library(tidyverse)
library(skimr)
library("haven") project-wondrous-starmie
Proposal
Data 1
Introduction and data
Identify the source of the data.
- NYTimes
State when and how it was originally collected (by the original data curator, not necessarily how you found the data).
Each participant was asked: How often do you wear a mask in public when you expect to be within six feet of another person?
July 2 - July 14, 2020
Write a brief description of the observations.
COUNTYFP: county FIPS code
NEVER: Share of people who responded never to the question
RARELY: Share of people who responded never to the question
SOMETIMES: Share of people who responded never to the question
FREQUENTLY: Share of people who responded never to the question
ALWAYS: Share of people who responded never to the question
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.)
- Are responses of counties affected by whether the county belongs to a “blue” or “red” state?
- A description of the research topic along with a concise statement of your hypotheses on this topic.
- Our group is researching practices with mask wearing at the start of the COVID pandemic (2020). We wanted to study whether or not counties belonging to either a red or blue state would affect how likely people would wear a mask. I hypothesize that there would be no notable difference - this was near the beginning of the covid pandemic, before it was politicized. Therefore, I do not believe that belonging to a red or blue state would particularly affect mask usage rates.
- Identify the types of variables in your research question. Categorical? Quantitative?
- COUNTYFP: Categorical
- NEVER: Quantitative
- RARELY: Quantitative
- SOMETIMES: Quantitative
- FREQUENTLY: Quantitative
- ALWAYS: Quantitative
Glimpse of data
mask_use_by_county <- read_csv("data/mask-use-by-county.csv") Rows: 3142 Columns: 6
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): COUNTYFP
dbl (5): NEVER, RARELY, SOMETIMES, FREQUENTLY, ALWAYS
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(mask_use_by_county)| Name | mask_use_by_county |
| Number of rows | 3142 |
| Number of columns | 6 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 5 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| COUNTYFP | 0 | 1 | 5 | 5 | 0 | 3142 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| NEVER | 0 | 1 | 0.08 | 0.06 | 0.00 | 0.03 | 0.07 | 0.11 | 0.43 | ▇▃▁▁▁ |
| RARELY | 0 | 1 | 0.08 | 0.06 | 0.00 | 0.04 | 0.07 | 0.12 | 0.38 | ▇▆▂▁▁ |
| SOMETIMES | 0 | 1 | 0.12 | 0.06 | 0.00 | 0.08 | 0.12 | 0.16 | 0.42 | ▅▇▂▁▁ |
| FREQUENTLY | 0 | 1 | 0.21 | 0.06 | 0.03 | 0.16 | 0.20 | 0.25 | 0.55 | ▂▇▃▁▁ |
| ALWAYS | 0 | 1 | 0.51 | 0.15 | 0.12 | 0.39 | 0.50 | 0.61 | 0.89 | ▁▆▇▅▂ |
Data 2
Introduction and data
- Identify the source of the data.
The Pew Research Center
State when and how it was originally collected (by the original data curator, not necessarily how you found the data).
The sample consisted of the American Trends Panel (ATP). The ATP is a national, probability-based online panel of adults living in households in the United States. In January of 2019, a questionnaire was mailed to all ATP panelists (5,817 people) that shared their address. All panelists with an email address received an email invitation and up to four email reminders if they did not respond to the survey. All ATP panelists that consented to SMS messages received an SMS invitation and up to four SMS reminders.
Write a brief description of the observations.
Each observation represents an American. ATP panelists were recruited from three large national, overlapping, dual-frame land-line and cellphone random-digit-dial (RDD) surveys and one national address-based sample (ABS) survey conducted for the Pew Research Center.
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 factors affect American’s trust in scientific experts the most?
- A description of the research topic along with a concise statement of your hypotheses on this topic.
In an era when science and society often appear to collide, it is important to gage the public’s confidence in our scientists. The area of science, political party, educational background, and religious background of an American are all examples of factors that can affect their trust in scientific experts. We hypothesize that the educational background and political party of Americans has the greatest factor in how much they trust scientific experts.
- Identify the types of variables in your research question. Categorical? Quantitative?
The variables in my research question are categorical.
