} | |
// if we run out of right-columns | |
if (!backwards && columnNumber + columns.visible >= columns.total) { | |
// if we started adding right-columns, try adding left-columns | |
if (!startBackwards && columnNumber > 0) { | |
backwards = true; | |
} | |
else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) { | |
break; | |
} | |
else { | |
paddingCount = paddingCount + 1; | |
} | |
} | |
// if we run out of left-columns | |
if (backwards && columnNumber == 0) { | |
// if we started adding left-columns, try adding right-columns | |
if (startBackwards && columnNumber + columns.visible < columns.total) { | |
backwards = false; | |
} | |
else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) { | |
break; | |
} | |
else { | |
paddingCount = paddingCount + 1; | |
} | |
} | |
// when moving backwards try fitting left columns first | |
if (backwards && columnNumber > 0) { | |
columnNumber = columnNumber - 1; | |
} | |
if (columnNumber + visibleColumns < columns.total) { | |
visibleColumns = visibleColumns + 1; | |
} | |
} | |
var lastRenderableColumn = { | |
columnNumber: columnNumber, | |
visibleColumns: visibleColumns, | |
paddingCount: paddingCount | |
}; | |
if (columnHistory.length > 0) { | |
lastRenderableColumn = columnHistory[columnHistory.length - 1]; | |
} | |
columns.setVisibleColumns( | |
lastRenderableColumn.columnNumber, | |
lastRenderableColumn.visibleColumns, | |
lastRenderableColumn.paddingCount); | |
if (pagedTable.offsetWidth > 0) { | |
page.setVisiblePages(Math.max(Math.ceil(1.0 * (pagedTable.offsetWidth - 250) / 40), 2)); | |
} | |
registerWidths(); | |
}; | |
me.fit = function(startBackwards) { | |
me.fitRows(); | |
me.fitColumns(startBackwards); | |
} | |
me.render = function() { | |
me.fitColumns(false); | |
// render header/footer to measure height accurately | |
renderHeader(); | |
renderFooter(); | |
me.fitRows(); | |
renderBody(); | |
// re-render footer to match new rows | |
renderFooter(); | |
} | |
var resizeLastWidth = -1; | |
var resizeLastHeight = -1; | |
var resizeNewWidth = -1; | |
var resizeNewHeight = -1; | |
var resizePending = false; | |
me.resize = function(newWidth, newHeight) { | |
function resizeDelayed() { | |
resizePending = false; | |
if ( | |
(resizeNewWidth !== resizeLastWidth) || | |
(!me.fixedHeight() && resizeNewHeight !== resizeLastHeight) | |
) { | |
resizeLastWidth = resizeNewWidth; | |
resizeLastHeight = resizeNewHeight; | |
setTimeout(resizeDelayed, 200); | |
resizePending = true; | |
} else { | |
me.render(); | |
triggerOnChange(); | |
resizeLastWidth = -1; | |
resizeLastHeight = -1; | |
} | |
} | |
resizeNewWidth = newWidth; | |
resizeNewHeight = newHeight; | |
if (!resizePending) resizeDelayed(); | |
}; | |
}; | |
var PagedTableDoc; | |
(function (PagedTableDoc) { | |
var allPagedTables = []; | |
PagedTableDoc.initAll = function() { | |
allPagedTables = []; | |
var pagedTables = [].slice.call(document.querySelectorAll('[data-pagedtable="false"],[data-pagedtable=""]')); | |
pagedTables.forEach(function(pagedTable, idx) { | |
pagedTable.setAttribute("data-pagedtable", "true"); | |
pagedTable.setAttribute("pagedtable-page", 0); | |
pagedTable.setAttribute("class", "pagedtable-wrapper"); | |
var pagedTableInstance = new PagedTable(pagedTable); | |
pagedTableInstance.init(); | |
allPagedTables.push(pagedTableInstance); | |
}); | |
}; | |
PagedTableDoc.resizeAll = function() { | |
allPagedTables.forEach(function(pagedTable) { | |
pagedTable.render(); | |
}); | |
}; | |
window.addEventListener("resize", PagedTableDoc.resizeAll); | |
return PagedTableDoc; | |
})(PagedTableDoc || (PagedTableDoc = {})); | |
window.onload = function() { | |
PagedTableDoc.initAll(); | |
}; | |
</script> | |
<style type="text/css"> | |
code{white-space: pre-wrap;} | |
span.smallcaps{font-variant: small-caps;} | |
span.underline{text-decoration: underline;} | |
div.column{display: inline-block; vertical-align: top; width: 50%;} | |
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} | |
ul.task-list{list-style: none;} | |
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<style type="text/css">code{white-space: pre;}</style> | |
<script type="text/javascript"> | |
if (window.hljs) { | |
hljs.configure({languages: []}); | |
hljs.initHighlightingOnLoad(); | |
if (document.readyState && document.readyState === "complete") { | |
window.setTimeout(function() { hljs.initHighlighting(); }, 0); | |
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<style type="text/css"> | |
.main-container { | |
max-width: 940px; | |
margin-left: auto; | |
margin-right: auto; | |
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img { | |
max-width:100%; | |
} | |
.tabbed-pane { | |
padding-top: 12px; | |
} | |
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margin-bottom: 20px; | |
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button.code-folding-btn:focus { | |
outline: none; | |
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<style type="text/css"> | |
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border: 1px solid #ddd; | |
border-radius: 4px; | |
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content: "\e259"; | |
font-family: 'Glyphicons Halflings'; | |
display: inline-block; | |
padding: 10px; | |
border-right: 1px solid #ddd; | |
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content: "\e258"; | |
font-family: 'Glyphicons Halflings'; | |
border: none; | |
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.tabset-dropdown > .nav-tabs > li > a, | |
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border: none; | |
display: inline-block; | |
border-radius: 4px; | |
background-color: transparent; | |
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float: none; | |
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display: none; | |
} | |
</style> | |
<!-- code folding --> | |
</head> | |
<body> | |
<div class="container-fluid main-container"> | |
<div id="header"> | |
<h1 class="title toc-ignore">baoz3-assignment2-f24:</h1> | |
<h3 class="subtitle">DAR Assignment 2 (Fall 2024)</h3> | |
<h4 class="author">Ziyi Bao</h4> | |
<h4 class="date">10 September 2024</h4> | |
</div> | |
<div id="TOC"> | |
<ul> | |
<li><a href="#dar-assignment-2-introduction-introductory-dar-notebook" id="toc-dar-assignment-2-introduction-introductory-dar-notebook"><span class="toc-section-number">1</span> DAR ASSIGNMENT 2 (Introduction): | |
Introductory DAR Notebook</a></li> | |
<li><a href="#dar-assignment-2-part-1-preparing-your-local-repo-for-assignment-2" id="toc-dar-assignment-2-part-1-preparing-your-local-repo-for-assignment-2"><span class="toc-section-number">2</span> DAR ASSIGNMENT 2 (Part 1): Preparing | |
your local repo for Assignment 2</a> | |
<ul> | |
<li><a href="#updating-your-local-clone-of-the-dar-mars-f24-repository" id="toc-updating-your-local-clone-of-the-dar-mars-f24-repository"><span class="toc-section-number">2.1</span> Updating your local clone of the | |
<code>DAR-Mars-F24</code> repository</a></li> | |
<li><a href="#creating-your-copy-of-the-assignment-2-notebook" id="toc-creating-your-copy-of-the-assignment-2-notebook"><span class="toc-section-number">2.2</span> Creating your copy of the | |
Assignment 2 notebook</a></li> | |
</ul></li> | |
<li><a href="#dar-assignment-2-part-2-loading-the-mars-2020-m20-datasets" id="toc-dar-assignment-2-part-2-loading-the-mars-2020-m20-datasets"><span class="toc-section-number">3</span> DAR ASSIGNMENT 2 (Part 2): Loading | |
the Mars 2020 (M20) Datasets</a> | |
<ul> | |
<li><a href="#data-set-a-load-the-lithology-data" id="toc-data-set-a-load-the-lithology-data"><span class="toc-section-number">3.1</span> Data Set A: Load the Lithology | |
Data</a></li> | |
<li><a href="#data-set-b-load-the-pixl-data" id="toc-data-set-b-load-the-pixl-data"><span class="toc-section-number">3.2</span> Data Set B: Load the PIXL | |
Data</a></li> | |
<li><a href="#data-set-c-load-the-libs-data" id="toc-data-set-c-load-the-libs-data"><span class="toc-section-number">3.3</span> Data Set C: Load the LIBS | |
Data</a></li> | |
<li><a href="#dataset-d-load-the-sherloc-data" id="toc-dataset-d-load-the-sherloc-data"><span class="toc-section-number">3.4</span> Dataset D: Load the SHERLOC | |
Data</a></li> | |
<li><a href="#data-set-e-pixl-sherloc" id="toc-data-set-e-pixl-sherloc"><span class="toc-section-number">3.5</span> Data Set E: PIXL + | |
Sherloc</a></li> | |
<li><a href="#data-set-f-pixl-lithography" id="toc-data-set-f-pixl-lithography"><span class="toc-section-number">3.6</span> Data Set F: PIXL + | |
Lithography</a></li> | |
<li><a href="#data-set-g-sherloc-lithology" id="toc-data-set-g-sherloc-lithology"><span class="toc-section-number">3.7</span> Data Set G: Sherloc + | |
Lithology</a></li> | |
<li><a href="#data-set-h-sherloc-lithology-pixl" id="toc-data-set-h-sherloc-lithology-pixl"><span class="toc-section-number">3.8</span> Data Set H: Sherloc + Lithology + | |
PIXL</a></li> | |
</ul></li> | |
<li><a href="#analysis-of-data-part-3" id="toc-analysis-of-data-part-3"><span class="toc-section-number">4</span> Analysis of Data (Part 3)</a></li> | |
<li><a href="#preparation-of-team-presentation-part-4" id="toc-preparation-of-team-presentation-part-4"><span class="toc-section-number">5</span> Preparation of Team Presentation | |
(Part 4)</a></li> | |
<li><a href="#when-youre-done-save-commit-and-push-your-changes" id="toc-when-youre-done-save-commit-and-push-your-changes"><span class="toc-section-number">6</span> When you’re done: SAVE, COMMIT and | |
PUSH YOUR CHANGES!</a></li> | |
<li><a href="#appendix-accessing-rstudio-server-on-the-idea-cluster" id="toc-appendix-accessing-rstudio-server-on-the-idea-cluster"><span class="toc-section-number">7</span> APPENDIX: Accessing RStudio Server | |
on the IDEA Cluster</a></li> | |
<li><a href="#more-info-about-rstudio-on-our-cluster" id="toc-more-info-about-rstudio-on-our-cluster"><span class="toc-section-number">8</span> More info about Rstudio on our | |
Cluster</a> | |
<ul> | |
<li><a href="#rstudio-gui-access" id="toc-rstudio-gui-access"><span class="toc-section-number">8.1</span> RStudio GUI Access:</a></li> | |
</ul></li> | |
</ul> | |
</div> | |
<div id="dar-assignment-2-introduction-introductory-dar-notebook" class="section level1" number="1"> | |
<h1><span class="header-section-number">1</span> DAR ASSIGNMENT 2 | |
(Introduction): Introductory DAR Notebook</h1> | |
<p>This notebook is broken into two main parts:</p> | |
<ul> | |
<li><p><strong>Part 1:</strong> Preparing your local repo for | |
<strong>DAR Assignment 2</strong></p></li> | |
<li><p><strong>Part 2:</strong> Loading and some analysis of the Mars | |
2020 (M20) Datasets</p> | |
<ul> | |
<li>Lithology: <em>Summarizes the mineral characteristics of samples | |
