davebraze

library(dplyr)

## Toy data
df <- data_frame(block=rep(1:3, each=4), item=rep(1:4,3),
                 cond=rep(LETTERS[1:2], 6),
                 stim=paste0(rep(letters[1:2],6), rep(1:6,each=2)))

## Randomize blocks and trials within blocks
df.rand <- df %>%
    group_by(block) %>%

davebraze

library(lme4)
library(nlme)
library(ggplot2)

data(Oxboys)
head(Oxboys, 12)

ggplot(aes(y=height, x=age), data=Oxboys) +
    geom_point() + geom_path(aes(group=Subject))

davebraze

library(car)
library(Hmisc)
library(perturb)

n <- 200

x0 <- rnorm(n)
x1 <- rnorm(n)
x2 <- x1 + rnorm(n)/2
x3 <- x2 + rnorm(n)/1.25

davebraze

p <- seq(0,1,.05)
odds <- p/(1-p)
logOdds <- log(odds)

cbind(p, odds, logOdds)

##          p      odds  logOdds
## [1,] 0.00  0.000000     -Inf
## [2,] 0.05  0.052632 -2.94444
## [3,] 0.10  0.111111 -2.19722

davebraze

library(tidyverse)

## toy fixation report
fixations <- tibble(fixidx=as.integer(1:3), start=c(100,250,372), end=c(202, 348, 426), 
                    x=sample(0:1000,3), y=sample(0:1000,3))

## convert to long format
fixations <- fixations %>%
    gather(start, end, -c(fixidx,x,y)) %>%
    arrange(fixidx) %>%

davebraze

lmerTest:: satterthwaite df estimation
pbkrtest:: kenward-roger df estimation; also parametric-bootstrap per Judd, Westfall & Kenny (2012)

mertools:: provides prediction intervals for parameters estimates in lme4 models.
semTools:: supplements lavaan. 
mcmcglmm:: bayesian mixed models.
glmmPQL::
plotmcmc::

GLMM FAQ: 

davebraze

##### Basic factor level ordering and (treatment) contrasts

## set up data.frame with 1 continuous variable and 1 factor with 8 levels.
set.seed(1234)
x <- rnorm(80)
fac <- factor(rep(LETTERS[8:1], 10))
df <- data.frame(x, fac)
df$x[as.integer(df$fac) %% 5 == 0] <- rnorm(10, 1)

head(df, 16) ## Note the order of factor levels in this data is reverse

davebraze

Intro

Getting the right tables for a project can be fiddly in R, both for content and format. What I really prefer is clean separation between functions for generating content of a table, from those to do with its formatting. I am often, but not always, working in the context of an rmarkdown based workflow. There, fine control over format details will usually require making use of tools peculiar to the output type of the document (pdf, html, etc). This can complicate things a bit.

Table Content

These methods and tools are primarily about getting table content right.

Summary tables from dataframes

dplyr::

davebraze

Intro

ggrepel::

https://cran.r-project.org/web/packages/ggrepel/

"Provides text and label geoms for 'ggplot2' that help to avoid overlapping text labels.

ggforce::

https://cran.r-project.org/web/packages/ggforce/

davebraze

Given a yaml block like this:

---
title: My Data Summary (DRAFT)
subtitle: Fall 2019 Data
author: David Braze
institute: Haskins Laboratories
date: "`r format(Sys.time(), '%B %d, %Y')`"