library(tidyverse)
library(skimr)
library(haven)An Analysis of NYPD Arrest Data
Proposal
Data 1
Introduction and data
Identify the source of the data.
https://data.cityofnewyork.us/Public-Safety/NYPD-Arrest-Data-Year-to-Date-/uip8-fykc
NYC Open Data
State when and how it was originally collected (by the original data curator, not necessarily how you found the data).
This data is year to date data manually collected every quarter by the Office of Management and Planning. It was last updated February 1st, 2023.
Write a brief description of the observations.
Each row is a different arrest case in NYC by the NYPD. There are 19 columns containing information regarding things like, age, sex, and race of the person arrested, the date and location of the arrest, and charges and classifications associated with the arrest.
Ethical concern?
An ethical concern for this dataset would be revealing too many details about a specific observation or making far fetched conclusions without hard evidence to back it up, since this dataset deals with crime and it may not be 100% accurate.
Research question
A well formulated research question. (You may include more than one research question if you want to receive feedback on different ideas for your project. However, one per data set is required.)
What effect does location have on a the likely perpetrators age, sex, and race for a crime committed in NYC.
or
How does a perpetrators age, sex, and race affect the level of offense they are convicted of.
A description of the research topic along with a concise statement of your hypotheses on this topic.
This research topic would look at how someones personal profile could change depending on the location they committed the crime. Do certain areas have larger amounts of younger people committing crime? Does a specific borough have the highest proportion of female perpetrators? These are all questions we could look at and answer with this data.
With the second research question we could look at how the level of offense for someone could change depending on the perpetrators characteristics.
People of minority status might be more likely to be tried for higher levels of offense due to discrimination in the legal/penal system.
Identify the types of variables in your research question. Categorical? Quantitative?
Date: Quantitative
Sex, race, age group, borough, and level of offense: Categorical
Glimpse of data
nypd_arrest_data_raw <- read_csv("data/NYPD_Arrest_Data__Year_to_Date_.csv")Rows: 189774 Columns: 19
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (10): ARREST_DATE, PD_DESC, OFNS_DESC, LAW_CODE, LAW_CAT_CD, ARREST_BORO...
dbl (9): ARREST_KEY, PD_CD, KY_CD, ARREST_PRECINCT, JURISDICTION_CODE, X_CO...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.skimr::skim(nypd_arrest_data_raw)| Name | nypd_arrest_data_raw |
| Number of rows | 189774 |
| Number of columns | 19 |
| _______________________ | |
| Column type frequency: | |
| character | 10 |
| numeric | 9 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| ARREST_DATE | 0 | 1.00 | 10 | 10 | 0 | 365 | 0 |
| PD_DESC | 0 | 1.00 | 6 | 54 | 0 | 243 | 0 |
| OFNS_DESC | 0 | 1.00 | 4 | 36 | 0 | 66 | 0 |
| LAW_CODE | 0 | 1.00 | 8 | 10 | 0 | 1058 | 0 |
| LAW_CAT_CD | 1747 | 0.99 | 1 | 1 | 0 | 5 | 0 |
| ARREST_BORO | 0 | 1.00 | 1 | 1 | 0 | 5 | 0 |
| AGE_GROUP | 0 | 1.00 | 3 | 5 | 0 | 5 | 0 |
| PERP_SEX | 0 | 1.00 | 1 | 1 | 0 | 2 | 0 |
| PERP_RACE | 0 | 1.00 | 5 | 30 | 0 | 7 | 0 |
| New Georeferenced Column | 0 | 1.00 | 21 | 42 | 0 | 34251 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ARREST_KEY | 0 | 1 | 247721027.46 | 5378637.00 | 238492434.00 | 243171338.25 | 247532420.50 | 252021557.75 | 261197497.00 | ▇▇▇▆▁ |
| PD_CD | 561 | 1 | 406.28 | 271.33 | 9.00 | 113.00 | 339.00 | 681.00 | 997.00 | ▇▆▃▅▂ |
| KY_CD | 570 | 1 | 246.62 | 149.78 | 101.00 | 110.00 | 235.00 | 344.00 | 995.00 | ▇▇▁▁▁ |
| ARREST_PRECINCT | 0 | 1 | 62.69 | 35.03 | 1.00 | 40.00 | 61.00 | 100.00 | 123.00 | ▆▇▆▅▇ |
| JURISDICTION_CODE | 0 | 1 | 0.94 | 7.94 | 0.00 | 0.00 | 0.00 | 0.00 | 97.00 | ▇▁▁▁▁ |
| X_COORD_CD | 0 | 1 | 1005207.28 | 21228.07 | 913554.00 | 991203.00 | 1005040.00 | 1017119.00 | 1067185.00 | ▁▁▇▇▂ |
| Y_COORD_CD | 0 | 1 | 208693.04 | 29606.36 | 121312.00 | 186655.75 | 207651.00 | 236145.75 | 271909.00 | ▁▃▇▆▃ |
| Latitude | 0 | 1 | 40.74 | 0.08 | 40.50 | 40.68 | 40.74 | 40.81 | 40.91 | ▁▃▇▆▃ |
| Longitude | 0 | 1 | -73.92 | 0.08 | -74.25 | -73.97 | -73.92 | -73.88 | -73.70 | ▁▁▇▇▂ |
nypd_arrest_data <- nypd_arrest_data_raw |> drop_na() |>
select(1:14) |>
select(!JURISDICTION_CODE) |>
select(!ARREST_PRECINCT)Data 2
Introduction and data
Identify the source of the data.
