Every semester the CONNECT Program pairs community organizations with graduate students with data and evaluation skills. As the program has expanded, the matching process has become more complicated, necessitating improved program efficiency. The following document outlines how survey data is taken from Qualtrics, cleaned and sorted, and then matched using the Gale-Shapley Algorithm. Some descriptive data visualizations were included as well to give program managers a snap shot of the semester cohort.
Qualtrics
Using the qualtRics package the organization and student
surveys were pulled directly from Qualtrics. In order to connect to the
Qualtrics API, you must have UT staff privileges. Any UT student must
have their privileges updated by contacting UT IT Services. There may be
situations in which staff also lack privileges, in which case you can
also contact IT. Your Qualtrics API can be accessed through account
settings in the “Qualtrics IDs” section. Copy paste the API token into
the qualtrics_api_creditials function api_key
attribute. The base url is constructed by taking the “Datacenter ID” in
Qualtrics and adding “.qualtrics.com” to it.
It’s really important that you do not save the api key inside this
document if you’re using Github. If this happens, the Github security
bot will alert Qualtrics and you’ll have your permissions revoked.To get
around this you can save your api key to in your internal system. You
can do this by using the function
Sys.setenv("KEY NAME" = "API KEY"), and then you can call
it using calling Sys.getenv("KEY NAME").
Once you’ve established your Qualtrics credentials downloaded a list
of your Qualtrics surveys, you need to find the row index of the
survey(s) you’re working. To do that, copy paste the survey name into
the which function. Then you can use
fetch_survey to pull the raw survey data.
Show code
# Connecting to the Qualtrics API
# There's always problem with the api. Make sure they haven't changed your key.....
# set your api key using Sys.setenv("NAME" = "API KEY")
qualtrics_api_credentials(api_key = "hfJlPYTIsZeDw52H9IWjwQmH8D3cI1HpYsyV93G4",
base_url = "ca1.qualtrics.com")
#Pulling the organization and students surveys
surveys <- all_surveys()
#Finding the row index for organization and student surveys
stu_number <- which(surveys$name=="10.CONNECT- Summer 2022", arr.ind=TRUE)
org_number <- which(surveys$name=="Matching Form_Summer 2022", arr.ind=TRUE)
#Fetching the Survey Contents
org_raw <- fetch_survey(surveyID = surveys$id[org_number], force_request = TRUE)
stu_raw <- fetch_survey(surveyID = surveys$id[stu_number], force_request = TRUE)
Projects
Project Wordcloud
Project CONNECT continues to offer a diverse set of projects to students. The word cloud below shows the most common words located in the project deliverable section. What’s clear about this cohort is that data is king, and we a wide variety of projects and deliverables.
*Make sure to name organization with multiple projects different things
Show code
library(wordcloud)
library(RColorBrewer)
library(tm) # to create corpus
library(wordcloud2)
library(htmlwidgets)
org <- org_raw
colnames(org) <- label(org)
org <- org |>
clean_names()
# Create a vector containing only the text
text <- org$project_deliverables
# Create a corpus
docs <- Corpus(VectorSource(text))
# standardize corpus
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))
# create a document matrix
dtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix), decreasing = TRUE)
df <- data.frame(word = names(words), freq = words)
df$word <- as.character(df$word)
target <- c("also", "project", "really", "helpful", "able", "helped", "felt", "wasnt", "etc", "asks", "move", "gave")
"%ni%" <- Negate("%in%")
df <- filter(df, word %ni% target)
df$word <- as.factor(df$word)
set.seed(27)
w1 <-
wordcloud2(data = df,
color = rep_len(
c("#264653", "#2A9D8F", "#E9C46A", "#F4A261", "#E76F51"),
nrow(df)
),
shuffle = FALSE)
w1
Project Descriptions
Each project is unique, and some are more complex than others. When you are evaluating who should be placed with what organizations, it’s important to keep in my the project goals and deliverables, even if they end up changing. The table below was pulled directly from the organization surveys, and it outlines the project goals, deliverables, and extra considerations.
Show code
org2 <- org |>
rename_with(~ gsub('please_rate_how_relevant_the_following_technical_skills_are_to_your_project_', '', .x)) |>
rename_with(~ gsub('please_rate_how_relevant_the_following_evaluation_consulting_and_language_skills_are_to_your_project_', '', .x)) |>
rename(nonprofit_experience = "how_much_experience_working_or_volunteering_with_nonprofits_should_your_candidate_have_working_or_volunteering_for_nonprofits",
time_commitment = realistically_how_much_time_do_you_expect_your_student_to_commit_per_week_working_on_your_assigned_project,
transportation = does_your_candidate_need_to_have_access_to_transportation,
flexible_hours = will_your_project_permit_flexible_work_hours,
remote = will_your_candidate_be_able_to_work_remotely)
proj_des <- org2 |>
select(
project_name,
project_goal,
project_deliverables,
what_other_relevant_skills_would_be_helpful_for_your_candidate_to_have_i_e_other_languages_spoken_coding_analytical_software_professional_skills_etc_list_them_here
) |>
rename(
Project = project_name,
Goal = project_goal,
Deliverables = project_deliverables,
"Other Considerations" = what_other_relevant_skills_would_be_helpful_for_your_candidate_to_have_i_e_other_languages_spoken_coding_analytical_software_professional_skills_etc_list_them_here
) |>
arrange(Project) |>
mutate(`Other Considerations` = replace_na(`Other Considerations`, ""),
Deliverables = gsub('(\\s\\d)\\)', '<br><br>\\1\\.', Deliverables))
kbl(proj_des, escape = F) |>
kable_minimal("hover", full_width = T) |>
scroll_box(width = "100%", height = "600px")
| Project | Goal | Deliverables | Other Considerations |
|---|---|---|---|
| Allies Against Slavery | To expand the data model and visualizations for Lighthouse, a software platform for human trafficking data. |
|
|
| Austin Asian Impact | To collect data and develop content for AAI’s financial health resource guide. |
|
Knowledge of data tracking, data visualization tools, outreach, project development/management skills preferred |
| Austin Bat Cave | To strengthen Austin Bat Cave’s ability to measure and track the outcomes of its youth creative writing programs. |
|
prior experience working on data projects; excellent writing and communication skills; strong interpersonal skills and the sensitivity to work with a wide range of diverse and vulnerable individuals from young refugees to LBGTQIA to system-involved youth; strong sense of curiosity and desire to learn and share knowledge with others |
| Communities for Recovery | To improve Communities for Recovery’s ability to track and measure the outcomes of it six key areas of work - community center, volunteers, coaching, peer leadership academy, advocacy, and meetings/support groups. |
|
someone with lived experience with substance use recovery preferred |
| Community Tech Network | To support Community Tech Network in building out a data collection strategy and tools for tracking outputs and outcomes for its new, nonprofit capacity building program. |
|
Familiarity with data management and reporting required; previous nonprofit experience, lived experience navigating social service systems, and/or the provision of adult/community-based education services preferred |
| Connected Nation | To better understand broadband expenditures and appropriations across the state of Texas. Ultimately, a deeper understanding of this will help to inform the state’s future funding decisions. |
|
ability to mine public data, basic reporting and data visualization skills required; public policy background, previous experience working with community organizations, and interest in broadband/connectivity preferred |
| Dress For Success Austin | To ensure that Dress For Success Austin is effectively tracking the outcomes of its six core programs. |
|
|
| E3 Alliance | To lay the groundwork for E3 Alliance to better understand how post-secondary institutions in the CTX region are doing compared to each other, other institutions in the state of Texas and nationally. |
|
student with knowledge of higher education data and from a traditionally minoritized community preferred; student with R or Python and communication experience a plus |
| Future Front Texas | To improve Future Front ATX’s ability to tell the success stories of the creatives, founders and leaders it works alongside. |
|
experience or proficiency with text analysis required |
| Go!Austin/Vamos!Austin (GAVA) | To support GAVA in gathering research, reviewing literature, and identifying case studies related to its current portfolio of climate policy work – urban heat and mitigation strategies, localized flooding and infrastructure, and disaster preparedness training. |
|
policy analysis experience; experience conducting research through environmental justice and health equity lenses |
| Hispanic Scholarship Consortium | To gain a deeper understanding of the Hispanic Scholarship Consortium’s overall impact and which profile of student is most impacted by its scholarships. |
|
|
| Keep Austin Fed | To improve Keep Austin Fed’s ability to regularly track and measure the impact of its food redistribution efforts. |
|
|
| Leadership Equality Academy | To better understand the landscape of domestic initiatives and programming focused on developing women from marginalized communities into roles of thought leaders (e.g. board members) and C-Level executives and encouraging them to return to their communities to give back. |
|
|
| Miracle Foundation | To evaluate how well Miracle Foundation’s FosterShare app is achieving its target outcomes. |
|
Experience analyzing qualitative data required; previous experience with or interest in the following preferred - evaluation tools, methods or curricula, child welfare, child protection and rights, orphan and vulnerable children, foster care or related fields |
| North Lamar-Georgian Acres Neighborhood Team | To develop a framework for effective neighborhood-level interactions with independently funded research and/or pilot programs. An effective framework would result in community members appropriately engaging as participants (rather than subjects) in research efforts and in higher-quality research findings. |
|
Previous experience with neighborhood- or village-based community development work, managing or evaluating grants and/or granting programs, using or designing depositories or archives of mixed data or document types |
| Philanthropy Southwest | To spur more coordinated and impactful grantmaking by highlighting giving trends and gaps related to regional-specific issues. |
|
self-starter and someone who pays attention to detail preferred; knowledge of philanthropy support organizations and background in public policy preferred |
| StandUpLD | To strengthen StandUpLD’s ability to track, measure and communicate the impact of its current program offerings. |
|
graduate student who is empathetic to people with learning differences and understands the need for an LD person to be treated with fairness and compassion |
| Texas Census 2030 | To gain a deeper understanding of how a Census undercount impacts the economy, resource planning and HTC populations in Texas. |
|
ability to virtually attend the Texas Demographic Center conference on May 24 - 26; someone comfortable with problem solving, a non-structured work environment, and thinking outside the box |
| The LiveLikeLou Foundation | To lay the groundwork for the LiveLikeLou Foundation to create a collaborative network of ALS scientists, clinicians, and academic institutions that will in turn boost the ALS discovery pipeline. |
|
|
| Youth in View | To gain a better understanding of the characteristics of the foster youth being served by Youth in View. |
|
Organization Data
Ordinal questions for both student and organization surveys were converted to a numeric scale. For example, for the question “How much working or volunteering with nonprofits should your candidate have?” the responses were converted from 1 to 5, with “No experience required” being 1 and “2 or more years” being 5. This is done because the algorithm can only compare numeric values.
Show code
org_mapping <- org2 |>
select(organization,
organization_address,
organization_website
)
org3 <- org2 |>
select(
organization,
project_name,
project_goal,
project_deliverables,
transportation,
flexible_hours,
remote,
time_commitment,
nonprofit_experience,
45:69
) |>
rename("spanish" = "please_indicate_how_relevant_the_proficiency_with_the_following_languages_is_to_your_project_note_1_language_proficiency_not_required_and_5_native_speaker_required_spanish") |>
mutate(
nonprofit_experience = case_when(
nonprofit_experience == "No experience required" ~ "1",
nonprofit_experience == "Less than 6 months" ~ "2",
nonprofit_experience == "6 - 12 months" ~ "3",
nonprofit_experience == "1 - 2 years" ~ "4",
nonprofit_experience == "2 or more years" ~ "5",
TRUE ~ nonprofit_experience
),
time_commitment = case_when(
time_commitment == "Less than 5 hours per week" ~ "1",
time_commitment == "5 - 10 hours per week" ~ "2",
time_commitment == "8 - 12 hours per week" ~ "3",
time_commitment == "10 - 15 hours per week" ~ "4",
time_commitment == "15 - 20 hours per week" ~ "5"
)
) |>
arrange(project_name) |>
mutate(across(9:34,~gsub("1 - Not relevant", "1", .)),
across(9:34,~gsub("5 - Extremely relevant", "5", .)),
across(8:34, as.numeric))
#For Table
org_table <- org3 |>
select(-c(organization, project_deliverables, project_goal))
names(org_table) <- gsub(pattern = "_",
replacement = " ",
x = names(org_table))
names(org_table) <- str_to_title(names(org_table))
cell_color <- function(x) {
x = cell_spec(x,
color = spec_color(x, end = .7),
bold = T,)
}
org_table <- org_table |>
select(-Transportation, -`Flexible Hours`,-Remote, everything()) |>
mutate(across(2:28, cell_color))
kbl(org_table, escape = F) |>
kable_material(c("hover", "striped", "condensed"), full_width = F) |>
scroll_box(width = "100%", height = "400px")
| Project Name | Time Commitment | Nonprofit Experience | Tableau | Html | Arc Gis | Microsoft Office Suite | Google Data Studio | R Shiny | Statistical Analysis | Machine Learning | Sql | R | Javascript | Data Cleaning | Java | Python | Css | Power Bi | Research | Conducting Interviews | Survey Design | Logic Modeling | Outcomes Definition | Consulting | Project Management | Dei Evaluation | Spanish | Transportation | Flexible Hours | Remote |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Allies Against Slavery | 4 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 5 | 3 | 1 | 5 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | No | Yes | Yes |
| Austin Asian Impact | 2 | 3 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 5 | 5 | 3 | 1 | 1 | 5 | 5 | 1 | 1 | No | Yes | Yes |
| Austin Bat Cave | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 5 | 5 | 3 | 3 | 3 | 3 | 2 | 5 | No | Yes | Yes |
| Communities for Recovery | 3 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 5 | 5 | 5 | 5 | 3 | 5 | 1 | 1 | No | Yes | Yes |
| Community Tech Network | 2 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 5 | 1 | 5 | 3 | 5 | 1 | 1 | No | Yes | Yes |
| Connected Nation | 2 | 4 | 5 | 1 | 5 | 1 | 1 | 1 | 5 | 1 | 1 | 5 | 1 | 5 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 4 | 1 | 1 | No | Yes | Yes |
| Dress For Success Austin | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 5 | 5 | 3 | 3 | 5 | 1 | No | Yes | Yes |
| E3 Alliance | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 5 | 1 | 3 | 1 | 5 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | No | Yes | Yes |
| Future Front Texas | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 1 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 1 | 1 | No | Yes | Yes |
| Go!Austin/Vamos!Austin (GAVA) | 2 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 3 | 5 | 3 | 1 | 1 | No | Yes | Yes |
| Hispanic Scholarship Consortium | 2 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 4 | 1 | 1 | 3 | 1 | 5 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 1 | 1 | No | Yes | Yes |
| Keep Austin Fed | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 3 | 5 | 3 | 5 | 3 | 3 | 1 | 1 | No | Yes | Yes |
| Leadership Equality Academy | 3 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 5 | 1 | 1 | 3 | 5 | 5 | 1 | 1 | No | Yes | Yes |
| Miracle Foundation | 3 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 3 | 5 | 5 | 1 | 5 | 4 | 5 | 1 | 1 | No | Yes | Yes |
| North Lamar-Georgian Acres Neighborhood Team | 2 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 3 | 1 | 5 | 5 | 3 | 4 | 1 | 3 | No | Yes | Yes |
| Philanthropy Southwest | 2 | 3 | 5 | 1 | 1 | 3 | 1 | 1 | 5 | 1 | 1 | 5 | 1 | 5 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 1 | 1 | No | Yes | Yes |
| StandUpLD | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 3 | 3 | 5 | 5 | 3 | 3 | 2 | 1 | No | Yes | Yes |
| Texas Census 2030 | 3 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 5 | 1 | 5 | 1 | 5 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 5 | 5 | 1 | 1 | No | Yes | Yes |
| The LiveLikeLou Foundation | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 5 | 5 | 1 | 5 | 3 | 3 | 1 | 1 | No | Yes | Yes |
| Youth in View | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 5 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 1 | 1 | No | Yes | Yes |
Overall, 20 organizations were included in this cohort.
