2d:null 2e:["$","title",null,{"children":"Think Stats - 1. Exploratory Data Analysis | Shana Matthews | Observable"}] 33:[{"id":"d362151580b59cb5bb5df99912899d6de323b3ddaacd29b7dd74ea0c49f89d60c204d2d79877596acc32c4d03d1c031343bc9b8b125ab41b40a05f51df47a1fa","href":"https://static.observableusercontent.com/files/d362151580b59cb5bb5df99912899d6de323b3ddaacd29b7dd74ea0c49f89d60c204d2d79877596acc32c4d03d1c031343bc9b8b125ab41b40a05f51df47a1fa","name":"2002FemPreg.dat","type":"application/octet-stream","lastModified":1780180268365.678,"size":6089664},{"id":"6a1eee9c63991eed1b726f22e307c3db5cd0b5a991aef2637e61b965e9ce83fb118f9471d7ed83d6863fdf60ea5d46e089165b1c8ee77c0c667ea88befb499ba","href":"https://static.observableusercontent.com/files/6a1eee9c63991eed1b726f22e307c3db5cd0b5a991aef2637e61b965e9ce83fb118f9471d7ed83d6863fdf60ea5d46e089165b1c8ee77c0c667ea88befb499ba","name":"2002FemPreg.dct","type":"application/octet-stream","lastModified":1780176929833.572,"size":28607}] 31:{"id":"ea885b1bcec91fae","slug":"think-stats-1","title":"Think Stats - 1. Exploratory Data Analysis","thumbnail":"a631a97cff4cebbb112a625941c582fbbbcb42a061555639e7fa9066ed99eca6","access_level":"public","create_time":"$D2026-05-30T21:33:56.160Z","update_time":"$D2026-06-14T01:14:11.539Z","view_time":null,"first_public_time":"$D2026-06-01T01:44:13.752Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"c2c4bf3817594e7a","login":"shanamatthews","name":"Shana Matthews","avatar_url":"https://avatars-next.observableusercontent.com/340ef6744a9ac2c729c6d78fcd6cba4605a6eb050c76e0528bc44eef6752a274","tier":"starter_2024"},"version":1248,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"543390bedabcfda6","login":"shanamatthews","name":"Shana Matthews","avatar_url":"https://avatars-next.observableusercontent.com/340ef6744a9ac2c729c6d78fcd6cba4605a6eb050c76e0528bc44eef6752a274","tier":null}],"members":[{"type":"individual","id":"543390bedabcfda6","login":"shanamatthews","name":"Shana Matthews","avatar_url":"https://avatars-next.observableusercontent.com/340ef6744a9ac2c729c6d78fcd6cba4605a6eb050c76e0528bc44eef6752a274","tier":null,"role":"editor"},{"type":"team","id":"c2c4bf3817594e7a","login":"shanamatthews","name":"Shana Matthews","avatar_url":"https://avatars-next.observableusercontent.com/340ef6744a9ac2c729c6d78fcd6cba4605a6eb050c76e0528bc44eef6752a274","tier":"starter_2024","role":"viewer"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":{"id":"d30a2b2544e10fcc","slug":"think-stats","title":"Think Stats","owner":{"id":"c2c4bf3817594e7a","login":"shanamatthews","name":"Shana Matthews"}},"type":"notebook","body":{"title":"Think Stats - 1. Exploratory Data Analysis","cells":[{"id":1226,"value":"# Think Stats - 1. Exploratory Data Analysis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1119,"value":"Todo:\n\n- [x] Replace import data step with @shanamatthews/parse-stata-dictionary-files\n- [x] Finish exercises\n- [ ] Figure out discrepancy with # of valid `totalwgt_lb` vals calculated","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":0,"value":"*A JavaScript + Arquero version of [Allen Downey's Think Stats Ch. 1](https://allendowney.github.io/ThinkStats/chap01.html). The ideas here are not my own, it's just a rewrite in JS.*\n\nI was working through Think Stats and remembered how much nicer it is to use JS + Observable for data exploration vs. Python + Jupyter. \n\nI'm redoing the entire data import, cleaning, validation, analysis in JS + Arquero. This helps me make sure I really understand what's happening and learn more about Arquero in the process. No AI was used in the making of this notebook, since it's just for learning.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":168,"value":"Import stata pregnancy data from \n- https://www.cdc.gov/nchs/nsfg/nsfg_cycle6.htm\n- https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NSFG/","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":170,"value":"## Import data\n\nThe book imports the STATA data by \n1. Grabbing the dct_file (column data) and dat_file (row data)\n2. Using an open source library, [`statadict`](https://github.com/atudomain/statadict) to parse the dct file into correctly formatted column data.\n3. Passing the formatted column data and the dat_file into `pandas.read_fwf` to create a Pandas `DataFrame`.\n\nThere is no equivalent JavaScript library (that I could find) to parse dct files, so I created [@shanamatthews/parse-stata-dictionary-files](https://observablehq.com/@shanamatthews/parse-stata-dictionary-files) to prep the dct column data into a format Arquero's `loadFixed` could use.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":302,"value":"### Import columns data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":293,"value":"dctImportText = FileAttachment(\"2002FemPreg.dct\").text()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1127,"value":"columnsClean = dctToTableInput(dctImportText)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":216,"value":"We have a total of 243 columns in the data set.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":541,"value":"### Prep for Arquero import","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":318,"value":"Our row data is stored as fixed width data in a text file. How to read in fixed width data like this? I would use `read_fwf()` in Pandas.