luisDVA

library(ggplot2)
library(dplyr)

# datos internos que vienen con ggplot2
animals <-
  msleep %>% slice_max(sleep_rem, n = 30) %>% # seleccionando top 30
  ggplot(aes(x = forcats::fct_reorder(name, sleep_total), y = sleep_total, label = name)) +
  geom_bar(stat = "identity") + xlab("") +
  geom_text(nudge_y = 2, hjust = 0) +
  theme(

luisDVA

## %######################################################%##
#                                                          #
####   Understanding a regular expression - your turn   ####
#                                                          #
## %######################################################%##

# Write regular expressions to match:
# Test with stringr, regexplain, or a web-based regex tester

# "cute, cuuute, cuuuuuute, and cuuuuuuuuute"

luisDVA

## %######################################################%##
#                                                          #
####        Regex for data cleaning - your turn         ####
#                                                          #
## %######################################################%##

# 1. Download CRAN package descriptions
# 2. Select Package name, author, description, and all variables that end in 'ports'
# 3. Filter rows for packages with names that:
#    - end in plot

luisDVA

## %######################################################%##
#                                                          #
####         Unusable variable names - your turn         ####
#                                                          #
## %######################################################%##

# - Import the Marine Protected Areas data (MPAS-your.csv)
# - Make the variable names usable by placing all header fragments in a single
#   header row
# - Clean the names for consistency

luisDVA

## %######################################################%##
#                                                          #
####               Whitespace - your turn               ####
#                                                          #
## %######################################################%##

# - Import the Marine Protected Areas data (MPAS-your.csv) from the previous lesson
# - check the Country variable for leading or trailing whitespace
# - Remove it if necessary.

luisDVA

## %######################################################%##
#                                                          #
####               Letter case - your turn               ####
#                                                          #
## %######################################################%##


# Import the Marine Protected Areas dataset (MPAS-your.csv)
# Summarize the number of Marine Protected Areas by country (Country full).

luisDVA

## %######################################################%##
#                                                          #
####          Missing, implicit, or misplaced           ####
####           grouping variables - your turn           ####
#                                                          #
## %######################################################%##

# Load the `primates2017` dataset bundled with 📦 `unheadr`
# Create a new column that groups the different species by taxonomic family.
# In biology, taxonomic families all end in the suffix "_DAE_"

luisDVA

## %######################################################%##
#                                                          #
####            Compound values - your turn             ####
#                                                          #
## %######################################################%##

# Import the Marine Protected Areas dataset (MPAS-your.csv)
# Separate the country codes variable (ISO3 and UN scheme)
# Unnest the Reference variable
# > Keep an eye on the separators

luisDVA

## %######################################################%##
#                                                          #
####               Duplicates - your turn               ####
#                                                          #
## %######################################################%##

# Load the messy Age of Empires units dataset bundled with `unheadr` (AOEunits_raw)
# Keep only units of Type "Cavalry"
# Identify duplicated records across all variables
# Remove duplicated records across all variables

luisDVA

## %######################################################%##
#                                                          #
####             Broken values - your turn              ####
#                                                          #
## %######################################################%##

# Load the raw Age of Empires units dataset from csv (aoe_raw.csv)
# Identify the broken values in both the 'Type' and 'Name' columns and unbreak them
# Clean up any separator-related issues arising from the 'unbreaking'