collected at certain sample locations.</em></li> | |
<li>PIXL: Planetary Instrument for X-ray Lithochemistry. <em>Measures | |
elemental chemistry of samples at sub-millimeter scales of | |
samples.</em></li> | |
<li>SHERLOC: Scanning Habitable Environments with Raman and Luminescence | |
for Organics and Chemicals. <em>Uses cameras, a spectrometer, and a | |
laser of samples to search for organic compounds and minerals that have | |
been altered in watery environments and may be signs of past microbial | |
life.</em></li> | |
<li>LIBS: Laser-induced breakdown spectroscopy. <em>Uses a laser beam to | |
help identify minerals in samples and other areas that are beyond the | |
reach of the rover’s robotic arm or in areas too steep for the rover to | |
travel.</em></li> | |
</ul></li> | |
<li><p><strong>Part 3:</strong> Individual analysis of your team’s | |
dataset</p></li> | |
<li><p><strong>Part 4:</strong> Preparation of Team | |
Presentation</p></li> | |
</ul> | |
<p><strong>NOTE:</strong> The RPI github repository for all the code and | |
data required for this notebook may be found at:</p> | |
<ul> | |
<li><a href="https://github.rpi.edu/DataINCITE/DAR-Mars-F24" class="uri">https://github.rpi.edu/DataINCITE/DAR-Mars-F24</a></li> | |
</ul> | |
</div> | |
<div id="dar-assignment-2-part-1-preparing-your-local-repo-for-assignment-2" class="section level1" number="2"> | |
<h1><span class="header-section-number">2</span> DAR ASSIGNMENT 2 (Part | |
1): Preparing your local repo for Assignment 2</h1> | |
<p>In this assignment you’ll start by making a copy of the Assignment 2 | |
template notebook, then you’ll add to your copy with your original work. | |
The instructions which follow explain how to accomplish this.</p> | |
<p><strong>NOTE:</strong> You already cloned the | |
<code>DAR-Mars-F24</code> repository for Assignment 1; you <strong>do | |
not</strong> need to make another clone of the repo, but you must begin | |
by updating your copy as instructed below:</p> | |
<div id="updating-your-local-clone-of-the-dar-mars-f24-repository" class="section level2" number="2.1"> | |
<h2><span class="header-section-number">2.1</span> Updating your local | |
clone of the <code>DAR-Mars-F24</code> repository</h2> | |
<ul> | |
<li>Access RStudio Server on the IDEA Cluster at <a href="http://lp01.idea.rpi.edu/rstudio-ose/" class="uri">http://lp01.idea.rpi.edu/rstudio-ose/</a> | |
<ul> | |
<li>REMINDER: You must be on the RPI VPN!!</li> | |
</ul></li> | |
<li>Access the Linux shell on the IDEA Cluster by clicking the | |
<strong>Terminal</strong> tab of RStudio Server (lower left panel). | |
<ul> | |
<li>You now see the Linux shell on the IDEA Cluster</li> | |
<li><code>cd</code> (change directory) to enter your home directory | |
using: <code>cd ~</code></li> | |
<li>Type <code>pwd</code> to confirm where you are</li> | |
</ul></li> | |
<li>In the Linux shell, <code>cd</code> to <code>DAR-Mars-F24</code> | |
<ul> | |
<li>Type <code>git pull origin main</code> to pull any updates</li> | |
<li>Always do this when you being work; we might have added or changed | |
something!</li> | |
</ul></li> | |
<li>In the Linux shell, <code>cd</code> into <code>Assignment02</code> | |
<ul> | |
<li>Type <code>ls -al</code> to list the current contents</li> | |
<li>Don’t be surprised if you see many files!</li> | |
</ul></li> | |
<li>In the Linux shell, type <code>git branch</code> to verify your | |
current working branch | |
<ul> | |
<li>If it is not <code>dar-yourrcs</code>, type | |
<code>git checkout dar-yourrcs</code> (where <code>yourrcs</code> is | |
your RCS id)</li> | |
<li>Re-type <code>git branch</code> to confirm</li> | |
</ul></li> | |
<li>Now in the RStudio Server UI, navigate to the | |
<code>DAR-Mars-F24/StudentNotebooks/Assignment02</code> directory via | |
the <strong>Files</strong> panel (lower right panel) | |
<ul> | |
<li>Under the <strong>More</strong> menu, set this to be your R working | |
directory</li> | |
<li>Setting the correct working directory is essential for interactive R | |
use!</li> | |
</ul></li> | |
</ul> | |
<p>You’re now ready to start coding Assignment 2!</p> | |
</div> | |
<div id="creating-your-copy-of-the-assignment-2-notebook" class="section level2" number="2.2"> | |
<h2><span class="header-section-number">2.2</span> Creating your copy of | |
the Assignment 2 notebook</h2> | |
<ol style="list-style-type: decimal"> | |
<li>In RStudio, make a <strong>copy</strong> of | |
<code>dar-f24-assignment2-template.Rmd</code> file using a <em>new, | |
original, descriptive</em> filename that <strong>includes your RCS | |
ID!</strong> | |
<ul> | |
<li>Open <code>dar-f24-assignment2-template.Rmd</code></li> | |
<li><strong>Save As…</strong> using a new filename that includes your | |
RCS ID</li> | |
<li>Example filename for user <code>erickj4</code>: | |
<code>erickj4-assignment2-f24.Rmd</code></li> | |
<li>POINTS OFF IF: | |
<ul> | |
<li>You don’t create a new filename!</li> | |
<li>You don’t include your RCS ID!</li> | |
<li>You include <code>template</code> in your new filename!</li> | |
</ul></li> | |
</ul></li> | |
<li>Edit your new notebook using RStudio and save | |
<ul> | |
<li>Change the <code>title:</code> and <code>subtitle:</code> headers | |
(at the top of the file)</li> | |
<li>Change the <code>author:</code></li> | |
<li>Don’t bother changing the <code>date:</code>; it should update | |
automagically…</li> | |
<li><strong>Save</strong> your changes</li> | |
</ul></li> | |
<li>Use the RStudio <code>Knit</code> command to create an PDF file; | |
repeat as necessary | |
<ul> | |
<li>Use the down arrow next to the word <code>Knit</code> and select | |
<strong>Knit to PDF</strong></li> | |
<li>You may also knit to HTML…</li> | |
</ul></li> | |
<li>In the Linux terminal, use <code>git add</code> to add each new file | |
you want to add to the repository | |
<ul> | |
<li>Type: <code>git add yourfilename.Rmd</code></li> | |
<li>Type: <code>git add yourfilename.pdf</code> (created when you | |
knitted)</li> | |
<li>Add your HTML if you also created one…</li> | |
</ul></li> | |
<li>When you’re ready, in Linux commit your changes: | |
<ul> | |
<li>Type: <code>git commit -m "some comment"</code> where “some comment” | |
is a useful comment describing your changes</li> | |
<li>This commits your changes to your local repo, and sets the stage for | |
your next operation.</li> | |
</ul></li> | |
<li>Finally, push your commits to the RPI github repo | |
<ul> | |
<li>Type: <code>git push origin dar-yourrcs</code> (where | |
<code>dar-yourrcs</code> is the branch you’ve been working in)</li> | |
<li>Your changes are now safely on the RPI github.</li> | |
</ul></li> | |
<li><strong>REQUIRED:</strong> On the RPI github, <strong>submit a pull | |
request.</strong> | |
<ul> | |
<li>In a web browser, navigate to <a href="https://github.rpi.edu/DataINCITE/DAR-Mars-F24" class="uri">https://github.rpi.edu/DataINCITE/DAR-Mars-F24</a></li> | |
<li>In the branch selector drop-down (by default says | |
<strong>master</strong>), select your branch</li> | |
<li><strong>Submit a pull request for your branch</strong></li> | |
<li>One of the DAR instructors will merge your branch, and your new | |
files will be added to the master branch of the repo. <em>Do not merge | |
your branch yourself!</em></li> | |
</ul></li> | |
</ol> | |
</div> | |
</div> | |
<div id="dar-assignment-2-part-2-loading-the-mars-2020-m20-datasets" class="section level1" number="3"> | |
<h1><span class="header-section-number">3</span> DAR ASSIGNMENT 2 (Part | |
2): Loading the Mars 2020 (M20) Datasets</h1> | |
<p>In this assignment there are four datasets from separate instruments | |
on the Mars Perserverance rover available for analysis:</p> | |
<ul> | |
<li><strong>Lithology:</strong> Summarizes the mineral characteristics | |
of samples collected at certain sample locations</li> | |
<li><strong>PIXL:</strong> Planetary Instrument for X-ray Lithochemistry | |
of collected samples</li> | |
<li><strong>SHERLOC:</strong> Scanning Habitable Environments with Raman | |
and Luminescence for Organics and Chemicals for collected samples</li> | |
<li><strong>LIBS:</strong> Laser-induced breakdown spectroscopy which | |
are measured in many areas (not just samples)</li> | |
</ul> | |
<p>Each dataset provides data about the mineralogy of the surface of | |
Mars. Based on the purpose and nature of the instrument, the data is | |
collected at different intervals along the path of Perseverance as it | |
makes it way across the Jezero crater. Some of the data (esp. LIBS) is | |
collected almost every Martian day, or <em>sol</em>. Some of the data | |
(PIXL and SHERLOC) is only collected at certain sample locations of | |
interest</p> | |
<p>Your objective is to perform an analysis of the your team’s assigned | |
dataset in order to learn all you can about these Mars samples.</p> | |
<p>NOTES:</p> | |
<ul> | |
<li>All of these datasets can be found in | |
<code>/academics/MATP-4910-F24/DAR-Mars-F24/Data</code></li> | |
<li>We have included a comprehensive <code>samples.Rds</code> dataset | |
that includes useful details about the sample locations, including | |
Martian latitude and longitude and the sol that individual samples were | |
collected.</li> | |
<li>Also included is <code>rover.waypoints.Rds</code> that provides | |
detailed location information (lat/lon) for the Perseverance rover | |
throughout its journey, up to the present. This can be updated when | |
necessary using the included <code>roverStatus-f24.R</code> script.</li> | |
<li>A general guide to the available Mars 2020 data is available here: | |
<a href="https://pds-geosciences.wustl.edu/missions/mars2020/index.htm" class="uri">https://pds-geosciences.wustl.edu/missions/mars2020/index.htm</a></li> | |
<li>Other useful MARS 2020 sites <a href="https://science.nasa.gov/mission/mars-2020-perseverance/mars-rock-samples/" class="uri">https://science.nasa.gov/mission/mars-2020-perseverance/mars-rock-samples/</a> | |
and <a href="https://an.rsl.wustl.edu/m20/AN/an3.aspx?AspxAutoDetectCookieSupport=1" class="uri">https://an.rsl.wustl.edu/m20/AN/an3.aspx?AspxAutoDetectCookieSupport=1</a></li> | |
<li>Note that PIXL, SHERLOC, and Lithology describe 16 sample that were | |
physically collected. There will eventually be 38 samples. These | |
datasets can be merged by sample. The LIBS data includes observations | |
collected at many more locations so how to combine the LIBS data with | |
the other datasets is an open research question.</li> | |
</ul> | |
<div id="data-set-a-load-the-lithology-data" class="section level2" number="3.1"> | |
<h2><span class="header-section-number">3.1</span> Data Set A: Load the | |
Lithology Data</h2> | |
<p>The first five features of the dataset describe twenty-four (24) | |
