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library(tidyverse) | |
get_label = function(var) tibble(var=var, label=d[[var]] %>% attr(which = "label")) | |
map(colnames(d), get_label) |> list_rbind() | |
var = "sex_rec" | |
get_value_labels <- function(var) { | |
value_labels <- attr(d[[var]], which="labels") | |
tibble(var=var, value=value_labels, label=names(value_labels)) |
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import subprocess | |
from pathlib import Path | |
from PyPDF2 import PdfReader, PdfWriter | |
outPdf=PdfWriter() | |
for inf in Path.cwd().glob("*.html"): | |
pdff = inf.with_suffix(".pdf") | |
if not pdff.exists(): | |
print(f"*1* {inf} -> {pdff}") | |
subprocess.check_call(["wkhtmltopdf", str(inf), str(pdff)]) |
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import csv | |
import sys | |
import numpy | |
from pyannote.audio.pipelines.utils.hook import ProgressHook | |
import collections | |
import whisper | |
from pyannote.audio import Pipeline | |
import torch | |
from pyannote.audio import Audio | |
from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding |
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library(tidyverse) | |
tidy_svd = function(long_data, rows_from, columns_from, values_from, ndimensions=10) { | |
# center the data | |
long_data[[values_from]] = long_data[[values_from]] - mean(long_data[[values_from]], na.rm=TRUE) | |
# pivot and cast to wide matrix | |
m <- long_data |> | |
select(all_of(c(rows_from, columns_from, values_from))) |> | |
na.omit() |> | |
pivot_wider(names_from=columns_from, values_from=values_from, values_fill = 0) |> |
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library(gh) | |
library(tidyverse) | |
get_all_pages <- function(url, ...) { | |
result <- list() | |
for (page in 1:99999) { | |
message(str_c("[", page, "] ", do.call(glue::glue, list(url, ...)))) | |
p <- gh(url, ..., page=page) | |
if (length(p) == 0) break | |
result[[as.character(page)]] <- p |
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--- | |
title: 'Lab 2: exploring the US elections (template)' | |
author: "(Your name)" | |
output: | |
pdf_document: | |
editor_options: | |
chunk_output_type: console | |
--- | |
```{r setup, include=FALSE} |
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library(psych) | |
library(tidyverse) | |
library(haven) | |
d = read_sav("~/Downloads/Project 2 GGD_February 21, 2023_03.46.sav") | |
cleaned = d |> filter(status != 1) |> | |
rename_with(~str_replace(., "Q37", "Vertrouwen"), starts_with("Q37")) |> | |
rename_with(~str_replace(., "Q38", "Privacygevoeligheid"), starts_with("Q38")) |> | |
rename_with(~str_replace(., "Q40", "Perceptie_medewerkers"), starts_with("Q40")) |> |
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<?xml version='1.0' encoding='utf-8'?> | |
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.2 20190208//EN" "JATS-archivearticle1.dtd"> | |
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="1.2" article-type="other"> | |
<front> | |
<journal-meta> | |
<journal-id/> | |
<journal-title-group> | |
<journal-title>Computational Communication Research</journal-title> | |
</journal-title-group> | |
<issn publication-format="electronic">2665-9085</issn> |
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* Encoding: UTF-8. | |
DATASET ACTIVATE DataSet1. | |
FREQUENCIES VARIABLES=Q14 Q37 A4.1_ A4.2_ A4.3_ Q37_1 Q37_2 Q37_3 Q37_4 Q37_5 Q37_6 Q37_7 Q37_8 | |
Q38_1 Q38_2 Q38_3 Q38_4 Q38_5 Q38_6 Q38_7 Q38_8 Q38_9 Q40_1 Q40_2 Q40_3 Q40_4 Q40_5 Q40_6 Q40_7 | |
Q40_8 Q15 | |
/STATISTICS=MEAN MEDIAN MODE | |
/ORDER=ANALYSIS. | |
*** hieronder de variabele veranderd (andere naam) |
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Text: Wij zijn Wouter van Atteveldt en Nel Ruigrok. | |
10.000 nieuwe stamcel- en bloeddonoren na oproep PSV-perschef Thijs Slegers. | |
Ongeneeslijk ziek Opvallend veel mannen meldden zich aan als donor na een oproep van de ongeneeslijk zieke Slegers, Matchis kreeg 7.000 nieuwe aanmeldingen, Sanquin 3.000. | |
Model: pdelobelle/robbert-v2-dutch-ner | |
NER output: | |
{'entity_group': 'PER', 'score': 0.9998577, 'word': ' Wouter van Atte', 'start': 9, 'end': 24, 'full_word': 'Wouter van Atteveldt'} | |
{'entity_group': 'PER', 'score': 0.9999995, 'word': ' Nel Ruig', 'start': 33, 'end': 41, 'full_word': 'Nel Ruigrok'} |
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