quiz1_ecekurtoglu

library(tidyverse)

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library(readxl)

data <- read_excel("~/ECON 465/QUIZ1_ECEKURTOGLU/data/Incites Researchers.xlsx")

New names:
• `` -> `...19`
• `` -> `...20`
• `` -> `...21`
• `` -> `...22`
• `` -> `...23`
• `` -> `...24`
• `` -> `...25`
• `` -> `...26`
• `` -> `...27`
• `` -> `...28`
• `` -> `...29`
• `` -> `...30`
• `` -> `...31`

izmir_data <- data |>
  filter(aff1 == "Izmir Ekonomi Universitesi")

nrow(izmir_data)  

[1] 630

I filtered the dataset and selected only the researchers whose first affiliation is Izmir Ekonomi Universitesi.

ggplot(izmir_data, aes(x = impact)) +
  geom_histogram(bins = 20) +
  labs(
    title = "Histogram of Impact",
    x = "Impact",
    y = "Frequency"
  ) +
  theme_minimal()

This histogram shows how the impact values are distributed.

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

This boxplot shows the distribution of impact and possible outliers.

The distribution of impact is right-skewed. Most researchers have low or medium impact values, but some researchers have much higher values. The center of the data looks low, because many values are close to the bottom of the boxplot. The spread is fairly wide since the data go from small values to much larger ones. There are clear outliers in the boxplot. These outliers show that a small number of researchers have much higher impact than the others. Overall, most researchers have relatively low impact, while a few researchers stand out with very high impact scores.