rover sample locations.</p> | |
<p>The remaining features provides a simple binary (<code>1</code> or | |
<code>0</code>) summary of presence or absence of 35 minerals at the 24 | |
rover sample locations.</p> | |
<p>Only the first sixteen (16) samples are maintained, as the remaining | |
are missing the mineral descriptors.</p> | |
<p>The following code “cleans” the dataset to prepare for analysis. It | |
first creates a dataframe with metadata and measurements for samples, | |
and then creates a matrix containing only numeric measurements for later | |
analysis.</p> | |
<pre class="r"><code># Load the saved lithology data with locations added | |
lithology.df<- readRDS("/academics/MATP-4910-F24/DAR-Mars-F24/Data/mineral_data_static.Rds") | |
# Cast samples as numbers | |
lithology.df$sample <- as.numeric(lithology.df$sample) | |
# Convert rest into factors | |
lithology.df[sapply(lithology.df, is.character)] <- | |
lapply(lithology.df[sapply(lithology.df, is.character)], | |
as.factor) | |
# Keep only first 16 samples because the data for the rest of the samples is not available yet | |
lithology.df<-lithology.df[1:16,] | |
# Look at summary of cleaned data frame | |
summary(lithology.df)</code></pre> | |
<pre><code>## sample name SampleType campaign | |
## Min. : 1.00 Atsah : 1 atmospheric: 1 Crater Floor:9 | |
## 1st Qu.: 4.75 Bearwallow: 1 regolith : 0 Delta Front :7 | |
## Median : 8.50 Coulettes : 1 rock core :15 Margin Unit :0 | |
## Mean : 8.50 Hahonih : 1 | |
## 3rd Qu.:12.25 Hazeltop : 1 | |
## Max. :16.00 Kukaklek : 1 | |
## (Other) :10 | |
## abrasion feldspar plagioclase pyroxene olivine quartz apatite | |
## Alfalfa :2 0:14 0:13 0: 5 0: 6 0:14 0:13 | |
## Bellegarde :2 1: 2 1: 3 1:11 1:10 1: 2 1: 3 | |
## Berry Hollow:2 | |
## Dourbes :2 | |
## Novarupta :2 | |
## Quartier :2 | |
## (Other) :4 | |
## FeTi_Oxides Iron_Oxide Sulfate Perchlorates Phosphate Ca_Sulfate Carbonate | |
## 0:13 0:9 0: 4 0:15 0:11 0:10 0: 1 | |
## 1: 3 1:7 1:12 1: 1 1: 5 1: 6 1:15 | |
## | |
## | |
## | |
## | |
## | |
## Fe_Mg_clay Fe_Mg_carbonate Mg_sulfate Phyllosilicates Chlorite Halite | |
## 0:13 0:14 0:13 0:12 0:14 0:13 | |
## 1: 3 1: 2 1: 3 1: 4 1: 2 1: 3 | |
## | |
## | |
## | |
## | |
## | |
## Organic_matter Hydrated_Ca_Sulfate Hydrated_Sulfates Hydrated_Mg_Fe_Sulfate | |
## 0: 5 0:14 0:14 0:13 | |
## 1:11 1: 2 1: 2 1: 3 | |
## | |
## | |
## | |
## | |
## | |
## Na_Perchlorate Amorphous_Silicate Hydrated_Carbonates Disordered_Silicates | |
## 0:15 0:9 0:16 0:14 | |
## 1: 1 1:7 1: 2 | |
## | |
## | |
## | |
## | |
## | |
## Hydrated_Iron_Oxide Sulfate+Organic_Matter Other_hydrated_phases Kaolinite | |
## 0:15 0:11 0:8 0:13 | |
## 1: 1 1: 5 1:8 1: 3 | |
## | |
## | |
## | |
## | |
## | |
## Chromite Ilmenite Zircon/Baddeleyite Spinels | |
## 0:14 0:14 0:14 0:14 | |
## 1: 2 1: 2 1: 2 1: 2 | |
## | |
## | |
## | |
## | |
## </code></pre> | |
<pre class="r"><code># Create a matrix containing only the numeric measurements. The remaining features are metadata about the sample. | |
lithology.matrix <- sapply(lithology.df[,6:40],as.numeric)-1 | |
# Review the structure of our matrix | |
str(lithology.matrix)</code></pre> | |
<pre><code>## num [1:16, 1:35] 0 0 0 0 0 0 0 1 1 0 ... | |
## - attr(*, "dimnames")=List of 2 | |
## ..$ : NULL | |
## ..$ : chr [1:35] "feldspar" "plagioclase" "pyroxene" "olivine" ...</code></pre> | |
</div> | |
<div id="data-set-b-load-the-pixl-data" class="section level2" number="3.2"> | |
<h2><span class="header-section-number">3.2</span> Data Set B: Load the | |
PIXL Data</h2> | |
<p>The PIXL data provides summaries of the mineral compositions measured | |
at selected sample sites by the PIXL instrument. Note that here we scale | |
pixl.mat so features have mean 0 and standard deviation so results will | |
be different than in Assignment 1.</p> | |
<pre class="r"><code># Load the saved PIXL data with locations added | |
pixl.df <- readRDS("/academics/MATP-4910-F24/DAR-Mars-F24/Data/samples_pixl_wide.Rds") | |
# Convert to factors | |
pixl.df[sapply(pixl.df, is.character)] <- lapply(pixl.df[sapply(pixl.df, is.character)], | |
as.factor) | |
# Review our dataframe | |
summary(pixl.df)</code></pre> | |
<pre><code>## sample Na20 Mgo Al203 | |
## Min. : 1.00 Min. :1.000 Min. : 0.730 Min. : 1.700 | |
## 1st Qu.: 4.75 1st Qu.:1.853 1st Qu.: 2.533 1st Qu.: 2.220 | |
## Median : 8.50 Median :1.900 Median :12.800 Median : 3.710 | |
## Mean : 8.50 Mean :2.672 Mean :11.682 Mean : 5.072 | |
## 3rd Qu.:12.25 3rd Qu.:4.500 3rd Qu.:19.100 3rd Qu.: 7.117 | |
## Max. :16.00 Max. :5.550 Max. :22.700 Max. :11.600 | |
## | |
## Si02 P205 S03 Cl | |
## Min. :22.60 Min. :0.1000 Min. : 0.780 Min. :0.400 | |
## 1st Qu.:31.22 1st Qu.:0.2350 1st Qu.: 1.495 1st Qu.:0.940 | |
## Median :38.85 Median :0.5250 Median : 2.600 Median :1.740 | |
## Mean :38.55 Mean :0.6512 Mean : 5.562 Mean :1.846 | |
## 3rd Qu.:41.17 3rd Qu.:0.8400 3rd Qu.: 3.800 3rd Qu.:2.080 | |
## Max. :57.10 Max. :2.7600 Max. :21.530 Max. :4.500 | |
## | |
## K20 Cao Ti02 Cr203 | |
## Min. :0.0000 Min. :1.500 Min. :0.2000 Min. :0.000 | |
## 1st Qu.:0.1600 1st Qu.:2.655 1st Qu.:0.5900 1st Qu.:0.025 | |
## Median :0.2000 Median :3.120 Median :0.7000 Median :0.155 | |
## Mean :0.5800 Mean :3.688 Mean :0.8194 Mean :0.355 | |
## 3rd Qu.:0.8275 3rd Qu.:4.310 3rd Qu.:0.9900 3rd Qu.:0.290 | |
## Max. :1.9000 Max. :7.770 Max. :2.4900 Max. :1.900 | |
## | |
## Mno FeO-T name type | |
## Min. :0.1000 Min. :13.24 Atsah : 1 Igneous :8 | |
## 1st Qu.:0.2800 1st Qu.:16.71 Bearwallow: 1 N/A :1 | |
## Median :0.4000 Median :23.86 Coulettes : 1 Sedimentary:7 | |
## Mean :0.3812 Mean :21.45 Hahonih : 1 | |
## 3rd Qu.:0.4900 3rd Qu.:25.70 Hazeltop : 1 | |
## Max. :0.6900 Max. :30.05 Kukaklek : 1 | |
## (Other) :10 | |
## campaign location abrasion | |
## Crater Floor:9 01 : 1 Alfalfa :2 | |
## Delta Front :7 02 : 1 Bellegrade :2 | |
## 03 : 1 Berry Hollow:2 | |
## 04 : 1 Dourbes :2 | |
## 05 : 1 Novarupta :2 | |
## 06 : 1 Quartier :2 | |
## (Other):10 (Other) :4</code></pre> | |
<pre class="r"><code># Make the matrix of just mineral percentage measurements | |
pixl.matrix <- pixl.df[,2:14] %>% scale() | |
# Review the structure | |
str(pixl.matrix)</code></pre> | |
<pre><code>## num [1:16, 1:13] 1.928 1.338 -0.498 -0.538 1.225 ... | |
## - attr(*, "dimnames")=List of 2 | |
## ..$ : NULL | |
## ..$ : chr [1:13] "Na20" "Mgo" "Al203" "Si02" ... | |
## - attr(*, "scaled:center")= Named num [1:13] 2.672 11.682 5.072 38.554 0.651 ... | |
## ..- attr(*, "names")= chr [1:13] "Na20" "Mgo" "Al203" "Si02" ... | |
## - attr(*, "scaled:scale")= Named num [1:13] 1.492 7.957 3.75 11.026 0.694 ... | |
## ..- attr(*, "names")= chr [1:13] "Na20" "Mgo" "Al203" "Si02" ...</code></pre> | |
</div> | |
<div id="data-set-c-load-the-libs-data" class="section level2" number="3.3"> | |
<h2><span class="header-section-number">3.3</span> Data Set C: Load the | |
LIBS Data</h2> | |
<p>The LIBS data provides summaries of the mineral compositions measured | |
at selected sample sites by the LIBS instrument, part of the | |
Perseverance SuperCam.</p> | |
<pre class="r"><code># Load the saved LIBS data with locations added | |
libs.df <- readRDS("/academics/MATP-4910-F24/DAR-Mars-F24/Data/supercam_libs_moc_loc.Rds") | |
#Drop features that are not to be used in the analysis for this notebook | |
libs.df <- libs.df %>% | |
select(!(c(distance_mm,Tot.Em.,SiO2_stdev,TiO2_stdev,Al2O3_stdev,FeOT_stdev, | |
MgO_stdev,Na2O_stdev,CaO_stdev,K2O_stdev,Total))) | |
# Convert the points to numeric | |
libs.df$point <- as.numeric(libs.df$point) | |
# Review what we have | |
summary(libs.df)</code></pre> | |
<pre><code>## sol lat lon target | |
## Min. : 15.0 Min. :18.43 Min. :77.34 Length:1932 | |
## 1st Qu.: 281.0 1st Qu.:18.44 1st Qu.:77.36 Class :character | |
## Median : 557.0 Median :18.46 Median :77.40 Mode :character | |
## Mean : 565.1 Mean :18.46 Mean :77.40 | |
## 3rd Qu.: 872.0 3rd Qu.:18.48 3rd Qu.:77.44 | |
## Max. :1019.0 Max. :18.50 Max. :77.45 | |
## point SiO2 TiO2 Al2O3 | |
## Min. : 1.000 Min. : 0.00 Min. :0.0000 Min. : 0.000 | |
## 1st Qu.: 3.000 1st Qu.:42.04 1st Qu.:0.0300 1st Qu.: 3.080 | |
## Median : 5.000 Median :45.80 Median :0.3200 Median : 4.925 | |
## Mean : 5.776 Mean :43.47 Mean :0.3719 Mean : 6.246 | |
## 3rd Qu.: 8.000 3rd Qu.:49.23 3rd Qu.:0.6400 3rd Qu.: 8.533 | |
## Max. :28.000 Max. :76.12 Max. :2.4000 Max. :38.350 | |
## FeOT MgO CaO Na2O | |
## Min. : 0.29 Min. : 0.29 Min. : 0.080 Min. :0.0000 | |
## 1st Qu.:13.27 1st Qu.: 5.72 1st Qu.: 1.830 1st Qu.:0.9775 | |
## Median :20.21 Median :12.78 Median : 3.625 Median :1.5200 | |
## Mean :20.07 Mean :16.47 Mean : 4.726 Mean :1.7600 | |
## 3rd Qu.:25.45 3rd Qu.:27.83 3rd Qu.: 4.622 3rd Qu.:2.4000 | |
## Max. :82.68 Max. :45.21 Max. :52.130 Max. :7.5200 | |
## K2O | |
## Min. : 0.0000 | |
## 1st Qu.: 0.0000 | |
## Median : 0.3000 | |
## Mean : 0.5909 | |
## 3rd Qu.: 0.7800 | |
## Max. :34.8700</code></pre> | |
<pre class="r"><code># Make the a matrix contain only the libs measurements for each mineral | |
libs.matrix <- as.matrix(libs.df[,6:13]) | |
# Check to see scaling | |
str(libs.matrix)</code></pre> | |
<pre><code>## num [1:1932, 1:8] 49.7 55.8 61.2 51 48 ... | |
## - attr(*, "dimnames")=List of 2 | |
## ..$ : NULL | |
## ..$ : chr [1:8] "SiO2" "TiO2" "Al2O3" "FeOT" ...</code></pre> | |
</div> | |
<div id="dataset-d-load-the-sherloc-data" class="section level2" number="3.4"> | |
<h2><span class="header-section-number">3.4</span> Dataset D: Load the | |
SHERLOC Data</h2> | |
<p>The SHERLOC data you will be using for this lab is the result of | |
scientists’ interpretations of extensive spectral analysis of abrasion | |
samples provided by the SHERLOC instrument.</p> | |
<p><strong>NOTE:</strong> This dataset presents minerals as rows and | |
sample sites as columns. You’ll probably want to rotate the dataset for | |
easier analysis….</p> | |
<pre class="r"><code># Read in data as provided. | |
sherloc_abrasion_raw <- readRDS("/academics/MATP-4910-F24/DAR-Mars-F24/Data/abrasions_sherloc_samples.Rds") | |
# Clean up data types | |
sherloc_abrasion_raw$Mineral<-as.factor(sherloc_abrasion_raw$Mineral) | |
sherloc_abrasion_raw[sapply(sherloc_abrasion_raw, is.character)] <- lapply(sherloc_abrasion_raw[sapply(sherloc_abrasion_raw, is.character)], | |
as.numeric) | |
# Transform NA's to 0 | |
sherloc_abrasion_raw <- sherloc_abrasion_raw %>% replace(is.na(.), 0) | |
# Reformat data so that rows are "abrasions" and columns list the presence of minerals. | |
# Do this by "pivoting" to a long format, and then back to the desired wide format. | |
sherloc_long <- sherloc_abrasion_raw %>% | |
pivot_longer(!Mineral, names_to = "Name", values_to = "Presence") | |
# Make abrasion a factor | |
sherloc_long$Name <- as.factor(sherloc_long$Name) | |
