Where to start?

Here's a YouTube tutorial:

Also here's a Twitter thread on switchboard's functionalities, but also see functions on CRAN here, and GitHub here.

Dashboards are great for data snapshots--but what if you need something more agile in R?

Generate real-time, dynamic visualizations for simulations or whatevs with the ?????????????????????? package ??

See tutorial:https://t.co/DAGqDCNOdI

??1/7 #Rstats #dataviz #DataScience pic.twitter.com/77N3i0BE5i

— Marc J. Lajeunesse (@LajeunesseLab) October 8, 2021



December 2, 2022 | correlation without causation via collider bias



### R code with switchboard v. 0.1

library(switchboard)
collider <- 0
min_XYC <- c(-10, -10); max_XYC <- c(10, 10);
N <- 250

for(simulation_length in 1:50000) {

  X <- rnorm(N, 0, 2); Y <- rnorm(N, 0, 2);
  C <- collider * X + collider * Y + rnorm(N, 0, 2)
  XYC <- data.frame(X, Y, C)
  betaXY_lm_XY <- lm(Y ~ X, data = XYC)$coefficients[2]
  betaXY_lm_XYC <- lm(Y ~ X + C, data = XYC)$coefficients[2]

  switchboard() %>%
    caption(c("collider bias",
              "When Y & X influence variation in a collider (C), the estimated slope between X & Y can be lower than the true slope when C is included in the linear model (lm)."), placeOnGrid = c(0,0), size = 2) %>%
    control_slider("collider", label = "collider", minimum = 0, maximum = 2,
                   placeOnGrid = c(2,2)) %>%
    eavesdropper_2D(c(XYC[1, 1], XYC[1, 2]), label = c("Y~X", ""), plotRegression = TRUE,
                    placeOnGrid = c(2, 1), minimum = min_XYC, maximum = max_XYC) %>%
    eavesdropper_2D(c(XYC[1, 1], XYC[1, 3]), label = c("X~C", ""), plotRegression = TRUE,
                    placeOnGrid = c(1, 2), minimum = min_XYC, maximum = max_XYC) %>%
    eavesdropper_2D(c(XYC[1, 2], XYC[1, 3]), label = c("Y~C", ""), plotRegression = TRUE,
                    placeOnGrid = c(3, 2), minimum = min_XYC, maximum = max_XYC) %>%
    eavesdropper_2D(c(betaXY_lm_XY, betaXY_lm_XYC), label = c("slope bias", ""), switch = TRUE,
                    placeOnGrid = c(3,3), minimum = c(-0.1, -1.0), maximum = c(0.1, 0.5)) %>%
    eavesdropper(eavesdrop = betaXY_lm_XY, label = c("lm(Y~X)"),
                 placeOnGrid = c(2,0), minimum = -1, maximum = 0.5) %>%
    eavesdropper(eavesdrop = betaXY_lm_XYC, label = c("lm(Y~X+C)"),
                 placeOnGrid = c(2,3),  minimum = -1, maximum = 0.5) %>%
    progress_image(round(collider / 2 * 100), maximum = 100,
                  file = "collider_DAG_switchboard.png",
                  placeOnGrid = c(1,3), fill = "vertical", honest = TRUE, size = 1)

}
switchboard_close()



Custom progress image [collider_DAG_switchboard.png] used in simulation here: DAG of collider bias



November 18, 2022 | effects of noise versus sampling error on regression



### R code with switchboard v. 0.1

library(switchboard)
library(MASS)

