diff --git a/code/00_load_dependencies.R b/code/00_load_dependencies.R
index 291af3f..2805836 100644
--- a/code/00_load_dependencies.R
+++ b/code/00_load_dependencies.R
@@ -33,4 +33,4 @@ if(length(new.packages)) install.packages(new.packages)
 lapply(list.of.packages, require, character.only = TRUE)
 
 # remove created variables for packages
-rm(list.of.packages,new.packages)
\ No newline at end of file
+rm(list.of.packages,new.packages)
diff --git a/code/01_city_demo_diversity.Rmd b/code/01_city_demo_diversity.Rmd
index 3cd23fa..943e537 100644
--- a/code/01_city_demo_diversity.Rmd
+++ b/code/01_city_demo_diversity.Rmd
@@ -3,7 +3,7 @@ title: "01_city_demo_diversity_metric_new"
 output: html_document
 ---
 
-```{r setup}
+```{r}
 # R version
 
 # using PUMS 2016-2020 5 Year Labor Force demographics
@@ -13,38 +13,112 @@ output: html_document
 # https://webapps.dol.gov/dolfaq/go-dol-faq.asp?faqid=111&faqsub=Labor+Force&faqtop=Statistics&topicid=6
 # 
 # ***FIRST***: load libraries from `00_load_dependencies.R`
+options(scipen = 999)
 ```
 
 ```{r}
-# source("envs.R") .
-options(scipen = 999)
 # read in data from mdat pums -----------
-# 2020 5-Year
+# 2018, 2019, 2020, 2021 5-Year
 # ----- ***TO DO***: GET NEW DATA -----
-t_18 <- fromJSON(paste0('https://api.census.gov/data/2018/acs/acs5/pums?get=PWGTP,PWGTP,RAC1P,SEX,ESR,HISP&ucgid=7950000US3603701,7950000US3603702,7950000US3603703,7950000US3603704,7950000US3603705,7950000US3603706,7950000US3603707,7950000US3603708,7950000US3603709,7950000US3603710,7950000US3603801,7950000US3603802,7950000US3603803,7950000US3603804,7950000US3603805,7950000US3603806,7950000US3603807,7950000US3603808,7950000US3603809,7950000US3603810,7950000US3603901,7950000US3603902,7950000US3603903,7950000US3604001,7950000US3604002,7950000US3604003,7950000US3604004,7950000US3604005,7950000US3604006,7950000US3604007,7950000US3604008,7950000US3604009,7950000US3604010,7950000US3604011,7950000US3604012,7950000US3604013,7950000US3604014,7950000US3604015,7950000US3604016,7950000US3604017,7950000US3604018,7950000US3604101,7950000US3604102,7950000US3604103,7950000US3604104,7950000US3604105,7950000US3604106,7950000US3604107,7950000US3604108,7950000US3604109,7950000US3604110,7950000US3604111,7950000US3604112,7950000US3604113,7950000US3604114&key=', Sys.getenv("KEY")))
+vars = "PWGTP,RAC1P,SEX,ESR,HISP"
+geographies=c("7950000US3603701","7950000US3603702","7950000US3603703","7950000US3603704","7950000US3603705","7950000US3603706","7950000US3603707","7950000US3603708","7950000US3603709","7950000US3603710","7950000US3603801","7950000US3603802","7950000US3603803","7950000US3603804","7950000US3603805","7950000US3603806","7950000US3603807","7950000US3603808","7950000US3603809","7950000US3603810","7950000US3603901","7950000US3603902","7950000US3603903","7950000US3604001","7950000US3604002","7950000US3604003","7950000US3604004","7950000US3604005","7950000US3604006","7950000US3604007","7950000US3604008","7950000US3604009","7950000US3604010","7950000US3604011","7950000US3604012","7950000US3604013","7950000US3604014","7950000US3604015","7950000US3604016","7950000US3604017","7950000US3604018","7950000US3604101","7950000US3604102","7950000US3604103","7950000US3604104","7950000US3604105","7950000US3604106","7950000US3604107","7950000US3604108","7950000US3604109","7950000US3604110","7950000US3604111","7950000US3604112","7950000US3604113","7950000US3604114")
+dataset = "acs/acs5/pums" #american community survey, 5 year, public microdata sample (PUMS)
 ```
 
 ```{r}
-# ----- GETTING NEW DATA (2019) -----
-t_19 <- fromJSON(paste0('https://api.census.gov/data/2019/acs/acs5/pums?get=PWGTP,PWGTP,RAC1P,SEX,ESR,HISP&ucgid=7950000US3603701,7950000US3603702,7950000US3603703,7950000US3603704,7950000US3603705,7950000US3603706,7950000US3603707,7950000US3603708,7950000US3603709,7950000US3603710,7950000US3603801,7950000US3603802,7950000US3603803,7950000US3603804,7950000US3603805,7950000US3603806,7950000US3603807,7950000US3603808,7950000US3603809,7950000US3603810,7950000US3603901,7950000US3603902,7950000US3603903,7950000US3604001,7950000US3604002,7950000US3604003,7950000US3604004,7950000US3604005,7950000US3604006,7950000US3604007,7950000US3604008,7950000US3604009,7950000US3604010,7950000US3604011,7950000US3604012,7950000US3604013,7950000US3604014,7950000US3604015,7950000US3604016,7950000US3604017,7950000US3604018,7950000US3604101,7950000US3604102,7950000US3604103,7950000US3604104,7950000US3604105,7950000US3604106,7950000US3604107,7950000US3604108,7950000US3604109,7950000US3604110,7950000US3604111,7950000US3604112,7950000US3604113,7950000US3604114&key=', Sys.getenv("KEY")))
+get_acs <- function(request, year) {
+  df = data.frame()
+  for (i in 1:length(geographies)) {
+    geography = geographies[i]
+    print(geography)
+    
+    request = paste(
+      "https://api.census.gov/data/",
+      year,
+      "/",
+      dataset,
+      "?get=",
+      vars,
+      "&ucgid=",
+      geography,
+      sep = ''
+    )
+    
+    r <- GET(request) # library(httr)
+    print(r$status_code)
+    content = content(r)
+    
+    p = length(content[[1]])
+    n = length(content) - 1
+    
+    M = matrix(unlist(content), ncol = p, byrow = TRUE)
+    tmp = data.frame(M[2:nrow(M), ])
+    colnames(tmp) = M[1, ]
+    
+    df = rbind(df, tmp)
+  }
+  return(df)
+}
 ```
 
