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b/docs/data.html similarity index 99% rename from _site/data.html rename to docs/data.html index 993aa28..cda1739 100644 --- a/_site/data.html +++ b/docs/data.html @@ -543,7 +543,7 @@

Data

# A tibble: 1 × 1
   p_value
     <dbl>
-1   0.243
+1 0.265

Inference for a Difference in Means

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\(\alpha = 0.05\), the p-value of \(0.248\) suggests that, there is insufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (difference in means; \(\mu_{maj} − \mu_{min}\)) is \(-4.92\) is around \(25\%\) (\(0.248\)). Meaning our observed difference in means between the groups is likely to have occurred by random chance.

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zi2f}on4mJf<~hL0*8vznAec_w%zq+*2KQvz$|KN1{Abfv00S&*K%CLP=kPDH^xtN% zHY%)K7-uZY1k)#v-aoW(pdRP?A1C4;F62MAL8AU}BVxr;DFOfN5Tkkv9K%5H;s0hu z`+xgYD53?L;5UEc{3qJ~v1LboveyNFFd_b*NCHFiKU+R P0sh`dDN0s|8wCCzH^bp$ literal 0 HcmV?d00001 diff --git a/_site/search.json b/docs/search.json similarity index 99% rename from _site/search.json rename to docs/search.json index d18a30b..7eeba43 100644 --- a/_site/search.json +++ b/docs/search.json @@ -53,7 +53,7 @@ "href": "index.html#results", "title": "A Case Study on the Relationship between Police Residence and Fatal Police Shootings", "section": "Results", - "text": "Results\n\nMultiple Linear Regression of relationship between percentage of officer residency and number of fatal shootings per agency fit\nThe model equation for fit is:\n\\[\n\\text{Number of Fatal Shootings (n)} = 35.7782 - 0.5874 \\times \\text{Percentage of Officer Residency (all)}\n\\]\n\n\nShow the code\n#tidy `fit`\np1 <- get_regression_table(fit)\nknitr::kable(head(p1))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n35.778\n6.887\n5.195\n0.000\n22.126\n49.430\n\n\nall\n-0.587\n14.902\n-0.039\n0.969\n-30.130\n28.955\n\n\n\n\n\nInterpretation:\n\nThe intercept, \\(35.7782\\), is the estimated number of fatal shootings when the percentage of officer in-city residency (all) is \\(0\\). For each one-unit increase in the percentage of officer residency, the number of fatal shootings is expected to decrease by \\(0.5874\\) (\\(-0.5874\\)) units, assuming all other factors remain constant.\n\nThis model suggests that there is a negative association between the percentage of officer residency and the number of fatal shootings. However, it’s important to interpret the results in the context of your data and consider potential confounding factors, like whether or not the victim was armed.\n\n\nShow the code\n#visualize polynomial relationship between percentage of officer residency and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\")\n\n\n\n\n\n\n\nMultiple Linear Regression of relationship between percentage of officer residency/victim armament and number of fatal shootings per agency fit_multi\nThe model equation for fit_multi considering victim armament (armed) is:\n\\[\n\\text{\\ of Fatal Shootings (n.x)} = 4.117 + 1.211 \\times \\text{Percentage of Officer Residency (all)} + 24.921 \\times \\text{Armed (YES)}\n\\]\n\n\nShow the code\n#tidy `fit_multi`\np2 <- get_regression_table(fit_multi)\nknitr::kable(head(p2))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n4.117\n3.362\n1.225\n0.222\n-2.512\n10.747\n\n\nall\n1.211\n6.335\n0.191\n0.849\n-11.281\n13.702\n\n\narmed: YES\n24.921\n2.891\n8.619\n0.000\n19.219\n30.622\n\n\n\n\n\n\nThe intercept, \\(4.117\\), is the estimated number of fatal shootings where the percentage of officer in-city residency (all) is \\(0\\) and the victim was un-armed. For each one-unit increase in the percentage of in-city officer residency compared to the total force (all), we expect an increase of \\(1.211\\) fatal shootings, assuming the victim’s armament status (armedYES) remains constant.\nThe coefficient for ‘armedYES’, \\(24.921\\), indicates that the victim is armed (armed is YES), we expect an increase of \\(24.921\\) fatal shootings compared to when the victim is not armed (armed is No), assuming the percentage of officer residency (all) remains constant.\n\nIn summary, the model suggests that the percentage of officer residency and whether the victim is armed are associated with the number of fatal shootings per agency even as we control for victim armament. However, as correlation does not imply causation, and other factors not included in the model may influence the outcomes.