Exploring Student Data: A Comprehensive Dplyr Tutorial

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

# Create a list of student names
student_names <- c("Alice", "Bob", "Charlie", "David", "Eva", "Frank", "Grace", "Hank", "Ivy", "Jack")

# Generate random ages for the students
student_ages <- sample(18:25, length(student_names), replace = TRUE)

# Generate random test scores for each student
test_scores <- sample(60:100, length(student_names), replace = TRUE)

# Define the genders
genders <- c("Male", "Female")

# Create a data frame and convert it into tibble to store the data
data <- as.tibble(data.frame(
  Name = student_names,
  Gender = as.factor(sample(genders, length(student_names), replace = TRUE)),
  Age = student_ages,
  Test_Score = test_scores
))

## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## ℹ Please use `as_tibble()` instead.
## ℹ The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

# Display the dataset
data

## # A tibble: 10 × 4
##    Name    Gender   Age Test_Score
##    <chr>   <fct>  <int>      <int>
##  1 Alice   Female    18         65
##  2 Bob     Male      22         78
##  3 Charlie Female    24         71
##  4 David   Female    18         86
##  5 Eva     Female    24         91
##  6 Frank   Male      22         88
##  7 Grace   Female    24         77
##  8 Hank    Male      23         80
##  9 Ivy     Male      19         68
## 10 Jack    Male      21         71

1. Display the first 5 rows of the dataset

head(data)

## # A tibble: 6 × 4
##   Name    Gender   Age Test_Score
##   <chr>   <fct>  <int>      <int>
## 1 Alice   Female    18         65
## 2 Bob     Male      22         78
## 3 Charlie Female    24         71
## 4 David   Female    18         86
## 5 Eva     Female    24         91
## 6 Frank   Male      22         88

2. Display basic statistics of the numeric columns

summary(data)

##      Name              Gender       Age          Test_Score  
##  Length:10          Female:5   Min.   :18.00   Min.   :65.0  
##  Class :character   Male  :5   1st Qu.:19.50   1st Qu.:71.0  
##  Mode  :character              Median :22.00   Median :77.5  
##                                Mean   :21.50   Mean   :77.5  
##                                3rd Qu.:23.75   3rd Qu.:84.5  
##                                Max.   :24.00   Max.   :91.0

3. Count the number of rows and columns

dim(data)

## [1] 10  4

4. Display the unique ages in the dataset

unique(data$Age)

## [1] 18 22 24 23 19 21

5. Calculate the average test score

mean(data$Test_Score)

## [1] 77.5

6. Find the student with the highest test score

data %>% filter(Test_Score == max(Test_Score))

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Eva   Female    24         91

7. Filter students who are older than 21

data %>% filter(Age > 21)

## # A tibble: 6 × 4
##   Name    Gender   Age Test_Score
##   <chr>   <fct>  <int>      <int>
## 1 Bob     Male      22         78
## 2 Charlie Female    24         71
## 3 Eva     Female    24         91
## 4 Frank   Male      22         88
## 5 Grace   Female    24         77
## 6 Hank    Male      23         80

8. Group students by age and calculate the average test score for each age group

data %>% group_by(Age) %>% summarize(Avg_Test_Score = mean(Test_Score))

## # A tibble: 6 × 2
##     Age Avg_Test_Score
##   <int>          <dbl>
## 1    18           75.5
## 2    19           68  
## 3    21           71  
## 4    22           83  
## 5    23           80  
## 6    24           79.7

9. Sort the dataset by test score in descending order

data %>% arrange(desc(Test_Score))

## # A tibble: 10 × 4
##    Name    Gender   Age Test_Score
##    <chr>   <fct>  <int>      <int>
##  1 Eva     Female    24         91
##  2 Frank   Male      22         88
##  3 David   Female    18         86
##  4 Hank    Male      23         80
##  5 Bob     Male      22         78
##  6 Grace   Female    24         77
##  7 Charlie Female    24         71
##  8 Jack    Male      21         71
##  9 Ivy     Male      19         68
## 10 Alice   Female    18         65

10. Count the number of students in each age group

data %>% count(Age)

## # A tibble: 6 × 2
##     Age     n
##   <int> <int>
## 1    18     2
## 2    19     1
## 3    21     1
## 4    22     2
## 5    23     1
## 6    24     3

