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# Load R packages for use | |
library("dplyr") | |
library("recipes") | |
library("rsample") | |
library("broom") | |
library("jsonlite") | |
library("sessioninfo") | |
# Sample data | |
sm_iris = initial_split(iris) |
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let | |
iterations = 10, // Number of iterations | |
url = | |
"https://api.hubapi.com/deals/v1/deal/paged?" & "hapikey=demo" & "&includeAssociations=true" & "&limit=1" & "&properties=dealname" & "&propertiesWithHistory=true" | |
, | |
FnGetOnePage = | |
(url) as record => | |
let | |
Source = Json.Document(Web.Contents(url)), | |
data = try Source[deals] otherwise null, |
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library(rtweet) #rtweet API creds should already be set up | |
library(stringi) | |
library(dplyr) | |
friends = get_friends(user="stephlocke") | |
followers = get_followers("stephlocke") | |
tweeps_id = distinct(bind_rows(friends, followers)) | |
tweeps_info = lookup_users(tweeps_id$user_id) | |
# A regex for a visit to Durham |
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[ | |
{ | |
"userId": 1, | |
"id": 1, | |
"title": "Analytics", | |
"body": "quia et suscipit\nsuscipit recusandae consequuntur expedita et cum\nreprehenderit molestiae ut ut quas totam\nnostrum rerum est autem sunt rem eveniet architecto" | |
}, | |
{ | |
"userId": 1, | |
"id": 2, |
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## ----message=FALSE------------------------------------------------------- | |
library(tabulizer) | |
library(tidyverse) | |
library(zoo) | |
library(tidytext) | |
## ------------------------------------------------------------------------ | |
"http://www.pass.org/Portals/0/Governance%202017/2018%20Budget.pdf?ver=2017-10-26-171625-747×tamp=1509063392150" %>% | |
tabulizer::extract_text() -> | |
rawtxt |
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--- | |
title: "sample" | |
output: | |
md_document: | |
variant: markdown_github | |
preserve_yaml: true | |
always_allow_html: yes | |
--- | |
```{r setup, include = FALSE, message = FALSE, warning = FALSE, error = FALSE} |
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library(tidyverse) | |
library(rvest) | |
"https://en.wikipedia.org/wiki/List_of_superhero_debuts" %>% | |
read_html() %>% | |
html_nodes(xpath = "//*[@id='mw-content-text']/div/table") %>% | |
map(html_table, fill = TRUE) %>% | |
map_df( ~ mutate(., `Year Debuted` = as.character(`Year Debuted`))) %>% | |
mutate(`Char_Team` = coalesce(`Character / Team / Series`, `Character / Team`)) %>% | |
select(Char_Team, Year_Debut = `Year Debuted`) %>% |
This hands-on one-day training takes you through the following topics – fundamentals of R, R for ETL, producing reports, and doing data science. Starting with some simple data manipulation, we’ll quickly progress to tools that can make an immediate impact in your day job.
- For ETL, we’ll look at ways to import and export data, transform it, and apply data quality checks along the way.
- For producing reports we’ll see how we can build web reports, PDFs, and interactive dashboards.
- For data science, we’ll look at how R supports you along the process.
By the end of the day, you’ll know some R (including some advanced stuff!) that you can add to your toolbelt.