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temario basico
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# Clase 1:
# 1.1) Introduction
# 1.1.1) R Projects & Paths
# 1.1.2) Installing & loading packages
# 1.1.3) Dataframes
# 1.1.4) Long & wide database formats [tidyr package]
# 1.1.5) The tidyverse
# 1.1.6) Pipes [magrittr package]
# 1.1.7) R benchmarking
# 1.2) Data import
# 1.2.1) From flatfiles (csv, txt) [readr package]
# 1.2.2) From excel [readxl package]
# 1.2.3) From other source of data [haven package]
# 1.3) Intro to data visualization
# 1.3.1) Framework for data visualization [ggplot2 package]
# 1.3.2) Basic plots: Scatterplot, Barplot
# 1.4) Funciones básicas de estadística
# 1.4.1) Density function (dnorm, dunif, dt, dchisq)
# 1.4.2) Distribution function (pnrom, punif, pt, pchisq)
# 1.4.3) Quantile function (qnorm, qunif, qt, qchisq)
# 1.4.4) Random samples (rnorm, runif, rt, rchisq)
# Clase 2:
# 2.1) More advanced plots
# 2.1.1) Heatmap
# 2.1.2) Circular barplot
# 2.1.3) Maps
# 2.1.4) Boxplot
# 2.2) Interactive graphs (plotly package)
# 2.2.1) Basic interactive graphs
# 2.3) Interactive dashboards (shiny/shinydashboard packages)
# 2.3.1) Basic interactive dashboards
# 2.3.2) BI tools comparison
# Clase 3:
# 3.1) Data transformation (dplyr package)
# 3.1.1) Basic operations: Select
# 3.1.2) Basic operations: Mutate
# 3.1.3) Basic operations: Filter
# 3.1.4) Basic operations: Arrange
# 3.1.5) Basic operations: Group by - mutate
# 3.1.6) Basic operations: Group by - summarise
# 3.1.7) Basic operations: Group by - mutate
# 3.2) Variables transformations
# 3.2.1) missing values
# 3.2.2) string transformation (stringr package)
# 3.2.3) factor transformation (forcats package)
# 3.2.4) dates/times transformation (lubridate/hms/zoo packages)
# Clase 4:
# 4.1) Basic statistics (stats package)
# 4.1.1) basic distributions
# 4.1.2) basic estimators
# 4.1.3) more complex statistics
# 4.2) Basic forecast understanding
# 4.2.1) ts object
# 4.2.2) Time series pattern
# 4.2.3) Error measures
# 4.3) More advanced forecast methods [forecast package]
# 4.3.1) Basic forecast funcions