mitchelloharawild

library(httr)
library(purrr)
token <- rtweet:::check_token(NULL)
dsw <- GET(url = "https://api.twitter.com/1.1/lists/members.json?slug=women-in-data-science&owner_screen_name=becomingdatasci&cursor=-1&count=5000&skip_status=1", token)
map_chr(content(dsw)$users, "screen_name") %>% sort %>% cat(sep = "\n")
#> ___dhara___
#> __Helma
#> _ajbc
#> _beenkim
#> _ehinosa

mschnetzer

library(tidyverse)
library(survey)
library(msthemes)
library(gganimate)

# Load HFCS and Non-core data
load("hfcs2014AT.rda")
load("Noncore AT/non-core-at.rda")

# Calculate estimated decile with mean from multiple imputation data

PaulC91

library(tidyverse)
library(officer)
library(mschart)

# function to make ppt deck for each class in mpg data
new_deck <- function (i) {
  data <- mpg %>% 
    filter(class == i)
  
  ppt_chart <- ms_scatterchart(data, x = "displ", y = "hwy", group = "cyl") %>% 

conormm

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs: