arvi1000

library(tidyverse)

adj_ftp <- function(rider_kg=70, watts_kg_ftp=3, extra_kg) {
  watts <-  watts_kg_ftp * rider_kg
  adj_wkf <- watts / (rider_kg + extra_kg)
  return(adj_wkf)
}


tibble(extra_kg = seq(0, 5, .1),

arvi1000

library(tidyverse)

dat <-
"name, bike_count
Reid, 4
John, 3
The_Meat, 8
MatthewS, 2
Pinche, 4
arvi1000, 4

arvi1000

setwd('~/Documents/personal/r_stuff/bcc/srt_sort/')

# processing function. input is lines of the file as char vector
fix_srt <- function(srt_file) {

  # caption chunks are delimited by a blank line ''. 
  # so... add a blank line to the start
  srt_file <- c('', srt_file)
  
  # ...now the cumulative sum of blanks so far is a vector we can split on

arvi1000

library(tidyverse)

lapply(1:5, function(x) {
  data.frame(team = LETTERS[x], 
             alignment_score = rnorm(20, mean=max(x, 3), sd=0.5),
             stringsAsFactors = F)
  }) %>%
  bind_rows() %>%
  ggplot(aes(x=team, y=alignment_score)) +
  geom_boxplot() +

arvi1000

devtools::install_github('arvi1000/rGridMap')
library(rGridMap)
library(ggplot2)

# a data.frame of states with random categorical value
set.seed(123)
my_dat <- data.frame(state.abb = c(state.abb, 'DC'), # don't forget DC!
                     value=sample(LETTERS[1:5], 51, replace=T))

plotGridMap(my_dat, fill_var = 'value',

arvi1000

library(tidyverse)

# zachary / zachery by year
z_dat <- babynames::babynames %>%
  filter(grepl('^zach(a|e)ry$', tolower(name)) & year >= 1950) %>%
  group_by(name, year) %>%
  summarise(n=sum(n))

# when was peak zachary? 1993
z_dat %>%

arvi1000

just to rename gist

arvi1000

library(tidyverse)

# Prep data
# data from https://poll.qu.edu/texas/release-detail?ReleaseID=2625
dat <- 
"challenger, d_pct, trump_pct
Biden, 48, 44
Sanders, 44, 47
Warren, 45, 46
Harris, 43, 47

arvi1000

library(babynames)
library(tidyverse)
library(ggrepel)

dat <- babynames %>%
  filter(sex=='M') %>%
  mutate(chars = nchar(name),
         last_char = substr(name, chars, chars)) %>%
  group_by(year, last_char) %>%
  summarise(prop = sum(prop))

arvi1000

library(babynames)
library(tidyverse)

babynames::babynames %>%
  filter(name=='Arya') %>%
  ggplot(aes(x=year, y=prop*100, color=sex)) +
  geom_line(size=1) +
  labs(title = 'Babies Named Arya', 
       subtitle = 'as a proportion of all births that year',
       y='%') +