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#######################
####  Reprex demo  ####
#######################

## 2020-04-24

# A simple reprex --------------------------------------------------------------

dat <- data.frame(a = 1:2, b = LETTERS[1:2])
mean(dat$a)

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############################################
####  Calculate number of code reviews  ####
############################################

## Needs a GitHub PAT or you'll hit rate limiting

library("gh")
library("purrr")
library("glue")

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library("cholera")
library("tidyverse")
library("magick")
library("grid")
library("egg")

# Get image
bree <- image_read("https://pbs.twimg.com/media/EBPHuylUIAAziJt?format=jpg")

# Add x/y coordinates of Broad St. pump and convert format

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## Follower accession chart for AMPADportal
## Inspired by: https://blog.ouseful.info/2013/04/05/estimated-follower-accession-charts-for-twitter/

library("rtweet")
library("tidyverse")

followers <- get_followers("AMPADPortal")
followers_df <- lookup_users(followers$user_id)

process_user_data <- function(data) {

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## Start with the code and data here: https://github.com/fivethirtyeight/data/blob/master/police-deaths/plot.R

## Then:
## Calculate total deaths by year
dat <- data_for_plot %>%
  group_by(year) %>%
  mutate(total = sum(count))

## Colors I chose
mycolors <- c("#00bf7b", "#59004d", "#ffcb89", "#a76e61", "#ac270d", "#7890ff",

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## Load tidyverse package, which contains ggplot2, dplyr, tidyr, etc.
library("tidyverse")

## Create some data
set.seed(432)
dat <- tibble(fert = c(rnorm(n = 10, mean = 0.6, sd = 0.2),
                       rnorm(n = 10, mean = 0.4, sd = 0.2)),
              treat = rep(c("25-25", "28-28"), each = 10))

## Plot boxplots with points on top + yellow X for mean & legend

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## Load ggplot package
library("ggplot2")

## Create sample data
dat <- structure(list(x = c(2.69753522456158, 9.77329733015504,
                            0.579014160879888, 6.01934352936223,
                            5.53224114519544, 7.52123757028021, 1.1418573057279,
                            5.81543114883825, 1.98635061332025,
                            8.98725295660552),
                      y = c(1.1341254551895, 8.69686821205542, 1.0851298507303,

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library("ggplot2")
library("dplyr")

###########################################################
####  This does not work to create stacked area chart  ####
###########################################################

## Create data
set.seed(40)
dat <- data.frame(x = rep(c(1:10), 3),

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# In response to: https://twitter.com/duffy_ma/status/628300075110936576

df <- data.frame(x=1:10, y = 1:10)
library("plyr")

df$y <- mapvalues(df$y, from = c(5), to = c(NA)) # make sure to keep `y` column numeric
lengthdata <- length(df$x)

for(i in 1:(lengthdata-1)) {
  if (is.na(df$y[i])) {  # use is.na to test for NAs