raw-tables.qmd

---
title: "HOMBRES Youth Data"
author: "Weize Wang, Ana Bravo"
format: pdf
editor: visual
---

```{r}
#| label: load-packages
#| code-summary: "packages"
#| message: false


library(haven)
library(gt)
library(gtsummary)
library(tidyverse)

```

```{r}
#| label: data-import
#| message: false
#| echo: false


```

```{r}
#| label: table-summary
#| tbl-cap: Summary of HOMBRES Youth Data Variables
#| message: false
#| echo: false

### table prep ##### 

table_1 <- 
  list(
    "tbl_summary-str:default_con_type" = "continuous2",
    "tbl_summary-str:continuous_stat" = c(
      "{median} ({p25} - {p75})",
      "{mean} ({sd})",
      "{min} - {max}"
  ),
  
  "tbl_summary-str:categorical_stat" = "({p}%)",
    "style_number-arg:big.mark" = "",
    "tbl_summary-fn:percent_fun" = function(x) style_percent(x, digits = 1)
  )



# subset for table #####

table_subset <- copy_clean_HOMBRES_Y %>% 
  select(
    Redcap_event_name, Language_pref, Total_household_n, Currently_in_school, MACV_RSP, MACV_REL, Machismo_SS,
    Caballerismo_SS, HSI_Y, FES_COFLT, FES_RELIG, FES_CONTROL, BAS_Americanism, BAS_Hispanicism,  SemiRural_or_urban, Parent_marital_status, Pastmonth_total_income, Group
  )



table_subset_2 <- table_subset %>% 
  mutate(Redcap_event_name = case_when(
    Redcap_event_name == "first_interview_arm_1" ~ "Baseline",
    Redcap_event_name == "second_post_interv_arm_1" ~ "6 month follow-up"
  )) %>% 
  mutate(Language_pref = case_when(
    Language_pref == "1" ~ "English",
    Language_pref == "2" ~ "Spanish",
    Language_pref == "3" ~ "Other"
  )) %>% 
  mutate(SemiRural_or_urban = case_when(
    SemiRural_or_urban == "1" ~ "Semi-rural",
    SemiRural_or_urban == "2" ~ "Urban"
  )) %>% 
  mutate(Pastmonth_total_income = case_when(
    Pastmonth_total_income == "1" ~ "0 - $999",
    Pastmonth_total_income == "2" ~ "$1,000 - $1,999",
    Pastmonth_total_income == "3" ~ "$2,000 or more"
  )) %>% 
  mutate(Group = case_when(
    Group == "I" ~ "Intervetion",
    Group == "C" ~ "Control"
  )) 



  
## table ####

gtsummary::set_gtsummary_theme(table_1)


table_subset_2 %>% 
  gtsummary::tbl_summary(
    by = Redcap_event_name,
    list(
      Language_pref ~ "Language Preference",
      Total_household_n ~ "Total household individuals",
      Currently_in_school ~ "Currently in school",
      MACV_RSP ~ "Respect subscale score",
      MACV_REL ~ "Religion score",
      Machismo_SS ~ "Machismo score",
      Caballerismo_SS ~ "Caballerismo score",
      HSI_Y ~ "Adolescent Hispanic stress score",
      FES_COFLT ~ "FES conflict score",
      FES_RELIG ~ "Moral religion score",
      FES_CONTROL ~"FES control score",
      BAS_Americanism ~ "Americanism score",
      BAS_Hispanicism ~ "Hispanicism score",
      SemiRural_or_urban ~ "Semi-rural or Urban",
      Parent_marital_status ~ "Parent marital status",
      Pastmonth_total_income ~ "Past month income"
    )
  ) %>% 
  bold_labels() %>% 
  modify_header(label ~ "")



#### table 2 modification of vairables ########

table_subset_2 <- table_subset %>% 
  mutate(Redcap_event_name = case_when(
    Redcap_event_name == "first_interview_arm_1" ~ "Baseline",
    Redcap_event_name == "second_post_interv_arm_1" ~ "6 month follow-up"
  )) %>% 
  mutate(Language_pref = case_when(
    Language_pref == "1" ~ "English",
    Language_pref == "2" ~ "Spanish",
    Language_pref == "3" ~ "Other"
  )) %>% 
  mutate(SemiRural_or_urban = case_when(
    SemiRural_or_urban == "1" ~ "Semi-rural",
    SemiRural_or_urban == "2" ~ "Urban"
  )) %>% 
  mutate(Pastmonth_total_income = case_when(
    Pastmonth_total_income == "1" ~ "0 - $999",
    Pastmonth_total_income == "2" ~ "$1,000 - $1,999",
    Pastmonth_total_income == "3" ~ "$2,000 or more"
  ))

  
```

```{r}
#| label: table-summary-2
#| tbl-cap: Summary of HOMBRES Youth Data Demographic
#| message: false
#| echo: false


### table prep ####
table_2 <- 
  list(
    "tbl_summary-str:default_con_type" = "continuous2",
    "tbl_summary-str:continuous_stat" = c(
      "{median} ({p25} - {p75})",
      "{mean} ({sd})",
      "{min} - {max}"
  ),
  
  "tbl_summary-str:categorical_stat" = "({p}%)",
    "style_number-arg:big.mark" = "",
    "tbl_summary-fn:percent_fun" = function(x) style_percent(x, digits = 1)
  )


## subset prep #### 
HOMBRES_with_demographic <- table_subset_2 %>% 
  filter(Redcap_event_name == "Baseline") %>% 
  select(
    Language_pref,
    Total_household_n, 
    Currently_in_school, 
    SemiRural_or_urban, 
    Parent_marital_status,
    Pastmonth_total_income,
    Group
  )



gtsummary::set_gtsummary_theme(table_2)


HOMBRES_with_demographic %>% 
  tbl_summary(by = Group,
    list(
      Language_pref ~ "Language Preference",
      Total_household_n ~ "Total household individuals",
      Currently_in_school ~ "Currently in school",
      SemiRural_or_urban ~ "Semi-rural or Urban",
      Parent_marital_status ~ "Parent marital status",
      Pastmonth_total_income ~ "Past month income",
      Group ~ "Group"
    )
  ) %>% 
  bold_labels() %>% 
  modify_header(label ~ "")



```