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building the history web

Jason Heppler hepplerj

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building the history web
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View twitterusers.py
#!/usr/bin/python3
import tweepy
import csv
consumer_key = ""
consumer_secret = ""
access_token = ""
access_token_secret = ""
@hepplerj
hepplerj / metro.R
Created Mar 16, 2020
Percentage of residents who take public transportation to work
View metro.R
library(tidycensus)
library(tidyverse)
# If not set, un-comment below and install your Census API key (https://api.census.gov/data/key_signup.html)
# census_api_key("YOUR KEY HERE", install = TRUE)
get_acs(geography = "metropolitan statistical area/micropolitan statistical area",
variables = "DP03_0021PE",
summary_var = "B01003_001",
survey = "acs1",
@hepplerj
hepplerj / census_cleanup.R
Created Mar 11, 2020
An example script for census data in R
View census_cleanup.R
library(tidyverse)
library(tidycensus)
# My recommendation is to use the tidycensus library to make getting this data
# easier than reading in the data from the Census website.
#
# Before you can begin, you'll need to get an API key from the Census Bureau.
# You can acquire one here:
#
# Once you have the API key, run the following in RStudio:
@hepplerj
hepplerj / messy.R
Created Feb 20, 2020
Messy data in R, for teaching the tidyverse
View messy.R
library(charlatan)
library(salty)
library(magrittr)
library(readr)
messydata <- ch_generate('name','job','phone_number', n = 200)
messydata <- messydata %>%
mutate(job = salt_capitalization(job)) %>%
mutate(phone_number = salt_na(phone_number)) %>%
@hepplerj
hepplerj / frequency_to_list.R
Created Dec 11, 2019
Turn a frequency table into a list of individual items
View frequency_to_list.R
library(tidyverse)
library(readxl)
data <- readxl::read_xlsx("data.xlsx")
reshaped <- data %>% gather(word, freq, 2:21)
reshaped <- reshaped %>% drop_na()
cleaned <- reshaped %>%
uncount(freq)
@hepplerj
hepplerj / geofilter.R
Created Nov 8, 2019
Checking points and filtering incorrect or unneeded data.
View geofilter.R
library(tidyverse)
library(maps)
library(mapdata)
data <- read_csv("~/Desktop/nplsuperfund.csv")
names(data) <- c("lat","lon","date")
# Filter down to USA extent to remove extraneous points
tidy <- data %>%
filter(lat < -67, lat > -125) %>%
@hepplerj
hepplerj / hex_logo.R
Last active Jan 14, 2020
Hex logo generator for R User Group
View hex_logo.R
library(hexSticker)
library(tidyverse)
library(tidycensus)
library(sf)
library(viridis)
options(tigris_use_cache = TRUE)
nebraska_raw <- get_acs(state = "NE",
geography = "tract",
@hepplerj
hepplerj / pandas.py
Created May 23, 2018
An evolving set of pandas snippets I find useful
View pandas.py
# Unique values in a dataframe column
df['column_name'].unique()
# Grab dataframe rows where column = value
df = df.loc[df.column == 'some_value']
# Grab dataframe rows where column value is present in a list
value_list = ['value1', 'value2', 'value3']
df = df.loc[:,df.columns.isin(valuelist)]
# or grab rows where a value is not present in a list
@hepplerj
hepplerj / README.md
Last active May 10, 2018
Add leaflet points on click
View README.md

Click on the map to add points. See the console for lat/long output.

View index.html
<!DOCTYPE html>
<head>
<meta charset="utf-8">
<script src="https://d3js.org/d3.v4.min.js"></script>
<script src="http://www.webglearth.com/v2/api.js"></script>
<script>
function map() {
var options = { zoom: 1.5, position: [47.19537,8.524404] };
var earth = new WE.map('earth_div', options);