32:T402,chart = Plot.plot({ marginLeft: 200, grid: false, x: { label: "Years to Parole Consideration or Projected Release", ticks: 0 }, y: { label: null, domain: d3 .sort( data.filter((d) => d.state_name != "NA"), (d) => -d.term ) .map((d) => d.state_name) }, marks: [ Plot.barX( data.filter((d) => d.state_name != "NA"), { x: "term", y: "state_name", fill: (d) => d.state_name == "Michigan" ? "#07A9E0" : d.state_name == "Michigan w/ Max Prod. Credits" ? "#07A9E0" : "#f15a2c", title: (d) => `${d.state_name}: ${format(d.term)} years` } ), Plot.text( data.filter((d) => d.state_name != "NA"), { x: "term", y: "state_name", dx: 16, text: (d) => `${format(d.term)}` } ) ], caption: `ASJ analysis of NCRP data. Only new admissions -- not admissions due to revocations or violations -- are considered.` })33:Te23,## Replication code ``` library(dplyr) library(lubridate) library(janitor) library(plotrix) library(tidylog) options(scipen=999) load("input/ICPSR_38492/DS0003/38492-0003-Data.rda") load("input/ICPSR_38492/DS0001/38492-0001-Data.rda") load("input/ICPSR_38492/DS0002/38492-0002-Data.rda") list_of_states <- read.csv("input/list_of_states.csv", header = TRUE) releases <- clean_names(da38492.0003) terms <- clean_names(da38492.0001) admissions <- clean_names(da38492.0002) rm(da38492.0003, da38492.0001, da38492.0002) factor_to_numeric <- function(data) { i <- sapply(data,levels) i <- i[!sapply(i,is.null)] i <- sapply(i, function(x) all(substring(x,1,1)=="(")) var.fix <- names(i)[i] for(xj in var.fix) #create a for-loop to fix these variable names. for each value "xj" in var.fix { data[,xj] <- gsub("\$([0-9]+)\$.*","\\1",data[,xj]) data[,xj] <- as.numeric(data[,xj]) } return (data)} releases <- factor_to_numeric(releases) terms <- factor_to_numeric(terms) admissions <- factor_to_numeric(admissions) ############### total_admissions <- admissions %>% filter(admityr %in% 2016:2018, admtype == 1) %>% group_by(state) %>% summarise(total_admissions_2016_2018 = n()) test_admissions <- admissions %>% filter(admityr %in% 2016:2018, admtype == 1) %>% group_by(state, admityr) %>% summarise(total = n(), missing_parole_elig = sum(is.na(parelig_year)), missing_projprisrel_year = sum(is.na(proj_prisrel_year)), missing_mandprisrel_year = sum(is.na(mand_prisrel_year))) %>% left_join(list_of_states) %>% group_by(state) %>% summarise(n = n()) data <- admissions %>% filter(admityr %in% 2016:2018, admtype == 1) %>% mutate(key_date = case_when(!is.na(parelig_year) ~ parelig_year, !is.na(proj_prisrel_year) ~ proj_prisrel_year, !is.na(mand_prisrel_year) ~ mand_prisrel_year, TRUE ~ NA)) %>% filter(!is.na(key_date)) data <- data %>% mutate(key_date = case_when(key_date > admityr + 25 ~ admityr + 25, TRUE ~ key_date), admit_min = as.Date(mdy(paste0("01/01/",admityr))), admit_max = as.Date(mdy(paste0("12/31/",admityr))), rel_dt_min = as.Date(mdy(paste0("01/01/",key_date))), rel_dt_max = as.Date(mdy(paste0("12/31/",key_date))), ts_min = unclass(rel_dt_min - admit_max), ts_max = unclass(rel_dt_max - admit_min), ts = (ts_min+ts_max)/2, ts_reform = case_when(state == 26 ~ ts * .8, TRUE ~ ts)) state_summaries <- data %>% group_by(state) %>% summarise(median_ts = median(ts)/365.25, median_ts_reform = median(ts_reform)/365.25, mean_ts = mean(ts)/365.25, mean_ts_reform = mean(ts_reform)/365.25, standard_error = std.error(ts)/365.25, n = n()) %>% left_join(list_of_states) %>% left_join(total_admissions) %>% mutate(pct_w_data = n/total_admissions_2016_2018) state_summary_condensed <- state_summaries %>% mutate(term = mean_ts) %>% select(state_name, term, standard_error) mi <- data.frame(state_name = "Michigan w/ Max