- H. Wickham: Official ggplot2 documentation
- H. Wickham: ggplot2 book
- W. Chang: R graphics cookbook and Cookbook for R
- Z. Ross: Beautiful plotting in R: A ggplot2 cheatsheet
- D. Koffman: Introduction to ggplot2
- R. Saccilotto: Tutorial: ggplot2
- R. Hartman: How to format plots for publication using ggplot2
- G. Williams: Visualising data with ggplot2
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/* | |
* Arduino Class for ArrayList | |
* Written: Obed Isai Rios | |
*/ | |
#include "Arduino.h" | |
#include "ArrayList.h" | |
ArrayList::ArrayList(char* init){ |
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--- flashall.sh | |
+++ (clipboard) | |
@@ -220,15 +220,14 @@ | |
flash-command --alt u-boot-env0 -D "${VARIANT_FILE}" | |
echo "Flashing U-Boot Environment Backup" | |
- flash-command --alt u-boot-env1 -D "${VARIANT_FILE}" -R | |
+ flash-command --alt u-boot-env1 -D "${VARIANT_FILE}" | |
echo "Rebooting to apply partition changes" | |
- dfu-wait no-prompt |
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library(sf) | |
library(RPostgreSQL) | |
dat <- data.frame(ID = 1:2, | |
long = c(-74.003234, -73.983317), | |
lat = c(40.731863, 40.722684)) | |
dat_sf <- st_as_sf(dat, coords = c("long", "lat"), | |
crs = 4326, | |
agr = "identity") | |
con <- dbConnect(PostgreSQL(), dbname = "mydb", host = "localhost") | |
st_write_db(con, dat_sf, "mydata") |
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{"x": "dogs and cats"} |
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var page = require('webpage').create(), | |
system = require('system'), | |
address; | |
if (system.args.length === 1) { | |
console.log('Usage: phantomjs url_requests.js http://some.url.com'); | |
phantom.exit(1); | |
} else { | |
address = system.args[1]; | |
var logUrl = function (req) { |
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# Export as Text Table | |
aws --region XXXXXXXXX cognito-idp list-users --user-pool-id XXXXXXXXXXXXX --output table > ~/users.txt | |
# Export as JSON | |
aws --region XXXXXXXXX cognito-idp list-users --user-pool-id XXXXXXXXXXXXX --output json > ~/users.json | |
# Export User Pool with more than 60 users (pagination) | |
aws --region XXXXXXXXX cognito-idp list-users --user-pool-id XXXXXXXXXXXXX --pagination-token INCREDIBLYLONGSTRINGHERE --output json > ~/users-2.json |
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library(bit64) | |
twepoch <- as.integer64('1288834974657') | |
base <- as.integer64(2) | |
datacenter_id_bits <- 5 | |
worker_id_bits <- 5 | |
sequence_id_bits <- 12 | |
max_datacenter_id <- 1 * base^datacenter_id_bits | |
max_worker_id <- 1 * base^worker_id_bits | |
max_sequence_id <- 1 * base^sequence_id_bits |
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library(purrr) | |
library(rrapply) | |
# Split a data frame into a nested list using a different column for each level. | |
# This uses rrapply::rrapply() to avoid having to do any nested loops (map, lapply, | |
# for loop, whatever). | |
# Nested lists can be useful for avoiding searching through your data: the data | |
# has already been indexed in the list. This can be pretty handy for saving time | |
# if you need to do lots of filtering stuff. |
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/* | |
* I add this to html files generated with pandoc. | |
*/ | |
html { | |
font-size: 100%; | |
overflow-y: scroll; | |
-webkit-text-size-adjust: 100%; | |
-ms-text-size-adjust: 100%; | |
} |
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