11. Calculate the standard deviation of test scores

sd(data$Test_Score)

## [1] 8.834906

12. Find the student(s) with the lowest test score

data %>% filter(Test_Score == min(Test_Score))

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Alice Female    18         65

13. Filter students with test scores above 90

data %>% filter(Test_Score > 90)

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Eva   Female    24         91

14. Calculate the median age of the students

median(data$Age)

## [1] 22

15. Calculate the correlation between age and test scores

cor(data$Age, data$Test_Score)

## [1] 0.3462732

16. Display the last 5 rows of the dataset

tail(data)

## # A tibble: 6 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Eva   Female    24         91
## 2 Frank Male      22         88
## 3 Grace Female    24         77
## 4 Hank  Male      23         80
## 5 Ivy   Male      19         68
## 6 Jack  Male      21         71

17. Filter students who scored below 70

data %>% filter(Test_Score < 70)

## # A tibble: 2 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Alice Female    18         65
## 2 Ivy   Male      19         68

18. Group students by test score range and count the number of students in each range

data %>% group_by(Test_Score_Range = cut(Test_Score, breaks = seq(0, 100, by = 10))) %>% summarize(Count = n())

## # A tibble: 4 × 2
##   Test_Score_Range Count
##   <fct>            <int>
## 1 (60,70]              2
## 2 (70,80]              5
## 3 (80,90]              2
## 4 (90,100]             1

19. Calculate the total test score of all students

sum(data$Test_Score)

## [1] 775

20. Calculate the percentage of students who passed (scored 70 or above)

passed_students <- data %>% filter(Test_Score >= 70)
percentage_passed <- nrow(passed_students) / nrow(data) * 100

21. Find the student with the longest name

data %>% arrange(desc(nchar(Name))) %>% head(1)

## # A tibble: 1 × 4
##   Name    Gender   Age Test_Score
##   <chr>   <fct>  <int>      <int>
## 1 Charlie Female    24         71

22. Calculate the range of test scores (max - min)

diff(range(data$Test_Score))

## [1] 26

23. Find the student with the highest age

data %>% filter(Age == max(Age))

## # A tibble: 3 × 4
##   Name    Gender   Age Test_Score
##   <chr>   <fct>  <int>      <int>
## 1 Charlie Female    24         71
## 2 Eva     Female    24         91
## 3 Grace   Female    24         77

24. Calculate the average age of male and female students (assuming a gender column)

data %>% group_by(Gender) %>% summarize(Avg_Age = mean(Age, na.rm = TRUE))

## # A tibble: 2 × 2
##   Gender Avg_Age
##   <fct>    <dbl>
## 1 Female    21.6
## 2 Male      21.4

25. Calculate the total number of male and female students (assuming a gender column)

data %>% count(Gender)

## # A tibble: 2 × 2
##   Gender     n
##   <fct>  <int>
## 1 Female     5
## 2 Male       5

26. Find the student with the closest age to the median age

median_age <- median(data$Age)
data %>% mutate(Age_Difference = abs(Age - median_age)) %>% arrange(Age_Difference) %>% head(1)

## # A tibble: 1 × 5
##   Name  Gender   Age Test_Score Age_Difference
##   <chr> <fct>  <int>      <int>          <dbl>
## 1 Bob   Male      22         78              0

27. Calculate the median test score

median(data$Test_Score)

## [1] 77.5

28. Filter students who are 20 years old and scored above 80

data %>% filter(Age == 20, Test_Score > 80)

## # A tibble: 0 × 4
## # ℹ 4 variables: Name <chr>, Gender <fct>, Age <int>, Test_Score <int>

29. Find the student with the highest test score for each age group

data %>% group_by(Age) %>% slice(which.max(Test_Score))

## # A tibble: 6 × 4
## # Groups:   Age [6]
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 David Female    18         86
## 2 Ivy   Male      19         68
## 3 Jack  Male      21         71
## 4 Frank Male      22         88
## 5 Hank  Male      23         80
## 6 Eva   Female    24         91

30. Calculate the correlation between age and test scores

cor(data$Age, data$Test_Score)

## [1] 0.3462732

31. Calculate the number of students in each age group, grouped in 5-year intervals

data %>% group_by(Age_Group = cut(Age, breaks = seq(18, 30, by = 5))) %>% summarize(Count = n())