\n\nArquero's [`fromFixed()`](https://idl.uw.edu/arquero/api/#fromFixed) looks like a great option.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":278,"value":"### Import row data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":119,"value":"fempregText = FileAttachment(\"2002FemPreg.dat\").text()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":309,"value":"fempregArr = fempregText.split(\"\\n\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":532,"value":"aqTableRaw = aq.fromFixed(fempregText, {\n positions: columnsClean.positions,\n names: columnsClean.names\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":567,"value":"aqTableRaw.view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":551,"value":"Now we have an Arquero table created from our STATA dataset.\n\nThe first couple rows of data look reasonable compared to the original and I can see that the number of columns (variables) and rows (values) in this dataset are correct.\n\n- Columns: 243\n- Rows: 13593\n\nMy confidence level that this import is otherwise correct is not high yet though. Let's do some basic checks to see if our results line up with original summary statistics/computed variables.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":578,"value":"## Validate data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":573,"value":"The NSFG dataset contains 243 variables in total. Here are some of the ones we’ll use for the explorations in this book.\n\n- `caseid` is the integer ID of the respondent.\n- `pregordr` is a pregnancy serial number: the code for a respondent’s first pregnancy is 1, for the second pregnancy is 2, and so on.\n- `prglngth` is the integer duration of the pregnancy in weeks.\n- outcome is an integer code for the outcome of the pregnancy. The code 1 indicates a live birth.\n- `birthord` is a serial number for live births: the code for a respondent’s first child is 1, and so on. For outcomes other than live birth, this field is blank.\n- `birthwgt_lb` and birthwgt_oz contain the pounds and ounces parts of the birth weight of the baby.\n- `agepreg` is the mother’s age at the end of the pregnancy.\n- `finalwgt` is the statistical weight associated with the respondent. It is a floating-point value that indicates the number of people in the U.S. population this respondent represents.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":602,"value":"One way to validate data is to compute basic statistics and compare them with published results. For example, the NSFG codebook includes tables that summarize each variable. Here is the table for outcome, which encodes the outcome of each pregnancy.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":600,"value":"\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

Value	Label	Total
1	LIVE BIRTH	9148
2	INDUCED ABORTION	1862
3	STILLBIRTH	120
4	MISCARRIAGE	1921
5	ECTOPIC PREGNANCY	190
6	CURRENT PREGNANCY	352

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":605,"value":"The “Total” column indicates the number of pregnancies with each outcome. \n\nTo check these totals against our version of the dataset, I'll group our table by the `outcome` column, then use Arquero's [`count()`](https://idl.uw.edu/arquero/api/verbs#count) rollup shorthand to view the counts per outcome. \n\nI've also applied an `orderby()` sort to sort on the `outcome` field.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":609,"value":"aqTableRaw.groupby(\"outcome\").count().orderby(\"outcome\").view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":618,"value":"That's a match!\n\nLet's do the same for `birthwght_lb`.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":641,"value":"\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

Value	Label	Total
.	inapplicable	4449
0-5	UNDER 6 POUNDS	1125
6	6 POUNDS	2223
7	7 POUNDS	3049
8	8 POUNDS	1889
9-95	9 POUNDS OR MORE	799
97	Not ascertained	1
98	REFUSED	1
99	DON'T KNOW	57
Total		13593

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":643,"value":"Birth weight is only recorded for pregnancies that ended in a live birth. The table indicates that there are 4449 cases where this variable is inapplicable. In addition, there is one case where the question was not asked, one where the respondent did not answer, and 57 cases where they did not know.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":648,"value":"aqTableRaw.groupby(\"birthwgt_lb\").count().orderby(\"birthwgt_lb\").view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":653,"value":"The counts for 6, 7, and 8 pounds are consistent with the codebook. \n\nTo check the counts for the weight range from 0 to 5 pounds, we can filter the results down to a weight range from 0 to 5 pounds.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":656,"value":"aqTableRaw\n .filter(\n (d) => d.birthwgt_lb !== null && d.birthwgt_lb >= 0 && d.birthwgt_lb <= 5\n )\n .groupby(\"birthwgt_lb\")\n .count()\n .orderby(\"birthwgt_lb\")\n .view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":666,"value":"Now let's get the total count from all 0-5 lb babies.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":668,"value":"aqTableRaw\n .filter(\n (d) => d.birthwgt_lb !