https://data.ca.gov/dataset/covid-19-hospital-data1
State of California – California Department of Public Health (CDPH)
State when and how it was originally collected (by the original data curator, not necessarily how you found the data).
CDPH requires all General Acute Care Hospitals (GACH) to report data every day via a tracking tool. Created June 23, 2022, but last updated March 15, 2023.
https://calhospital.org/streamlining-data/
Write a brief description of the observations.
Each observation is a report of a California hospital’s COVID data by county and date.
Research question
A well formulated research question. (You may include more than one research question if you want to receive feedback on different ideas for your project. However, one per data set is required.)
What date ranges were hospitals most inundated with COVID patients, is there a specific county that was hit the hardest?
Do hospitals with more beds tend to have a higher percentage of COVID patients than those with less beds?
A description of the research topic along with a concise statement of your hypotheses on this topic.
The research topic would be to gain insight on the impact of COVID on California. In particular if higher occupancy hospitals had a harder time accommodating COVID patients and if certain counties were hit harder in this regard.
Hypothesis:
Los Angeles and San Francisco counties were hit the hardest by COVID 19 because of their high population (would need an external dataset to prove)
Hospitals with a higher capacity tend to have a higher percentage of COVID patients because of better access to COVID 19 facilities.
Identify the types of variables in your research question. Categorical? Quantitative?
Categorical: county, todays_date
Quantitative: hospitalized_covid_confirmed_patients, hospitalized_suspected_covid_patients, hospitalized_covid_patients, all_hospital_beds, icu_covid_confirmed_patients, icu_suspected_covid_patients, icu_available_beds
Glimpse of data
cali_covid_data <- read_csv(paste("data/covid-19-hospital-data/statewide-covid",
"-19-hospital-county-data.csv", sep = ""))Rows: 60533 Columns: 9
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): county
dbl (7): hospitalized_covid_confirmed_patients, hospitalized_suspected_covi...
date (1): todays_date
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.skimr::skim(cali_covid_data)| Name | cali_covid_data |
| Number of rows | 60533 |
| Number of columns | 9 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| Date | 1 |
| numeric | 7 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| county | 0 | 1 | 4 | 15 | 0 | 56 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| todays_date | 0 | 1 | 2020-03-29 | 2023-03-14 | 2021-09-20 | 1081 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| hospitalized_covid_confirmed_patients | 9 | 1.00 | 82.02 | 297.16 | 0 | 2 | 12 | 60 | 8098 | ▇▁▁▁▁ |
| hospitalized_suspected_covid_patients | 10 | 1.00 | 11.02 | 41.14 | 0 | 0 | 0 | 9 | 1350 | ▇▁▁▁▁ |
| hospitalized_covid_patients | 1285 | 0.98 | 93.05 | 323.61 | 0 | 2 | 14 | 73 | 8422 | ▇▁▁▁▁ |
| all_hospital_beds | 1375 | 0.98 | 1205.36 | 2865.07 | 0 | 56 | 278 | 1047 | 54966 | ▇▁▁▁▁ |
| icu_covid_confirmed_patients | 31 | 1.00 | 18.36 | 68.57 | 0 | 0 | 2 | 12 | 1731 | ▇▁▁▁▁ |
| icu_suspected_covid_patients | 32 | 1.00 | 1.49 | 6.37 | -2 | 0 | 0 | 1 | 244 | ▇▁▁▁▁ |
| icu_available_beds | 804 | 0.99 | 39.61 | 104.95 | -110 | 2 | 8 | 27 | 1502 | ▇▁▁▁▁ |
Data 3
Introduction and data
Identify the source of the data.
https://www.pewresearch.org/hispanic/dataset/american-trends-panel-wave-86/
Pew Research Center
State when and how it was originally collected (by the original data curator, not necessarily how you found the data).