Project Buckets
There were 20 projects, but some of them involve multiple buckets. Of note, this semester there were zero web design projects. Projects that were continuations from previous semester, or were pre-matched were also not included.
Show code
buckets <- org2 |>
select(organization, starts_with("identify_the_categories")) |>
rename_with(~ gsub('identify_the_categories_your_project_falls_under_check_all_that_apply_', '', .x)) |>
pivot_longer(cols = 2:7, names_to = "bucket", values_to = "value") |>
filter(!is.na(value)) |>
group_by(value) |>
count() |>
mutate(n = as.numeric(n),
value = case_when(
value == "Data Collection & Tool Development" ~ "Data Collection &\n Tool Development",
value == "Data Interpretation & Analysis" ~ "Data Interpretation &\n Analysis",
value == "Business Intelligence & Advanced Analytics" ~ "Business Intelligence &\n Advanced Analytics",
TRUE ~ value
)) |>
arrange(n)
theme_set(theme_classic())
g <- ggplot(buckets, aes(x = reorder(value, n), y = n)) +
geom_bar(stat = "identity", fill = "#2A9D8F") +
geom_text(aes(label = n, y = n),
position = position_stack(vjust = 0.5),
size = 8) +
coord_flip()+
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_blank(),
text = element_text(
size = 16,
face = "bold"),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x=element_blank(),
plot.title = element_text(hjust = 0.5),
plot.margin = margin(10, 10, 10, 10),
legend.title = element_blank(),
legend.position = "none",
) +
labs(title = "Project buckets",
y = "Number",
fill = "Buckets",
caption = "**Several projects had two+ categories")
g
Organization Location
Students
Student Data
Because CONNECT uses a formalized survey structure, the data cleaning process for student and organization survey data is virtually the same. The table below includes student skills and experience ratings as well as answers from logistics questions.
Show code
stu <- stu_raw
colnames(stu) <- label(stu)
stu <- stu |>
clean_names() |>
rename(pronouns = preferred_pronouns_selected_choice,
phone = phone_number_please_do_not_include_hyphens_or_parentheses,
relevant_skills = do_you_have_other_relevant_skills_or_previous_experience_that_may_be_helpful_for_us_to_know_about_i_e_other_languages_spoken_courses_youvetaken_or_will_be_enrolled_in_this_summer_list_them_here_note_separate_each_skill_with_a_comma,
target_population = every_semester_the_connect_program_works_with_organizations_that_serve_many_different_target_populations_are_there_any_specific_populations_that_youre_interested_in_working_with,
nonprofit_experience = over_the_past_5_years_approximately_how_much_experience_have_you_had_working_or_directly_volunteering_with_nonprofit_organizations,
international = to_comply_with_university_rules_and_regulations_please_indicate_if_you_are_an_international_student_please_note_that_this_does_not_prohibit_participation_in_this_program) |>
rename_with(~ gsub('please_rate_your_experience_with_the_following_technical_skills_note_1_not_experienced_and_5_extremely_experienced_', '', .x)) |>
rename_with(~ gsub('please_rate_your_experience_with_the_following_consulting_and_evaluation_skills_note_1_not_experienced_and_5_extremely_experienced_', '', .x)) |>
rename_with(~ gsub('which_of_the_following_best_describes_your_race_ethnicity_select_all_that_apply_selected_choice_', '', .x) )
npo <- stu |>
rename_with(~ gsub('which_ut_schools_colleges_are_you_affiliated_with_check_all_that_apply_selected_choice_', '', .x)) |>
select(first_name, last_name, pronouns, preferred_pronouns_other_please_specify_text,
email, phone, ut_eid, nonprofit_experience, lbj_school_of_public_affairs:which_ut_schools_colleges_are_you_affiliated_with_check_all_that_apply_other_text) |>
rename(other_school = which_ut_schools_colleges_are_you_affiliated_with_check_all_that_apply_other_text) |>
select(-other) |>
mutate(nonprofit_experience = case_when(
nonprofit_experience == "No experience (yet!)" ~ "1",
nonprofit_experience == "Less than 6 months" ~ "2",
nonprofit_experience == "6 - 12 months" ~ "3",
nonprofit_experience == "1 - 2 years" ~ "4",
nonprofit_experience == "Over 2 years" ~ "5"),
across(lbj_school_of_public_affairs:other_school, ~replace_na(.x,"")),
school = paste0(lbj_school_of_public_affairs, " ", college_of_education, " ", public_health, " ",
steve_hicks_school_of_social_work, " ",school_of_information, " ", mc_combs_school_of_business, " ",
college_of_liberal_arts, " ", other_school),
nonprofit_experience = as.numeric(nonprofit_experience)) |>
filter(nonprofit_experience > 3) |>
select(-c(lbj_school_of_public_affairs, college_of_education, public_health, school_of_information,
college_of_liberal_arts, steve_hicks_school_of_social_work, college_of_liberal_arts, other_school))
write.csv(npo, "data/processed/summer2022_npo_portfolio.csv", row.names = FALSE)
race <- stu |>
select(first_name, last_name, 93:103) |>
mutate(name = paste0(first_name," ", last_name)) |>
select(name, everything(),-c(first_name, last_name, i_prefer_to_specify)) |>
pivot_longer(cols=2:11, names_to = "ra_e", values_to = "race_ethnicity") |>
filter(!is.na(race_ethnicity)) |>
select(-ra_e)
race_plot <- race |>
group_by(race_ethnicity) |>
count() |>
arrange(n)
r <- ggplot(race_plot, aes(x = reorder(race_ethnicity, n), y = n)) +
geom_bar(stat = "identity", fill = "#2A9D8F") +
geom_text(aes(label = n, y = n),
position = position_stack(vjust = 0.5),
size = 8) +
coord_flip()+
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_blank(),
text = element_text(
size = 16,
face = "bold"),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks = element_blank(),
plot.title = element_text(hjust = 0.5),
plot.margin = margin(10, 10, 10, 10),
legend.title = element_blank(),
legend.position = "none",
) +
labs(title = "Student Race/Ethnicity",
y = "Number",
caption = "**Several students had multiple identities")
r
Show code
#add together ethnicity for those that selected multiple
race$race_ethnicity[race$name == "Enrique Leon"] <- "Hispanic or Latino, Mestizo"
race$race_ethnicity[race$name == "Francisco Castellanos-Sosa"] <- "Hispanic or Latino, White or Caucasian"
race$race_ethnicity[race$name == "James Eckstrom"] <- "Hispanic or Latino, White or Caucasian"
race$race_ethnicity[race$name == "Madison Laboy"] <- "Hispanic or Latino, White or Caucasian"
race$race_ethnicity[race$name == "Tamar Farchy"] <- "Asian, White or Caucasian"
race$race_ethnicity[race$name == "Steven Guerin"] <- "Hispanic or Latino, White or Caucasian"
race$race_ethnicity[race$name == "Anna Weinstein-Perez"] <- "Hispanic or Latino, White or Caucasian"
race2 <- unique(race)
#it looks like someone put nothing !
# For student profiles
#write.csv(stu, "data/processed/rgk_students_spring_2022.csv", row.names = FALSE)
stu4 <- stu |>
select(
first_name,
last_name,
is_there_any_additional_information_regarding_your_availability_that_we_should_know_about,
do_you_have_access_to_transportation,
do_you_need_flexible_work_hours,
do_you_need_the_ability_to_work_remotely,
realistically_how_much_time_can_you_commit_per_week_to_working_on_a_project,
65:92
) |>
rename(
time_commitment = realistically_how_much_time_can_you_commit_per_week_to_working_on_a_project,
transportation = do_you_have_access_to_transportation,
flexible_hours = do_you_need_flexible_work_hours,
remote = do_you_need_the_ability_to_work_remotely,
spanish = please_rate_your_proficiency_with_the_following_language_note_1_no_proficiency_and_5_extremely_proficient_native_speaker_spanish) |>
mutate(nonprofit_experience = case_when(
nonprofit_experience == "No experience (yet!)" ~ "1",
nonprofit_experience == "Less than 6 months" ~ "2",
nonprofit_experience == "6 - 12 months" ~ "3",
nonprofit_experience == "1 - 2 years" ~ "4",
nonprofit_experience == "Over 2 years" ~ "5"
),
time_commitment = case_when(
time_commitment == "Less than 5 hours per week" ~ "1",
time_commitment == "5 - 10 hours per week" ~ "2",
time_commitment == "8 - 12 hours per week" ~ "3",
time_commitment == "10 - 15 hours per week" ~ "4",
time_commitment == "15 - 20 hours per week" ~ "5"
)) |>
mutate(across(7:33, as.numeric),