pop_rho <- 0; pop_X <- 0; pop_Y <- 0; pop_SD <- 0.5;
pop_N <- 142
dino_X <- c(55.3846,  51.5385,  46.1538,  42.8205,  40.7692,  38.7179,  35.641,  
            33.0769,  28.9744,  26.1538,  23.0769,  22.3077,  22.3077,  23.3333,  
            25.8974,  29.4872,  32.8205,  35.3846,  40.2564,  44.1026,  46.6667, 
            50,  53.0769,  56.6667,  59.2308,  61.2821,  61.5385,  61.7949,  
            57.4359,  54.8718,  52.5641,  48.2051,  49.4872,  51.0256,  45.3846,  
            42.8205,  38.7179,  35.1282,  32.5641,  30,  33.5897,  36.6667,  
            38.2051,  29.7436,  29.7436,  30,  32.0513,  35.8974,  41.0256,  
            44.1026,  47.1795,  49.4872,  51.5385,  53.5897,  55.1282,  56.6667,  
            59.2308,  62.3077,  64.8718,  67.9487,  70.5128,  71.5385,  71.5385,  
            69.4872,  46.9231,  48.2051,  50,  53.0769,  55.3846,  56.6667,  
            56.1538,  53.8462,  51.2821,  50,  47.9487,  29.7436,  29.7436,  
            31.2821,  57.9487,  61.7949,  64.8718,  68.4615,  70.7692,  72.0513,  
            73.8462,  75.1282,  76.6667,  77.6923,  79.7436,  81.7949,  83.3333,  
            85.1282,  86.4103,  87.9487,  89.4872,  93.3333,  95.3846,  98.2051,  
            56.6667,  59.2308,  60.7692,  63.0769,  64.1026,  64.359,  74.359,  
            71.2821,  67.9487,  65.8974,  63.0769,  61.2821,  58.7179,  55.1282,  
            52.3077,  49.7436,  47.4359,  44.8718,  48.7179,  51.2821,  54.1026,  
            56.1538,  52.0513,  48.7179,  47.1795,  46.1538,  50.5128,  53.8462,  
            57.4359,  60,  64.1026,  66.9231,  71.2821,  74.359,  78.2051,  
            67.9487,  68.4615,  68.2051,  37.6923,  39.4872,  91.2821,  50,  47.9487,  44.1026 )
dino_Y <- c(97.1795,  96.0256,  94.4872,  91.4103,  88.3333,  84.8718,  79.8718,
            77.5641,  74.4872,  71.4103,  66.4103,  61.7949,  57.1795,  52.9487,
            51.0256,  51.0256,  51.0256,  51.4103,  51.4103,  52.9487,  54.1026, 
            55.2564,  55.641,  56.0256,  57.9487,  62.1795,  66.4103,  69.1026, 
            55.2564,  49.8718,  46.0256,  38.3333,  42.1795,  44.1026,  36.4103, 
            32.5641,  31.4103,  30.2564,  32.1795,  36.7949,  41.4103,  45.641, 
            49.1026,  36.0256,  32.1795,  29.1026,  26.7949,  25.2564,  25.2564, 
            25.641,  28.718,  31.4103,  34.8718,  37.5641,  40.641,  42.1795,  
            44.4872,  46.0256,  46.7949,  47.9487,  53.718,  60.641,  64.4872,
            69.4872,  79.8718,  84.1026,  85.2564,  85.2564,  86.0256,  86.0256, 
            82.9487,  80.641,  78.718,  78.718,  77.5641,  59.8718,  62.1795, 
            62.5641,  99.4872,  99.1026,  97.5641,  94.1026,  91.0256,  86.4103,
            83.3333,  79.1026,  75.2564,  71.4103,  66.7949,  60.2564,  55.2564, 
            51.4103,  47.5641,  46.0256,  42.5641,  39.8718,  36.7949,  33.718,  
            40.641,  38.3333,  33.718,  29.1026,  25.2564,  24.1026,  22.9487, 
            22.9487,  22.1795,  20.2564,  19.1026,  19.1026,  18.3333,  18.3333,
            18.3333,  17.5641,  16.0256,  13.718,  14.8718,  14.8718,  14.8718, 
            14.1026,  12.5641,  11.0256,  9.8718,  6.0256,  9.4872,  10.2564, 
            10.2564,  10.641,  10.641,  10.641,  10.641,  10.641,  10.641,  8.718, 
            5.2564,  2.9487,  25.7692,  25.3846,  41.5385,  95.7692,  95,  92.6923)

for(i in 1:1e6) {

  cov_XY <- matrix(c(pop_SD, 0, 0, pop_SD), nrow = 2, ncol = 2)
  sample_XY <- MASS::mvrnorm(142, mu = c(pop_X, pop_Y), Sigma = cov_XY)