The data was collected by the Pew Research Center through self-administered web surveys from March 15 to March 28, 2021. Data was collected from The American Trends Panel, a nationally representative panel of randomly selected US adults, in this dataset specifically those who self-identified as Hispanic.
Write a brief description of the observations.
Each row is a different respondent of the survey, which are uniquely identified with the QKEY variable. There are 192 columns, mainly containing responses to questions regarding their feelings to the treatment of Hispanics in the US and financial concerns. For example, some columns include how financially secure they believe their children will be in the future or how they feel Joe Biden is handleing his job as President.
Research question
A well formulated research question. (You may include more than one research question if you want to receive feedback on different ideas for your project. However, one per data set is required.)
How do feelings of self-identified Hispanics in the US towards the importance of the deportation of illegal immigrants vary based on what generation of immigrant they are (1st, 2nd, 3rd, etc) and their skin color?
A description of the research topic along with a concise statement of your hypotheses on this topic.
The research topic would focus on how the different experiences of Hispanics in the United States shapes their view on the treatment of new immigrants. These different experiences would be shaped by their skin color and by how long their family has been in the United States. This research topic would be specifically looking at feelings towards the deportation of illegal immigrants.
Hypothesis: For self-identified Hispanics in the US, the longer their family has been in the US and the lighter their skin color, the more likely they are to want illegal immigrants deported.
Identify the types of variables in your research question. Categorical? Quantitative?
Categorical: Skin Color, Generation, Importance of Deportation
Quantitative: Number of Self-identified Hispanics
Glimpse of data
hispanic_survey_data <- read_sav("data/ATP_W86.sav")
skimr::skim(hispanic_survey_data)| Name | hispanic_survey_data |
| Number of rows | 3375 |
| Number of columns | 192 |
| _______________________ | |
| Column type frequency: | |
| numeric | 190 |
| POSIXct | 2 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| QKEY | 0 | 1.00 | 1.061579e+11 | 1.00804e+11 | 124 | 3331.5 | 2.015017e+11 | 2.01901e+11 | 2.02001e+11 | ▇▁▁▁▇ |
| DEVICE_TYPE_W86 | 0 | 1.00 | 1.760000e+00 | 5.30000e-01 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 3.00000e+00 | ▃▁▇▁▁ |
| LANG_W86 | 0 | 1.00 | 1.370000e+00 | 4.80000e-01 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 2.00000e+00 | ▇▁▁▁▅ |
| XPANEL_W86 | 0 | 1.00 | 1.560000e+00 | 5.00000e-01 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 2.00000e+00 | ▆▁▁▁▇ |
| FORM_W86 | 0 | 1.00 | 1.500000e+00 | 5.00000e-01 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 2.00000e+00 | ▇▁▁▁▇ |
| SATIS_W86 | 0 | 1.00 | 2.410000e+00 | 9.61000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| PERSFIN_W86 | 0 | 1.00 | 3.080000e+00 | 6.87000e+00 | 1 | 2.0 | 3.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| PERSFNCB_W86 | 0 | 1.00 | 2.790000e+00 | 9.89000e+00 | 1 | 1.0 | 1.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| FINKIDS_W86 | 0 | 1.00 | 3.000000e+00 | 9.90000e+00 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| HISP_W86 | 0 | 1.00 | 1.000000e+00 | 0.00000e+00 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 1.00000e+00 | ▁▁▇▁▁ |
