Exercise 3
Zhicheng Zhong
2024-03-30
Load packages
library(readr)
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library(wru)##
## Please cite as:
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## Khanna K, Bertelsen B, Olivella S, Rosenman E, Rossell Hayes A, Imai K
## (2024). _wru: Who are You? Bayesian Prediction of Racial Category Using
## Surname, First Name, Middle Name, and Geolocation_. R package version
## 3.0.1, <https://CRAN.R-project.org/package=wru>.
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## Note that wru 2.0.0 uses 2020 census data by default.
## Use the argument `year = "2010"`, to replicate analyses produced with earlier package versions.library(lubridate)
library(dplyr)
#library(predictrace)
library(gtsummary)
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library(babynames)
library(rethnicity)## ══ WARNING! ════════════════════════════════════════════════════════════════════
## The method provided by this package has its limitations and anyone must use them cautiously and responsibly. You should also agree that you only intend to use the method for academic research purpose and not for commercial use. You would also agree NOT to discriminate anyone based on race or color or any characteristic, with the information provided by this package. Please refer to the documentation for details: https://fangzhou-xie.github.io/rethnicity/index.html
## ════════════════════════════════════════════════════════════════════════════════library(dplyr)
library(ggplot2)import data
data_path <- "C:/Users/tsong/Downloads/672_project_data/"
applications <- read_parquet(paste0(data_path,"app_data_sample.parquet"))
edges <- read_csv(paste0(data_path,"edges_sample.csv"))## Rows: 32906 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): application_number
## dbl (2): ego_examiner_id, alter_examiner_id
## date (1): advice_date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Since we will rely on names to assign gender and race, we need to remove those rows with insufficient information
applications <- applications %>%drop_na(examiner_name_last,examiner_name_first, filing_date, examiner_art_unit,examiner_name_middle, examiner_id, application_number )No NA left in these two columns
sum(is.na(applications$examiner_name_first))## [1] 0sum(is.na(applications$examiner_name_last))## [1] 0inspect how many samples there are in the dataset. Sample it down to fit my PC
dim(applications)## [1] 1537478 16applications <- applications %>% slice_sample(n = 10000)Predict gender based on first name (did not work)
# examiner_names_gender <- examiner_names %>%
# do(results = gender(.$examiner_name_first, method = "ssa")) %>%
# unnest(cols = c(results), keep_empty = TRUE) %>%
# select(
# examiner_name_first = name,
# gender,
# proportion_female
# )
#
# examiner_names_gender <- examiner_names_gender %>%
# select(examiner_name_first, gender)
#
# # joining gender back to the dataset
# applications <- applications %>%
## left_join(examiner_names_gender, by = "examiner_name_first")
#
# # cleaning up
# rm(examiner_names)
# rm(examiner_names_gender)
# gc()Using babynames package worked (reminder: names in babyname are in lowercase. convert both columns to lowercase before joining)
examiner_names_gender <- applications %>%
distinct(examiner_name_first) %>%
mutate(examiner_name_first = tolower(examiner_name_first)) %>%
inner_join(babynames %>% mutate(name = tolower(name)), by = c("examiner_name_first" = "name")) %>%
group_by(examiner_name_first) %>%
summarize(
gender = ifelse(sum(sex == "F") > sum(sex == "M"), "Female", "Male"),
proportion_female = sum(sex == "F") / sum(sex %in% c("F", "M"))
) %>%
ungroup()
examiner_names_gender## # A tibble: 1,078 × 3
## examiner_name_first gender proportion_female
## <chr> <chr> <dbl>
## 1 aaron Male 0.389
## 2 abdel Male 0
## 3 abdou Male 0
## 4 abdullah Male 0
## 5 abdullahi Male 0
## 6 abigail Female 0.780
## 7 abu Male 0
## 8 adam Male 0.299
## 9 addison Male 0.207
## 10 adnan Male 0
## # ℹ 1,068 more rowsNow we can join it to the dataset
examiner_names_gender <- examiner_names_gender %>%
select(examiner_name_first, gender)
# joining gender back to the dataset
applications <- applications %>% mutate(examiner_name_first = tolower(examiner_name_first)) %>%
left_join(examiner_names_gender, by = "examiner_name_first")
# cleaning up
rm(examiner_names_gender)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 1679062 89.7 5429402 290.0 4781512 255.4
## Vcells 13848481 105.7 44193435 337.2 68108624 519.7Inspect the dataset. Now we have a gender column. Great.
head(applications)## # A tibble: 6 × 17
## application_number filing_date examiner_name_last examiner_name_first
## <chr> <date> <chr> <chr>
## 1 09598706 2000-06-21 NGUYEN cam linh
## 2 13521752 2012-07-12 PIERCE jeremy
## 3 10465706 2003-06-19 PARKER frederick
## 4 15126795 2016-09-16 NGO ricky
## 5 13389358 2012-03-30 WARD paul
## 6 15267795 2016-09-16 MARCHESCHI michael
## # ℹ 13 more variables: examiner_name_middle <chr>, examiner_id <dbl>,
## # examiner_art_unit <dbl>, uspc_class <chr>, uspc_subclass <chr>,
## # patent_number <chr>, patent_issue_date <date>, abandon_date <date>,
## # disposal_type <chr>, appl_status_code <dbl>, appl_status_date <chr>,
## # tc <dbl>, gender <chr>Predict race based on family name (did not work)
# examiner_surnames <- applications %>%
# select(surname = examiner_name_last) %>%
# distinct()
#
# examiner_race <- wru::predict_race(voter.file = examiner_surnames, surname.only = T) %>%
# as_tibble()
#
# examiner_race <- examiner_race %>%
# mutate(max_race_p = pmax(pred.asi, pred.bla, pred.his, pred.oth, pred.whi)) %>%
# mutate(race = case_when(
# max_race_p == pred.asi ~ "Asian",
# max_race_p == pred.bla ~ "black",
# max_race_p == pred.his ~ "Hispanic",
# max_race_p == pred.oth ~ "other",
# max_race_p == pred.whi ~ "white",
# TRUE ~ NA_character_
# ))
#
# examiner_race <- examiner_race %>%
# select(surname,race)
#
# applications <- applications %>%
# left_join(examiner_race, by = c("examiner_name_last" = "surname"))
#
# rm(examiner_race)
# rm(examiner_surnames)
# gc()Using the predict_ethnicity function from rethnicity package seems to solve the problem.
predict_ethnicity(lastnames = applications$examiner_name_last[1], method = "lastname")## lastname prob_asian prob_black prob_hispanic prob_white race
## 1 NGUYEN 0.9992425 0.0001792363 0.0002078512 0.0003703848 asianmaking a function for appending only race to the dataset later
pred_eth <- function(name_combo) {
predicted <- predict_ethnicity(lastnames = name_combo, method = "lastname")
predicted <- predicted %>% select(race)
if (is.na(predicted$race) || predicted$race == "Unknown") {
predicted$race <- "white"
}
return(predicted)}
pred_eth(applications$examiner_name_last[1]) ## race
## 1 asianNow we can apply it to get the full list of race for the dataset.
race = sapply(applications$examiner_name_last, pred_eth)
race_vector <- vector()
for (i in seq_along(race)) {
race_vector[i] <- race[[i]]
}
applications <- applications %>%
add_column(race = race_vector)
rm(race)
rm(race_vector)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 1686629 90.1 5429402 290.0 5429402 290.0
## Vcells 13876539 105.9 44193435 337.2 68108624 519.7attach it to the dataset as a column and move on.
Get examiner’s tenure and append it to datasets. It works just by copying the starter code.