  sample_XY[, 1] <- sample_XY[, 1] + dino_X * 1.2
  sample_XY[, 2] <- sample_XY[, 2] + dino_Y

  sample_XY <- sample_XY[sample(142, pop_N), ]
  random_sample <- sample.int(nrow(sample_XY), 1)

  switchboard() %>%
    caption(c("Monte Carlo Simulation",
              "Noise is assumed independent in regression, but sampling error will erodes this."),
            placeOnGrid = c(1,1), size = 2) %>%
    control_slider_pair(c("pop_SD", "pop_N"), minimum = c(0.5, 10), maximum = c(20, 142),
                        label = c("noise", "sample size"), placeOnGrid = c(2, 3)) %>%
    eavesdropper_2D(c(sample_XY[random_sample, 1], sample_XY[random_sample, 2]), inject = c("pop_X", "pop_Y"),
                minimum = c(0, 0), maximum = c(120, 100),
                plotRegression = TRUE, plotSampleSize = ctrl_N, switch = TRUE,
                forget = nrow(sample_XY) * 6, placeOnGrid = c(1, 4), size = 2) %>%
    eavesdropper(cor(sample_XY[,1], sample_XY[,2]), label = "correlation", minimum = -1, maximum = 1, placeOnGrid = c(1,7), size = 2)

}
switchboard_close()



November 9, 2022 | p-value dance when effect is null

library(switchboard)
library(MASS)

pop_rho <- 0; pop_X <- 0; pop_Y <- 0;
sample_XY <- 0; cov_XY <- matrix(c(1, pop_rho, pop_rho, 1), nrow = 2, ncol = 2);

for(simulation_length in 1:5000) {

  sample_XY <- rbind(MASS::mvrnorm(1, mu = c(pop_X, pop_Y), Sigma = cov_XY), sample_XY)
  ttest <- stats::t.test(sample_XY[, 1], sample_XY[, 2])

  switchboard() %>%
    caption(c("drifting p-values",
              "Impact of adding more data in absence of an effect."),
            placeOnGrid = c(1, 1), extendRow = 2) %>%
    eavesdropper(ttest$p.value, label = "p-value", minimum = 0, maximum = 1,
                 size = 2, placeOnGrid = c(1, 3)) %>%
    progress_pikachu(simulation_length, maximum = 5000, closeAtMaximum = FALSE,
                     fill = "horizontal", placeOnGrid = c(2, 1)) %>%
    number(simulation_length, label = "sample size",  placeOnGrid = c(2, 2))

}
switchboard_close()



November 4, 2022 | dance of regression with small sample sizes


(Click here to also see toot on Mastodon).

### R code with switchboard v. 0.1

library(switchboard)
library(MASS)

pop_rho <- 0; pop_X <- 0; pop_Y <- 0;
pop_forget <- 400 #milliseconds
ctrl_regression <- FALSE; ctrl_N <- FALSE;

# NOTE: if swichboard window is closed early, the simulation will continue to run in background until i == 1e5
for(i in 1:1e5) {

  cov_XY <- matrix(c(1, pop_rho, pop_rho, 1), nrow = 2, ncol = 2)
  sample_XY <- MASS::mvrnorm(1, mu = c(pop_X, pop_Y), Sigma = cov_XY)
  
  switchboard() %>%
    caption(c("Monte Carlo Simulation", 
              "Slide correlation and sample size to see how regression estimation is impacted."), 
            placeOnGrid = c(1,1), size = 2) %>%
    control_switch_pair(c("ctrl_regression", "ctrl_N"), 
                        label = c("plot slope", "plot N"), placeOnGrid = c(1, 3)) %>%
    control_slider_pair(c("pop_rho", "pop_forget"), minimum = c(-1, 4), maximum = c(1, 3000),
                        label = c("correlation", "sample size"), placeOnGrid = c(2, 3)) %>%
    injector_2D(c(sample_XY[1], sample_XY[2]), inject = c("pop_X", "pop_Y"),
                minimum = c(-5, -5), maximum = c(5, 5),
                plotRegression = ctrl_regression, plotSampleSize = ctrl_N, switch = TRUE,
                forget = pop_forget, placeOnGrid = c(1, 4), size = 2) 
  
}
switchboard_close()