| SELFNAT_W86 | 0 | 1.00 | 6.820000e+00 | 1.95600e+01 | 1 | 2.0 | 2.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| AFROLATINO_W86 | 0 | 1.00 | 2.510000e+00 | 7.64000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| POL1JB_W86 | 0 | 1.00 | 4.100000e+00 | 1.65100e+01 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| POL1JBSTR_W86 | 99 | 0.97 | 2.710000e+00 | 1.16300e+01 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| ST2_W86 | 0 | 1.00 | 3.890000e+00 | 8.73000e+00 | 1 | 3.0 | 3.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| ASYLUMHA_MOD_W86 | 0 | 1.00 | 2.100000e+00 | 7.69000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| BRDERJOB_MOD_W86 | 0 | 1.00 | 4.900000e+00 | 1.41200e+01 | 1 | 2.0 | 3.000000e+00 | 3.50000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| IMMVAL_b_W86 | 0 | 1.00 | 4.170000e+00 | 1.25800e+01 | 1 | 2.0 | 3.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| IMMVAL_c_W86 | 0 | 1.00 | 2.740000e+00 | 1.00300e+01 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| IMMVAL_d_W86 | 0 | 1.00 | 3.240000e+00 | 1.16600e+01 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| IMMVAL_g_W86 | 0 | 1.00 | 3.510000e+00 | 1.19800e+01 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| IMMVAL_h_W86 | 0 | 1.00 | 3.410000e+00 | 1.19900e+01 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| IMMVAL_i_W86 | 0 | 1.00 | 2.690000e+00 | 1.00400e+01 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| IMMVAL_j_W86 | 0 | 1.00 | 2.710000e+00 | 9.76000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| IMMVAL_k_W86 | 0 | 1.00 | 3.140000e+00 | 1.10500e+01 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| WORRYDPORT_W86 | 0 | 1.00 | 3.780000e+00 | 8.94000e+00 | 1 | 2.0 | 3.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| LAN1_W86 | 0 | 1.00 | 2.300000e+00 | 8.24000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| LAN2_W86 | 0 | 1.00 | 2.660000e+00 | 9.62000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| LAN3_W86 | 0 | 1.00 | 2.340000e+00 | 8.72000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| LAN4_W86 | 0 | 1.00 | 2.510000e+00 | 9.91000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| HISPSIT_W86 | 0 | 1.00 | 2.930000e+00 | 8.00000e+00 | 1 | 2.0 | 3.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| HISPDISCR_a_W86 | 0 | 1.00 | 2.340000e+00 | 7.84000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| HISPDISCR_b_W86 | 0 | 1.00 | 2.300000e+00 | 6.89000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| HISPDISCR_c_W86 | 233 | 0.93 | 2.180000e+00 | 6.26000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| HISPDISCR_d_W86 | 0 | 1.00 | 2.360000e+00 | 7.28000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| HISPDISCR_e_W86 | 0 | 1.00 | 2.230000e+00 | 6.90000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| HISPDISCR_f_W86 | 0 | 1.00 | 2.100000e+00 | 6.27000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| HISPDISCR_g_W86 | 0 | 1.00 | 2.340000e+00 | 6.47000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| HISPDISCR_h_W86 | 0 | 1.00 | 2.160000e+00 | 6.91000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| HISPDISCR_i_W86 | 0 | 1.00 | 2.430000e+00 | 7.83000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO1_a_W86 | 1774 | 0.47 | 2.450000e+00 | 9.43000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO1_c_W86 | 1774 | 0.47 | 3.120000e+00 | 1.02500e+01 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO1_d_W86 | 1774 | 0.47 | 1.510000e+00 | 6.00000e+00 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO1_e_W86 | 1774 | 0.47 | 3.590000e+00 | 1.22900e+01 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO1_f_W86 | 1774 | 0.47 | 1.970000e+00 | 8.10000e+00 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO1_g_W86 | 1774 | 0.47 | 2.090000e+00 | 8.09000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO2_a_W86 | 3230 | 0.04 | 3.340000e+00 | 1.13900e+01 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO2_c_W86 | 3230 | 0.04 | 2.140000e+00 | 5.90000e-01 | 1 | 2.0 | 2.000000e+00 | 3.00000e+00 | 3.00000e+00 | ▂▁▇▁▃ |
| USVSCOO2_d_W86 | 3230 | 0.04 | 1.320000e+00 | 7.10000e-01 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 3.00000e+00 | ▇▁▁▁▂ |
