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library(ggplot2)
library(dplyr)
library(tidyr)
library(purrr)
library(scales)
library(patchwork)

theme_set(theme_minimal())

# define functions -------------------------------------------------------------

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library(dplyr)     # for data wrangling
library(tidyr)     # same
library(purrr)     # for functional programming
library(rlang)     # for tidyeval
library(ggplot2)   # for dataviz
library(ggsci)     # for nice colours
library(scales)    # for displaying percentages
library(brms)      # for Bayesian models
library(tidybayes) # for extracting posterior draws and predictions
library(yardstick) # for generating ROC curves

gongcastro

from machine import Pin, PWM
from time import sleep

# Create a dictionary of Morse Code. s is for Short (or dots), l is for Long (or dashes)
MorseCodes = {
    ' ': '',
    'a': 'sl',
    'b': 'lsss',
    'c': 'lsls',
    'd': 'lss',

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Contributing to Transcriptase

We love your input! We want to make contributing to this project as easy and transparent as possible, whether it's:

• Reporting a bug
• Discussing the current state of the code
• Submitting a fix
• Proposing new features
• Becoming a maintainer

We Develop with Github

gongcastro

# get audio duration

# set up ----
library(audio)
library(purrr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(PraatR)
library(stringr)

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# animate the Beta distribution

# parameter vectors
x = collect(0.01:0.01:0.99); # sampling space
α = collect(0.1:0.1:10);
β = collect(0.1:0.1:10);
# only β=5 is used, but I don't want to mess up the code

# extract probability densities for all combinations of parameters
y = zeros(length(x), length(α), length(β)); # pre-alocate

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# extract lexical frequencies from CHILDES

# you may need to install the following packages:
# install.packages(c("dplyr", "stringr", "tidyr", "chidesr"))

get_childes_frequency <- function(
  token, # word(s) form to look up, e.g. c("table", "mesa")
  languages = c("cat", "spa"), # languages in which to look up the word form 
  ... # other arguments (see ?childesr::get_speaker_statistics)
){

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#### 2020-10-17-visualising-polynomial-regression -----

#### set up -------------------------------------------

# load packages
library(tidyverse)
library(gganimate)
library(data.table)
library(magick)
library(here)

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#### mask ----------------------------------------------------------------------

# import packages
library(tidyverse)
library(readxl)
library(stringdist)

#### import data ---------------------------------------------------------------
# you can download the data from https://drive.google.com/file/d/18SeJTiM2-JXR9SOqEg22wdkvNL3OxG3u/view?usp=sharing

gongcastro

library(tidyverse)
library(gganimate)

x <- seq(-4*pi, 4*pi, by = pi/50)
k <- seq(4*pi, -4*pi, by = -pi/50)
y <- t(sapply(x, function(x) sin(x-k)))
d <- as.data.frame(cbind(as.matrix(x), y))
colnames(d) <- c("x", k)
d <- pivot_longer(d, -x, "k", values_to = "y") %>%
  mutate_at(vars(k), as.numeric)