# to work with dates
examiner_dates <- applications %>%
select(examiner_id, filing_date, appl_status_date)
examiner_dates <- examiner_dates %>%
mutate(start_date = ymd(filing_date), end_date = as_date(dmy_hms(appl_status_date)))
examiner_dates <- examiner_dates %>%
group_by(examiner_id) %>%
summarise(
earliest_date = min(start_date, na.rm = TRUE),
latest_date = max(end_date, na.rm = TRUE),
tenure_days = interval(earliest_date, latest_date) %/% days(1)
) %>%
filter(year(latest_date)<2018)
applications <- applications %>%
left_join(examiner_dates, by = "examiner_id")
rm(examiner_dates)
gc()## used (Mb) gc trigger (Mb) max used (Mb)
## Ncells 1703291 91.0 5429402 290.0 5429402 290.0
## Vcells 13945031 106.4 44193435 337.2 68108624 519.7after inspecting the dataset, still some NA in gender. For simplicity just drop them
applications = applications %>% drop_na(gender)Select two art units 211 and 249. Tenure days are calculate across race and gender. In workgroup 1 (211), there’s no significant gender difference in tenure days by measure of both median and mean statitics. Hispanic examiners are all males. Asian and White are the majority groups. In work gorup 2, there’s a big gender difference in tenure days by both measure.Minority female examiners have longer tenure. Hispanics are underrepresented. Both work groups emlpoy more male than female examiner across all races.
Female examiners process more applications on average in both groups, execept in work group 1 where white male examimers have more workload.
# Filter the data for workgroup 1 and workgroup 2
workgroup1 <- applications %>%
filter(substr(examiner_art_unit, 1, 3) == "211")
workgroup2 <- applications %>%
filter(substr(examiner_art_unit, 1, 3) == "249")
# Calculate mean tenure differences for workgroup 1
mean_diff_workgroup1 <- workgroup1 %>%
select(race, gender, tenure_days,application_number,examiner_id ) %>%
group_by(race, gender) %>%
summarise(
mean_tenure = mean(tenure_days, na.rm = TRUE), median_tenure = median(tenure_days, na.rm = TRUE),
num_application = n_distinct(application_number), num_employee = n_distinct(examiner_id), .groups = 'drop') %>% mutate(average_applications = num_application/num_employee )
# Calculate mean tenure differences for workgroup 2
mean_diff_workgroup2 <- workgroup2 %>%
select(race, gender, tenure_days,application_number,examiner_id ) %>%
group_by(race, gender) %>%
summarise(
mean_tenure = mean(tenure_days, na.rm = TRUE), median_tenure = median(tenure_days, na.rm = TRUE),
num_application = n_distinct(application_number), num_employee = n_distinct(examiner_id), .groups = 'drop') %>% mutate(average_applications = num_application/num_employee )
plot_mean_tenure_race_gender_workgroup1 <- ggplot(mean_diff_workgroup1, aes(x = race, y = mean_tenure, fill = gender)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Mean Tenure by Race and Gender - Workgroup 1",
x = "Race",
y = "Mean Tenure (Days)",
fill = "Gender") +
theme_minimal()
# Plot the mean tenure by race and gender for workgroup 2
plot_mean_tenure_race_gender_workgroup2 <- ggplot(mean_diff_workgroup2, aes(x = race, y = mean_tenure, fill = gender)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Mean Tenure by Race and Gender - Workgroup 2",
x = "Race",
y = "Mean Tenure (Days)",
fill = "Gender") +
theme_minimal()
# Show the plots
print(plot_mean_tenure_race_gender_workgroup1)
print(plot_mean_tenure_race_gender_workgroup2)
# Plot the differences in mean tenure by race and gender for workgroup 1
plot_median_diff_workgroup1 <- ggplot(mean_diff_workgroup1, aes(x = race, y = median_tenure, fill = gender)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = " Median Tenure by Race and Gender - Workgroup 1",
x = "Race",
y = "Median Tenure (Days)",
fill = "Gender") +
theme_minimal()
# Plot the differences in mean tenure by race and gender for workgroup 2
plot_median_diff_workgroup2 <- ggplot(mean_diff_workgroup2, aes(x = race, y = median_tenure, fill = gender)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = " Median Tenure by Race and Gender - Workgroup 2",
x = "Race",
y = "Median Tenure (Days)",
fill = "Gender") +
theme_minimal()
# Show the plots
print(plot_median_diff_workgroup1)
print(plot_median_diff_workgroup2)
# Plot the differences in num_emlopyee by race and gender for workgroup 1
plot_employee_workgroup1 <- ggplot(mean_diff_workgroup1, aes(x = race, y =num_employee , fill = gender)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = " #employee by Race and Gender - Workgroup 1",
x = "Race",
y = "#employee",
fill = "Gender") +
theme_minimal()
# Plot the differences in num_employee by race and gender for workgroup 2
plot_employee_workgroup2 <- ggplot(mean_diff_workgroup2, aes(x = race, y =num_employee , fill = gender)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = " #employee by Race and Gender - Workgroup 2",
x = "Race",
y = "#employee",
fill = "Gender") +
theme_minimal()
# Show the plots
print(plot_employee_workgroup1)
print(plot_employee_workgroup2)
# Plot the differences in average_applications by race and gender for workgroup 1
plot_app_workgroup1 <- ggplot(mean_diff_workgroup1, aes(x = race, y =average_applications , fill = gender)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "average applications # by Race and Gender - Workgroup 1",
x = "Race",
y = "average applications # ",
fill = "Gender") +
theme_minimal()
# Plot the differences in average_applications by race and gender for workgroup 2
plot_app_workgroup2 <- ggplot(mean_diff_workgroup2, aes(x = race, y =average_applications , fill = gender)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "average applications # by Race and Gender - Workgroup 2",
x = "Race",
y = "average applications # ",
fill = "Gender") +
theme_minimal()
# Show the plots
print(plot_app_workgroup1)
print(plot_app_workgroup2)
Summary statistics shown below:
print(mean_diff_workgroup1)## # A tibble: 7 × 7
## race gender mean_tenure median_tenure num_application num_employee
## <chr> <chr> <dbl> <dbl> <int> <int>
## 1 asian Female 4140. 4263 23 6
## 2 asian Male 3965. 4062 76 22
## 3 black Female 4101. 5405 13 3
## 4 black Male 2965. 2987 28 12
## 5 hispanic Male 5098. 5900 26 6
## 6 white Female 4591. 5923 4 2
## 7 white Male 4006. 4242 87 25
## # ℹ 1 more variable: average_applications <dbl>print(mean_diff_workgroup2)## # A tibble: 7 × 7
## race gender mean_tenure median_tenure num_application num_employee
## <chr> <chr> <dbl> <dbl> <int> <int>
## 1 asian Female 2646. 2620 6 4
## 2 asian Male 2625. 2214 32 18
## 3 black Female 3330. 3108 4 2
## 4 black Male 3102. 3891 26 13
## 5 hispanic Male 3276. 2979 12 5
## 6 white Female 2886 2886 1 1
## 7 white Male 2961. 3216 24 11
## # ℹ 1 more variable: average_applications <dbl>Move on to edge sample where a multi-dimensional advice network can be created by joining the examiners’ race, gender and work unit to the dataset.
applications_grouped <- applications %>%
group_by(examiner_id, gender, race, examiner_art_unit, tenure_days) %>%
summarize()## `summarise()` has grouped output by 'examiner_id', 'gender', 'race',
## 'examiner_art_unit'. You can override using the `.groups` argument.advice_linkage_with_info <- edges %>%
inner_join(select(applications_grouped, examiner_id, gender, race, examiner_art_unit,tenure_days),
by = c("ego_examiner_id" = "examiner_id")) %>%
inner_join(select(applications_grouped, examiner_id, gender, race, examiner_art_unit, tenure_days),
by = c("alter_examiner_id" = "examiner_id")) %>%
mutate(examiner_art_unit.x = substr(examiner_art_unit.x, 1, 3),
examiner_art_unit.y = substr(examiner_art_unit.y, 1, 3))## Warning in inner_join(., select(applications_grouped, examiner_id, gender, : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 2235 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.## Warning in inner_join(., select(applications_grouped, examiner_id, gender, : Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 9 of `x` matches multiple rows in `y`.