Prod. Credits", term = 3.55, standard_error = 0.4) state_summary_condensed <- rbind(state_summary_condensed, mi) %>% arrange(-term) write.csv(state_summary_condensed, "output/2023 - 0525 Michigan NCRP Admissions Time To Parole Consideration.csv")``` 34:[{"id":"f46947276f8fab61cdd70d3e9f66fe87221ff86621f48dae1dd6c498a31c7f17edc9b726f3e87f474be6c1c73e5d930fc42ab03a3f29a3b0a94e12c234306bb7","href":"https://static.observableusercontent.com/files/f46947276f8fab61cdd70d3e9f66fe87221ff86621f48dae1dd6c498a31c7f17edc9b726f3e87f474be6c1c73e5d930fc42ab03a3f29a3b0a94e12c234306bb7","name":"2023 - 0525 Michigan NCRP Admissions Time To Parole Consideration@1.csv","type":"text/csv","lastModified":1685045240923.07,"size":1409},{"id":"f42428492061a1dd92dd5e576cf1c02b0e793d1d80e7025ef86c570e2da4c7df321914876e6b788ff6ab87cc2e9ffbd678fd654702480e4b885663710b8ef09a","href":"https://static.observableusercontent.com/files/f42428492061a1dd92dd5e576cf1c02b0e793d1d80e7025ef86c570e2da4c7df321914876e6b788ff6ab87cc2e9ffbd678fd654702480e4b885663710b8ef09a","name":"2023 - 0525 Michigan NCRP Admissions Time To Parole Consideration@2.csv","type":"text/csv","lastModified":1685456376057.253,"size":2187}] 30:{"title":"Michigan has the longest prison terms in the country. Even if productivity credits become law and are implemented to their maximum extent, Michigan will remain in the top 9 states with the longest minimum sentences.","cells":[{"id":0,"value":"# Michigan has the longest prison terms in the country. Even if productivity credits become law and are implemented to their maximum extent, Michigan will remain in the top 9 states with the longest minimum sentences.","pinned":false,"hidden":false,"mode":"md"},{"id":38,"value":"#### Average Expected Years to Parole Consideration or to Projected Release, by State, 2016-2018 Admissions to Prison","pinned":false,"hidden":false,"mode":"md"},{"id":7,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":73,"value":"### Other data points\n\n* People in Michigan [serve longer prison terms](https://observablehq.com/@cyrusobrien/michigan-has-the-longest-prison-terms-in-the-country) than people anywhere else in the country.","pinned":false,"hidden":false,"mode":"md"},{"id":101,"value":"### Notes on the data:\n\n* This is a May 25, 2023 analysis of [NCRP prison admission data](https://www.icpsr.umich.edu/web/NACJD/studies/38491). It analyses new court admissions by state for 2016, 2017, and 2018. NCRP does not provide precise sentencing data, so years to parole consideration and years to projected release were calculated using the variables admityr, and parelig_year, proj_prisrel_year, or mand_prisrel_year. The uncertainty for each case is ±365 days.\n* Sentences longer than 25 years were coded as 25 years.\n* Michigan's estimated average time to parole consideration if productivity credits were implemented to their maximum extent intentionally overstates their impact. This figure assumes that all people in Michigan prisons will receive the equivalent of 20% of their minimum term in productivity credits. ","pinned":false,"hidden":false,"mode":"md"},{"id":65,"value":"## Data, Logic, and Dependencies","pinned":false,"hidden":false,"mode":"md"},{"id":5,"value":"data = FileAttachment(\n \"2023 - 0525 Michigan NCRP Admissions Time To Parole Consideration@2.csv\"\n).csv({ typed: true })","pinned":false,"hidden":false,"mode":"ojs"},{"id":16,"value":"format = d3.format(\".2\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":26,"value":"import { Plot } from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":40,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":53,"value":"html`