| USVSCOO2_e_W86 | 3230 | 0.04 | 3.630000e+00 | 1.13400e+01 | 1 | 2.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO2_f_W86 | 3230 | 0.04 | 2.190000e+00 | 8.14000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO2_g_W86 | 3230 | 0.04 | 2.080000e+00 | 8.14000e+00 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO3_a_W86 | 1746 | 0.48 | 3.580000e+00 | 1.31000e+01 | 1 | 1.0 | 1.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO3_c_W86 | 1746 | 0.48 | 3.540000e+00 | 1.19400e+01 | 1 | 2.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO3_d_W86 | 1746 | 0.48 | 2.340000e+00 | 1.02400e+01 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO3_e_W86 | 1746 | 0.48 | 4.230000e+00 | 1.44800e+01 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO3_f_W86 | 1746 | 0.48 | 2.850000e+00 | 1.20300e+01 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOO3_g_W86 | 1746 | 0.48 | 3.180000e+00 | 1.29300e+01 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| MIGUSAGAIN1_W86 | 1774 | 0.47 | 2.390000e+00 | 1.06100e+01 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| MIGUSAGAIN2_W86 | 3230 | 0.04 | 2.020000e+00 | 8.13000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV7_W86 | 1678 | 0.50 | 2.880000e+00 | 5.80000e+00 | 1 | 2.0 | 3.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV7_MOD_W86 | 1697 | 0.50 | 2.960000e+00 | 6.29000e+00 | 1 | 2.0 | 3.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACATTN_W86 | 0 | 1.00 | 3.030000e+00 | 1.05400e+01 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACEATTN_a_W86 | 0 | 1.00 | 2.780000e+00 | 9.88000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACEATTN_b_W86 | 0 | 1.00 | 3.030000e+00 | 9.42000e+00 | 1 | 2.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACEATTN_c_W86 | 0 | 1.00 | 3.530000e+00 | 1.14900e+01 | 1 | 2.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV21_MOD_W86 | 0 | 1.00 | 3.580000e+00 | 6.45000e+00 | 1 | 2.0 | 3.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| DISCRPROB_W86 | 0 | 1.00 | 3.430000e+00 | 1.28900e+01 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| DISCRCOO1_W86 | 3230 | 0.04 | 4.000000e+00 | 1.13500e+01 | 1 | 1.0 | 3.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| DISCRCOO2_W86 | 1774 | 0.47 | 4.530000e+00 | 1.28800e+01 | 1 | 2.0 | 3.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV5_MOD_a_W86 | 0 | 1.00 | 2.430000e+00 | 9.07000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV5_MOD_b_W86 | 0 | 1.00 | 4.690000e+00 | 8.95000e+00 | 1 | 4.0 | 4.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV5_MOD_c_W86 | 0 | 1.00 | 2.420000e+00 | 8.75000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV5_MOD_d_W86 | 0 | 1.00 | 4.350000e+00 | 9.02000e+00 | 1 | 3.0 | 4.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV5_MOD_e_W86 | 0 | 1.00 | 3.510000e+00 | 9.76000e+00 | 1 | 1.0 | 2.000000e+00 | 5.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV5_MOD_f_W86 | 0 | 1.00 | 4.930000e+00 | 1.04700e+01 | 1 | 3.0 | 4.000000e+00 | 5.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV5_MOD_g_W86 | 0 | 1.00 | 4.160000e+00 | 1.12700e+01 | 1 | 1.0 | 2.000000e+00 | 5.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV5_MOD_h_W86 | 0 | 1.00 | 4.760000e+00 | 1.06500e+01 | 1 | 3.0 | 3.000000e+00 | 5.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV36_W86 | 0 | 1.00 | 3.400000e+00 | 8.64000e+00 | 1 | 2.0 | 3.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV36_MOD_W86 | 0 | 1.00 | 3.390000e+00 | 8.80000e+00 | 1 | 2.0 | 3.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVID_FUT_W86 | 0 | 1.00 | 2.490000e+00 | 1.08400e+01 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| FINANCES_a_W86 | 0 | 1.00 | 2.930000e+00 | 1.02600e+01 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| FINANCES_b_W86 | 0 | 1.00 | 2.490000e+00 | 8.35000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| FINANCES_c_W86 | 0 | 1.00 | 2.460000e+00 | 8.68000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| FINANCES_e_W86 | 0 | 1.00 | 2.370000e+00 | 8.02000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| FINANCES_f_W86 | 0 | 1.00 | 2.690000e+00 | 