## ℹ Row 537 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.# Print the resulting dataset
print(advice_linkage_with_info)## # A tibble: 16,398 × 12
## application_number advice_date ego_examiner_id alter_examiner_id gender.x
## <chr> <date> <dbl> <dbl> <chr>
## 1 09402488 2008-11-17 84356 63519 Male
## 2 09402488 2008-11-17 84356 63519 Male
## 3 09491624 2008-05-29 91688 67669 Female
## 4 09491624 2008-05-29 91688 67669 Female
## 5 09491624 2008-05-29 91688 67669 Female
## 6 09491624 2008-05-29 91688 67669 Female
## 7 09491624 2008-05-29 91688 67669 Female
## 8 09491624 2008-05-29 91688 67669 Female
## 9 09495207 2008-04-25 61797 78036 Male
## 10 09495207 2008-04-25 61797 78036 Male
## # ℹ 16,388 more rows
## # ℹ 7 more variables: race.x <chr>, examiner_art_unit.x <chr>,
## # tenure_days.x <dbl>, gender.y <chr>, race.y <chr>,
## # examiner_art_unit.y <chr>, tenure_days.y <dbl>With this merged dataset we can visualize the network shape before deciding which centrality measures to use To make sure the number of vertices match the number of attribute vector, a summary table needs to be obtained for both ego and alter and then merge them to remove duplicates.
g <- graph_from_data_frame(advice_linkage_with_info[,c("ego_examiner_id","alter_examiner_id")], directed = TRUE)
# Create summarized tables for ego and alter examiners
ego <- advice_linkage_with_info %>%
group_by(ego_examiner_id) %>%
summarize(race = names(which.max(table(race.x))),
work_unit = names(which.max(table(examiner_art_unit.x))),
tenure = mean(tenure_days.x),
gender = names(which.max(table(gender.x))))
alter <- advice_linkage_with_info %>%
group_by(alter_examiner_id) %>%
summarize(race = names(which.max(table(race.y))),
work_unit = names(which.max(table(examiner_art_unit.y))),
tenure = mean(tenure_days.y),
gender = names(which.max(table(gender.y))))
# Rename columns in ego and alter datasets
ego <- rename(ego, examiner_id = ego_examiner_id)
alter <- rename(alter, examiner_id = alter_examiner_id)
# Merge ego and alter datasets so that only unique examiner_id remains
examiner_attrs <- full_join(ego, alter, by = "examiner_id") %>%
distinct(examiner_id, .keep_all = TRUE) %>%
mutate(gender = coalesce(gender.x, gender.y),
race = coalesce(race.x, race.y),
work_unit = coalesce(work_unit.x, work_unit.y),
tenure = coalesce(tenure.x, tenure.y)) %>%
select(-ends_with(".x"), -ends_with(".y"))
sum(is.na(examiner_attrs))## [1] 0# Add examiner attributes to the graph object after verifying no null
V(g)$race <- examiner_attrs$race
V(g)$work_unit <- examiner_attrs$work_unit
V(g)$gender <- examiner_attrs$genderWe can plot the network by race. it is hard to see the pattern.
# Plot the network
vertex_colors <- ifelse(V(g)$race == "white", "pink",
ifelse(V(g)$race == "asian", "yellow",
ifelse(V(g)$race == "black", "brown",
ifelse(V(g)$race == "hispanic", "orange", "gray"))))
# Add a legend for the vertex colors and shapes
plot(g, vertex.label = NA, vertex.color = vertex_colors, vertex.size = 5,
edge.arrow.size = 0.5, edge.width = 0.5, layout = layout_with_fr)
by gender. also it is hard to see the pattern. Need to further break it down
# Set vertex colors based on gender
vertex_colors <- ifelse(V(g)$gender == "Female", "pink", "gray")
plot(g, vertex.label = NA, vertex.color = vertex_colors, vertex.size = 5,
edge.arrow.size = 0.5, edge.width = 0.5, layout = layout_with_fr)
by work unit. Inter-unit adivosry does not seem to be common in the netowork.
# Set vertex colors based on work unit
vertex_colors <- ifelse(V(g)$work_unit == 161, "darkgreen",
ifelse(V(g)$work_unit == 163, "darkblue",
ifelse(V(g)$work_unit == 165, "red",
ifelse(V(g)$work_unit == 211, 'brown',
ifelse(V(g)$work_unit == 249, 'yellow', 'gray')))))
# Subset the graph to include only vertices in work units 161 and 249
g_sub <- induced_subgraph(g, V(g)$work_unit %in% c(161, 163, 165, 211, 249))
# Plot the subgraph using igraph
plot(g_sub, vertex.label = NA, vertex.color = vertex_colors[V(g_sub)], vertex.size = 10,
edge.arrow.size = 0.5, edge.width = 0.5, layout = layout_with_fr)
White examiners have more interconnected clusters than black and asian examiners, whose clusters are more isolated from one another. No cluster in hispanic groups.
# Set vertex colors based on gender
vertex_colors <- ifelse(V(g)$gender == "Female", "pink", "gray")
for(r in unique(applications$race)){
# Subset the graph to include only vertices in work units 161 and 249
g_sub <- induced_subgraph(g, V(g)$race %in% c(r))
# Plot the subgraph using igraph
plot(g_sub, vertex.label = NA, vertex.color = vertex_colors[V(g_sub)], vertex.size = 5,
edge.arrow.size = 0.5, edge.width = 0.5, layout = layout_with_fr)
title(r)
}
In the same units, cross cultrue comminication does exist. But more
often it is within the same race.
