• Free and open source

• Leading software for statistics, data analysis, and machine learning (https://www.r-bloggers.com/r-passes-sas-in-scholarly-use-finally/)

• Many packages available on CRAN

• Support for reproducible research (rmarkdown), interactive analyses (shiny)

• Integrated Development Environment (IDE) for R

• Syntax highlighting, code completion, and smart indentation

• Easily manage multiple working directories using projects

• Workspace browser and data viewer

• Plot history, zooming, and flexible image and PDF export

• Integrated R help and documentation

http://r4ds.had.co.nz/index.html

The tidyverse is a set of packages that work in harmony because they share common data representations and API design.

It includes:

• ggplot2, for data visualisation
• dplyr, for data manipulation
• tidyr, for data tidying
• readr, for data import
• purrr, for functional programming
• tibble, for tibbles, a modern re-imagining of data frames
• and many more …

Dataset

marathon.Rdata

load(url("http://alecri.github.io/downloads/data/marathon.Rdata"))

Reference (article)

"Hyponatremia among Runners in the Boston Marathon", New England Journal of Medicine, 2005, Volume 352:1550-1556.

Descriptive abstract

Hyponatremia has emerged as an important cause of race-related death and life-threatening illness among marathon runners. We studied a cohort of marathon runners to estimate the incidence of hyponatremia and to identify the principal risk factors.

https://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html

5 functions (verbs) to manipulate data

• Pick observations by their values (filter())
  
• Reorder the rows (arrange())
  
• Pick variables by their names (select())
  
• Create new variables with functions of existing variables (mutate())
  
• Collapse many values down to a single summary (summarise())

# create a variable timeh (time in hours)
marathon = mutate(data = marathon, timeh = runtime/60)
# select only a few variables
marathon_sub = select(data = marathon, id, female, age, na, bmi, timeh)
# select only female with bmi > 30
female_30 = filter(data = marathon_sub, female == "female", bmi > 30)
# sorting by (descending) na levels and timeh
arrange(female_30, desc(na), timeh)

Equivalent to

marathon %>% 
   mutate(timeh = runtime/60) %>%
   select(id, female, age, na, bmi, timeh) %>%
   filter(female == "female", bmi > 30) %>%
   arrange(desc(na), timeh)

http://www.cookbook-r.com/Graphs/

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

1. first argument is the dataset to use in the plot

2. the geom_functions add different layer to the plot

3. each geom_function takes a mapping argument (paired with the aes())

# an example
ggplot(data = marathon, aes(x = wtdiff, y = na, color = female)) +
   geom_point(aes(size = runtime), shape = 18) + 
   geom_smooth(method = "lm", se = F)