erichare

# Sets theme according to color of logo
set_color_theme <- function(logo_loc, low_sat = 0.042, high_sat = 0.866, medium_sat = 0.28, 
                            low_val = 0.99, high_val = 0.38, medium_val = 0.65) {  
    # Read image/logo as a list
    img1 <- list(readPNG(file.path(logo_loc)))
    # Find the average pixel value
    rgb <- rowMeans(sapply(img1, function(im) apply(im, 3, mean) * 255))
    # Convert to HSV - hue, saturation, value
    img_hsv <- rgb2hsv(rgb[1:3])[,1]
    

erichare

library(tidyverse)
library(gganimate)
library(cowplot)

iris_tall <- iris %>%
    cbind(iris %>%
              rename_all(function(.) paste0(., "_2")) %>%
              select(-Species_2)) %>%
    gather(key = Variable, value = Value, c(1:(ncol(iris) - 1))) %>%
    gather(key = Variable2, value = Value2, c(2:ncol(iris))) %>%

erichare

library(tidyverse)
library(cowplot)
library(bartext) # devtools::install_github("Omni-Analytics-Group/bartext")

mpg_manu <- mpg %>%
    distinct(manufacturer, model) %>%
    mutate_all(tools::toTitleCase) %>%
    mutate(model = gsub(" 4wd| 2wd| Pickup| Wagon|Toyota ", "", model))

p1 <- ggplot(mpg_manu, aes(x = manufacturer)) +

erichare

library(pdftools)
library(tidyverse)

parse_tables <- function(url, remove_last = TRUE) {
    
    my_data <- pdf_data(url)
    
    lapply(my_data, function(my_data2) {
        header_row <- my_data2 %>%
            filter(y == min(y))

erichare

library(tidyverse)

polygon_values <- tibble(
    id = 1:2,
    value = c(-2, 2)
)

polygon_positions <- tibble(
    id = c(1, 1, 1, 2, 2, 2, 2),
    x = c(1, 2, 3, 5, 6, 7, 6),

erichare

library(magick)
library(tidyverse)

im <- image_read("line.jpg")

im_proc <- im %>%
    image_channel("saturation") %>%
    image_threshold("white", "30%") %>%
    image_negate()

erichare

library(tidyverse)
library(fable)

fable_series <- AirPassengers %>%
    as_tsibble() %>%
    model(
        ets = ETS(box_cox(value, 0.3)),
        arima = ARIMA(value),
        snaive = SNAIVE(value)
    ) %>%

erichare

library(tidyverse)
library(clean)

unclean_tbl <- tibble(
    Logical = c("Yes", "No", "Depends", "Unknown"),
    Factor = c("bachelor's", "Bachelor's Degree", "Master's Degree", "PhD"),
    Currency = c("$20", "$ 25", "26.02", "$27.0"),
    Numeric = c("~15", "about 10", "20", "15")
)

erichare

library(ggplot2)

ggplot(data = diamonds, aes(x = carat, y = price, colour = clarity)) +
    geom_point() +
    scale_y_continuous(labels = scales::dollar,
                       breaks = scales::breaks_width(2000)) +
    scale_x_continuous(breaks = scales::breaks_width(.5))

ggplot(data = diamonds, aes(x = carat, y = price, colour = clarity)) +
    geom_point() +

erichare

library(tidyverse)
library(ggrepel)

ln <- read_csv("Investment_Bubble_Cycle_Line.csv") %>%
    mutate(Y = Y + 50 + 28)
cv <- read_csv("Investment_Bubble_Cycle.csv") %>%
    mutate(Y = Y + 50)

mapping = tibble(
    X = cv$X[c(20, 50, 70, 75, 105, 125, 138, 150, 175, 165, 185, 208, 225, 255, 280)],