// Numbas version: exam_results_page_options
{"name": "Querying a data frame (TV set)", "extensions": ["stats", "json_from_file"], "custom_part_types": [], "resources": [["question-resources/tv_u1CHb9S.json", "/srv/numbas/media/question-resources/tv_u1CHb9S.json"]], "navigation": {"allowregen": true, "showfrontpage": false, "preventleave": false, "typeendtoleave": false}, "question_groups": [{"pickingStrategy": "all-ordered", "questions": [{"name": "Querying a data frame (TV set)", "tags": [], "metadata": {"description": "<p>This question takes in a json file containing a&nbsp;tv data frame in the R package ncldata (<a href=\"https://rdrr.io/rforge/ncldata/\">https://rdrr.io/rforge/ncldata/</a>).&nbsp;</p>", "licence": "Creative Commons Attribution 4.0 International"}, "statement": "<p>Run the following commands&nbsp;to load the <code>tv</code>&nbsp;data frame:</p>\n<pre>install.packages(\"ncldata\", repos=\"http://R-Forge.R-project.org\")<br/>library(ncldata)<br/>data(tv)</pre>\n<p><em></em>Take a look at the help for the data frame with:</p>\n<pre>help(tv)</pre>\n<p></p>", "advice": "<h4>a)</h4>\n<p>The dimensions of the data frame can be found with the function <code>dim</code></p>\n<pre>dim(tv)</pre>\n<p>Or individually, the number of rows and columns can be found with</p>\n<pre>nrow(tv)<br/>ncol(tv)</pre>\n<h4>b)</h4>\n<p>The row with a title <em>{random_prog['Title']}</em> can be isolated with</p>\n<pre>tv[tv<span class=\"dollar\"></span>Title=='{random_prog['Title']}',]</pre>\n<p>Note the capital T for the column title. If you aren't sure about the column names use</p>\n<pre>colnames(tv)</pre>\n<p>We can either read off the rating, or we can isolate the value using the $ notation</p>\n<pre>tv[tv<span class=\"dollar\"></span>Title=='{random_prog['Title']}',]$Rating</pre>\n<h4>c)</h4>\n<p>Following the method in part <strong>b)</strong>,</p>\n<pre>tv[tv<span class=\"dollar\"></span>Title=='{random_prog2['Title']}',]$Votes</pre>\n<h4>d)</h4>\n<p>Using the square bracket notation above, the subset of tv shows with a start year greater than or equal to {year}&nbsp;can be found with</p>\n<pre>tv[tv<span class=\"dollar\"></span>StartYear &gt;= {year},]</pre>\n<p>Or,&nbsp;using the function <code>subset</code>,</p>\n<pre>subset(tv,StartYear &gt;= {year})</pre>\n<p>We can use <code>nrow</code> to find the number of tv shows in the subset, either by assigning the above to a new data frame, or querying it directly:</p>\n<pre>nrow(subset(tv,StartYear &gt;= {year}))</pre>\n<h4>e)</h4>\n<p>The subset of&nbsp;TV shows which both started and ended in the {1900+(decade*10)}s can be found with</p>\n<pre>subset(tv,StartYear &gt;={1900+(decade*10)} &amp; EndYear &lt;={1909+(decade*10)})</pre>\n<p>Again, <code>nrow</code> can be used to find the number of shows in this subset.</p>\n<h4>f)</h4>\n<p>The subset of TV shows which started and ended in the same year, but have a rating greater than {random_rating} is</p>\n<pre>subset(tv,StartYear == EndYear &amp; Rating &gt; {random_rating})</pre>\n<h4>g)</h4>\n<p>The&nbsp;mean rating of TV shows which started&nbsp;have started since (and including) {year2} can be found with</p>\n<pre>mean(subset(tv,StartYear &gt;= {year2})$Rating)</pre>\n<h4>h)</h4>\n<p>The subset of TV shows which are exactly {random_length} minutes long can be found with</p>\n<pre>subset(tv, Length=={random_length})</pre>\n<p>Note that for this example, you will find a different (and incorrect) answer if you use square brackets to subset. Take a look at the output of the following command to see the problem:</p>\n<pre>tv[tv<span class=\"dollar\"></span>Length == {random_length},]$Length</pre>", "rulesets": {}, "extensions": ["json_from_file", "stats"], "variables": {"year2": {"name": "year2", "group": "Ungrouped