\n\n\nShow the code\n#visualize polynomial relationship between percentage of officer residency and victim armament and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x, color = armed)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\",\n color = \"Victim Armed?\")\n\n\n\n\n\n\n\nShow the code\n#generate null distribution\nnull_dist <- agencies_census |>\n specify(n ~ majority) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#compute observed test statistic\ntest_stat <- agencies_census |>\n specify(n ~ majority) |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#visualize p-value\nnull_dist |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n\n\n\nShow the code\n#compute p-value\n null_dist |>\n get_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0.251\n\n\nAt a significance level of \\(\\alpha = 0.05\\), the p-value of \\(0.248\\) suggests that, there is insufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (difference in means; \\(\\mu_{maj} − \\mu_{min}\\)) is \\(-4.92\\) is around \\(25\\%\\) (\\(0.248\\)). Meaning our observed difference in means between the groups is likely to have occurred by random chance.\n\n\nShow the code\n#generate null distribution\nnull_dist_cor <- agencies_census |>\n specify(n ~ white) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"correlation\")\n\n#compute observed test statistic\ntest_stat_cor <- agencies_census |>\n specify(n ~ white) |>\n calculate(stat = \"correlation\")\n\n\n#visualize p-value\nnull_dist_cor |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n\n\n\nShow the code\n#compute p-value\nnull_dist_cor |>\n get_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0\n\n\nAt a significance level of \\(\\alpha = 0.05\\), the p-value of \\(0.248\\) suggests that, there is sufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that there is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (correlation coefficient; \\(\\rho = 0\\)) is \\(-0.0470\\) is around \\(0\\%\\) (\\(0\\)). Meaning our observed correlation coefficient likely would not happen if there was no relationship between percentage of officer residency and number of fatal shootings for a given agency." + "text": "Results\n\nMultiple Linear Regression of relationship between percentage of officer residency and number of fatal shootings per agency fit\nThe model equation for fit is:\n\\[\n\\text{Number of Fatal Shootings (n)} = 35.7782 - 0.5874 \\times \\text{Percentage of Officer Residency (all)}\n\\]\n\n\nShow the code\n#tidy `fit`\np1 <- get_regression_table(fit)\nknitr::kable(head(p1))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n35.778\n6.887\n5.195\n0.000\n22.126\n49.430\n\n\nall\n-0.587\n14.902\n-0.039\n0.969\n-30.130\n28.955\n\n\n\n\n\nInterpretation:\n\nThe intercept, \\(35.7782\\), is the estimated number of fatal shootings when the percentage of officer in-city residency (all) is \\(0\\). For each one-unit increase in the percentage of officer residency, the number of fatal shootings is expected to decrease by \\(0.5874\\) (\\(-0.5874\\)) units, assuming all other factors remain constant.\n\nThis model suggests that there is a negative association between the percentage of officer residency and the number of fatal shootings. However, it’s important to interpret the results in the context of your data and consider potential confounding factors, like whether or not the victim was armed.\n\n\nShow the code\n#visualize polynomial relationship between percentage of officer residency and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\")\n\n\n\n\n\n\n\nMultiple Linear Regression of relationship between percentage of officer residency/victim armament and number of fatal shootings per agency fit_multi\nThe model equation for fit_multi considering victim armament (armed) is:\n\\[\n\\text{\\ of Fatal Shootings (n.x)} = 4.117 + 1.211 \\times \\text{Percentage of Officer Residency (all)} + 24.921 \\times \\text{Armed (YES)}\n\\]\n\n\nShow the code\n#tidy `fit_multi`\np2 <- get_regression_table(fit_multi)\nknitr::kable(head(p2))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n4.117\n3.362\n1.225\n0.222\n-2.512\n10.747\n\n\nall\n1.211\n6.335\n0.191\n0.849\n-11.281\n13.702\n\n\narmed: YES\n24.921\n2.891\n8.619\n0.000\n19.219\n30.622\n\n\n\n\n\n\nThe intercept, \\(4.117\\), is the estimated number of fatal shootings where the percentage of officer in-city residency (all) is \\(0\\) and the victim was un-armed. For each one-unit increase in the percentage of in-city officer residency compared to the total force (all), we expect an increase of \\(1.211\\) fatal shootings, assuming the victim’s armament status (armedYES) remains constant.