vertex_colors <- ifelse(V(g)$race == "white", "pink",
ifelse(V(g)$race == "asian", "yellow",
ifelse(V(g)$race == "black", "brown",
ifelse(V(g)$race == "hispanic", "orange", "gray"))))
for(r in unique(examiner_attrs$work_unit)){
# Subset the graph to include only vertices in work units 161 and 249
g_sub <- induced_subgraph(g, V(g)$work_unit %in% c(r))
# Plot the subgraph using igraph
plot(g_sub, vertex.label = NA, vertex.color = vertex_colors[V(g_sub)], vertex.size = 10,
edge.arrow.size = 0.5, edge.width = 0.5, layout = layout_with_fr)
title(r)
}
Therefore, betweenness would be a better measure in this network characterized by clusters formed between races. Degree centrality can also measure the connectivity within one’s own ethnic group. After getting the centrality measures, we join it to the examiner_attrs table to see its relationship with tenure days
# Calculate centrality scores for examiners
selected_centrality_scores <- data.frame(
degree = degree(g),
betweenness = betweenness(g)
)
selected_centrality_scores <- selected_centrality_scores %>%
rownames_to_column("examiner_id") %>%
mutate(examiner_id = as.numeric(examiner_id)) %>%
left_join(examiner_attrs, by = "examiner_id")
# Print the centrality scores for examiners in the selected workgroups
print(selected_centrality_scores)## examiner_id degree betweenness gender race work_unit tenure
## 1 84356 42 18.0000000 Male white 165 4206
## 2 91688 46 0.2222222 Female white 161 4846
## 3 61797 84 0.0000000 Male white 241 4751
## 4 94270 20 7.0000000 Female black 214 2269
## 5 60128 8 1.0000000 Male black 249 4149
## 6 75772 12 1.4000000 Female asian 218 5868
## 7 67713 2 0.0000000 Female black 218 6161
## 8 90730 33 38.8647654 Male asian 244 5371
## 9 97910 124 28.0000000 Male white 164 6016
## 10 73119 45 0.0000000 Male white 245 2523
## 11 67570 68 315.4657210 Male white 214 6013
## 12 68584 18 0.0000000 Female asian 244 1688
## 13 91989 18 0.0000000 Male white 161 3642
## 14 68593 17 9.5063796 Male asian 244 3655
## 15 84860 6 0.0000000 Male asian 215 2069
## 16 70581 8 0.0000000 Male black 212 4742
## 17 95938 28 0.0000000 Male asian 215 5809
## 18 90049 134 821.6498707 Male white 245 2472
## 19 99654 19 0.0000000 Female asian 245 3034
## 20 73364 156 0.0000000 Male black 161 4739
## 21 72573 298 3038.1880166 Male black 214 5500
## 22 95600 64 422.9013072 Male white 215 3327
## 23 99182 9 0.0000000 Female black 215 5920
## 24 62778 44 9.0000000 Female white 177 6027
## 25 67189 44 73.0000000 Male white 215 5048
## 26 60449 6 23.0000000 Male asian 247 3340
## 27 62919 124 1503.9288556 Male black 246 1617
## 28 63962 43 0.0000000 Male asian 245 1733
## 29 60501 27 0.0000000 Female asian 244 1898
## 30 72848 1 0.0000000 Male white 163 3425
## 31 76927 100 6.5000000 Female black 177 5572
## 32 92812 18 0.0000000 Male white 213 5301
## 33 97590 8 0.0000000 Male asian 212 4586
## 34 76621 2 0.0000000 Female asian 216 3792
## 35 85308 32 241.3944444 Female white 215 5486
## 36 84460 23 163.0000000 Male asian 219 1559
## 37 94230 6 0.0000000 Female white 246 1112
## 38 78821 30 0.0000000 Male asian 241 3256
## 39 93839 42 0.0000000 Male white 164 1172
## 40 74224 33 0.0000000 Female white 164 2288
## 41 69116 34 0.0000000 Male asian 245 5090
## 42 59811 29 0.0000000 Male black 161 5805
## 43 84516 6 0.0000000 Male white 244 5284
## 44 64660 20 0.0000000 Male white 214 3260
## 45 65566 19 15.0000000 Male asian 215 4512
## 46 72332 9 0.0000000 Male white 172 5713
## 47 94753 54 75.0000000 Male white 242 3469
## 48 66685 62 159.7361895 Male asian 215 4987
## 49 61918 24 67.0000000 Male black 212 5363
## 50 67690 116 0.0000000 Male black 162 5494
## 51 60768 4 0.0000000 Male white 219 1542
## 52 90588 12 0.0000000 Female asian 161 6293
## 53 97703 35 31.0000000 Male asian 242 1420
## 54 97586 2 0.0000000 Female black 163 6029
## 55 96764 39 95.6068217 Male asian 245 3496
## 56 93859 40 6.3333333 Female black 178 4716
## 57 75615 320 0.0000000 Female white 241 4463
## 58 64623 18 0.0000000 Female asian 245 1412
## 59 73847 39 0.0000000 Male asian 247 4116
## 60 98330 12 0.0000000 Male black 241 1574
## 61 76536 208 402.4657210 Female white 244 5623
## 62 68752 8 0.0000000 Male white 219 1700
## 63 73150 21 0.0000000 Male white 219 3540
## 64 97309 6 0.0000000 Male hispanic 243 4796
## 65 94129 18 84.0000000 Male asian 215 5728
## 66 59211 25 8.0000000 Male white 164 4781
## 67 75036 15 7.0000000 Male white 161 2350
## 68 61827 4 0.0000000 Female white 217 4034
## 69 60078 24 3.0000000 Female white 177 5613
## 70 67543 21 92.4207635 Male hispanic 215 5711
## 71 94419 6 0.0000000 Male asian 165 4373
## 72 94046 15 0.0000000 Male white 164 3502
## 73 64053 738 9.0000000 Male asian 173 6330
## 74 71385 5 0.0000000 Male white 163 5687
## 75 63623 76 0.0000000 Male black 214 3568
## 76 98582 62 30.0000000 Male white 171 5557
## 77 69365 4 1.0000000 Male black 242 5541
## 78 75341 216 0.0000000 Female white 171 6117
## 79 97202 5 1.0000000 Male asian 244 477
## 80 69446 34 0.0000000 Female asian 216 2521
## 81 61925 10 8.0000000 Male black 213 5901
## 82 68532 112 124.9585716 Male white 218 3330
## 83 93781 36 14.0000000 Female asian 213 4703
## 84 71195 1 0.0000000 Female asian 164 4424
## 85 81117 13 0.0000000 Male white 164 3962
## 86 97543 12 0.0000000 Female black 164 2623
## 87 93802 54 0.0000000 Female white 241 3260
## 88 76977 44 0.0000000 Male black 211 3483
## 89 84896 3 0.0000000 Female white 164 2070
## 90 96926 5 0.0000000 Male asian 248 1187
## 91 75730 9 0.0000000 Male asian 164 1197
## 92 99763 92 9.0000000 Female white 165 4984
## 93 97242 3 0.0000000 Female white 163 5383
## 94 72979 71 0.0000000 Male asian 246 2982
## 95 92784 14 0.0000000 Male black 164 5392
## 96 95604 20 2.8000000 Female white 167 1205
## 97 59574 8 0.0000000 Male black 165 2788
## 98 69849 15 0.3600000 Male asian 163 6076
## 99 95116 14 2.0000000 Male black 241 3875
## 100 75940 66 0.0000000 Male white 219 5097
## 101 98678 14 65.0000000 Male black 213 5248
## 102 80410 16 6.0000000 Male white 213 3627
## 103 59699 3 0.0000000 Female asian 244 793
## 104 62114 12 3.0000000 Female asian 214 5432
## 105 70755 37 0.0000000 Male asian 245 1869
## 106 66510 