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read_csv ("test.csv", col_names=FALSE, col_types = cols(.default = "c", time = "i"))
# This should set the default type for all columns as character, while time would be parsed as integer.

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data_filter <- filter(myData,
                   grepl('Search', field))

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# devtools::install_github("tidyverse/googlesheets4")
library(googlesheets4)
library(here)
# library(dplyr)
data <- read_sheet("https://docs.google.com/spreadsheets/d/1AkYjbnLbWW83LTm6jcsRjg78hRVxWsSKQv1eSssDHSM/edit#gid=0")
# reads from: Reading from 'emergency_tenant_protections_current_do_not_edit_me'

data$lat[data$lat == "NULL"] <- NA
data$lon[data$lon == "NULL"] <- NA

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library(tidyverse)
library(v)

data_dir %>% 
  dir_ls(regexp = "\\.csv$") %>% 
  map_dfr(read_csv, .id = "source") %>% 
  mutate(Month_Year = myd(Month_Year, truncated = 1))

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#!/bin/sh 

DIR="$HOME/.oh-my-zsh/custom/themes"
mkdir -p $DIR
cd $DIR

sed "s/➜/→/g;s/✗/×/g" $HOME/.oh-my-zsh/themes/robbyrussell.zsh-theme > robbyrussell.zsh-theme

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ggplot(data = storms, aes(x = pressure)) +
  geom_density(fill = 'cyan', color = 'cyan') +
  labs(title = 'The pressure variable is strongly left-skewed') +
  theme(text = element_text(family = 'Gill Sans', color = "#444444")
        ,panel.background = element_rect(fill = '#444B5A')
        ,panel.grid.minor = element_line(color = '#4d5566')
        ,panel.grid.major = element_line(color = '#586174')
        ,plot.title = element_text(size = 24)
        ,axis.title = element_text(size = 18, color = '#555555')
        ,axis.title.y = element_text(vjust = .5, angle = 0)

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`r if(show.text){"la la la"}`

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empdataCA4 <-  lapply(split(empdataCA3, empdataCA3$reg), function(x) {
 x.Date <- (c(CurrentYear, CurrentYear + 1, CurrentYear + 2, CurrentYear + 3, CurrentYear +4 ,CurrentYear +5))
 val <- as.ts(na.approx(zoo(x = c(x$Pct[1],NA,NA,NA,NA,x$Pct[2]), x.Date)))
 val2 <-predict(td(val~1, to='quarterly', method='denton-cholette', conversion = "last"))
 # change the options for interpolation as needed
 data.frame(yearQtr= as.yearqtr(time(val2)), test=val2)})

df.dplyr <- as.data.frame(bind_rows(empdataCA4, .id = "groups"))
df.dplyr0 <- as.data.frame(stringr::str_split_fixed(df.dplyr$groups, "_", n=2))

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## data input (number of reads mapped to each category)
total=100
rRNA=5 # mapped to nuclear rRNA regions
mtRNA=7  # mapped to mitochondria genome
# for the rest of above, then we divide into different category, like http://www.biomedcentral.com/1741-7007/8/149 did.
intergenic=48 
introns=12
exons=30
upstream=3
downstream=6

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install.packages('data.table', 'dplyr', 'plyr')
library(dplyr)

data_acs <- data.table::fread("./data/usa_00035.csv")
data_acs <- tbl_df(data_acs)

## make an example age dummy variable
data_acs$AGE_VAR <- ifelse(data_acs$AGE < 15, 1, 0)
data_acs$AGE_VAR <- ifelse(data_acs$AGE >= 24 & data_acs$AGE <= 36 , 2, data_acs$AGE_VAR)
data_acs$AGE_VAR <- ifelse(data_acs$AGE > 36 & data_acs$AGE <= 51 , 3, data_acs$AGE_VAR)