dylanjm

#!/bin/bash
# Take all .html files in directory and covert them to .docx files for grading
# Check all .html files in directory for the string "Passed all tests with no errors"
# This way we can quickly sort files of people who may have had more trouble 
# Move files that pass into a seperate directory than those with failed tests.
str="Passed all tests with no errors"
for file in *.html; do
    pandoc -o "${file%.*}.docx" -s -S "$file"
    if grep -q "$str" "$file"; then
	mv "${file%.*}.docx" ../docx/testPass/

dylanjm

math335_theme <- function() {
theme_bw() + theme(panel.border = element_blank(),
                   panel.grid.major = element_blank(),
                   panel.grid.minor = element_blank(),
                   axis.line = element_line(colour = "black"),
                   plot.title = element_text(face="bold"),
                   axis.title = element_text(face="bold"),
                   plot.caption = element_text(face="italic", size = 8, color = "#4d4d4d"))
}

dylanjm

library(tidyverse)
library(rio)
library(rvest)
library(janitor)


# Rcode to go and fetch country codes
country_codes <- read_html("http://web.stanford.edu/~chadj/countrycodes6.3") %>% 
  html_text() %>% 
  str_extract_all("[A-Z]{3}") %>% 

dylanjm

library(blscrapeR)
library(tidyverse)
library(albersusa)
library(ggalt)
library(wesanderson)
library(cowplot)
library(tidycensus)

# The original blscrapeR::qcew_api() is not really suited to take multiple years
# or niac codes at once. Here is a function that grabs it all!

dylanjm

library(tidyverse)

read_lines(here::here("input_data/data04a.txt")) %>% 
  str_match("\\[(\\d+)-(\\d+-\\d+) (\\d+):(\\d+)\\] (.*$)") %>% 
  as_tibble() %>% 
  set_names(c("raw_data", "year", "date", "hour", "min", "comment")) %>% 
  mutate(id = str_extract_all(comment, "(\\d+)", simplify = T) %>% 
           ifelse(. == "", NA, .)) %>% 
  arrange(year, date, hour, min) %>% 
  fill(id) %>% 

dylanjm

library(tidyverse)
library(hrbrthemes)
library(patchwork)

r_tweets <- read_rds(here::here("data/data_2019/week01_rstats_tweets.rds"))
tt_tweets <- read_rds(here::here("data/data_2019/week01_tidy_tuesday_tweets.rds"))

(top_users <- tt_tweets %>% 
  count(screen_name) %>% 
  top_n(10, n) %>% 

dylanjm

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

url = 'https://python-graph-gallery.com/wp-content/uploads/gapminderData.csv'

def add_cols(grp):
  grp['popl'] = grp['pop'].apply(lambda x: x/10**5)
  grp['gdp_wt'] = np.average(grp['gdpPercap'], weights = grp['pop'])

dylanjm

structure(list(y = c(-428.846931773699, 2814.14354795795, 1041.57062019406, 
413.053552770623, -2800.67011782234, -2198.16050963969, 801.924652033519, 
732.090097673507, -18875.6488077687, -2832.38372468581, 173.46142036292, 
-3786.71578136507, -1997.72041336496, 1592.56310472052, -486.657881249919, 
593.68112891115, -21.9813204920439, 864.966591019571, -5237.93696303717, 
-9202.40040067778, 425.320418694913, 797.650940028017, -123.700827591037, 
-216.376695426808, 305.222605237999, 3.28251012543717, 991.586724694986, 
1433.13448159131, 4629.28834528504, 643.801261103721, -1171.77757782828, 
3346.60023414888, 2039.8742756452, 1695.36368497713, 1100.78951900258, 
-284.355910530996, 1033.8900351293, 1000.68508364355, 202.456374187071, 

dylanjm

#!/usr/bin/env python3

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

def amort(principal, rate, n):
    return principal * (rate * ((1 + rate) ** n)) / ((1 + rate) ** n - 1)

dylanjm

#!/usr/bin/env python3

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import textwrap
from tabulate import tabulate

def amort(principal, rate, n):
    return principal * (rate * ((1 + rate) ** n)) / ((1 + rate) ** n - 1)