luisDVA

# tidytuesday, jan. 14th 2025
library(dplyr)
library(tidyr)
library(stringr)
library(readr)
library(stringi)

conf2024 <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-01-14/conf2024.csv')
conf2023 <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-01-14/conf2023.csv')

luisDVA

# exploring comments about loaded packages
library(bigrquery) # CRAN v1.4.1
library(dplyr) # CRAN v1.1.1
library(stringr) # CRAN v1.5.0
library(readr) # CRAN v2.1.4
library(tidyr) # CRAN v1.3.0
library(rlang) # CRAN v1.1.0
library(purrr) # CRAN v1.0.1

luisDVA

library(ggsvg)
library(ggplot2)

# the svg that I need to use 
svg_url <- "https://raw.githubusercontent.com/luisDVA/luisdva.github.io/master/assets/images/Cat-green.svg"
cat_svg <- paste(readLines(svg_url), collapse = "\n")
# toy data
dat <-
  expand.grid(y=1:2,x=seq_len(2))
dat$qual <- letters[1:4]  

luisDVA

# lista 1
dat1a <- tibble(id=c("A","B","C","D","E"),val=rnorm(5))
dat1b <- tibble(id=c("A","B","C","D","E"),val=rnorm(5))

dat1 <- list(dat1a,dat1b)
dat1

# lista dos
dat2a <- tibble(id=c("A","D","E"),label=rnorm(3))
dat2b_1 <- tibble(id=c("A","B","C","E"),label=rnorm(4))

luisDVA

## %######################################################%##
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####        Regex for data cleaning 1 - your turn        ####
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## %######################################################%##

# After running the code below: 

library(ggplot2)
library(dplyr)

luisDVA

##%######################################################%##
#                                                          #
####               Regex in R - Your Turn               ####
#                                                          #
##%######################################################%##


# Match the following regular expressions against the test vector below using `str_detect`.  
## Can you explain the matches?

luisDVA

## %######################################################%##
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####           Coalesce and Fill - Your Turn            ####
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## %######################################################%##

# Load the fish landings data 'fish-landings.csv'
# Fill the 'Fish' and 'Lake' columns
# Reorder the numeric variables ('Comission reported total' first)
# create a new column, coalescing the three numeric variables

luisDVA

## %######################################################%##
#                                                          #
####             Pivoting Data - Your Turn              ####
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## %######################################################%##

# Load the dog ranks data ("dogranks_your.csv")
# Pivot the data (wide to long and back to wide)

# load packages -----------------------------------------------------------

luisDVA

## %######################################################%##
#                                                          #
####   Working with columns with 'across' - Your Turn   ####
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## %######################################################%##

# Load the midwest data bundled with ggplot2
# Keep only rows for Ohio (OH)
# Subset the 'county' column and all columns that match the string 'pop' (hint: use a selection helper)
# Square-root transform all numeric variables

luisDVA

## %######################################################%##
#                                                          #
####         Useful dplyr functions - your turn         ####
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## %######################################################%##


# Load the mammal sleep data bundled with ggplot2
# Select "name" and "conservation" columns and those that include the string 'sleep' in their name
# Create a new column that contains the values of 'sleep_total' multiplied by 3