== null && d.birthwgt_lb >= 0 && d.birthwgt_lb <= 5\n )\n .count()\n .view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":695,"value":"Again, this is consistent with the codebook. My confidence is now high that I did this data import right.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":713,"value":"## Clean data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":912,"value":"There's still a lot of cleaning left to do. Since Arquero tables are read-only, we'll want to get our data back into objects we can edit. We'll put it back into an Arquero table when we're done.\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":720,"value":"birthsDataArr = aqTableRaw.objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":731,"value":"birthsDataArr","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":697,"value":"1. In `birthwgt_lb` the values 97, 98, and 99 represent cases where the birth weight is unknown. The value 51 pounds is also clearly wrong. I'll replace these values with NaN.\n\n2. In `birthwgt_oz` the values 97, 98, and 99 also represent cases where the birth weight is unknown. \n\n3. `agepreg` contains the mother’s age at the end of the pregnancy. According to the codebook, it is an integer number of centiyears (hundredths of a year). I'll convert that to years","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":727,"value":"// Clean up data\n// - birthwgt_lb: unknown birth weight and obvious incorrect birth weight\n// - birthwgt_oz: unknown birth weight\n// - agepreg: transform age at end of pregnancy from centiyears to years\nbirthsDataArrClean1 = birthsDataArr.map((d) => {\n let toRet = {\n ...d,\n agepreg: d.agepreg !== null ? d.agepreg / 100 : NaN,\n birthwgt_lb: [51, 97, 98, 99].includes(d.birthwgt_lb) ? NaN : d.birthwgt_lb,\n birthwgt_oz: [97, 98, 99].includes(d.birthwgt_oz) ? NaN : d.birthwgt_oz\n };\n return toRet;\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":933,"value":"What else? \n\n`birthwgt_lb` and `birthwgt_oz` contain birth weights with the pounds and ounces in separate columns. It will be more convenient to combine them into as single column that contains weights in pounds and fractions of a pound.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":936,"value":"birthsDataArrClean2 = birthsDataArrClean1.map((d) => ({\n ...d,\n totalwgt_lb:\n // there are potential real values where birthwgt_lb != NaN that we could use - apparently 46 of them\n // also 1 where birthwgt_oz != NaN, but that one seems leff valid\n d.birthwgt_lb && d.birthwgt_oz ? d.birthwgt_lb + d.birthwgt_oz / 16.0 : NaN\n}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":866,"value":"birthsDataArrClean2","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":956,"value":"Looking good, let's get it back into an Arquero table.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":958,"value":"birthsTable = aq.from(birthsDataArrClean2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1005,"value":"## Summary statistics","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1008,"value":"A **statistic** is a number derived from a dataset, usually intended to quantify some aspect of the data. Examples include the count, mean, variance, and standard deviation.\n\nI'll use a filter to return the number of values of `totalwgt_lb` that are not NaN.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1012,"value":"// 5599 values of totalwgt_lb are NaN\nbirthsTable.column(\"totalwgt_lb\").filter((d) => !d)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1070,"value":"// 7994 values of totalwgt_lb are valid\nweights = birthsTable.column(\"totalwgt_lb\").filter((d) => d)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1074,"value":"We do start to disagree with the Python version of this dataset here.\n\nI'm sure it has something to do with how I've chosen to calculate `totalwgt_lb` vs. Pandas does (`preg[\"totalwgt_lb\"] = preg[\"birthwgt_lb\"] + preg[\"birthwgt_oz\"] / 16.0`)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1079,"value":"// calculate the mean\nbirthsTable\n .filter((d) => d.totalwgt_lb)\n .rollup({\n mean_weight_lbs: (d) => op.mean(d.totalwgt_lb) // op.average is available as a synonym\n })\n .view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1096,"value":"Our mean weight is very similar though (7.28 vs. 7.26), so I'm confident nothing is too wrong.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1098,"value":"Variance is a statistic that quantifies the spread of a set of values. It is the mean of the squared deviations, which are the distances of each point from the mean.\n\n`squaredDeviations = (weights - mean) ** 2`\n\nWe can compute the mean of the squared deviations like this.\n\n```\nvar = squared_deviations.sum() / n\n```","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1113,"value":"In this dataset, the variance of the birth weights is about 1.98, but that value is hard to interpret – for one thing, it is in units of pounds squared. Variance is useful in some computations, but not a good way to describe a dataset. A better option is the **standard deviation**, which is the square root of variance.