48 0.0000000 Male asian 165 3322
## 107 69853 12 0.0000000 Male asian 214 4587
## 108 70037 8 1.0000000 Female black 165 995
## 109 69319 18 0.0000000 Male black 241 4958
## 110 78019 2 0.0000000 Male asian 163 5414
## 111 95091 3 0.0000000 Male white 163 5110
## 112 77971 2 0.0000000 Female black 165 3651
## 113 71388 84 36.5666667 Female black 177 4417
## 114 59509 4 0.0000000 Male asian 211 5465
## 115 97111 44 0.0000000 Male white 218 4993
## 116 63986 3 0.0000000 Male white 242 460
## 117 66435 7 0.0000000 Male white 161 3032
## 118 81959 112 0.0000000 Male white 161 5363
## 119 94658 21 3.0000000 Male white 172 4573
## 120 80740 2 0.0000000 Female black 163 5876
## 121 92704 16 0.0000000 Female asian 216 3449
## 122 99003 16 0.0000000 Female white 173 2050
## 123 77182 2 0.0000000 Male white 214 6158
## 124 64618 3 0.0000000 Male white 243 4620
## 125 60802 2 0.0000000 Male asian 244 992
## 126 71370 2 0.0000000 Female asian 243 2694
## 127 60715 6 1.0000000 Male hispanic 161 5012
## 128 59693 3 0.0000000 Female white 164 2254
## 129 67901 12 0.0000000 Male white 174 2920
## 130 78057 43 0.0000000 Male white 245 752
## 131 71996 43 0.0000000 Female asian 219 3709
## 132 99447 12 0.0000000 Female black 243 1912
## 133 78326 118 860.2622222 Male asian 216 6168
## 134 90956 732 6.0000000 Male hispanic 173 4625
## 135 76004 9 65.0000000 Male hispanic 217 1658
## 136 75118 6 0.0000000 Male asian 161 5159
## 137 99483 180 559.1978787 Male asian 215 4183
## 138 61103 2 0.0000000 Male white 245 3349
## 139 75216 6 0.0000000 Male asian 245 5702
## 140 68678 96 0.0000000 Male black 178 4458
## 141 93845 9 1.0000000 Male white 244 2220
## 142 78302 7 0.0000000 Male asian 161 4664
## 143 99379 87 0.0000000 Female black 165 5880
## 144 95217 2 0.0000000 Male black 164 1395
## 145 59491 18 0.0000000 Male asian 219 4575
## 146 99019 13 0.0000000 Female asian 216 5175
## 147 66971 8 0.0000000 Female black 163 688
## 148 59771 159 132.0000000 Male black 173 4681
## 149 93745 30 0.0000000 Male asian 165 4454
## 150 93896 450 0.0000000 Female asian 179 5207
## 151 76959 8 2.0000000 Male asian 173 5368
## 152 64374 4 2.0000000 Male white 161 1392
## 153 96047 3 0.0000000 Male white 245 3108
## 154 87031 33 8.0000000 Male asian 248 3811
## 155 96898 14 0.0000000 Female white 162 4847
## 156 79564 18 1.0000000 Male white 173 5410
## 157 87897 35 0.0000000 Male black 164 4886
## 158 99070 134 35.0000000 Female asian 217 2408
## 159 94987 92 15.0000000 Male asian 216 5039
## 160 91398 42 0.0000000 Male black 212 5039
## 161 94257 6 0.0000000 Female black 163 5882
## 162 90331 10 1.2000000 Male asian 164 3185
## 163 73186 49 0.0000000 Male hispanic 242 3565
## 164 93421 10 0.0000000 Female black 164 4104
## 165 71930 144 0.0000000 Male black 211 5355
## 166 80730 231 0.0000000 Male white 179 2142
## 167 84858 3 0.0000000 Male asian 248 1614
## 168 59338 15 178.0000000 Male asian 164 6244
## 169 66903 1 0.0000000 Male asian 218 1552
## 170 68625 24 0.0000000 Female black 245 3692
## 171 63977 2 0.0000000 Male white 163 4401
## 172 97884 8 0.0000000 Female black 172 6207
## 173 73612 2 0.0000000 Male hispanic 245 3143
## 174 95871 29 1320.2086707 Male white 214 1518
## 175 71119 87 223.0163934 Female asian 171 6054
## 176 65558 6 0.0000000 Male asian 212 5264
## 177 69873 6 0.0000000 Male asian 219 2806
## 178 63622 2 0.0000000 Female black 161 4293
## 179 96939 52 2.7777778 Female asian 161 5544
## 180 62583 7 0.0000000 Male white 164 2366
## 181 69580 10 151.0000000 Male black 212 3087
## 182 73469 1 0.0000000 Female white 163 4325
## 183 71381 17 59.0000000 Male black 216 2264
## 184 97697 12 0.0000000 Male asian 245 2357
## 185 66336 2 0.0000000 Female black 164 2710
## 186 60045 122 8.0000000 Female black 217 4972
## 187 99346 3 0.0000000 Male asian 219 4906
## 188 63394 3 0.0000000 Female white 164 3036
## 189 97750 22 0.0000000 Male hispanic 241 3607
## 190 67421 42 0.0000000 Male white 242 2491
## 191 75682 4 0.0000000 Male asian 217 3935
## 192 98883 12 0.0000000 Male white 216 4352
## 193 94816 7 192.0000000 Male black 218 800
## 194 61417 7 3.0000000 Female white 162 6109
## 195 96172 231 5.3333333 Female white 177 3727
## 196 94238 1 0.0000000 Female black 173 1429
## 197 77772 24 0.0000000 Female black 177 4281
## 198 94579 5 0.0000000 Female white 163 4390
## 199 59840 4 0.0000000 Male hispanic 242 3032
## 200 97755 98 0.0000000 Male asian 175 4314
## 201 88202 75 6.0000000 Female black 171 6075
## 202 90166 6 0.0000000 Male white 211 5383
## 203 64073 1 0.0000000 Male white 163 3998
## 204 81211 6 0.0000000 Male black 161 4720
## 205 74334 28 0.0000000 Male asian 172 3754
## 206 94466 12 0.0000000 Male white 179 1973
## 207 60682 9 0.0000000 Male white 245 848
## 208 83601 27 0.0000000 Male black 179 1331
## 209 62581 14 11.1764706 Male white 161 4236
## 210 96496 4 0.0000000 Male asian 165 5018
## 211 89023 56 15.0000000 Male white 177 5843
## 212 62152 110 0.0000000 Male asian 172 4074
## 213 72052 152 0.0000000 Male white 173 2740
## 214 62862 6 0.0000000 Male hispanic 177 3702
## 215 74579 36 8.0000000 Male asian 178 3681
## 216 60575 12 0.0000000 Female asian 179 2329
## 217 95973 27 3.0000000 Male black 215 4457
## 218 63190 6 1.0000000 Female black 163 3113
## 219 78586 64 0.0000000 Male black 241 4124
## 220 94341 27 0.0000000 Female black 171 5247
## 221 59866 45 0.0000000 Male white 171 3235
## 222 92902 4 0.0000000 Female black 162 4653
## 223 97456 82 0.0000000 Male white 175 4016
## 224 80063 25 1.0000000 Male white 165 1023
## 225 76021 12 0.0000000 Male white 174 3115
## 226 93968 255 0.0000000 Male black 172 3270
## 227 99465 18 0.0000000 Female asian 174 4096
## 228 92143 138 499.0000000 Female white 172 2307
## 229 97902 7 22.0000000 Male asian 241 3573
## 230 71107 17 0.0000000 Female white 174 2058
## 231 68166 8 0.0000000 Female asian 162 5150
## 232 73920 280 0.0000000 Female black 165 2671
## 233 81846 4 0.0000000 Male black 244 2280
## 234 77773 74 0.0000000 Male asian 246 1468
## 235 65654 16 2.0000000 Female black 164 4838
## 236 71856 18 0.0000000 Male black 165 4215
## 237 98840 10 0.0000000 Female white 179 1320
## 238 88509 6 0.0000000 Male white 178 4842
