library(readxl)
library(ggplot2)

df <- read_excel("Incites Researchers.xlsx")
ieu <- subset(df, grepl("zmir Ekonomi", aff1))

Descriptive Statistics

summary(ieu$impact)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.2409  0.6888  0.7804 33.0800

Histogram

ggplot(ieu, aes(x = impact)) +
  geom_histogram(bins = 30, fill = "steelblue", color = "white") +
  labs(title = "Histogram of Impact", x = "Impact", y = "Count") +
  theme_minimal()

Boxplot

ggplot(ieu, aes(y = impact)) +
  geom_boxplot(fill = "steelblue") +
  labs(title = "Boxplot of Impact", y = "Impact") +
  theme_minimal()

Interpretation

The distribution of impact scores is strongly right-skewed. The median (0.24) is much lower than the mean (0.69), indicating that most researchers have low impact scores while a few have very high ones. The middle 50% of researchers fall between 0 and 0.78 (IQR = 0.78), showing that scores are tightly concentrated near zero. There are 45 outliers with unusually high impact, with the maximum reaching 33.08. Overall, the distribution is highly unequal — a small number of researchers drive most of the scientific impact at the institution.