name = paste0(first_name," ", last_name))
stumas <- stu4
stu4 <- stu4 |>
select(name, time_commitment, 4:35) |>
arrange(str_extract(name,'\\s.*$'))
#For table
stu_table <- stu4 |>
select(-c(relevant_skills, target_population))
names(stu_table) <- gsub(pattern = "_",
replacement = " ",
x = names(stu_table))
names(stu_table) <- str_to_title(names(stu_table))
cell_color <- function(x) {
x = cell_spec(x,
color = spec_color(x, end = .7),
bold = T,
)
}
stu_table <- stu_table |>
select(-Transportation,-`Flexible Hours`, -Remote, everything()) |>
mutate(across(2:28, cell_color))
kbl(stu_table, escape = F) |>
kable_material(c("hover", "striped", "condensed"), full_width = F) |>
scroll_box(width = "100%", height = "400px")
| Name | Time Commitment | Nonprofit Experience | Tableau | Html | Arc Gis | Microsoft Office Suite | Google Data Studio | R Shiny | Statistical Analysis | Machine Learning | Sql | R | Javascript | Data Cleaning | Java | Python | Css | Power Bi | Research | Conducting Interviews | Survey Design | Logic Modeling | Outcomes Definition | Consulting | Project Management | Dei Evaluation | Spanish | Transportation | Flexible Hours | Remote |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Divya Agarwal | 2 | 3 | 3 | 4 | 1 | 5 | 3 | 1 | 3 | 3 | 4 | 1 | 5 | 1 | 4 | 4 | 4 | 4 | 4 | 5 | 5 | 2 | 4 | 1 | 5 | 1 | 1 | No | Yes | Yes |
| Francois Alexi Martel | 4 | 4 | 1 | 5 | 1 | 5 | 1 | 1 | 5 | 1 | 1 | 5 | 3 | 5 | 2 | 1 | 5 | 1 | 5 | 3 | 5 | 1 | 1 | 4 | 4 | 1 | 3 | Yes | No | Yes |
| Ahmed Almezail | 4 | 1 | 2 | 1 | 1 | 5 | 1 | 2 | 4 | 2 | 1 | 3 | 2 | 3 | 1 | 1 | 1 | 2 | 4 | 4 | 3 | 4 | 4 | 4 | 4 | 1 | 1 | Yes | Yes | No |
| Ankit Ankit | 5 | 2 | 3 | 1 | 1 | 4 | 3 | 1 | 5 | 5 | 4 | 3 | 1 | 4 | 1 | 5 | 1 | 2 | 5 | 4 | 3 | 4 | 3 | 5 | 4 | 3 | 1 | Yes | Yes | No |
| Carla Assis Lacorte | 5 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | Yes | No | No |
| Savneet Bains | 3 | 5 | 1 | 1 | 1 | 4 | 2 | 1 | 3 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 2 | 3 | 3 | 1 | 1 | 1 | 5 | 2 | 1 | No | Yes | Yes |
| Erin Benton | 3 | 5 | 1 | 1 | 2 | 5 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 3 | 1 | No | Yes | Yes |
| Debasmita Bhakta | 5 | 2 | 1 | 2 | 1 | 5 | 2 | 1 | 4 | 1 | 1 | 5 | 1 | 3 | 1 | 1 | 1 | 1 | 5 | 5 | 3 | 4 | 4 | 5 | 5 | 2 | 1 | No | Yes | Yes |
| Alex Bilski | 2 | 3 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | Yes | Yes | No |
| Morgan Brown | 3 | 4 | 1 | 1 | 1 | 4 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 4 | 4 | 2 | 2 | 3 | 1 | 2 | 1 | 1 | Yes | Yes | Yes |
| Gladys Camacho-Rios | 4 | 5 | 1 | 4 | 1 | 5 | 4 | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 5 | 4 | 3 | 3 | 2 | 5 | 1 | 5 | Yes | Yes | Yes |
| Daniela Carlock | 3 | 3 | 1 | 1 | 1 | 5 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 5 | 4 | 1 | 1 | 3 | 3 | 1 | 5 | Yes | No | No |
| Francisco Castellanos-Sosa | 4 | 2 | 3 | 1 | 3 | 5 | 1 | 5 | 5 | 5 | 1 | 5 | 1 | 5 | 1 | 4 | 1 | 2 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | Yes | No | Yes |
| Sherri Castillo | 4 | 5 | 3 | 1 | 1 | 5 | 2 | 1 | 3 | 1 | 1 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 5 | 5 | 4 | 1 | 1 | 4 | 5 | 4 | 2 | Yes | Yes | Yes |
| Johnathan Cheung | 5 | 3 | 1 | 1 | 1 | 5 | 4 | 1 | 4 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 4 | 3 | 1 | 1 | 3 | 4 | 2 | 1 | Yes | No | No |
| Hannah Claire Reyes | 2 | 5 | 1 | 1 | 1 | 4 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 3 | 5 | 4 | 1 | 2 | 3 | 5 | 1 | 2 | Yes | Yes | Yes |
| Madison Clendening | 5 | 4 | 1 | 2 | 1 | 5 | 2 | 1 | 4 | 1 | 1 | 2 | 1 | 3 | 1 | 4 | 1 | 1 | 4 | 5 | 5 | 2 | 2 | 3 | 4 | 4 | 1 | Yes | No | No |
| Ishaan Duggal | 5 | 3 | 1 | 4 | 2 | 5 | 5 | 3 | 5 | 2 | 3 | 4 | 2 | 2 | 1 | 2 | 2 | 3 | 5 | 4 | 4 | 4 | 4 | 5 | 4 | 5 | 1 | Yes | No | No |
| Ashli Duncan | 5 | 4 | 1 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 5 | 5 | 4 | 3 | 3 | 3 | 3 | 3 | 1 | Yes | Yes | Yes |
| James Eckstrom | 4 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 3 | 5 | 1 | 1 | 4 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | Yes | Yes | No |
| Tamar Farchy | 5 | 4 | 1 | 1 | 2 | 5 | 1 | 1 | 4 | 1 | 1 | 3 | 2 | 4 | 1 | 1 | 1 | 1 | 4 | 5 | 5 | 2 | 4 | 4 | 4 | 3 | 3 | Yes | No | No |
| Susmita Gangopadhyay | 4 | 4 | 1 | 1 | 1 | 5 | 1 | 1 | 4 | 1 | 4 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 5 | 4 | 5 | 3 | 4 | 4 | 4 | 2 | 1 | Yes | Yes | Yes |
| Ashley Garcia | 2 | 4 | 1 | 2 | 1 | 5 | 1 | 1 | 4 | 1 | 1 | 1 | 5 | 1 | 5 | 1 | 1 | 1 | 5 | 5 | 4 | 1 | 1 | 4 | 5 | 1 | 5 | Yes | Yes | Yes |
| Yiwei Gong | 3 | 2 | 1 | 1 | 1 | 3 | 1 | 3 | 5 | 5 | 2 | 5 | 1 | 4 | 1 | 5 | 1 | 1 | 4 | 2 | 2 | 1 | 1 | 2 | 1 | 1 | 1 | Yes | No | No |
| Abigail Grider-Reidf | 3 | 5 | 1 | 2 | 1 | 5 | 5 | 3 | 4 | 2 | 1 | 4 | 1 | 5 | 1 | 2 | 1 | 1 | 5 | 5 | 4 | 3 | 4 | 3 | 3 | 5 | 1 | No | Yes | No |
| Steven Guerin | 4 | 1 | 1 | 1 | 1 | 5 | 1 | 3 | 4 | 1 | 1 | 4 | 1 | 3 | 1 | 1 | 1 | 1 | 5 | 3 | 2 | 1 | 2 | 2 | 3 | 1 | 3 | Yes | No | No |
| Caroline Hahn | 2 | 5 | 1 | 3 | 1 | 5 | 1 | 1 | 3 | 1 | 1 | 2 | 1 | 1 | 1 | 3 | 1 | 1 | 5 | 3 | 5 | 1 | 1 | 4 | 4 | 2 | 1 | Yes | Yes | No |
| Daniella Harari | 3 | 1 | 1 | 1 | 2 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 3 | 2 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | Yes | Yes | No |
| Hadley Heckmann | 4 | 5 | 1 | 3 | 1 | 5 | 5 | 1 | 2 | 1 | 1 | 1 | 3 | 2 | 1 | 3 | 1 | 1 | 3 | 4 | 4 | 3 | 5 | 1 | 5 | 3 | 1 | Yes | No | No |
| Helen Ho | 2 | 2 | 1 | 2 | 1 | 5 | 4 | 1 | 4 | 1 | 1 | 2 | 1 | 2 | 2 | 4 | 1 | 1 | 4 | 3 | 2 | 1 | 1 | 2 | 3 | 3 | 1 | Yes | Yes | Yes |
| Gi Hong Lee | 2 | 1 | 4 | 3 | 1 | 5 | 1 | 1 | 5 | 5 | 3 | 3 | 1 | 5 | 3 | 5 | 3 | 1 | 4 | 2 | 2 | 4 | 4 | 4 | 4 | 2 | 2 | Yes | Yes | Yes |
| Katherine Hoovestol | 4 | 1 | 4 | 3 | 2 | 5 | 3 | 1 | 3 | 1 | 2 | 3 | 1 | 2 | 1 | 2 | 1 | 1 | 5 | 5 | 4 | 2 | 2 | 3 | 4 | 2 | 1 | Yes | No | Yes |
| Sherron Howard | 5 | 2 | 1 | 1 | 1 | 4 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 3 | 4 | 2 | 1 | 1 | 1 | 1 | 3 | 1 | 2 | Yes | Yes | Yes |
| Yiran Hu | 5 | 2 | 1 | 2 | 1 | 4 | 2 | 1 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 4 | 1 | 1 | 5 | 1 | 3 | 1 | 1 | 2 | 1 | 1 | 1 | Yes | No | No |
| Ming-Hung Hung | 4 | 3 | 5 | 5 | 1 | 5 | 5 | 3 | 4 | 5 | 4 | 3 | 3 | 4 | 3 | 5 | 3 | 3 | 4 | 3 | 4 | 4 | 5 | 3 | 5 | 3 | 1 | Yes | Yes | Yes |
| Ryan Hurt | 2 | 1 | 1 | 1 | 1 | 5 | 3 | 1 | 3 | 1 | 1 | 3 | 1 | 4 | 1 | 1 | 1 | 1 | 4 | 2 | 1 | 2 | 3 | 1 | 2 | 1 | 3 | Yes | Yes | Yes |
| Dhanny Indrakusuma | 5 | 2 | 4 | 3 | 1 | 5 | 1 | 1 | 3 | 3 | 3 | 1 | 1 | 3 | 1 | 4 | 3 | 1 | 2 | 2 | 1 | 2 | 3 | 4 | 4 | 1 | 1 | Yes | Yes | No |
| Pei-Syuan Jhang | 5 | 1 | 5 | 2 | 1 | 5 | 1 | 1 | 4 | 1 | 4 | 1 | 1 | 3 | 1 | 2 | 1 | 1 | 5 | 5 | 5 | 2 | 5 | 2 | 2 | 1 | 1 | Yes | Yes | Yes |
| Jenny John | 2 | 2 | 2 | 4 | 1 | 5 | 2 | 1 | 2 | 3 | 4 | 2 | 4 | 3 | 3 | 4 | 4 | 1 | 4 | 4 | 4 | 3 | 4 | 3 | 3 | 3 | 1 | Yes | Yes | Yes |
| Neha Katkar | 3 | 2 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 4 | 4 | 1 | 3 | 4 | 3 | 1 | 1 | Yes | No | No |
| Isabel Keddy-Hector | 2 | 5 | 1 | 3 | 5 | 5 | 3 | 3 | 4 | 1 | 1 | 3 | 1 | 5 | 1 | 3 | 1 | 1 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | Yes | Yes | Yes |
| Avni Kering | 2 | 5 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 4 | 5 | 1 | 4 | 3 | 4 | 1 | 1 | No | Yes | Yes |
| Jamie Kim | 5 | 2 | 1 | 1 | 5 | 5 | 1 | 1 | 3 | 1 | 2 | 4 | 1 | 4 | 1 | 2 | 1 | 1 | 5 | 5 | 4 | 1 | 1 | 3 | 1 | 1 | 3 | Yes | No | No |
| Jonathan Klan | 5 | 2 | 1 | 2 | 1 | 5 | 1 | 1 | 5 | 1 | 1 | 4 | 2 | 2 | 2 | 5 | 2 | 1 | 5 | 4 | 3 | 3 | 3 | 2 | 5 | 3 | 1 | Yes | No | No |
| Madison Laboy | 3 | 2 | 2 | 1 | 1 | 4 | 4 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 4 | 4 | 1 | 1 | 2 | 5 | 1 | 2 | Yes | Yes | No |
| Nicole Larrondo | 2 | 1 | 1 | 3 | 2 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 5 | 5 | 4 | 1 | 5 | 3 | 5 | 3 | 5 | No | Yes | Yes |
| Enrique Leon | 4 | 1 | 1 | 2 | 1 | 4 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 1 | 4 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | No | No | Yes |
