In this example, I will work with the world population per country dataset, downloaded from the internet. This lesson is based on a youtube video [1].

Importing data

using DataFrames
using CSV

wp = DataFrame(CSV.File("./world_pop.csv"))
first(wp, 5)

describe(wp)

names(wp)

8-element Vector{String}:
 "Country"
 "Population 2024"
 "Population 2023"
 "Area (km2)"
 "Density (/km2)"
 "Growth Rate"
 "World %"
 "World Rank"

Data wrangling

wp.id = 1:nrow(wp)
first(wp, 5)

colnames = [:country, :pop2024, :pop2023, :area, :density, :growth_rate, :world_perc, :world_rank, :id]
rename!(wp, colnames)
describe(wp)

wp_clean = select!(wp, :id, :country, :pop2024, :growth_rate);

Be careful, the bang (!) modifies also the original table! Remember that we are not making copies, but creating new pointers to the same objects in memory.

describe(wp_clean)

describe(wp)

Subsetting

It is possible to check whether a string (i.e. country name) or value is present by using the in operator.

"Tanzania" in wp.country

true

We can get the index where a specific country is by using the findall() or findfirst() functions.

# with anonymous functions
findall(x -> x == "Tanzania", wp.country)

# or using the == function
findall(==("Tanzania"), wp.country)

1-element Vector{Int64}:
 21

And this allows us to subset our dataframe in several ways:

# using any of the possibles ways with findall() or findfirst()
wp[findall(==("Tanzania"), wp.country), :]

# or using broadcasting, similar as R syntax
wp[wp.country .== "Tanzania", :]

The wp.country .== "Tanzania" statement returns a vector of 0s and 1s, that is used for selecting the rows.

Footnotes:

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