Glimpse of data
# add code here
library("haven")
library(readr)
data2 <- read_sav("data/ATP W42.sav")
csv2 <- write_csv(x=data2, file="from_sav_data.csv")
skimr::skim(csv2)| Name | csv2 |
| Number of rows | 4464 |
| Number of columns | 195 |
| _______________________ | |
| Column type frequency: | |
| numeric | 195 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| QKEY | 0 | 1.00 | 1.601233e+11 | 8.164814e+10 | 100363.00 | 2.015016e+11 | 2.01801e+11 | 2.018011e+11 | 2.018012e+11 | ▂▁▁▁▇ |
| Device_Type_W42 | 0 | 1.00 | 1.830000e+00 | 9.400000e-01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 3.000000e+00 | 3.000000e+00 | ▇▁▂▁▆ |
| LANG_W42 | 0 | 1.00 | 9.020000e+00 | 1.400000e-01 | 9.00 | 9.000000e+00 | 9.00000e+00 | 9.000000e+00 | 1.000000e+01 | ▇▁▁▁▁ |
| FORM_W42 | 0 | 1.00 | 1.500000e+00 | 5.000000e-01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 2.000000e+00 | ▇▁▁▁▇ |
| PAST_W42 | 2238 | 0.50 | 1.380000e+00 | 3.640000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| FUTURE_W42 | 2238 | 0.50 | 1.660000e+00 | 6.240000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| DOV_FUTURE_W42 | 2238 | 0.50 | 1.500000e+00 | 5.000000e-01 | 1.00 | 1.000000e+00 | 1.50000e+00 | 2.000000e+00 | 2.000000e+00 | ▇▁▁▁▇ |
| FUTURE_BOE_OE1_W42 | 3351 | 0.25 | 4.128000e+01 | 7.856000e+01 | 10.00 | 1.200000e+01 | 1.40000e+01 | 6.100000e+01 | 9.970000e+02 | ▇▁▁▁▁ |
| FUTURE_BOE_OE2_W42 | 4061 | 0.09 | 3.375000e+01 | 2.267000e+01 | 10.00 | 1.400000e+01 | 2.50000e+01 | 4.500000e+01 | 1.020000e+02 | ▇▅▂▁▁ |
| FUTURE_BOE_OE3_W42 | 4289 | 0.04 | 4.654000e+01 | 7.655000e+01 | 10.00 | 2.000000e+01 | 4.00000e+01 | 5.000000e+01 | 9.970000e+02 | ▇▁▁▁▁ |
| FUTURE_WOE_OE1_W42 | 3351 | 0.25 | 7.873000e+01 | 1.229500e+02 | 10.00 | 2.300000e+01 | 7.30000e+01 | 9.900000e+01 | 9.970000e+02 | ▇▁▁▁▁ |
| FUTURE_WOE_OE2_W42 | 4283 | 0.04 | 4.207000e+01 | 2.884000e+01 | 10.00 | 2.000000e+01 | 2.80000e+01 | 5.100000e+01 | 1.060000e+02 | ▇▃▂▂▂ |
| FUTURE_WOE_OE3_W42 | 4419 | 0.01 | 4.291000e+01 | 2.983000e+01 | 10.00 | 2.000000e+01 | 3.10000e+01 | 5.100000e+01 | 1.050000e+02 | ▇▃▂▁▂ |
| SC1_W42 | 2226 | 0.50 | 1.750000e+00 | 5.110000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SC1POS_W42_OE1_W42 | 2811 | 0.37 | 3.538000e+01 | 3.134000e+01 | 10.00 | 1.300000e+01 | 2.00000e+01 | 5.200000e+01 | 1.040000e+02 | ▇▂▁▁▂ |
| SC1POS_W42_OE2_W42 | 3657 | 0.18 | 4.012000e+01 | 2.630000e+01 | 10.00 | 1.300000e+01 | 3.20000e+01 | 5.600000e+01 | 1.060000e+02 | ▇▇▅▃▂ |
| SC1POS_W42_OE3_W42 | 4053 | 0.09 | 4.591000e+01 | 2.616000e+01 | 10.00 | 2.100000e+01 | 4.10000e+01 | 6.100000e+01 | 1.030000e+02 | ▇▇▆▅▂ |
| SC1NEG_W42_OE1_W42 | 3927 | 0.12 | 9.749000e+01 | 1.156200e+02 | 10.00 | 6.700000e+01 | 9.90000e+01 | 1.080000e+02 | 9.990000e+02 | ▇▁▁▁▁ |
| SC1NEG_W42_OE2_W42 | 4402 | 0.01 | 7.235000e+01 | 3.844000e+01 | 10.00 | 4.500000e+01 | 6.85000e+01 | 1.090000e+02 | 1.280000e+02 | ▅▅▂▂▇ |
| SC1NEG_W42_OE3_W42 | 4449 | 0.00 | 7.680000e+01 | 3.354000e+01 | 13.00 | 5.850000e+01 | 7.10000e+01 | 1.110000e+02 | 1.240000e+02 | ▃▃▇▂▇ |
| CONFa_W42 | 0 | 1.00 | 3.020000e+00 | 5.040000e+00 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| CONFb_W42 | 0 | 1.00 | 2.800000e+00 | 4.420000e+00 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| CONFc_W42 | 0 | 1.00 | 2.110000e+00 | 5.290000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| CONFd_F1_W42 | 2238 | 0.50 | 1.940000e+00 | 4.170000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| CONFd_F2_W42 | 2226 | 0.50 | 2.080000e+00 | 5.480000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| CONFe_W42 | 0 | 1.00 | 2.710000e+00 | 5.470000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| CONFf_W42 | 0 | 1.00 | 2.510000e+00 | 6.830000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| CONFg_W42 | 0 | 1.00 | 2.800000e+00 | 4.620000e+00 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| POLICY1_W42 | 0 | 1.00 | 2.540000e+00 | 1.059000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| POLICY2_W42 | 0 | 1.00 | 2.760000e+00 | 1.124000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| POLICY3_W42 | 0 | 1.00 | 2.570000e+00 | 7.300000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ1_F1A_W42 | 2238 | 0.50 | 2.040000e+00 | 7.190000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ2_F1A_W42 | 2238 | 0.50 | 2.280000e+00 | 5.460000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ3_F1Aa_W42 | 2567 | 0.42 | 2.490000e+00 | 8.920000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ3_F1Ab_W42 | 2567 | 0.42 | 2.890000e+00 | 1.156000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ3_F1Ac_W42 | 2567 | 0.42 | 2.800000e+00 | 9.940000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ3_F1Ad_W42 | 2567 | 0.42 | 1.600000e+00 | 6.740000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Aa_W42 | 2238 | 0.50 | 2.680000e+00 | 1.008000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Ab_W42 | 2238 | 0.50 | 2.760000e+00 | 9.640000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Ac_W42 | 2238 | 0.50 | 3.490000e+00 | 1.081000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Ad_W42 | 2238 | 0.50 | 3.840000e+00 | 1.240000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Ae_W42 | 2238 | 0.50 | 2.710000e+00 | 9.430000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ5_F1A_W42 | 2238 | 0.50 | 3.780000e+00 | 1.116000e+01 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ6_F1A_W42 | 2238 | 0.50 | 2.630000e+00 | 1.089000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ7_F1A_W42 | 2238 | 0.50 | 4.500000e+00 | 1.741000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ8_F1A_W42 | 2238 | 0.50 | 3.400000e+00 | 1.001000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ1_F1B_W42 | 2238 | 0.50 | 2.320000e+00 | 7.740000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ2_F1B_W42 | 2238 | 0.50 | 2.410000e+00 | 6.180000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ3_F1Ba_W42 | 2645 | 0.41 | 2.820000e+00 | 1.015000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ3_F1Bb_W42 | 2645 | 0.41 | 2.830000e+00 | 1.137000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ3_F1Bc_W42 | 2645 | 0.41 | 3.310000e+00 | 1.197000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ3_F1Bd_W42 | 2645 | 0.41 | 1.770000e+00 | 7.930000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Ba_W42 | 2238 | 0.50 | 3.320000e+00 | 1.246000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Bb_W42 | 2238 | 0.50 | 3.310000e+00 | 1.194000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Bc_W42 | 2238 | 0.50 | 3.900000e+00 | 1.256000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Bd_W42 | 2238 | 0.50 | 4.040000e+00 | 1.335000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Be_W42 | 2238 | 0.50 | 3.250000e+00 | 1.177000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ5_F1B_W42 | 2238 | 0.50 | 3.910000e+00 | 1.133000e+01 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ6_F1B_W42 | 2238 | 0.50 | 3.120000e+00 | 1.282000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ7_F1B_W42 | 2238 | 0.50 | 4.580000e+00 | 1.752000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ8_F1B_W42 | 2238 | 0.50 | 4.180000e+00 | 1.269000e+01 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ1_F1C_W42 | 2238 | 0.50 | 2.260000e+00 | 6.240000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ2_F1C_W42 | 2238 | 0.50 | 2.440000e+00 | 5.450000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ3_F1Ca_W42 | 2772 | 0.38 | 2.470000e+00 | 8.170000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ3_F1Cb_W42 | 2772 | 0.38 | 2.130000e+00 | 7.490000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ3_F1Cc_W42 | 2772 | 0.38 | 2.700000e+00 | 9.120000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ3_F1Cd_W42 | 2772 | 0.38 | 1.760000e+00 | 7.510000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Ca_W42 | 2238 | 0.50 | 4.150000e+00 | 1.483000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Cb_W42 | 2238 | 0.50 | 3.870000e+00 | 1.353000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Cc_W42 | 2238 | 0.50 | 4.590000e+00 | 1.448000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Cd_W42 | 2238 | 0.50 | 4.580000e+00 | 