## # A tibble: 3 × 2
##   Age_Group Count
##   <fct>     <int>
## 1 (18,23]       5
## 2 (23,28]       3
## 3 <NA>          2

32. Calculate the average age of students with test scores above 90

data %>% filter(Test_Score > 90) %>% summarize(Avg_Age = mean(Age))

## # A tibble: 1 × 1
##   Avg_Age
##     <dbl>
## 1      24

33. Find the student with the lowest test score in the age group of 22

data %>% filter(Age == 22) %>% arrange(Test_Score) %>% head(1)

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Bob   Male      22         78

34. Calculate the percentage of students who are 25 years old

data %>% filter(Age == 25) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 1 × 1
##   Percentage
##        <dbl>
## 1          0

35. Group students by age and gender and calculate the average test score for each group

data %>% group_by(Age, Gender) %>% summarize(Avg_Test_Score = mean(Test_Score, na.rm = TRUE))

## `summarise()` has grouped output by 'Age'. You can override using the `.groups`
## argument.

## # A tibble: 6 × 3
## # Groups:   Age [6]
##     Age Gender Avg_Test_Score
##   <int> <fct>           <dbl>
## 1    18 Female           75.5
## 2    19 Male             68  
## 3    21 Male             71  
## 4    22 Male             83  
## 5    23 Male             80  
## 6    24 Female           79.7

36. Calculate the median test score for students aged 21 or younger

data %>% filter(Age <= 21) %>% summarize(Median_Test_Score = median(Test_Score))

## # A tibble: 1 × 1
##   Median_Test_Score
##               <dbl>
## 1              69.5

37. Find the student with the closest test score to the median test score

median_test_score <- median(data$Test_Score)
data %>% mutate(Test_Score_Difference = abs(Test_Score - median_test_score)) %>% arrange(Test_Score_Difference) %>% head(1)

## # A tibble: 1 × 5
##   Name  Gender   Age Test_Score Test_Score_Difference
##   <chr> <fct>  <int>      <int>                 <dbl>
## 1 Bob   Male      22         78                   0.5

38. Calculate the average age of students who scored below 70

data %>% filter(Test_Score < 70) %>% summarize(Avg_Age = mean(Age))

## # A tibble: 1 × 1
##   Avg_Age
##     <dbl>
## 1    18.5

39. Calculate the total number of students for each unique age

data %>% group_by(Age) %>% tally()

## # A tibble: 6 × 2
##     Age     n
##   <int> <int>
## 1    18     2
## 2    19     1
## 3    21     1
## 4    22     2
## 5    23     1
## 6    24     3

40. Calculate the variance of test scores

var(data$Test_Score)

## [1] 78.05556

41. Calculate the percentage of students who are 24 years old and scored between 70 and 80

data %>% filter(Age == 24, Test_Score >= 70, Test_Score <= 80) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 1 × 1
##   Percentage
##        <dbl>
## 1         20

42. Find the student with the highest test score among students aged 23

data %>% filter(Age == 23) %>% arrange(desc(Test_Score)) %>% head(1)

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Hank  Male      23         80

43. Calculate the percentage of male and female students (assuming a gender column)

data %>% group_by(Gender) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 2 × 2
##   Gender Percentage
##   <fct>       <dbl>
## 1 Female         50
## 2 Male           50

44. Calculate the interquartile range (IQR) of test scores

IQR(data$Test_Score)

## [1] 13.5

45. Find the student with the lowest test score for each age group

data %>% group_by(Age) %>% filter(Test_Score == min(Test_Score))

## # A tibble: 6 × 4
## # Groups:   Age [6]
##   Name    Gender   Age Test_Score
##   <chr>   <fct>  <int>      <int>
## 1 Alice   Female    18         65
## 2 Bob     Male      22         78
## 3 Charlie Female    24         71
## 4 Hank    Male      23         80
## 5 Ivy     Male      19         68
## 6 Jack    Male      21         71

46. Calculate the coefficient of variation (CV) for test scores

cv <- sd(data$Test_Score) / mean(data$Test_Score) * 100
cv

## [1] 11.39988

47. Find the student with the highest age and test score

data %>% arrange(desc(Age), desc(Test_Score)) %>% head(1)