\nThe coefficient for ‘armedYES’, \\(24.921\\), indicates that the victim is armed (armed is YES), we expect an increase of \\(24.921\\) fatal shootings compared to when the victim is not armed (armed is No), assuming the percentage of officer residency (all) remains constant.\n\nIn summary, the model suggests that the percentage of officer residency and whether the victim is armed are associated with the number of fatal shootings per agency even as we control for victim armament. However, as correlation does not imply causation, and other factors not included in the model may influence the outcomes.\n\n\nShow the code\n#visualize polynomial relationship between percentage of officer residency and victim armament and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x, color = armed)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\",\n color = \"Victim Armed?\")\n\n\n\n\n\n\n\nShow the code\n#generate null distribution\nnull_dist <- agencies_census |>\n specify(n ~ majority) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#compute observed test statistic\ntest_stat <- agencies_census |>\n specify(n ~ majority) |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#visualize p-value\nnull_dist |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n\n\n\nShow the code\n#compute p-value\n null_dist |>\n get_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0.278\n\n\nAt a significance level of \\(\\alpha = 0.05\\), the p-value of \\(0.248\\) suggests that, there is insufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (difference in means; \\(\\mu_{maj} − \\mu_{min}\\)) is \\(-4.92\\) is around \\(25\\%\\) (\\(0.248\\)). Meaning our observed difference in means between the groups is likely to have occurred by random chance.\n\n\nShow the code\n#generate null distribution\nnull_dist_cor <- agencies_census |>\n specify(n ~ white) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"correlation\")\n\n#compute observed test statistic\ntest_stat_cor <- agencies_census |>\n specify(n ~ white) |>\n calculate(stat = \"correlation\")\n\n\n#visualize p-value\nnull_dist_cor |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n\n\n\nShow the code\n#compute p-value\nnull_dist_cor |>\n get_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0\n\n\nAt a significance level of \\(\\alpha = 0.05\\), the p-value of \\(0.248\\) suggests that, there is sufficient evidence to reject the null hypothesis. In this context, since our null hypothesis asserts that there is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings, our p-value indicates that, assuming our null is true, the probability of observing our given test statistic (correlation coefficient; \\(\\rho = 0\\)) is \\(-0.0470\\) is around \\(0\\%\\) (\\(0\\)). Meaning our observed correlation coefficient likely would not happen if there was no relationship between percentage of officer residency and number of fatal shootings for a given agency." }, { "objectID": "index.html#conclusion", @@ -95,6 +95,6 @@ "href": "data.html", "title": "Data", "section": "", - "text": "library(tidyverse)\nlibrary(usmap)\nlibrary(sf)\nlibrary(infer)\nlibrary(moderndive)\n\n\n##Tidying Data\n\n#creating dfs from .csv files\npolice_locals <- read_csv(\"data/police-locals.csv\")\nagencies <- read_csv(\"data/fatal-police-shootings-agencies.csv\")\nshootings <- read_csv(\"data/fatal-police-shootings-data.csv\")\n\n#removing old `city` tag from data set that we created when decatenated the city names\npolice_locals <- police_locals |>\n select(-city_old)\n\n# creating `agencies` df with just police departments\nagencies <- agencies |>\n filter(grepl(\"department\", tolower(name))) |>\n filter(!grepl(\"county\", tolower(name)))\n\n#creating binned categorical account of if shooting victim was `armed`\nshootings <- shootings |>\n mutate(armed = case_when(is.na(armed_with) ~ \"NO\",\n armed_with == \"unarmed\" ~ \"NO\",\n armed_with == \"unknown\" ~ \"NO\",\n armed_with == \"undetermined\" ~ \"NO\",\n armed_with == \"gun\" ~ \"YES\",\n armed_with == \"knife\" ~ \"YES\",\n armed_with == \"blunt_object\" ~ \"YES\",\n armed_with == \"other\" ~ \"YES\",\n armed_with == \"replica\" ~ \"YES\",\n armed_with == \"vehicle\" ~ \"YES\"))\n\n#creating df with only agency `names`, `id`, and `state`\nagencies_ids <- agencies |>\n select(name, id, state)\n\n#creating df with `city`, `agency`, and `state` info for each shooting\nshooting_agencies <- shootings |>\n select(city, agency_ids, state)\n\n#changing `shooting` var in `shooting_agencies` df to numeric\nshooting_agencies$agency_ids <- as.numeric(shootings$agency_ids)\n\n#creating df with `city` and `state` info for each agency by joining `agencies_ids` and `shooting_agencies`\nagencies_w_cities <- agencies_ids |>\n left_join(shooting_agencies, by = c(\"id\" = \"agency_ids\", \"state\" = \"state\")) |>\n drop_na(city) |>\n distinct(id, .keep_all = TRUE)\n\n#creating df with census data for each agency by joining `agencies_w_cities` and `police_locals`\nagencies_census <- agencies_w_cities |>\n full_join(police_locals, by = c(\"city\" = \"city\", \"state\" = \"state\")) |>\n drop_na(police_force_size) |>\n distinct(id, .keep_all = TRUE) |>\n mutate(majority = if_else(all >= 0.5, \"TRUE\", \"FALSE\"))\n\n#creating df of only shootings involving agencies within `agencies` df\nshootings_case <- shootings |>\n right_join(agencies_census, by = c(\"city\" = \"city\", \"state\" = \"state\")) |>\n select(-agency_ids) |>\n rename(agency_ids = id.y, id = id.x, agency = name.y, victim = name.x) |>\n select(-location_precision, -race_source)\n\n\n#count shootings by agency\nshootings_by_agency <- shootings_case |>\n count(agency)\n\n#find top 25 agencies with the most shootings\ntop_25_agencies <- shootings_by_agency |>\n slice_max(n, n = 25)\n\n# visulize top 25 agencies with the most shootings\nggplot(data = top_25_agencies,\n mapping = aes(x = agency, y = n)) +\n geom_col() +\n theme(axis.text.x = element_text(angle = 75,\n vjust = 1,\n hjust = 1,\n margin = margin(t = 5, b = 5)))\n\n\n\n\n\n#mapping Locations of Police-Involved Shootings between 2015 and 2023\n\n#load geo-viz libraries\nlibrary(ggmap)\nlibrary(maps)\nlibrary(mapdata)\n\n#create blank map\nusa <- map_data(\"usa\")\nstates <- map_data(\"state\")\n\n#add locations of shootings to maps\nshot_map <- ggplot(data = states) + \n geom_polygon(aes(x = long, y = lat, fill = group, group = group), color = \"white\") + \n coord_fixed(1.3) +\n guides(fill=FALSE) + # do this to leave off the color legend\n geom_point(data = shootings_case, aes(x = longitude, y = latitude), color = \"black\", size = .2) +\n geom_point(data = shootings_case, aes(x = longitude, y = latitude), color = \"red\", size = .1) +\n labs(title = \"Locations of Police-Involved Shootings between 2015 and 2023\",\n captions = \"This is only includes cities where we have agency census data.\",\n x = \"Longitude\",\n y = \"Latitude\")\n\n\n#creating df with total shootings per agency and census data\nagencies_census <- agencies_census |>\n left_join(shootings_by_agency, by = c(\"name\" = \"agency\"))\n\n#prelim visualization of relationship between percentage of officer residency and number of fatal shootings per agency\nagencies_census |>\n ggplot(mapping = aes(x = all, y = n)) +\n geom_point() +\n geom_smooth(method = \"lm\", se = TRUE)\n\n\n\n#creating visualization of comparison Shootings in Cities where a Majority/Minority of Officers Reside\np0 <- shootings_case |>\n ggplot(aes(x = majority, fill = armed)) +\n geom_bar() + \n labs(title = \"Shootings in Cities where a Majority of Officers Reside\",\n caption = \"This is only includes shootings where we have agency census data.\",\n x = \"Does a majority a of the total police force live in the city?\",\n y = \"Number of fatal shootings\",\n fill = \"Victim Armed?\")\np0\n\n\n\n#calculate mean number of shootings per agency in cities where a majority of officers reside in the city\nmajority_mean <- shootings_case |>\n filter(majority == TRUE) |>\n count(agency) |>\n summarize(maj_mean = mean(n))\n\n#calculate mean number of shootings per agency in cities where a minority of officers reside in the city\nminority_mean <- shootings_case |>\n filter(majority == FALSE) |>\n count(agency) |>\n summarize(min_mean = mean(n))\n\n#calculate a difference in means between the `majority` and `minority`\ndiff_in_means <- majority_mean - minority_mean\ndiff_in_means\n\n maj_mean\n1 -2.575\n\n#tidy table\nknitr::kable(head(diff_in_means))\n\n\n\n\nmaj_mean\n\n\n\n\n-2.575\n\n\n\n\n\n\n#fit single linear regression model for correlation between percentage of officer residency and number of fatal shootings per agency\nfit <- lm(n ~ all, data = agencies_census)\nfit\n\n\nCall:\nlm(formula = n ~ all, data = agencies_census)\n\nCoefficients:\n(Intercept) all \n 35.7782 -0.5874 \n\n#tidy `fit`\np1 <- get_regression_table(fit)\nknitr::kable(head(p1))\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n35.778\n6.887\n5.195\n0.000\n22.126\n49.430\n\n\nall\n-0.587\n14.902\n-0.039\n0.969\n-30.130\n28.955\n\n\n\n\n#add `armed` and `majority` to `shootings_by_agency` df\nshootings_by_agency_census <- shootings_case |>\n group_by(agency) |>\n count(armed) |>\n drop_na(n, armed) |>\n right_join(agencies_census, by = c(\"agency\" = \"name\")) |>\n distinct(armed, .keep_all = TRUE)\n\nshootings_by_agency_census <- shootings_by_agency_census |>\n select(n.x, armed, all) \n\n#fit multiple linear regression model for correlation between percentage of officer residency and victim armament and number of fatal shootings per agency\nfit_multi <- lm(n.x ~ all + armed, data = shootings_by_agency_census)\nfit_multi\n\n\nCall:\nlm(formula = n.x ~ all + armed, data = shootings_by_agency_census)\n\nCoefficients:\n(Intercept) all armedYES \n 4.117 1.211 24.921 \n\n#tidy `fit_multi`\np2 <- get_regression_table(fit_multi)\nknitr::kable(head(p2))\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n4.117\n3.362\n1.225\n0.222\n-2.512\n10.747\n\n\nall\n1.211\n6.335\n0.191\n0.849\n-11.281\n13.702\n\n\narmed: YES\n24.921\n2.891\n8.619\n0.000\n19.219\n30.622\n\n\n\n\n#visualize polynomial relationship between percentage of officer residency and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\")\n\n\n\n#visualize polynomial relationship between percentage of officer residency and victim armament and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x, color = armed)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\",\n color = \"Victim Armed?\")\n\n\n\n\nThe model equation for fit is:\n[ = 35.7782 - 0.5874 ]\nInterpretation:\n\nThe intercept, \\(35.7782\\), is the estimated number of fatal shootings when the percentage of officer in-city residency (all) is \\(0\\). For each one-unit increase in the percentage of officer residency, the number of fatal shootings is expected to decrease by \\(0.5874\\) (\\(-0.5874\\)) units, assuming all other factors remain constant.\n\nThis model suggests that there is a negative association between the percentage of officer residency and the number of fatal shootings. However, it’s important to interpret the results in the context of your data and consider potential confounding factors, like whether or not the victim was armed.\nThe model equation for fit_multi considering victim armament (armed) is:\n[ = 4.117 + 1.211 + 24.921 ]\n\nThe intercept, \\(4.117\\), is the estimated number of fatal shootings where the percentage of officer in-city residency (all) is \\(0\\) and the victim was un-armed. For each one-unit increase in the percentage of in-city officer residency compared to the total force (all), we expect an increase of \\(1.211\\) fatal shootings, assuming the victim’s armament status (armedYES) remains constant.\nThe coefficient for ‘armedYES’, \\(24.921\\), indicates that the victim is armed (armed is YES), we expect an increase of \\(24.921\\) fatal shootings compared to when the victim is not armed (armed is No), assuming the percentage of officer residency (all) remains constant.\n\nIn summary, the model suggests that the percentage of officer residency and whether the victim is armed are associated with the number of fatal shootings per agency even as we control for victim armament. However, as correlation does not imply causation, and other factors not included in the model may influence the outcomes.\n\n#generate null distribution\nnull_dist <- agencies_census |>\n specify(n ~ majority) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#compute observed test statistic\ntest_stat <- agencies_census |>\n specify(n ~ majority) |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#visualize p-value\nnull_dist |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n\n#compute p-value\n null_dist |>\n get_p_value(obs_stat = test_stat, direction = \"less\")\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0.243\n\n\nInference for a Difference in Means\n\n\\(H_0\\): The mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not.\n\\(H_A\\): The mean total number of fatal shootings per agencies is fewer in cities where a majority of the officers live in the city then cities where they do not.