emmagrimaldi

annotations = [dict(
  
              # text I want to display. I used <br> to break it into two lines
              text = 'All US storm events that caused more than $50k of economic damage,<br> from 2000 until today', 
              
              # font and border characteristics
              font = dict(color = '#FFFFFF', size = 14), borderpad = 10, 
              
              # positional arguments
              x = 0.05, y = 0.05, xref = 'paper', yref = 'paper', align = 'left', 

emmagrimaldi

layout = dict(
    height = 800,
    # top, bottom, left and right margins
    margin = dict(t = 0, b = 0, l = 0, r = 0),
    font = dict(color = '#FFFFFF', size = 11),
    paper_bgcolor = '#000000',
    mapbox = dict(
        # here you need the token from Mapbox
        accesstoken = mapbox_access_token,
        bearing = 0,

emmagrimaldi

updatemenus=list([
    # drop-down 1: map styles menu
    # buttons containes as many dictionaries as many alternative map styles I want to offer
    dict(
        buttons=list([
            dict(
                args=['mapbox.style', 'dark'],
                label='Dark',
                method='relayout'
            ),                    

ryanrosenberg

library(tidyverse)

regs_tossups <- read_tsv("tossups.tsv")

regs_games_played <- regs_tossups %>% 
  filter(!is.na(team), packet != "S", !is.na(buzz_location_pct)) %>% 
  distinct(team, packet) %>% 
  count(team)

# Change based on set categories

DFoly

import mysql.connector
from mysql.connector import Error
import tweepy
import json
from dateutil import parser
import time
import os
import subprocess

#importing file which sets env variable

ryanrosenberg

library(tidyverse)
library(sf)
library(lubridate)

chicago_neighborhoods <- read_sf("chi_community_shapefiles") %>% 
  mutate(community_area = as.integer(area_numbe)) 
chicago_potholes <- read_csv("chicago_potholes.csv") %>% 
  janitor::clean_names() %>% 
  mutate_at(vars(creation_date, completion_date), mdy) %>% 
  mutate(time_to_fill = difftime(completion_date, creation_date))

mkearney

#' Conditionally apply expressions on a data object
#' 
#' @param .data Input data
#' @param condition A logical value to determine whether to use .if or .else
#' @param .if Formula or function to apply to intput data when condition is TRUE
#' @param .else Formula or function to apply to intput data when condition is FALSE
#' @return Output of appropriate .if/.else call
#' @export
#' @importFrom rlang as_closure
do_if_else <- function(.data, condition, .if, .else = identity) {

sergiospagnuolo

library(tidyverse)

# Distribuição de contribuintes previdenciários
# ref http://dados.gov.br/dataset/estatisticas-contrib-pessoas-fisicas-por-uf/resource/6b498c82-dbd9-42b4-8a7e-d04967c6cff1
# manual das colunas http://www.previdencia.gov.br/dados-abertos/aeps-2007-anuario-estatistico-da-previdencia-social-2007/anuario-estatistico-da-previdencia-social-2007-contribuintes-da-previdencia-social/

d <- read.csv("CTB21.csv", header = T, fileEncoding = "ISO-8859-1", dec = ",")

ufs_abrev <- c("Acre", "Alagoas", "Amazonas", "Amapá", "Bahia", "Ceará", "Distrito Federal", "Espírito Santo", "Goiás", "Maranhão", "Mato Grosso", "Mato Grosso do Sul", "Minas Gerais", "Pará", "Paraíba", "Paraná", "Pernambuco", "Piauí", "Rio de Janeiro", "Rio Grande do Norte", "Rondônia", "Rio Grande do Sul", "Roraima", "Santa Catarina", "Sergipe", "São Paulo", "Tocantins")
ufs_siglas <- c("AC", "AL", "AM", "AP", "BA", "CE", "DF", "ES", "GO", "MA", "MT", "MS", "MG", "PA", "PB", "PR", "PE", "PI", "RJ", "RN", "RO", "RS", "RR", "SC", "SE", "S

rpaskin

import random

def non_random_choice(list):
	return random.choice(list)

def programar(algo):
		tecnicas = ['algoritmos', 'IA', 'um chatbot', 'um framework', ' alguma tecnologia']
		print(f'* TODO * Resolver {algo} com {non_random_choice(tecnicas)}')

def usar_ferramenta_para_resolver(algo):

mathiasfls

library(rJava)
library(coreNLP)
initCoreNLP()

#in this case, 'test' is a data frame with a col named 'text'

for (i in 1:dim(test)[1]) {
  cat(paste0(i / dim(test)[1] * 100, '% completed'))
  ann <- annotateString(paste(test$text[i]), format = c("obj"), outputFile = NA, includeXSL = FALSE)
  gd <- getDependency(ann)