embruna

library(rvest)

webpage <- read_html("https://www.computerworld.com/article/3109890/these-r-packages-import-sports-weather-stock-data-and-more.html")

tbls <- html_nodes(webpage, "table")
tbls_ls <- webpage %>%
  html_nodes("table") %>%
  .[1:1] %>%
  html_table(fill = FALSE)

embruna

# text mining resources ---------------------------------------------------

# * [Text Mining with R](https://www.tidytextmining.com/) by Julia Silge and David Robinson
# * [`gutenbergr`](https://cran.r-project.org/web/packages/gutenbergr/index.html): Download and Process Public Domain Works from [Project Gutenberg](https://www.gutenberg.org/). Tutorial can be found [here](https://docs.ropensci.org/gutenbergr/)

# install or load the libraries -------------------------------------------
library(tidyverse)

# organizing and analyzing the text files

embruna

---
title: "title text"
date: "updated: `r Sys.Date()`" # using `r Sys.Date()` updates the exact date that you save as the update 
header-includes:
- \usepackage[default]{sourcesanspro} # font choice: https://tug.org/FontCatalogue/sourcesanspro/
- \AtBeginDocument{\let\maketitle\relax} # this removes default title section 
- \usepackage{fancyhdr} # modify header into left, center, right
- \pagestyle{fancy} # cues the use of fancy header
# to modify the Left Header [L]: 
- \fancyhead[L]{LAS 6292 Spring `r format(Sys.time(), '%Y')`, EM Bruna} 

embruna

# posts / tutorials --------------------------------------------------------

# https://www.adrianbruegger.com/post/import-qualtrics-csv-files/
# https://www3.nd.edu/~sberry5/qualtricsAPI.html
# https://blogs.uoregon.edu/rclub/2016/05/18/bringing-in-qualtrics-and-other-data/
# https://druedin.com/2019/12/01/getting-qualtrics-data-into-r/


# the 'qualtRics' package -------------------------------------------------

embruna

# ------------------------------------------------------------------------
## script name: wk_4_live_coding
## author: Emilio Bruna (embruna@ufl.edu)
## description: examples of using Tidyverse and BaseR for data clean-up


# introductory notes ------------------------------------------------------

# 1) Use pound symbol (#, a.k.a hashtag) to mark a line of your R script as a 
# comment that R should ignore and not a command to be executed.

embruna

#### 1. Sign up at GitHub.com ################################################

## If you do not have a GitHub account, sign up here:
## https://github.com/join

# ----------------------------------------------------------------------------

#### 2. Install git ##########################################################

## If you do not have git installed, please do so: