-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathREADME.Rmd
178 lines (105 loc) · 12.7 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
---
output: md_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
# OpenCaseStudies: Influence of Multicollinearity on Measured Impact of Right-to-Carry Gun Laws Part 1
<!-- badges: start -->
[](https://github.com/opencasestudies/ocs-bp-RTC-wrangling/actions)
[](https://github.com/opencasestudies/ocs-bp-RTC-wrangling/actions)
<!-- badges: end -->
### Important links
- HTML: https://www.opencasestudies.org/ocs-bp-RTC-wrangling
- GitHub: https://github.com/opencasestudies/ocs-bp-RTC-wrangling
- Bloomberg American Health Initiative: https://americanhealth.jhu.edu/open-case-studies
### Disclaimer
The purpose of the [Open Case
Studies](https://opencasestudies.github.io) project is **to demonstrate
the use of various data science methods, tools, and software in the
context of messy, real-world data**. A given case study does not cover
all aspects of the research process, is not claiming to be the most
appropriate way to analyze a given dataset, and should not be used in
the context of making policy decisions without external consultation
from scientific experts.
### License
This case study is part of the [OpenCaseStudies](https://opencasestudies.github.io) project.
This work is licensed under the Creative Commons Attribution-NonCommercial 3.0 ([CC BY-NC 3.0](https://creativecommons.org/licenses/by-nc/3.0/us/)) United States License.
### Citation
To cite this case study:
Wright, Carrie and Ontiveros, Michael and Jager, Leah and Taub, Margaret and Hicks, Stephanie. (2020). https://github.com/opencasestudies/ocs-bp-RTC-wrangling. Influence of Multicollinearity on Measured Impact of Right-to-Carry Gun Laws Part 1 (Version v1.0.0).
### Acknowledgments
We would like to acknowledge [Daniel Webster](https://www.jhsph.edu/faculty/directory/profile/739/daniel-webster) for assisting in framing the major direction of the case study.
We would also like to acknowledge the [Bloomberg American Health Initiative](https://americanhealth.jhu.edu/) for funding this work.
### Title
Influence of Multicollinearity on Measured Impact of Right-to-Carry Gun Laws Part 1
### Motivation
The influence of the implementation of less restrictive right-to-carry gun laws on violent crime is a historically controversial topic. One reason for the controversy, is concern that some earlier reports examining this topic may have used methods that were inappropriate.
One of the major concerns is that an earlier report included multiple demographic variables that were collinear with one another. This resulted in different a very coefficient estimate for right-to-carry gun law adoption than other reports that did not include collinear variables. This phenomenon is called [multicollinearity](https://en.wikipedia.org/wiki/Multicollinearity), and it can result in aberrant findings for particular explanatory variables, despite not altering the overall predictive power of a model.
In the next case study we perform a simplified analysis similar to those of these reports on this topic to explore the influence of multicollinearity on coefficient estimate stability. We however, do not recreate the previous analyses.
The reports that we use as a guide for our analysis are:
1. John J. Donohue et al., Right‐to‐Carry Laws and Violent Crime: A Comprehensive Assessment Using Panel Data and a State‐Level Synthetic Control Analysis. *Journal of Empirical Legal Studies*, 16,2 (2019).
2. David B. Mustard & John Lott. Crime, Deterrence, and Right-to-Carry Concealed Handguns. *Coase-Sandor Institute for Law & Economics* Working Paper No. 41, (1996).
### Motivating question
1) How does the inclusion of different numbers of age groups influence the results of an analysis of right to carry laws and violence rates?
### Data
In this case study, we perform analyses similar to those in [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"} article and the [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"} article, however **we do not try to recreate them**, instead we perform simplified analyses to allow us to focus on multicollinearity.
Therefore we use a subset of the **explanatory variables** used by each article including:
1. Data about state demographics in terms of population compositions for age, sex, race, as well as overall population values from the US Census Bureau:
Data | Link
---------- |-------------
**years 1977 to 1979** | [link](https://www2.census.gov/programs-surveys/popest/tables/1900-1980/state/asrh/)
**years 1980 to 1989** | [link](https://www2.census.gov/programs-surveys/popest/tables/1980-1990/counties/asrh/) * county data was used for this decade which also has state information
**years 1990 to 1999** | [link](https://www2.census.gov/programs-surveys/popest/tables/1990-2000/state/asrh/)
**years 2000 to 2010** | [link](https://www.census.gov/data/datasets/time-series/demo/popest/intercensal-2000-2010-state.html) <br> [technical documentation](https://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2000-2010/intercensal/state/st-est00int-alldata.pdf){target="_blank"}
Six demographic variables are created for the [Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"}-like analysis and 36 were created for the [Lott and Mustard](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"}-like analysis.
To use this data, we also need [Federal Information Processing Standard (FIPS) state codes](https://en.wikipedia.org/wiki/Federal_Information_Processing_Standard_state_code){target="_blank", to identify what demographic data corresponds to what state. This is also available from the [US Census Bureau](https://www.census.gov/geographies/reference-files/2014/demo/popest/2014-geocodes-state.html){target="_blank"}.
2. Police staffing data, which was downloaded from the [Federal Bureau of Investigation](https://crime-data-explorer.fr.cloud.gov/downloads-and-docs){target="_blank"}
3. Unemployment data, which was downloaded from the [U.S. Bureau of Labor Statistics](https://data.bls.gov/cgi-bin/dsrv?la){target="_blank"}.
4. Poverty data, extracted from Table 21 from this [US Census Bureau Poverty Data ](https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-poverty-people.html){target="_blank"}
5. Right-to-carry law data, which is available in a table in the [Donohue paper](https://www.nber.org/papers/w23510.pdf){target="_blank"}
Finally our outcome of interest is violent crime rates. The violent crime data was downloaded from the [FBI uniform crime reporting system](https://www.ucrdatatool.gov/Search/Crime/State/StatebyState.cfm){target="_blank"}
#### Learning Objectives
The skills, methods, and concepts that students will be familiar with by the end of this case study are:
<u>**Data Science Learning Objectives:**</u>
1. Data import of many different file types with special cases (`readr`, `readxl`, `pdftools`)
2. Joining data from multiple sources (`dplyr`)
3. Working with character strings (`stringr`)
4. Data comparisons (`dplyr` and `janitor`)
5. Reshaping data into different formats (`tidyr`)
#### Data import
In this case study particularly covers many different types of data import. We cover data import using the Tidyverse `readr` package to import the various types of text files, the Tidyverse `readxl` package to import excel files, and the Tidyverse `pdftools` package to import a PDF file. We demonstrate special cases where header rows need to be skipped, or column data types need to be specified. We also use the the `list.files()` base function together with the `map()` function of the Tidyverse `purrr` package to efficiently perform data importation of multiple files with one command.
#### Data wrangling
This case study is covers many details about wrangling data from excel files with unusual header structures and with similar data in multiple tables within the same file. This involves using the `stringr` package to split, subset, detect, extract, and modify patterns of text. This also involves using the `tidyr` package to change data shape and using the `dplyr` package to summarize, select, filter, modify, and join data. They case study also covers using various `map_*()` functions of the `purrr` package to perform functions across tibbles within lists and the `across()` function of the `dplyr` package to perform functions across columns of an individual tibble. This case study provides especially diverse material about data wrangling.
See [this case study](https://github.com/opencasestudies/ocs-bp-RTC-analysis) for part 2 which includes the following sections:
#### Data Visualization
The next [case study](https://github.com/opencasestudies/ocs-bp-RTC-analysis) demonstrates how to make correlation plots and scatter plots with error bars. We also show how to add formulas and arrows to plots. The instruction about data visualization assumes that students have some familiarity with `ggplot2`.
### Analysis
The next [case study](https://github.com/opencasestudies/ocs-bp-RTC-analysis) covers balanced panel regression model data analysis with fixed effects. In doing so we provide an introduction to longitudinal analysis in general, as well as use of the `plm` package. We also show how to calculate [Variance inflation factor (VIF)](https://en.wikipedia.org/wiki/Variance_inflation_factor) values using the `car` package to quantify the severity of multicollinearity. As another assessment of multicollinearity, we demonstrate how to perform simulations to evaluate the stability of coefficient estimates.
### Other notes and resources
[Tidyverse](https://www.tidyverse.org/){target="_blank"}
The articles used to motivate this case study are:
[Mustard and Lott](https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1150&context=law_and_economics){target="_blank"}
[Donohue, et al.](https://www.nber.org/papers/w23510.pdf){target="_blank"}
[See here for a list of studies on this topic ](https://en.wikipedia.org/wiki/More_Guns,_Less_Crime){target="_blank"}
<u>**Packages used in this case study:** </u>
Package | Use in this case study
---------- |-------------
[here](https://github.com/jennybc/here_here){target="_blank"} | to easily load and save data
[readxl](https://readxl.tidyverse.org/){target="_blank"} | to import the data in the excel files
[readr](https://readr.tidyverse.org/){target="_blank"} | to import the CSV file data
[pdftools](https://github.com/ropensci/pdftools){target="_blank"} | to import data from a pdf file
[dplyr](https://dplyr.tidyverse.org/){target="_blank"} | to arrange/filter/select/compare specific subsets of the data
[magrittr](https://cran.r-project.org/web/packages/magrittr/vignettes/magrittr.html){target="_blank"} | to use the compound assignment pipe operator `%<>%`
[tidyr](https://tidyr.tidyverse.org/){target="_blank"} | to rearrange data in wide and long formats
[stringr](https://stringr.tidyverse.org/articles/stringr.html){target="_blank"} | to manipulate the character strings within the data
[purrr](https://purrr.tidyverse.org/){target="_blank"} | to import the data in all the different excel and csv files efficiently
[forcats](https://forcats.tidyverse.org/){target="_blank"} | to allow for reordering of factors in plots
[tibble](https://tibble.tidyverse.org/){target="_blank"} | to create data objects that we can manipulate with `dplyr`/`stringr`/`tidyr`/`purrr`
#### For users
There is a [`Makefile`](Makefile) in this folder that allows you to type `make` to knit the case study contained in the `index.Rmd` to `index.html` and it will also knit the [`README.Rmd`](README.Rmd) to a markdown file (`README.md`).
#### For instructors
Instructors can skip the Data Import and Data Wrangling and start at the Data Exploration section, which gives a brief introduction of the data before the Data Analysis section, by skipping to this [this case study](https://github.com/opencasestudies/ocs-bp-RTC-analysis).
#### Target audience
This case study is intended for individuals or classes with some familiarity with R programming.
Part 2 of this case study is intended for individuals or classes with some familiarity with regression. See this [case study](https://opencasestudies.github.io/ocs-bp-diet/){target="_blank"} for an introduction to regression.
#### Suggested homework
Ask students to import and wrangle similar datasets to those used here.