{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# More with R" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**By [Ryan Menezes](https://twitter.com/ryanvmenezes) (Los Angeles Times) & [Christine Zhang](https://twitter.com/christinezhang) (Knight-Mozilla / Los Angeles Times)**\n", "\n", "*IRE conference -- New Orleans, LA* \n", " \n", "June 18, 2016 \n", "\n", "This workshop is a continuation of our previous session, [Getting Started with R](Getting%20started%20with%20R.ipynb)\n", "\n", "To recap, in Getting Started with R, we cleaned and merged two census datasets with some demographic information on Louisiana for the years 2000 and 2010, with a view toward understanding population changes pre- and post-Hurricane Katrina, which took place in August 2005.\n", "\n", "As in our last session, we will use [The Times-Picayune](http://www.nola.com/politics/index.ssf/2011/02/new_orleans_officials_2010_pop.html) article as the inspiration for our analysis.\n", "\n", "** In this session, we will: **\n", "\n", "* Use scatterplots and histograms to better see trends in our data\n", "* Query our data for insights we could write about\n", "* Group our data to perform aggregate calculations\n", "* Use R's built-in regression tools to visualize trendlines\n", "\n", "** Here are some questions we will set out to answer: **\n", "\n", "* How many census tracts in New Orleans had fewer people in 2010 than in 2000?\n", "* Which parishes (Lousiana lingo for counties) saw the most dramatic population changes in that time period?\n", "* How did the occupancy of Louisiana homes change in that time period?\n", "* Which parishes saw the most dramatic occupancy changes?\n", "* Is there a relationship between population and occupancy rate?\n", "\n", "The following code and annotations were written in a Jupyter notebook. The code is best run in RStudio version 0.99.902 using R version 3.3.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's read in the census data file we created in our last workshop and visually inspect the first six rows using `head`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
fips.codetractparishstatepopulation.00total.housing.units.00occupied.housing.units.00vacant.housing.units.00population.10total.housing.units.10occupied.housing.units.10vacant.housing.units.10
122001960100Census Tract 9601Acadia ParishLouisiana618824102236174621325742345229
222001960200Census Tract 9602Acadia ParishLouisiana505619091764145598823622144218
322001960300Census Tract 9603Acadia ParishLouisiana314912461145101358214271286141
422001960400Census Tract 9604Acadia ParishLouisiana561721761991185658426042362242
522001960500Census Tract 9605Acadia ParishLouisiana492717961692104609323492178171
622001960600Census Tract 9606Acadia ParishLouisiana565422922073219597225042306198
\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllllll}\n", " & fips.code & tract & parish & state & population.00 & total.housing.units.00 & occupied.housing.units.00 & vacant.housing.units.00 & population.10 & total.housing.units.10 & occupied.housing.units.10 & vacant.housing.units.10\\\\\n", "\\hline\n", "\t1 & 22001960100 & Census Tract 9601 & Acadia Parish & Louisiana & 6188 & 2410 & 2236 & 174 & 6213 & 2574 & 2345 & 229\\\\\n", "\t2 & 22001960200 & Census Tract 9602 & Acadia Parish & Louisiana & 5056 & 1909 & 1764 & 145 & 5988 & 2362 & 2144 & 218\\\\\n", "\t3 & 22001960300 & Census Tract 9603 & Acadia Parish & Louisiana & 3149 & 1246 & 1145 & 101 & 3582 & 1427 & 1286 & 141\\\\\n", "\t4 & 22001960400 & Census Tract 9604 & Acadia Parish & Louisiana & 5617 & 2176 & 1991 & 185 & 6584 & 2604 & 2362 & 242\\\\\n", "\t5 & 22001960500 & Census Tract 9605 & Acadia Parish & Louisiana & 4927 & 1796 & 1692 & 104 & 6093 & 2349 & 2178 & 171\\\\\n", "\t6 & 22001960600 & Census Tract 9606 & Acadia Parish & Louisiana & 5654 & 2292 & 2073 & 219 & 5972 & 2504 & 2306 & 198\\\\\n", "\\end{tabular}\n" ], "text/plain": [ " fips.code tract parish state population.00\n", "1 22001960100 Census Tract 9601 Acadia Parish Louisiana 6188\n", "2 22001960200 Census Tract 9602 Acadia Parish Louisiana 5056\n", "3 22001960300 Census Tract 9603 Acadia Parish Louisiana 3149\n", "4 22001960400 Census Tract 9604 Acadia Parish Louisiana 5617\n", "5 22001960500 Census Tract 9605 Acadia Parish Louisiana 4927\n", "6 22001960600 Census Tract 9606 Acadia Parish Louisiana 5654\n", " total.housing.units.00 occupied.housing.units.00 vacant.housing.units.00\n", "1 2410 2236 174\n", "2 1909 1764 145\n", "3 1246 1145 101\n", "4 2176 1991 185\n", "5 1796 1692 104\n", "6 2292 2073 219\n", " population.10 total.housing.units.10 occupied.housing.units.10\n", "1 6213 2574 2345\n", "2 5988 2362 2144\n", "3 3582 1427 1286\n", "4 6584 2604 2362\n", "5 6093 2349 2178\n", "6 5972 2504 2306\n", " vacant.housing.units.10\n", "1 229\n", "2 218\n", "3 141\n", "4 242\n", "5 171\n", "6 198" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "census <- read.csv('census_comparison.csv')\n", "head(census)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Grouping and summing populations by parish\n", "\n", "Recall that our information is organized by census tract. We'll use dplyr's `group_by` to group the data by parish. We'll use dplyr's `summarise_each` to add up the columns. \n", "\n", "Then we'll calculate the percent change between the two years:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Warning message:\n", ": package ‘dplyr’ was built under R version 3.2.4\n", "Attaching package: ‘dplyr’\n", "\n", "The following objects are masked from ‘package:stats’:\n", "\n", " filter, lag\n", "\n", "The following objects are masked from ‘package:base’:\n", "\n", " intersect, setdiff, setequal, union\n", "\n" ] } ], "source": [ "## if dplyr was not installed we would have to run this\n", "# install.packages('dplyr')\n", "\n", "## to import the package and all of its functions \n", "library('dplyr')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "parishes <- census %>% # notice the use of \"%>%\"\n", "group_by(parish) %>% # this tells R to group our data by parishes\n", "summarise_each( \n", " # sum all the columns \n", " funs(sum(., na.rm = TRUE)),\n", " # except the non-numerical ones\n", " -fips.code, -tract, -state\n", ") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You'll notice that we used a strange symbol, `%>%`, to accomplish this. This is what R calls the \"pipe operator.\" It has gained popularity in recent years as an alternative to nesting functions. \n", "\n", "If we were to accomplish the same task without piping, we would need to write the following unwieldy line of code:\n", "\n", "```\n", "summarise_each(group_by(census, parish), funs(sum(., na.rm = TRUE)), -fips.code, -tract, -state)\n", "```" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
parishpopulation.00total.housing.units.00occupied.housing.units.00vacant.housing.units.00population.10total.housing.units.10occupied.housing.units.10vacant.housing.units.10perc.pop.diff
1Acadia Parish588612320921142206761773253872284125464.947249
2Allen Parish25440915781021055257649733851612171.273585
3Ascension Parish76627291722669124811072154078437790299439.91804
4Assumption Parish233889635823913962342110351873616150.141098
5Avoyelles Parish414811657614736184042073180421543226101.427159
6Beauregard Parish329861450112104239735654150401315918818.08828
