Results, September 30 2023
2023-09-30
library(readxl)
mydata <- read_xlsx("./Rezultati.xlsx")
mydata <- as.data.frame(mydata)library(pastecs)
round(stat.desc(mydata[4:7]), 2)## R_Exam Statistics Economics Total
## nbr.val 35.00 35.00 35.00 35.00
## nbr.null 0.00 0.00 0.00 0.00
## nbr.na 0.00 0.00 0.00 0.00
## min 10.00 4.00 8.00 31.00
## max 20.00 20.00 70.00 108.00
## range 10.00 16.00 62.00 77.00
## sum 595.00 480.00 1549.00 2624.00
## median 17.00 14.00 45.00 72.00
## mean 17.00 13.71 44.26 74.97
## SE.mean 0.44 0.63 3.34 3.69
## CI.mean.0.95 0.89 1.28 6.80 7.51
## var 6.71 13.92 391.37 477.44
## std.dev 2.59 3.73 19.78 21.85
## coef.var 0.15 0.27 0.45 0.29Averages of 2022 Generation
- R_Exam: 17.51 points
- Statistics: 12.77 points
- Economics: 47.18 points
- Total: 77.46 points
library(ggplot2)
ggplot(mydata, aes(x = Total)) +
geom_histogram(binwidth = 5, color = "black", fill = "pink") +
ylab("Frequency") +
xlab("Total points")
print(mydata[order(-mydata$Total), c(1, 4:8)], row.names = FALSE)## Student_ID R_Exam Statistics Economics Total Grade
## 19332963 20 18 70 108 10
## 19618436 20 16 70 106 10
## 19329311 20 20 65 105 10
## 19229524 20 16 68 104 10
## 19235632 17 16 70 103 10
## 19568904 20 12 70 102 10
## 19227375 20 12 69 101 10
## 19573322 18 16 65 99 10
## 19377040 15 12 66 93 10
## 19629029 20 20 52 92 10
## 19566100 19 18 53 90 10
## 19568511 18 12 60 90 10
## 19376969 19 10 61 90 10
## 19568773 15 14 53 82 9
## 19310190 15 12 50 77 8
## 19618441 18 12 47 77 8
## 19260302 15 14 45 74 8
## 19566933 18 14 40 72 8
## 19632094 14 18 40 72 8
## 19232715 19 8 42 69 7
## 19568134 10 4 55 69 7
## 19629139 19 10 40 69 7
## 19226240 17 14 37 68 7
## 19592455 17 14 37 68 7
## 19566561 15 12 35 62 7
## 19628968 17 18 25 60 7
## 19628973 13 8 35 56 6
## 19629060 18 18 19 55 6
## 19566399 15 16 23 54 6
## 19224568 15 18 19 52 6
## 19620288 17 12 22 51 6
## 19629076 20 8 14 42 5
## 19238104 13 14 14 41 5
## 19628989 16 14 10 40 5
## 19632115 13 10 8 31 5