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Eva   Female    24         91

48. Calculate the average test score for male and female students (assuming a gender column)

data %>% group_by(Gender) %>% summarize(Avg_Test_Score = mean(Test_Score, na.rm = TRUE))

## # A tibble: 2 × 2
##   Gender Avg_Test_Score
##   <fct>           <dbl>
## 1 Female             78
## 2 Male               77

49. Calculate the number of students who are 22 years old and scored above 75

data %>% filter(Age == 22, Test_Score > 75) %>% summarize(Count = n())

## # A tibble: 1 × 1
##   Count
##   <int>
## 1     2

50. Find the student with the highest test score among students aged 21 or younger

data %>% filter(Age <= 21) %>% arrange(desc(Test_Score)) %>% head(1)

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 David Female    18         86

51. Calculate the percentage of students who are 23 years old and scored 60 or below

data %>% filter(Age == 23, Test_Score <= 60) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 1 × 1
##   Percentage
##        <dbl>
## 1          0

52. Find the student with the highest age and the lowest test score

data %>% arrange(desc(Age), Test_Score) %>% head(1)

## # A tibble: 1 × 4
##   Name    Gender   Age Test_Score
##   <chr>   <fct>  <int>      <int>
## 1 Charlie Female    24         71

53. Calculate the median age of students who scored above 80

data %>% filter(Test_Score > 80) %>% summarize(Median_Age = median(Age))

## # A tibble: 1 × 1
##   Median_Age
##        <int>
## 1         22

54. Calculate the average test score for each unique age and gender combination

data %>% group_by(Age, Gender) %>% summarize(Avg_Test_Score = mean(Test_Score, na.rm = TRUE))

## `summarise()` has grouped output by 'Age'. You can override using the `.groups`
## argument.

## # A tibble: 6 × 3
## # Groups:   Age [6]
##     Age Gender Avg_Test_Score
##   <int> <fct>           <dbl>
## 1    18 Female           75.5
## 2    19 Male             68  
## 3    21 Male             71  
## 4    22 Male             83  
## 5    23 Male             80  
## 6    24 Female           79.7

55. Calculate the range of ages (max - min)

diff(range(data$Age))

## [1] 6

56. Find the student with the lowest age

data %>% filter(Age == min(Age))

## # A tibble: 2 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Alice Female    18         65
## 2 David Female    18         86

57. Calculate the percentage of students who are 19 years old and scored 70 or above

data %>% filter(Age == 19, Test_Score >= 70) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 1 × 1
##   Percentage
##        <dbl>
## 1          0

58. Find the student with the lowest test score in the age group of 24

data %>% filter(Age == 24) %>% arrange(Test_Score) %>% head(1)

## # A tibble: 1 × 4
##   Name    Gender   Age Test_Score
##   <chr>   <fct>  <int>      <int>
## 1 Charlie Female    24         71

59. Calculate the standard error of the mean (SEM) for test scores

stderror <- function(x) sd(x)/sqrt(length(x))
stderror(data$Test_Score)

## [1] 2.793842

60. Calculate the percentage of students who are 20 years old and scored 75 or above

data %>% filter(Age == 20, Test_Score >= 75) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 1 × 1
##   Percentage
##        <dbl>
## 1          0

61. Calculate the percentage of students who are 21 years old and scored below 60

data %>% filter(Age == 21, Test_Score < 60) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 1 × 1
##   Percentage
##        <dbl>
## 1          0

62. Find the student with the highest age in the age group of 24

data %>% filter(Age == 24) %>% arrange(desc(Age)) %>% head(1)

## # A tibble: 1 × 4
##   Name    Gender   Age Test_Score
##   <chr>   <fct>  <int>      <int>
## 1 Charlie Female    24         71

63. Calculate the average test score for students who are 22 years old or older

data %>% filter(Age >= 22) %>% summarize(Avg_Test_Score = mean(Test_Score, na.rm = TRUE))

## # A tibble: 1 × 1
##   Avg_Test_Score
##            <dbl>
## 1           80.8

64. Find the student with the highest test score in the age group of 20

data %>% filter(Age == 20) %>% arrange(desc(Test_Score)) %>% head(1)

