File size: 3,867 Bytes
2f6b10b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
library(ggplot2)
library(lme4)
library(stringr)
library(dplyr)
library(tidyr)
library(scales)
library(readr)
library(tidyverse)
library(fs)
library(purrr)
library(svglite)
library(seewave)
library(gganimate)
library(transformr)
library(magick)
library(rsvg)
library(gifski)
library(cowplot)
library(gsignal)
library(colorspace)
library(tidyr)
library(dplyr)
library(plotly)
library(trelliscopejs)
library(stringr)
blog_width = 2400
blog_height =1000
blog_dpi = 175
windowsFonts("Gentium Book Plus" = windowsFont("Gentium Book Plus"))
cbbPalette <- c("#FB5607", "#8338EC", "#FF006E", "#FFBE0B", "#3A86FF", '#6EC200', '#BA3200', "#009E73")
very_light_yellow = '#FFE819'
light_yellow = '#F9D213'
base_yellow = '#F2BC0C'
dark_yellow = '#BD9105'
very_dark_yellow = '#A07A00'
sky_blue = '#2D99FF'
very_light_blue = '#1265B2'
light_blue = '#0A4B89'
base_blue = '#053561'
dark_blue = '#01192F'
very_dark_blue = '#000C17'
very_light_red = '#FF4619'
light_red = '#D43610'
base_red = '#AA2809'
dark_red = '#761A03'
very_dark_red = '#5C1200'
theme_memcauliffe <- function (base_family="Gentium Book Plus") {
base_size = 12
half_line <- base_size/2
theme_bw(base_size=base_size, base_family=base_family) %+replace%
theme(
plot.background = element_rect(fill = "#003566"),
panel.background = element_rect(fill = "#003566", color = '#003566'),
panel.border = element_rect(color='#000814', fill='NA'),
panel.grid.major = element_line(size = 0.25, linetype = 'solid',
colour = "#FFC300"),
panel.grid.minor = element_line(size = 0.125, linetype = 'solid',
colour = "#FFD60A"),
axis.text = element_text(size = rel(1.2), colour = "#FFD60A"),
axis.text.y = element_text(margin = margin(r = 0.8*half_line/2),
hjust = 1, colour = "#FFD60A"),
axis.title = element_text(colour = "#FFC300", size = rel(1.6)),
plot.title = element_text(colour = "#FFC300", size = rel(1.8), hjust=0,
margin = margin(b = half_line * 1.2)),
strip.background = element_rect(fill = "#FFC300", colour = NA),
strip.text = element_text(colour = "#000814", size = rel(1.2), margin = margin( b = 3, t = 3, l = 4, r = 4)),
legend.background = element_blank(),
legend.key = element_blank(),
legend.title = element_text(size = rel(1.2), colour = "#FFC300"),
legend.text = element_text(size = rel(1), colour = "#FFC300"),
)
}
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
conf.interval=.95, .drop=TRUE) {
require(dplyr)
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
dots <- lapply(groupvars, as.symbol)
functions <- list("n()", paste("mean(",measurevar,")", sep = ''), as.formula(paste("~median(",measurevar,")", sep = '')), paste("sd(",measurevar,")", sep = ''))
# hack to deal with median specified as formula
names <- lapply(functions, function(x){if(length(x)>1){as.character(x[2])}else{x[1]}})
names <- lapply(names, as.name)
datac <- data %>% group_by_(.dots = dots) %>% summarise_(.dots = functions) %>% rename_(.dots = setNames(names, c('N', 'mean', 'median', 'sd')))
# Rename the "mean" column
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
|