2b:null 2c:["$","title",null,{"children":"LaiMicoleA2 | Micole | Observable"}] 31:T61f,## Aviation Dataset This data is from the [NTSB's downloadable dataset](https://www.ntsb.gov/Pages/AviationQuery.aspx), avall events, retrieved on Jan 28, 2023. It contains information starting from 2008 about aircraft incidents and accidents that have occurred. More information about the specifics of each report and other fields (some have been reduced from gist) are available through the [Aircraft Accident Data Sources and Trends](https://onlinepubs.trb.org/onlinepubs/acrp/acrp_rpt_027AircraftAccidentData.pdf) report. Information about various data fields is outlined in the following table: | Variable | Field | Definition | Key | |---|---|---| | **Event ID** | ev_id |Event ID number | | **Event Type** | ev_type | Accident or Incident | ACC, INC | | **Event Date** | ev_date | Year-Month-Day | | **Event City** | ev_city | Nearest city to event | | **Latitude** | latitude | Event location latitude | | **Longitude** | longitude | Event location longitude | | **Light Conditions** | light_cond | Day, Night, Near Dark | DAYL, NITE, NDRK | | **Sky Conditions (Lowest Non-Ceiling)** | sky_cond_nonceil | Clear, Scattered, Few, Broken, Overcast, Unknown | CLER, SCAT, FEW, BKN, OVCT, UNK | | **Highest Injury** | ev_highest_injury| None, Minor, Serious, Fatal | NONE, MINR, SERS, FATL | | **Total Fatalities** | inj_tot_f | | **Total Minor** | inj_tot_m | | **Total None** | inj_tot_n | | **Total Serious** | inj_tot_s | | **Total Injuries (All)** | inj_tot_t | **Sky conditions** refers to the types of clouds that were present during the reported event.32:[] 2f:{"id":"4428538b9e1ffa9a","slug":null,"title":"LaiMicoleA2","thumbnail":"a4ac4bdb059febc6e4cfca0d1a67d11291d4a23c904e2110ff2cc27a40407528","access_level":"public","create_time":"$D2023-02-02T05:26:21.592Z","update_time":"$D2023-11-04T02:32:12.409Z","view_time":null,"first_public_time":"$D2023-02-02T05:26:21.592Z","fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"c82cd4ee74fcb22f","login":"wstl","name":"Micole","avatar_url":"https://avatars-next.observableusercontent.com/d7629f2847ab0acb13a5cc5a3509ac2605148fb251714ce070c6dff2946548b4","tier":"starter_2024"},"version":805,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"4dd9c05b2974e20b","login":"wstl","name":"Micole","avatar_url":"https://avatars-next.observableusercontent.com/d7629f2847ab0acb13a5cc5a3509ac2605148fb251714ce070c6dff2946548b4","tier":null}],"members":[{"type":"individual","id":"4dd9c05b2974e20b","login":"wstl","name":"Micole","avatar_url":"https://avatars-next.observableusercontent.com/d7629f2847ab0acb13a5cc5a3509ac2605148fb251714ce070c6dff2946548b4","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"LaiMicoleA2","cells":[{"id":0,"value":"# LaiMicoleA2\n## [IAT 355] Assignment 2\n\nExploring aircraft incidents from NTSB reports.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":16,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":22,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":47,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":26,"value":"## Parsing Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":18,"value":"avall = d3.csv(\"https://gist.githubusercontent.com/mwstl/12f8dd472538e9808042460b05d0a605/raw/1a4cf7a973d40dbc077d3a5e3fb432fcb1df323f/NTSB_Avall_Events_Reduced\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":32,"value":"avallMap = avall.map(d => {\n var a = d;\n a.totFatal = +d.inj_tot_f;\n a.totMinor = +d.inj_tot_m;\n a.totNone = +d.inj_tot_n;\n a.totSerious = +d.inj_tot_s;\n a.totAll = +d.inj_tot_t;\n a.year = +d.ev_year;\n a.month = +d.ev_month;\n\n return a;\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":736,"value":"avallUnknown = {\n for (let i=0; i< avall.length; i++)\n {\n if (avall[i].light_cond == \"\") {\n avall[i].light_cond = \"Unknown\";\n }\n if (avall[i].sky_cond_nonceil == \"\") {\n avall[i].sky_cond_nonceil = \"Unknown\";\n }\n if (avall[i].sky_cond_ceil == \"\") {\n avall[i].sky_cond_ceil = \"Unknown\";\n }\n if (avall[i].ev_highest_injury == \"\") {\n avall[i].ev_highest_injury = \"Unknown\";\n }\n }\n return avall;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":34,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":77,"value":"## Operations","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":378,"value":"injuries = avallMap.map(function(d){return +d.totAll});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":360,"value":"fatalInjr = d3.sum(avallMap.map(function(d) {return d.totFatal}));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":354,"value":"allInjr = d3.sum(avallMap.map(function(d) {return d.totAll}));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":386,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":88,"value":"## Min/Max and more
\nFinding out the min, max, mean, median, and mode number of total injuries per incident across all reported incidents.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":36,"value":"{\n return (\"Minimum number of injuries: \" + d3.min(injuries) +\n \"\\n Maximum number of injuries: \" + d3.max(injuries) +\n \"\\n Mean number of injuries: \" + Math.round(d3.mean(injuries)) + \n \"\\n Median number of injuries: \" + d3.median(injuries) +\n \"\\n Mode number of injuries: \" + d3.mode(injuries));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":110,"value":"vl.markTick()\n .data(avallMap)\n .encode(\n vl.x().fieldQ('totAll').title(\"Total of all injuries\")\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":251,"value":"In this tick bar, we can see that most of the total injuries lie somewhere below 200 and gradually spread out between 200-400.