1.463000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ4_F1Ce_W42 | 2238 | 0.50 | 3.630000e+00 | 1.293000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ5_F1C_W42 | 2238 | 0.50 | 4.300000e+00 | 1.284000e+01 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ6_F1C_W42 | 2238 | 0.50 | 3.310000e+00 | 1.344000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ7_F1C_W42 | 2238 | 0.50 | 5.520000e+00 | 1.986000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| RQ8_F1C_W42 | 2238 | 0.50 | 4.420000e+00 | 1.330000e+01 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ1_F2A_W42 | 2226 | 0.50 | 1.650000e+00 | 4.670000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ2_F2A_W42 | 2226 | 0.50 | 1.860000e+00 | 5.470000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ3_F2Aa_W42 | 2337 | 0.48 | 2.290000e+00 | 9.900000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ3_F2Ab_W42 | 2337 | 0.48 | 2.410000e+00 | 9.430000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ3_F2Ac_W42 | 2337 | 0.48 | 2.900000e+00 | 1.070000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ3_F2Ad_W42 | 2337 | 0.48 | 2.750000e+00 | 1.209000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Aa_W42 | 2226 | 0.50 | 1.980000e+00 | 6.200000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Ab_W42 | 2226 | 0.50 | 1.900000e+00 | 5.480000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Ac_W42 | 2226 | 0.50 | 3.140000e+00 | 8.430000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Ad_W42 | 2226 | 0.50 | 4.100000e+00 | 1.315000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Ae_W42 | 2226 | 0.50 | 2.060000e+00 | 7.440000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ5_F2A_W42 | 2226 | 0.50 | 2.920000e+00 | 7.100000e+00 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ6_F2A_W42 | 2226 | 0.50 | 2.350000e+00 | 9.640000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ7_F2A_W42 | 2226 | 0.50 | 3.030000e+00 | 1.312000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ8_F2A_W42 | 2226 | 0.50 | 2.600000e+00 | 6.820000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ1_F2B_W42 | 2226 | 0.50 | 2.150000e+00 | 6.870000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ2_F2B_W42 | 2226 | 0.50 | 2.420000e+00 | 5.440000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ3_F2Ba_W42 | 2777 | 0.38 | 3.190000e+00 | 1.176000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ3_F2Bb_W42 | 2777 | 0.38 | 2.740000e+00 | 1.056000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ3_F2Bc_W42 | 2777 | 0.38 | 3.000000e+00 | 1.105000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ3_F2Bd_W42 | 2777 | 0.38 | 1.920000e+00 | 8.570000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Ba_W42 | 2226 | 0.50 | 3.960000e+00 | 1.453000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Bb_W42 | 2226 | 0.50 | 4.220000e+00 | 1.520000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Bc_W42 | 2226 | 0.50 | 5.550000e+00 | 1.743000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Bd_W42 | 2226 | 0.50 | 5.200000e+00 | 1.675000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Be_W42 | 2226 | 0.50 | 4.100000e+00 | 1.509000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ5_F2B_W42 | 2226 | 0.50 | 5.600000e+00 | 1.656000e+01 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ6_F2B_W42 | 2226 | 0.50 | 4.410000e+00 | 1.701000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ7_F2B_W42 | 2226 | 0.50 | 4.960000e+00 | 1.869000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ8_F2B_W42 | 2226 | 0.50 | 5.580000e+00 | 1.718000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ1_F2C_W42 | 2226 | 0.50 | 1.990000e+00 | 5.510000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ2_F2C_W42 | 2226 | 0.50 | 1.910000e+00 | 2.960000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ3_F2Ca_W42 | 2423 | 0.46 | 2.370000e+00 | 9.130000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ3_F2Cb_W42 | 2423 | 0.46 | 2.940000e+00 | 1.155000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ3_F2Cc_W42 | 2423 | 0.46 | 2.870000e+00 | 