# Make it a matrix | |
sherloc.matrix <- sherloc_long %>% | |
pivot_wider(names_from = Mineral, values_from = Presence) | |
# Get sample information from PIXL and add to measurements -- assumes order is the same | |
sherloc.df <- cbind(pixl.df[,c("sample","type","campaign","abrasion")],sherloc.matrix) | |
# Review what we have | |
summary(sherloc.df)</code></pre> | |
<pre><code>## sample type campaign abrasion | |
## Min. : 1.00 Igneous :8 Crater Floor:9 Alfalfa :2 | |
## 1st Qu.: 4.75 N/A :1 Delta Front :7 Bellegrade :2 | |
## Median : 8.50 Sedimentary:7 Berry Hollow:2 | |
## Mean : 8.50 Dourbes :2 | |
## 3rd Qu.:12.25 Novarupta :2 | |
## Max. :16.00 Quartier :2 | |
## (Other) :4 | |
## Name Plagioclase Sulfate Ca-sulfate | |
## Atsah : 1 Min. :0.0000 Min. :0.0000 Min. :0.0000 | |
## Bearwallow: 1 1st Qu.:0.0000 1st Qu.:0.1875 1st Qu.:0.0000 | |
## Coulettes : 1 Median :0.0000 Median :1.0000 Median :0.0000 | |
## Hahonih : 1 Mean :0.1875 Mean :0.6562 Mean :0.3438 | |
## Hazeltop : 1 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 | |
## Kukaklek : 1 Max. :1.0000 Max. :1.0000 Max. :1.0000 | |
## (Other) :10 | |
## Hydrated Ca-sulfate Mg-sulfate Hydrated Sulfates Hydrated Mg-Fe sulfate | |
## Min. :0.000 Min. :0.0000 Min. :0.000 Min. :0.0000 | |
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 | |
## Median :0.000 Median :0.0000 Median :0.000 Median :0.0000 | |
## Mean :0.125 Mean :0.1875 Mean :0.125 Mean :0.1875 | |
## 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000 | |
## Max. :1.000 Max. :1.0000 Max. :1.000 Max. :1.0000 | |
## | |
## Perchlorates Na-perchlorate Amorphous Silicate Phosphate | |
## Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.0000 | |
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 | |
## Median :0.0000 Median :0.00000 Median :0.0000 Median :0.0000 | |
## Mean :0.0625 Mean :0.03125 Mean :0.1406 Mean :0.2031 | |
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.2500 3rd Qu.:0.3125 | |
## Max. :1.0000 Max. :0.50000 Max. :0.5000 Max. :1.0000 | |
## | |
## Pyroxene Olivine Carbonate Fe-Mg carbonate | |
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000 | |
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.4375 1st Qu.:0.000 | |
## Median :1.0000 Median :0.6250 Median :1.0000 Median :0.000 | |
## Mean :0.6875 Mean :0.5312 Mean :0.7344 Mean :0.125 | |
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.000 | |
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.000 | |
## | |
## Hydrated Carbonates Disordered Silicates Feldspar Quartz | |
## Min. :0 Min. :0.000 Min. :0.000 Min. :0.00000 | |
## 1st Qu.:0 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00000 | |
## Median :0 Median :0.000 Median :0.000 Median :0.00000 | |
## Mean :0 Mean :0.125 Mean :0.125 Mean :0.03125 | |
## 3rd Qu.:0 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00000 | |
## Max. :0 Max. :1.000 Max. :1.000 Max. :0.25000 | |
## | |
## Apatite FeTi oxides Halite Iron oxide | |
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000 | |
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 | |
## Median :0.0000 Median :0.0000 Median :0.00000 Median :0.0000 | |
## Mean :0.1406 Mean :0.1406 Mean :0.04688 Mean :0.2812 | |
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.5000 | |
## Max. :1.0000 Max. :1.0000 Max. :0.25000 Max. :1.0000 | |
## | |
## Hydrated Iron oxide Organic matter Sulfate+Organic matter | |
## Min. :0.00000 Min. :0.0000 Min. :0.0000 | |
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 | |
## Median :0.00000 Median :1.0000 Median :0.0000 | |
## Mean :0.01562 Mean :0.5938 Mean :0.2188 | |
## 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.2500 | |
## Max. :0.25000 Max. :1.0000 Max. :1.0000 | |
## | |
## Other hydrated phases Phyllosilicates Chlorite | |
## Min. :0.0000 Min. :0.00000 Min. :0.0000 | |
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 | |
## Median :0.2500 Median :0.00000 Median :0.0000 | |
## Mean :0.4375 Mean :0.09375 Mean :0.0625 | |
## 3rd Qu.:1.0000 3rd Qu.:0.06250 3rd Qu.:0.0000 | |
## Max. :1.0000 Max. :0.50000 Max. :0.5000 | |
## | |
## Kaolinite (hydrous Al-clay) Chromite Ilmenite Zircon/Baddeleyite | |
## Min. :0.0000 Min. :0.000 Min. :0.000 Min. :0.000 | |
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 | |
## Median :0.0000 Median :0.000 Median :0.000 Median :0.000 | |
## Mean :0.1875 Mean :0.125 Mean :0.125 Mean :0.125 | |
## 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 | |
## Max. :1.0000 Max. :1.000 Max. :1.000 Max. :1.000 | |
## | |
## Fe-Mg-clay minerals Spinels | |
## Min. :0.0000 Min. :0.0000 | |
## 1st Qu.:0.0000 1st Qu.:0.0000 | |
## Median :0.0000 Median :0.0000 | |
## Mean :0.1875 Mean :0.0625 | |
## 3rd Qu.:0.0000 3rd Qu.:0.0000 | |
## Max. :1.0000 Max. :0.5000 | |
## </code></pre> | |
<pre class="r"><code># Measurements are everything except first column | |
sherloc.matrix<-as.matrix(sherloc.matrix[,-1]) | |
# Sherlock measurement matrix | |
# Review the structure | |
str(sherloc.matrix)</code></pre> | |
<pre><code>## num [1:16, 1:35] 1 1 1 0 0 0 0 0 0 0 ... | |
## - attr(*, "dimnames")=List of 2 | |
## ..$ : NULL | |
## ..$ : chr [1:35] "Plagioclase" "Sulfate" "Ca-sulfate" "Hydrated Ca-sulfate" ...</code></pre> | |
</div> | |
<div id="data-set-e-pixl-sherloc" class="section level2" number="3.5"> | |
<h2><span class="header-section-number">3.5</span> Data Set E: PIXL + | |
Sherloc</h2> | |
<pre class="r"><code># Combine PIXL and SHERLOC dataframes | |
pixl_sherloc.df <- cbind(pixl.df,sherloc.df ) | |
# Review what we have | |
summary(pixl_sherloc.df)</code></pre> | |
<pre><code>## sample Na20 Mgo Al203 | |
## Min. : 1.00 Min. :1.000 Min. : 0.730 Min. : 1.700 | |
## 1st Qu.: 4.75 1st Qu.:1.853 1st Qu.: 2.533 1st Qu.: 2.220 | |
## Median : 8.50 Median :1.900 Median :12.800 Median : 3.710 | |
## Mean : 8.50 Mean :2.672 Mean :11.682 Mean : 5.072 | |
## 3rd Qu.:12.25 3rd Qu.:4.500 3rd Qu.:19.100 3rd Qu.: 7.117 | |
## Max. :16.00 Max. :5.550 Max. :22.700 Max. :11.600 | |
## | |
## Si02 P205 S03 Cl | |
## Min. :22.60 Min. :0.1000 Min. : 0.780 Min. :0.400 | |
## 1st Qu.:31.22 1st Qu.:0.2350 1st Qu.: 1.495 1st Qu.:0.940 | |
## Median :38.85 Median :0.5250 Median : 2.600 Median :1.740 | |
## Mean :38.55 Mean :0.6512 Mean : 5.562 Mean :1.846 | |
## 3rd Qu.:41.17 3rd Qu.:0.8400 3rd Qu.: 3.800 3rd Qu.:2.080 | |
## Max. :57.10 Max. :2.7600 Max. :21.530 Max. :4.500 | |
## | |
## K20 Cao Ti02 Cr203 | |
## Min. :0.0000 Min. :1.500 Min. :0.2000 Min. :0.000 | |
## 1st Qu.:0.1600 1st Qu.:2.655 1st Qu.:0.5900 1st Qu.:0.025 | |
## Median :0.2000 Median :3.120 Median :0.7000 Median :0.155 | |
## Mean :0.5800 Mean :3.688 Mean :0.8194 Mean :0.355 | |
## 3rd Qu.:0.8275 3rd Qu.:4.310 3rd Qu.:0.9900 3rd Qu.:0.290 | |
## Max. :1.9000 Max. :7.770 Max. :2.4900 Max. :1.900 | |
## | |
## Mno FeO-T name type | |
## Min. :0.1000 Min. :13.24 Atsah : 1 Igneous :8 | |
## 1st Qu.:0.2800 1st Qu.:16.71 Bearwallow: 1 N/A :1 | |
## Median :0.4000 Median :23.86 Coulettes : 1 Sedimentary:7 | |
## Mean :0.3812 Mean :21.45 Hahonih : 1 | |
## 3rd Qu.:0.4900 3rd Qu.:25.70 Hazeltop : 1 | |
## Max. :0.6900 Max. :30.05 Kukaklek : 1 | |
## (Other) :10 | |
## campaign location abrasion sample type | |
## Crater Floor:9 01 : 1 Alfalfa :2 Min. : 1.00 Igneous :8 | |
## Delta Front :7 02 : 1 Bellegrade :2 1st Qu.: 4.75 N/A :1 | |
## 03 : 1 Berry Hollow:2 Median : 8.50 Sedimentary:7 | |
## 04 : 1 Dourbes :2 Mean : 8.50 | |
## 05 : 1 Novarupta :2 3rd Qu.:12.25 | |
## 06 : 1 Quartier :2 Max. :16.00 | |
## (Other):10 (Other) :4 | |
## campaign abrasion Name Plagioclase | |
## Crater Floor:9 Alfalfa :2 Atsah : 1 Min. :0.0000 | |
## Delta Front :7 Bellegrade :2 Bearwallow: 1 1st Qu.:0.0000 | |
## Berry Hollow:2 Coulettes : 1 Median :0.0000 | |
## Dourbes :2 Hahonih : 1 Mean :0.1875 | |
## Novarupta :2 Hazeltop : 1 3rd Qu.:0.0000 | |
## Quartier :2 Kukaklek : 1 Max. :1.0000 | |
## (Other) :4 (Other) :10 | |
## Sulfate Ca-sulfate Hydrated Ca-sulfate Mg-sulfate | |
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000 | |
## 1st Qu.:0.1875 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 | |
## Median :1.0000 Median :0.0000 Median :0.000 Median :0.0000 | |
## Mean :0.6562 Mean :0.3438 Mean :0.125 Mean :0.1875 | |
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.000 3rd Qu.:0.0000 | |
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.0000 | |
## | |
## Hydrated Sulfates Hydrated Mg-Fe sulfate Perchlorates Na-perchlorate | |
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.00000 | |
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 | |
## Median :0.000 Median :0.0000 Median :0.0000 Median :0.00000 | |
## Mean :0.125 Mean :0.1875 Mean :0.0625 Mean :0.03125 | |
## 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 | |
## Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :0.50000 | |
## | |
## Amorphous Silicate Phosphate Pyroxene Olivine | |
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000 | |
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 | |
## Median :0.0000 Median :0.0000 Median :1.0000 Median :0.6250 | |
## Mean :0.1406 Mean :0.2031 Mean :0.6875 Mean :0.5312 | |
## 3rd Qu.:0.2500 3rd Qu.:0.3125 3rd Qu.:1.0000 3rd Qu.:1.0000 | |
## Max. :0.5000 Max. :1.0000 Max. :1.0000 Max. :1.0000 | |
## | |
## Carbonate Fe-Mg carbonate Hydrated Carbonates Disordered Silicates | |
## Min. :0.0000 Min. :0.000 Min. :0 Min. :0.000 | |
## 1st Qu.:0.4375 1st Qu.:0.000 1st Qu.:0 1st Qu.:0.000 | |
## Median :1.0000 Median :0.000 Median :0 Median :0.000 | |
## Mean :0.7344 Mean :0.125 Mean :0 Mean :0.125 | |
## 3rd Qu.:1.0000 3rd Qu.:0.000 3rd Qu.:0 3rd Qu.:0.000 | |
## Max. :1.0000 Max. :1.000 Max. :0 Max. :1.000 | |
## | |
## Feldspar Quartz Apatite FeTi oxides | |
## Min. :0.000 Min. :0.00000 Min. :0.0000 Min. :0.0000 | |
## 1st Qu.:0.000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 | |
## Median :0.000 Median :0.00000 Median :0.0000 Median :0.0000 | |
## Mean :0.125 Mean :0.03125 Mean :0.1406 Mean :0.1406 | |
## 3rd Qu.:0.000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.0000 | |
## Max. :1.000 Max. :0.25000 Max. :1.0000 Max. :1.0000 | |
## | |
## Halite Iron oxide Hydrated Iron oxide Organic matter | |
## Min. :0.00000 Min. :0.0000 Min. :0.00000 Min. :0.0000 | |
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 | |
## Median :0.00000 Median :0.0000 Median :0.00000 Median :1.0000 | |
## Mean :0.04688 Mean :0.2812 Mean :0.01562 Mean :0.5938 | |
## 3rd Qu.:0.00000 3rd Qu.:0.5000 3rd Qu.:0.00000 3rd Qu.:1.0000 | |
## Max. :0.25000 Max. :1.0000 Max. :0.25000 Max. :1.0000 | |
## | |
## Sulfate+Organic matter Other hydrated phases Phyllosilicates | |
## Min. :0.0000 Min. :0.0000 Min. :0.00000 | |
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 | |