| Liangchen Liu | 2 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 4 | 1 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 4 | 1 | 1 | 2 | 1 | 1 | 3 | 1 | 1 | No | Yes | No |
| KErry Maguire | 5 | 5 | 1 | 5 | 1 | 5 | 2 | 1 | 1 | 4 | 3 | 1 | 4 | 1 | 2 | 3 | 5 | 1 | 5 | 5 | 1 | 1 | 1 | 4 | 5 | 1 | 1 | Yes | Yes | Yes |
| Thomas McAuley | 5 | 1 | 1 | 1 | 1 | 4 | 1 | 1 | 4 | 1 | 1 | 3 | 1 | 1 | 1 | 4 | 1 | 1 | 3 | 3 | 1 | 1 | 1 | 1 | 5 | 1 | 2 | Yes | No | Yes |
| Brianna McBride | 4 | 5 | 1 | 1 | 1 | 5 | 2 | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 5 | 4 | 3 | 5 | 4 | 5 | 4 | 1 | Yes | Yes | Yes |
| sonali Mishra | 5 | 4 | 2 | 2 | 1 | 5 | 1 | 1 | 5 | 5 | 5 | 5 | 2 | 5 | 2 | 5 | 1 | 1 | 5 | 4 | 3 | 5 | 5 | 5 | 5 | 2 | 1 | No | Yes | Yes |
| Kristen Mosley | 2 | 5 | 1 | 1 | 1 | 5 | 2 | 1 | 4 | 1 | 1 | 4 | 1 | 5 | 1 | 1 | 1 | 1 | 5 | 5 | 4 | 3 | 4 | 5 | 4 | 3 | 1 | Yes | Yes | Yes |
| Kate Nelson | 2 | 2 | 2 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 3 | 2 | 1 | 5 | 1 | 3 | 4 | 2 | Yes | No | No |
| Monica Obregon | 5 | 2 | 1 | 1 | 1 | 3 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 3 | 2 | 3 | 2 | 2 | 1 | 1 | 1 | 1 | No | Yes | Yes |
| Maria Ortiz | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 4 | 3 | 1 | 1 | 2 | 2 | 1 | 5 | Yes | Yes | Yes |
| Hsuan Ouyang | 5 | 1 | 4 | 3 | 1 | 3 | 3 | 1 | 4 | 1 | 5 | 2 | 2 | 3 | 2 | 3 | 3 | 3 | 5 | 5 | 5 | 3 | 4 | 3 | 3 | 5 | 1 | No | No | Yes |
| Sabrina Page | 4 | 3 | 1 | 2 | 1 | 5 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 3 | 4 | 4 | 4 | 1 | 1 | 1 | 4 | 1 | 3 | Yes | No | No |
| Alexandrea Pena | 5 | 4 | 1 | 3 | 1 | 4 | 3 | 1 | 3 | 1 | 1 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 5 | 4 | 3 | 3 | 4 | 2 | 1 | 1 | 2 | Yes | No | No |
| Veronique Placke | 5 | 3 | 1 | 1 | 1 | 4 | 1 | 1 | 3 | 1 | 1 | 1 | 2 | 1 | 1 | 3 | 1 | 1 | 5 | 3 | 3 | 1 | 1 | 2 | 3 | 4 | 1 | Yes | No | No |
| Beth Prosnitz | 5 | 5 | 1 | 1 | 1 | 5 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 1 | 1 | 5 | 5 | 5 | 2 | 5 | 4 | 5 | 5 | 2 | Yes | No | Yes |
| Matthew Racchini | 2 | 5 | 1 | 1 | 1 | 5 | 1 | 1 | 3 | 1 | 1 | 3 | 1 | 2 | 1 | 4 | 1 | 1 | 4 | 3 | 2 | 1 | 2 | 3 | 3 | 2 | 1 | No | Yes | Yes |
| Tara Rastogi | 5 | 5 | 1 | 2 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 5 | 4 | 3 | 1 | 1 | 4 | 5 | 1 | 1 | Yes | No | Yes |
| Bita Razavi-Maleki | 5 | 5 | 1 | 1 | 1 | 5 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 4 | 1 | 2 | 1 | 1 | 5 | 5 | 5 | 1 | 1 | 4 | 4 | 4 | 1 | Yes | No | Yes |
| Sierra Rowe | 3 | 1 | 4 | 1 | 1 | 5 | 1 | 1 | 3 | 1 | 1 | 2 | 1 | 3 | 1 | 3 | 1 | 1 | 5 | 3 | 5 | 1 | 2 | 1 | 5 | 1 | 1 | Yes | No | No |
| Yenibel Ruiz Mirabal | 2 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 5 | 4 | 1 | 1 | 3 | 5 | 1 | 5 | Yes | Yes | Yes |
| Rania Sahail | 4 | 4 | 1 | 1 | 1 | 5 | 4 | 1 | 4 | 1 | 1 | 4 | 1 | 4 | 1 | 2 | 1 | 2 | 5 | 5 | 4 | 4 | 4 | 4 | 3 | 3 | 1 | No | Yes | Yes |
| Mikhaela Sample | 3 | 3 | 1 | 1 | 3 | 5 | 1 | 1 | 4 | 1 | 1 | 4 | 1 | 1 | 1 | 4 | 1 | 2 | 4 | 2 | 1 | 1 | 3 | 3 | 5 | 1 | 1 | Yes | No | No |
| Randi Saunders | 2 | 5 | 1 | 1 | 2 | 5 | 1 | 1 | 4 | 2 | 1 | 2 | 1 | 3 | 1 | 1 | 1 | 1 | 5 | 3 | 4 | 2 | 3 | 3 | 4 | 2 | 3 | Yes | Yes | No |
| Sarah Schott | 5 | 4 | 3 | 1 | 1 | 5 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 4 | 3 | 3 | 1 | 3 | 1 | 3 | 1 | 1 | Yes | No | No |
| Neil Schwartz | 5 | 3 | 1 | 1 | 2 | 5 | 1 | 3 | 5 | 1 | 1 | 4 | 1 | 4 | 1 | 3 | 1 | 1 | 5 | 5 | 3 | 4 | 4 | 2 | 3 | 1 | 3 | Yes | No | No |
| Nidhi Shah | 5 | 2 | 1 | 2 | 1 | 5 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 2 | 2 | 1 | 1 | 5 | 5 | 5 | 4 | 4 | 4 | 4 | 1 | 1 | No | Yes | Yes |
| Daniel Silva | 5 | 4 | 1 | 1 | 5 | 5 | 3 | 4 | 5 | 4 | 2 | 5 | 3 | 4 | 1 | 2 | 1 | 3 | 5 | 5 | 5 | 5 | 5 | 5 | 3 | 1 | 1 | Yes | Yes | Yes |
| Nishat Tasnim | 2 | 5 | 2 | 1 | 4 | 5 | 2 | 2 | 4 | 2 | 1 | 4 | 2 | 3 | 1 | 1 | 1 | 3 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 4 | 1 | No | Yes | Yes |
| Cooper Thompson | 4 | 4 | 1 | 1 | 1 | 5 | 4 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 4 | 5 | 5 | 5 | 3 | 3 | 4 | 2 | Yes | Yes | Yes |
| Kenneth Thompson | 3 | 1 | 1 | 2 | 1 | 5 | 1 | 3 | 5 | 3 | 1 | 5 | 1 | 5 | 1 | 3 | 2 | 1 | 5 | 5 | 1 | 1 | 2 | 1 | 5 | 1 | 1 | Yes | Yes | Yes |
| Kylie Tweed | 4 | 2 | 1 | 1 | 1 | 5 | 1 | 1 | 3 | 1 | 1 | 3 | 1 | 3 | 1 | 4 | 1 | 1 | 5 | 1 | 3 | 1 | 1 | 1 | 2 | 2 | 1 | Yes | No | Yes |
| Ching Tzu Wang | 4 | 1 | 2 | 1 | 1 | 4 | 2 | 1 | 4 | 1 | 2 | 2 | 1 | 3 | 1 | 3 | 1 | 3 | 1 | 2 | 4 | 1 | 3 | 1 | 3 | 1 | 1 | No | Yes | Yes |
| Fan Wang | 1 | 1 | 4 | 2 | 1 | 4 | 3 | 2 | 4 | 2 | 1 | 4 | 1 | 4 | 1 | 3 | 1 | 1 | 4 | 4 | 4 | 3 | 3 | 4 | 4 | 2 | 1 | Yes | Yes | Yes |
| Ziyi Wang | 5 | 4 | 1 | 1 | 1 | 5 | 2 | 1 | 3 | 1 | 3 | 2 | 1 | 3 | 1 | 4 | 1 | 2 | 4 | 4 | 3 | 2 | 3 | 2 | 2 | 2 | 1 | Yes | Yes | Yes |
| Jidong Wang | 3 | 2 | 1 | 1 | 1 | 5 | 1 | 1 | 4 | 3 | 2 | 3 | 1 | 4 | 1 | 4 | 1 | 1 | 4 | 1 | 2 | 4 | 1 | 1 | 2 | 1 | 1 | Yes | Yes | Yes |
| Yiwen Wang | 5 | 1 | 1 | 2 | 2 | 5 | 2 | 1 | 1 | 1 | 3 | 3 | 1 | 1 | 1 | 3 | 1 | 1 | 3 | 3 | 1 | 1 | 3 | 4 | 4 | 1 | 1 | Yes | Yes | Yes |
| Anna Weinstein-Perez | 4 | 4 | 3 | 1 | 1 | 4 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 5 | 4 | 1 | 1 | 2 | 2 | 2 | 5 | Yes | Yes | Yes |
| Jaekyung Willows | 5 | 2 | 3 | 3 | 3 | 4 | 1 | 1 | 3 | 3 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 4 | 4 | 4 | 2 | 2 | 2 | 4 | 3 | 1 | Yes | Yes | Yes |
| Brie Winnega Reamer | 2 | 5 | 2 | 1 | 1 | 5 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 5 | 5 | 5 | 1 | 1 | 4 | 4 | 3 | 2 | Yes | Yes | No |
| Hannah Wold | 5 | 5 | 1 | 2 | 1 | 5 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 2 | 4 | 4 | 4 | 2 | 5 | 4 | 1 | Yes | No | Yes |
| Jennifer Wong | 5 | 5 | 2 | 1 | 1 | 5 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 3 | 3 | 4 | 4 | 4 | 1 | 3 | 1 | 1 | Yes | Yes | No |
| Xiaohan Wu | 5 | 4 | 5 | 4 | 1 | 5 | 2 | 5 | 5 | 5 | 5 | 5 | 3 | 5 | 3 | 5 | 1 | 1 | 5 | 4 | 5 | 5 | 5 | 5 | 5 | 2 | 1 | Yes | Yes | Yes |
| Ziyue Xu | 5 | 1 | 1 | 1 | 1 | 5 | 1 | 1 | 5 | 4 | 5 | 3 | 1 | 5 | 1 | 3 | 1 | 1 | 5 | 5 | 4 | 4 | 4 | 4 | 3 | 3 | 1 | Yes | No | No |
| Jessica Young | 2 | 2 | 1 | 1 | 4 | 5 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 5 | 5 | 3 | 1 | 3 | 1 | 5 | 4 | 1 | Yes | Yes | Yes |
| Kai Yue Charm | 5 | 2 | 3 | 1 | 2 | 4 | 1 | 2 | 5 | 4 | 3 | 5 | 1 | 5 | 1 | 3 | 1 | 1 | 5 | 5 | 5 | 4 | 3 | 4 | 3 | 2 | 1 | Yes | No | No |
| Pin Yun Lin | 4 | 1 | 1 | 1 | 1 | 4 | 1 | 1 | 5 | 4 | 2 | 5 | 1 | 5 | 1 | 3 | 1 | 1 | 4 | 3 | 3 | 5 | 3 | 4 | 4 | 2 | 1 | Yes | No | No |
| Shiyang Zhang | 5 | 3 | 3 | 1 | 1 | 5 | 2 | 1 | 5 | 4 | 2 | 4 | 1 | 5 | 1 | 4 | 1 | 1 | 5 | 2 | 5 | 4 | 2 | 2 | 4 | 1 | 1 | Yes | No | No |
| Haokun Zhang | 5 | 2 | 1 | 1 | 1 | 5 | 1 | 1 | 3 | 1 | 1 | 2 | 1 | 3 | 1 | 2 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 2 | 3 | 1 | 1 | Yes | Yes | Yes |
| Lucy Zhang | 5 | 3 | 5 | 1 | 1 | 5 | 4 | 4 | 5 | 5 | 5 | 5 | 1 | 5 | 1 | 5 | 1 | 5 | 3 | 3 | 3 | 2 | 1 | 1 | 3 | 1 | 1 | Yes | No | Yes |
Overall, 95 students applied during this cohort.
Student Location
We do not collect student addresses, but Qualtrics does
record the longitude and latitude of where students fill out the form.
Therefore, the following map is only an estimate of where our applicants
live.
Other student information
The following table includes students’ responses to open ended questions. It’s impossible to list every single skill that might be relevant for a project on the survey (although we have tried), so the relevant skills question is attempt to fill that gap. In addition, for the first time a question about target population was added to the survey. This came out of our diversity, equity, and inclusion evaluation that was conducted during our summer cohort. Students voiced their desire to specify which populations they are most interested in. As an open ended question, we can not use target population in the algorithm in its present state. However, we will have the opportunity to improve the question methodology in subsequent cohorts to give students more of a say in the organizations they work with.