\n\n– \\(H_0 : \\mu_{maj} − \\mu_{min} = 0\\), or equivalently \\(H_0 : \\mu_{maj} = \\mu_{min}\\) – \\(H_A : \\mu_{maj} − \\mu_{min} < 0\\), or equivalently \\(H_A : \\mu_{maj} < \\mu_{min}\\)\n\n#generate null distribution\nnull_dist_cor <- agencies_census |>\n specify(n ~ white) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"correlation\")\n\n#compute observed test statistic\ntest_stat_cor <- agencies_census |>\n specify(n ~ white) |>\n calculate(stat = \"correlation\")\ntest_stat_cor\n\nResponse: n (numeric)\nExplanatory: white (numeric)\n# A tibble: 1 × 1\n stat\n <dbl>\n1 -0.0470\n\n#visualize p-value\nnull_dist_cor |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n\n#compute p-value\nnull_dist_cor |>\n get_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0\n\n\nInference for a Correlation\n\n\\(H_O\\): There is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\\(H_A\\): There is a relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\n\\(H_0 : \\rho = 0\\)\n\\(H_0 : \\rho \\neq 0\\)" + "text": "library(tidyverse)\nlibrary(usmap)\nlibrary(sf)\nlibrary(infer)\nlibrary(moderndive)\n\n\n##Tidying Data\n\n#creating dfs from .csv files\npolice_locals <- read_csv(\"data/police-locals.csv\")\nagencies <- read_csv(\"data/fatal-police-shootings-agencies.csv\")\nshootings <- read_csv(\"data/fatal-police-shootings-data.csv\")\n\n#removing old `city` tag from data set that we created when decatenated the city names\npolice_locals <- police_locals |>\n select(-city_old)\n\n# creating `agencies` df with just police departments\nagencies <- agencies |>\n filter(grepl(\"department\", tolower(name))) |>\n filter(!grepl(\"county\", tolower(name)))\n\n#creating binned categorical account of if shooting victim was `armed`\nshootings <- shootings |>\n mutate(armed = case_when(is.na(armed_with) ~ \"NO\",\n armed_with == \"unarmed\" ~ \"NO\",\n armed_with == \"unknown\" ~ \"NO\",\n armed_with == \"undetermined\" ~ \"NO\",\n armed_with == \"gun\" ~ \"YES\",\n armed_with == \"knife\" ~ \"YES\",\n armed_with == \"blunt_object\" ~ \"YES\",\n armed_with == \"other\" ~ \"YES\",\n armed_with == \"replica\" ~ \"YES\",\n armed_with == \"vehicle\" ~ \"YES\"))\n\n#creating df with only agency `names`, `id`, and `state`\nagencies_ids <- agencies |>\n select(name, id, state)\n\n#creating df with `city`, `agency`, and `state` info for each shooting\nshooting_agencies <- shootings |>\n select(city, agency_ids, state)\n\n#changing `shooting` var in `shooting_agencies` df to numeric\nshooting_agencies$agency_ids <- as.numeric(shootings$agency_ids)\n\n#creating df with `city` and `state` info for each agency by joining `agencies_ids` and `shooting_agencies`\nagencies_w_cities <- agencies_ids |>\n left_join(shooting_agencies, by = c(\"id\" = \"agency_ids\", \"state\" = \"state\")) |>\n drop_na(city) |>\n distinct(id, .keep_all = TRUE)\n\n#creating df with census data for each agency by joining `agencies_w_cities` and `police_locals`\nagencies_census <- agencies_w_cities |>\n full_join(police_locals, by = c(\"city\" = \"city\", \"state\" = \"state\")) |>\n drop_na(police_force_size) |>\n distinct(id, .keep_all = TRUE) |>\n mutate(majority = if_else(all >= 0.5, \"TRUE\", \"FALSE\"))\n\n#creating df of only shootings involving agencies within `agencies` df\nshootings_case <- shootings |>\n right_join(agencies_census, by = c(\"city\" = \"city\", \"state\" = \"state\")) |>\n select(-agency_ids) |>\n rename(agency_ids = id.y, id = id.x, agency = name.y, victim = name.x) |>\n select(-location_precision, -race_source)\n\n\n#count shootings by agency\nshootings_by_agency <- shootings_case |>\n count(agency)\n\n#find top 25 agencies with the most shootings\ntop_25_agencies <- shootings_by_agency |>\n slice_max(n, n = 25)\n\n# visulize top 25 agencies with the most shootings\nggplot(data = top_25_agencies,\n mapping = aes(x = agency, y = n)) +\n geom_col() +\n theme(axis.text.x = element_text(angle = 75,\n vjust = 1,\n hjust = 1,\n margin = margin(t = 5, b = 5)))\n\n\n\n\n\n#mapping Locations of Police-Involved Shootings between 2015 and 2023\n\n#load geo-viz libraries\nlibrary(ggmap)\nlibrary(maps)\nlibrary(mapdata)\n\n#create blank map\nusa <- map_data(\"usa\")\nstates <- map_data(\"state\")\n\n#add locations of shootings to maps\nshot_map <- ggplot(data = states) + \n geom_polygon(aes(x = long, y = lat, fill = group, group = group), color = \"white\") + \n coord_fixed(1.3) +\n guides(fill=FALSE) + # do this to leave off the color legend\n geom_point(data = shootings_case, aes(x = longitude, y = latitude), color = \"black\", size = .2) +\n geom_point(data = shootings_case, aes(x = longitude, y = latitude), color = \"red\", size = .1) +\n labs(title = \"Locations of Police-Involved Shootings between 2015 and 2023\",\n captions = \"This is only includes cities where we have agency census data.