Dyr og Data

Learning objectives

In section of the data wrangling topic you will

• Understand how to create data frames

• How to access the elements of data frames

• Create and work with tibbles

What are data frames?

Data frames are R’s equivalent of an Excel spreadsheet

Data are arranged in rows and columns

• rows — observations

• columns — variables

Data frames are a special case of a list, where each component of the list is required to be a vector of the same length

Each column of the data frame can be of a difference type

What are data frames

Creating data frames

Use the data.frame() function

people <- data.frame(name   = c('Ada', 'Bob', 'Chris', 'Diya', 'Emma'),
                     height = c(64, 74, 69, 69, 71),
                     weight = c(135, 156, 139, 144, 152))
people

   name height weight
1   Ada     64    135
2   Bob     74    156
3 Chris     69    139
4  Diya     69    144
5  Emma     71    152

Investigating data frames

There are many functions for working with data frames

Useful functions include

• head()
• tail()
• nrow()
• ncol()

Investigating data frames

head() shows the first few rows of data frame (default is 6)

head(penguins, n = 5)

# A tibble: 5 × 8
  species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
1 Adelie  Torgersen           39.1          18.7               181        3750
2 Adelie  Torgersen           39.5          17.4               186        3800
3 Adelie  Torgersen           40.3          18                 195        3250
4 Adelie  Torgersen           NA            NA                  NA          NA
5 Adelie  Torgersen           36.7          19.3               193        3450
# ℹ 2 more variables: sex <fct>, year <int>

tail() works similarly, but shows the last n rows.

Accessing data frames

Can appear complicated because data frames are lists and matrix-like objects

people$height

[1] 64 74 69 69 71

people[['height']]

[1] 64 74 69 69 71

people[[2]]

[1] 64 74 69 69 71

people[2]

  height
1     64
2     74
3     69
4     69
5     71

Accessing data frames

Matrix-like access

Can use:

• row names and column names — people['Ada', 'height']

• row & column number (index) — people[2, 3]

• mixtures — people[2, 'height']

• extracting one or more rows — people[2, ]

• extracting one or more columns — people[, 'height']

• Can use numeric, character, logical indices

Access data frames

rownames(people) <- people$name
people['Ada', ]

    name height weight
Ada  Ada     64    135

people[, 'height']

[1] 64 74 69 69 71

people[, c('height','weight')]

      height weight
Ada       64    135
Bob       74    156
Chris     69    139
Diya      69    144
Emma      71    152

people[2:4, ]

       name height weight
Bob     Bob     74    156
Chris Chris     69    139
Diya   Diya     69    144

Tibbles

Tibbles are a modern implementation of the data frame concept

Widely used in the Tidyverse — a collection of packages that all share common philosophy and approaches to working with data

Coerce a data frame to be a tibble with as_tibble() (from tibble 📦)

Load data directly to tibbles using the readr, vroom, & readxl 📦📦📦

Tibbles

people <- tibble::as_tibble(people)
people

# A tibble: 5 × 3
  name  height weight
  <chr>  <dbl>  <dbl>
1 Ada       64    135
2 Bob       74    156
3 Chris     69    139
4 Diya      69    144
5 Emma      71    152

Tibbles

penguins

# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# ℹ 334 more rows
# ℹ 2 more variables: sex <fct>, year <int>

Tibbles

There are some differences

• people$name extracts a vector as usual but never partial matches — people$nam is an error

• people[ , "height"] extracts a 1-column data frame — [r, c] never drops empty dimensions

• tibbles are much faster for large data sets

• tibbles print differently — never again swamp your R session by accidentally printing a large data frame