\n" ], "text/latex": [ "\\begin{tabular}{r|llllllllll}\n", " & parish & population.00 & total.housing.units.00 & occupied.housing.units.00 & vacant.housing.units.00 & population.10 & total.housing.units.10 & occupied.housing.units.10 & vacant.housing.units.10 & perc.pop.diff\\\\\n", "\\hline\n", "\t1 & Acadia Parish & 58861 & 23209 & 21142 & 2067 & 61773 & 25387 & 22841 & 2546 & 4.947249\\\\\n", "\t2 & Allen Parish & 25440 & 9157 & 8102 & 1055 & 25764 & 9733 & 8516 & 1217 & 1.273585\\\\\n", "\t3 & Ascension Parish & 76627 & 29172 & 26691 & 2481 & 107215 & 40784 & 37790 & 2994 & 39.91804\\\\\n", "\t4 & Assumption Parish & 23388 & 9635 & 8239 & 1396 & 23421 & 10351 & 8736 & 1615 & 0.141098\\\\\n", "\t5 & Avoyelles Parish & 41481 & 16576 & 14736 & 1840 & 42073 & 18042 & 15432 & 2610 & 1.427159\\\\\n", "\t6 & Beauregard Parish & 32986 & 14501 & 12104 & 2397 & 35654 & 15040 & 13159 & 1881 & 8.08828\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [6 x 10]\n", "\n", " parish population.00 total.housing.units.00\n", " (fctr) (int) (int)\n", "1 Acadia Parish 58861 23209\n", "2 Allen Parish 25440 9157\n", "3 Ascension Parish 76627 29172\n", "4 Assumption Parish 23388 9635\n", "5 Avoyelles Parish 41481 16576\n", "6 Beauregard Parish 32986 14501\n", "Variables not shown: occupied.housing.units.00 (int), vacant.housing.units.00\n", " (int), population.10 (int), total.housing.units.10 (int),\n", " occupied.housing.units.10 (int), vacant.housing.units.10 (int), perc.pop.diff\n", " (dbl)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parishes$perc.pop.diff <- (parishes$population.10 - parishes$population.00) / parishes$population.00 * 100\n", "\n", "head(parishes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can answer the question: **Which parishes (Lousiana lingo for counties) saw the most dramatic population changes?**\n", "\n", "We'll do this by arranging the columns by the percent change in population, using dplyr's `arrange`. To make the output easier to see, we'll only select a few of the columns." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\t\n", "\n", "
parishpopulation.00population.10perc.pop.diff
1St. Bernard Parish6722935897-46.60489
2Cameron Parish99916839-31.54839
3Orleans Parish484674343829-29.05974
4Tensas Parish66185252-20.64068
5East Carroll Parish94217759-17.64144
6Plaquemines Parish2675723042-13.88422
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " & parish & population.00 & population.10 & perc.pop.diff\\\\\n", "\\hline\n", "\t1 & St. Bernard Parish & 67229 & 35897 & -46.60489\\\\\n", "\t2 & Cameron Parish & 9991 & 6839 & -31.54839\\\\\n", "\t3 & Orleans Parish & 484674 & 343829 & -29.05974\\\\\n", "\t4 & Tensas Parish & 6618 & 5252 & -20.64068\\\\\n", "\t5 & East Carroll Parish & 9421 & 7759 & -17.64144\\\\\n", "\t6 & Plaquemines Parish & 26757 & 23042 & -13.88422\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [6 x 4]\n", "\n", " parish population.00 population.10 perc.pop.diff\n", " (fctr) (int) (int) (dbl)\n", "1 St. Bernard Parish 67229 35897 -46.60489\n", "2 Cameron Parish 9991 6839 -31.54839\n", "3 Orleans Parish 484674 343829 -29.05974\n", "4 Tensas Parish 6618 5252 -20.64068\n", "5 East Carroll Parish 9421 7759 -17.64144\n", "6 Plaquemines Parish 26757 23042 -13.88422" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parishes %>% \n", "select(parish, population.00, population.10, perc.pop.diff) %>% # select the columns of interest\n", "arrange(perc.pop.diff) %>%\n", "head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results of a query like this one could turn into a paragraph in a story. The Times-Picayune did just this with the following sentence:\n", "\n", "> Hard-hit St. Bernard saw the most dramatic population decline, losing 47 percent of its population compared with 2000. Plaquemines Parish's population also fell, though only by 14 percent.\n", "\n", "\n", "For now, we'll focus on just New Orleans, or the Orleans Parish. To help us filter the data set to only include the census tracts in this parish, we are going to import a package called dplyr.\n", "\n", "We'll filter the data and run the `str` command, which gives you the structure of the variable as defined by R:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "'data.frame':\t211 obs. of 12 variables:\n", " $ fips.code : num 2.21e+10 2.21e+10 2.21e+10 2.21e+10 2.21e+10 ...\n", " $ tract : Factor w/ 793 levels \"Census Tract 1\",..: 1 159 368 453 596 597 598 599 600 601 ...