9.28000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| FINANCES_g_W86 | 0 | 1.00 | 2.880000e+00 | 1.01300e+01 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| FINANCES_h_W86 | 0 | 1.00 | 3.280000e+00 | 1.15000e+01 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVHELP1_a_W86 | 0 | 1.00 | 2.370000e+00 | 8.85000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVHELP1_b_W86 | 0 | 1.00 | 2.670000e+00 | 9.73000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVHELP1_c_W86 | 0 | 1.00 | 2.650000e+00 | 9.44000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVHELP1_d_W86 | 0 | 1.00 | 2.520000e+00 | 8.84000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVHELP2_a_W86 | 0 | 1.00 | 2.660000e+00 | 9.13000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVHELP2_b_W86 | 0 | 1.00 | 2.870000e+00 | 9.56000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVHELP2_c_W86 | 0 | 1.00 | 2.690000e+00 | 9.13000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVHELP2_d_W86 | 0 | 1.00 | 2.710000e+00 | 8.65000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| CVDECON13_a_W86 | 0 | 1.00 | 3.020000e+00 | 9.71000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| CVDECON13_b_W86 | 0 | 1.00 | 3.160000e+00 | 1.05200e+01 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| CVDECON13_c_W86 | 0 | 1.00 | 3.720000e+00 | 1.23900e+01 | 1 | 2.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| CVDECON14_a_W86 | 0 | 1.00 | 2.820000e+00 | 8.67000e+00 | 1 | 2.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| CVDECON14_b_W86 | 0 | 1.00 | 2.980000e+00 | 9.72000e+00 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| CVDECON14_c_W86 | 0 | 1.00 | 3.080000e+00 | 9.70000e+00 | 1 | 2.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVID_WORK_SIT_MOD_W86 | 0 | 1.00 | 6.700000e+00 | 1.94500e+01 | 1 | 1.0 | 3.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVID_SELF_a_W86 | 0 | 1.00 | 2.020000e+00 | 5.81000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVID_SELF_b_W86 | 0 | 1.00 | 2.440000e+00 | 8.01000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVID_SELF_c_W86 | 0 | 1.00 | 2.420000e+00 | 7.47000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVID_SELF_e_W86 | 0 | 1.00 | 2.760000e+00 | 8.96000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVTOLL_W86 | 0 | 1.00 | 3.230000e+00 | 7.32000e+00 | 1 | 1.0 | 3.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVLEAV_W86 | 1774 | 0.47 | 5.030000e+00 | 1.42600e+01 | 1 | 3.0 | 3.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| CHILD_LT12_W86 | 0 | 1.00 | 2.020000e+00 | 5.57000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| PARENTK12_W86 | 0 | 1.00 | 2.950000e+00 | 6.88000e+00 | 1 | 2.0 | 3.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| CVCHILDCARE_W86 | 2382 | 0.29 | 3.330000e+00 | 7.52000e+00 | 1 | 2.0 | 3.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| INSTRUCTION1_MOD_W86 | 2189 | 0.35 | 3.290000e+00 | 9.71000e+00 | 1 | 2.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVIDSCHL4_MOD_W86 | 2244 | 0.34 | 2.420000e+00 | 7.12000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| RACESURV56_W86 | 0 | 1.00 | 3.020000e+00 | 8.75000e+00 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| OPENRACE_W86_DETAILED_1_W86 | 0 | 1.00 | 3.312000e+01 | 4.63800e+01 | 0 | 0.0 | 1.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_2_W86 | 0 | 1.00 | 3.290000e+01 | 4.65300e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_3_W86 | 0 | 1.00 | 3.293000e+01 | 4.65100e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_4_W86 | 0 | 1.00 | 3.281000e+01 | 4.66000e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_5_W86 | 0 | 1.00 | 3.280000e+01 | 4.66000e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_6_W86 | 0 | 1.00 | 3.307000e+01 | 4.64100e+01 | 0 | 0.0 | 1.