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0214-lreding:deck_gl_geojson lreding$ ap run | |
+ yarn add deck.gl | |
yarn add v0.23.2 | |
warning No license field | |
[1/4] 🔍 Resolving packages... | |
[2/4] 🚚 Fetching packages... | |
[3/4] 🔗 Linking dependencies... | |
[4/4] 📃 Building fresh packages... | |
warning Your current version of Yarn is out of date. The latest version is "0.23.3" while you're on "0.23.2". | |
success Saved 1 new dependency. |
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swagger: '2.0' | |
info: | |
description: >- | |
This is a simple example of a REST API deployed on Anaconda Enterprise and | |
documented with Swagger UI. | |
version: 2017.06.02 | |
title: quotes | |
termsOfService: 'http://swagger.io/terms/' | |
contact: | |
email: lreding@continuum.io |
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swagger: '2.0' | |
info: | |
description: >- | |
This API implments some simple natural language processing tasks like | |
tokenization, sentiment analysis, and retrieving parts of speech from text. | |
version: 2017.06.08 | |
title: NLP with Anaconda | |
termsOfService: '' | |
contact: | |
email: lreding@continuum.io |
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random_csv = "http://datadryad.org/bitstream/handle/10255/dryad.37977/Cranby%20E4%20pheno.csv?sequence=1" | |
# read in a csv | |
df <- read.csv(random_csv) | |
# print it to the console | |
print(df) | |
# print the raw characters from the csv file to the console | |
readLines(random_csv) |
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swagger: '2.0' | |
info: | |
description: >- | |
This API implments some simple natural language processing tasks like | |
tokenization, sentiment analysis, finding the most common words, and | |
retrieving parts of speech from text. | |
version: 2017.06.14 | |
title: Natural Language Processing with Anaconda | |
termsOfService: '' | |
contact: |
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df <- read_csv("http://datadryad.org/bitstream/handle/10255/dryad.152300/forDryad.csv?sequence=1") | |
df %<>% unite(uni, source, target) | |
edges <- df %>% | |
group_by(uni) %>% | |
count %>% | |
separate(col = "uni", into = c("source", "target"), sep = "_") | |
edges <- rename(edges, number = n) | |
net <-graph_from_data_frame(edges) |
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library(ggplot2) | |
library(cowplot) # github version | |
# create dataframe with x and y with different ranges | |
df <- data.frame(x = rnorm(20, mean = 5, sd = 2), y = rnorm(20, mean = 20, sd = 5)) | |
# create scatterplot | |
p <- ggplot(df, aes(x = x, y = y)) + | |
geom_point() |
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data_frame(x=1:10) %>% | |
mutate( | |
# x2 = x*2, | |
x3 = x*3) %>% | |
filter(x>5) |
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#!/usr/bin/env bash | |
# this script downloads an example animation from the internet and creates looped version of the animation that you could then use in a mate choice study | |
# the advantage of the script as opposed to doing this in something like iMovie: | |
### time. On my computer this script takes <30 seconds to execute. Doing this in iMovie could easily take half an hour. | |
### repeatability. I can send this script to you and you can make the video yourself. | |
### easy to remake videos when you make a mistake | |
### easy to keep track of what you've done. A plain text script like this allows you do _version control_ your work, allowing you to revert to a previous version and always know when you changed parts of your code | |
# make a directory to keep things tidy |
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# the goal: | |
## download the animation | |
## use ffmpeg to make it playable | |
## change the speed of the animation | |
## randomally change the speed of the animation | |
## concatenate multiple speeds together | |
# make a directory to keep things tidy | |
mkdir create_video | |
cd create_video |
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