 ```{r}
-# ----- GETTING NEW DATA (2020) -----
-t_20 <- fromJSON(paste0('https://api.census.gov/data/2020/acs/acs5/pums?get=PWGTP,PWGTP,RAC1P,SEX,ESR,HISP&ucgid=7950000US3603701,7950000US3603702,7950000US3603703,7950000US3603704,7950000US3603705,7950000US3603706,7950000US3603707,7950000US3603708,7950000US3603709,7950000US3603710,7950000US3603801,7950000US3603802,7950000US3603803,7950000US3603804,7950000US3603805,7950000US3603806,7950000US3603807,7950000US3603808,7950000US3603809,7950000US3603810,7950000US3603901,7950000US3603902,7950000US3603903,7950000US3604001,7950000US3604002,7950000US3604003,7950000US3604004,7950000US3604005,7950000US3604006,7950000US3604007,7950000US3604008,7950000US3604009,7950000US3604010,7950000US3604011,7950000US3604012,7950000US3604013,7950000US3604014,7950000US3604015,7950000US3604016,7950000US3604017,7950000US3604018,7950000US3604101,7950000US3604102,7950000US3604103,7950000US3604104,7950000US3604105,7950000US3604106,7950000US3604107,7950000US3604108,7950000US3604109,7950000US3604110,7950000US3604111,7950000US3604112,7950000US3604113,7950000US3604114&key=', Sys.getenv("KEY")))
+# ---- GETTING NEW DATA (2018) ----
+request = paste0('https://api.census.gov/data/2018/acs/acs5/pums?get=PWGTP,RAC1P,SEX,ESR,HISP&ucgid=7950000US3603701,7950000US3603702,7950000US3603703,7950000US3603704,7950000US3603705,7950000US3603706,7950000US3603707,7950000US3603708,7950000US3603709,7950000US3603710,7950000US3603801,7950000US3603802,7950000US3603803,7950000US3603804,7950000US3603805,7950000US3603806,7950000US3603807,7950000US3603808,7950000US3603809,7950000US3603810,7950000US3603901,7950000US3603902,7950000US3603903,7950000US3604001,7950000US3604002,7950000US3604003,7950000US3604004,7950000US3604005,7950000US3604006,7950000US3604007,7950000US3604008,7950000US3604009,7950000US3604010,7950000US3604011,7950000US3604012,7950000US3604013,7950000US3604014,7950000US3604015,7950000US3604016,7950000US3604017,7950000US3604018,7950000US3604101,7950000US3604102,7950000US3604103,7950000US3604104,7950000US3604105,7950000US3604106,7950000US3604107,7950000US3604108,7950000US3604109,7950000US3604110,7950000US3604111,7950000US3604112,7950000US3604113,7950000US3604114&key=', Sys.getenv("KEY"))
+year = "2018"
+t_18 <- get_acs(request, year)
 ```
 