## 239 97955 48 1.0000000 Male white 172 4187
## 240 88092 6 0.0000000 Male black 171 4007
## 241 95210 14 0.0000000 Female asian 176 4215
## 242 64272 16 0.0000000 Male hispanic 248 4473
## 243 75233 12 0.0000000 Male white 246 4471
## 244 61485 200 2972.5081838 Male asian 244 3293
## 245 62284 21 0.0000000 Male black 179 366
## 246 81877 31 0.0000000 Male asian 174 3009
## 247 97975 52 134.0000000 Male asian 245 3130
## 248 92612 19 0.0000000 Female white 171 3516
## 249 61698 20 22.0000000 Male black 178 6065
## 250 63842 9 0.0000000 Male white 173 4898
## 251 63577 23 0.0000000 Male white 174 3778
## 252 84895 2 0.0000000 Male white 214 4805
## 253 67034 94 8.0000000 Male hispanic 172 2420
## 254 94956 140 49.0000000 Male black 174 3961
## 255 94905 17 0.0000000 Male black 161 5272
## 256 95411 33 0.0000000 Male black 242 2442
## 257 97473 49 0.0000000 Female white 177 3502
## 258 97705 33 0.0000000 Male white 173 2595
## 259 76629 30 0.0000000 Male hispanic 172 3824
## 260 89312 54 0.0000000 Male asian 171 1959
## 261 65121 45 0.0000000 Male black 172 3095
## 262 90352 13 120.0000000 Male asian 244 3687
## 263 60147 6 0.0000000 Female asian 242 5332
## 264 70799 26 0.0000000 Male black 179 1707
## 265 78056 5 0.0000000 Male white 176 2335
## 266 76154 82 0.0000000 Male black 174 3207
## 267 64864 184 5.0000000 Male black 174 3275
## 268 64951 14 0.0000000 Female black 172 4978
## 269 98297 393 1.9836066 Male asian 171 5593
## 270 63030 1 0.0000000 Female white 163 5675
## 271 99518 50 0.0000000 Male black 177 5799
## 272 69835 11 0.0000000 Male asian 247 1577
## 273 86212 2 0.0000000 Male white 176 4300
## 274 67300 138 0.0000000 Male black 179 4319
## 275 99457 58 0.0000000 Male white 171 2797
## 276 94093 4 0.0000000 Male asian 218 1266
## 277 75238 38 0.0000000 Male white 174 3354
## 278 78313 4 0.0000000 Female asian 212 4253
## 279 68017 7 0.0000000 Male black 177 3463
## 280 91233 55 0.0000000 Male white 171 4066
## 281 76396 146 0.0000000 Male asian 242 4340
## 282 93636 18 0.0000000 Male white 178 3177
## 283 94161 140 7.0000000 Female asian 174 1960
## 284 98253 16 0.0000000 Male white 178 1239
## 285 75774 58 0.0000000 Male white 173 3792
## 286 97083 18 0.0000000 Male asian 172 1366
## 287 75824 3 0.0000000 Male white 161 2829
## 288 63613 11 0.0000000 Male black 171 2148
## 289 67281 4 0.0000000 Male black 161 4076
## 290 94994 20 0.0000000 Female asian 171 4413
## 291 67978 10 0.0000000 Male white 161 4386
## 292 62892 11 0.0000000 Male black 178 1151
## 293 93653 24 0.0000000 Male black 176 3703
## 294 68788 50 0.0000000 Male asian 174 4223
## 295 59359 30 0.0000000 Male white 173 2785
## 296 94208 15 0.0000000 Male white 211 3030
## 297 69870 50 0.0000000 Male black 216 1335
## 298 71647 12 0.0000000 Female black 241 4179
## 299 83475 118 0.0000000 Male black 177 2003
## 300 93715 24 0.0000000 Female asian 173 4074
## 301 91064 48 0.0000000 Male black 178 1213
## 302 99047 1 0.0000000 Male white 162 3798
## 303 98045 37 0.0000000 Male white 179 1693
## 304 92238 32 0.0000000 Female asian 176 3599
## 305 64183 16 0.0000000 Female asian 245 4084
## 306 96438 231 0.0000000 Male white 212 2886
## 307 83670 121 0.0000000 Male asian 165 4334
## 308 70227 39 0.0000000 Male hispanic 173 1481
## 309 68463 60 0.0000000 Male white 174 4516
## 310 93875 56 102.0000000 Male white 212 3535
## 311 75301 22 0.0000000 Male black 242 3015
## 312 74186 11 0.0000000 Male black 247 1871
## 313 95814 51 0.0000000 Female white 173 6204
## 314 64472 4 0.0000000 Male white 215 932
## 315 61759 26 0.0000000 Female white 179 1145
## 316 89550 34 105.0000000 Male white 176 4315
## 317 61384 12 0.0000000 Male asian 247 1694
## 318 67050 148 0.0000000 Male black 174 2170
## 319 94603 6 0.0000000 Male asian 245 763
## 320 59987 79 0.0000000 Male asian 173 1346
## 321 72153 18 0.0000000 Male white 179 2958
## 322 71932 92 0.0000000 Male hispanic 213 3077
## 323 96532 45 0.0000000 Female asian 176 2156
## 324 77791 18 0.0000000 Male white 173 3235
## 325 97287 80 6.6666667 Male white 176 2743
## 326 75461 39 6.0000000 Male black 171 5845
## 327 92108 3 0.0000000 Female white 179 2371
## 328 60943 5 0.0000000 Male asian 246 943
## 329 78051 24 0.0000000 Female black 177 3387
## 330 94724 57 0.0000000 Female white 242 819
## 331 61126 42 27.0000000 Male black 174 3501
## 332 60691 24 0.0000000 Male asian 245 4156
## 333 93498 7 0.0000000 Male asian 249 2203
## 334 92475 1 0.0000000 Male black 211 1863
## 335 67752 34 0.0000000 Male asian 242 2687
## 336 97517 31 159.4430029 Female asian 241 2548
## 337 99879 42 0.0000000 Female black 173 3108
## 338 62658 8 0.0000000 Male asian 215 2933
## 339 92674 5 0.0000000 Female black 243 4520
## 340 67376 52 6.0000000 Male black 172 5612
## 341 98594 14 0.0000000 Male white 218 1923
## 342 99311 21 81.0000000 Male asian 241 2541
## 343 91758 11 0.0000000 Male white 212 4872
## 344 59533 3 1.0000000 Male white 244 81
## 345 87028 98 439.6666667 Male asian 212 5407
## 346 88532 2 0.0000000 Female black 216 4907
## 347 65271 26 12.1666667 Male white 177 4933
## 348 71445 12 0.0000000 Female asian 161 4371
## 349 63970 87 5.0000000 Male black 171 5633
## 350 94445 8 0.0000000 Male black 244 2440
## 351 96266 10 1.0689655 Male white 211 5493
## 352 95183 68 0.0000000 Male asian 241 3684
## 353 76151 1 0.0000000 Male hispanic 219 4233
## 354 61519 8 0.0000000 Male asian 163 639
## 355 71174 31 275.0000000 Female white 173 5464
## 356 76318 2 0.0000000 Male white 165 1886
## 357 88225 84 0.0000000 Male asian 245 4255
## 358 93548 32 0.0000000 Male black 244 4595
## 359 95328 12 0.0000000 Male black 212 5945
## 360 82386 2 0.0000000 Male black 219 5094
## 361 99835 1 0.0000000 Male black 242 2617
## 362 63182 7 0.0000000 Male white 245 4339
## 363 64917 63 222.0000000 Male asian 175 2363
## 364 61889 13 4.0000000 Male black 219 3934
## 365 71278 3 0.0000000 Male black 216 2528
## 366 64454 1 0.0000000 Male white 248 4850
## 367 86852 4 0.0000000 Male white 213 2394
## 368 92187 4 0.0000000 Female asian 161 4154
## 369 94852 24 0.0000000 Male hispanic 212 4458
## 370 85754 13 2.0000000 Male black 246 4551
## 371 62073 16 1.4444444 Male asian 217 4367
## 372 