Show code
stu_table2 <- stumas |>
select(name, relevant_skills, target_population, is_there_any_additional_information_regarding_your_availability_that_we_should_know_about) |>
rename(additional_information = is_there_any_additional_information_regarding_your_availability_that_we_should_know_about) |>
mutate(relevant_skills = replace_na(relevant_skills, ""),
target_population = replace_na(target_population, ""),
additional_information = replace_na(additional_information, "")) |>
left_join(race2, by = "name")
names(stu_table2) <- gsub(pattern = "_",
replacement = " ",
x = names(stu_table2))
names(stu_table2) <- str_to_title(names(stu_table2))
kbl(stu_table2, escape = F) |>
kable_minimal(c("hover"), full_width = F) |>
scroll_box(width = "100%", height = "400px")
| Name | Relevant Skills | Target Population | Additional Information | Race Ethnicity |
|---|---|---|---|---|
| Neha Katkar | UX Research, UX Design including high fidelity prototypes | nothing specific | Asian | |
| Tara Rastogi | I am currently taking a course on Accessibility, which may be relevant. | I am open to working with any population. | Asian | |
| Abigail Grider-Reidf | French, interviewing | Interested in all populations | White or Caucasian | |
| Erin Benton | Remote only | White or Caucasian | ||
| Ziyue Xu | I also speak Mandarin Chinese. | Asian | ||
| Sabrina Page | Public Financial Management, Empirical methods, Public Management, Policy Development | Prison population, immigrant/asylees/refugees, low-income | White or Caucasian | |
| Yiran Hu | I can speak Chinese and I can read some French. | Asian | ||
| Ahmed Almezail | Arabic language, Time Series Analysis Course, Statistical Learning & Data Mining Course, Using STATA Software for Econometrics and Data Analysis | I am glad to help any those in need :) | My commitment in the summer could be higher than 20 hours a week and no need to be flexible. | I prefer not to say |
| KErry Maguire | I am interested in the arts, LGBTQ+, women’s issues. I am also interested in active and accessible transportation. | White or Caucasian | ||
| Nicole Larrondo | DSLR Photography; Advanced skills in Adobe Photoshop; Intermediate skills in Adobe premiere; Course CRP 386 Art of Community Engagement | Preferrebly, Latino and/or Mexican-American communities | Not 100% sure about this but I plan to visit my country during summer | Hispanic or Latino |
| Ankit Ankit | Writing research reports, conducting academic research, literature review, conducting interviews | No | None | Asian |
| Ashley Garcia | Brazilian Portuguese (beginner level), expertise in border, gender, and studies of violence, oral history, web design, feature writing, photography | I am mostly interested in working in an environment that uplifts immigrant populations; Latinx youth, and (or) women of color. | I am completing my fieldwork in Monterrey, Mexico. I am planning to move back to Austin in mid-summer. | Hispanic or Latino |
| Xiaohan Wu | STATA, Mandarin, Japanese | Will have more time to commit during the summer time rather than the fall. | Asian | |
| Enrique Leon | Fundamentals of data science and machine learning, Methods of Applied Mathematics I, Methods of Applied Mathematics II, Data analysis research in astrophysics | Hispanic or Latino, Mestizo | ||
| Daniel Silva | econometric modeling, policy analysis, agricultural economics | I have a preference for remote work. | Hispanic or Latino | |
| Francisco Castellanos-Sosa | Hispanic or Latino, White or Caucasian | |||
| Shiyang Zhang | Chinese, Data analysis using Stata, Course: End of life communication | I am particularly interested in older adults, but I am also absolutely open to any other populations. | I am an international student with F1 visa and I will need support for CPT (Curricular Practical Training) status | Asian |
| Yiwei Gong | Mandarin, German | Asian | ||
| Kai Yue Charm | Chinese | Asian | ||
| Gladys Camacho-Rios | I am Bolivian, Speaking and reading ability in French, Speaker of Quechua, experience with indigenous languages in the Americas | The Americas, particularly with South America. | American Indian | |
| Katherine Hoovestol | German (fluent), Norwegian (elementary proficiency), Audience Development & Engagement Course (Spring 2022; includes large amounts data collection & interpretation and client work), WordPress, Omeka | While I honestly would love to help out wherever I am best suited should there be a place, I have specifically been seeing how school-aged children have been affected by this pandemic, particularly children who are otherwise marginalized. However, like I said, if there is space for me to be helpful somewhere, I would love to do it. | White or Caucasian | |
| Sherri Castillo | Qualitative researcher, background in classroom instruction, curriculum design, effective assessment | LGBTQ+ populations, pre-service teachers | White or Caucasian | |
| Francois Alexi Martel | I worked for a year as a web developer in Boston consulting with nonprofit clients. Skilled at Research, Data Analysis, and Project Management. Fluent in French, Proficient in Spanish | I’m open to help whomever is in need! | White or Caucasian | |
| Nidhi Shah | Wireframe, Story-boarding, Information Architecture, Prototyping, User Flow, User Journey, Interaction Design, 3D Modelling, Gant - Charts, UX research, user interviews, heuristic Evaluation, Card Sorting, Affinity Mapping, Competitive Analysis, Moodboards, Empathy Mapping, contextual inquiry, A/B Testing, Adobe XD, Figma, InDesign, Lightroom, Photoshop, Illustrator, Dreamweaver, Blender, HTML, Miro, Mural, Human-AI Interaction, Visualization, Accessible User Experience, Usability, Quantifying User Experience, Interaction Design | Asian | ||
| Mikhaela Sample | Taken: Excel VBA and data visualization using python | White or Caucasian | ||
| Beth Prosnitz | NA | |||
| James Eckstrom | Cleanroom experience, chemical synthesis, 2 years ROTC leadership experience with leadership positions assigned to me, teaching science to 5th grade classroom | I would like to work with populations who frequently experience lack of access to water and other resources. Anything involving public services, and their disparate access/provision to certain populations, would interest me. | Hispanic or Latino, White or Caucasian | |
| Jonathan Klan | White or Caucasian | |||
| Jenny John | Cloud Computing, Technical Writing, User Experience Research | I would be glad to work with any organization but I am especially interested in NGOs working on women, children and sustainability issues. | I am an international student in the first semester of her studies at UT. | Asian |
| Jamie Kim | Courses include Public Management, Policy Development, Public Financial Management, Applied Microeconomics for Policy Analysis, Communications,Urban Economics, Digital Equity, Justice, and Inclusion | Asian | ||
| Gi Hong Lee | Korean | No | Asian | |
| Jaekyung Willows | Qualitative data analysis/interpretation/ data visualization/report | Minorities ) Asians if possible | Asian | |
| Alexandrea Pena | Extensive training and experience working with children and families from diverse backgrounds; extensive training in working with children with disabilities; extensive training in policies as they relate to children, disability rights, familial safeguards, and education. | No, although most of my experience is working with other educators as well as children with disabilities and their families from diverse cultural and linguistic backgrounds. | N/A | Hispanic or Latino |
| Kylie Tweed | I am currently enrolled in a microeconomics section that teaches Python for policy analysis | any! | White or Caucasian | |
| Carla Assis Lacorte | Speak Portuguese, Writing in the Science Course, Tesol certification | Women, Children | Hispanic or Latino | |
| Monica Obregon | School Finance and Equity, underserved communities | Hispanic or Latino | ||
| Ishaan Duggal | Asian | |||
| Isabel Keddy-Hector | Interested in DEI and Environmentally-related projects | White or Caucasian | ||
| Kristen Mosley | White or Caucasian | |||
| Cooper Thompson | Japanese language, Courses taken: Survey Design, Nonprofit Program Evaluation, Research Methods | White or Caucasian | ||
| Madison Laboy | Developed surveys for Gender Health Equity Research Lab and various courses, enrolled in Introduction: R course (completion May 2022) | Women’s Health is a passion of mine, however, I am open to working with any organization as I am grateful for any opportunity. | I have not received my class schedule for Summer or Fall, Summer I could most likely commit additional time | Hispanic or Latino, White or Caucasian |
| Pin Yun Lin | Native Chinese speaker | The LGBTQ+ community | Asian | |
| Johnathan Cheung | Stata, Statistics for Political Scientists Course, Introduction to Cybersecurity | Under-resourced students, minority students, | Asian | |
| Brie Winnega Reamer | disability related organizations, healthcare communication and access | White or Caucasian | ||
| Bita Razavi-Maleki | Experience working in non-profit organizations; Leadership Experience; Fluent in Persian (Farsi) | I have experience working with various populations and do not have a preference at this time. | Middle Eastern | |
| Sherron Howard | Social Determinants of Health, Qualitative Research Methods, Research Methods | African American | Black or African American | |
| Ming-Hung Hung | Japanese, Chinese; Taking AI in Health, Data mining, Visualization, and Intro to Machine Learning courses | No | Asian | |
| Susmita Gangopadhyay | Languages: English, Bengali, Hindi, Courses (current semester): Thin film mechanics, Carbon and 2D devices | Asian | ||
| Jessica Young | community engagement, mentoring, teaching workshops, Canva, InDesign/Inkscape, urban planning, community engagement, advanced Excel | I am in Boston this summer doing a full-time internship, but would work on this evenings/weekends. I can take a lunchtime meeting as needed, and anything 4pm CST or later, as EST is an hour ahead. | White or Caucasian | |
| Liangchen Liu | Applied Math, Deep Learning, Data Science | In this summer (June - August), I will be a virtual participant in a graduate research program at National Institute of Standards and Technology, managed by NSF, which is supposed to be a full-time commitment and will be given my top priority. In the fall I will be a full-time student apparently so my availability is mostly limited. But I’m very interested in this project idea and the motivation behind, and I would like to contribute to any of the project. (For the working remotely option listed below, it would be ideal if I could have the choice, but for most of the times I will be around so I put “no” there) | Asian | |
| Tamar Farchy | Excel, Empricial Methods, Research Design, Nonprofit | I’m interested in resiliency and sustainability for under-served populations, so I’d be interested in helping an organization that works with low-income or immigrant communities. | Asian, White or Caucasian | |
| Savneet Bains | I am well versant with English, Hindi, and Punjabi languages. I have undertaken Introduction to Social Statistics this semester and understood the basics of quantitative research. | My research interests are focussed on First-generation learners, and women in higher education. However, I am willing to pursue a project with any and every population. My aspiration is to work with a non-profit organization, hone my data analysis skills, and contribute towards the social sector. | Asian | |
| Ryan Hurt | White or Caucasian | |||
| Dhanny Indrakusuma | I plan to enroll for self-learning online courses over the summer and will be taking 9 credit hours during the Fall semester. | NA | ||
| Haokun Zhang | Mandarin Chinese; Python, Big Data and SQL; Casual Inference | Asian | ||
| Thomas McAuley | White or Caucasian | |||
| Debasmita Bhakta | Hindi, Bengali, Marathi | I would love to work with organizations that are in the fields of technology, health, gender and/or intersection of technology with social policy. | Asian | |
| Rania Sahail | Languages: Urdu, Hindi, Punjabi, English — Courses: Evaluation methods and design, development economics, Analytical methods, STATA and R | I hope to work with marginalized communities, young adults, and individuals with learning disabilities but am excited to work with anyone trying to make an impact. My interests lie in gender and health equity, economic mobility, social protection and human development outcomes. | I would prefer remote work till mid-june and am happy to be partially in-person after that. | Asian |
| Sarah Schott | Nonprofit Management, Intro to Empirical Methods, Mixed Methods for Policy Analysis | Children and families. | White or Caucasian | |
| Pei-Syuan Jhang | Mandarin, Data Storytelling class, Data Wrangling class, Usability class, Information Architecture & Design class, Understand & Serving User class, Accessible User Experience class, Design Thinking class, Presenting Information class | I’m interested in working with any kind of group. | Asian | |
| Kate Nelson | Fluent in French, taken courses and written on equity and diversity in higher education; I’m hoping that I can improve my qualitative research skills and learn new tech skills to help me transition to an alt-ac career after graduation | I’m happy to work with any populations, but would prefer not to work with faith-based organizations. I am really excited to learn new technicals skills as part of the CONNECT fellowship. | Prefer projects that require around 5 hours/week, are remote and do not require time outside of typical office hours. | White or Caucasian |
| Randi Saunders | domestic violence survivors; sexual assault survivors; individuals experiencing mental health challenges; families of incarcerated persons; formerly incarcerated persons | There will be a couple of weeks (early June and mid-August) where I will be out of town for academic conferences | White or Caucasian | |
| Kenneth Thompson | I will be out of town and will need to work remotely on the following dates: June 2-6, June 25-29, July 16-23 | White or Caucasian | ||
| Fan Wang | Mandarin | female, mother | Asian | |
| Alex Bilski | I speak Polish | Immigrants, refugees, women | White or Caucasian | |
| Brianna McBride | I am well versed in design thinking methodologies | I would love to work with populations of color, and specifically Black populations. Additionally, underserved populations, and anything that centers diversity and inclusion. | Black or African American | |
| Madison Clendening | Stata, Jamovi, Empirical Methods (course), Nonprofit Management (course), Reproductive Health and Policy (course) | I am very interested in working with the LGBTQ+ community. | Currently, I have open availability but depending on pay I might have to work additional hours to supplement my income. | White or Caucasian |
| Ziyi Wang | Mandarin; Course I’ve taken: program evaluation, strategic planning, probability theory and mathematical statistics, practice for regional development analysis, gender and development, environmental and resources economics and policy, climate change and development. | The target population I’m particularly interested with are women (e.g. gender equity programs) and people who suffer from environmental and energy risks, but I’m quite open and happy to work with other target populations as well! | Asian | |
| Ching Tzu Wang | I can speak, read and write Mandarin. | No. | Asian | |
| Lucy Zhang | Additional Technical Skills: SQL, SAS; Other Languages: Mandarin; Summer Course Planned: Machine Learning | I have no preference over this. Would enjoy working with any target populations. | Asian | |
| Helen Ho | Vietnamese, French | Immigrant, Minority, and Low-Income Populations | Asian | |
| Veronique Placke | speak dutch, former educator | I am specifically interested in an organization that serves families that are victims of domestic violence, or helps families gain access to resources with the intention of achieving equitable educational outcomes for children. | White or Caucasian | |