## # A tibble: 0 × 4
## # ℹ 4 variables: Name <chr>, Gender <fct>, Age <int>, Test_Score <int>

65. Calculate the percentage of students who are 18 years old and scored 80 or above

data %>% filter(Age == 18, Test_Score >= 80) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 1 × 1
##   Percentage
##        <dbl>
## 1         10

66. Find the student with the lowest test score for each gender

data %>% group_by(Gender) %>% filter(Test_Score == min(Test_Score))

## # A tibble: 2 × 4
## # Groups:   Gender [2]
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Alice Female    18         65
## 2 Ivy   Male      19         68

67. Calculate the number of students who are 21 years old and scored 65 or above

data %>% filter(Age == 21, Test_Score >= 65) %>% summarize(Count = n())

## # A tibble: 1 × 1
##   Count
##   <int>
## 1     1

68. Find the student with the highest age and the lowest test score in the age group of 22

data %>% filter(Age == 22) %>% arrange(desc(Age), Test_Score) %>% head(1)

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Bob   Male      22         78

69. Calculate the median test score for male and female students (assuming a gender column)

data %>% group_by(Gender) %>% summarize(Median_Test_Score = median(Test_Score, na.rm = TRUE))

## # A tibble: 2 × 2
##   Gender Median_Test_Score
##   <fct>              <int>
## 1 Female                77
## 2 Male                  78

70. Find the student with the highest age and the highest test score

data %>% arrange(desc(Age), desc(Test_Score)) %>% head(1)

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Eva   Female    24         91

71. Calculate the range of test scores for male and female students (assuming a gender column)

data %>% group_by(Gender) %>% summarize(Test_Score_Range = max(Test_Score) - min(Test_Score))

## # A tibble: 2 × 2
##   Gender Test_Score_Range
##   <fct>             <int>
## 1 Female               26
## 2 Male                 20

72. Find the student with the highest test score for each gender

data %>% group_by(Gender) %>% filter(Test_Score == max(Test_Score))

## # A tibble: 2 × 4
## # Groups:   Gender [2]
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Eva   Female    24         91
## 2 Frank Male      22         88

73. Calculate the percentage of students who are 25 years old and scored below 75

data %>% filter(Age == 25, Test_Score < 75) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 1 × 1
##   Percentage
##        <dbl>
## 1          0

74. Find the student with the highest test score in the age group of 23

data %>% filter(Age == 23) %>% arrange(desc(Test_Score)) %>% head(1)

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Hank  Male      23         80

75. Calculate the average age of students who scored 75 or above

data %>% filter(Test_Score >= 75) %>% summarize(Avg_Age = mean(Age, na.rm = TRUE))

## # A tibble: 1 × 1
##   Avg_Age
##     <dbl>
## 1    22.2

76. Calculate the percentage of students who are 19 years old and scored below 70

data %>% filter(Age == 19, Test_Score < 70) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 1 × 1
##   Percentage
##        <dbl>
## 1         10

77. Find the student with the highest test score for each unique age and gender combination

data %>% group_by(Age, Gender) %>% filter(Test_Score == max(Test_Score))

## # A tibble: 6 × 4
## # Groups:   Age, Gender [6]
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 David Female    18         86
## 2 Eva   Female    24         91
## 3 Frank Male      22         88
## 4 Hank  Male      23         80
## 5 Ivy   Male      19         68
## 6 Jack  Male      21         71

78. Calculate the average age of students who scored 80 or above

data %>% filter(Test_Score >= 80) %>% summarize(Avg_Age = mean(Age, na.rm = TRUE))

## # A tibble: 1 × 1
##   Avg_Age
##     <dbl>
## 1    21.8

79. Find the student with the highest age in the age group of 23

data %>% filter(Age == 23) %>% arrange(desc(Age)) %>% head(1)

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Hank  Male      23         80

80. Calculate the percentage of students who are 22 years old and scored 60 or below

data %>% filter(Age == 22, Test_Score <= 60) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 1 × 1
##   Percentage
##        <dbl>
## 1          0

81. Find the student with the lowest test score in the age group of 25

data %>% filter(Age == 25) %>% arrange(Test_Score) %>% head(1)

## # A tibble: 0 × 4
## # ℹ 4 variables: Name <chr>, Gender <fct>, Age <int>, Test_Score <int>

82. Calculate the average test score for students who are 24 years old or older

data %>% filter(Age >= 24) %>% summarize(Avg_Test_Score = mean(Test_Score, na.rm = TRUE))

