In jorvlan/openvis: Create visualizations based on best open science practices

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

if (!require(devtools)) {
  install.packages('devtools')
}

devtools::install_github('jorvlan/openvis')

library(openvis)

df_1x1 <- data_1x1(
  array_1 = iris$Sepal.Length[1:50],
  array_2 = iris$Sepal.Length[51:100],
  jit_distance = .09,
  jit_seed = 321)

head(df_1x1)
tail(df_1x1)

figure_1x1_v <- raincloud_1x1(df_1x1, ort = 'v') +

  scale_x_continuous(breaks=c(1,2), labels=c("Group1", "Group2"), limits=c(0, 3)) +
  xlab("Groups") + 
  ylab("Score") +
  theme_classic()

figure_1x1_v

figure_1x1_h <- raincloud_1x1(df_1x1, ort = 'h') +

  scale_x_continuous(breaks=c(1,2), labels=c("Group1", "Group2"), limits=c(0, 3)) +
  xlab("Groups") + 
  ylab("Score") +
  theme_classic()

figure_1x1_h

figure_1x1_rm <- raincloud_1x1_repmes(df_1x1, line_alpha = .5, line_color = 'gray', alpha = .3, size = 2, align_clouds = FALSE) +

  scale_x_continuous(breaks=c(1,2), labels=c("Before", "After"), limits=c(0, 3)) +
  xlab("States") + 
  ylab("Score") +
  theme_classic()

figure_1x1_rm

figure_1x1_rm_2.0 <- raincloud_1x1_repmes(df_1x1, line_alpha = .9, line_color = 'black', alpha = .8, size = 2, align_clouds = TRUE) +

  scale_x_continuous(breaks=c(1,2), labels=c("Before", "After"), limits=c(0, 3)) +
  xlab("States") + 
  ylab("Score") +
  theme_classic()

figure_1x1_rm_2.0

df_2x2_1.0 <- data_2x2(
  array_1 = iris$Sepal.Length[1:50],
  array_2 = iris$Sepal.Length[51:100],
  array_3 = iris$Sepal.Length[101:150],
  array_4 = iris$Sepal.Length[81:130],
  label_1 = 'congruent',
  label_2 = 'incongruent',
  jit_distance = .05,
  jit_seed = 321,
  spread_x_ticks = TRUE) # FALSE if 2 x-ticks

head(df_2x2_1.0)
tail(df_2x2_1.0)

figure_2x2_1.0 <- raincloud_2x2_repmes(df_2x2_1.0, line_color = 'gray', line_alpha = .5, size = 2, alpha = .5, spread_x_ticks = TRUE) +

  scale_x_continuous(breaks=c(1,2,3,4),
                     labels=c("low-congr", "high-congr", "low-incongr", "high-incongr"),
                     limits=c(0, 5)) +
  xlab("Conditions") +
  ylab("Score") +
  theme_classic()

figure_2x2_1.0

df_2x2_2.0 <- data_2x2(
  array_1 = iris$Sepal.Length[1:50],
  array_2 = iris$Sepal.Length[51:100],
  array_3 = iris$Sepal.Length[101:150],
  array_4 = iris$Sepal.Length[81:130],
  label_1 = 'congruent',
  label_2 = 'incongruent',
  jit_distance = .05,
  jit_seed = 321,
  spread_x_ticks = FALSE)

head(df_2x2_2.0)
tail(df_2x2_2.0)

figure_2x2_2.0 <- raincloud_2x2_repmes(df_2x2_2.0,line_color = 'gray', line_alpha = .5, size = 2, alpha = .5, spread_x_ticks = FALSE) +

  scale_x_continuous(breaks=c(1,2), labels=c("Before", "After"), limits=c(0, 3)) +
  xlab("States") +
  ylab("Score") +
  theme_classic()

figure_2x2_2.0

df_2x3 <- data_2x2(
  array_1 = iris$Sepal.Length[1:50],
  array_2 = iris$Sepal.Length[51:100],
  array_3 = iris$Sepal.Length[101:150],
  array_4 = iris$Sepal.Length[81:130],
  array_5 = iris$Sepal.Length[21:70],
  array_6 = iris$Sepal.Length[41:90],
  label_1 = 'Drug',
  label_2 = 'Placebo',
  jit_distance = .07,
  jit_seed = 321)

head(df_2x3)
tail(df_2x3)

figure_2x3_v <- raincloud_2x3_repmes(df_2x3, size = 1, alpha = .5, ort = 'v') +

  scale_x_continuous(breaks=c(1,2,3),
                     labels=c("Time-1", "Time-2", "Time-3"),
                     limits=c(0, 4)) +
  xlab("States") +
  ylab("Score") +
  theme_classic()

figure_2x3_v

figure_2x3_h <- raincloud_2x3_repmes(df_2x3, size = 1, alpha = .6, ort = 'h') +

  scale_x_continuous(breaks=c(1,2,3),
                     labels=c("1", "2", "3"),
                     limits=c(0, 4)) +
  xlab("States") +
  ylab("Score") +
  theme_classic()

figure_2x3_h