\nAlthough there is not enough separation in detail between each of the ticks, we see roughly where the min/max values are. However, it does not give us precise values when compared to the function calculations.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":453,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":185,"value":"## Sum
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":437,"value":"#### Total of All Fatal Injuries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":375,"value":"The sum of all fatal injuries across reports.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":97,"value":"{\n return (\"The total sum of all fatal injuries is \" + fatalInjr + \".\");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":445,"value":"vl.markBar()\n .data(avallMap)\n .encode(\n vl.x().fieldQ('totFatal').aggregate('max').title('Maximum fatal injuries (in a single incident)'),\n vl.y().fieldN('ev_year').title('Year')\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":467,"value":"I also took a look at the maximum number of fatalities in a single accident across the years. The number of fatalities varies greatly because air accidents are not common, nor do they have a specific pattern to them. As each accident is dependent on various factors, fatalities are somewhat randomized, however, we can see that 2017 seems to have been the safest year.
\nIf I were to search for some sort of pattern, the most prominent thing that pops out to me is that when the number of fatalities are lowest, the following year has a sudden increase in fatalities (in a single accident). For example, 2013-2014 and 2017-2018. However, we would have to analyse more accidents from prior years in order to draw any conclusions.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":187,"value":"Most fatalities and corresponding accident.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":40,"value":"{\n let num = 0;\n let city;\n let country;\n let date;\n\n for (let i = 0; i < avallMap.length; i++) {\n if (avallMap[i].totFatal > num) {\n num = avallMap[i].totFatal;\n city = avallMap[i].ev_city;\n country = avallMap[i].ev_country;\n date = avallMap[i].ev_date;\n }\n }\n\n // country names are abbreviated, so I opted to change the output if the accident is outside of the US\n if (country != \"USA\") {\n country = \" outside of the US\";\n } else {\n let temp = country;\n country = (\" in the country of \" + temp);\n }\n\n return (\n \"The highest number of fatalities in a single accident is: \" + num + \".\" +\n \"\\n This accident occured on \" + date + country + \", near the city of \" + city + \".\"\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":315,"value":"{\n let percent;\n let num = 0;\n \n for (let i = 0; i < avallMap.length; i++) {\n if (avallMap[i].totFatal > num) {\n num = avallMap[i].totFatal;\n }\n }\n\n percent = (num / fatalInjr); \n let rounded = Math.round((percent) * 10000) / 100;\n\n return (\n \"This single accident with the most fatalities accounts for \" + rounded + \"% of the total fatal injuries in this dataset.\"\n )\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":440,"value":"#### Total of All Types of Injuries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":349,"value":"The sum of all (comprehensive) injuries across reports.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":351,"value":"{\n return (\"The total sum of all injuries in this dataset is \" + allInjr + \".\");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":420,"value":"vl.markBar()\n .data(avallMap)\n .encode(\n vl.x().fieldQ('totAll').aggregate('max').title('Maximum total injuries (in a single incident)'),\n vl.y().fieldN('ev_year').title('Year')\n // vl.color().fieldQ('totFatal')\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":475,"value":"Compared to the previous chart of fatalities, the total injuries seem to be less varied as opposed to the major jumps present in the fatal injuries chart. We can still see sudden peaks during the years when there were major counts of fatalities, but overall, most of the accidents have been somewhat reducing in the number. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":393,"value":"Most total injuries and corresponding incident:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":395,"value":"{\n let num = 0;\n let city;\n let country;\n let date;\n let state;\n\n for (let i = 0; i < avallMap.length; i++) {\n if (avallMap[i].totAll > num) {\n num = avallMap[i].totAll;\n city = avallMap[i].ev_city;\n country = avallMap[i].ev_country;\n date = avallMap[i].ev_date;\n state = avallMap[i].ev_state;\n }\n }\n\n // country names are abbreviated, so I opted to change the output if the accident is outside of the US\n if (country != \"USA\") {\n country = \" outside of the US\";\n } else {\n let temp = country;\n country = (\" in the country of \" + temp + \", in \" + state);\n }\n\n return (\n \"The highest number of injuries in a single incident is: \" + num + \".