1.027000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ3_F2Cd_W42 | 2423 | 0.46 | 2.310000e+00 | 9.630000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Ca_W42 | 2226 | 0.50 | 2.850000e+00 | 1.142000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Cb_W42 | 2226 | 0.50 | 3.030000e+00 | 1.176000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Cc_W42 | 2226 | 0.50 | 5.420000e+00 | 1.745000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Cd_W42 | 2226 | 0.50 | 5.900000e+00 | 1.875000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ4_F2Ce_W42 | 2226 | 0.50 | 2.560000e+00 | 1.048000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ5_F2C_W42 | 2226 | 0.50 | 4.650000e+00 | 1.259000e+01 | 1.00 | 3.000000e+00 | 3.00000e+00 | 4.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ6_F2C_W42 | 2226 | 0.50 | 3.480000e+00 | 1.474000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ7_F2C_W42 | 2226 | 0.50 | 4.640000e+00 | 1.794000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| PQ8_F2C_W42 | 2226 | 0.50 | 5.090000e+00 | 1.561000e+01 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM4a_W42 | 0 | 1.00 | 2.360000e+00 | 9.770000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM4b_W42 | 0 | 1.00 | 2.290000e+00 | 8.250000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| Q6F1_W42 | 2238 | 0.50 | 2.570000e+00 | 8.510000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| Q7F1_W42 | 2238 | 0.50 | 2.650000e+00 | 9.450000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| Q8F1_W42 | 2238 | 0.50 | 3.240000e+00 | 9.600000e+00 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| Q9F1_W42 | 2238 | 0.50 | 2.840000e+00 | 7.680000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| Q6F2_W42 | 2226 | 0.50 | 3.010000e+00 | 1.025000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| Q7F2_W42 | 2226 | 0.50 | 2.440000e+00 | 9.440000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| Q8F2_W42 | 2226 | 0.50 | 3.450000e+00 | 1.018000e+01 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| Q9F2_W42 | 2226 | 0.50 | 2.830000e+00 | 7.920000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM5a_W42 | 0 | 1.00 | 2.600000e+00 | 1.208000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM5b_W42 | 0 | 1.00 | 4.860000e+00 | 1.792000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM5c_W42 | 0 | 1.00 | 3.380000e+00 | 1.433000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM5d_W42 | 0 | 1.00 | 4.690000e+00 | 1.666000e+01 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM5e_W42 | 0 | 1.00 | 5.130000e+00 | 1.799000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM5f_W42 | 0 | 1.00 | 4.640000e+00 | 1.790000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM5g_W42 | 0 | 1.00 | 4.900000e+00 | 1.856000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM5h_W42 | 0 | 1.00 | 6.260000e+00 | 2.056000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM5i_W42 | 0 | 1.00 | 5.440000e+00 | 1.907000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM5j_W42 | 0 | 1.00 | 5.440000e+00 | 1.907000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM2_W42 | 0 | 1.00 | 2.660000e+00 | 1.126000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| SCM3_W42 | 0 | 1.00 | 2.650000e+00 | 1.077000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| POP1_W42 | 0 | 1.00 | 3.110000e+00 | 1.263000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| POP2_W42 | 0 | 1.00 | 3.280000e+00 | 7.790000e+00 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| POP3_W42 | 0 | 1.00 | 3.340000e+00 | 1.251000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| KNOW1_W42 | 0 | 1.00 | 2.920000e+00 | 5.530000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| KNOW1_CORRECT_W42 | 0 | 1.00 | 7.000000e-01 | 4.600000e-01 | 0.00 | 0.000000e+00 | 1.00000e+00 | 1.000000e+00 | 1.000000e+00 | ▃▁▁▁▇ |
| KNOW2_W42 | 0 | 1.00 | 2.610000e+00 | 7.670000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| KNOW2_CORRECT_W42 | 0 | 1.00 | 6.300000e-01 | 4.800000e-01 | 0.00 | 0.000000e+00 | 1.00000e+00 | 1.000000e+00 | 1.000000e+00 | ▅▁▁▁▇ |
