Steve Haroz steveharoz

## Texas congressional district simulation.R
library(tidyverse)

# arbitrary number
district_count = 38

# population from stephanie's figure
# https://twitter.com/evergreendata/status/1450862060972216320
population = c(
  rep("White", 40),
  rep("Latino", 39),

## data simulation
library(tidyverse)
library(magrittr)
library(afex)
library(Superpower)

set.seed(555)
SUBJECT_COUNT = 3 # per between-subject condition

#### ground-truth parameters for 3w x 2w x 2b (100 repetitions)

## ggdist psychometric functions.R
library(tidyverse)
library(ggdist)

# make some data
expand_grid(
  condition = c("A", "B"),
  stimulus = seq(-3,3,0.3),
  repetitions = 1:20
) %>%
  # make some noisy response data

## readme.txt
reference: https://dplyr.tidyverse.org/reference/storms.html

visualization: https://imgur.com/a/TMONnmS

source: https://www.nhc.noaa.gov/data/#hurdat

## _words before and after.R
library(tidyverse)

PATH = "peerj_reviews_txt/"
ignored_words = c("the", "dear", "original")
filenames = dir(path = PATH, pattern="*.txt", recursive = TRUE)


preceeding_words = sapply(filenames, function(f) {
  words = read_file(paste0(PATH, f)) %>%

## example within subject.csv

          
            subject
            x
            color
            rep
            y

            
              H
              1
              0
              1
              10.390916389674972

            
              J
              1
              0
              1
              11.877635692199044

            
              A
              1
              0
              1
              6.40831056135098

            
              G
              1
              0
              1
              10.143656530161934

            
              F
              1
              0
              1
              9.858462287495191

            
              B
              1
              0
              1
              8.726973066068064

            
              C
              1
              0
              1
              9.09479144777112

            
              D
              1
              0
              1
              9.689029831578754

            
              E
              1
              0
              1
              9.806302728246905

## How many p-values just under threshold.R
# simulate true effect sizes vs p in 0.01-0.05 range
library(tidyverse)

SUBJECT_COUNT = 16
TEST_COUNT = 16

# test if the p-value is in the specified range
inRange = function(p) {
  (p > 0.01) & (p < 0.05)
}

## bootstrap per condition and plot with ggdist.R
library(tidyverse)
library(ggdist) # for stat_histinterval()

############# make the dataset ###################

# 1 sample per subject per condition
data_per_subject_condition = expand_grid(
  subjectID = paste0("S", 1:50),
  independent_variable_A = c("circle", "square"),
  independent_variable_B = 1:3

## VR Gender.R
library(tidyverse)

# Mind the Gap:The Underrepresentation of Female Participants and Authors in Virtual Reality Research
# Tabitha C. Peck,Laura E. Sockol, and Sarah M. Hancock

# Table 3
data = read_csv(
"Study,N,CohensD,CI.Low,CI.Hi,p,SAMD,FemaleParticipants
Arafat et al. [4],16,0.42,0.19,0.65,<.001,−.07,81%
Ariza et al. [5],18,0.12,-0.09,0.33,0.263,−.68,22%

## sewer-covid19.csv
X,COVID-19 New Cases,Rep 1-N1,Rep 2-N1,Rep 1-N2,Rep 2-N2,Hospital admissions,virus RNA/ml
0,0.0,0.01771832019916774,0.025327583188293947,0.0012636319937241525,0.021536686448187715,0.22274881516587694,0.011848341232227562
1,0.0,0.01771832019916774,0.013927423563501674,0.0,0.01771832019916774,0.2819905213270144,0.014218009478673148
2,0.0,0.06076422295302489,0.10633738965673233,0.008872894878409794,0.006345630953625507,0.07819905213270138,0.06161137440758302
3,0.044047743576570264,0.0,0.1164739172696972,0.0012636319937241525,0.036727742012376295,0.22274881516587694,0.0
4,0.07024387534899187,0.15191055715279342,0.05950059026999374,0.0898827021364172,0.036727742012376295,0.2819905213270144,0.12796208530805683
5,0.022630983907802882,0.09240996608905253,0.08482817343043705,0.07342801384045856,0.08861906947427432,0.3388625592417062,0.08293838862559255
6,0.1726258132144233,0.17215614066131332,0.24558415520075633,0.28102079504903993,0.14682855819289167,0.22274881516587694,0.20616113744075823
7,0.0,0.15822871709356356,0
	library(tidyverse)