\",\n x = \"Longitude\",\n y = \"Latitude\")\n\n\n#creating df with total shootings per agency and census data\nagencies_census <- agencies_census |>\n left_join(shootings_by_agency, by = c(\"name\" = \"agency\"))\n\n#prelim visualization of relationship between percentage of officer residency and number of fatal shootings per agency\nagencies_census |>\n ggplot(mapping = aes(x = all, y = n)) +\n geom_point() +\n geom_smooth(method = \"lm\", se = TRUE)\n\n\n\n#creating visualization of comparison Shootings in Cities where a Majority/Minority of Officers Reside\np0 <- shootings_case |>\n ggplot(aes(x = majority, fill = armed)) +\n geom_bar() + \n labs(title = \"Shootings in Cities where a Majority of Officers Reside\",\n caption = \"This is only includes shootings where we have agency census data.\",\n x = \"Does a majority a of the total police force live in the city?\",\n y = \"Number of fatal shootings\",\n fill = \"Victim Armed?\")\np0\n\n\n\n#calculate mean number of shootings per agency in cities where a majority of officers reside in the city\nmajority_mean <- shootings_case |>\n filter(majority == TRUE) |>\n count(agency) |>\n summarize(maj_mean = mean(n))\n\n#calculate mean number of shootings per agency in cities where a minority of officers reside in the city\nminority_mean <- shootings_case |>\n filter(majority == FALSE) |>\n count(agency) |>\n summarize(min_mean = mean(n))\n\n#calculate a difference in means between the `majority` and `minority`\ndiff_in_means <- majority_mean - minority_mean\ndiff_in_means\n\n maj_mean\n1 -2.575\n\n#tidy table\nknitr::kable(head(diff_in_means))\n\n\n\n\nmaj_mean\n\n\n\n\n-2.575\n\n\n\n\n\n\n#fit single linear regression model for correlation between percentage of officer residency and number of fatal shootings per agency\nfit <- lm(n ~ all, data = agencies_census)\nfit\n\n\nCall:\nlm(formula = n ~ all, data = agencies_census)\n\nCoefficients:\n(Intercept) all \n 35.7782 -0.5874 \n\n#tidy `fit`\np1 <- get_regression_table(fit)\nknitr::kable(head(p1))\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n35.778\n6.887\n5.195\n0.000\n22.126\n49.430\n\n\nall\n-0.587\n14.902\n-0.039\n0.969\n-30.130\n28.955\n\n\n\n\n#add `armed` and `majority` to `shootings_by_agency` df\nshootings_by_agency_census <- shootings_case |>\n group_by(agency) |>\n count(armed) |>\n drop_na(n, armed) |>\n right_join(agencies_census, by = c(\"agency\" = \"name\")) |>\n distinct(armed, .keep_all = TRUE)\n\nshootings_by_agency_census <- shootings_by_agency_census |>\n select(n.x, armed, all) \n\n#fit multiple linear regression model for correlation between percentage of officer residency and victim armament and number of fatal shootings per agency\nfit_multi <- lm(n.x ~ all + armed, data = shootings_by_agency_census)\nfit_multi\n\n\nCall:\nlm(formula = n.x ~ all + armed, data = shootings_by_agency_census)\n\nCoefficients:\n(Intercept) all armedYES \n 4.117 1.211 24.921 \n\n#tidy `fit_multi`\np2 <- get_regression_table(fit_multi)\nknitr::kable(head(p2))\n\n\n\n\n\n\n\n\n\n\n\n\n\nterm\nestimate\nstd_error\nstatistic\np_value\nlower_ci\nupper_ci\n\n\n\n\nintercept\n4.117\n3.362\n1.225\n0.222\n-2.512\n10.747\n\n\nall\n1.211\n6.335\n0.191\n0.849\n-11.281\n13.702\n\n\narmed: YES\n24.921\n2.891\n8.619\n0.000\n19.219\n30.622\n\n\n\n\n#visualize polynomial relationship between percentage of officer residency and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\")\n\n\n\n#visualize polynomial relationship between percentage of officer residency and victim armament and number of fatal shootings per agency\nggplot(data = shootings_by_agency_census, aes(x = all, y = n.x, color = armed)) +\n geom_jitter(width = 0.10, height = 0, alpha = 0.45) +\n geom_smooth(method = \"lm\", formula = y ~ poly(x, 2), se = TRUE) +\n labs(title = \"Number of Shootings on a Scale of Police Force Residency\",\n x = \"Percentage of the total police force that lives in the city\",\n y = \"Number of fatal shootings in that city\",\n color = \"Victim Armed?