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1105,"value":"We can calculate it in Arquero with [`op.stdev()`](https://idl.uw.edu/arquero/api/op#stdev)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1110,"value":"birthsTable\n .filter((d) => d.totalwgt_lb)\n .rollup({\n mean_weight_lbs: (d) => op.mean(d.totalwgt_lb), // op.average is available as a synonym\n standard_deviation: (d) => op.stdev(d.totalwgt_lb)\n })\n .view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1116,"value":"In this dataset, the standard deviation of birth weights is about 1.4 pounds. Informally, values that are one or two standard deviations from the mean are common – values farther from the mean are rare.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":962,"value":"## Interpretation","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":969,"value":"To work with data effectively, you have to think on two levels at the same time: the level of statistics and the level of context. As an example, let’s select the rows in the pregnancy file with `caseid` 10229.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":971,"value":"id10229 = birthsTable.filter((d) => d.caseid === 10229)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":981,"value":"id10229.view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":987,"value":"The result is an Arquero `Table` that contains only the rows where the query is `True`. This respondent reported seven pregnancies – here are their outcomes, which are recorded in chronological order.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":977,"value":"id10229.array(\"outcome\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":989,"value":"The outcome code `1` indicates a live birth. Code `4` indicates a miscarriage – that is, a pregnancy loss, usually with no known medical cause.\n\nStatistically this respondent is not unusual. Pregnancy loss is common and there are other respondents who reported as many instances. But remembering the context, this data tells the story of a woman who was pregnant six times, each time ending in miscarriage. Her seventh and most recent pregnancy ended in a live birth. If we consider this data with empathy, it is natural to be moved by the story it tells.\n\nEach row in the NSFG dataset represents a person who provided honest answers to many personal and difficult questions. We can use this data to answer statistical questions about family life, reproduction, and health. At the same time, we have an obligation to consider the people represented by the data, and to afford them respect and gratitude.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":995,"value":"## Exercises","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":997,"value":"Select the `birthord` column from `preg`, print the value counts, and compare to results published in the codebook at https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NSFG/Cycle6Codebook-Pregnancy.pdf.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1175,"value":"|Value|Label|Total|\n|-|-|-|\n|.|INAPPLICABLE|4445|\n|1|1ST BIRTH|4413|\n|2|2ND BIRTH|2874|\n|3|3RD BIRTH|1234|\n|4|4TH BIRTH|421|\n|5|5TH BIRTH|126|\n|6|6TH BIRTH|50|\n|7|7TH BIRTH|20|\n|8|8TH BIRTH|7|\n|9|9TH BIRTH|2|\n|10|10TH BIRTH|1|","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1160,"value":"birthsTable.groupby(\"birthord\").count().orderby(\"birthord\").view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":999,"value":"Create a new column named `totalwgt_kg` that contains birth weight in kilograms (there are approximately 2.2 pounds per kilogram). Compute the mean and standard deviation of the new column.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1183,"value":"birthsTable\n .derive({ totalwgt_kg: (d) => d.totalwgt_lb * 2.2 })\n .rollup({\n mean_wgt_kg: (d) => op.mean(d.totalwgt_kg),\n stdev_wgt_kg: (d) => op.stdev(d.totalwgt_kg)\n })\n .view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1002,"value":"What are the pregnancy lengths for the respondent with `caseid` 2298?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1206,"value":"id2298 = birthsTable.filter((d) => d.caseid === 2298)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1212,"value":"id2298.view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1198,"value":"// in weeks of gestation\nid2298.array(\"wksgest\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1202,"value":"What was the birth weight of the first baby born to the respondent with `caseid` 5013?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1215,"value":"id5013 = birthsTable.filter((d) => d.caseid === 5013)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1235,"value":"id5013.orderby(\"pregordr\").view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1219,"value":"// in pounds\nid5013.orderby(\"pregordr\").array(\"totalwgt_lb\")[0]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1240,"value":"Plot.plot({\n marks: [\n Plot.auto(null, {})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":56,"value":"## Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1124,"value":"import { dctToTableInput } from \"@shanamatthews/parse-stata-dictionary-files\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":337,"value":"import { aq, op } from \"@uwdata/arquero\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1245,"value":"birthsDataArrCleanFinal = birthsDataArrClean2","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W33","stdlib":"1","titling":"auto"}} 32:"$31:body"