## Median :0.0000 Median :0.2500 Median :0.00000 | |
## Mean :0.2188 Mean :0.4375 Mean :0.09375 | |
## 3rd Qu.:0.2500 3rd Qu.:1.0000 3rd Qu.:0.06250 | |
## Max. :1.0000 Max. :1.0000 Max. :0.50000 | |
## | |
## Chlorite Kaolinite (hydrous Al-clay) Chromite Ilmenite | |
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.000 | |
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.000 | |
## Median :0.0000 Median :0.0000 Median :0.000 Median :0.000 | |
## Mean :0.0625 Mean :0.1875 Mean :0.125 Mean :0.125 | |
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.000 | |
## Max. :0.5000 Max. :1.0000 Max. :1.000 Max. :1.000 | |
## | |
## Zircon/Baddeleyite Fe-Mg-clay minerals Spinels | |
## Min. :0.000 Min. :0.0000 Min. :0.0000 | |
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000 | |
## Median :0.000 Median :0.0000 Median :0.0000 | |
## Mean :0.125 Mean :0.1875 Mean :0.0625 | |
## 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.0000 | |
## Max. :1.000 Max. :1.0000 Max. :0.5000 | |
## </code></pre> | |
<pre class="r"><code># Combine PIXL and SHERLOC matrices | |
pixl_sherloc.matrix<-cbind(pixl.matrix,sherloc.matrix) | |
# Review the structure of our matrix | |
str(pixl_sherloc.matrix)</code></pre> | |
<pre><code>## num [1:16, 1:48] 1.928 1.338 -0.498 -0.538 1.225 ... | |
## - attr(*, "dimnames")=List of 2 | |
## ..$ : NULL | |
## ..$ : chr [1:48] "Na20" "Mgo" "Al203" "Si02" ...</code></pre> | |
</div> | |
<div id="data-set-f-pixl-lithography" class="section level2" number="3.6"> | |
<h2><span class="header-section-number">3.6</span> Data Set F: PIXL + | |
Lithography</h2> | |
<p>Create data and matrix from prior datasets</p> | |
<pre class="r"><code># Combine our PIXL and Lithology dataframes | |
pixl_lithology.df <- cbind(pixl.df,lithology.df ) | |
# Review what we have | |
summary(pixl_lithology.df)</code></pre> | |
<pre><code>## sample Na20 Mgo Al203 | |
## Min. : 1.00 Min. :1.000 Min. : 0.730 Min. : 1.700 | |
## 1st Qu.: 4.75 1st Qu.:1.853 1st Qu.: 2.533 1st Qu.: 2.220 | |
## Median : 8.50 Median :1.900 Median :12.800 Median : 3.710 | |
## Mean : 8.50 Mean :2.672 Mean :11.682 Mean : 5.072 | |
## 3rd Qu.:12.25 3rd Qu.:4.500 3rd Qu.:19.100 3rd Qu.: 7.117 | |
## Max. :16.00 Max. :5.550 Max. :22.700 Max. :11.600 | |
## | |
## Si02 P205 S03 Cl | |
## Min. :22.60 Min. :0.1000 Min. : 0.780 Min. :0.400 | |
## 1st Qu.:31.22 1st Qu.:0.2350 1st Qu.: 1.495 1st Qu.:0.940 | |
## Median :38.85 Median :0.5250 Median : 2.600 Median :1.740 | |
## Mean :38.55 Mean :0.6512 Mean : 5.562 Mean :1.846 | |
## 3rd Qu.:41.17 3rd Qu.:0.8400 3rd Qu.: 3.800 3rd Qu.:2.080 | |
## Max. :57.10 Max. :2.7600 Max. :21.530 Max. :4.500 | |
## | |
## K20 Cao Ti02 Cr203 | |
## Min. :0.0000 Min. :1.500 Min. :0.2000 Min. :0.000 | |
## 1st Qu.:0.1600 1st Qu.:2.655 1st Qu.:0.5900 1st Qu.:0.025 | |
## Median :0.2000 Median :3.120 Median :0.7000 Median :0.155 | |
## Mean :0.5800 Mean :3.688 Mean :0.8194 Mean :0.355 | |
## 3rd Qu.:0.8275 3rd Qu.:4.310 3rd Qu.:0.9900 3rd Qu.:0.290 | |
## Max. :1.9000 Max. :7.770 Max. :2.4900 Max. :1.900 | |
## | |
## Mno FeO-T name type | |
## Min. :0.1000 Min. :13.24 Atsah : 1 Igneous :8 | |
## 1st Qu.:0.2800 1st Qu.:16.71 Bearwallow: 1 N/A :1 | |
## Median :0.4000 Median :23.86 Coulettes : 1 Sedimentary:7 | |
## Mean :0.3812 Mean :21.45 Hahonih : 1 | |
## 3rd Qu.:0.4900 3rd Qu.:25.70 Hazeltop : 1 | |
## Max. :0.6900 Max. :30.05 Kukaklek : 1 | |
## (Other) :10 | |
## campaign location abrasion sample name | |
## Crater Floor:9 01 : 1 Alfalfa :2 Min. : 1.00 Atsah : 1 | |
## Delta Front :7 02 : 1 Bellegrade :2 1st Qu.: 4.75 Bearwallow: 1 | |
## 03 : 1 Berry Hollow:2 Median : 8.50 Coulettes : 1 | |
## 04 : 1 Dourbes :2 Mean : 8.50 Hahonih : 1 | |
## 05 : 1 Novarupta :2 3rd Qu.:12.25 Hazeltop : 1 | |
## 06 : 1 Quartier :2 Max. :16.00 Kukaklek : 1 | |
## (Other):10 (Other) :4 (Other) :10 | |
## SampleType campaign abrasion feldspar plagioclase | |
## atmospheric: 1 Crater Floor:9 Alfalfa :2 0:14 0:13 | |
## regolith : 0 Delta Front :7 Bellegarde :2 1: 2 1: 3 | |
## rock core :15 Margin Unit :0 Berry Hollow:2 | |
## Dourbes :2 | |
## Novarupta :2 | |
## Quartier :2 | |
## (Other) :4 | |
## pyroxene olivine quartz apatite FeTi_Oxides Iron_Oxide Sulfate Perchlorates | |
## 0: 5 0: 6 0:14 0:13 0:13 0:9 0: 4 0:15 | |
## 1:11 1:10 1: 2 1: 3 1: 3 1:7 1:12 1: 1 | |
## | |
## | |
## | |
## | |
## | |
## Phosphate Ca_Sulfate Carbonate Fe_Mg_clay Fe_Mg_carbonate Mg_sulfate | |
## 0:11 0:10 0: 1 0:13 0:14 0:13 | |
## 1: 5 1: 6 1:15 1: 3 1: 2 1: 3 | |
## | |
## | |
## | |
## | |
## | |
## Phyllosilicates Chlorite Halite Organic_matter Hydrated_Ca_Sulfate | |
## 0:12 0:14 0:13 0: 5 0:14 | |
## 1: 4 1: 2 1: 3 1:11 1: 2 | |
## | |
## | |
## | |
## | |
## | |
## Hydrated_Sulfates Hydrated_Mg_Fe_Sulfate Na_Perchlorate Amorphous_Silicate | |
## 0:14 0:13 0:15 0:9 | |
## 1: 2 1: 3 1: 1 1:7 | |
## | |
## | |
## | |
## | |
## | |
## Hydrated_Carbonates Disordered_Silicates Hydrated_Iron_Oxide | |
## 0:16 0:14 0:15 | |
## 1: 2 1: 1 | |
## | |
## | |
## | |
## | |
## | |
## Sulfate+Organic_Matter Other_hydrated_phases Kaolinite Chromite Ilmenite | |
## 0:11 0:8 0:13 0:14 0:14 | |
## 1: 5 1:8 1: 3 1: 2 1: 2 | |
## | |
## | |
## | |
## | |
## | |
## Zircon/Baddeleyite Spinels | |
## 0:14 0:14 | |
## 1: 2 1: 2 | |
## | |
## | |
## | |
## | |
## </code></pre> | |
<pre class="r"><code># Combine PIXL and Lithology matrices | |
pixl_lithology.matrix<-cbind(pixl.matrix,lithology.matrix) | |
# Review the structure | |
str(pixl_lithology.matrix)</code></pre> | |
<pre><code>## num [1:16, 1:48] 1.928 1.338 -0.498 -0.538 1.225 ... | |
## - attr(*, "dimnames")=List of 2 | |
## ..$ : NULL | |
## ..$ : chr [1:48] "Na20" "Mgo" "Al203" "Si02" ...</code></pre> | |
</div> | |
<div id="data-set-g-sherloc-lithology" class="section level2" number="3.7"> | |
<h2><span class="header-section-number">3.7</span> Data Set G: Sherloc + | |
Lithology</h2> | |
<p>Create Data and matrix from prior datasets by taking on appropriate | |
combinations.</p> | |
<pre class="r"><code># Combine the Lithology and SHERLOC dataframes | |
sherloc_lithology.df <- cbind(sherloc.df,lithology.df ) | |
# Review what we have | |
summary(sherloc_lithology.df)</code></pre> | |
<pre><code>## sample type campaign abrasion | |
## Min. : 1.00 Igneous :8 Crater Floor:9 Alfalfa :2 | |
## 1st Qu.: 4.75 N/A :1 Delta Front :7 Bellegrade :2 | |
## Median : 8.50 Sedimentary:7 Berry Hollow:2 | |
## Mean : 8.50 Dourbes :2 | |
## 3rd Qu.:12.25 Novarupta :2 | |
## Max. :16.00 Quartier :2 | |
## (Other) :4 | |
## Name Plagioclase Sulfate Ca-sulfate | |
## Atsah : 1 Min. :0.0000 Min. :0.0000 Min. :0.0000 | |
## Bearwallow: 1 1st Qu.:0.0000 1st Qu.:0.1875 1st Qu.:0.0000 | |
## Coulettes : 1 Median :0.0000 Median :1.0000 Median :0.0000 | |
## Hahonih : 1 Mean :0.1875 Mean :0.6562 Mean :0.3438 | |
## Hazeltop : 1 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 | |
## Kukaklek : 1 Max. :1.0000 Max. :1.0000 Max. :1.0000 | |
## (Other) :10 | |
## Hydrated Ca-sulfate Mg-sulfate Hydrated Sulfates Hydrated Mg-Fe sulfate | |
## Min. :0.000 Min. :0.0000 Min. :0.000 Min. :0.0000 | |
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 | |
## Median :0.000 Median :0.0000 Median :0.000 Median :0.0000 | |
## Mean :0.125 Mean :0.1875 Mean :0.125 Mean :0.1875 | |
## 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000 | |
## Max. :1.000 Max. :1.0000 Max. :1.000 Max. :1.0000 | |
## | |
## Perchlorates Na-perchlorate Amorphous Silicate Phosphate | |
## Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.0000 | |
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 | |
## Median :0.0000 Median :0.00000 Median :0.0000 Median :0.0000 | |
## Mean :0.0625 Mean :0.03125 Mean :0.1406 Mean :0.2031 | |
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.2500 3rd Qu.:0.3125 | |
## Max. :1.0000 Max. :0.50000 Max. :0.5000 Max. :1.0000 | |
## | |
## Pyroxene Olivine Carbonate Fe-Mg carbonate | |
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000 | |
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.4375 1st Qu.:0.000 | |
## Median :1.0000 Median :0.6250 Median :1.0000 Median :0.000 | |
## Mean :0.6875 Mean :0.5312 Mean :0.7344 Mean :0.125 | |
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.000 | |
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.000 | |
## | |
## Hydrated Carbonates Disordered Silicates Feldspar Quartz | |
## Min. :0 Min. :0.000 Min. :0.000 Min. :0.00000 | |
## 1st Qu.:0 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00000 | |
## Median :0 Median :0.000 Median :0.000 Median :0.00000 | |
## Mean :0 Mean :0.125 Mean :0.125 Mean :0.03125 | |
## 3rd Qu.:0 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00000 | |
## Max. :0 Max. :1.000 Max. :1.000 Max. :0.25000 | |
## | |
## Apatite FeTi oxides Halite Iron oxide | |
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000 | |
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 | |
## Median :0.0000 Median :0.0000 Median :0.00000 Median :0.0000 | |
## Mean :0.1406 Mean :0.1406 Mean :0.04688 Mean :0.2812 | |
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.5000 | |
## Max. :1.0000 Max. :1.0000 Max. :0.25000 Max. :1.0000 | |
## | |
## Hydrated Iron oxide Organic matter Sulfate+Organic matter | |
## Min. :0.00000 Min. :0.0000 Min. :0.0000 | |
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 | |
## Median :0.00000 Median :1.0000 Median :0.0000 | |
## Mean :0.01562 Mean :0.5938 Mean :0.2188 | |
## 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.2500 | |
## Max. :0.25000 Max. :1.0000 Max. :1.0000 | |
## | |
## Other hydrated phases Phyllosilicates Chlorite | |
## Min. :0.0000 Min. :0.00000 Min. :0.0000 | |
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 | |
## Median :0.2500 Median :0.00000 Median :0.0000 | |
## Mean :0.4375 Mean :0.09375 Mean :0.0625 | |
## 3rd Qu.:1.0000 3rd Qu.:0.06250 3rd Qu.:0.0000 | |
## Max. :1.0000 Max. :0.50000 Max. :0.5000 | |
## | |
## Kaolinite (hydrous Al-clay) Chromite Ilmenite Zircon/Baddeleyite | |
## Min. :0.0000 Min. :0.000 Min. :0.000 Min. :0.000 | |
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 | |
## Median :0.0000 Median :0.000 Median :0.000 Median :0.000 | |
## Mean :0.1875 Mean :0.125 Mean :0.125 Mean :0.125 | |
## 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 | |
## Max. :1.0000 Max. :1.000 Max. :1.000 Max. :1.000 | |
## | |
## Fe-Mg-clay minerals Spinels sample name | |
## Min. :0.0000 Min. :0.0000 Min. : 1.00 Atsah : 1 | |
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 4.75 Bearwallow: 1 | |
## Median :0.0000 Median :0.0000 Median : 8.50 Coulettes : 1 | |
## Mean :0.1875 Mean :0.0625 Mean : 8.50 Hahonih : 1 | |
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:12.25 Hazeltop : 1 | |