\n", " $ parish : Factor w/ 64 levels \"Acadia Parish\",..: 36 36 36 36 36 36 36 36 36 36 ...\n", " $ state : Factor w/ 1 level \"Louisiana\": 1 1 1 1 1 1 1 1 1 1 ...\n", " $ population.00 : int 2381 1347 1468 2564 2034 2957 2342 5131 2902 4400 ...\n", " $ total.housing.units.00 : int 1408 691 719 1034 704 1106 978 2100 992 1641 ...\n", " $ occupied.housing.units.00: int 1145 496 559 873 506 1011 671 1886 893 1593 ...\n", " $ vacant.housing.units.00 : int 263 195 160 161 198 95 307 214 99 48 ...\n", " $ population.10 : int 2455 1197 1231 2328 849 2534 1605 3925 2205 4346 ...\n", " $ total.housing.units.10 : int 1513 738 641 1137 328 1108 922 1795 994 1644 ...\n", " $ occupied.housing.units.10: int 1229 496 467 911 269 923 498 1456 804 1544 ...\n", " $ vacant.housing.units.10 : int 284 242 174 226 59 185 424 339 190 100 ...\n" ] } ], "source": [ "orleans <- census %>% filter(parish == 'Orleans Parish')\n", "orleans %>% str() # this is equivalent to str(orleans)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scatterplots\n", "\n", "Plotting our data can reveal some interesting trends about the New Orleans population in 2000 and its population in 2010. R has a `plot` function where we can specify the x and y variables we want to plot:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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higher populations in 2010. \n", "\n", "We can better examine the relationship if we draw a 45-degree line with `abline(0, 1)`. If a census tract's population in 2010 was exactly the same as its population in 2000, it would fall on this line. If its 2010 population was lower than its 2000 population, it would fall below this line.\n", "\n", "This gives us a quick way to visually inspect the changes in population.\n", "\n", "While we're at it, we'll add some x and y limits, labels and a title." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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\n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "Plot with title “Census Tracts in Orleans Parish”" ] }, "metadata": { "image/svg+xml": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "plot(\n", " orleans$population.00,\n", " orleans$population.10, \n", " xlim = c(0, 8000), \n", " ylim = c(0, 8000),\n", " xlab = '2000 population',\n", " ylab = '2010 population',\n", " main = 'Census Tracts in Orleans Parish'\n", ")\n", "\n", "abline(0, 1) # Draw a line with y-intercept of 0 and slope of 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most of the points fall below the line, indicating most census tracts saw population drops. This makes sense with our previous finding that New Orleans overall saw a 29% drop in population between 2000 and 2010.\n", "\n", "We can further quantify this by answering the question: **How many census tracts in New Orleans had fewer people in 2010 than in 2000?**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "orleans$pop.diff <- orleans$population.10 - orleans$population.00" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After calculating the difference, we now need to run a query that asks a programming equivalent version of the question above: \"How many numbers in the pop.diff column are less than 0?\"" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pop.drops.orleans <- sum(orleans$pop.diff < 0, na.rm = TRUE)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1] \"In New Orleans, 135 tracts had fewer people in 2010 than in 2000.\"\n" ] } ], "source": [ "print(paste('In New Orleans,', pop.drops.orleans, 'tracts had fewer people in 2010 than in 2000.'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Changes in Louisiana housing occupancy\n", "\n", "We can calculate the occupancy rate (or the percent of housing units that are occupied) for both years and the percentage point change, and write all of these variables to new columns in our data frame:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "parishes$perc.occupied.00 <- parishes$occupied.housing.units.00 / parishes$total.housing.units.00 * 100\n", "parishes$perc.occupied.10 <- parishes$occupied.housing.units.10 / parishes$total.housing.units.10 * 100\n", "parishes$perc.occupied.diff <- parishes$perc.occupied.10 - parishes$perc.occupied.00" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now answer the question, **Which parishes saw the most dramatic occupancy changes?