\" +\n \"\\n This incident occured on \" + date + country + \", near the city of \" + city + \".\"\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":400,"value":"{\n let percent;\n let num = 0;\n \n for (let i = 0; i < avallMap.length; i++) {\n if (avallMap[i].totAll > num) {\n num = avallMap[i].totAll;\n }\n }\n\n percent = (num / allInjr); \n let rounded = Math.round((percent) * 10000) / 100;\n\n return (\n \"This single incident with the most injuries accounts for \" + rounded + \"% of the total injuries in this dataset.\"\n )\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":411,"value":"{\n let percent = (fatalInjr / allInjr);\n let rounded = Math.round((percent) * 10000) / 100;\n\n return (\n \"The total number of fatal injuries accounts for \" + rounded + \"% of the total for all types of injuries in this dataset.\"\n )\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":561,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":416,"value":"## Average","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":524,"value":"Occurrences of accidents throughout the year","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":526,"value":"vl.markBar()\n .data(avallMap)\n .encode(\n vl.x().fieldN('month').title('Event Month'),\n vl.y().count()\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":534,"value":"{\n let month = avallMap.map(function(d){return +d.ev_month});\n let avg = Math.round(d3.mean(month));\n \n return (\n \"The average amount of reported incidents occur around the \" + avg + \" month.\"\n )\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":541,"value":"There seems to be quite an apparent bell curve when looking at when the most accidents occur throughout the year. The highest amount of incidents happens in July, with the numbers rising/falling around it. This time is during the summer months when school is off and people generally take holidays. This may be a reason for the visible increase in reported incidents.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":545,"value":"I also tried charting the average number of total injuries, however, this value is heavily skewed towards the lower end of the 10s. This makes it difficult to see and compare with other numbers as the maximum total number of injuries is almost 600, whereas most of the reports are around 1-10. So, it doesn't make much sense to me to chart this type of field as formulas will give us a much more precise reading.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":510,"value":"vl.markBar()\n .data(avallMap)\n .encode(\n vl.x().fieldQ('totAll').bin({ step: 10 }).title('Total of all injuries (binned in 10s)'),\n vl.y().count()\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":552,"value":"{\n return (\"The average number of injuries is \" + Math.round(d3.mean(injuries)));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":103,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":161,"value":"## Count of dimension
\nFinding the total count of reported incidents across the years.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":149,"value":"vl.markBar()\n .data(avallMap)\n .encode(\n vl.y().count().title('Total Reports'),\n vl.x().field('year').title('Year')\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":200,"value":"Looking at the general heights of the bars, we can see that over the years, the amount of incidents has gone down. During this time, air travel has also increased, indicating somewhat of an inverse trend. This tells us that air travel is getting safer and safer over the years.\nWe can also note a substantial decrease in 2020 possibly due to the pandemic, although there is still quite a number of incidents.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":638,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":754,"value":"vl.markBar()\n .data(avallMap)\n .encode(\n vl.x().fieldN('ev_highest_injury').title('Highest Type of Injury').bin(false),\n vl.y().count().title('Total Reports')\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":775,"value":"This dimension shows the highest type of injury that has occurred in an accident. We can see that for the majority of accidents, people were not injured. However, the second highest statistic is the amount of fatalities. By nature of these accidents, these types of injuries may include both people within the aircraft, and people on the ground. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":190,"value":"vl.markBar()\n .data(avallMap)\n .encode(\n vl.x().fieldN('ev_dow').title('Day of the Week').sort([\"Su\", \"Mo\", \"Tu\", \"We\", \"Th\", \"Fr\", \"Sa\"]),\n vl.y().count().title('Total Reports')\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":603,"value":"This chart shows the number of reported accidents according to which day of the week the occurred. The days are sorted via custom order according to the [documentation](https://vega.github.io/vega-lite/docs/sort.html).