## Max. :1.0000 Max. :0.5000 Max. :16.00 Kukaklek : 1 | |
## (Other) :10 | |
## SampleType campaign abrasion feldspar plagioclase | |
## atmospheric: 1 Crater Floor:9 Alfalfa :2 0:14 0:13 | |
## regolith : 0 Delta Front :7 Bellegarde :2 1: 2 1: 3 | |
## rock core :15 Margin Unit :0 Berry Hollow:2 | |
## Dourbes :2 | |
## Novarupta :2 | |
## Quartier :2 | |
## (Other) :4 | |
## pyroxene olivine quartz apatite FeTi_Oxides Iron_Oxide Sulfate Perchlorates | |
## 0: 5 0: 6 0:14 0:13 0:13 0:9 0: 4 0:15 | |
## 1:11 1:10 1: 2 1: 3 1: 3 1:7 1:12 1: 1 | |
## | |
## | |
## | |
## | |
## | |
## Phosphate Ca_Sulfate Carbonate Fe_Mg_clay Fe_Mg_carbonate Mg_sulfate | |
## 0:11 0:10 0: 1 0:13 0:14 0:13 | |
## 1: 5 1: 6 1:15 1: 3 1: 2 1: 3 | |
## | |
## | |
## | |
## | |
## | |
## Phyllosilicates Chlorite Halite Organic_matter Hydrated_Ca_Sulfate | |
## 0:12 0:14 0:13 0: 5 0:14 | |
## 1: 4 1: 2 1: 3 1:11 1: 2 | |
## | |
## | |
## | |
## | |
## | |
## Hydrated_Sulfates Hydrated_Mg_Fe_Sulfate Na_Perchlorate Amorphous_Silicate | |
## 0:14 0:13 0:15 0:9 | |
## 1: 2 1: 3 1: 1 1:7 | |
## | |
## | |
## | |
## | |
## | |
## Hydrated_Carbonates Disordered_Silicates Hydrated_Iron_Oxide | |
## 0:16 0:14 0:15 | |
## 1: 2 1: 1 | |
## | |
## | |
## | |
## | |
## | |
## Sulfate+Organic_Matter Other_hydrated_phases Kaolinite Chromite Ilmenite | |
## 0:11 0:8 0:13 0:14 0:14 | |
## 1: 5 1:8 1: 3 1: 2 1: 2 | |
## | |
## | |
## | |
## | |
## | |
## Zircon/Baddeleyite Spinels | |
## 0:14 0:14 | |
## 1: 2 1: 2 | |
## | |
## | |
## | |
## | |
## </code></pre> | |
<pre class="r"><code># Combine the Lithology and SHERLOC matrices | |
sherloc_lithology.matrix<-cbind(sherloc.matrix,lithology.matrix) | |
# Review the resulting matrix | |
str(sherloc_lithology.matrix)</code></pre> | |
<pre><code>## num [1:16, 1:70] 1 1 1 0 0 0 0 0 0 0 ... | |
## - attr(*, "dimnames")=List of 2 | |
## ..$ : NULL | |
## ..$ : chr [1:70] "Plagioclase" "Sulfate" "Ca-sulfate" "Hydrated Ca-sulfate" ...</code></pre> | |
</div> | |
<div id="data-set-h-sherloc-lithology-pixl" class="section level2" number="3.8"> | |
<h2><span class="header-section-number">3.8</span> Data Set H: Sherloc + | |
Lithology + PIXL</h2> | |
<p>Create data frame and matrix from prior datasets by making on | |
appropriate combinations.</p> | |
<pre class="r"><code># Combine the Lithology and SHERLOC dataframes | |
sherloc_lithology_pixl.df <- cbind(sherloc.df,lithology.df, pixl.df ) | |
# Review what we have | |
summary(sherloc_lithology_pixl.df)</code></pre> | |
<pre><code>## sample type campaign abrasion | |
## Min. : 1.00 Igneous :8 Crater Floor:9 Alfalfa :2 | |
## 1st Qu.: 4.75 N/A :1 Delta Front :7 Bellegrade :2 | |
## Median : 8.50 Sedimentary:7 Berry Hollow:2 | |
## Mean : 8.50 Dourbes :2 | |
## 3rd Qu.:12.25 Novarupta :2 | |
## Max. :16.00 Quartier :2 | |
## (Other) :4 | |
## Name Plagioclase Sulfate Ca-sulfate | |
## Atsah : 1 Min. :0.0000 Min. :0.0000 Min. :0.0000 | |
## Bearwallow: 1 1st Qu.:0.0000 1st Qu.:0.1875 1st Qu.:0.0000 | |
## Coulettes : 1 Median :0.0000 Median :1.0000 Median :0.0000 | |
## Hahonih : 1 Mean :0.1875 Mean :0.6562 Mean :0.3438 | |
## Hazeltop : 1 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 | |
## Kukaklek : 1 Max. :1.0000 Max. :1.0000 Max. :1.0000 | |
## (Other) :10 | |
## Hydrated Ca-sulfate Mg-sulfate Hydrated Sulfates Hydrated Mg-Fe sulfate | |
## Min. :0.000 Min. :0.0000 Min. :0.000 Min. :0.0000 | |
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 | |
## Median :0.000 Median :0.0000 Median :0.000 Median :0.0000 | |
## Mean :0.125 Mean :0.1875 Mean :0.125 Mean :0.1875 | |
## 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000 | |
## Max. :1.000 Max. :1.0000 Max. :1.000 Max. :1.0000 | |
## | |
## Perchlorates Na-perchlorate Amorphous Silicate Phosphate | |
## Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.0000 | |
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 | |
## Median :0.0000 Median :0.00000 Median :0.0000 Median :0.0000 | |
## Mean :0.0625 Mean :0.03125 Mean :0.1406 Mean :0.2031 | |
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.2500 3rd Qu.:0.3125 | |
## Max. :1.0000 Max. :0.50000 Max. :0.5000 Max. :1.0000 | |
## | |
## Pyroxene Olivine Carbonate Fe-Mg carbonate | |
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000 | |
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.4375 1st Qu.:0.000 | |
## Median :1.0000 Median :0.6250 Median :1.0000 Median :0.000 | |
## Mean :0.6875 Mean :0.5312 Mean :0.7344 Mean :0.125 | |
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.000 | |
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.000 | |
## | |
## Hydrated Carbonates Disordered Silicates Feldspar Quartz | |
## Min. :0 Min. :0.000 Min. :0.000 Min. :0.00000 | |
## 1st Qu.:0 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.00000 | |
## Median :0 Median :0.000 Median :0.000 Median :0.00000 | |
## Mean :0 Mean :0.125 Mean :0.125 Mean :0.03125 | |
## 3rd Qu.:0 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.00000 | |
## Max. :0 Max. :1.000 Max. :1.000 Max. :0.25000 | |
## | |
## Apatite FeTi oxides Halite Iron oxide | |
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000 | |
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 | |
## Median :0.0000 Median :0.0000 Median :0.00000 Median :0.0000 | |
## Mean :0.1406 Mean :0.1406 Mean :0.04688 Mean :0.2812 | |
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.5000 | |
## Max. :1.0000 Max. :1.0000 Max. :0.25000 Max. :1.0000 | |
## | |
## Hydrated Iron oxide Organic matter Sulfate+Organic matter | |
## Min. :0.00000 Min. :0.0000 Min. :0.0000 | |
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 | |
## Median :0.00000 Median :1.0000 Median :0.0000 | |
## Mean :0.01562 Mean :0.5938 Mean :0.2188 | |
## 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.2500 | |
## Max. :0.25000 Max. :1.0000 Max. :1.0000 | |
## | |
## Other hydrated phases Phyllosilicates Chlorite | |
## Min. :0.0000 Min. :0.00000 Min. :0.0000 | |
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 | |
## Median :0.2500 Median :0.00000 Median :0.0000 | |
## Mean :0.4375 Mean :0.09375 Mean :0.0625 | |
## 3rd Qu.:1.0000 3rd Qu.:0.06250 3rd Qu.:0.0000 | |
## Max. :1.0000 Max. :0.50000 Max. :0.5000 | |
## | |
## Kaolinite (hydrous Al-clay) Chromite Ilmenite Zircon/Baddeleyite | |
## Min. :0.0000 Min. :0.000 Min. :0.000 Min. :0.000 | |
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 | |
## Median :0.0000 Median :0.000 Median :0.000 Median :0.000 | |
## Mean :0.1875 Mean :0.125 Mean :0.125 Mean :0.125 | |
## 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.000 | |
## Max. :1.0000 Max. :1.000 Max. :1.000 Max. :1.000 | |
## | |
## Fe-Mg-clay minerals Spinels sample name | |
## Min. :0.0000 Min. :0.0000 Min. : 1.00 Atsah : 1 | |
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 4.75 Bearwallow: 1 | |
## Median :0.0000 Median :0.0000 Median : 8.50 Coulettes : 1 | |
## Mean :0.1875 Mean :0.0625 Mean : 8.50 Hahonih : 1 | |
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:12.25 Hazeltop : 1 | |
## Max. :1.0000 Max. :0.5000 Max. :16.00 Kukaklek : 1 | |
## (Other) :10 | |
## SampleType campaign abrasion feldspar plagioclase | |
## atmospheric: 1 Crater Floor:9 Alfalfa :2 0:14 0:13 | |
## regolith : 0 Delta Front :7 Bellegarde :2 1: 2 1: 3 | |
## rock core :15 Margin Unit :0 Berry Hollow:2 | |
## Dourbes :2 | |
## Novarupta :2 | |
## Quartier :2 | |
## (Other) :4 | |
## pyroxene olivine quartz apatite FeTi_Oxides Iron_Oxide Sulfate Perchlorates | |
## 0: 5 0: 6 0:14 0:13 0:13 0:9 0: 4 0:15 | |
## 1:11 1:10 1: 2 1: 3 1: 3 1:7 1:12 1: 1 | |
## | |
## | |
## | |
## | |
## | |
## Phosphate Ca_Sulfate Carbonate Fe_Mg_clay Fe_Mg_carbonate Mg_sulfate | |
## 0:11 0:10 0: 1 0:13 0:14 0:13 | |
## 1: 5 1: 6 1:15 1: 3 1: 2 1: 3 | |
## | |
## | |
## | |
## | |
## | |
## Phyllosilicates Chlorite Halite Organic_matter Hydrated_Ca_Sulfate | |
## 0:12 0:14 0:13 0: 5 0:14 | |
## 1: 4 1: 2 1: 3 1:11 1: 2 | |
## | |
## | |
## | |
## | |
## | |
## Hydrated_Sulfates Hydrated_Mg_Fe_Sulfate Na_Perchlorate Amorphous_Silicate | |
## 0:14 0:13 0:15 0:9 | |
## 1: 2 1: 3 1: 1 1:7 | |
## | |
## | |
## | |
## | |
## | |
## Hydrated_Carbonates Disordered_Silicates Hydrated_Iron_Oxide | |
## 0:16 0:14 0:15 | |
## 1: 2 1: 1 | |
## | |
## | |
## | |
## | |
## | |
## Sulfate+Organic_Matter Other_hydrated_phases Kaolinite Chromite Ilmenite | |
## 0:11 0:8 0:13 0:14 0:14 | |
## 1: 5 1:8 1: 3 1: 2 1: 2 | |
## | |
## | |
## | |
## | |
## | |
## Zircon/Baddeleyite Spinels sample Na20 Mgo | |
## 0:14 0:14 Min. : 1.00 Min. :1.000 Min. : 0.730 | |
## 1: 2 1: 2 1st Qu.: 4.75 1st Qu.:1.853 1st Qu.: 2.533 | |
## Median : 8.50 Median :1.900 Median :12.800 | |
## Mean : 8.50 Mean :2.672 Mean :11.682 | |
## 3rd Qu.:12.25 3rd Qu.:4.500 3rd Qu.:19.100 | |
## Max. :16.00 Max. :5.550 Max. :22.700 | |
## | |
## Al203 Si02 P205 S03 | |
## Min. : 1.700 Min. :22.60 Min. :0.1000 Min. : 0.780 | |
## 1st Qu.: 2.220 1st Qu.:31.22 1st Qu.:0.2350 1st Qu.: 1.495 | |
## Median : 3.710 Median :38.85 Median :0.5250 Median : 2.600 | |
## Mean : 5.072 Mean :38.55 Mean :0.6512 Mean : 5.562 | |
## 3rd Qu.: 7.117 3rd Qu.:41.17 3rd Qu.:0.8400 3rd Qu.: 3.800 | |
## Max. :11.600 Max. :57.10 Max. :2.7600 Max. :21.530 | |
## | |
## Cl K20 Cao Ti02 | |
## Min. :0.400 Min. :0.0000 Min. :1.500 Min. :0.2000 | |
## 1st Qu.:0.940 1st Qu.:0.1600 1st Qu.:2.655 1st Qu.:0.5900 | |
## Median :1.740 Median :0.2000 Median :3.120 Median :0.7000 | |
## Mean :1.846 Mean :0.5800 Mean :3.688 Mean :0.8194 | |
## 3rd Qu.:2.080 3rd Qu.:0.8275 3rd Qu.:4.310 3rd Qu.:0.9900 | |
## Max. :4.500 Max. :1.9000 Max. :7.770 Max. :2.4900 | |
## | |
## Cr203 Mno FeO-T name | |
## Min. :0.000 Min. :0.1000 Min. :13.24 Atsah : 1 | |
## 1st Qu.:0.025 1st Qu.:0.2800 1st Qu.:16.71 Bearwallow: 1 | |
## Median :0.155 Median :0.4000 Median :23.86 Coulettes : 1 | |
## Mean :0.355 Mean :0.3812 Mean :21.45 Hahonih : 1 | |
## 3rd Qu.:0.290 3rd Qu.:0.4900 3rd Qu.:25.70 Hazeltop : 1 | |
## Max. :1.900 Max. :0.6900 Max. :30.05 Kukaklek : 1 | |
## (Other) :10 | |
## type campaign location abrasion | |
## Igneous :8 Crater Floor:9 01 : 1 Alfalfa :2 | |
## N/A :1 Delta Front :7 02 : 1 Bellegrade :2 | |
## Sedimentary:7 03 : 1 Berry Hollow:2 | |
## 04 : 1 Dourbes :2 | |
## 05 : 1 Novarupta :2 | |
## 06 : 1 Quartier :2 | |
## (Other):10 (Other) :4</code></pre> | |
<pre class="r"><code># Combine the Lithology, SHERLOC and PIXLmatrices | |
sherloc_lithology_pixl.matrix<-cbind(sherloc.matrix,lithology.matrix,pixl.matrix) | |
# Review the resulting matrix | |
str(sherloc_lithology_pixl.matrix)</code></pre> | |
<pre><code>## num [1:16, 1:83] 1 1 1 0 0 0 0 0 0 0 ... | |
## - attr(*, "dimnames")=List of 2 | |
## ..$ : NULL | |
## ..$ : chr [1:83] "Plagioclase" "Sulfate" "Ca-sulfate" "Hydrated Ca-sulfate" ...</code></pre> | |
</div> | |
</div> | |