| KNOW3_W42 | 0 | 1.00 | 3.920000e+00 | 7.760000e+00 | 1.00 | 3.000000e+00 | 3.00000e+00 | 4.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| KNOW3_CORRECT_W42 | 0 | 1.00 | 5.700000e-01 | 5.000000e-01 | 0.00 | 0.000000e+00 | 1.00000e+00 | 1.000000e+00 | 1.000000e+00 | ▆▁▁▁▇ |
| KNOW4_W42 | 0 | 1.00 | 3.040000e+00 | 7.710000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| KNOW4_CORRECT_W42 | 0 | 1.00 | 6.400000e-01 | 4.800000e-01 | 0.00 | 0.000000e+00 | 1.00000e+00 | 1.000000e+00 | 1.000000e+00 | ▅▁▁▁▇ |
| KNOW5_W42 | 0 | 1.00 | 3.480000e+00 | 8.800000e+00 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| KNOW5_CORRECT_W42 | 0 | 1.00 | 6.100000e-01 | 4.900000e-01 | 0.00 | 0.000000e+00 | 1.00000e+00 | 1.000000e+00 | 1.000000e+00 | ▅▁▁▁▇ |
| KNOW6_W42 | 0 | 1.00 | 3.790000e+00 | 6.940000e+00 | 1.00 | 3.000000e+00 | 3.00000e+00 | 4.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| KNOW6_CORRECT_W42 | 0 | 1.00 | 5.200000e-01 | 5.000000e-01 | 0.00 | 0.000000e+00 | 1.00000e+00 | 1.000000e+00 | 1.000000e+00 | ▇▁▁▁▇ |
| KNOW7_W42 | 0 | 1.00 | 4.600000e+00 | 8.050000e+00 | 1.00 | 4.000000e+00 | 4.00000e+00 | 4.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| KNOW7_CORRECT_W42 | 0 | 1.00 | 8.000000e-01 | 4.000000e-01 | 0.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 1.000000e+00 | ▂▁▁▁▇ |
| KNOW9_W42 | 0 | 1.00 | 2.730000e+00 | 7.540000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| KNOW9_CORRECT_W42 | 0 | 1.00 | 6.000000e-01 | 4.900000e-01 | 0.00 | 0.000000e+00 | 1.00000e+00 | 1.000000e+00 | 1.000000e+00 | ▅▁▁▁▇ |
| KNOW10_W42 | 0 | 1.00 | 4.060000e+00 | 8.300000e+00 | 1.00 | 3.000000e+00 | 3.00000e+00 | 5.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| KNOW10_CORRECT_W42 | 0 | 1.00 | 4.000000e-01 | 4.900000e-01 | 0.00 | 0.000000e+00 | 0.00000e+00 | 1.000000e+00 | 1.000000e+00 | ▇▁▁▁▅ |
| KNOW11_W42 | 0 | 1.00 | 2.090000e+00 | 7.380000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| KNOW11_CORRECT_W42 | 0 | 1.00 | 8.200000e-01 | 3.800000e-01 | 0.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 1.000000e+00 | ▂▁▁▁▇ |
| KNOW12_W42 | 0 | 1.00 | 3.370000e+00 | 8.080000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| KNOW12_CORRECT_W42 | 0 | 1.00 | 5.800000e-01 | 4.900000e-01 | 0.00 | 0.000000e+00 | 1.00000e+00 | 1.000000e+00 | 1.000000e+00 | ▆▁▁▁▇ |
| KNOW_INDEX_W42 | 0 | 1.00 | 6.870000e+00 | 3.310000e+00 | 0.00 | 4.000000e+00 | 7.00000e+00 | 1.000000e+01 | 1.100000e+01 | ▃▂▃▃▇ |
| KNOW14_W42 | 0 | 1.00 | 2.820000e+00 | 8.810000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_METRO | 0 | 1.00 | 1.130000e+00 | 3.400000e-01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 2.000000e+00 | ▇▁▁▁▁ |
| F_CREGION | 0 | 1.00 | 2.670000e+00 | 1.000000e+00 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 4.000000e+00 | ▃▅▁▇▅ |
| F_AGECAT | 0 | 1.00 | 2.650000e+00 | 3.060000e+00 | 1.00 | 2.000000e+00 | 3.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_SEX | 0 | 1.00 | 1.650000e+00 | 2.960000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_EDUCCAT | 0 | 1.00 | 2.230000e+00 | 4.660000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_EDUCCAT2 | 0 | 1.00 | 3.720000e+00 | 4.790000e+00 | 1.00 | 2.000000e+00 | 3.00000e+00 | 5.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_HISP | 0 | 1.00 | 2.320000e+00 | 6.810000e+00 | 1.00 | 2.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_RACECMB | 0 | 1.00 | 3.870000e+00 | 1.502000e+01 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_RACETHN | 0 | 1.00 | 1.740000e+00 | 1.290000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 2.000000e+00 | 9.000000e+00 | ▇▂▁▁▁ |
| F_NATIVITY | 0 | 1.00 | 1.470000e+00 | 4.470000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_CITIZEN | 0 | 1.00 | 1.250000e+00 | 4.640000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_MARITAL | 0 | 1.00 | 3.020000e+00 | 5.750000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 5.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_RELIG | 0 | 1.00 | 5.320000e+00 | 8.290000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 1.000000e+01 | 9.900000e+01 | ▇▁▁▁▁ |