\nWe can see that most reported incidents occur over the weekend, particularly on Saturday.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":577,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":579,"value":"## Histogram","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":606,"value":"vl.markBar()\n .data(avallMap)\n .encode(\n vl.x().fieldN('totFatal').title('Total Fatalities (un-binned)').bin(false),\n vl.y().count().title('Total Reports')\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":620,"value":"This is a histogram of the number of fatalities per report. It is un-binned because most of the values are 0, and are visible on the chart until around 10. Binning the results into other numbers will make reading the number of fatalities less accurate, such as in the following chart.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":626,"value":"vl.markBar()\n .data(avallMap)\n .encode(\n vl.x().fieldN('totFatal').title('Total Fatalities (binned in 5s)').bin({ step: 5 }),\n vl.y().count().title('Total Reports')\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":664,"value":"Below are the histograms of the different types of injuries: None, Minor, Serious, and All.\nThese are all un-binned because for this type of dataset, most of the reports are for smaller numbers of injuries/fatalities, and larger incidents have a more visible impact when we compare the min/max. These large accidents also stretch the range of our data.
\nSomething that I find weird is that for all of the charts, not including the total for all injuries, the mode is *0*, however, for the total injuries, the mode is *1*.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":652,"value":"vl.markBar()\n .data(avallMap)\n .encode(\n vl.x().fieldN('totNone').title('No Injuries (un-binned)'),\n vl.y().count().title('Total Reports')\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":669,"value":"{\n let none = avallMap.map(function(d){return +d.totNone});\n let median = d3.median(none);\n \n return (\n \"The median for no reported injuries is \" + median + \".\"\n )\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":647,"value":"vl.markBar()\n .data(avallMap)\n .encode(\n vl.x().fieldN('totMinor').title('Total Minor Injuries (un-binned)').bin(false),\n vl.y().count().title('Total Reports')\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":674,"value":"{\n let none = avallMap.map(function(d){return +d.totMinor});\n let median = d3.median(none);\n \n return (\n \"The median for minor injuries is \" + median + \".\"\n )\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":654,"value":"vl.markBar()\n .data(avallMap)\n .encode(\n vl.x().fieldN('totSerious').title('Total Serious Injuries (un-binned)').bin(false),\n vl.y().count().title('Total Reports')\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":678,"value":"{\n let none = avallMap.map(function(d){return +d.totSerious});\n let median = d3.median(none);\n \n return (\n \"The median for serious injuries is \" + median + \".\"\n )\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":657,"value":"vl.markBar()\n .data(avallMap)\n .encode(\n vl.x().fieldN('totAll').title('Total All Injuries (un-binned)').bin(false),\n vl.y().count().title('Total Reports')\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":681,"value":"{\n let none = avallMap.map(function(d){return +d.totAll});\n let median = d3.median(none);\n \n return (\n \"The median for all injuries is \" + median + \".\"\n )\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":687,"value":"When printing out the median through formulas, I saw that there were inconsistencies between the charts and the calculated median. This is visible in the charts for no reported injuries and for all reported injuries. Perhaps this is because incidents can have a mix of the different types of injuries, which alters the output for different fields. For example, an accident can have 1 minor injury, 23 serious ones, and 1 fatality, resulting in 25 total injuries. In which case, looking at these fields in isolation will not tell us the whole story. This may explain the inconsistencies between the charts and calculations.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":691,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":693,"value":"## Challenges and issues","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":695,"value":"Most of the data is filled in, but I encountered some issues when finding certain states. As some of the incidents are located outside of the US, their *ev_state* dimension may have been left empty. So, when I was calculating where the highest number of injuries/fatalities was, I used an if statement to check which country the crash was in and then correspondingly change the output text.\n\nFor other data fields that were unfilled, I simply parsed them and added an if statement to check if they had nothing filled in, in which case, I set the output to \"Unknown\".","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":12,"value":"### Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"d3 = require(\"d3@7\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"import {vl} from '@vega/vega-lite-api'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":785,"value":"vl.markBar()\n .data(phil)\n .encode(\n vl.x().bin({ step: 5 }),\n vl.y().count().title('Total Reports')\n )\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":787,"value":"phil = [47607350, 39270150, 17116279, 11710818, 11692308, 8000000, 7723000, 5370370, 4343920, 4310250, 2226240, 2019706, 2019706, 2019706, 2019706, 1930681, 559782, 559782, 559782, 500000]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":792,"value":"import {Histogram} from \"@d3/histogram\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":796,"value":"Histogram(phil)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":798,"value":"d3.median(phil)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":800,"value":"d3.mean(phil)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":802,"value":"sat = [{\"460 to 480\": 0}, {\"480 to 500\": 5.8}, {\"500 to 520\": 5.8}, {\"520 to 540\": 9.8}, {\"540 to 560\": 8.4}, {\"560 to 580\": 4.5}, {\"580 to 600\": 2}, {\"600 to 620\": 4.5}, {\"620 to 640\": 4.5}, {\"640 to 660\": 3}];","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":804,"value":"Histogram(sat)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W32","stdlib":"1","titling":"auto"}} 30:"$2f:body"