 ```{r}
-convert_df <- function(t) {
-  df=t
-  df[1,1:ncol(df)]
-  df<-as.data.frame(df[1:nrow(df), 1:ncol(df)])
-  names(df)<-t[1,1:ncol(t)]
-  df=df[-1,-1]
-}
-df_18 = convert_df(t_18)
-df_19 = convert_df(t_19)
-df_20 = convert_df(t_20)
+# ----- GETTING NEW DATA (2019) ----
+request = paste0('https://api.census.gov/data/2019/acs/acs5/pums?get=PWGTP,RAC1P,SEX,ESR,HISP&ucgid=7950000US3603701,7950000US3603702,7950000US3603703,7950000US3603704,7950000US3603705,7950000US3603706,7950000US3603707,7950000US3603708,7950000US3603709,7950000US3603710,7950000US3603801,7950000US3603802,7950000US3603803,7950000US3603804,7950000US3603805,7950000US3603806,7950000US3603807,7950000US3603808,7950000US3603809,7950000US3603810,7950000US3603901,7950000US3603902,7950000US3603903,7950000US3604001,7950000US3604002,7950000US3604003,7950000US3604004,7950000US3604005,7950000US3604006,7950000US3604007,7950000US3604008,7950000US3604009,7950000US3604010,7950000US3604011,7950000US3604012,7950000US3604013,7950000US3604014,7950000US3604015,7950000US3604016,7950000US3604017,7950000US3604018,7950000US3604101,7950000US3604102,7950000US3604103,7950000US3604104,7950000US3604105,7950000US3604106,7950000US3604107,7950000US3604108,7950000US3604109,7950000US3604110,7950000US3604111,7950000US3604112,7950000US3604113,7950000US3604114&key=', Sys.getenv("KEY"))
+year = "2019"
+t_19 <- get_acs(request, year)
+```
+
+```{r}
+# ----- GETTING NEW DATA (2020) ----
+request = paste0('https://api.census.gov/data/2020/acs/acs5/pums?get=PWGTP,RAC1P,SEX,ESR,HISP&ucgid=7950000US3603701,7950000US3603702,7950000US3603703,7950000US3603704,7950000US3603705,7950000US3603706,7950000US3603707,7950000US3603708,7950000US3603709,7950000US3603710,7950000US3603801,7950000US3603802,7950000US3603803,7950000US3603804,7950000US3603805,7950000US3603806,7950000US3603807,7950000US3603808,7950000US3603809,7950000US3603810,7950000US3603901,7950000US3603902,7950000US3603903,7950000US3604001,7950000US3604002,7950000US3604003,7950000US3604004,7950000US3604005,7950000US3604006,7950000US3604007,7950000US3604008,7950000US3604009,7950000US3604010,7950000US3604011,7950000US3604012,7950000US3604013,7950000US3604014,7950000US3604015,7950000US3604016,7950000US3604017,7950000US3604018,7950000US3604101,7950000US3604102,7950000US3604103,7950000US3604104,7950000US3604105,7950000US3604106,7950000US3604107,7950000US3604108,7950000US3604109,7950000US3604110,7950000US3604111,7950000US3604112,7950000US3604113,7950000US3604114&key=', Sys.getenv("KEY"))
+year = "2020"
+t_20 <- get_acs(request, year)
+```
+
+```{r}
+# ----- GETTING NEW DATA (2021) ----
+request = paste0('https://api.census.gov/data/2021/acs/acs5/pums?get=PWGTP,RAC1P,SEX,ESR,HISP&ucgid=7950000US3603701,7950000US3603702,7950000US3603703,7950000US3603704,7950000US3603705,7950000US3603706,7950000US3603707,7950000US3603708,7950000US3603709,7950000US3603710,7950000US3603801,7950000US3603802,7950000US3603803,7950000US3603804,7950000US3603805,7950000US3603806,7950000US3603807,7950000US3603808,7950000US3603809,7950000US3603810,7950000US3603901,7950000US3603902,7950000US3603903,7950000US3604001,7950000US3604002,7950000US3604003,7950000US3604004,7950000US3604005,7950000US3604006,7950000US3604007,7950000US3604008,7950000US3604009,7950000US3604010,7950000US3604011,7950000US3604012,7950000US3604013,7950000US3604014,7950000US3604015,7950000US3604016,7950000US3604017,7950000US3604018,7950000US3604101,7950000US3604102,7950000US3604103,7950000US3604104,7950000US3604105,7950000US3604106,7950000US3604107,7950000US3604108,7950000US3604109,7950000US3604110,7950000US3604111,7950000US3604112,7950000US3604113,7950000US3604114&key=', Sys.getenv("KEY"))
+year = "2021"
+t_21 <- get_acs(request, year)
+```
+
+```{r}
+# # source("envs.R") .
+# options(scipen = 999)
+# # read in data from mdat pums -----------
+# # 2020 5-Year
+# # ----- ***TO DO***: GET NEW DATA -----
+# t_18 <- fromJSON(paste0('https://api.census.gov/data/2018/acs/acs5/pums?get=PWGTP,PWGTP,RAC1P,SEX,ESR,HISP&ucgid=7950000US3603701,7950000US3603702,7950000US3603703,7950000US3603704,7950000US3603705,7950000US3603706,7950000US3603707,7950000US3603708,7950000US3603709,7950000US3603710,7950000US3603801,7950000US3603802,7950000US3603803,7950000US3603804,7950000US3603805,7950000US3603806,7950000US3603807,7950000US3603808,7950000US3603809,7950000US3603810,7950000US3603901,7950000US3603902,7950000US3603903,7950000US3604001,7950000US3604002,7950000US3604003,7950000US3604004,7950000US3604005,7950000US3604006,7950000US3604007,7950000US3604008,7950000US3604009,7950000US3604010,7950000US3604011,7950000US3604012,7950000US3604013,7950000US3604014,7950000US3604015,7950000US3604016,7950000US3604017,7950000US3604018,7950000US3604101,7950000US3604102,7950000US3604103,7950000US3604104,7950000US3604105,7950000US3604106,7950000US3604107,7950000US3604108,7950000US3604109,7950000US3604110,7950000US3604111,7950000US3604112,7950000US3604113,7950000US3604114&key=', Sys.getenv("KEY")))
+```
+
+```{r}
+# # ----- GETTING NEW DATA (2019) -----
+# t_19 <- fromJSON(paste0('https://api.census.gov/data/2019/acs/acs5/pums?get=PWGTP,PWGTP,RAC1P,SEX,ESR,HISP&ucgid=7950000US3603701,7950000US3603702,7950000US3603703,7950000US3603704,7950000US3603705,7950000US3603706,7950000US3603707,7950000US3603708,7950000US3603709,7950000US3603710,7950000US3603801,7950000US3603802,7950000US3603803,7950000US3603804,7950000US3603805,7950000US3603806,7950000US3603807,7950000US3603808,7950000US3603809,7950000US3603810,7950000US3603901,7950000US3603902,7950000US3603903,7950000US3604001,7950000US3604002,7950000US3604003,7950000US3604004,7950000US3604005,7950000US3604006,7950000US3604007,7950000US3604008,7950000US3604009,7950000US3604010,7950000US3604011,7950000US3604012,7950000US3604013,7950000US3604014,7950000US3604015,7950000US3604016,7950000US3604017,7950000US3604018,7950000US3604101,7950000US3604102,7950000US3604103,7950000US3604104,7950000US3604105,7950000US3604106,7950000US3604107,7950000US3604108,7950000US3604109,7950000US3604110,7950000US3604111,7950000US3604112,7950000US3604113,7950000US3604114&key=', Sys.getenv("KEY")))
+```
+
+```{r}
+# # ----- GETTING NEW DATA (2020) -----
+# t_20 <- fromJSON(paste0('https://api.census.gov/data/2020/acs/acs5/pums?get=PWGTP,PWGTP,RAC1P,SEX,ESR,HISP&ucgid=7950000US3603701,7950000US3603702,7950000US3603703,7950000US3603704,7950000US3603705,7950000US3603706,7950000US3603707,7950000US3603708,7950000US3603709,7950000US3603710,7950000US3603801,7950000US3603802,7950000US3603803,7950000US3603804,7950000US3603805,7950000US3603806,7950000US3603807,7950000US3603808,7950000US3603809,7950000US3603810,7950000US3603901,7950000US3603902,7950000US3603903,7950000US3604001,7950000US3604002,7950000US3604003,7950000US3604004,7950000US3604005,7950000US3604006,7950000US3604007,7950000US3604008,7950000US3604009,7950000US3604010,7950000US3604011,7950000US3604012,7950000US3604013,7950000US3604014,7950000US3604015,7950000US3604016,7950000US3604017,7950000US3604018,7950000US3604101,7950000US3604102,7950000US3604103,7950000US3604104,7950000US3604105,7950000US3604106,7950000US3604107,7950000US3604108,7950000US3604109,7950000US3604110,7950000US3604111,7950000US3604112,7950000US3604113,7950000US3604114&key=', Sys.getenv("KEY")))
+```
+
+```{r}
+# convert_df <- function(t) {
+#   df=t
+#   df[1,1:ncol(df)]
+#   df<-as.data.frame(df[1:nrow(df), 1:ncol(df)])
+#   names(df)<-t[1,1:ncol(t)]
+#   df=df[-1,-1]
+# }
+# df_18 = convert_df(t_18)
+# df_19 = convert_df(t_19)
+# df_20 = convert_df(t_20)
 ```
 
 ```{r}
@@ -71,7 +145,7 @@ recode_df <- function(df) {
                                      "9" = "Other"),
                       SEX = recode(SEX, "1" = "Male",
                                         "2" = "Female"),
-                      HISP = case_when(HISP != "1" ~ "Hispanic or Latino", 
+                      HISP = case_when(HISP != "01" ~ "Hispanic or Latino", # changed from "1" to "01" in 2021
                                        TRUE ~ "Non-Hispanic"),
                       race_ethnicity = case_when(HISP == "Non-Hispanic" ~ race_ethnicity,
                                                  TRUE ~ "Hispanic or Latino"),
@@ -90,9 +164,10 @@ recode_df <- function(df) {
 ```
 