71175 15 32.5000000 Male asian 212 5297
## 373 94427 11 3.0000000 Male white 245 3011
## 374 70767 12 0.0000000 Female black 162 5861
## 375 94513 5 9.0000000 Male white 212 4323
## 376 90135 100 0.0000000 Male hispanic 215 2647
## 377 71021 5 2.0000000 Female black 216 4735
## 378 97657 171 505.0000000 Male asian 172 5357
## 379 71861 22 0.0000000 Male black 165 4117
## 380 72087 28 2.0000000 Male black 212 1850
## 381 61609 3 0.0000000 Male white 247 3079
## 382 59581 76 0.0000000 Male asian 218 499
## 383 71858 2 0.0000000 Male white 164 5245
## 384 84867 8 0.0000000 Female asian 173 4043
## 385 73898 2 0.0000000 Male white 175 1405
## 386 80376 9 0.0000000 Male asian 218 5102
## 387 76580 52 0.0000000 Male black 211 2946
## 388 96234 32 0.0000000 Male white 171 4336
## 389 88443 6 1.0000000 Male white 163 5194
## 390 74319 13 0.0000000 Male white 241 1476
## 391 67462 2 0.0000000 Male white 243 3967
## 392 84547 33 0.0000000 Male asian 247 2122
## 393 68259 17 0.0000000 Male asian 218 1563
## 394 95721 8 0.0000000 Female white 178 3544
## 395 68338 63 22.2285878 Female white 213 5923
## 396 97772 1 0.0000000 Female white 164 5153
## 397 92162 2 0.0000000 Male asian 165 4880
## 398 67506 10 0.0000000 Male hispanic 171 4773
## 399 59664 3 0.0000000 Male asian 212 5308
## 400 75568 10 0.0000000 Male black 173 1452
## 401 63129 16 0.0000000 Female black 248 4018
## 402 76443 3 0.0000000 Male white 242 1163
## 403 69090 190 480.8660369 Male asian 211 2921
## 404 77169 7 4.0000000 Male asian 247 21
## 405 59941 8 0.0000000 Male white 242 4758
## 406 95900 2 0.0000000 Male white 173 1364
## 407 92487 128 0.0000000 Male asian 171 4283
## 408 71833 29 0.0000000 Male asian 216 1372
## 409 64704 23 58.5555556 Female asian 244 3214
## 410 96243 2 66.0000000 Female asian 212 4096
## 411 62167 4 0.0000000 Male asian 241 2905
## 412 94793 6 0.0000000 Male black 245 2667
## 413 69889 28 0.0000000 Male white 212 4899
## 414 64940 27 4.0000000 Male asian 173 5796
## 415 97673 2 0.0000000 Male black 219 4362
## 416 69463 316 0.0000000 Male white 241 4286
## 417 96062 7 0.0000000 Male black 245 1971
## 418 60675 13 0.0000000 Male hispanic 161 2340
## 419 80053 7 0.0000000 Male asian 211 632
## 420 67812 21 0.0000000 Male hispanic 218 1695
## 421 66503 4 0.0000000 Male white 172 4530
## 422 96199 6 0.0000000 Male white 216 4625
## 423 85437 8 1.0000000 Female asian 161 6048
## 424 71780 39 3.1484741 Male asian 214 5321
## 425 68384 51 9.0000000 Male black 171 5819
## 426 99007 6 1.0000000 Male black 212 3899
## 427 63219 56 6.0000000 Female white 178 5473
## 428 98852 228 221.0000000 Male black 175 5914
## 429 86683 6 0.0000000 Male hispanic 174 20
## 430 83034 6 0.0000000 Male asian 172 3247
## 431 68445 1 0.0000000 Male white 164 4255
## 432 64473 3 3.2000000 Female asian 217 4851
## 433 71721 2 0.0000000 Male black 243 3297
## 434 96225 5 0.0000000 Male hispanic 218 4482
## 435 65111 72 0.0000000 Male white 165 6156
## 436 59647 2 0.0000000 Male black 241 4377
## 437 97268 1 0.0000000 Female asian 165 4099
## 438 99300 2 0.0000000 Male white 161 3555
## 439 62519 2 0.0000000 Male asian 243 4030
## 440 84267 64 0.0000000 Male black 172 4243
## 441 82438 3 0.0000000 Male white 243 4623
## 442 96075 4 0.0000000 Male asian 245 3522
## 443 63027 28 0.0000000 Male white 172 3324
## 444 72099 46 5.0000000 Female asian 216 2357
## 445 97481 10 0.0000000 Male white 177 3211
## 446 83323 3 0.0000000 Female black 179 1361
## 447 75933 189 8.0000000 Male black 173 5601
## 448 80925 15 12.0000000 Male asian 248 2352
## 449 80908 22 0.0000000 Male black 177 3452
## 450 69464 24 0.0000000 Male asian 171 4028
## 451 68598 12 0.0000000 Female black 177 3301
## 452 74002 2 0.0000000 Female white 216 1466
## 453 87065 7 0.0000000 Male white 246 2941
## 454 86115 4 0.0000000 Male white 164 4168
## 455 84289 6 0.0000000 Male black 174 3663
## 456 71655 16 0.0000000 Male white 173 1006
## 457 93626 48 0.0000000 Male white 172 2647
## 458 93732 42 8.0000000 Male white 177 5669
## 459 88678 18 0.0000000 Female asian 249 1735
## 460 75367 92 13.0000000 Male white 175 5768
## 461 66626 10 0.0000000 Male hispanic 242 2147
## 462 96314 2 0.0000000 Male asian 213 1533
## 463 92890 62 6.0000000 Female white 175 4129
## 464 63765 8 0.0000000 Female asian 241 1746
## 465 96253 1 0.0000000 Female white 244 2151
## 466 78715 6 0.0000000 Male white 172 2259
## 467 99154 8 1.0000000 Male white 213 5371
## 468 68719 7 0.0000000 Male asian 172 2130
## 469 90563 64 8.4606061 Male black 211 5512
## 470 60838 16 2.0000000 Male asian 241 3891
## 471 59870 18 2.0000000 Male black 171 4876
## 472 69711 12 0.0000000 Female asian 175 5266
## 473 78379 8 0.4333333 Male white 171 3232
## 474 71632 10 0.0000000 Female asian 211 2324
## 475 99424 2 0.0000000 Male white 162 4556
## 476 86685 46 0.0000000 Male white 241 3624
## 477 99929 6 0.0000000 Male black 171 2994
## 478 85940 4 0.0000000 Female black 172 4115
## 479 97072 2 0.0000000 Male white 164 3827
## 480 67801 30 0.0000000 Male black 241 3177
## 481 97650 18 4.0000000 Male white 176 767
## 482 60164 6 0.0000000 Male white 244 4577
## 483 85599 9 0.0000000 Male white 179 1080
## 484 59447 24 0.0000000 Female white 179 4242
## 485 66482 6 1.0000000 Male asian 218 2858
## 486 94849 2 0.0000000 Male black 211 1380
## 487 89394 13 0.0000000 Male white 244 4681
## 488 96125 30 2.0000000 Male white 172 6118
## 489 63649 4 0.0000000 Female white 172 5978
## 490 66251 6 0.0000000 Female white 179 4287
## 491 96906 30 0.0000000 Female asian 242 2677
## 492 60702 9 0.0000000 Male asian 242 3351
## 493 70176 70 197.0000000 Female asian 173 3421
## 494 67173 2 0.0000000 Female white 163 4502
## 495 59539 4 0.0000000 Male asian 177 1261
## 496 59497 5 0.0000000 Male white 164 4609
## 497 94211 3 0.0000000 Male white 171 3998
## 498 70672 40 246.4444444 Male asian 215 1708
## 499 93546 36 0.0000000 Female asian 244 4347
## 500 70370 34 0.0000000 Male asian 215 4607
## 501 70897 11 1.0000000 Female asian 247 2330
## 502 67343 6 0.0000000 Female asian 211 2840
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## 996 95784 2 0.0000000 Male white 164 4512
