expersso

library(tidyverse)
library(gtools)
library(topicmodels)
library(tidytext)

rcat <- function(n, x, p) {
  sample(x, size = n, replace = TRUE, prob = p)
}

generate_document <- function(z, k, w, beta) {

expersso

library(tidyverse)
library(patchwork)

strip_lblr <- function(s) as_labeller(function(x) paste0(s, " = ", x))
p_brks <- seq(0, 1, .25) %>% set_names() %>% set_names(MASS::fractions(.))
x <- 4

df <- crossing(k = 0:x, p = seq(0, 1, .05)) %>% 
  mutate(PX_k = dbinom(k, x, p))

expersso

library(tidyverse)
library(scales)
library(ggrepel)
library(patchwork)
library(ggthemes)
library(pwt9)

years <- c(1955, 2014)

pwt9.0 <- pwt9.0 %>% 

expersso

library(tidyverse)
library(patchwork)

plot_pair <- function(data, x, y) {
  ggplot(data, aes_string(x = x, y = y, color = "Species", shape = "Species")) + 
    geom_point() +
    scale_color_brewer(palette = "Dark2") +
    theme(legend.position = "none", plot.title = element_text(size = 7)) +
    labs(x = NULL, y = NULL, title = paste0(y, " ~ ", x))

expersso

# A rewrite of https://gist.github.com/seanjtaylor/f0f4815427a076c2544fc6a1a80e1dbd
# using some tidyverse tricks to create lagged variables

library(tidyverse)
library(glmnet)
library(broom)

wide_ <- "https://github.com/johnmyleswhite/room_temperatures/raw/master/temperatures.csv" %>% 
  read_csv() %>% 
  mutate(room = str_replace(room, "Room ", "r"),

expersso

library(tidyverse)
library(gganimate)

f <- function(n, x) 1 - cos(2 * pi * n * x)
g <- function(.f, x) map_dbl(x, ~integrate(function(z) .f(z), 0, .x)$value)

df <- cross_df(list(
  x = seq(0, 1, length.out = 100),
  n = c(1, 2, 3, 5, 10, 15, 25)
)) %>% 

expersso

library(tidyverse)
set.seed(1)

make_regression_equation <- function(df) {
  df %>% 
    summarise(eq = paste0(round(estimate, 1), term, collapse = " + ")) %>% 
    mutate(eq = paste0("y = ", eq),
           eq = str_remove(eq, "\\(Intercept\\)")) %>% 
    pull(eq)
}

expersso

# Inspired by http://www.brodrigues.co/blog/2018-09-11-human_to_machine/

library(tidyverse)
library(readxl)

# Extract all data from one sheet into tidy dataframe
get_sheet <- function(sheet, path = "time-use.xlsx") {
  
  ranges <- list(
    col  = "C3:BS5",

expersso

library(tidyverse)

add_units <- function(p, unit, scale = "x", outside = TRUE) {
  
  params <- ggplot_build(p)$layout$panel_params[[1]]
  
  if(scale == "x") {
    brks <- params$x.major_source 
    scale <- scale_x_continuous
  } else {

expersso

# 1987-1990: old federal states (Bundesländer)
# 1991-1992: old federal states and Berlin metropolitan area
# from 1993: all federal states

library(tidyverse)

nms <- c("key", "crime", "year", "total", "per_100k_pre2013_census", 
         "per_100k_post2013_census", "attempts_total", 
         "attempts_share_of_total", "with_firearm_threat", "with_firearm_shot", 
         "percent_cases_solved", "total_suspects", "non_german_suspects",