tl;dr

Details

Introducing Free Week 

Free Access for Individuals 

• DataCamp Workspace
• DataCamp Projects
• Data Scientist and Data Analyst Certifications
• DataCamp Jobs
• DataCamp Competitions
• R-programming challenges

• Data visualization
• Data manipulation
• A/B Testing
• R for Finance
• Machine learning
• Regression
• Web scraping

Free Access for Teams

• Build learning programs with their team to create custom learning paths just for your team (eg. if your team specializes in R, their team will help create R-specialized learning pathways)
• Report on your ROI using DataCamp’s visualization and spreadsheet tools to thoroughly understand your team’s progress
• Upskill your company by investing in professional development for all roles and skill levels
• Get started and scale using DataCamp’s integrations

Hello!

The all-virtual useR! 2022 conference opens next week, on 20 June, with 6 keynotes, 18 tutorials, and dozens of talks and posters to choose from.

Keynote speakers include Paola Moraga, Amanda Cox, the Afrimapr project, Julia Silge, Sebastian Meyer, and Mine Dogucu.

See the program overview at the conference website:
https://user2022.r-project.org/program/overview/

If you haven’t yet enrolled, sign up here
(free for people from low income countries, and price ranges from $6 to $85, depending on the country and if student/academia or industry):
https://user2022.r-project.org/participate/registration/

Sincerely,
The organizing committee of useR! 2022
— 

If curious, here are the fees

Conference Fees

Tutorial Fees

You may go ahead and sign-up here:
https://user2022.r-project.org/participate/registration/

[ES: Desplazándose hacia abajo por favor para el texto en español.
FR: Faites défiler svp pour le text français.]

Hello!

The all-virtual useR! 2022 conference opens on 20 June – less than 1 month from now – with 6 keynotes, 18 tutorials, and dozens of talks and posters to choose from. Tutorial spots are first-come, first-reserved, and some sessions are already sold out!

If you’ve already registered, we thank you – we’re looking forward to welcoming you at the new conference platform next month.

If you haven’t yet enrolled, now’s the time! Sign up at our website: https://user2022.r-project.org/participate/registration/

We’re excited about this year’s lineup, and we can’t wait to share it all with you!

Sincerely,
The organizing committee of useR! 2022

 — 

¡Hola!

La conferencia completamente virtual useR! 2022 abre el 20 de junio, en menos de un mes, con 6 charlas principales, 18 tutoriales y decenas de charlas y posters para elegir. Los espacios disponibles para tutoriales son por orden de llegada. ¡Algunas sesiones ya están agotadas!

Agradecemos a las personas que ya se registraron: las personas que hacemos useR! estamos ansiosas por saludarte en la nueva plataforma de la conferencia el próximo mes.

Si aún no te has inscrito ¡ahora es el momento! Regístrate ahora en nuestro sitio web: https://user2022.r-project.org/participate/registration/ 

¡El equipo está muy entusiasmado con el programa de este año y no vemos la hora de compartirlo contigo! 

Sinceramente,
Comité organizador de useR! 2022

— 

Bonjour!

La conférence virtuelle useR! 2022 s’ouvre le 20 juin, dans moins d’un mois, avec 6 discours luminaires, 18 tutoriels et des dizaines de courts discours et d’affiches. Certaines séances sont déjà complètes.

Aux personnes déjà inscrites: nous vous remercions. Nous nous réjouissons de vous accueillir sur la nouvelle plateforme de conference le mois prochain.

Si vous n’êtes pas déjà inscrit, c’est le moment! 

https://user2022.r-project.org/participate/registration/

Nous sommes ravis du programme de cette année et nous avons hate de le partager avec vous.

