GT_Workflow.Rmd

---
title: "GT"
output: html_notebook
---

With GT, it is simple to produce nice-looking display tables.

Features include annotations, table element styling, and text transformations that serve to communicate the subject matter more clearly. Tables can have:

A Table Header (optional; with a title and possibly a subtitle)

A Stub and the Stub Head (optional; contains row labels, optionally within row groups having row group labels and possibly summary labels when a summary is present)

Column Labels (contains column labels, optionally under spanner column labels)

Table Body (contains columns and rows of cells)

Table Footer (optional; possibly with footnotes and source notes)

This first part is creating a tidy tibble to pass into GT using openFDA data.


```{r include=FALSE}

library(tidyverse)
library(openfda)
library(ggthemes)
library(gt)

get_adverse <- function(gender, brand_name) {
  fda_query("/drug/event.json") %>%
    fda_filter("patient.drug.openfda.brand_name", brand_name) %>% 
    fda_filter("patient.patientsex", gender) %>% 
    fda_count("patient.reaction.reactionmeddrapt.exact") %>% 
    fda_exec()
}

total_events<- function(brand_name){
  fda_query("/drug/event.json") %>%
    fda_filter("patient.drug.openfda.brand_name", brand_name) %>% 
    fda_count("patient.reaction.reactionmeddrapt.exact") %>% 
    fda_exec()
}

serious_events<- function(brand_name){
  fda_query("/drug/event.json") %>%
    fda_filter("patient.drug.openfda.brand_name", brand_name) %>% 
    fda_count("serious") %>% 
    fda_exec()
}

drug<- "Keytruda"
female<-get_adverse(2, drug) %>% mutate(sex = 'female')
male<-get_adverse(1, drug)%>% mutate(sex = 'male')

all<- total_events(drug) 
total_count<- sum(all$count)
serious<-serious_events(drug) %>% filter(term==1) %>% pull(count)

```

Here is a Raw gt Table

```{r}
gt_tbl <- gt(all[1:5,])

# Show the gt Table
gt_tbl
```


Now we will take out table and add the title and subtitle.



```{r}
gt_tbl <-
  gt_tbl %>%
  tab_header(
    title = "Adverse Events",
    subtitle = "The top five most reported events"
  )

# Show the gt Table
gt_tbl
```

Now we are going to bold the title and subtitle.

```{r}
gt_tbl <-
  gt_tbl %>%
  tab_header(
    title = md("**Adverse Events**"),
    subtitle = md("The top ten most reported events")
  )

gt_tbl
```
Now we are going to add some source notes.

```{r}
gt_tbl <-
  gt_tbl %>%
  tab_source_note(
    source_note = "Source: https://open.fda.gov/"
  ) %>%
  tab_source_note(
    source_note = md("Reference: https://open.fda.gov/about/")
  )

# Show the gt Table
gt_tbl
```
Now we are going to add a footnote only to rows 3 and 4.

```{r}
gt_tbl <-
  gt_tbl %>%
  tab_footnote(
    footnote = "3rd and 4th place",
    locations = cells_body(
      columns = term,
      rows = 3:4
    )
  )

# Show the gt Table
gt_tbl
```

Now we are going to add a footnote based on logic. Adding footnotes to the largest and smallest observations.

```{r}

largest <-
  all[1:5,] %>%
  arrange(desc(count)) %>%
  slice(1) %>%
  pull(term)

# Create two additional footnotes, using the
# `columns` and `where` arguments of `data_cells()`
gt_tbl <-
  gt_tbl %>%
  tab_footnote(
    footnote = md("The **largest** by term."),
    locations = cells_body(
      columns = count,
      rows = term == largest)
  ) %>%
  tab_footnote(
    footnote = "The lowest by population.",
    locations = cells_body(
      columns = count,
      rows = count == min(count))
  )

# Show the gt Table
gt_tbl
```

Here is a table with the parts combined and broken out by male and female.

### Events By Sex

```{r}
rbind(male,female) %>%  
  mutate(term=str_to_title(term)) %>% 
  pivot_wider(names_from=sex, values_from=count) %>% 
  mutate(Total=female+male) %>% 
  top_n(10, Total) %>% 
  gt(rowname_col="term") %>% tab_header(
    title = md("Adverse Events"),
    subtitle = "The top ten most reported events"
) %>% tab_source_note("All data queried from openFDA") %>% 
  tab_stubhead(label= 'Patient Reaction') %>% 
  tab_spanner(label = "By sex",
              columns = c(female, male)) %>% 
  tab_spanner(label= 'Total Cases',
              columns = Total) %>% 
    fmt_number(columns = c(female, male, Total), sep_mark = ',', decimals=0) %>% 
  cols_label(Total = '',
             female = 'Female',
             male= 'Male')
```