variables", "definition": "random(2011..2017)", "description": "", "templateType": "anything"}, "random_prog_id": {"name": "random_prog_id", "group": "Ungrouped variables", "definition": "random(20..length(getTVEnded()))", "description": "", "templateType": "anything"}, "tv_in_decade": {"name": "tv_in_decade", "group": "Ungrouped variables", "definition": "len(filter(prog[\"StartYear\"] >= 1900+(decade*10) and prog[\"EndYear\"] <= 1909+(decade*10),prog,getTVEnded()))", "description": "", "templateType": "anything"}, "columns_in_data": {"name": "columns_in_data", "group": "Ungrouped variables", "definition": "len(getTVData()[1])-1", "description": "", "templateType": "anything"}, "random_prog": {"name": 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{"name": "random_rating", "group": "Ungrouped variables", "definition": "random(5..9)", "description": "", "templateType": "anything"}, "random_length": {"name": "random_length", "group": "Ungrouped variables", "definition": "random(20..90#10)", "description": "", "templateType": "anything"}, "tv_exact_length": {"name": "tv_exact_length", "group": "Ungrouped variables", "definition": "length(filter(prog[\"Length\"]=random_length,prog,getTVData()))", "description": "", "templateType": "anything"}, "decade": {"name": "decade", "group": "Ungrouped variables", "definition": "random(6..9)", "description": "", "templateType": "anything"}, "category2": {"name": "category2", "group": "Movie Categories", "definition": "categories[1]", "description": "", "templateType": "anything"}, "random_prog2": {"name": "random_prog2", "group": "Ungrouped variables", "definition": "getTVEnded()[random_prog_id2-1]", "description": "", "templateType": "anything"}, "tv_after_year": {"name": "tv_after_year", "group": "Ungrouped variables", "definition": "length(filter(prog[\"StartYear\"]>=year,prog,getTVData()))", "description": "", "templateType": "anything"}, "category1": {"name": "category1", "group": "Movie Categories", "definition": "categories[0]", "description": "", "templateType": "anything"}, "year": {"name": "year", "group": "Ungrouped variables", "definition": "random(1990..2017)", "description": "", "templateType": "anything"}, "categories": {"name": "categories", "group": "Movie Categories", "definition": "shuffle([\"Action\", \"Animation\", \"Comedy\", \"Drama\", \"Documentary\", \"Romance\"])", "description": "<p>The 7 movie categories in the data frame</p>", "templateType": "anything"}}, "variablesTest": {"condition": "", "maxRuns": 100}, "ungrouped_variables": ["year", "year2", "random_prog_id", "random_prog_id2", "random_length", "decade", "random_rating", "tv_after_year", "rows_in_data", "columns_in_data", "tv_in_decade", "random_prog", "one_year_shows", "rating_since_year", "tv_exact_length", "random_prog2"], "variable_groups": [{"name": "Movie Categories", "variables": ["categories", "category1", "category2"]}], "functions": {"getTVData": {"parameters": [], "type": "list", "language": "jme", "definition": "json_from_file('resources/question-resources/tv_u1CHb9S.json')"}, "getTVEnded": {"parameters": [], "type": "list", "language": "jme", "definition": "filter(tv['EndYear']<>'NA',tv,getTVData())"}}, "preamble": {"js": "question.signals.on('HTMLAttached',function() {\n  $(\"span.dollar\").html(\"$\");\n});", "css": ""}, "parts": [{"type": "gapfill", "useCustomName": false, "customName": "", "marks": 0, "showCorrectAnswer": true, "showFeedbackIcon": true, "scripts": {}, "variableReplacements": [], "variableReplacementStrategy": "originalfirst", "adaptiveMarkingPenalty": 0, "customMarkingAlgorithm": "", "extendBaseMarkingAlgorithm": true, "unitTests": [], "prompt": "<p>How many rows and columns are there in&nbsp;the <code>tv</code> data 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