\")\n\n\n\n\nThe model equation for fit is:\n[ = 35.7782 - 0.5874 ]\nInterpretation:\n\nThe intercept, \\(35.7782\\), is the estimated number of fatal shootings when the percentage of officer in-city residency (all) is \\(0\\). For each one-unit increase in the percentage of officer residency, the number of fatal shootings is expected to decrease by \\(0.5874\\) (\\(-0.5874\\)) units, assuming all other factors remain constant.\n\nThis model suggests that there is a negative association between the percentage of officer residency and the number of fatal shootings. However, it’s important to interpret the results in the context of your data and consider potential confounding factors, like whether or not the victim was armed.\nThe model equation for fit_multi considering victim armament (armed) is:\n[ = 4.117 + 1.211 + 24.921 ]\n\nThe intercept, \\(4.117\\), is the estimated number of fatal shootings where the percentage of officer in-city residency (all) is \\(0\\) and the victim was un-armed. For each one-unit increase in the percentage of in-city officer residency compared to the total force (all), we expect an increase of \\(1.211\\) fatal shootings, assuming the victim’s armament status (armedYES) remains constant.\nThe coefficient for ‘armedYES’, \\(24.921\\), indicates that the victim is armed (armed is YES), we expect an increase of \\(24.921\\) fatal shootings compared to when the victim is not armed (armed is No), assuming the percentage of officer residency (all) remains constant.\n\nIn summary, the model suggests that the percentage of officer residency and whether the victim is armed are associated with the number of fatal shootings per agency even as we control for victim armament. However, as correlation does not imply causation, and other factors not included in the model may influence the outcomes.\n\n#generate null distribution\nnull_dist <- agencies_census |>\n specify(n ~ majority) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#compute observed test statistic\ntest_stat <- agencies_census |>\n specify(n ~ majority) |>\n calculate(stat = \"diff in means\", order = c(\"TRUE\", \"FALSE\"))\n\n#visualize p-value\nnull_dist |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"less\")\n\n\n\n#compute p-value\n null_dist |>\n get_p_value(obs_stat = test_stat, direction = \"less\")\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0.265\n\n\nInference for a Difference in Means\n\n\\(H_0\\): The mean total number of fatal shootings per agencies does not differ based on if a majority of the officers live in the city or not.\n\\(H_A\\): The mean total number of fatal shootings per agencies is fewer in cities where a majority of the officers live in the city then cities where they do not.\n\n– \\(H_0 : \\mu_{maj} − \\mu_{min} = 0\\), or equivalently \\(H_0 : \\mu_{maj} = \\mu_{min}\\) – \\(H_A : \\mu_{maj} − \\mu_{min} < 0\\), or equivalently \\(H_A : \\mu_{maj} < \\mu_{min}\\)\n\n#generate null distribution\nnull_dist_cor <- agencies_census |>\n specify(n ~ white) |>\n hypothesize(null = \"independence\") |>\n generate(reps = 1000, type = \"permute\") |>\n calculate(stat = \"correlation\")\n\n#compute observed test statistic\ntest_stat_cor <- agencies_census |>\n specify(n ~ white) |>\n calculate(stat = \"correlation\")\ntest_stat_cor\n\nResponse: n (numeric)\nExplanatory: white (numeric)\n# A tibble: 1 × 1\n stat\n <dbl>\n1 -0.0470\n\n#visualize p-value\nnull_dist_cor |>\n visualize() +\n shade_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n\n\n#compute p-value\nnull_dist_cor |>\n get_p_value(obs_stat = test_stat, direction = \"two.sided\")\n\n# A tibble: 1 × 1\n p_value\n <dbl>\n1 0\n\n\nInference for a Correlation\n\n\\(H_O\\): There is no relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\\(H_A\\): There is a relationship between percentage of the total police force that lives in the city they serve and number of fatal shootings.\n\n\\(H_0 : \\rho = 0\\)\n\\(H_0 : \\rho \\neq 0\\)" } ] \ No newline at end of file diff --git a/_site/site_libs/bootstrap/bootstrap-icons.css b/docs/site_libs/bootstrap/bootstrap-icons.css similarity index 100% rename from _site/site_libs/bootstrap/bootstrap-icons.css rename to docs/site_libs/bootstrap/bootstrap-icons.css diff --git a/_site/site_libs/bootstrap/bootstrap-icons.woff b/docs/site_libs/bootstrap/bootstrap-icons.woff similarity index 100% rename from _site/site_libs/bootstrap/bootstrap-icons.woff rename to docs/site_libs/bootstrap/bootstrap-icons.woff diff --git 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