Nothing
## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
library(JWileymisc)
library(ggplot2)
library(data.table)
## ----eval = FALSE, echo = TRUE, results = "hide"------------------------------
#
# egltable(c("mpg", "hp", "qsec", "wt", "vs"),
# data = mtcars)
#
## ----echo = FALSE, results = "asis"-------------------------------------------
pander::pandoc.table(
egltable(c("mpg", "hp", "qsec", "wt", "vs"),
data = mtcars),
caption = "Example descriptive statistics table.",
justify = "left")
## ----eval = FALSE, echo = TRUE, results = "hide"------------------------------
#
# egltable(c("mpg", "hp", "qsec", "wt", "vs"),
# data = mtcars, strict=FALSE)
#
## ----echo = FALSE, results = "asis"-------------------------------------------
pander::pandoc.table(
egltable(c("mpg", "hp", "qsec", "wt", "vs"),
data = mtcars, strict=FALSE),
caption = "Example descriptive statistics table with automatic categorical variables.",
justify = "left")
## ----eval = FALSE, echo = TRUE, results = "hide"------------------------------
#
# egltable(c("mpg", "hp", "qsec", "wt", "vs"),
# g = "am", data = mtcars, strict = FALSE)
#
## ----echo = FALSE, results = "asis"-------------------------------------------
pander::pandoc.table(
egltable(c("mpg", "hp", "qsec", "wt", "vs"),
g = "am", data = mtcars, strict = FALSE),
caption = "Example descriptive statistics table by group.",
justify = "left")
## ----eval = FALSE, echo = TRUE, results = "hide"------------------------------
#
# egltable(c("mpg", "hp", "qsec", "wt", "vs"),
# g = "am", data = mtcars, strict = FALSE,
# parametric = FALSE)
#
## ----echo = FALSE, results = "asis"-------------------------------------------
pander::pandoc.table(
egltable(c("mpg", "hp", "qsec", "wt", "vs"),
g = "am", data = mtcars, strict = FALSE,
parametric = FALSE),
caption = "Example descriptive statistics table by group.",
justify = "left")
## ----eval = FALSE, echo = TRUE, results = "hide"------------------------------
# ## example with paired data
# egltable(
# vars = "extra",
# g = "group",
# data = sleep,
# idvar = "ID",
# paired = TRUE)
#
## ----echo = FALSE, results = "asis"-------------------------------------------
pander::pandoc.table(
egltable(
vars = "extra",
g = "group",
data = sleep,
idvar = "ID",
paired = TRUE),
caption = "Example parametric descriptive statistics for paired data.",
justify = "left")
## ----eval = FALSE, echo = TRUE, results = "hide"------------------------------
# egltable(
# vars = "extra",
# g = "group",
# data = sleep,
# idvar = "ID",
# paired = TRUE,
# parametric = FALSE)
#
## ----echo = FALSE, results = "asis"-------------------------------------------
pander::pandoc.table(
egltable(
vars = "extra",
g = "group",
data = sleep,
idvar = "ID",
paired = TRUE,
parametric = FALSE),
caption = "Example non parametric descriptive statistics for paired data.",
justify = "left")
## -----------------------------------------------------------------------------
## paired categorical data example
## using data on chick weights to create categorical data
tmp <- subset(ChickWeight, Time %in% c(0, 20))
tmp$WeightTertile <- cut(tmp$weight,
breaks = quantile(tmp$weight, c(0, 1/3, 2/3, 1), na.rm = TRUE),
include.lowest = TRUE)
## ----eval = FALSE, echo = TRUE, results = "hide"------------------------------
# egltable(c("weight", "WeightTertile"), g = "Time",
# data = tmp,
# idvar = "Chick", paired = TRUE)
## ----echo = FALSE, results = "asis"-------------------------------------------
pander::pandoc.table(
egltable(c("weight", "WeightTertile"), g = "Time",
data = tmp,
idvar = "Chick", paired = TRUE),
caption = "Continuous and categorical paired data.",
justify = "left")
## -----------------------------------------------------------------------------
m <- SEMSummary(~ mpg + hp + qsec + wt, data = mtcars)
corTab <- APAStyler(m, type = "cor", stars = TRUE)
## ----echo = FALSE, results = "asis"-------------------------------------------
pander::pandoc.table(
corTab$table,
caption = "Example correlation table.",
justify = "left")
## -----------------------------------------------------------------------------
plot(m) +
ggtitle("Order by hierarchical clustering")
plot(m, order = "asis") +
ggtitle("Order as written")
## -----------------------------------------------------------------------------
plot(m, type = "p") +
ggtitle("Numbers are p-values")
## -----------------------------------------------------------------------------
mg <- SEMSummary(~ Sepal.Length + Petal.Length +
Sepal.Width + Petal.Width | Species,
data = iris)
plot(mg)
## -----------------------------------------------------------------------------
## simulate some likert style data
set.seed(1234)
d <- data.table(
Happy = sample(1:5, 200, TRUE, c(.1, .2, .4, .2, .1)),
Cheerful = sample(1:5, 200, TRUE, c(.1, .2, .2, .4, .1)),
Peaceful = sample(1:5, 200, TRUE, c(.1, .1, .2, .4, .2)),
Sad = sample(1:5, 200, TRUE, c(.1, .3, .3, .2, .1)),
Hopeless = sample(1:5, 200, TRUE, c(.3, .3, .2, .2, 0)),
Angry = sample(1:5, 200, TRUE, c(.4, .3, .2, .08, .02)))
dmeans <- melt(d, measure.vars = names(d))[,
.(Mean = mean(value, na.rm = TRUE)), by = variable]
dmeans[, Low := paste0(variable, "\nNot at all")]
dmeans[, High := paste0(variable, "\nExtremely")]
dmeans[, variable := as.integer(factor(variable))]
## view the summarised data
print(dmeans)
gglikert("Mean", "variable", "Low", "High", data = dmeans,
xlim = c(1, 5),
title = "Average Affect Ratings")
## -----------------------------------------------------------------------------
## create a grouping variable
dg <- cbind(d, Group = ifelse(
d$Happy > mean(d$Happy, na.rm = TRUE),
"General Population", "Depressed"))
dgmeans <- melt(dg, measure.vars = names(d), id.vars = "Group")[,
.(Mean = mean(value, na.rm = TRUE)), by = .(variable, Group)]
dgmeans[, Low := paste0(variable, "\nNot at all")]
dgmeans[, High := paste0(variable, "\nExtremely")]
dgmeans[, variable := as.integer(factor(variable))]
## view the summarised data
print(dgmeans)
gglikert("Mean", "variable", "Low", "High",
colour = "Group",
data = dgmeans,
xlim = c(1, 5),
title = "Average Affect Ratings") +
scale_colour_manual(
values = c("Depressed" = "black",
"General Population" = "grey70"))
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