sampleRcode/OLDsc/Sample_Summary.R

In netique/ShinyItemAnalysis: Test and Item Analysis via Shiny

# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# * TOTAL SCORES ######
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

library(ggplot2)
library(psych)
library(ShinyItemAnalysis)

# loading data
data(GMAT, package = "difNLR")
data <- GMAT[, 1:20]

# total score calculation
score <- rowSums(data)

# summary of total score
tab <- describe(score)[, c("n", "min", "max", "mean", "median", "sd", "skew", "kurtosis")]
tab$kurtosis <- tab$kurtosis + 3
tab

# colors by cut-score
cut <- median(score) # cut-score
color <- c(rep("red", cut - min(score)), "gray", rep("blue", max(score) - cut))
df <- data.frame(score)

# histogram
ggplot(df, aes(score)) +
  geom_histogram(binwidth = 1, fill = color, col = "black") +
  xlab("Total score") +
  ylab("Number of respondents") +
  theme_app()

# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# * STANDARD SCORES ######
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

# loading data
data(GMAT, package = "difNLR")
data <- GMAT[, 1:20]

# scores calculations (unique values)
score <- rowSums(data)               # Total score
tosc <- sort(unique(score))          # Levels of total score
perc <- ecdf(score)(tosc)            # Percentiles
sura <- 100 * (tosc / max(score))    # Success rate
zsco <- sort(unique(scale(score)))   # Z-score
tsco <- 50 + 10 * zsco               # T-score

cbind(tosc, perc, sura, zsco, tsco)