lukereding

What's new in Python 3.8

The walrus operator

:= lets you assign variables as part of larger expressions.

Example:

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import requests
import pandas as pd
import altair as alt
from typing import List, Tuple


def download_comments(repo: str, user: str) -> List[str]:
    url = f"https://api.github.com/repos/{user}/{repo}/issues/comments"
    res = requests.get(url)
    if res.ok:

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library(tidyverse)

## testing overall incidence of intransitivity

# number of intransitive fish in each experiment per the results
intransitive <- c(9, 6, 6)

# total number of fish in each experiment
total <- c(10, 11, 23) + intransitive

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# edited from https://github.com/aschinchon/travelling-salesman-portrait/blob/master/frankenstein_TSP.R

library(imager)
library(dplyr)
library(ggplot2)
library(scales)
library(TSP)

urlfile <- "https://www.dropbox.com/s/4td7zbqohyw0f3g/creepy.jpg?dl=0"

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require(tidyverse)

# computes things you want to know across the entire trial
do_it <- function(x) {
  name <- x$filename[1]
  fish_name <- name %>% stringr::str_match(pattern = "_S[a-z]+?_([A-Za-z]+)_") %>% as.vector %>% .[2]
  
  temp <- x %>% 
  mutate(time_zone = lead(time) - time) %>% 
  group_by(code) %>%

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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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#!/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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data_frame(x=1:10)  %>%  
  mutate(
    # x2 = x*2,  
    x3 = x*3) %>%  
  filter(x>5)  

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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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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)