Tidy tabular data

• Tidy tabular data has
  • One variable per column
  • One observation per row
  • One value per cell

Data wrangling

“The Herd Quitter.” Oil C.M. Russell, Montana Historical Society MacKay Collection (source)

Data wrangling

• Aggregation through summaries
  • E.g. variables are summarized over multiple observations
• Transformation of data
  • E.g. new variables are created on existing data
• Arranging data
  • E.g. sorting data based on values of observations

dplyr

• dplyr provides verbs for wrangling data
• “Translate thoughts to code”
• Using the package we can
  • mutate (create) new variables
  • select variables
  • filter observations
  • summarise values
  • arrange observations or rows

R functions

• Functions are created to perform a specific task
• Functions exists in packages, or as user specified in your environment
• Function (can) take arguments as input

1important_function(arg1 = "A", arg2 = "B")
2important_function("A", "B")
3important_function()

R functions in tidyverse

• Functions that are specifically written for the tidyverse are “pipeable”, the data argument has the first position, an example

1pipe_function(data = my.data, arg2 = "a", arg3 = "b", arg4 = "etc")
2pipe_function(my.data, arg2 = "a", arg3 = "b", arg4 = "etc")
3pipe_function(my.data)

Data pipes

# This is equivalent...
data |>
        pipe_function() 
# ... to this
pipe_function(data) 

Why pipe?

• Piping makes code more readable, an example

# No pipes
print(fun_c(fun_b(fun_a(data))))

# Using pipes
data |>
        fun_a() |>
        fun_b() |>
        fun_c() |>
        print()

Pipes in R

Two pipe operators in action

library(dplyr)

data |>
 filter(var1 > 10) |>
 mutate(var3 = var1 + var2) |>
 select(var1, varX) |>
 print()

library(dplyr)

data %>% 
 filter(var1 > 10) %>% 
 mutate(var3 = var1 + var2) %>% 
 select(var1, varX) %>% 
 print()

Data placeholder

• If the data argument is not the first argument in a function, use a placeholder

data %>% 
        fun(argument)

## Is equivalent to
data %>% 
        fun(., argument)


## If the data is to be used in another place we need the placeholder
data %>% 
        fun(argument, data = .) 

Data placeholder a realistic example, and saving output

library(exscidata); library(tidyverse)
1model1 <- cyclingstudy %>%
2        filter(timepoint == "pre") %>%
3        mutate(VO2max.kg = VO2.max / weight.T1) %>%
4        lm(tte ~ VO2max.kg, data = .)
5summary(model1)