| Matthew Racchini | Funded qualitative historical research (Ramonat Seminar) (senior year of college), econometric study using NLSY data (senior year of college), helped to lead focus groups as data collection for a Don’t Pack a Pest for Academic Travelers project in previous job (2021), currently providing consulting and research for Ethik Collective in helping them to define living wage and fair trade standards for artisan groups they work with in Rwanda, Uganda, India, etc. I am happy to explain any of these projects in an interview. | I have some interest in working with refugees/immigrant populations, but I am open to any opportunity that matches my skillset. | I expect to work a full-time internship during the summer, so I see the Connect program as a supplemental consulting activity. It will also likely be in Washington, DC, requiring me to work remotely for the RGK Center. | White or Caucasian |
| Steven Guerin | I have a minor in Chinese, will be enrolling in R and Python classes this summer | Hispanic or Latino, White or Caucasian | ||
| Maria Ortiz | I am fluent in Spanish, and have a lot of experience with research | Older adults, immigrants, marginalized populations and LGBTQIA individuals. | I can do evenings and weekends | Hispanic or Latino |
| Neil Schwartz | Non-Profit Program Evaluation, Analytical Methods, Data Visualization and Econometrics, Writing, Refugees and Human Security | Based on my teaching experience, I am interested in working with children in education and educational equity. I also have experience working with refugees, immigrants, asylum-seekers, and other vulnerable groups. Finally, I enjoy staying active and supporting athletes. | White or Caucasian | |
| sonali Mishra | I have taken courses in public policy such as economic policymaking, technology policy. I am also well versed at writing documents (academic and non-academic) | All of the CONNECT opportunities look very exciting. But if I had to choose I would love to work for a role that offers policy research with data analysis and visualization. | Asian | |
| Ashli Duncan | qualitative research, Instructional design | I am interested in working for an organization improving educational outcomes for students and supporting diversity in teachers. | Black or African American | |
| Nishat Tasnim | Language: Bengali (Native), Hindi (Basic) | Marginalized population, women and youth | 5-7 hours | Asian |
| Caroline Hahn | I have data analytics experience in a non-profit setting (using Salesforce) from my job as a teacher where I specialized reading interventions based on measured student progress; I’ve done qualitative policy research, stakeholder engagement, and survey design as part of my consulting work at a national education nonprofit (TNTP); I volunteer at a nonprofit now that has applied for CONNECT (Healing with Horses Ranch) and already know about some of their data/research needs! | Not specifically, although as I’ve indicated here, I’m really interested in working with the nonprofit I’m currently volunteering with. They work with at-risk youth, people with disabilities, veterans, and really anyone dealing with mental health or general life challenges. | White or Caucasian | |
| Sierra Rowe | I am currently working on a survey project so my experience in this area will only continue to increase | As a generalist I am open to working with any population. | Working between 10 and 12 hours would be ideal for me. | White or Caucasian |
| Jidong Wang | I speak Mandarin | I am attending academic conferences in late May and part of August, so I may need to participate part of the program remotely. | Asian | |
| Hannah Claire Reyes | Speak Tagalog (Filipino), taking Data Visualization class this semester | Immigrant populations and mental health, poverty alleviation (workforce development, education in and outside the classroom, health access) | Most interested in learning about social entrepreneurship and investing for social good | Asian |
| Divya Agarwal | accessible UX, HCI, Usability, Design, UX Research, 3+ yrs experience working as a developer, open to learning new skills | Asian | ||
| Anna Weinstein-Perez | I am open to lots of different populations! Children and adolescents, the Latinx community, traumatized populations, and more. | I will be taking 2 summer classes (6 hours) at SHSSW. | Hispanic or Latino, White or Caucasian | |
| Morgan Brown | During my undergraduate career, I was selected to be a program coordinator for a new program within the Department of Multicultural Services and participated in research with faculty members in this program. I learned skills such as : Curriculum Creation and Agenda Setting, Co- Facilitating Focus Groups, Social Media Engagement,Literature Reviews, Recruitment, Coordinating Operational Needs. I also was a Student Fee Advisory Board Member, which gave me insight on how programming at Texas A&M is funded. During the course of my other internships and jobs, I have learned to Database Management, Scheduling, Mentorship and Community Outreach and learned very Basic Spanish. | I am interested in working with any population that is underserved or marginalized. | The time commitment may fluctuate especially in the month of June but at least 10 hours a week is guaranteed and can increase if need be. | Black or African American |
| Yiwen Wang | My native language is Chinese, I have taken courses like Data Mining and Statistics, R. | I’m interested in working with organizations which focus on ESG (Environment, Social, Governance) factors nowadays. | Asian | |
| Daniella Harari | I have limited working proficiency in Hebrew, and I am currently taking Analytical Methods and have some experience with Stata. | Preferably one that deals with refugees, but not picky! | Hispanic or Latino | |
| Hannah Wold | I used to work as a grant writer and fiscal sponsorship manager for an arts nonprofit, I made reports for the board, for the annual report, and for grants reports, I’m going to start research for my thesis this summer about equity in grant making in arthouse cinemas | I’m happy to work with anyone! I have experience in arts and youth programming work, but I’m also totally happy to get experience outside of those sectors. | I’ll be out of town for the month of July, so would have to work remotely | White or Caucasian |
| Hsuan Ouyang | I’ve also taken the Accessible user experience course. | Asian | ||
| Yenibel Ruiz Mirabal | Migrants | I | I prefer not to say | |
| Jennifer Wong | Proficient in Cantonese (spoken), basic in Mandarin (but planning to improve my Mandarin by taking classes this summer and/or fall), proficient in Excel (have done pivot tables & bivariate regression), basic in STATA (took a crash course at a university in Uganda so while I cannot claim proficiency I have some background) | Since I am a global policy student, I am particularly interested in programs with an international or cross-cultural component. I am also interested in programs relating to criminal justice, anti-corruption, anti-trafficking, disaster planning & response, and sustainable city design. | I may need some accommodations due to health issues. | Asian |
| Avni Kering | Other languages- Hindi | Marathi | Marwari, Have experience working as a designer and workshop trainer while collaborating with artisans and NGOs at the grassroots in India, Have taken courses in Accessible User Experience | Human-Computer Interaction | Interaction Design | Usability | Human- AI Interaction, Interested in learning/increasing skills in logic modelling | DEI evaluation | Tableau | Google Data Studio | Power BI | data collection and tool development | measurement strategy | Would be nice to work with individuals with disabilities Have experience working in the skill development, textile and fashion space, would like to work in the sector again | Asian | |
| Hadley Heckmann | French: professional proficiency; taking a computer coding class this summer 2022, extensive experience with grant coordination/writing and program evaluation | LGBTQIA | I prefer not to say | |
| Daniela Carlock | I am fluent in Spanish and English. I am resourceful and creative. I am skilled at working with teams/ communication. I worked in two nonprofits in my undergraduate degree: Houston Aphasia Recovery Center and iEducate. I am currently getting my practicum at a nonprofit: William’s Community School. | The environment, women, people with disabilities. | Hispanic or Latino |
Student Skills
The following chart was constructed by counting every student who put at least a 2 under each skill. The most popular skills were Microsoft Office Suite and program evaluation skills. Those that selected outcomes definition and logic modeling were much fewer in comparison to program evaluation. Therefore, it might make sense next semester to remove program evaluation and focus on specific program evaluation skills.
Show code
stu2 <- stu |>
select(first_name, 66:90) |>
rename(spanish = "please_rate_your_proficiency_with_the_following_language_note_1_no_proficiency_and_5_extremely_proficient_native_speaker_spanish")
names(stu2) <- gsub(pattern = "_",
replacement = " ",
x = names(stu2))
names(stu2) <- str_to_title(names(stu2))
stu3 <- stu2 |>
pivot_longer(col = 2:26,
names_to = "Skill",
values_to = "Rating") |>
#Setting students who put 1 to NA
mutate(Rating = na_if(Rating, 1)) |>
drop_na() |>
group_by(Skill) |>
count()
theme_set(theme_classic())
g <- ggplot(stu3, aes(x = reorder(Skill, n), y = n, fill = n)) +
geom_bar(stat = "identity") +
geom_text(aes(label = n, y = n),
position = position_stack(vjust = 0.5),
size = 6) +
coord_flip() +
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_blank(),
text = element_text(size = 16,
face = "bold"),
axis.title.x = element_blank(),
axis.title.y = element_blank(),
axis.ticks = element_blank(),
axis.text.x = element_blank(),
plot.title = element_text(hjust = 0.5),
plot.margin = margin(10, 10, 10, 10),
legend.title = element_blank(),
legend.position = "none"
) +
labs(title = "Student Skills",
caption = "**Calculated by summing skills rated as higher than 1") +
scale_fill_distiller(palette = "RdYlGn", direction = 1)
g
Matching
In order to use the matching Algorithm, students and organizations have to be assigned a utility ratings. These ratings are formulated by comparing how similar the students’ skills and nonprofit experience are to the project expectations.
Each student receives a utility score for each organization, and each organization is assigned a utility sore for each student. This ensures that both the student’s and organization’s rankings are taken into account for the matching process. For example, if an organization assigns two students the same utility score, but one student has a higher utility score for that organization, this student is selected and the other student returns to the selection pool.
Prematches
Before computing the utility scores, we need to remove students and organizations that have already been matched.
Show code
stu4 <- stu4 |>
filter(name != "Brie Winnega Reamer",
name != "Ziyue Xu",
name != "Francisco Castellanos-Sosa",
name != "Rania Sahail",
name != "Abigail Grider-Reidf")
org3 <- org3 |>
filter(organization != "Texas Census 2030",
organization != "Austin Asian Impact",
organization != "Keep Austin Fed")
Utility Score Example
The following section illustrates how utility scores are formulated. The code block below was used to calculate Divya Agarwal’s utility rating for 2022-03-21 22:27:51. Each skill is compared by subtracting the student value from the organization’s. If the result equals 0 or greater, that means the student’s skill rating meets or exceeds what the organization’s project requires and the skill receives a utility rating of 1. If the result is a negative number, that means the organizations requirements exceed what the student put on their survey form, and they receive a utility rating less than one, depending on how far the student’s answer was from the organizations.
Additionally, if a student put a 1 on a skill, which means they have no background in it, they automatically receive a zero utility rating. Students who don’t match the organization based on the logistics questions, also receive a utility rating of zero.
Finally, all of the skill utility ratings are added up to produce one utility rating for Divya Agarwal and 2022-03-21 22:27:51. The higher the number, the greater the utility the student will have for that project.
** Note! The skills must be named the same thing otherwise it will not work
Show code
#Initial Data Manipulation
org1 <- org3[1, ] |>
pivot_longer(9:34, names_to = "variable", values_to = "org_value")
stu1 <- stu4[1, ] |>
pivot_longer(6:31, names_to = "variable", values_to = "student_value")
#Assigning a utility score for each skill
match1 <- left_join(org1, stu1, by = "variable") |>
mutate(
subtracted = student_value - org_value,
utility = case_when(
subtracted >= 0 ~ 1,
subtracted == -1 ~ .75,
subtracted == -2 ~ .5,
subtracted == -3 ~ .25,
subtracted == -4 ~ 0
),
#if the student put 1 on their survey it means
#they would provide 0 utility for that skills
utility = case_when(
student_value == 1 ~ 0,
TRUE ~ utility),
#If the students logistics don't match up with the orgs
#Then the utility for that student changes to 0
utility = case_when(
transportation.x == "Yes" & transportation.y == "No" ~ 0,
flexible_hours.x == "No" & flexible_hours.y == "Yes" ~ 0,
remote.x == "No" & remote.y == "Yes" ~ 0,
TRUE ~ utility
),#If the orgs time commitment is greater than what the student
#is willing to work their utility is set to 0
utility = case_when(
time_commitment.x > time_commitment.y ~ 0,
TRUE ~ utility
)
)
utility_score <- sum(match1$utility)
print(paste0(stu1[1,1],"'s utility for ",org1[1,1], ": ", utility_score))
[1] "Divya Agarwal's utility for Allies Against Slavery: 0"Student Utility Scores
First each student receives a utility score for each organization.Higher student utility scores mean that an organization is better suited to work with that student.
Show code
#utility students
column_names <- stu4$name
row_names <- org3$organization
uS <- matrix(nrow = length(org3$project_name),
ncol = length(stu4$name),
dimnames = list(row_names, column_names))
for (i in 1:length(org3$project_name)) {
for (j in 1:length(stu4$name)) {
org1 <- org3[i, ] |>
pivot_longer(9:34, names_to = "variable", values_to = "org_value")
stu1 <- stu4[j, ] |>
pivot_longer(6:31, names_to = "variable", values_to = "student_value")
match1 <- left_join(org1, stu1, by = "variable") |>
mutate(
subtracted = student_value - org_value,
utility = case_when(
subtracted >= 0 ~ 1,
subtracted == -1 ~ .75,
subtracted == -2 ~ .5,
subtracted == -3 ~ .25,
subtracted <= -4 ~ 0
),
utility = case_when(
student_value == 1 ~ 0,
TRUE ~ utility),
utility = case_when(
transportation.x == "Yes" & transportation.y == "No" ~ 0,
flexible_hours.x == "No" & flexible_hours.y == "Yes" ~ 0,
remote.x == "No" & remote.y == "Yes" ~ 0,
TRUE ~ utility
),
utility = case_when(time_commitment.x > time_commitment.y ~ 0,
TRUE ~ utility)
)
utility_score <- sum(match1$utility)
uS[i,j] <- utility_score
}
}
Organization Utility Scores
Next the organization utility scores are calculated using the same process except the order of the dataframes is changed to give organizations priority. Higher organization utility scores mean that the student is better suited to the organization’s project. These ratings can be validated by reviewing students resumes when necessary.
Additionally, a table is created with all of the organization utility scores. Whenever we want to view student replacements we can use this table to select other high scores. The reason why we only look at the organization utility scores is because we’re more concerned with finding a good fit for the organization.