 ```{r}
-df_18_1 <- recode_df(df_18)
-df_19_1 <- recode_df(df_19)
-df_20_1 <- recode_df(df_20)
+df_18_1 <- recode_df(t_18)
+df_19_1 <- recode_df(t_19)
+df_20_1 <- recode_df(t_20)
+df_21_1 <- recode_df(t_21)
 ```
 
 ```{r}
@@ -107,6 +182,7 @@ pw_value <- function(df) {
 df_18_2 <- pw_value(df_18_1)
 df_19_2 <- pw_value(df_19_1)
 df_20_2 <- pw_value(df_20_1)
+df_21_2 <- pw_value(df_21_1)
 ```
 
 ```{r, include=FALSE}
@@ -114,15 +190,17 @@ df_20_2 <- pw_value(df_20_1)
 # should be around 8 million/year
 sum(as.integer(df_18_1$PWGTP))
 sum(as.integer(df_19_1$PWGTP))
-sum(as.integer(df_20_1$PWGTP)) # looking good
+sum(as.integer(df_20_1$PWGTP)) 
+sum(as.integer(df_21_1$PWGTP))# looking good
 ```
 
 ```{r}
 # individual level demos ----- 
 # whereas df_[year]_1 is aggregated by total of x demographic
-write.csv(df_18_2, '../data/output/city_demo_by-pums_2018.csv', row.names = F)
+write.csv(df_18_2, '../data/output/city_demo_by-pums_2018_2.csv', row.names = F)
 write.csv(df_19_2, '../data/output/city_demo_by-pums_2019.csv', row.names = F)
 write.csv(df_20_2, '../data/output/city_demo_by-pums_2020.csv', row.names = F)
+write.csv(df_21_2, '../data/output/city_demo_by-pums_2021.csv', row.names = F)
 ```
 
 ```{r}
@@ -144,6 +222,7 @@ filter_race <- function(df2) {
 race_18 <- filter_race(df_18_2)
 race_19 <- filter_race(df_19_2)
 race_20 <- filter_race(df_20_2)
+race_21 <- filter_race(df_21_2)
 
 # ----- ***TO DO***: AGGREGATE, FILTER FOR 2019 & 2020 -----
 
@@ -159,6 +238,7 @@ filter_gender <- function(df2) {
 sex_18 <- filter_gender(df_18_2)
 sex_19 <- filter_gender(df_19_2)
 sex_20 <- filter_gender(df_20_2)
+sex_21 <- filter_gender(df_21_2)
 ```
 
 ```{r}
@@ -174,37 +254,44 @@ get_var <- function(yr, group, demo) {
 city_female_18 <- get_var(sex_18, sex_18$SEX, "Female")
 city_female_19 <- get_var(sex_19, sex_19$SEX, "Female")
 city_female_20 <- get_var(sex_20, sex_20$SEX, "Female")
+city_female_21 <- get_var(sex_21, sex_21$SEX, "Female")
 
 # male:
 city_male_18 <- get_var(sex_18, sex_18$SEX, "Male")
 city_male_19 <- get_var(sex_19, sex_19$SEX, "Male")
 city_male_20 <- get_var(sex_20, sex_20$SEX, "Male")
+city_male_21 <- get_var(sex_21, sex_21$SEX, "Male")
 
 # ---- RACE ----
 # nh white:
 city_white_18 <- get_var(race_18, race_18$race_ethnicity, "NH White") 
 city_white_19 <- get_var(race_19, race_19$race_ethnicity, "NH White") 
 city_white_20 <- get_var(race_20, race_20$race_ethnicity, "NH White") 
+city_white_21 <- get_var(race_21, race_21$race_ethnicity, "NH White") 
 
 # nh black: 
 city_black_18 <- get_var(race_18, race_18$race_ethnicity, "NH Black") 
 city_black_19 <- get_var(race_19, race_19$race_ethnicity, "NH Black") 
 city_black_20 <- get_var(race_20, race_20$race_ethnicity, "NH Black") 
+city_black_21 <- get_var(race_21, race_21$race_ethnicity, "NH Black") 
 
 # hispanic or latino:
 city_hispanic_18 <- get_var(race_18, race_18$race_ethnicity, "Hispanic or Latino") 
 city_hispanic_19 <- get_var(race_19, race_19$race_ethnicity, "Hispanic or Latino") 
 city_hispanic_20 <- get_var(race_20, race_20$race_ethnicity, "Hispanic or Latino") 
+city_hispanic_21 <- get_var(race_21, race_21$race_ethnicity, "Hispanic or Latino") 
 
 # asian:
 city_asian_18 <- get_var(race_18, race_18$race_ethnicity, "NH Asian") 
 city_asian_19 <- get_var(race_19, race_19$race_ethnicity, "NH Asian") 
 city_asian_20 <- get_var(race_20, race_20$race_ethnicity, "NH Asian") 
+city_asian_21 <- get_var(race_21, race_21$race_ethnicity, "NH Asian") 
 
 # sor:
 city_sor_18 <- get_var(race_18, race_18$race_ethnicity, "NH Other")
 city_sor_19 <- get_var(race_19, race_19$race_ethnicity, "NH Other")
 city_sor_20 <- get_var(race_20, race_20$race_ethnicity, "NH Other")
+city_sor_21 <- get_var(race_21, race_21$race_ethnicity, "NH Other")
 ```
 
 ```{r}
@@ -213,6 +300,7 @@ city_sor_20 <- get_var(race_20, race_20$race_ethnicity, "NH Other")
 wp_18 <- read.csv("../data/input/agency_diversity_2018.csv") %>% janitor::clean_names()
 wp_19 <- read.csv("../data/input/agency_diversity_2019.csv") %>% janitor::clean_names()
 wp_20 <- read.csv("../data/input/agency_diversity_2020.csv") %>% janitor::clean_names()
+wp_21 <- read.csv("../data/input/agency_diversity_2021.csv") %>% janitor::clean_names()
 ```
 
 ```{r}
@@ -262,6 +350,7 @@ wp$d5 <- abs(wp$x_female - city_female) / (wp$x_female + city_female) /2  +
 wp_18 <- run_calc(wp_18, city_female_18, city_male_18, city_white_18, city_black_18, city_hispanic_18, city_asian_18, city_sor_18)
 wp_19 <- run_calc(wp_19, city_female_19, city_male_19, city_white_19, city_black_19, city_hispanic_19, city_asian_19, city_sor_19)
 wp_20 <- run_calc(wp_20, city_female_20, city_male_20, city_white_20, city_black_20, city_hispanic_20, city_asian_20, city_sor_20)
+wp_21 <- run_calc(wp_21, city_female_21, city_male_21, city_white_21, city_black_21, city_hispanic_21, city_asian_21, city_sor_21)
 ```
 
