stevenworthington

https://docs.python.org/3/reference/index.html
https://docs.python.org/3/library/index.html

https://www.cs.put.poznan.pl/csobaniec/software/python/py-qrc.html

https://perso.limsi.fr/pointal/python:abrege
https://perso.limsi.fr/pointal/python:memento
https://perso.limsi.fr/pointal/_media/python:cours:abregepython.pdf
https://perso.limsi.fr/pointal/_media/python:cours:abregepython-english.pdf
https://perso.limsi.fr/pointal/_media/python:cours:mementopython3.pdf

stevenworthington

# Function definition
add_event <- function(start, end = NULL, label = NULL, line = 0.5, las = 1, col = scales::alpha("steelblue", 0.5)) {
  if(is.null(end)) {
    end <- paste(start, "23:59:59")
    start <- paste(start, "00:00:00")
  }
  
  start <- as.POSIXct(start)
  end <- as.POSIXct(end)
  

stevenworthington

library(lme4)

head(sleepstudy)
summary(sleepstudy)
sapply(sleepstudy, class)

# Fit model using the original data:
d <- sleepstudy
d$Subject <- factor(rep(1:18, each=10))
fm1 <- lmer(Reaction ~ Days + (Days|Subject), d)

stevenworthington

from PIL import Image
from PIL.ExifTags import TAGS, GPSTAGS

def get_exif_data(image):
    """Returns a dictionary from the exif data of an PIL Image item. Also converts the GPS Tags"""
    exif_data = {}
    info = image._getexif()
    if info:
        for tag, value in info.items():
            decoded = TAGS.get(tag, tag)

stevenworthington

data(mtcars)
library(ggplot2)

plot1 <- ggplot(mtcars, aes(x = hp, y = mpg)) +
    geom_point() +
    geom_smooth(method = "lm") +
    facet_wrap(~ cyl) +
    theme_bw()
    

stevenworthington

x <- c(
	'knitr', #  A general-purpose package for dynamic report generation in R.
# 'sqldf', # For running SQL statements on R data frames, optimized for convenience.
	'randomForest', # Classification and regression based on a forest of trees using random inputs.
	'arm', # R functions for processing lm, glm, svy.glm, mer and polr outputs.
	'ggplot2', # An implementation of the Grammar of Graphics.
	'gridExtra', # misc. high-level Grid functions
	'plyr', # Tools for splitting, applying and combining data.
	'tree', # Classification and regression trees.
	'gbm', # Generalized Boosted Regression Models

stevenworthington

# set working directory
dir_path <-  "path_to_text_files"
setwd(dir_path)

# create vector of filenames
filenames <- list.files(dir_path)

# read in files to a list
docList <- lapply(filenames, scan, what = "character", sep = "\n")

stevenworthington

library(sp)
library(maptools)

# get North Carolina shape data
NC <- readShapePoly(system.file("shapes/sids.shp", package = "maptools")[1],
          IDvar = "FIPSNO", proj4string = CRS("+proj=longlat +ellps=clrk66"))

# plot polygons
plot(NC, border = "blue", axes = TRUE, las = 1)

stevenworthington

# list with character vectors
text <- list(a = "all day I play @sworth with R",
             b = "all night I play @sworth with R")

# extract letters after "@" in a single character vector
sub("^.*@(\\w+).*", "\\1", text$a) 

# extract letters after "@" in a list of character vectors
gsub("^.*@(\\w+).*", "\\1", text)

stevenworthington

# Note: requires loading the "socsub" data frame (not a bundled R dataset)


# ------------------------------------------------------------------------------------
# pairwise comparisons including interactions


# use lm model to get design matrix
model1 <- lm(agro.rec.tot ~ sex*ageclass + loggrpmem, offset = logtimeage, data = socsub)