	# arbitrary number
	district_count = 38

	# population from stephanie's figure
	# https://twitter.com/evergreendata/status/1450862060972216320
	population = c(
	rep("White", 40),
	rep("Latino", 39),
	library(tidyverse)
	library(magrittr)
	library(afex)
	library(Superpower)

	set.seed(555)
	SUBJECT_COUNT = 3 # per between-subject condition

	#### ground-truth parameters for 3w x 2w x 2b (100 repetitions)
	library(tidyverse)
	library(ggdist)

	# make some data
	expand_grid(
	condition = c("A", "B"),
	stimulus = seq(-3,3,0.3),
	repetitions = 1:20
	) %>%
	# make some noisy response data
	reference: https://dplyr.tidyverse.org/reference/storms.html

	visualization: https://imgur.com/a/TMONnmS

	source: https://www.nhc.noaa.gov/data/#hurdat
	library(tidyverse)

	PATH = "peerj_reviews_txt/"
	ignored_words = c("the", "dear", "original")
	filenames = dir(path = PATH, pattern="*.txt", recursive = TRUE)



	preceeding_words = sapply(filenames, function(f) {
	words = read_file(paste0(PATH, f)) %>%
subject	x	color	rep	y
H	1	0	1	10.390916389674972
J	1	0	1	11.877635692199044
A	1	0	1	6.40831056135098
G	1	0	1	10.143656530161934
F	1	0	1	9.858462287495191
B	1	0	1	8.726973066068064
C	1	0	1	9.09479144777112
D	1	0	1	9.689029831578754
E	1	0	1	9.806302728246905
	# simulate true effect sizes vs p in 0.01-0.05 range
	library(tidyverse)

	SUBJECT_COUNT = 16
	TEST_COUNT = 16

	# test if the p-value is in the specified range
	inRange = function(p) {
	(p > 0.01) & (p < 0.05)
	}
	library(tidyverse)
	library(ggdist) # for stat_histinterval()

	############# make the dataset ###################

	# 1 sample per subject per condition
	data_per_subject_condition = expand_grid(
	subjectID = paste0("S", 1:50),
	independent_variable_A = c("circle", "square"),
	independent_variable_B = 1:3
	library(tidyverse)

	# Mind the Gap:The Underrepresentation of Female Participants and Authors in Virtual Reality Research
	# Tabitha C. Peck,Laura E. Sockol, and Sarah M. Hancock

	# Table 3
	data = read_csv(
	"Study,N,CohensD,CI.Low,CI.Hi,p,SAMD,FemaleParticipants
	Arafat et al. [4],16,0.42,0.19,0.65,<.001,−.07,81%
	Ariza et al. [5],18,0.12,-0.09,0.33,0.263,−.68,22%
	X,COVID-19 New Cases,Rep 1-N1,Rep 2-N1,Rep 1-N2,Rep 2-N2,Hospital admissions,virus RNA/ml
	0,0.0,0.01771832019916774,0.025327583188293947,0.0012636319937241525,0.021536686448187715,0.22274881516587694,0.011848341232227562
	1,0.0,0.01771832019916774,0.013927423563501674,0.0,0.01771832019916774,0.2819905213270144,0.014218009478673148
	2,0.0,0.06076422295302489,0.10633738965673233,0.008872894878409794,0.006345630953625507,0.07819905213270138,0.06161137440758302
	3,0.044047743576570264,0.0,0.1164739172696972,0.0012636319937241525,0.036727742012376295,0.22274881516587694,0.0
	4,0.07024387534899187,0.15191055715279342,0.05950059026999374,0.0898827021364172,0.036727742012376295,0.2819905213270144,0.12796208530805683
	5,0.022630983907802882,0.09240996608905253,0.08482817343043705,0.07342801384045856,0.08861906947427432,0.3388625592417062,0.08293838862559255
	6,0.1726258132144233,0.17215614066131332,0.24558415520075633,0.28102079504903993,0.14682855819289167,0.22274881516587694,0.20616113744075823
	7,0.0,0.15822871709356356,0