## 997 84313 1 0.0000000 Male hispanic 177 2183
## 998 79043 1 0.0000000 Female asian 242 538
## 999 61209 2 0.0000000 Male asian 214 4734
## 1000 79221 4 0.0000000 Male white 212 2621
## 1001 64851 2 0.0000000 Male asian 162 1877
## 1002 98656 2 0.0000000 Male hispanic 217 4493
## 1003 64548 12 0.0000000 Male asian 175 4033
## 1004 97477 4 0.0000000 Male white 161 642
## 1005 76081 6 0.0000000 Male asian 171 5540
## 1006 97322 5 0.0000000 Male white 211 3457
## 1007 71557 5 0.0000000 Male black 174 3679
## 1008 94224 3 0.0000000 Female asian 246 1114
## 1009 59878 20 0.0000000 Male white 218 6133
## 1010 60077 24 0.0000000 Female white 173 6034
## 1011 63363 24 0.0000000 Female white 175 5236
## 1012 95794 2 0.0000000 Female black 165 5690
## 1013 87554 12 0.0000000 Male asian 172 2890
## 1014 96329 1 0.0000000 Male white 163 462
## 1015 65403 30 0.0000000 Male white 171 5927
## 1016 92688 3 0.0000000 Male white 162 2628
## 1017 62095 1 0.0000000 Male hispanic 171 2584
## 1018 95646 1 0.0000000 Female black 216 2655
## 1019 61105 2 0.0000000 Male asian 164 4151
## 1020 70142 2 0.0000000 Female asian 163 4175
## 1021 68658 3 0.0000000 Male asian 213 728
## 1022 72355 2 0.0000000 Female white 164 5685
## 1023 97442 2 0.0000000 Male hispanic 219 934
## 1024 70856 4 0.0000000 Male white 165 6087
## 1025 98394 6 0.0000000 Male asian 176 4587
## 1026 96643 3 0.0000000 Female white 163 5096
## 1027 97957 5 0.0000000 Female white 172 366
## 1028 63101 20 0.0000000 Male black 179 4872
## 1029 95065 6 0.0000000 Male white 172 6140
## 1030 91512 1 0.0000000 Male white 214 197
## 1031 64915 6 0.0000000 Male black 173 4545
## 1032 96874 2 0.0000000 Male black 214 1911
## 1033 74725 3 0.0000000 Male white 179 1466
## 1034 97349 2 0.0000000 Male asian 211 2186
## 1035 68694 8 0.0000000 Male black 161 5905
## 1036 60709 6 0.0000000 Male black 161 4637
## 1037 64823 18 0.0000000 Male white 177 6124
## 1038 60580 1 0.0000000 Male white 218 2226
## 1039 94650 4 0.0000000 Female white 173 4361
## 1040 61435 5 0.0000000 Male black 172 2738
## 1041 62206 2 0.0000000 Male black 172 2354
## 1042 60309 3 0.0000000 Male white 172 5318
## 1043 70423 4 0.0000000 Male asian 172 4666
## 1044 67259 12 0.0000000 Male hispanic 172 5660
## 1045 61182 6 0.0000000 Male white 176 4635
## 1046 70629 1 0.0000000 Male asian 216 2270
## 1047 93178 3 0.0000000 Female black 162 4970
## 1048 62064 18 0.0000000 Male asian 171 4885
## 1049 95807 1 0.0000000 Male white 218 1635
## 1050 59599 2 0.0000000 Male white 217 3099
## 1051 74227 2 0.0000000 Male white 161 1899
## 1052 81049 1 0.0000000 Male hispanic 212 1057
## 1053 66769 6 0.0000000 Male white 172 6251
## 1054 92710 2 0.0000000 Female asian 173 2918
## 1055 64507 6 0.0000000 Male black 164 4261
## 1056 73327 9 0.0000000 Male white 175 5871
## 1057 60610 1 0.0000000 Male asian 165 5652
## 1058 91747 7 0.0000000 Male hispanic 163 3295
## 1059 68737 1 0.0000000 Female asian 167 1442
## 1060 97639 1 0.0000000 Male asian 219 2147
## 1061 76516 6 0.0000000 Male asian 178 4488
## 1062 65260 4 0.0000000 Female black 178 3642
## 1063 95782 6 0.0000000 Male asian 177 6070
## 1064 93854 4 0.0000000 Male asian 214 4619
## 1065 82433 6 0.0000000 Male asian 163 3650
## 1066 68231 6 0.0000000 Male black 216 3408
## 1067 63326 4 0.0000000 Male white 173 4757
## 1068 95863 6 0.0000000 Male black 178 827
## 1069 61477 1 0.0000000 Male black 217 3304
## 1070 98679 2 0.0000000 Male black 248 3260
## 1071 99340 12 0.0000000 Female white 163 5102
## 1072 67321 2 0.0000000 Male hispanic 212 2182
## 1073 67666 4 0.0000000 Female white 213 5599
## 1074 59181 12 0.0000000 Male black 216 5710
## 1075 61538 1 0.0000000 Male black 219 1769
## 1076 73274 2 0.0000000 Male white 173 5020
## 1077 98895 1 0.0000000 Male asian 242 3963
## 1078 63713 2 0.0000000 Male asian 178 2445
## 1079 61164 2 0.0000000 Male white 213 968
## 1080 93072 1 0.0000000 Male white 162 763
## 1081 94954 2 0.0000000 Male white 177 4733
## 1082 96110 1 0.0000000 Male white 176 969
## 1083 60879 4 0.0000000 Female white 173 6028
## 1084 59632 18 0.0000000 Female black 162 5478
## 1085 62325 2 0.0000000 Male white 217 1301
## 1086 63164 2 0.0000000 Male black 174 5093
## 1087 95525 1 0.0000000 Female white 163 1090
## 1088 69867 2 0.0000000 Male white 216 2894
## 1089 67280 4 0.0000000 Male white 213 5650
## 1090 74937 6 0.0000000 Male white 211 5643
## 1091 97741 2 0.0000000 Male white 174 1931
## 1092 77743 12 0.0000000 Male white 173 6006
## 1093 59816 6 0.0000000 Male white 175 5999
## 1094 64004 2 0.0000000 Male white 164 4945
## 1095 94390 2 0.0000000 Female white 171 4499
## 1096 99014 4 0.0000000 Male asian 174 952
## 1097 68339 2 0.0000000 Female white 162 2922
## 1098 71086 3 0.0000000 Female black 178 192
## 1099 97649 8 0.0000000 Male white 177 5622
## 1100 62464 8 0.0000000 Male white 172 6064
## 1101 92706 1 0.0000000 Male black 218 1216
## 1102 67620 1 0.0000000 Male white 164 797
## 1103 99892 1 0.0000000 Female white 164 2981
## 1104 61047 3 0.0000000 Female black 172 90
## 1105 99850 2 0.0000000 Male white 174 4890
## 1106 62135 2 0.0000000 Male black 216 2608
## 1107 65880 2 0.0000000 Male asian 217 1254
## 1108 99191 3 0.0000000 Male white 162 4917
## 1109 63316 3 0.0000000 Male white 165 5176
## 1110 97340 6 0.0000000 Female asian 215 5550
## 1111 97939 1 0.0000000 Male asian 248 1555
## 1112 59589 2 0.0000000 Female black 172 4718
## 1113 76347 2 0.0000000 Female asian 176 1774
## 1114 93682 12 0.0000000 Female white 178 4381
## 1115 82959 1 0.0000000 Male hispanic 212 3168
## 1116 74027 2 0.0000000 Male white 218 4039
## 1117 99535 6 0.0000000 Male white 162 3936
## 1118 63938 6 0.0000000 Male white 171 4158A correlation matrix is caculated and degree has more correlation with tenure days. In other words, a examiner who gives more advice is more likely to have longer tenure.
cor_matrix <- cor(selected_centrality_scores[, c("degree", "betweenness","tenure")])
print(cor_matrix)## degree betweenness tenure
## degree 1.0000000 0.23888097 0.16246474
## betweenness 0.2388810 1.00000000 0.02694365
## tenure 0.1624647 0.02694365 1.00000000heatmap(
cor_matrix,
Rowv = NA,
Colv = NA,
col = colorRampPalette(c("white", "darkred"))(25),
margins = c(15, 10)
)