| F_BORN | 1678 | 0.62 | 3.910000e+00 | 1.494000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_ATTEND | 0 | 1.00 | 4.580000e+00 | 6.550000e+00 | 1.00 | 3.000000e+00 | 5.00000e+00 | 6.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_PARTY_FINAL | 0 | 1.00 | 3.740000e+00 | 1.206000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_PARTYLN_FINAL | 2788 | 0.38 | 1.436000e+01 | 3.291000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_PARTYSUM_FINAL | 0 | 1.00 | 1.950000e+00 | 1.680000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.000000e+00 | ▇▁▁▁▁ |
| F_INCOME | 0 | 1.00 | 9.460000e+00 | 1.959000e+01 | 1.00 | 3.000000e+00 | 6.00000e+00 | 8.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_INCOME_RECODE | 0 | 1.00 | 6.310000e+00 | 2.014000e+01 | 1.00 | 1.000000e+00 | 2.00000e+00 | 3.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_REG | 138 | 0.97 | 1.610000e+00 | 4.510000e+00 | 1.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_IDEO | 0 | 1.00 | 5.450000e+00 | 1.525000e+01 | 1.00 | 2.000000e+00 | 3.00000e+00 | 4.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| F_ACSWEB | 0 | 1.00 | 9.200000e-01 | 2.700000e-01 | 0.00 | 1.000000e+00 | 1.00000e+00 | 1.000000e+00 | 1.000000e+00 | ▁▁▁▁▇ |
| F_VOLSUM | 0 | 1.00 | 1.850000e+00 | 4.140000e+00 | 1.00 | 1.000000e+00 | 2.00000e+00 | 2.000000e+00 | 9.900000e+01 | ▇▁▁▁▁ |
| WEIGHT_W42 | 0 | 1.00 | 1.000000e+00 | 8.500000e-01 | 0.11 | 4.300000e-01 | 7.50000e-01 | 1.270000e+00 | 4.680000e+00 | ▇▃▁▁▁ |
Data 3
Introduction and data
Identify the source of the data.
Los Angeles Open Data
State when and how it was originally collected (by the original data curator, not necessarily how you found the data).
The dataset was originally collected by the Los Angeles Police Department from the time period of 2020 to present. The data was collected in the police department’s record management system and was processed by the City of Los Angeles.
Write a brief description of the observations.
The following data set includes information about crimes that were reported in LAPD from 2020 to present. The data set contains 682K rows and 28 columns. The data set has types of crimes such as assault, theft, and murder. Each row represents each crime incident and its own unique data features with categories such as date, time, geographical location, and other details about that crime. They also provided the current status of the crime: whether the case remains open or it is closed. In terms of the information about the people related, they provide demographics such as age, gender, and race of the victims and potential suspects in the field. In terms of formulating the research question, some variables to consider are ‘Date Reported’, ‘Time Occurred’, ‘Area ID’, ‘Crime Code’, and ‘Crime Code Description’.
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 is the correlation among the types of crime, location, and time of day in the city of Los Angeles between 2020 to present?
A description of the research topic along with a concise statement of your hypotheses on this topic.
First, the target population for this research is from all reported crimes in Los Angeles between 2020 to present. This research question tries to explore the relationship between three variables mentioned in the instruction: types of crime, location, and time of the day. These three factors can be analyzed to find the relationship between how the geographical aspect of the city and the time shifts the stages of criminals to commit a crime and specify which types of crimes can be hugely affected by location or the time of the day. The question can be answered with the following data from the code chunk below, which contains information about variables such as ‘Crime Code Description’, ‘Date reported’, ‘Time Occurred’, ‘Area ID’. These variables can be well utilized to identify the three variables mentioned in the research question, and generate statistics such as regression or description to analyze the data.
Identify the types of variables in your research question. Categorical? Quantitative?