** \n", "\n", "All we have to do is arrange the data by `perc.occupied.diff`, our new variable for the percentage point change in occupancy rates.\n", "\n", "Note that this time, we'll arrange the data using `arrange(desc(perc.occupied.diff))` to first see the most dramatic increases in occupancy rates - parishes where occupancy rates went up the most. `arrange`'s default is to arrange in ascending order." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "parish.occupancy.rates <- parishes %>% \n", "select(parish, perc.occupied.00, perc.occupied.10, perc.occupied.diff) %>% \n", "arrange(desc(perc.occupied.diff)) # arrange(desc()) arranges in descending order" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use `head` to show the first few rows of the data. We specify n = 3 to show the first 3 rows." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
parishperc.occupied.00perc.occupied.10perc.occupied.diff
1St. Helena Parish76.9368384.135927.199093
2Cameron Parish67.3163471.667134.350789
3Beauregard Parish83.4701187.493354.023246
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " & parish & perc.occupied.00 & perc.occupied.10 & perc.occupied.diff\\\\\n", "\\hline\n", "\t1 & St. Helena Parish & 76.93683 & 84.13592 & 7.199093\\\\\n", "\t2 & Cameron Parish & 67.31634 & 71.66713 & 4.350789\\\\\n", "\t3 & Beauregard Parish & 83.47011 & 87.49335 & 4.023246\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [3 x 4]\n", "\n", " parish perc.occupied.00 perc.occupied.10 perc.occupied.diff\n", " (fctr) (dbl) (dbl) (dbl)\n", "1 St. Helena Parish 76.93683 84.13592 7.199093\n", "2 Cameron Parish 67.31634 71.66713 4.350789\n", "3 Beauregard Parish 83.47011 87.49335 4.023246" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "head(parish.occupancy.rates, n = 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It might be more interesting for us to see parishes where the occupancy rates went *down* the most. \n", "\n", "We can see this using the `tail` command, which shows us the last few rows of the data. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\t\n", "\t\n", "\t\n", "\n", "
parishperc.occupied.00perc.occupied.10perc.occupied.diff
1Tensas Parish71.9261764.70063-7.225543
2Orleans Parish87.5215674.86098-12.66058
3St. Bernard Parish93.7775378.72454-15.05298
\n" ], "text/latex": [ "\\begin{tabular}{r|llll}\n", " & parish & perc.occupied.00 & perc.occupied.10 & perc.occupied.diff\\\\\n", "\\hline\n", "\t1 & Tensas Parish & 71.92617 & 64.70063 & -7.225543\\\\\n", "\t2 & Orleans Parish & 87.52156 & 74.86098 & -12.66058\\\\\n", "\t3 & St. Bernard Parish & 93.77753 & 78.72454 & -15.05298\\\\\n", "\\end{tabular}\n" ], "text/plain": [ "Source: local data frame [3 x 4]\n", "\n", " parish perc.occupied.00 perc.occupied.10 perc.occupied.diff\n", " (fctr) (dbl) (dbl) (dbl)\n", "1 Tensas Parish 71.92617 64.70063 -7.225543\n", "2 Orleans Parish 87.52156 74.86098 -12.660584\n", "3 St. Bernard Parish 93.77753 78.72454 -15.052984" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tail(parish.occupancy.rates, n = 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Histograms\n", "\n", "Maybe we want to know more about the overall trends in parishes' occupancy rates. For example, did most Louisiana parishes see decreases or increases in occupancy? \n", "\n", "We can explore this question using a histogram of perc.occupied.diff. A histogram is a good plot for understanding the distribution of a variable. On the x-axis, it plots the range of values of perc.occupied.diff (the change in occupancy over the two years) and on the y-axis, it plots how frequently these values occurred in our data." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", "\n", "\n", " \n", " \n", "\n", "\n", " \n", " \n", "\n", "\n", " \n", " \n", "\n", "\n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "Plot with title “Histogram of parish.occupancy.rates$perc.occupied.diff”" ] }, "metadata": { "image/svg+xml": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "hist(parish.occupancy.rates$perc.occupied.diff)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we did above, let's improve on this default plot that R spit out.\n", "\n", "The histogram grouped `perc.occupied.diff` by bins, which are equal-sized intervals of the variable's values. Here we have six bins, but we would understand the distribution in greater detail if we had more bins. To do this, we need to add an argument called `breaks`. \n", "\n", "Let's also draw a vertical line at 0 to quickly see how many parishes saw drops in occupancy rates. We'll also add some labels." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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" \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", " \n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", "\n", "\n", " \n", "\n", "\n", " \n", " \n", "\n", "\n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "Plot with title “Difference in occupancy rates, by parish”" ] }, "metadata": { "image/svg+xml": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "hist(\n", " parish.occupancy.rates$perc.occupied.diff, \n", " breaks = 20, \n", " main = 'Difference in occupancy rates, by parish',\n", " xlab = 'Percent point change in occupancy between 2000 and 2010', \n", " ylab = 'Number of parishes'\n", ")\n", "abline(v = 0, lwd = 5) # draw a vertical line at 0 with width of 5 pixels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can see that most of the parishes' occupancy rates went down.\n", "\n", "### Relationships and trend lines\n", "\n", "Let's examine the relationship between two of the variables in our data frame. We can run the `names` command to find the names of our columns. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
    \n", "\t
  1. 'parish'
    2. \n", "\t
  2. 'population.00'
    3. \n", "\t
  3. 'total.housing.units.00'
    4. \n", "\t
  4. 'occupied.housing.units.00'
    5. \n", "\t
  5. 'vacant.housing.units.00'
    6. \n", "\t
  6. 'population.10'
    7. \n", "\t
  7. 'total.housing.units.10'
    8. \n", "\t
  8. 'occupied.housing.units.10'
    9. \n", "\t
  9. 'vacant.housing.units.10'
    10. \n", "\t
  10. 'perc.pop.diff'
    11. \n", "\t
  11. 'perc.occupied.00'
    12. \n", "\t
  12. 'perc.occupied.10'
    13. \n", "\t
  13. 'perc.occupied.diff'
    14. \n", "
\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'parish'\n", "\\item 'population.00'\n", "\\item 'total.housing.units.00'\n", "\\item 'occupied.housing.units.00'\n", "\\item 'vacant.housing.units.00'\n", "\\item 'population.10'\n", "\\item 'total.housing.units.10'\n", "\\item 'occupied.housing.units.10'\n", "\\item 'vacant.housing.units.10'\n", "\\item 'perc.pop.diff'\n", "\\item 'perc.occupied.00'\n", "\\item 'perc.occupied.10'\n", "\\item 'perc.occupied.diff'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'parish'\n", "2. 'population.00'\n", "3. 'total.housing.units.00'\n", "4. 'occupied.housing.units.00'\n", "5. 'vacant.housing.units.00'\n", "6. 'population.10'\n", "7. 'total.housing.units.10'\n", "8. 'occupied.housing.units.10'\n", "9. 'vacant.housing.units.10'\n", "10. 'perc.pop.diff'\n", "11. 'perc.occupied.00'\n", "12. 'perc.occupied.10'\n", "13. 