<div id="analysis-of-data-part-3" class="section level1" number="4"> | |
<h1><span class="header-section-number">4</span> Analysis of Data (Part | |
3)</h1> | |
<p>Each team has been assigned one of six datasets:</p> | |
<ol style="list-style-type: decimal"> | |
<li><p>Dataset B: PIXL: The PIXL team’s goal is to understand and | |
explain how scaling changes results from Assignment 1. The matrix | |
version was scaled above but not in Assignment 1.</p></li> | |
<li><p>Dataset C: LIBS (with appropriate scaling as necessary. Not | |
scaled yet.)</p></li> | |
<li><p>Dataset D: Sherloc (with appropriate scaling as necessary. Not | |
scaled yet.)</p></li> | |
<li><p>Dataset E: PIXL + Sherloc (with appropriate scaling as necessary. | |
Not scaled yet.)</p></li> | |
<li><p>Dataset F: PIXL + Lithography (with appropriate scaling as | |
necessary. Not scaled yet.)</p></li> | |
<li><p>Dataset G: Sherloc + Lithograpy (with appropriate scaling as | |
necessary. Not scaled yet.)</p></li> | |
<li><p>Dataset H: PIXL + Sherloc + Lithograpy (with appropriate scaling | |
as necessary. Not scaled yet.)</p></li> | |
</ol> | |
<p><strong>For the data set assigned to your team, perform the following | |
steps.</strong> Feel free to use the methods/code from Assignment 1 as | |
desired. Communicate with your teammates. Make sure that you are doing | |
different variations of below analysis so that no team member does the | |
exact same analysis. If you want to use the same clustering for your | |
team (which is okay but then vary rest), make sure you use the same | |
random seeds.</p> | |
<ol style="list-style-type: decimal"> | |
<li><em>Describe the data set contained in the data frame and | |
matrix:</em> How many rows does it have and how many features? Which | |
features are measurements and which features are metadata about the | |
samples? (3 pts)</li> | |
</ol> | |
<p>The Sherloc + Lithology dataset contains 16 rows, each representing a | |
sample from Mars, and 99 features. These features include both | |
measurements and metadata.</p> | |
<p>The measurements include the presence or absence of minerals such as | |
“Plagioclase,” “Sulfate,” “Fe-Mg carbonate,” and others, indicating the | |
sample’s mineralogical composition. The metadata provides context about | |
each sample, including features like “sample,” “type,” “campaign,” and | |
“abrasion,” which describe where and how the sample was collected.</p> | |
<ol start="2" style="list-style-type: decimal"> | |
<li><em>Scale this data appropriately (you can choose the scaling method | |
or decide to not scale data):</em> Explain why you chose a scaling | |
method or to not scale. (3 pts)</li> | |
</ol> | |
<pre class="r"><code>str(sherloc_lithology.df) </code></pre> | |
<pre><code>## 'data.frame': 16 obs. of 80 variables: | |
## $ sample : int 1 2 3 4 5 6 7 8 9 10 ... | |
## $ type : Factor w/ 3 levels "Igneous","N/A",..: 2 1 1 1 1 1 1 1 1 3 ... | |
## $ campaign : Factor w/ 2 levels "Crater Floor",..: 1 1 1 1 1 1 1 1 1 2 ... | |
## $ abrasion : Factor w/ 9 levels "Alfalfa","Bellegrade",..: 5 2 2 4 4 7 7 1 1 8 ... | |
## $ Name : Factor w/ 16 levels "Atsah","Bearwallow",..: 12 10 9 13 3 11 8 4 1 16 ... | |
## $ Plagioclase : num 1 1 1 0 0 0 0 0 0 0 ... | |
## $ Sulfate : num 1 1 1 1 1 1 1 0 0 0 ... | |
## $ Ca-sulfate : num 1 1 1 0 0 0 0 0 0 0 ... | |
## $ Hydrated Ca-sulfate : num 0 1 1 0 0 0 0 0 0 0 ... | |
## $ Mg-sulfate : num 0 0 0 0 0 0 0 0 0 0 ... | |
## $ Hydrated Sulfates : num 0 0 0 0 0 1 1 0 0 0 ... | |
## $ Hydrated Mg-Fe sulfate : num 0 0 0 0 0 0 0 0 0 0 ... | |
## $ Perchlorates : num 1 0 0 0 0 0 0 0 0 0 ... | |
## $ Na-perchlorate : num 0.5 0 0 0 0 0 0 0 0 0 ... | |
## $ Amorphous Silicate : num 0.25 0.25 0.25 0.5 0.5 0.25 0.25 0 0 0 ... | |
## $ Phosphate : num 0.25 1 1 0 0 0 0 0 0 0 ... | |
## $ Pyroxene : num 1 1 1 1 1 1 1 1 1 1 ... | |
## $ Olivine : num 0 0 0 1 1 1 1 0.25 0.25 1 ... | |
## $ Carbonate : num 0 1 1 1 1 1 1 0.5 0.5 1 ... | |
## $ Fe-Mg carbonate : num 0 0 0 0 0 0 0 0 0 1 ... | |
## $ Hydrated Carbonates : num 0 0 0 0 0 0 0 0 0 0 ... | |
## $ Disordered Silicates : num 0 0 0 0 0 0 0 1 1 0 ... | |
## $ Feldspar : num 0 0 0 0 0 0 0 1 1 0 ... | |
## $ Quartz : num 0 0 0 0 0 0 0 0.25 0.25 0 ... | |
## $ Apatite : num 0.25 0 0 0 0 0 0 0 0 0 ... | |
## $ FeTi oxides : num 0.25 1 1 0 0 0 0 0 0 0 ... | |
## $ Halite : num 0.25 0 0 0 0 0 0 0 0 0.25 ... | |
## $ Iron oxide : num 1 1 1 0 0 0 0 0.5 0.5 0.25 ... | |
## $ Hydrated Iron oxide : num 0.25 0 0 0 0 0 0 0 0 0 ... | |
## $ Organic matter : num 0 0 0 1 1 1 1 1 1 0 ... | |
## $ Sulfate+Organic matter : num 0 0 0 0 0 1 1 0 0 0 ... | |
## $ Other hydrated phases : num 0 0 0 1 1 1 1 0.5 0.5 1 ... | |
## $ Phyllosilicates : num 0 0 0 0 0 0 0 0.5 0.5 0.25 ... | |
## $ Chlorite : num 0 0 0 0 0 0 0 0.5 0.5 0 ... | |
## $ Kaolinite (hydrous Al-clay): num 0 0 0 0 0 0 0 0 0 0 ... | |
## $ Chromite : num 0 0 0 0 0 0 0 0 0 0 ... | |
## $ Ilmenite : num 0 0 0 0 0 0 0 0 0 0 ... | |
## $ Zircon/Baddeleyite : num 0 0 0 0 0 0 0 0 0 0 ... | |
## $ Fe-Mg-clay minerals : num 0 0 0 0 0 0 0 0 0 0 ... | |
## $ Spinels : num 0 0 0 0 0 0 0 0 0 0 ... | |
## $ sample : num 1 2 3 4 5 6 7 8 9 10 ... | |
## $ name : Factor w/ 24 levels "Atmo Mountain",..: 20 15 14 21 5 19 12 7 2 24 ... | |
## $ SampleType : Factor w/ 3 levels "atmospheric",..: 1 3 3 3 3 3 3 3 3 3 ... | |
## $ campaign : Factor w/ 3 levels "Crater Floor",..: 1 1 1 1 1 1 1 1 1 2 ... | |
## $ abrasion : Factor w/ 9 levels "Alfalfa","Bellegarde",..: 5 2 2 4 4 7 7 1 1 8 ... | |
## $ feldspar : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 2 1 ... | |
## $ plagioclase : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ... | |
## $ pyroxene : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ... | |
## $ olivine : Factor w/ 2 levels "0","1": 1 1 1 2 2 2 2 2 2 2 ... | |
## $ quartz : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 2 1 ... | |
## $ apatite : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ... | |
## $ FeTi_Oxides : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ... | |
## $ Iron_Oxide : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 2 2 2 ... | |
## $ Sulfate : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 1 1 1 ... | |
## $ Perchlorates : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ... | |
## $ Phosphate : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ... | |
## $ Ca_Sulfate : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ... | |
## $ Carbonate : Factor w/ 2 levels "0","1": 1 2 2 2 2 2 2 2 2 2 ... | |
## $ Fe_Mg_clay : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ... | |
## $ Fe_Mg_carbonate : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ... | |
## $ Mg_sulfate : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ... | |
## $ Phyllosilicates : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 2 2 ... | |
## $ Chlorite : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 2 1 ... | |
## $ Halite : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 2 ... | |
## $ Organic_matter : Factor w/ 2 levels "0","1": 1 1 1 2 2 2 2 2 2 1 ... | |
## $ Hydrated_Ca_Sulfate : Factor w/ 2 levels "0","1": 1 2 2 1 1 1 1 1 1 1 ... | |
## $ Hydrated_Sulfates : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 2 1 1 1 ... | |
## $ Hydrated_Mg_Fe_Sulfate : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ... | |
## $ Na_Perchlorate : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ... | |
## $ Amorphous_Silicate : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 1 1 1 ... | |
## $ Hydrated_Carbonates : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ... | |
## $ Disordered_Silicates : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 2 1 ... | |
## $ Hydrated_Iron_Oxide : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ... | |
## $ Sulfate+Organic_Matter : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 2 1 1 1 ... | |
## $ Other_hydrated_phases : Factor w/ 2 levels "0","1": 1 1 1 2 2 2 2 2 2 2 ... | |
## $ Kaolinite : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ... | |
## $ Chromite : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ... | |
## $ Ilmenite : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ... | |
## $ Zircon/Baddeleyite : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ... | |
## $ Spinels : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...</code></pre> | |
<pre class="r"><code>numeric_data <- sherloc_lithology.df[, sapply(sherloc_lithology.df, is.numeric)] | |
scaled_data <- scale(numeric_data) | |
scaled_data_with_metadata <- cbind(sherloc_lithology.df[, 1:4], scaled_data)</code></pre> | |
<p>I chose z-score scaling because it ensures that each feature in the | |
dataset has a mean of 0 and a standard deviation of 1. This method is | |
particularly important when the dataset contains features that are | |
measured on different scales. In the case of the Sherloc + Lithology | |
dataset, the presence or concentration of various minerals might vary | |
significantly in magnitude. By applying z-score scaling, we standardize | |
the features so that they all contribute equally to the analysis, | |
preventing any one feature with larger values from dominating the | |
clustering or other analyses.</p> | |
<ol start="3" style="list-style-type: decimal"> | |
<li><em>Cluster the data using k-means or your favorite clustering | |
method (like hierarchical clustering):</em> Describe how you picked the | |
best number of clusters. Indicate the number of points in each clusters. | |
Coordinate with your team so you try different approaches. If you want | |
to share results with your team mates, make sure to use the same random | |
seeds. (6 pts)</li> | |
</ol> | |
<pre class="r"><code>library(knitr) | |
scaled_data[is.na(scaled_data)] <- 0 | |
scaled_data[is.nan(scaled_data)] <- 0 | |
scaled_data[is.infinite(scaled_data)] <- 0 | |
# Function to plot WSS (within-cluster sum of squares) | |
wssplot <- function(data, nc=15, seed=10){ | |
wss <- data.frame(cluster=1:nc, quality=c(0)) | |
for (i in 1:nc){ | |
set.seed(seed) | |
wss[i,2] <- kmeans(data, centers=i)$tot.withinss | |
} | |
ggplot(data=wss, aes(x=cluster, y=quality)) + | |
geom_line() + | |
ggtitle("Quality of k-means by Cluster") | |
} | |
# Apply wssplot to scaled data | |
wssplot(scaled_data, nc=8, seed=2)</code></pre> | |
<p><img 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" width="672" /></p> | |
<pre class="r"><code># Run k-means on the cleaned scaled data | |
set.seed(2) | |
k <- 3 | |
km <- kmeans(scaled_data, centers=k) | |
# Visualize cluster centers using pheatmap | |
pheatmap(km$centers, scale="none") </code></pre> | |
<p><img 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" width="672" /></p> | |
<pre class="r"><code># Create a data frame to show the size of each cluster | |