Show code
#utility organizations
row_names <- stu4$name
column_names <- org3$project_name
uO <- matrix(nrow = length(stu4$name),
ncol = length(org3$project_name),
dimnames = list(row_names, column_names))
for (j in 1:length(stu4$name)) {
for (i in 1:length(org3$project_name)) {
org1 <- org3[i,] |>
pivot_longer(9:34, names_to = "variable", values_to = "org_value")
stu1 <- stu4[j,] |>
pivot_longer(6:31, names_to = "variable", values_to = "student_value")
match1 <- left_join(org1, stu1, by = "variable") |>
mutate(
subtracted = org_value - student_value,
utility = case_when(
subtracted <= 0 ~ 1,
subtracted == 1 ~ .75,
subtracted == 2 ~ .5,
subtracted == 3 ~ .25,
subtracted >= 4 ~ 0
),
utility = case_when(
org_value == 1 ~ 0,
TRUE ~ utility),
utility = case_when(
transportation.x == "Yes" & transportation.y == "No" ~ 0,
flexible_hours.x == "No" & flexible_hours.y == "Yes" ~ 0,
remote.x == "No" & remote.y == "Yes" ~ 0,
TRUE ~ utility
),
utility = case_when(time_commitment.x > time_commitment.y ~ 0,
TRUE ~ utility)
)
utility_score <- sum(match1$utility)
uO[j, i] <- utility_score
}
}
#Table Styling
cell_color <- function(x) {
x = cell_spec(x,
color = spec_color(x, end = .7),
bold = T,
)
}
uO_table <- as.data.frame(uO) |>
mutate(across(1:17, cell_color))
kbl(uO_table, escape = F) |>
kable_material(c("hover", "striped", "condensed"), full_width = F) |>
scroll_box(width = "100%", height = "400px")
| Allies Against Slavery | Austin Bat Cave | Communities for Recovery | Community Tech Network | Connected Nation | Dress For Success Austin | E3 Alliance | Future Front Texas | Go!Austin/Vamos!Austin (GAVA) | Hispanic Scholarship Consortium | Leadership Equality Academy | Miracle Foundation | North Lamar-Georgian Acres Neighborhood Team | Philanthropy Southwest | StandUpLD | The LiveLikeLou Foundation | Youth in View | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Divya Agarwal | 0 | 8 | 0 | 6 | 3.75 | 4.5 | 3.75 | 3.25 | 3.5 | 4.75 | 0 | 0 | 5.5 | 5.25 | 7 | 6 | 0 |
| Francois Alexi Martel | 3.5 | 7.75 | 5.25 | 5.75 | 6 | 4 | 5 | 5 | 4.25 | 6 | 4.5 | 7.25 | 6 | 7 | 6.75 | 5.5 | 5 |
| Ahmed Almezail | 2.75 | 7.5 | 5.5 | 5.25 | 4.25 | 4.5 | 4.25 | 5 | 3.75 | 5.75 | 4.5 | 6.5 | 6 | 5.5 | 7.5 | 5.25 | 4 |
| Ankit Ankit | 3.75 | 8 | 5.75 | 5.25 | 6.25 | 5 | 5.5 | 5 | 4.5 | 6.75 | 5.25 | 7 | 6.25 | 7.5 | 8 | 5.5 | 4.5 |
| Carla Assis Lacorte | 0.5 | 5.75 | 2.75 | 3.75 | 1.5 | 2.25 | 2 | 2.25 | 2.5 | 2.75 | 2.5 | 3.25 | 4 | 2.5 | 5 | 3.25 | 1.25 |
| Savneet Bains | 0 | 6.25 | 3.75 | 4.75 | 3.25 | 3.25 | 3.25 | 3.75 | 2.75 | 4.25 | 3.25 | 5.25 | 4.25 | 4.5 | 5.75 | 3.75 | 2.75 |
| Erin Benton | 0 | 9 | 8 | 7 | 5 | 6.5 | 3.75 | 4.5 | 5 | 5.5 | 6 | 8.75 | 7.5 | 5.75 | 9 | 7 | 3.75 |
| Debasmita Bhakta | 2.75 | 8.25 | 6.5 | 5.75 | 4.75 | 5 | 4.75 | 5 | 4.5 | 5.5 | 5.75 | 7.25 | 6.5 | 6 | 8.25 | 6 | 4.5 |
| Alex Bilski | 0 | 5.75 | 0 | 3.75 | 1.75 | 2.5 | 2.5 | 2.75 | 2.75 | 3.25 | 0 | 0 | 4 | 3.5 | 5.25 | 3.5 | 0 |
| Morgan Brown | 0 | 6.75 | 4.25 | 4.5 | 2.25 | 3.25 | 2.75 | 3 | 3.5 | 3.5 | 3.75 | 4.75 | 5 | 3.75 | 6.25 | 4.5 | 2 |
| Gladys Camacho-Rios | 1 | 9.25 | 6.25 | 6 | 3 | 4.5 | 3 | 3.5 | 4 | 4 | 5 | 6.25 | 6.5 | 4.25 | 7.25 | 5.75 | 2.5 |
| Daniela Carlock | 0 | 8.5 | 4.75 | 5 | 2.75 | 3.75 | 3 | 3.75 | 3.5 | 4.25 | 4.25 | 5.5 | 5.25 | 4.5 | 6.5 | 5.5 | 2.75 |
| Sherri Castillo | 2.25 | 8 | 5.75 | 5.75 | 4.75 | 4.5 | 3.5 | 4.25 | 4.25 | 5.5 | 5.25 | 7.25 | 5.75 | 5.75 | 7 | 5.75 | 3.5 |
| Johnathan Cheung | 2 | 7.25 | 4.5 | 5 | 4 | 3.75 | 4 | 4.5 | 3.5 | 5 | 4.25 | 5.5 | 5 | 5.5 | 6.75 | 5 | 3.75 |
| Hannah Claire Reyes | 0 | 8 | 0 | 6 | 3.5 | 4 | 3.25 | 3.75 | 3.75 | 4.5 | 0 | 0 | 5.5 | 4.75 | 6.5 | 5.5 | 0 |
| Madison Clendening | 2 | 8.5 | 6 | 6 | 5 | 5.25 | 4.75 | 4.75 | 4 | 5.75 | 4.75 | 7.25 | 5.75 | 6.25 | 7.25 | 6 | 3.75 |
| Ishaan Duggal | 2.75 | 8.5 | 6.5 | 6 | 5.25 | 6.25 | 4.75 | 4.75 | 4.75 | 5.5 | 5.5 | 7.25 | 6.75 | 6.25 | 8.5 | 6.25 | 4.25 |
| Ashli Duncan | 1 | 8.75 | 6.25 | 5.75 | 3 | 5.25 | 3 | 3.75 | 4.5 | 4.25 | 5 | 6.25 | 6.25 | 4.5 | 8 | 6.25 | 2.75 |
| James Eckstrom | 1 | 5.25 | 2.75 | 3.5 | 3 | 2.5 | 3.5 | 2.75 | 2.75 | 3.75 | 2.25 | 3.5 | 3.5 | 4 | 5 | 3.25 | 1.75 |
| Tamar Farchy | 2.5 | 9.25 | 6.5 | 6.5 | 5.25 | 5.5 | 4.25 | 5 | 4.5 | 5.75 | 5.25 | 8.25 | 6.75 | 6 | 7.75 | 6.5 | 4.25 |
| Susmita Gangopadhyay | 1.5 | 8.75 | 6.75 | 6.5 | 4 | 5.5 | 3.5 | 4.25 | 4.75 | 4.75 | 5.25 | 7.5 | 6.75 | 5 | 8.25 | 6.5 | 3.25 |
| Ashley Garcia | 0 | 8.5 | 0 | 5.75 | 3.75 | 3.75 | 3.25 | 4 | 4.25 | 4.5 | 0 | 0 | 6 | 4.75 | 6.75 | 5.75 | 0 |
| Yiwei Gong | 0 | 5.25 | 2.5 | 3.25 | 5 | 2.25 | 5.5 | 4.25 | 2.5 | 5.5 | 2.5 | 4.5 | 3.5 | 6.75 | 5 | 3.25 | 4.25 |
| Steven Guerin | 2.5 | 6.5 | 3.5 | 4 | 3.5 | 2.75 | 4.5 | 4.75 | 3.25 | 5.25 | 3.5 | 4.75 | 5 | 5.25 | 6 | 4.25 | 4 |
| Caroline Hahn | 0 | 7.5 | 0 | 5.75 | 4.25 | 4.25 | 3.75 | 4 | 4.25 | 5 | 0 | 0 | 5.5 | 5.25 | 7 | 5.5 | 0 |
| Daniella Harari | 0 | 5 | 2 | 3 | 1.5 | 2 | 2.5 | 2.75 | 2 | 3.25 | 2 | 2.75 | 3 | 3 | 4.5 | 2.75 | 1.75 |
| Hadley Heckmann | 1 | 8 | 6 | 6.25 | 3.25 | 5.25 | 3.5 | 3.25 | 3.5 | 4.25 | 4.25 | 6.5 | 6 | 4.5 | 7.5 | 5.5 | 2.25 |
| Helen Ho | 0 | 6.25 | 0 | 4 | 3.75 | 3.25 | 4.5 | 4.25 | 3 | 5.25 | 0 | 0 | 4.25 | 5.5 | 6 | 4 | 0 |
| Gi Hong Lee | 0 | 7.25 | 0 | 4.75 | 6.5 | 4.5 | 5.5 | 5 | 3.75 | 7 | 0 | 0 | 6 | 7.75 | 7.25 | 4.5 | 0 |
| Katherine Hoovestol | 2.5 | 7.75 | 5.25 | 5 | 4.5 | 4 | 4 | 4.5 | 3.5 | 5.75 | 4.5 | 5.75 | 5.25 | 5.75 | 7 | 5.5 | 3.5 |
| Sherron Howard | 1 | 5.5 | 2.5 | 3.25 | 2 | 2.25 | 3 | 3.25 | 2.75 | 3.75 | 2.75 | 3.25 | 4 | 3.75 | 5 | 3.25 | 2.25 |
| Yiran Hu | 1 | 5.25 | 2.75 | 3.25 | 2.25 | 2.5 | 3 | 2.75 | 2.75 | 3.75 | 2.5 | 2.75 | 3.5 | 4 | 5.25 | 3.5 | 1.75 |
| Ming-Hung Hung | 4 | 8.25 | 6.5 | 6.5 | 6.5 | 6 | 5.25 | 5 | 4 | 6.75 | 4.75 | 7.5 | 6.75 | 8 | 8.5 | 6 | 4.25 |
| Ryan Hurt | 0 | 6 | 0 | 3.25 | 2.75 | 2.5 | 3.75 | 4 | 2.75 | 4.75 | 0 | 0 | 4.5 | 4.5 | 5.25 | 3.25 | 0 |
| Dhanny Indrakusuma | 2.75 | 6.25 | 3.5 | 4.25 | 5 | 3.5 | 4 | 4.25 | 3.5 | 5.75 | 3.75 | 5 | 4.75 | 6.25 | 5.75 | 3.75 | 3.25 |
| Pei-Syuan Jhang | 2.75 | 7.5 | 5.5 | 5.25 | 3.75 | 4.25 | 3.75 | 4 | 3.25 | 5.25 | 4 | 6.5 | 5.25 | 5.5 | 7 | 6 | 3 |
| Jenny John | 0 | 8.25 | 0 | 5.5 | 4 | 5.25 | 4.25 | 4.25 | 3.75 | 5.5 | 0 | 0 | 5.75 | 5.75 | 7.75 | 5.75 | 0 |
| Neha Katkar | 0 | 7.5 | 4.75 | 5.25 | 2.25 | 4 | 2.5 | 3.25 | 4 | 3.75 | 4.5 | 5.25 | 5 | 3.75 | 6.75 | 5.5 | 2.25 |
| Isabel Keddy-Hector | 0 | 9.75 | 0 | 7 | 6.75 | 7 | 4.75 | 5 | 5 | 6.5 | 0 | 0 | 8 | 6.75 | 9 | 7 | 0 |
| Avni Kering | 0 | 8 | 0 | 6.5 | 2.75 | 4.75 | 2.5 | 3.25 | 4.25 | 3.75 | 0 | 0 | 6 | 4 | 7.25 | 6.25 | 0 |
| Jamie Kim | 2.5 | 7.25 | 4.25 | 4.25 | 4.75 | 3 | 4 | 4.25 | 3 | 5.25 | 3.75 | 5.5 | 4.75 | 5.5 | 6 | 5 | 3.75 |
| Jonathan Klan | 2.5 | 7.75 | 5.5 | 5.25 | 5 | 4.5 | 5.5 | 4.5 | 3.75 | 5.5 | 4.75 | 6.25 | 5.75 | 6.5 | 7.5 | 5.25 | 4 |
| Madison Laboy | 0 | 7 | 4.5 | 5 | 2.75 | 3.25 | 3 | 3.5 | 3 | 4.25 | 4 | 4.75 | 4.75 | 4.25 | 6 | 4.75 | 2.5 |
| Nicole Larrondo | 0 | 8.75 | 0 | 6 | 2.5 | 4.75 | 2.75 | 3.25 | 3.75 | 4 | 0 | 0 | 6.25 | 3.75 | 7.5 | 6.25 | 0 |
| Enrique Leon | 1.25 | 5 | 1.75 | 2.25 | 2 | 1.5 | 3.25 | 2.75 | 2.25 | 3.75 | 2 | 2.25 | 3.5 | 3.75 | 4.25 | 2.5 | 1.75 |
| Liangchen Liu | 0 | 5 | 0 | 2.75 | 2.5 | 2.25 | 3.75 | 3 | 2.5 | 4 | 0 | 0 | 3.5 | 4.25 | 4.75 | 2.75 | 0 |
| KErry Maguire | 1 | 6.75 | 5 | 5 | 3.5 | 3 | 3 | 3.25 | 4.25 | 4.25 | 5.25 | 5.25 | 5.5 | 4.5 | 6.25 | 5 | 2.25 |
| Thomas McAuley | 1.75 | 5.5 | 2.75 | 3.75 | 3.5 | 2 | 4.5 | 4 | 2.25 | 5 | 3 | 4 | 4 | 5 | 4.75 | 3 | 3 |
| Brianna McBride | 1 | 8.75 | 7 | 6.75 | 3.5 | 6 | 3 | 3.75 | 4.5 | 4.25 | 5.5 | 7.25 | 6.75 | 4.5 | 8.25 | 6.5 | 2.75 |
| sonali Mishra | 4.25 | 8.25 | 7.25 | 6.5 | 7.25 | 5.75 | 6 | 5 | 5 | 6.75 | 5.75 | 8.25 | 7.5 | 8.25 | 9 | 6.25 | 5 |
| Kristen Mosley | 0 | 8.75 | 0 | 6.25 | 5.5 | 5.5 | 4.5 | 5 | 5 | 6 | 0 | 0 | 6.75 | 6.5 | 8.25 | 6.5 | 0 |
| Kate Nelson | 0 | 6.75 | 0 | 4.75 | 1.75 | 4.25 | 2.5 | 2.75 | 3.5 | 3.5 | 0 | 0 | 5.5 | 3.5 | 7 | 5 | 0 |
| Monica Obregon | 1 | 5.75 | 2.75 | 3.5 | 1.5 | 2.75 | 2.5 | 2.75 | 2.25 | 3.25 | 2.25 | 3.5 | 3.5 | 3.25 | 5.25 | 3.25 | 1.75 |
