This document provides an introduction to Julia and DataFrames. It demonstrates basic Julia syntax for characters, strings, and the NA type to represent missing values. It then shows how to create and access a DataFrame, including mixed indexing of rows and columns by number and name. As an example, it loads the iris dataset to demonstrate creating a DataFrame from external data.

1. Github Stats

2. Commits

3. Code Frequency

4. Strings

5. Character
julia> ’x’
’x’
julia> typeof(ans)
Char

6. Character
julia> ’A’ < ’a’
True
julia> ’A’ + 1
’B’

7. String
julia> str = “Hello, world.”
“Hello, world.”
julia> str[1]
’H’
julia> str[4:9]
"lo, wo“

8. String
julia> greet = "Hello"
"Hello“
julia> whom = "world"
"world“
julia> string(greet, ", ", whom, ".n")
"Hello, world.n"

9. Introduction to DataFrames

10. julia> Pkg.add("DataFrames")
julia> Pkg.add("RDatasets")
julia> using DataFrames
julia> using RDatasets

11. julia> v = @data([NA, 3, 2, 5, 4])
julia> mean(v)

12. The NA type:
Represents a missing value
Like NULL in some systems
Poisons other values
Like NaN for floating point numbers

13. julia> 1 + NA
NA
julia> 1 > NA
NA
julia> isna(NA)
true

14. A DataFrame is a list of DataVector's
DataFrame's allow mixed indexing:
Columns by number
Columns by name
Rows + Columns by number + number
Rows + Columns by number + name

15. df = DataFrame()
df[:A] = 1:8
df[:B] = ["M", "F", "F", "M", "F", "M", "M", "F"]
df

16. using Rdatasets
iris = dataset("datasets", "iris")
head(iris)