 ```{r}
@@ -299,7 +388,8 @@ run_calc_2 <- function(wp, city_female, city_male, city_white, city_black, city_
 
 ```{r}
 # for github chart
-write.csv(run_calc_2(wp_18, city_female_18, city_male_18, city_white_18, city_black_18, city_hispanic_18, city_asian_18, city_sor_18), '../data/output/agency_diversity_calculations_2018.csv', row.names = F)
-write.csv(run_calc_2(wp_19, city_female_19, city_male_19, city_white_19, city_black_19, city_hispanic_19, city_asian_19, city_sor_19), '../data/output/agency_diversity_calculations_2019.csv', row.names = F)
-write.csv(run_calc_2(wp_20, city_female_20, city_male_20, city_white_20, city_black_20, city_hispanic_20, city_asian_20, city_sor_20), '../data/output/agency_diversity_calculations_2020.csv', row.names = F)
+# write.csv(run_calc_2(wp_18, city_female_18, city_male_18, city_white_18, city_black_18, city_hispanic_18, city_asian_18, city_sor_18), '../data/output/agency_diversity_calculations_2018.csv', row.names = F)
+# write.csv(run_calc_2(wp_19, city_female_19, city_male_19, city_white_19, city_black_19, city_hispanic_19, city_asian_19, city_sor_19), '../data/output/agency_diversity_calculations_2019.csv', row.names = F)
+# write.csv(run_calc_2(wp_20, city_female_20, city_male_20, city_white_20, city_black_20, city_hispanic_20, city_asian_20, city_sor_20), '../data/output/agency_diversity_calculations_2020.csv', row.names = F)
+write.csv(run_calc_2(wp_21, city_female_21, city_male_21, city_white_21, city_black_21, city_hispanic_21, city_asian_21, city_sor_21), '../data/output/agency_diversity_calculations_2021.csv', row.names = F)
 ```
\ No newline at end of file
diff --git a/code/02_agency_demo_improv.Rmd b/code/02_agency_demo_improv.Rmd
index 2e885c6..9423a77 100644
--- a/code/02_agency_demo_improv.Rmd
+++ b/code/02_agency_demo_improv.Rmd
@@ -251,7 +251,7 @@ relative_plot <- cp %>%  filter(agency_name =='DSNY',gender2!="Other") %>%
   ggplot(aes(x = data_year, y = diff_middle)) +
   facet_wrap(~gender2, nrow=1) +
   geom_hline(yintercept=0, size = 0.5, color="grey") +
-  geom_point(aes(color=data_year)) + geom_label_repel(data = ~ subset(., data_year %in% c(2019,2020)), aes(label = round(diff_middle, digits=0))) + # diff_middle rounded to 0 to make more sense
+  geom_point(aes(color=data_year)) + geom_label_repel(data = ~ subset(., data_year %in% c(2019,2020,2021)), aes(label = round(diff_middle, digits=0))) + # diff_middle rounded to 0 to make more sense
   scale_color_discrete(name = "Year") + theme_nycc(facet=TRUE) +
   ggtitle("Relative Difference") + xlab("Year") + ylab("Number") +  easy_plot_title_size(14)
 