‘Crime Code Description’ = Categorical
‘Area ID’ = Categorical
‘Time Occurred’ = Quantitative
‘Date reported’ = Quantitative
Glimpse of data
# add code here
la_crime <- read_csv("data/la_crime_2020_to_present.csv")Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
dat <- vroom(...)
problems(dat)Rows: 682335 Columns: 28
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (17): DR_NO, Date Rptd, DATE OCC, TIME OCC, AREA, AREA NAME, Rpt Dist No...
dbl (10): Part 1-2, Crm Cd, Vict Age, Premis Cd, Weapon Used Cd, Crm Cd 1, C...
lgl (1): Crm Cd 4
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(la_crime)| Name | la_crime |
| Number of rows | 682335 |
| Number of columns | 28 |
| _______________________ | |
| Column type frequency: | |
| character | 17 |
| logical | 1 |
| numeric | 10 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| DR_NO | 0 | 1.00 | 4 | 9 | 0 | 682335 | 0 |
| Date Rptd | 0 | 1.00 | 22 | 22 | 0 | 1168 | 0 |
| DATE OCC | 0 | 1.00 | 22 | 22 | 0 | 1168 | 0 |
| TIME OCC | 0 | 1.00 | 4 | 4 | 0 | 1439 | 0 |
| AREA | 0 | 1.00 | 2 | 2 | 0 | 21 | 0 |
| AREA NAME | 0 | 1.00 | 6 | 11 | 0 | 21 | 0 |
| Rpt Dist No | 0 | 1.00 | 4 | 4 | 0 | 1194 | 0 |
| Crm Cd Desc | 0 | 1.00 | 5 | 56 | 0 | 137 | 0 |
| Mocodes | 93773 | 0.86 | 4 | 49 | 0 | 233172 | 0 |
| Vict Sex | 89346 | 0.87 | 1 | 1 | 0 | 4 | 0 |
| Vict Descent | 89351 | 0.87 | 1 | 1 | 0 | 19 | 0 |
| Premis Desc | 357 | 1.00 | 4 | 63 | 0 | 306 | 0 |
| Weapon Desc | 445186 | 0.35 | 3 | 46 | 0 | 79 | 0 |
| Status | 0 | 1.00 | 2 | 2 | 0 | 6 | 0 |
| Status Desc | 0 | 1.00 | 3 | 12 | 0 | 6 | 0 |
| LOCATION | 0 | 1.00 | 1 | 40 | 0 | 61184 | 0 |
| Cross Street | 571229 | 0.16 | 1 | 34 | 0 | 9082 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| Crm Cd 4 | 682335 | 0 | NaN | : |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| Part 1-2 | 0 | 1.00 | 1.42 | 0.49 | 1.00 | 1.00 | 1.00 | 2.00 | 2.00 | ▇▁▁▁▆ |
| Crm Cd | 0 | 1.00 | 500.57 | 207.84 | 110.00 | 330.00 | 442.00 | 626.00 | 956.00 | ▂▇▂▆▂ |
| Vict Age | 0 | 1.00 | 29.99 | 21.68 | -2.00 | 12.00 | 31.00 | 45.00 | 120.00 | ▆▇▃▁▁ |
| Premis Cd | 8 | 1.00 | 304.67 | 216.29 | 101.00 | 101.00 | 203.00 | 501.00 | 971.00 | ▇▁▅▁▁ |
| Weapon Used Cd | 445186 | 0.35 | 362.21 | 123.83 | 101.00 | 308.00 | 400.00 | 400.00 | 516.00 | ▂▁▁▇▃ |
| Crm Cd 1 | 9 | 1.00 | 500.31 | 207.63 | 110.00 | 330.00 | 442.00 | 626.00 | 956.00 | ▂▇▂▆▂ |
| Crm Cd 2 | 631351 | 0.07 | 956.52 | 113.17 | 210.00 | 998.00 | 998.00 | 998.00 | 999.00 | ▁▁▁▁▇ |
| Crm Cd 3 | 680618 | 0.00 | 983.33 | 52.55 | 434.00 | 998.00 | 998.00 | 998.00 | 999.00 | ▁▁▁▁▇ |
| LAT | 0 | 1.00 | 33.96 | 1.96 | 0.00 | 34.01 | 34.06 | 34.16 | 34.33 | ▁▁▁▁▇ |
| LON | 0 | 1.00 | -117.96 | 6.81 | -118.67 | -118.43 | -118.32 | -118.27 | 0.00 | ▇▁▁▁▁ |