'perc.occupied.diff'\n", "\n", "\n" ], "text/plain": [ " [1] \"parish\" \"population.00\" \n", " [3] \"total.housing.units.00\" \"occupied.housing.units.00\"\n", " [5] \"vacant.housing.units.00\" \"population.10\" \n", " [7] \"total.housing.units.10\" \"occupied.housing.units.10\"\n", " [9] \"vacant.housing.units.10\" \"perc.pop.diff\" \n", "[11] \"perc.occupied.00\" \"perc.occupied.10\" \n", "[13] \"perc.occupied.diff\" " ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "names(parishes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use population.10 (the 2010 population) and perc.occupied.10 (the 2010 occupancy rate). \n", "\n", "We can draw the straight line that best fits the relationship between these two variables. In order to do this, we can use `abline`, but with an additional argument, `lm`, which stands for \"linear model.\" We have to tell R that `parishes$perc.occupied.10` is our y-variable and `parishes$population.10` is our x variable, this time in a slightly different format than the plot. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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For one, the straight line would cross 100 percent, which is impossible. A better fit for this relationship would be a curved line.\n", "\n", "To fit a curve, we have to do a logarithmic transformation of the 2010 population, then make that the x-variable in our model. A logarithmic transformation is a statistical concept that you can read more about [here](https://infoactive.co/data-design/ch11.html)." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "curved.model <- lm(parishes$perc.occupied.10 ~ log(parishes$population.10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting the curve is slightly more complicated. For each x-value, we want to plot the predicted y-value. In R, we have to first sort the data points of the curve. The `lines` function will then connect all of the points." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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" \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n" ], "text/plain": [ "plot without title" ] }, "metadata": { "image/svg+xml": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "predicted.values <- predict(curved.model)\n", "\n", "plot(parishes$population.10, parishes$perc.occupied.10)\n", "lines(sort(parishes$population.10), sort(predicted.values))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The curve appears to fit the data better than the line. For example, the linear trend was bad at estimating the relationship between population and occupancy rate for parishes with very low populations.\n", "\n", "What's most interesting about this plot is the presence of a large outlier. Let's finalize a better version of this plot by adding text labels for each parish so we can identify which parish has a high population but a very low occupancy rate." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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" \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n" ], "text/plain": [ "Plot with title “Louisiana parishes in 2010”" ] }, "metadata": { "image/svg+xml": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "options(scipen=5) # to prevent axes from appearing in scientific notation\n", "plot(\n", " parishes$population.10, parishes$perc.occupied.10, \n", " type = \"n\", # type = \"n\" tells R not to plot the points\n", " xlab = \"Population\",\n", " ylab = \"Occupancy rate (%)\",\n", " main = \"Louisiana parishes in 2010\"\n", ")\n", "text(\n", " parishes$population.10, \n", " parishes$perc.occupied.10, \n", " labels = parishes$parish, cex = 0.6\n", ") # adding text where the points should be\n", "lines(sort(parishes$population.10), sort(predicted.values), col = \"red\", lwd = 2)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "This concludes our workshop, More with R. We hope you found it useful!\n", "\n", "\n", "Any questions?\n", "\n", "* christine.zhang@latimes.com or [@christinezhang](https://twitter.com/christinezhang) on Twitter\n", "* ryan.menezes@latimes.com or [@ryanvmenezes](https://twitter.com/ryanvmenezes) on Twitter" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.2.2" } }, "nbformat": 4, "nbformat_minor": 0 }