diff --git a/data/input/agency_diversity_2021.csv b/data/input/agency_diversity_2021.csv
new file mode 100644
index 0000000..ac9f3dc
--- /dev/null
+++ b/data/input/agency_diversity_2021.csv
@@ -0,0 +1,73 @@
+Agency,# Employees,%Female,%Male,%White,%Black,%Hispanic,%Asian,%SOR,check gender,check eth/race
+DCAS,2298,39,61,26,34,25,12,3,100,100
+DOITT,1673,38,62,35,26,15,20,4,100,100
+MAYORALTY,1243,59,41,42,20,16,18,4,100,100
+BOE,946,48,52,31,31,18,4,16,100,100
+NYCERS,484,61,39,30,35,11,22,2,100,100
+TRS,341,59,41,35,28,13,22,2,100,100
+OPA,146,61,39,27,33,20,18,2,100,100
+NYCPPF,147,55,45,34,28,17,19,2,100,100
+DORIS,62,56,44,39,21,15,23,3,100,101
+CLERK,68,71,29,22,9,47,22,0,100,100
+FDNYPF,46,70,30,39,26,7,28,0,100,100
+OCB,16,69,31,62,12,19,0,6,100,99
+DOF,2036,52,48,28,38,12,19,2,100,99
+COMPTROLLER,798,57,42,34,25,15,22,5,99,101
+TLC,594,40,60,22,34,25,15,3,100,99
+FISA,425,34,66,35,13,7,42,2,100,99
+DCA,388,60,40,28,23,27,19,3,100,100
+SBS,283,59,41,30,32,18,17,2,100,99
+BIC,82,51,49,43,17,20,17,4,100,101
+NYCTAX,51,51,49,53,14,10,24,0,100,101
+ACTUARY,43,44,56,49,7,7,35,2,100,100
+IBO,38,47,50,61,11,8,13,8,97,101
+MWFA,11,73,27,45,0,27,27,0,100,99
+COUNCIL,747,51,48,41,22,22,10,4,99,99
+BP-BK,120,64,35,30,41,17,3,9,99,100
+CFB,117,54,46,41,12,15,25,8,100,101
+BP-QNS,118,64,36,44,21,16,8,10,100,99
+BP-MAN,93,58,42,30,31,27,8,4,100,100
+BP-BX,91,59,41,23,20,46,1,10,100,100
+PA,73,53,45,23,40,18,4,15,98,100
+BP-SI,49,59,41,76,8,8,0,8,100,100
+PUBADMIN,46,57,43,35,26,30,4,4,100,99
+NYCHA,11937,39,61,16,51,24,5,4,100,100
+PARKS,6977,33,67,35,31,25,7,3,100,101
+HPD,2390,51,49,25,38,21,13,3,100,100
+LPC,75,72,28,69,9,8,7,7,100,100
+DCLA,79,62,38,39,19,19,15,8,100,100
+DOE,149419,78,22,44,21,23,8,5,100,101
+SCA,861,37,63,41,20,15,23,2,100,101
+NYCHH,41903,69,31,16,44,19,20,1,100,100
+HRA,12205,71,29,14,56,18,10,2,100,100
+DOHMH,6906,71,29,24,40,17,16,4,100,101
+ACS,7276,71,29,12,62,18,5,3,100,100
+DHS,2145,50,50,9,64,20,4,3,100,100
+DYCD,539,63,37,21,39,22,13,5,100,100
+DFTA,338,72,28,27,29,21,20,3,100,100
+DSNY,9359,9,91,51,21,22,5,1,100,100
+DEP,5976,46,48,48,21,14,15,2,94,100
+DOT,5759,26,74,40,31,16,11,2,100,100
+DOB,1674,36,64,34,31,15,16,4,100,100
+DDC,1226,38,62,31,23,15,29,2,100,100
+DCP,297,47,52,53,14,13,17,3,99,100
+LAW,1821,63,37,46,28,13,9,4,100,100
+DA-MAN,1553,57,43,55,14,19,8,4,100,100
+DA-BK,1174,56,44,39,29,20,8,5,100,101
+DA-BX,1049,61,39,35,26,31,6,3,100,101
+DA-QNS,794,59,41,50,15,18,10,6,100,99
+OATH,670,61,39,49,28,12,8,4,100,101
+DA-NARC,214,50,50,50,18,16,9,7,100,100
+CCRB,219,53,47,40,20,21,11,9,100,101
+DA-SI,213,59,41,67,10,15,3,5,100,100
+CCHR,128,57,41,27,24,23,18,9,98,101
+BOC,24,58,42,33,38,17,12,0,100,100
+COIB,24,54,46,50,4,17,21,8,100,100
+NYCCSC,15,67,33,40,40,13,7,0,100,100
+EEPC,13,69,31,15,46,8,15,15,100,99
+NYPD,54685,35,65,36,25,27,10,2,100,100
+FDNY,17564,12,88,62,13,19,4,1,100,99
+DOC,10372,44,56,12,59,22,5,2,100,100
+DOP,1084,69,31,11,63,20,3,2,100,99
+DOI,341,55,45,43,26,16,10,5,100,100
+NYCEM,206,50,49,53,18,11,10,8,99,100
\ No newline at end of file
diff --git a/data/output/agency_diversity_calculations_2021.csv b/data/output/agency_diversity_calculations_2021.csv
new file mode 100644
index 0000000..aa202bd
--- /dev/null
+++ b/data/output/agency_diversity_calculations_2021.csv
@@ -0,0 +1,73 @@
+"agency","x_employees","x_female","x_male","x_white","x_black","x_hispanic","x_asian","x_sor","check_gender","check_eth_race","d1","d2","d4","d3","d5","d3_norm","d4_norm","d3_gender","d3_race","d4_gender","d4_race","d3_gender_norm","d3_race_norm","d3_norm_high","d3_gender_norm_high","d3_race_norm_high","d3_gender_race_norm_high"
+"DCAS",2298,39,61,26,34,25,12,3,100,100,47.9368027752685,1.79170353094362,-1.5,21.8786294384801,0.42475226044697,22.6428236307202,0,14.3252436021833,16.5366811012498,0.102742506594418,0.322009753852552,23.5843110767982,20.7554857164242,FALSE,FALSE,FALSE,FALSE
+"DOITT",1673,38,62,35,26,15,20,4,100,100,46.2683133687191,1.6261770466082,1.5,21.3088604553259,0.416834110604331,21.5251606484579,100,15.7394571645564,14.3644360164138,0.11316942458188,0.30366468602245,26.1315007075716,15.935287643634,FALSE,FALSE,FALSE,FALSE
+"MAYORALTY",1243,59,41,42,20,16,18,4,100,100,42.0725568069446,1.32521055476509,0.5,19.6228538786967,0.346330466015075,18.2178778825241,66.6666666666667,13.9590276452786,13.791371996398,0.0993618978987054,0.24696856811637,22.9247066769605,14.6636622160298,FALSE,FALSE,FALSE,FALSE
+"BOE",946,48,52,31,31,18,4,16,100,100,48.4696272381126,5.02810186909947,-0.5,21.8689170628514,0.840343845515184,22.6237717640323,33.3333333333333,1.59732154082544,21.8105042902976,0.011304083387486,0.829039762127698,0.659604399837658,32.4580659407012,FALSE,FALSE,FALSE,FALSE
+"NYCERS",484,61,39,30,35,11,22,2,100,100,67.9354985273758,2.88719944594681,-0.5,28.8591559587266,0.748820619004576,36.3358752341791,33.3333333333333,16.7874547700248,23.474076020037,0.119935848457789,0.628884770546787,28.0190859385073,36.1495210631528,FALSE,FALSE,FALSE,FALSE
+"TRS",341,59,41,35,28,13,22,2,100,100,51.217581332863,2.27770216447472,0.5,22.5506862365162,0.610356456101299,23.9611351347698,66.6666666666667,13.9590276452786,17.7109852051248,0.0993618978987054,0.510994558202594,22.9247066769605,23.3612590786851,FALSE,FALSE,FALSE,FALSE
+"OPA",146,61,39,27,33,20,18,2,100,100,55.9354985273758,2.27069713668524,-0.5,23.7381188537703,0.576103638469119,26.2904115950096,33.3333333333333,16.7874547700248,16.7833146029053,0.119935848457789,0.45616779001133,28.0190859385073,21.30276389312,FALSE,FALSE,FALSE,FALSE