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_7_W86 | 0 | 1.00 | 3.284000e+01 | 4.65700e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_8_W86 | 0 | 1.00 | 3.281000e+01 | 4.65900e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_9_W86 | 0 | 1.00 | 3.280000e+01 | 4.66000e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_10_W86 | 0 | 1.00 | 3.281000e+01 | 4.65900e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_11_W86 | 0 | 1.00 | 3.280000e+01 | 4.66000e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_12_W86 | 0 | 1.00 | 3.282000e+01 | 4.65800e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_13_W86 | 0 | 1.00 | 3.282000e+01 | 4.65800e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_14_W86 | 0 | 1.00 | 3.281000e+01 | 4.65900e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_15_W86 | 0 | 1.00 | 3.280000e+01 | 4.66000e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_16_W86 | 0 | 1.00 | 3.281000e+01 | 4.65900e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_17_W86 | 0 | 1.00 | 3.282000e+01 | 4.65900e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_DETAILED_18_W86 | 0 | 1.00 | 3.281000e+01 | 4.65900e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_COLLAPSED_1_W86 | 0 | 1.00 | 3.312000e+01 | 4.63800e+01 | 0 | 0.0 | 1.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_COLLAPSED_2_W86 | 0 | 1.00 | 3.290000e+01 | 4.65300e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_COLLAPSED_3_W86 | 0 | 1.00 | 3.293000e+01 | 4.65100e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_COLLAPSED_4_W86 | 0 | 1.00 | 3.308000e+01 | 4.64000e+01 | 0 | 0.0 | 1.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_COLLAPSED_5_W86 | 0 | 1.00 | 3.289000e+01 | 4.65400e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| OPENRACE_W86_COLLAPSED_other_W86 | 0 | 1.00 | 3.290000e+01 | 4.65300e+01 | 0 | 0.0 | 0.000000e+00 | 9.90000e+01 | 9.90000e+01 | ▇▁▁▁▃ |
| MOTHERNAT_W86 | 0 | 1.00 | 3.460000e+00 | 8.92000e+00 | 1 | 2.0 | 3.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| FATHERNAT_W86 | 0 | 1.00 | 4.120000e+00 | 1.18900e+01 | 1 | 2.0 | 3.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| KNOWUNAUTH_W86 | 0 | 1.00 | 3.330000e+00 | 1.34200e+01 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| CLRSHAPES_W86 | 0 | 1.00 | 3.430000e+00 | 1.03900e+01 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| SKINCLR_W86 | 0 | 1.00 | 7.020000e+00 | 1.94800e+01 | 1 | 2.0 | 3.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| REG_W86 | 708 | 0.79 | 1.980000e+00 | 8.66000e+00 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_METRO | 13 | 1.00 | 1.040000e+00 | 2.00000e-01 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 2.00000e+00 | ▇▁▁▁▁ |
| F_CREGION | 0 | 1.00 | 3.080000e+00 | 9.30000e-01 | 1 | 3.0 | 3.000000e+00 | 4.00000e+00 | 4.00000e+00 | ▂▁▁▇▆ |
| F_CDIVISION | 0 | 1.00 | 6.280000e+00 | 2.38000e+00 | 1 | 5.0 | 7.000000e+00 | 9.00000e+00 | 9.00000e+00 | ▂▂▆▅▇ |
| F_AGECAT | 0 | 1.00 | 2.790000e+00 | 6.49000e+00 | 1 | 2.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_GENDER | 0 | 1.00 | 2.040000e+00 | 6.50000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_EDUCCAT | 0 | 1.00 | 2.080000e+00 | 2.51000e+00 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_EDUCCAT3 | 0 | 1.00 | 1.550000e+00 | 2.48000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_HISP | 0 | 1.00 | 1.000000e+00 | 0.00000e+00 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 1.00000e+00 | ▁▁▇▁▁ |
| F_HISP_ORIGIN | 0 | 1.00 | 8.560000e+00 | 2.25200e+01 | 1 | 1.0 | 1.000000e+00 | 6.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_YEARSINUS | 0 | 1.00 | 7.500000e+00 | 2.21400e+01 | 1 | 1.0 | 2.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_RACECMB | 0 | 1.00 | 6.860000e+00 | 2.07000e+01 | 1 | 1.0 | 1.000000e+00 | 5.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_RACETHNMOD | 0 | 1.00 | 3.000000e+00 | 0.00000e+00 | 3 | 3.0 | 3.000000e+00 | 3.00000e+00 | 3.00000e+00 | ▁▁▇▁▁ |
