original <- read_xlsx("C:/Users/Dulce Si/Documents/UTA/Bi-National Health Fair/Clean BNHF Data/2024_HF_clean_18+.xlsx")

df <- original %>% 
  select(gender, hispanic, race, immigrant, borncountry, birthyear, marital_status,
         edu, children, hf_attend, commute_min, commute_type, hf_aware, insurance,
         insurance_type, barrier_health, barrier_health_detail, informal_info,
         informal_info_detail, informal_facility, informal_facility_detail,
         informal_behavior, informal_behavior_detail)

Datasets

I only included the dataset with particpants over the age of 18. Then I selected key variables to perform descriptive statistics

Data Cleaning and Mutating Variables

df <- df %>% 
  mutate(gender = recode(gender,
                         "1" = "Female",
                         "0" = "Male"))
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `gender = recode(gender, `1` = "Female", `0` = "Male")`.
## Caused by warning:
## ! Unreplaced values treated as NA as `.x` is not compatible.
## Please specify replacements exhaustively or supply `.default`.
df <- df %>% 
  mutate(immigrant = recode(immigrant,
                            "1" = "Immigrant",
                            "0" = "U.S. Native"))
df <- df %>% 
  mutate(hispanic = recode(hispanic,
                           "1" = "Hispanic",
                           "0" = "Non-Hispanic"),
         insurance = recode(insurance,
                            "1" = "Insurance",
                            "0" = "No Insurance"))
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `hispanic = recode(hispanic, `1` = "Hispanic", `0` =
##   "Non-Hispanic")`.
## Caused by warning:
## ! Unreplaced values treated as NA as `.x` is not compatible.
## Please specify replacements exhaustively or supply `.default`.
df <- mutate_if(df, is.character, as.factor)

df$age <- 2025 - df$birthyear

Descriptives

n = 88 observations

Ethnicity

Characteristic N = 881
hispanic
    Hispanic 85 (99%)
    Non-Hispanic 1 (1.2%)
    Unknown 2
1 n (%)

Immigration Status

Characteristic N = 871
immigrant
    Immigrant 80 (92%)
    U.S. Native 7 (8.0%)
1 n (%)

Insurance Status

Characteristic N = 841
insurance
    Insurance 14 (17%)
    No Insurance 70 (83%)
1 n (%)
Characteristic N = 491
insurance_type
    Does not know 7 (14%)
    Employer-Sponsored 2 (4.1%)
    Indian Health Service 0 (0%)
    Medicare 1 (2.0%)
    Military 0 (0%)
    None 35 (71%)
    Prefer not to answer 0 (0%)
    Private 2 (4.1%)
    State-sponsored 2 (4.1%)
1 n (%)

Gender

Characteristic N = 881
gender
    Female 61 (70%)
    Male 26 (30%)
    Unknown 1
1 n (%)

Education

Characteristic N = 751
edu
    Some College 13 (18%)
    College Graduate 14 (19%)
    High school diploma 22 (30%)
    na 0 (0%)
    Less than HS 25 (34%)
    Unknown 1
1 n (%)

Gender and Insurance

df %>% 
  drop_na(barrier_health_detail) %>% 
  select(barrier_health_detail) %>%
  mutate(barrier_health_detail = recode(barrier_health_detail,
                                        "multiple barriers" = "Multiple Barriers",
                                        "appointment" = "Appointment",
                                        "no insurance" = "No Insurance",
                                        "other barrier" = "Other",
                                        "no barrier" = "None",
                                        "paying for services" = "Affordability",
                                        "appointment" = "Difficulty finding Appointments",
                                        "language translation" = "Translation",
                                        "languange translation" = "Translation",
                                        "citizenship status" = "Citizenship Status",
                                        "paying for serivces" = "Affordability",
                                        "distance" = "Distance")) %>% 
                                        