+"NYCPPF",147,55,45,34,28,17,19,2,100,100,35.9354985273758,1.7578169309342,-0.5,15.866182556625,0.470770612029582,10.8487640884338,33.3333333333333,8.30217339578622,13.5207124748088,0.0588055096297518,0.41196510239983,12.735948153867,14.0630704534088,FALSE,FALSE,FALSE,FALSE
+"DORIS",62,56,44,39,21,15,23,3,100,101,40.217581332863,1.652814705836,0.5,18.1521698256981,0.41748439497108,15.3329733453543,66.6666666666667,9.71638695815932,15.3327458030288,0.0688864819631795,0.348597913007901,15.2831377846404,18.0839609217498,FALSE,FALSE,FALSE,FALSE
+"CLERK",68,71,29,22,9,47,22,0,100,100,98.2482899564004,4.021187068201,-0.5,41.2357669612547,1.26282543692859,60.6139254130602,33.3333333333333,30.9295903937557,27.2717603934436,0.227941318468119,1.03488411846047,53.4909822462413,44.5765586131731,FALSE,FALSE,FALSE,FALSE
+"FDNYPF",46,70,30,39,26,7,28,0,100,100,89.217581332863,3.89992271211231,0.5,38.9448228677112,1.2607540924624,56.119992813199,66.6666666666667,29.5153768313827,25.4075138236483,0.216632617621866,1.04412147484053,50.9437926154679,40.4398072927707,FALSE,FALSE,FALSE,FALSE
+"OCB",16,69,31,62,12,19,0,6,100,99,100.555953481825,3.90778560377003,-0.5,43.6673206544792,1.17918457903946,65.3836788265502,33.3333333333333,28.1011632690096,33.4239362743165,0.205458188321147,0.973726390718314,48.3966029846945,58.2281978924901,FALSE,FALSE,FALSE,FALSE
+"DOF",2036,52,48,28,38,12,19,2,100,99,50.9354985273758,2.49356586909324,-0.5,24.9283425563144,0.639677905916052,28.6251630684588,33.3333333333333,4.05953270866694,24.5955779926437,0.0287088935128417,0.61096901240321,5.09437926154679,38.6381268308566,FALSE,FALSE,FALSE,FALSE
+"COMPTROLLER",798,57,42,34,25,15,22,5,99,101,42.4683229902199,1.861135180535,0.5,19.0413874376226,0.455210947485837,17.0772690817103,66.6666666666667,11.8588074510539,14.8977555822507,0.0848372903121327,0.370373657173705,19.1419264495575,17.1187203793661,FALSE,FALSE,FALSE,FALSE
+"TLC",594,40,60,22,34,25,15,3,100,99,47.4534061003879,1.69964867607212,-0.5,22.7063426664605,0.40787734218025,24.2664719201047,33.3333333333333,12.9110300398102,18.6784180432349,0.0923864840559099,0.315490858124341,21.0371214460248,25.5079864136278,FALSE,FALSE,FALSE,FALSE
+"FISA",425,34,66,35,13,7,42,2,100,99,87.0576399171126,4.03338192905872,-0.5,40.6903136904514,0.953842923455373,59.543960309035,33.3333333333333,21.3963114140488,34.6106845656132,0.155726985521608,0.798115937933764,36.3202592306652,60.8615848277527,FALSE,FALSE,FALSE,FALSE
+"DCA",388,60,40,28,23,27,19,3,100,100,35.9354985273758,1.24635680163466,-0.5,17.4213575222791,0.311110239482318,13.899406614516,33.3333333333333,15.3732412076517,8.19555688714502,0.109618875326163,0.201491364156155,25.4718963077339,2.24658396201309,FALSE,FALSE,FALSE,FALSE
+"SBS",283,59,41,30,32,18,17,2,100,99,48.9354985273758,2.05998532612714,-0.5,21.0524254452724,0.533503580046247,21.0221358672452,33.3333333333333,13.9590276452786,15.7591295548676,0.0993618978987054,0.434141682147542,22.9247066769605,19.0301037641158,FALSE,FALSE,FALSE,FALSE
+"BIC",82,51,49,43,17,20,17,4,100,101,25.0725568069446,0.967062338056126,0.5,12.0786650385714,0.244395555897194,3.41914217190807,66.6666666666667,2.64531914629385,11.7854332007041,0.0187052624254691,0.225690293471725,2.54718963077339,10.2124967445844,FALSE,FALSE,FALSE,FALSE
+"NYCTAX",51,51,49,53,14,10,24,0,100,101,58.5397323441005,3.18070298869549,-0.5,27.9510277351152,1.06482577876729,34.5544843043688,33.3333333333333,2.64531914629385,27.8255680636251,0.0187052624254691,1.04612051634182,2.54718963077339,45.8054543225663,FALSE,FALSE,FALSE,FALSE
+"ACTUARY",43,44,56,49,7,7,35,2,100,100,80.0576399171126,3.86348316224681,-0.5,35.4088745170702,1.03059479364791,49.1838508055639,33.3333333333333,7.25417579031782,34.6578321331382,0.051538049594134,0.979056744053772,10.8483629229312,60.9662049812092,FALSE,FALSE,FALSE,FALSE
+"IBO",38,47,50,61,11,8,13,8,97,101,63.8149072683312,3.25483874298099,-1.5,34.3711581729535,0.786728698310507,47.1482588758536,0,2.30053957111781,34.2940815860683,0.0153911389807623,0.771337559329744,1.9261950496458,60.159044823176,FALSE,FALSE,FALSE,FALSE
+"MWFA",11,73,27,45,0,27,27,0,100,99,94.5397323441005,4.0965251401757,-0.5,42.7025775994152,1.46491915800661,63.4912318315057,33.3333333333333,33.7580175185019,26.1516039059672,0.25099689402772,1.21392226397889,58.585361507788,42.090938468444,FALSE,FALSE,FALSE,FALSE
+"COUNCIL",747,51,48,41,22,22,10,4,99,99,23.2338024705876,0.957331169440133,0.5,10.3356340985968,0.253839812562669,0,66.6666666666667,3.42618732693395,9.75123441523352,0.0238570986320919,0.229982713930577,3.95363868885604,5.69862253497248,FALSE,FALSE,FALSE,FALSE
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+"HRA",12205,71,29,14,56,18,10,2,100,100,115.418895202256,4.36467609910251,-1.5,52.5559564066651,1.0207672592403,82.8196913803329,0,30.9295903937557,42.4910460202355,0.227941318468119,0.792825940772179,53.4909822462413,78.3480569397743,TRUE,FALSE,TRUE,TRUE
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+"DHS",2145,50,50,9,64,20,4,3,100,100,89.4188952022564,4.13185668945958,-1.5,52.2630986077452,0.977137787985655,82.2452193915145,0,1.23110558392075,52.2485966809075,0.00870589080899861,0.968431897176656,0,100,TRUE,FALSE,TRUE,TRUE
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+"DSNY",9359,9,91,51,21,22,5,1,100,100,115.218885580756,3.70991948295667,-0.5,60.2145986711285,1.18080674619021,97.8429397607467,33.3333333333333,56.7516504733761,20.1258058639353,0.486603147439598,0.694203598750617,100,28.7197308123714,TRUE,TRUE,FALSE,TRUE
+"DEP",5976,46,48,48,21,14,15,2,94,100,35.476535119369,1.52216864713482,-0.5,19.4167215605485,0.438066640714579,17.8135272276906,33.3333333333333,4.24659022414135,18.9466500370935,0.0309650900130787,0.4071015507015,5.43129511107034,26.1031915076035,FALSE,FALSE,FALSE,FALSE
