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tables_pharma_viz.R
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library(tidyverse)
library(here)
library(reactable)
pharma_tidy$drug_use <- factor(pharma_tidy$drug_use ,
levels = c("At least one",
"At least three",
"At least five"))
jump_range <- c("1988–1994", "2015–2018")
jump_range2 <- c("1999–2002", "2015–2018")
one_all_table <- pharma_tidy %>%
filter(drug_use == "At least one" , Sex == "Both") %>%
select(Year_Range, Pop_Percent) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
one_all_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
one_all_table
one_sex_table <- pharma_tidy %>%
filter(drug_use == "At least one" , Sex != "Both") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
one_sex_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
one_sex_table
one_sex_18_table <- Age_Range_Sex_Tidy %>%
filter(Age_Range == "Under 18 years",
drug_use == "At least one") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
one_sex_18_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
one_sex_18_table
one_sex_44_table <- Age_Range_Sex_Tidy %>%
filter(Age_Range == "18–44 years",
drug_use == "At least one") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
one_sex_44_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
one_sex_44_table
one_sex_64_table <- Age_Range_Sex_Tidy %>%
filter(Age_Range == "45–64 years",
drug_use == "At least one") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
one_sex_64_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
one_sex_64_table
one_sex_65_table <- Age_Range_Sex_Tidy %>%
filter(Age_Range == "65 years and over",
drug_use == "At least one") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
one_sex_65_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
one_sex_65_table
# At least three ----------------------------------------------------------
three_all_table <- pharma_tidy %>%
filter(drug_use == "At least three" , Sex == "Both") %>%
select(Year_Range, Pop_Percent) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
three_all_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
three_all_table
three_sex_table <- pharma_tidy %>%
filter(drug_use == "At least three" , Sex != "Both") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
three_sex_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
three_sex_table
three_sex_18_table <- Age_Range_Sex_Tidy %>%
filter(Age_Range == "Under 18 years",
drug_use == "At least three") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
three_sex_18_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
three_sex_18_table
three_sex_44_table <- Age_Range_Sex_Tidy %>%
filter(Age_Range == "18–44 years",
drug_use == "At least three") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
three_sex_44_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
three_sex_44_table
three_sex_64_table <- Age_Range_Sex_Tidy %>%
filter(Age_Range == "45–64 years",
drug_use == "At least three") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
three_sex_64_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
three_sex_64_table
three_sex_65_table <- Age_Range_Sex_Tidy %>%
filter(Age_Range == "65 years and over",
drug_use == "At least three") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
three_sex_65_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
three_sex_65_table
# At least five -----------------------------------------------------------
five_all_table <- pharma_tidy %>%
filter(drug_use == "At least five" , Sex == "Both") %>%
select(Year_Range, Pop_Percent) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
five_all_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
five_all_table
five_sex_table <- pharma_tidy %>%
filter(drug_use == "At least five" , Sex != "Both") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
five_sex_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
five_sex_table
five_sex_18_table <- Age_Range_Sex_Tidy %>%
filter(Age_Range == "Under 18 years",
drug_use == "At least five") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
five_sex_18_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
five_sex_18_table
five_sex_44_table <- Age_Range_Sex_Tidy %>%
filter(Age_Range == "18–44 years",
drug_use == "At least five") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
five_sex_44_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
five_sex_44_table
five_sex_64_table <- Age_Range_Sex_Tidy %>%
filter(Age_Range == "45–64 years",
drug_use == "At least five") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
five_sex_64_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
five_sex_64_table
five_sex_65_table <- Age_Range_Sex_Tidy %>%
filter(Age_Range == "65 years and over",
drug_use == "At least five") %>%
select(Sex, Year_Range, Pop_Percent) %>%
arrange(Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
five_sex_65_table %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
five_sex_65_table
# Jump Table --------------------------------------------------------------
jump_all_table <- pharma_tidy %>%
filter(Sex == "Both", Year_Range %in% jump_range) %>%
rename(Prescripts = drug_use) %>%
select(Prescripts, Year_Range, Pop_Percent) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
jump_all_table2 <- jump_all_table %>%
mutate(Delta = `2015–2018` - `1988–1994`,
Ratio = `2015–2018` /`1988–1994`) %>%
mutate(across(where(is.numeric), round, 2))
jump_all_table2 %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
jump_all_table
jump_all_table2
jump_sex_table <- pharma_tidy %>%
filter(Sex != "Both", Year_Range %in% jump_range) %>%
rename(Prescripts = drug_use) %>%
select(Prescripts, Sex, Year_Range, Pop_Percent) %>%
arrange(Prescripts, Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
jump_sex_table2 <- jump_sex_table %>%
mutate(Delta = `2015–2018` - `1988–1994`,
Ratio = `2015–2018` /`1988–1994`) %>%
mutate(across(where(is.numeric), round, 2))
jump_sex_table2 %>%
reactable(., highlight = TRUE,
striped = TRUE,
compact = TRUE,
theme = reactableTheme(
stripedColor = "#EDEDED",
highlightColor = "#FFE4E1") )
pharma_delta <- pharma_tidy %>%
filter(Sex != "Both", Year_Range %in% jump_range) %>%
rename(Prescripts = drug_use) %>%
select(Prescripts, Sex, Year_Range, Pop_Percent) %>%
arrange(Prescripts, Sex) %>%
pivot_wider(names_from = "Year_Range", values_from = "Pop_Percent")
pharma_delta$Prescripts <- factor(pharma_delta$Prescripts ,
levels = c("At least one",
"At least three",
"At least five"))
pdelta <- (pharma_delta[, 4] - pharma_delta[, 3]) %>% pull()
pratio <- (pharma_delta[, 4] / pharma_delta[, 3]) %>% pull()
pharma_delta$Delta <- pdelta
pharma_delta$Ratio <- pratio
pharma_delta <- pharma_delta %>%
mutate(across(where(is.numeric), round, 2))
pharma_delta %>%
arrange(Prescripts) %>%
knitr::kable(., caption = "Delta and Ratio")
# List tables -------------------------------------------------------------
jump_all_table
jump_all_table2
jump_sex_table
jump_sex_table2
one_all_table
one_sex_table
one_sex_18_table
one_sex_44_table
one_sex_64_table
one_sex_65_table
three_all_table
three_sex_table
three_sex_18_table
three_sex_44_table
three_sex_64_table
three_sex_65_table
five_all_table
five_sex_table
five_sex_18_table
five_sex_44_table
five_sex_64_table
five_sex_65_table
tab_dash <- c("jump_all_table",
"jump_all_table2",
'jump_sex_table',
'jump_sex_table2',
"one_sex_table",
"one_sex_18_table",
"one_sex_44_table",
"one_sex_64_table",
"one_sex_65_table",
"three_sex_table",
"three_sex_18_table",
"three_sex_44_table",
"three_sex_64_table",
"three_sex_65_table",
"five_sex_table",
"five_sex_18_table",
"five_sex_44_table",
"five_sex_64_table",
"five_sex_65_table",
"one_all_table",'three_all_table',
"five_all_table",
"pharma_delta")
save(list = tab_dash,
file = here::here("data", "tidy_data", "pharma_tabs.rda"))