  tbl_summary()
Characteristic N = 761
barrier_health_detail
    Appointment 4 (5.3%)
    Citizenship Status 4 (5.3%)
    Distance 1 (1.3%)
    Translation 2 (2.6%)
    Multiple Barriers 36 (47%)
    None 8 (11%)
    No Insurance 9 (12%)
    Other 2 (2.6%)
    Affordability 10 (13%)
1 n (%) 
df %>% 
  drop_na(barrier_health) %>% 
  mutate(barrier_health = recode(barrier_health,
                              "0" = "No",
                              "1" = "Yes")) %>% 
  ggplot(aes(barrier_health)) +
  geom_bar(alpha = 0.7)+
  theme_bw()+
  theme(plot.title = element_text(vjust = 1, hjust = .5),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())+
  labs(title = "Attendees with Barriers to Healthcare",
       x = "Barrier to healthcare",
       y = "Number")

df %>% 
  drop_na(informal_info_detail) %>% 
  select(informal_info_detail) %>%
  tbl_summary()
Characteristic N = 771
informal_info_detail
    church 1 (1.3%)
    clinic 9 (12%)
    community events 17 (22%)
    employer 1 (1.3%)
    family/friend 6 (7.8%)
    multiple places 21 (27%)
    online 11 (14%)
    other 11 (14%)
1 n (%) 
df %>% 
  drop_na(informal_info) %>% 
  mutate(informal_info = recode(informal_info,
                              "0" = "No",
                              "1" = "Yes")) %>% 
  ggplot(aes(informal_info)) +
  geom_bar(alpha = 0.7)+
  theme_bw()+
  theme(plot.title = element_text(vjust = 1, hjust = .5),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())+
  labs(title = "Attendees Who Rely on Informal access to Healthcare Information",
       x = "Rely on Informal Information",
       y = "Number")

df %>% 
  drop_na(informal_facility_detail) %>% 
   select(informal_facility_detail) %>%
   mutate(informal_facility_detail = recode(informal_facility_detail,
                      "clinic" = "Clinic",
                      "other" = "Other",
                      "community event" = "Community Event",
                      "pharmacy" = "Pharmacy",
                      "church" = "Church",
                      "multiple places" = "Multiple Places",
                      "other" = "Other",
                      "none" = "None")) %>% 
                      
  tbl_summary()
Characteristic N = 791
informal_facility_detail
    Church 2 (2.5%)
    Clinic 21 (27%)
    Community Event 4 (5.1%)
    ER 8 (10%)
    Multiple Places 28 (35%)
    None 3 (3.8%)
    Other 7 (8.9%)
    Pharmacy 6 (7.6%)
1 n (%) 
df %>% 
  drop_na(informal_facility) %>% 
  mutate(informal_facility = recode(informal_facility,
                              "0" = "No",
                              "1" = "Yes")) %>% 
  ggplot(aes(informal_facility)) +
  geom_bar(alpha = 0.7)+
  theme_bw()+
  theme(plot.title = element_text(vjust = 1, hjust = .5),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())+
  labs(title = "Attendees who use Informal Facilities to Receive Healthcare",
       x = "Informal Facility Use",
       y = "Number")

df %>% 
  drop_na(informal_behavior_detail) %>% 
  select(informal_behavior_detail) %>%
   mutate(informal_behavior_detail = recode(informal_behavior_detail,
                      "non-medical purchase" = "Unregulated Purchasing",
                      "natural remedy" = "Natural Remedy",
                      "self-medicate" = "Self-Medicate",
                      "med outside US" = "Medical Tourism",
                      "cut/skip doses" = "Cut/Skip Doses",
                      "multiple" = "Multiple Behaviors",
                      "none" = "None"
                                            )) %>% 
  tbl_summary()
Characteristic N = 771
informal_behavior_detail
    Cut/Skip Doses 7 (9.1%)
    Medical Tourism 4 (5.2%)
    Multiple Behaviors 26 (34%)
    Natural Remedy 22 (29%)
    Unregulated Purchasing 3 (3.9%)
    None 11 (14%)
    Self-Medicate 4 (5.2%)
1 n (%) 
df %>% 
  drop_na(informal_behavior) %>% 
  mutate(informal_behavior = recode(informal_behavior,
                              "0" = "No",
                              "1" = "Yes")) %>% 
  ggplot(aes(informal_behavior)) +
  geom_bar(alpha = 0.7)+
  theme_bw()+
  theme(plot.title = element_text(vjust = 1, hjust = .5),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank())+
  labs(title = "Attendees who exhibit at least one informal healthcare behavior",
       x = "Informal Medical Behavior",
       y = "Number")