+"DOT",5759,26,74,40,31,16,11,2,100,100,79.2188855807556,2.75290526784635,-0.5,36.8423349601782,0.74220867048273,51.9957372387813,33.3333333333333,32.7100199130335,16.9532369359636,0.246544611301199,0.495664059181531,56.6977762768523,21.6798204729917,FALSE,FALSE,FALSE,FALSE
+"DOB",1674,36,64,34,31,15,16,4,100,100,50.986230563232,1.67465491285682,0.5,24.6605778092977,0.423405732211702,28.0999137927985,66.6666666666667,18.5678842893026,16.2289177367899,0.134263776824344,0.289141955387357,31.2258799691184,20.0725607616846,FALSE,FALSE,FALSE,FALSE
+"DDC",1226,38,62,31,23,15,29,2,100,100,56.4534061003879,2.5599588267434,-0.5,24.8493347338858,0.628811365517415,28.4701807492235,33.3333333333333,15.7394571645564,19.2291165912997,0.11316942458188,0.515641940935535,26.1315007075716,26.7299830007536,FALSE,FALSE,FALSE,FALSE
+"DCP",297,47,52,53,14,13,17,3,99,100,45.0576399171126,1.78195448351008,-0.5,24.3521415756557,0.47851834363834,27.4948830331007,33.3333333333333,2.41047503436376,24.2325485541552,0.0165658855027544,0.461952458135585,2.12420366693303,37.8325668081945,FALSE,FALSE,FALSE,FALSE
+"LAW",1821,63,37,46,28,13,9,4,100,100,67.2338024705876,2.26205494683077,0.5,28.476561168885,0.607772775431618,35.5853745111576,66.6666666666667,19.615881894771,20.6429579638104,0.140776392618365,0.466996382813253,33.1134652000541,29.8672880436243,FALSE,FALSE,FALSE,FALSE
+"DA-MAN",1553,57,43,55,14,19,8,4,100,100,57.5559534818252,2.04635147422488,-0.5,26.410188943505,0.53712015254453,31.5319637215824,33.3333333333333,11.1306005205324,23.9501108991995,0.0790020456000698,0.45811810694446,17.8303274154138,37.205839442298,FALSE,FALSE,FALSE,FALSE
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+"DA-BX",1049,61,39,35,26,31,6,3,100,101,43.6916184255305,1.72065959863652,0.5,20.2296968685977,0.487441703171515,19.4082655061238,66.6666666666667,16.7874547700248,11.2881352640603,0.119935848457789,0.367505854713726,28.0190859385073,9.10899577568072,FALSE,FALSE,FALSE,FALSE
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+"OATH",670,61,39,49,28,12,8,4,100,101,68.2338024705876,2.37420957037141,0.5,28.780016605708,0.646929711116098,36.1806349035815,66.6666666666667,16.7874547700248,23.3767131600916,0.119935848457789,0.526993862658308,28.0190859385073,35.9334734941439,FALSE,FALSE,FALSE,FALSE
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+"CCHR",128,57,41,27,24,23,18,9,98,101,41.4683229902199,2.5386952878816,0.5,16.8122889883908,0.490823934184127,12.7046533476406,66.6666666666667,12.6242766277781,11.1031842574328,0.0907995669494473,0.400024367234679,20.5206398217845,8.69859066442959,FALSE,FALSE,FALSE,FALSE
+"BOC",24,58,42,33,38,17,12,0,100,100,53.4188952022564,2.83576240382523,-1.5,24.4744062457443,0.917543847099435,27.7347182993871,0,12.5448140829055,21.014856666346,0.0891584183218061,0.828385428777629,20.3775170461871,30.6925289069823,FALSE,FALSE,FALSE,FALSE
+"COIB",24,54,46,50,4,17,21,8,100,100,62.0725568069446,3.38942360025754,0.5,26.6594966021109,0.858932586006763,32.0210074383461,66.6666666666667,6.88795983341313,25.7543155298534,0.0487529779088462,0.810179608097916,10.1887585230936,41.2093580803682,FALSE,FALSE,FALSE,FALSE
+"NYCCSC",15,67,33,40,40,13,7,0,100,100,86.7009780077435,3.90699687993149,-0.5,36.52560871832,1.23936754036338,51.3744447386589,33.3333333333333,25.2727361442634,26.3706067436124,0.183478611068985,1.0558889292944,43.3022237231477,42.5769043768592,FALSE,FALSE,FALSE,FALSE
+"EEPC",13,69,31,15,46,8,15,15,100,99,115.46832299022,6.37897909755514,0.5,48.1691944332461,1.17333725863681,74.2145871808296,66.6666666666667,28.1011632690096,39.1228311255248,0.205458188321147,0.967879070315661,48.3966029846945,70.8740094404547,TRUE,FALSE,TRUE,TRUE
+"NYPD",54685,35,65,36,25,27,10,2,100,100,41.2188855807556,1.63927295900133,-0.5,21.2339702664592,0.457227172970054,21.3782555091721,33.3333333333333,19.9820978516757,7.18312318723082,0.14494556362794,0.312281609342114,33.7730695998918,0,FALSE,FALSE,FALSE,FALSE
+"FDNY",17564,12,88,62,13,19,4,1,100,99,130.541036591993,4.40254117427422,-1.5,61.314237863467,1.35109518681829,100,0,52.5090097862569,31.6581688675228,0.437379153571817,0.913716033246476,92.3584311076798,54.3099712105016,TRUE,TRUE,FALSE,TRUE
+"DOC",10372,44,56,12,59,22,5,2,100,100,87.9368027752685,4.09289324331566,-1.5,46.732868355061,0.986974738881492,71.3970794969985,0,7.25417579031782,46.1664154802453,0.051538049594134,0.935436689287358,10.8483629229312,86.5036784723551,TRUE,FALSE,TRUE,TRUE
+"DOP",1084,69,31,11,63,20,3,2,100,99,126.418895202256,5.11995130156007,-1.5,57.9691997618556,1.27710972409569,93.438348925679,0,28.1011632690096,50.7025910970868,0.205458188321147,1.07165153577454,48.3966029846945,96.5694234100578,TRUE,FALSE,TRUE,TRUE
+"DOI",341,55,45,43,26,16,10,5,100,100,43.2338024705876,1.84435563152464,0.5,17.9687840222059,0.474174813940181,14.9732424191444,66.6666666666667,8.30217339578622,15.9358437537201,0.0588055096297518,0.415369304310429,12.735948153867,19.4222314511296,FALSE,FALSE,FALSE,FALSE
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diff --git a/visuals/DOP_diversity_by-race.png b/visuals/DOP_diversity_by-race.png
index ea91c81..51a1830 100644
Binary files a/visuals/DOP_diversity_by-race.png and b/visuals/DOP_diversity_by-race.png differ
diff --git a/visuals/DSNY_diversity_by-gender.png b/visuals/DSNY_diversity_by-gender.png
index a359ce8..f047875 100644
Binary files a/visuals/DSNY_diversity_by-gender.png and b/visuals/DSNY_diversity_by-gender.png differ