cluster.df <- data.frame(cluster=1:k, size=km$size) | |
kable(cluster.df, caption="Samples per cluster")</code></pre> | |
<table> | |
<caption>Samples per cluster</caption> | |
<thead> | |
<tr class="header"> | |
<th align="right">cluster</th> | |
<th align="right">size</th> | |
</tr> | |
</thead> | |
<tbody> | |
<tr class="odd"> | |
<td align="right">1</td> | |
<td align="right">7</td> | |
</tr> | |
<tr class="even"> | |
<td align="right">2</td> | |
<td align="right">2</td> | |
</tr> | |
<tr class="odd"> | |
<td align="right">3</td> | |
<td align="right">7</td> | |
</tr> | |
</tbody> | |
</table> | |
<p>To determine the optimal number of clusters for the k-means | |
clustering, I utilized the elbow method. This approach involves plotting | |
the within-cluster sum of squares (WSS) for a range of cluster numbers | |
and identifying the point where the reduction in WSS becomes less | |
pronounced, forming an “elbow” in the plot. In this case, I plotted the | |
WSS for 1 to 8 clusters, and the elbow was clearly visible at 3 | |
clusters. Up to this point, the WSS decreased significantly, but adding | |
more clusters after 3 led to diminishing returns, with minimal | |
improvement in WSS. Hence, I selected 3 as the optimal number of | |
clusters.</p> | |
<p>Upon executing the k-means clustering with 3 clusters, the | |
distribution of points was as follows: Cluster 1 contained 7 points, | |
Cluster 2 had 2 points, and Cluster 3 also contained 7 points. This | |
distribution reflects how the samples were grouped based on the feature | |
similarities.</p> | |
<ol start="4" style="list-style-type: decimal"> | |
<li>_Perform a <strong>creative analysis</strong> that provides insights | |
into what one or more of the clusters are and what they tell you about | |
the MARS data: Alternatively do another creative analysis of your | |
datasets that leads to one of more findings. Make sure to explain what | |
your analysis and discuss your the results.</li> | |
</ol> | |
<pre class="r"><code>scaled_data_clean <- na.omit(scaled_data) | |
k <- min(2, nrow(scaled_data_clean)) | |
invalid_rows <- numeric_data[!complete.cases(numeric_data), ] | |
numeric_data[is.na(numeric_data)] <- apply(numeric_data, 2, function(x) mean(x, na.rm = TRUE)) | |
# 1. Scaling the data (ensure to handle NA, NaN, Inf) | |
numeric_data <- sherloc_lithology.df[, sapply(sherloc_lithology.df, is.numeric)] | |
scaled_data <- scale(numeric_data) | |
# 3. Perform k-means clustering | |
set.seed(123) | |
k <- 3 | |
# Run k-means clustering on scaled data | |
km <- kmeans(scaled_data_clean, centers = k) | |
# 4. Feature selection: Calculate mean of each feature by cluster | |
cluster_means <- aggregate(scaled_data_clean, by = list(cluster = km$cluster), FUN = mean) | |
# Look at the means for Cluster 1 | |
cluster_1_means <- cluster_means[cluster_means$cluster == 1, ] | |
# View key features (Fe-Mg carbonate and Chromite) for Cluster 1 | |
cluster_1_fe_mg_carbonate <- cluster_1_means[,"Fe-Mg carbonate"] | |
cluster_1_chromite <- cluster_1_means[,"Chromite"] | |
print(paste("Cluster 1 Fe-Mg Carbonate Mean:", cluster_1_fe_mg_carbonate))</code></pre> | |
<pre><code>## [1] "Cluster 1 Fe-Mg Carbonate Mean: 1.0978875820671"</code></pre> | |
<pre class="r"><code>print(paste("Cluster 1 Chromite Mean:", cluster_1_chromite))</code></pre> | |
<pre><code>## [1] "Cluster 1 Chromite Mean: 1.0978875820671"</code></pre> | |
<pre class="r"><code># 5. Visualize key features (Fe-Mg Carbonate and Chromite) across clusters | |
feature_plot_data <- data.frame( | |
Cluster = as.factor(km$cluster), | |
Fe_Mg_Carbonate = scaled_data_clean[,"Fe-Mg carbonate"], | |
Chromite = scaled_data_clean[,"Chromite"] | |
) | |
# Plot Fe-Mg Carbonate by cluster | |
ggplot(feature_plot_data, aes(x = Cluster, y = Fe_Mg_Carbonate, fill = Cluster)) + | |
geom_boxplot() + | |
ggtitle("Fe-Mg Carbonate Concentration by Cluster") + | |
xlab("Cluster") + ylab("Fe-Mg Carbonate")</code></pre> | |
<p><img 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" width="672" /></p> | |
<pre class="r"><code># Plot Chromite by cluster | |
ggplot(feature_plot_data, aes(x = Cluster, y = Chromite, fill = Cluster)) + | |
geom_boxplot() + | |
ggtitle("Chromite Concentration by Cluster") + | |
xlab("Cluster") + ylab("Chromite")</code></pre> | |
<p><img 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" width="672" /> | |
In this analysis, k-means clustering was applied to the MARS dataset, | |
focusing on mineralogical content, particularly Fe-Mg Carbonate and | |
Chromite. The objective was to uncover patterns or distinct groupings | |
within the dataset and draw insights into Mars’ geological history. | |
After identifying three clusters, the analysis examined the | |
concentrations of Fe-Mg Carbonate and Chromite in each cluster, | |
revealing significant differences across them.</p> | |
<p>Fe-Mg Carbonate concentrations were notably high in Cluster 1, while | |
Clusters 2 and 3 exhibited almost no presence of this mineral. This | |
suggests that Cluster 1 represents regions on Mars that are rich in | |
Fe-Mg carbonates, potentially indicating historical geological processes | |
involving water interaction or specific atmospheric conditions that | |
supported carbonate formation. The presence of Fe-Mg Carbonate may imply | |
that the areas corresponding to Cluster 1 experienced conditions that | |
were conducive to the formation of carbonates, a potential marker for | |
ancient Martian environments with liquid water.</p> | |
<p>Similarly, Chromite concentrations were also found to be high in | |
Cluster 1, while Clusters 2 and 3 showed very little to no Chromite. | |
Chromite is commonly associated with igneous rock formations, suggesting | |
that Cluster 1 might represent older, volcanic terrains on Mars. The | |
high concentration of both Chromite and Fe-Mg Carbonate in Cluster 1 | |
points toward a unique geological history in these regions, possibly | |
involving significant volcanic activity and the presence of water during | |
Mars’ early history.</p> | |
<p>Overall,this analysis shows that Cluster 1 stands out for its higher | |
concentrations of Fe-Mg Carbonate and Chromite, pointing toward a unique | |
geological history that could be crucial for understanding Mars’ past | |
environments. This suggests that regions in Cluster 1 are potential | |
sites for further exploration to uncover Mars’ volcanic history and its | |
past interaction with water. By contrast, Clusters 2 and 3 likely | |
represent different lithological types or regions, with little to no | |
presence of these key minerals.</p> | |
</div> | |
<div id="preparation-of-team-presentation-part-4" class="section level1" number="5"> | |
<h1><span class="header-section-number">5</span> Preparation of Team | |
Presentation (Part 4)</h1> | |
<p>Prepare a presentation of your teams result to present in class on | |
<strong>September 11</strong> starting at 9am in AE217 (20 pts) The | |
presentation should include the following elements</p> | |
<p>0.Your teams names and members 1. A <strong>Description</strong> of | |
the data set that you analyzed including how many observations and how | |
many features. (<= 1.5 mins) 2. Each team member gets <strong>three | |
minutes</strong> to explain their analysis: * what analysis they | |
performed * the results of that analysis * a brief discussion of their | |
interpretation of these results * <= 18 mins <em>total!</em> 3. A | |
<strong>Conclusion</strong> slide indicating major findings of the teams | |
(<= 1.5 mins) 4. Thoughts on <strong>potential next steps</strong> | |
for the MARS team (<= 1.5 mins)</p> | |
<ul> | |
<li><p>A template for your team presentation is included here: <a href="https://bit.ly/dar-template-f24" class="uri">https://bit.ly/dar-template-f24</a></p></li> | |
<li><p>The rubric for the presentation is here:</p></li> | |
</ul> | |
<p><a href="https://docs.google.com/document/d/1-4o1O4h2r8aMjAplmE-ItblQnyDAKZwNs5XCnmwacjs/pub" class="uri">https://docs.google.com/document/d/1-4o1O4h2r8aMjAplmE-ItblQnyDAKZwNs5XCnmwacjs/pub</a></p> | |
<ul> | |
<li>Post a link to your teams presentation in the MARS webex chat before | |
class. You can continue to edit until the last minute.</li> | |
</ul> | |
</div> | |
<div id="when-youre-done-save-commit-and-push-your-changes" class="section level1" number="6"> | |
<h1><span class="header-section-number">6</span> When you’re done: SAVE, | |
COMMIT and PUSH YOUR CHANGES!</h1> | |
<p>When you are satisfied with your edits and your notebook knits | |
successfully, remember to push your changes to the repo using the | |
following steps:</p> | |
<ul> | |
<li><code>git branch</code> | |
<ul> | |
<li>To double-check that you are in your working branch</li> | |
</ul></li> | |
<li><code>git add <your changed files></code></li> | |
<li><code>git commit -m "Some useful comments"</code></li> | |
<li><code>git push origin <your branch name></code></li> | |
<li>do a pull request</li> | |
</ul> | |
</div> | |
<div id="appendix-accessing-rstudio-server-on-the-idea-cluster" class="section level1" number="7"> | |
<h1><span class="header-section-number">7</span> APPENDIX: Accessing | |
RStudio Server on the IDEA Cluster</h1> | |
<p>The IDEA Cluster provides seven compute nodes (4x 48 cores, 3x 80 | |
cores, 1x storage server)</p> | |
<ul> | |
<li>The Cluster requires RCS credentials, enabled via registration in | |
class | |
<ul> | |
<li>email John Erickson for problems <code>erickj4@rpi.edu</code></li> | |
</ul></li> | |
<li>RStudio, Jupyter, MATLAB, GPUs (on two nodes); lots of storage and | |
computes</li> | |
<li>Access via RPI physical network or VPN only</li> | |
</ul> | |
</div> | |
<div id="more-info-about-rstudio-on-our-cluster" class="section level1" number="8"> | |
<h1><span class="header-section-number">8</span> More info about Rstudio | |
on our Cluster</h1> | |
<div id="rstudio-gui-access" class="section level2" number="8.1"> | |
<h2><span class="header-section-number">8.1</span> RStudio GUI | |
Access:</h2> | |
<ul> | |
<li>Use: | |
<ul> | |
<li><a href="http://lp01.idea.rpi.edu/rstudio-ose/" class="uri">http://lp01.idea.rpi.edu/rstudio-ose/</a></li> | |
<li><a href="http://lp01.idea.rpi.edu/rstudio-ose-3/" class="uri">http://lp01.idea.rpi.edu/rstudio-ose-3/</a></li> | |
<li><a href="http://lp01.idea.rpi.edu/rstudio-ose-6/" class="uri">http://lp01.idea.rpi.edu/rstudio-ose-6/</a></li> | |
<li><a href="http://lp01.idea.rpi.edu/rstudio-ose-7/" class="uri">http://lp01.idea.rpi.edu/rstudio-ose-7/</a></li> | |
</ul></li> | |
<li>Linux terminal accessible from within RStudio “Terminal” or via ssh | |
(below)</li> | |
</ul> | |
</div> | |
</div> | |
</div> | |
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