Markdown syntax

---
title: "Document title"
author: Your Name
---
# Header 1
## Header 2
### Header 3
Text **bold**, *italics*
- list 1
- list 2

1. number 1
2. number 2
Footnote[^1]
[^1]: Footnote content
![image caption](path/to/image.png)
[Link](https://google.com)
|   | Column 1 | Column 2 |
|:--|----:|--------:|
| Row 1 | Cell | Cell |
| Row 2 | Cell | Cell |

R Markdown code chunks

---
title: "My report"
author: "Me!"
bibliography: my_references.bib
output:
    html_document:
        theme: sandstone
---
Cite: [@Hoejsgaard2006a]
Code chunk.
```{r chunk-label, chunk.options}
# R Code here
plot(iris)
library(knitr)
# Table
kable(iris)
```

read_csv repeats twice, wrangling four times

nhanes_2009 <- read_csv(here::here("data/nhanes-2009_10.csv"))
nhanes_2009 %>% 
    mutate(ProblemBMIs = !between(BMI, 18.5, 40)) %>%
    filter(!is.na(ProblemBMIs)) %>%
    select(Age, Poverty, Pulse, BPSysAve, BPDiaAve, TotChol,
           SleepHrsNight, PhysActiveDays, ProblemBMIs) %>%
    gather(Measurement, Value, -ProblemBMIs) %>%
    na.omit() %>%
    ggplot(aes(y = Value, x = Measurement, colour = ProblemBMIs)) +
    geom_jitter(position = position_dodge(width = 0.6)) +
    scale_color_viridis_d(end = 0.8) +
    labs(y = "", x = "") +
    theme_minimal() +
    theme(legend.position = c(0.85, 0.85)) +
    coord_flip()

jitter repeats twice

nhanes_2009 <- read_csv(here::here("data/nhanes-2009_10.csv")) 
nhanes_2009 %>% 
    mutate(ProblemBMIs = !between(BMI, 18.5, 40)) %>% 
    filter(!is.na(ProblemBMIs)) %>% 
    select(Age, Poverty, Pulse, BPSysAve, BPDiaAve, TotChol,
           SleepHrsNight, PhysActiveDays, ProblemBMIs) %>% 
    gather(Measurement, Value, -ProblemBMIs) %>% 
    na.omit() %>% 
    ggplot(aes(y = Value, x = Measurement, colour = ProblemBMIs)) +
    geom_jitter(position = position_dodge(width = 0.6)) +
    scale_color_viridis_d(end = 0.8) +
    labs(y = "", x = "") +
    theme_minimal() +
    theme(legend.position = c(0.85, 0.85)) +
    coord_flip()

Move code into functions

read_mutate_gather <- function(.file_path) {
    .file_path %>%
        read_csv() %>% 
        mutate(ProblemBMIs = !between(BMI, 18.5, 40)) %>% 
        filter(!is.na(ProblemBMIs)) %>% 
        select(Age, Poverty, Pulse, BPSysAve, BPDiaAve, TotChol,
               SleepHrsNight, PhysActiveDays, ProblemBMIs) %>% 
        gather(Measurement, Value, -ProblemBMIs) %>% 
        na.omit() 
} 
plot_jitter <- function(.dataset) {
    .dataset %>%
        ggplot(aes(y = Value, x = Measurement, colour = ProblemBMIs)) +
        geom_jitter(position = position_dodge(width = 0.6)) +
        scale_color_viridis_d(end = 0.8) +
        labs(y = "", x = "") +
        theme_minimal() +
        theme(legend.position = c(0.85, 0.85)) +
        coord_flip()
}

density repeats twice

nhanes_2009 %>% 
    mutate(ProblemBMIs = !between(BMI, 18.5, 40)) %>% 
    filter(!is.na(ProblemBMIs)) %>% 
    select(Age, Poverty, Pulse, BPSysAve, BPDiaAve, TotChol,
           SleepHrsNight, PhysActiveDays, ProblemBMIs) %>% 
    gather(Measurement, Value, -ProblemBMIs) %>% 
    na.omit() %>% 
    ggplot(aes(x = Value, fill = ProblemBMIs)) +
    geom_density(alpha = 0.35) +
    facet_wrap(~Measurement, scales = "free") +
    scale_fill_viridis_d(end = 0.8) +
    labs(y = "", x = "") +
    theme_minimal() +
    theme(legend.position = c(0.85, 0.15),
          strip.text = element_text(face = "bold"))

Move code into functions

plot_density <- function(.dataset) {
    .dataset %>%
        ggplot(aes(x = Value, fill = ProblemBMIs)) +
        geom_density(alpha = 0.35) +
        facet_wrap(~Measurement, scales = "free") +
        scale_fill_viridis_d(end = 0.8) +
        labs(y = "", x = "") +
        theme_minimal() +
        theme(legend.position = c(0.85, 0.15),
              strip.text = element_text(face = "bold")) 
}

Two dataframes, two figures each.

# Start with file paths:
files <- 
    c(here::here("data/nhanes-2009_10.csv"),
      here::here("data/nhanes-2011_12.csv"))

# Apply wrangling to each data file:
data_list <- 
    files %>% 
    map(read_mutate_gather)

# Apply figure to each data file:
# Plot the jitters
map(data_list, plot_jitter)
# Plot the density
map(data_list, plot_density)

Parallel processing

# Start with file paths:
files <- 
    c(here::here("data/nhanes-2009_10.csv"),
      here::here("data/nhanes-2011_12.csv"))
library(furrr)
plan(multiprocess)
# Apply wrangling to each data file:
data_list <- 
    files %>% 
    future_map(read_mutate_gather)

# Apply figure to each data file:
# Plot the jitters
future_map(data_list, plot_jitter)
# Plot the density
future_map(data_list, plot_density)