kjhealy

## Using position_nudge() to make showing a background 
## distribution a little nicer, by pushing it very slightly
## to the right. Most of the work is getting the penguins
## data to the point where I can demonstrate position_nudge()

library(tidyverse)
library(palmerpenguins)

# Classify every penguin's flipper length into 
# bins with widths of 10mm; then sum up

kjhealy

```{r}
library(tidyverse)
library(here)
library(fs)
library(socviz)
```

```{r}
here()
```

kjhealy

library(tidyverse)
library(babynames)

babynames %>%
    filter(sex == "M") %>%
    mutate(endletter = str_sub(name, -1)) %>%
    group_by(year, endletter) %>%
    summarize(letter_count = n()) %>%
    mutate(letter_prop = letter_count / sum(letter_count), 
           letter_rc = case_when(

kjhealy

## Context: https://twitter.com/grant_mcdermott/status/1493400952878952448

options(collapse_mask = "all") # NB: see `help('collapse-options')`

library(dplyr)
library(data.table)
library(collapse) # Needs to come after library(dplyr) for collapse_mask to work

flights = fread('https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv')

kjhealy

library(tidyverse)
library(cowplot)

df <- read_csv("https://osf.io/qmhgw/download")

out <- df %>%
  ggplot(mapping = aes(x = year,
                       y = oneper,
                       group = country,
                       color = Region)) +

kjhealy

# UK map v. quick example


## Libraries
library(tidyverse)
library(sf)
#> Linking to GEOS 3.8.1, GDAL 3.1.4, PROJ 6.3.1


## Get the map data

kjhealy

This is my personal list of functions that I wrote for converting mov files to mp4!

Command Flags

Flag	Options	Description
-codec:a	libfaac, libfdk_aac, libvorbis	Audio Codec
-quality	best, good, realtime	Video Quality
-b:a	128k, 192k, 256k, 320k	Audio Bitrate
-codec:v	mpeg4, libx264, libvpx-vp9	Video Codec

kjhealy

library(tidyverse)
library(gapminder)
library(ggalt) ## install with install.packages("ggalt", repos = "http://cran.rstudio.com")

## Make some sample data with three countries that each has nine measures
example_wide <- gapminder %>%
  select(country, year, lifeExp, pop, gdpPercap) %>%
  filter(country %in% c("United States", "Canada", "Mexico"), 
         year %in% c(1957, 1977, 2007)) %>%
  pivot_wider(names_from = year, values_from = lifeExp:gdpPercap) 

kjhealy

## Get current Apple COVID data
## Kieran Healy / @kjhealy

## E.g. apple_covid <- get_apple_data()

## 1. Find today's URL
## Thanks to David Cabo (@dcabo) for alerting me to the existence of the json file. 
get_apple_target <- function(cdn_url = "https://covid19-static.cdn-apple.com",
                             json_file = "covid19-mobility-data/current/v3/index.json") {
  tf <- tempfile(fileext = ".json")

kjhealy

library(dplyr, warn.conflicts = FALSE)
library(gapminder)
probs <- c(0.1, 0.5, 0.9)

gapminder %>% 
  group_by(continent) %>% 
  summarise(
    probs = probs,
    across(is.numeric & !year, ~ quantile(.x, probs))
  )