Amicalement,
Comité d’organisation d’useR! 2022

library(tidyverse)
library(readxl)

# Original data source - https://www.nasdaq.com/market-activity/funds-and-etfs/ung/historical

# Download reformatted data (columns/headings) from my github site and save to a local drive

# https://github.com/123blee/Stealth_Curves.io/blob/main/UNG_prices_4_11_2022.xlsx

# Load your local data file
ung <- read_excel("... Insert your local file path here .../UNG_prices_4_11_2022.xlsx")
ung


# Convert 'Date and Time' to 'Date' column
ung[["Date"]] <- as.Date(ung[["Date"]])
ung

bars <- nrow(ung)

# Add bar indicator as first tibble column
ung %
  add_column(t = 1:nrow(ung), .before = "Date")
ung

# Add 40 future days to the tibble for projection of the Stealth Curve once added
future <- 40
ung %
  add_row(t = (bars+1):(bars+future))

# Market Pivot Lows using 'Low' Prices
# Chart 'Low' UNG prices
xmin <- 2250
xmax <- bars + future
ymin <- 0
ymax <- 25
plot.new()
background <- c("azure1")
chart_title_low <- c("Natural Gas (UNG) \nDaily Low Prices ($)")
u <- par("usr") 
rect(u[1], u[3], u[2], u[4], col = background) 
par(ann=TRUE)
par(new=TRUE)
t <- ung[["t"]]
Price <- ung[["Low"]]
plot(x=t, y=Price, main = chart_title_low, type="l", col = "blue", 
     
     ylim = c(ymin, ymax) ,
     
     xlim = c(xmin, xmax ) ) 

# Add 1st Stealth Support Curve to tibble
# Stealth Support Curve parameters
a <-   -444.56  
b <-      6.26  
c <-  -2555.01  

ung %
  mutate(Stealth_Curve_Low_1 = a/(t + c) + b)
ung

# Omit certain Stealth Support Curve values from charting
ung[["Stealth_Curve_Low_1"]][1:2275] <- NA
ung[["Stealth_Curve_Low_1"]][2550:xmax] <- NA
ung

# Add 1st Stealth Curve to chart
lines(t, ung[["Stealth_Curve_Low_1"]])

# Add 2nd Stealth Support Curve to tibble
# Stealth Support Curve parameters
a <-   -277.30  
b <-      8.70  
c <-  -2672.65 

ung %
  mutate(Stealth_Curve_Low_2 = a/(t + c) + b)
ung

# Omit certain Stealth Support Curve values from charting
ung[["Stealth_Curve_Low_2"]][1:2550] <- NA
ung[["Stealth_Curve_Low_2"]][2660:xmax] <- NA
ung

# Add 2nd Stealth Curve to chart
lines(t, ung[["Stealth_Curve_Low_2"]])

```{r setup}
  library(tidyverse)
  library(openxlsx)

  make_one_sheet <- function(){ 
    sheet_name <- knitr::opts_current$get()$label
    addWorksheet(wb, sheet = sheet_name)
    writeData(wb, sheet = sheet_name, x = df_to_excel) 
    insertPlot(wb, sheet = sheet_name, 
      startCol = ncol(df_to_excel) + 2 ) 
    saveWorkbook(wb, file ="my_excel.xlsx", 
      overwrite = TRUE) 
  }

  wb <- createWorkbook()
```

```{r dot_plot}
  plot_data <- iris %>% filter(Sepal.Length > 5) 

  plot_data %>% ggplot(aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +  
    geom_point() 

  df_to_excel <- plot_data %>% 
    select(Species, Sepal.Length, Sepal.Width)  

  make_one_sheet()
```

```{r histogram}
  plot_data <- iris %>%  group_by(Species) %>%
    mutate(Group = cut(Petal.Length, breaks = 0:7)) %>% 
    group_by(Group, Species) %>% tally()

  plot_data %>% ggplot(aes(x = Group, y = n, fill = Species)) +
    geom_bar(stat = "identity", position = "stack")

  df_to_excel <- plot_data %>%
    pivot_wider(id_cols = "Group", names_from = "Species", values_from = "n")
  
  make_one_sheet()
```