❝本节来介绍一款R包「rempsyc」即可用来进行统计分析又可用来进行图表绘制,内容很是丰富,原文文档链接见下方,各位观众老爷可以去参考官方文档了解具体细节。❞
❝https://rempsyc.remi-theriault.com/#nice-apa-tables❞
install.packages("rempsyc")
library(rempsyc)
library(tidyverse)
t.tests <- nice_t_test(data = mtcars,
response = c("mpg", "disp", "drat", "wt"),
group = "am")
nice_table(t.tests)
nice_contrasts(data = mtcars,
response = c("mpg", "disp"),
group = "cyl",
covariates = "hp") -> contrasts
contrasts
nice_table(contrasts, highlight = .001)
model1 <- lm(mpg ~ cyl + wt * hp, mtcars)
model2 <- lm(qsec ~ disp + drat * carb, mtcars)
mods <- nice_lm(list(model1, model2))
nice_table(mods, highlight = TRUE)
model1 <- lm(mpg ~ gear * wt, mtcars)
model2 <- lm(disp ~ gear * wt, mtcars)
my.models <- list(model1, model2)
simple.slopes <- nice_lm_slopes(my.models, predictor = "gear", moderator = "wt")
nice_table(simple.slopes)
nice_violin(data = ToothGrowth,group = "dose",response = "len",
xlabels = c("Low", "Medium", "High"),
comp1 = 1,comp2 = 3,
has.d = TRUE,d.y = 28)
ggsave('niceplot.pdf', width = 7, height = 7, unit = 'in', dpi = 300)
model <- lm(mpg ~ wt * cyl + gear, data = mtcars)
nice_table(nice_assumptions(model))
nice_normality(data = iris,variable = "Sepal.Length",
group = "Species",grid = FALSE,
shapiro = TRUE,histogram = TRUE)