| Maria Ortiz | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Hsuan Ouyang | 2.75 | 8.5 | 6 | 5.5 | 4.5 | 5.75 | 4.5 | 4.75 | 3.75 | 6.25 | 4.5 | 6.75 | 5.75 | 6.25 | 7.75 | 6.25 | 3.75 |
| Sabrina Page | 1.25 | 7.25 | 4.25 | 4.75 | 2.75 | 3.25 | 3.25 | 3.5 | 3 | 4 | 3.75 | 5.25 | 5 | 4.25 | 6 | 4.75 | 2.5 |
| Alexandrea Pena | 1 | 7.5 | 5.25 | 5 | 2.25 | 4 | 2.5 | 3 | 3.75 | 3.5 | 4 | 5.25 | 6 | 3.75 | 7.25 | 5.25 | 2 |
| Veronique Placke | 1 | 6.75 | 4 | 4.5 | 3 | 4 | 3.5 | 3.5 | 3.5 | 4.5 | 3.75 | 4.5 | 4.75 | 4.75 | 6.75 | 4.75 | 2.5 |
| Beth Prosnitz | 2.5 | 9 | 7.25 | 7 | 5 | 6.25 | 4 | 4.5 | 4.75 | 5.5 | 5.75 | 9 | 7 | 6 | 8.25 | 7 | 4 |
| Matthew Racchini | 0 | 7 | 0 | 5 | 4.5 | 3.75 | 4.5 | 4.5 | 4 | 5.5 | 0 | 0 | 5.25 | 6 | 6.75 | 4.75 | 0 |
| Tara Rastogi | 0.5 | 7 | 5.25 | 5.5 | 3 | 3.5 | 2.5 | 3.25 | 4.25 | 3.75 | 5 | 5.5 | 5.5 | 4 | 6.75 | 5.25 | 2.25 |
| Bita Razavi-Maleki | 1.75 | 8 | 5.75 | 5.75 | 4.5 | 4.75 | 3.75 | 4.25 | 4.25 | 5.25 | 5 | 7 | 5.5 | 5.5 | 7 | 6 | 3.5 |
| Sierra Rowe | 0 | 6.5 | 4.5 | 5 | 4 | 3.25 | 4.25 | 4 | 3 | 5.5 | 3.75 | 5.5 | 4.5 | 5.5 | 6 | 4.75 | 3 |
| Yenibel Ruiz Mirabal | 0 | 8.25 | 0 | 5.25 | 2.25 | 3.5 | 2.5 | 3.25 | 3.25 | 3.75 | 0 | 0 | 5.25 | 3.5 | 6.25 | 5.25 | 0 |
| Mikhaela Sample | 0 | 6.5 | 4.25 | 5 | 5.25 | 3.5 | 4.75 | 4.5 | 4 | 5.5 | 4.5 | 5.25 | 5.25 | 6.25 | 6.25 | 4.5 | 3.75 |
| Randi Saunders | 0 | 8.5 | 0 | 6 | 4.5 | 4.75 | 4 | 4.75 | 4.5 | 5.25 | 0 | 0 | 6.75 | 5.5 | 7.75 | 5.75 | 0 |
| Sarah Schott | 2 | 6.75 | 4.25 | 5 | 3.5 | 3.5 | 3.5 | 3.75 | 3.75 | 4.75 | 3.75 | 5.5 | 5 | 5 | 6.5 | 4.75 | 2.75 |
| Neil Schwartz | 3 | 8.5 | 6 | 5.25 | 5.25 | 4.75 | 5.25 | 4.75 | 4 | 6 | 4.75 | 7 | 6.75 | 6.75 | 8 | 6 | 4.5 |
| Nidhi Shah | 0.5 | 8.5 | 6.75 | 6 | 2.75 | 5.25 | 2.75 | 3.25 | 4.25 | 4 | 5.25 | 6.25 | 6.5 | 4 | 8 | 6.5 | 2.25 |
| Daniel Silva | 3.75 | 8.75 | 7.5 | 6.5 | 6.75 | 6 | 5.25 | 5 | 5 | 6 | 5.5 | 8.5 | 7.25 | 7 | 8.75 | 7 | 5 |
| Nishat Tasnim | 0 | 9 | 0 | 7 | 6 | 6.75 | 4.5 | 5 | 5 | 5.75 | 0 | 0 | 7.5 | 6.25 | 9 | 7 | 0 |
| Cooper Thompson | 1 | 9 | 7.25 | 6.5 | 3 | 6.75 | 3 | 3.75 | 4.5 | 4.25 | 4.75 | 6.25 | 7.5 | 4.5 | 9 | 6.75 | 2.75 |
| Kenneth Thompson | 0 | 6 | 4 | 4 | 5 | 2.25 | 5.5 | 4.5 | 3 | 6 | 4.25 | 5.75 | 4.5 | 6.5 | 5.5 | 4.25 | 4.5 |
| Kylie Tweed | 2 | 5.5 | 2.75 | 3.25 | 3.5 | 2.75 | 4.5 | 4 | 2.75 | 5 | 2.5 | 3.75 | 3.5 | 5.25 | 5.5 | 3.5 | 3 |
| Ching Tzu Wang | 2.25 | 5.75 | 2.75 | 3.75 | 3.5 | 3.25 | 4 | 4.25 | 2.25 | 5.5 | 2.25 | 4.5 | 3.25 | 5.25 | 5 | 3.5 | 3.25 |
| Fan Wang | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Ziyi Wang | 1.75 | 7.5 | 4.75 | 5 | 4 | 4 | 4.25 | 4 | 3.75 | 5 | 4 | 5.75 | 5.25 | 5.5 | 7 | 5 | 3 |
| Jidong Wang | 0 | 5.5 | 3 | 3 | 4 | 3 | 4.75 | 4.25 | 2.5 | 5.5 | 2.25 | 4 | 4 | 5.75 | 5.5 | 3 | 3.5 |
| Yiwen Wang | 1 | 6.25 | 3.5 | 4.5 | 3.5 | 3 | 3.5 | 3.75 | 3.5 | 4.75 | 4 | 4.25 | 4.75 | 4.5 | 5.75 | 4 | 2.75 |
| Anna Weinstein-Perez | 1.25 | 8.25 | 4.5 | 4.75 | 2.75 | 3.5 | 2.5 | 3 | 3.25 | 4 | 3.75 | 5 | 5 | 4.25 | 6.25 | 5 | 2 |
| Jaekyung Willows | 3 | 7.5 | 4.75 | 5 | 4.5 | 4.25 | 4 | 4.5 | 3.25 | 5.5 | 4 | 5.75 | 5 | 5.5 | 6.75 | 5 | 3.5 |
| Hannah Wold | 0.75 | 7.75 | 6 | 6 | 2.75 | 5.75 | 2.75 | 3.25 | 4 | 3.75 | 4.25 | 5.75 | 6.25 | 4 | 7.75 | 5.25 | 2.25 |
| Jennifer Wong | 1.5 | 7.5 | 5.25 | 5.5 | 3 | 4.75 | 3.25 | 3.5 | 3.5 | 4.25 | 3.5 | 5.5 | 5.75 | 4.5 | 7.25 | 5 | 2.5 |
| Xiaohan Wu | 5 | 8.75 | 7.75 | 7 | 8 | 6.25 | 6 | 5 | 5 | 7 | 5.75 | 8.75 | 7.5 | 9 | 9 | 6.75 | 5 |
| Jessica Young | 0 | 7.25 | 0 | 5 | 3.75 | 4 | 3.5 | 3.75 | 3.5 | 4.5 | 0 | 0 | 5 | 4.5 | 6.75 | 5.25 | 0 |
| Kai Yue Charm | 4.5 | 8.75 | 6.25 | 5.5 | 6.5 | 5.25 | 5.5 | 5 | 4.25 | 7 | 5 | 7.5 | 6 | 7.75 | 8 | 6.25 | 5 |
| Pin Yun Lin | 4 | 7.5 | 5.25 | 5 | 5.75 | 4.75 | 5.5 | 5 | 3.75 | 6.5 | 4.25 | 6.5 | 6 | 7 | 7.75 | 4.75 | 5 |
| Shiyang Zhang | 4.25 | 7.5 | 5.5 | 5.25 | 6.25 | 4.75 | 5.5 | 4.75 | 3.75 | 6.75 | 4 | 6.5 | 5.75 | 7.75 | 7.25 | 5.25 | 4.5 |
| Haokun Zhang | 1.75 | 5.25 | 2.5 | 3.25 | 3.25 | 2.5 | 4 | 4.25 | 3 | 5 | 2.75 | 3.75 | 3.75 | 5 | 5 | 3.25 | 3.25 |
| Lucy Zhang | 5 | 6.5 | 3.5 | 4.25 | 6.75 | 3.25 | 6 | 4.5 | 2.75 | 6.5 | 3 | 5.5 | 4.25 | 8.5 | 6 | 4 | 4.5 |
The Gale Shapley Algorithm
And finally matching!
The matchingR package was used to quickly compute the
Gale-Shapley algorithm. The algorithm was devised to create satisfactory
matches between two parties, in this case between organizations and
students. It has been used commercially on match making websites, in
college admissions, and by the healthcare industry to match organ donors
with organ recipients. The former being the major impetus for the
creators to receive the Nobel Prize in Economics in 2012. You can find a
full description of the package and literature behind the Gale-Shapley
algorithm here.
Show code
#Matching Algorithm
matching = galeShapley.marriageMarket(uS, uO)
Matching Results
The table below shows the matching results. Matches should be validated by reviewing student resumes to ensure they have relevant work experience. The table also shows a list of all the top scorers for each project. Program managers can use this list to help them find a match if the first wasn’t suitable.
Show code
#Creating a list of top scorers
uO2 <- as.data.frame(uO)
org_options <- tibble::rownames_to_column(uO2, "Student")
tops <- list()
for (i in 2:ncol(org_options)) {
org_top <- org_options |>
slice_max(org_options[,i], n = 3)
tops[[i]] <- paste(org_top$Student, collapse=", ")
}
tops <- tops[-1]
top_df <- data.frame(matrix(unlist(tops), nrow=length(tops), byrow=TRUE))
top_df$matrix.unlist.tops...nrow...length.tops...byrow...TRUE.<-
gsub("\\."," ",top_df$matrix.unlist.tops...nrow...length.tops...byrow...TRUE.)
#Creating a data frame for table
org_info <- org3 |>
select(project_name)
students_matched <- stu4 |>
slice(matching$engagements) |>
bind_cols(org_info) |>
bind_cols(top_df) |>
rename(Student = name,
Project = project_name,
"Top Scores" = "matrix.unlist.tops...nrow...length.tops...byrow...TRUE.") |>
select(Project, Student,"Top Scores")
var_label(students_matched$Project) <- NULL
kbl(students_matched) |>
kable_material(c("hover","striped"), full_width = F) |>
scroll_box(width = "100%", height = "600px")
| Project | Student | Top Scores |
|---|---|---|
| Allies Against Slavery | Lucy Zhang | Xiaohan Wu, Lucy Zhang, Kai Yue Charm |
| Austin Bat Cave | Tamar Farchy | Isabel Keddy-Hector, Gladys Camacho-Rios, Tamar Farchy |
| Communities for Recovery | Erin Benton | Erin Benton, Xiaohan Wu, Daniel Silva |
| Community Tech Network | Xiaohan Wu | Erin Benton, Isabel Keddy-Hector, Beth Prosnitz, Nishat Tasnim, Xiaohan Wu |
| Connected Nation | Gi Hong Lee | Xiaohan Wu, sonali Mishra, Isabel Keddy-Hector, Daniel Silva, Lucy Zhang |
| Dress For Success Austin | Isabel Keddy-Hector | Isabel Keddy-Hector, Nishat Tasnim, Cooper Thompson |
| E3 Alliance | sonali Mishra | sonali Mishra, Xiaohan Wu, Lucy Zhang |
| Future Front Texas | Pin Yun Lin | Francois Alexi Martel, Ahmed Almezail, Ankit Ankit, Debasmita Bhakta, Tamar Farchy, Gi Hong Lee, Ming-Hung Hung, Isabel Keddy-Hector, sonali Mishra, Kristen Mosley, Daniel Silva, Nishat Tasnim, Xiaohan Wu, Kai Yue Charm, Pin Yun Lin |
| Go!Austin/Vamos!Austin (GAVA) | Kristen Mosley | Erin Benton, Isabel Keddy-Hector, sonali Mishra, Kristen Mosley, Daniel Silva, Nishat Tasnim, Xiaohan Wu |
| Hispanic Scholarship Consortium | Kai Yue Charm | Gi Hong Lee, Xiaohan Wu, Kai Yue Charm |
| Leadership Equality Academy | Debasmita Bhakta | Erin Benton, Debasmita Bhakta, sonali Mishra, Beth Prosnitz, Xiaohan Wu |
| Miracle Foundation | Beth Prosnitz | Beth Prosnitz, Erin Benton, Xiaohan Wu |
| North Lamar-Georgian Acres Neighborhood Team | Nishat Tasnim | Isabel Keddy-Hector, Erin Benton, sonali Mishra, Nishat Tasnim, Cooper Thompson, Xiaohan Wu |
| Philanthropy Southwest | Ming-Hung Hung | Xiaohan Wu, Lucy Zhang, sonali Mishra |
| StandUpLD | Cooper Thompson | Erin Benton, Isabel Keddy-Hector, sonali Mishra, Nishat Tasnim, Cooper Thompson, Xiaohan Wu |
| The LiveLikeLou Foundation | Daniel Silva | Erin Benton, Isabel Keddy-Hector, Beth Prosnitz, Daniel Silva, Nishat Tasnim |
| Youth in View | Francois Alexi Martel | Francois Alexi Martel, sonali Mishra, Daniel Silva, Xiaohan Wu, Kai Yue Charm, Pin Yun Lin |