| F_CITIZEN | 0 | 1.00 | 2.160000e+00 | 9.63000e+00 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_CITIZEN2 | 1898 | 0.44 | 2.530000e+00 | 1.04200e+01 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_BIRTHPLACE_EXPANDED | 0 | 1.00 | 7.190000e+00 | 1.95200e+01 | 1 | 2.0 | 2.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_RELIG | 0 | 1.00 | 5.030000e+00 | 9.70000e+00 | 1 | 2.0 | 2.000000e+00 | 9.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_BORN | 908 | 0.73 | 4.760000e+00 | 1.71500e+01 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_RELIGCAT1 | 0 | 1.00 | 2.940000e+00 | 8.98000e+00 | 1 | 2.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_PARTY_FINAL | 0 | 1.00 | 5.390000e+00 | 1.68800e+01 | 1 | 2.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_PARTYLN_FINAL | 2003 | 0.41 | 1.678000e+01 | 3.52600e+01 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▂ |
| F_PARTYSUM_FINAL | 0 | 1.00 | 2.190000e+00 | 1.82000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.00000e+00 | ▇▁▁▁▁ |
| F_INC_SDT1 | 0 | 1.00 | 8.420000e+00 | 1.98900e+01 | 1 | 1.0 | 4.000000e+00 | 8.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_INTFREQ | 30 | 0.99 | 1.990000e+00 | 5.89000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| F_VOLSUM | 0 | 1.00 | 2.810000e+00 | 1.00000e+01 | 1 | 2.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| CVDECON13_TOTAL_W86 | 0 | 1.00 | 2.920000e+00 | 1.10500e+01 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| CVDECON13_14_TOTAL_W86 | 0 | 1.00 | 2.910000e+00 | 1.16700e+01 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| PRIMARY_LANGUAGE_W86 | 0 | 1.00 | 1.970000e+00 | 7.60000e-01 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 3.00000e+00 | ▆▁▇▁▅ |
| COVID_SELFSUM_W86 | 0 | 1.00 | 2.430000e+00 | 8.36000e+00 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| COVID_SELFSUM2_W86 | 0 | 1.00 | 2.150000e+00 | 8.21000e+00 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| HISPDISCR_NUM_W86 | 0 | 1.00 | 1.160000e+00 | 3.60000e-01 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 2.00000e+00 | ▇▁▁▁▂ |
| HISPDISCR_NUM3_W86 | 0 | 1.00 | 1.530000e+00 | 5.00000e-01 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 2.00000e+00 | ▇▁▁▁▇ |
| SUM_HISPDISCR_NUM_W86 | 0 | 1.00 | 1.780000e+00 | 2.31000e+00 | 0 | 0.0 | 1.000000e+00 | 3.00000e+00 | 8.00000e+00 | ▇▂▁▁▁ |
| USVSCOOCMB_A_W86 | 0 | 1.00 | 3.040000e+00 | 1.14400e+01 | 1 | 1.0 | 1.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOOCMB_C_W86 | 0 | 1.00 | 3.280000e+00 | 1.09000e+01 | 1 | 2.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOOCMB_D_W86 | 0 | 1.00 | 1.900000e+00 | 8.24000e+00 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOOCMB_E_W86 | 0 | 1.00 | 3.900000e+00 | 1.33600e+01 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOOCMB_F_W86 | 0 | 1.00 | 2.400000e+00 | 1.01900e+01 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| USVSCOOCMB_G_W86 | 0 | 1.00 | 2.620000e+00 | 1.07200e+01 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| MIGUSAGAINCMB_W86 | 1629 | 0.52 | 2.360000e+00 | 1.04200e+01 | 1 | 1.0 | 1.000000e+00 | 1.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| DISCRCOOCMB_W86 | 1629 | 0.52 | 4.480000e+00 | 1.27500e+01 | 1 | 2.0 | 3.000000e+00 | 4.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| NATIVITY1_W86 | 0 | 1.00 | 5.650000e+00 | 1.97900e+01 | 1 | 1.0 | 1.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| IMMGEN_W86 | 0 | 1.00 | 6.420000e+00 | 2.07200e+01 | 1 | 1.0 | 2.000000e+00 | 3.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| NATIVITY2_W86 | 0 | 1.00 | 5.690000e+00 | 1.97800e+01 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| IMMGEN2_W86 | 0 | 1.00 | 6.310000e+00 | 2.07400e+01 | 1 | 1.0 | 2.000000e+00 | 2.00000e+00 | 9.90000e+01 | ▇▁▁▁▁ |
| WEIGHT_W86 | 0 | 1.00 | 1.000000e+00 | 1.33000e+00 | 0 | 0.2 | 5.600000e-01 | 1.18000e+00 | 7.58000e+00 | ▇▁▁▁▁ |
Variable type: POSIXct
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| INTERVIEW_START_W86 | 0 | 1 | 2021-03-15 20:10:39 | 2021-03-28 20:18:10 | 2021-03-17 13:23:24 | 3286 |
| INTERVIEW_END_W86 | 0 | 1 | 2021-03-15 20:21:01 | 2021-03-28 22:07:52 | 2021-03-17 17:59:33 | 3299 |