## # A tibble: 1 × 1
##   Avg_Test_Score
##            <dbl>
## 1           79.7

83. Find the student with the highest age and the lowest test score in the age group of 20

data %>% filter(Age == 20) %>% arrange(desc(Age), Test_Score) %>% head(1)

## # A tibble: 0 × 4
## # ℹ 4 variables: Name <chr>, Gender <fct>, Age <int>, Test_Score <int>

84. Calculate the median test score for students who are 21 years old or younger

data %>% filter(Age <= 21) %>% summarize(Median_Test_Score = median(Test_Score))

## # A tibble: 1 × 1
##   Median_Test_Score
##               <dbl>
## 1              69.5

85. Find the student with the highest test score in the age group of 22

data %>% filter(Age == 22) %>% arrange(desc(Test_Score)) %>% head(1)

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Frank Male      22         88

86. Calculate the percentage of students who are 23 years old and scored 70 or above

data %>% filter(Age == 23, Test_Score >= 70) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 1 × 1
##   Percentage
##        <dbl>
## 1         10

87. Find the student with the lowest test score in the age group of 24

data %>% filter(Age == 24) %>% arrange(Test_Score) %>% head(1)

## # A tibble: 1 × 4
##   Name    Gender   Age Test_Score
##   <chr>   <fct>  <int>      <int>
## 1 Charlie Female    24         71

88. Calculate the average age of students who scored 90 or above

data %>% filter(Test_Score >= 90) %>% summarize(Avg_Age = mean(Age, na.rm = TRUE))

## # A tibble: 1 × 1
##   Avg_Age
##     <dbl>
## 1      24

89. Find the student with the highest test score for each unique age and gender combination

data %>% group_by(Age, Gender) %>% filter(Test_Score == max(Test_Score))

## # A tibble: 6 × 4
## # Groups:   Age, Gender [6]
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 David Female    18         86
## 2 Eva   Female    24         91
## 3 Frank Male      22         88
## 4 Hank  Male      23         80
## 5 Ivy   Male      19         68
## 6 Jack  Male      21         71

90. Calculate the range of ages for male and female students (assuming a gender column)

data %>% group_by(Gender) %>% summarize(Age_Range = max(Age) - min(Age))

## # A tibble: 2 × 2
##   Gender Age_Range
##   <fct>      <int>
## 1 Female         6
## 2 Male           4

91. Find the student with the lowest test score for each age group

data %>% group_by(Age) %>% filter(Test_Score == min(Test_Score))

## # A tibble: 6 × 4
## # Groups:   Age [6]
##   Name    Gender   Age Test_Score
##   <chr>   <fct>  <int>      <int>
## 1 Alice   Female    18         65
## 2 Bob     Male      22         78
## 3 Charlie Female    24         71
## 4 Hank    Male      23         80
## 5 Ivy     Male      19         68
## 6 Jack    Male      21         71

92. Calculate the percentage of students who are 24 years old and scored 80 or above

data %>% filter(Age == 24, Test_Score >= 80) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 1 × 1
##   Percentage
##        <dbl>
## 1         10

93. Find the student with the highest test score in the age group of 21

data %>% filter(Age == 21) %>% arrange(desc(Test_Score)) %>% head(1)

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Jack  Male      21         71

94. Calculate the average test score for students who are 25 years old or older

data %>% filter(Age >= 25) %>% summarize(Avg_Test_Score = mean(Test_Score, na.rm = TRUE))

## # A tibble: 1 × 1
##   Avg_Test_Score
##            <dbl>
## 1            NaN

95. Find the student with the lowest age in the age group of 23

data %>% filter(Age == 23) %>% arrange(Age) %>% head(1)

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Hank  Male      23         80

96. Calculate the median test score for male and female students (assuming a gender column)

data %>% group_by(Gender) %>% summarize(Median_Test_Score = median(Test_Score, na.rm = TRUE))

## # A tibble: 2 × 2
##   Gender Median_Test_Score
##   <fct>              <int>
## 1 Female                77
## 2 Male                  78

97. Find the student with the lowest age and the lowest test score

data %>% arrange(Age, Test_Score) %>% head(1)

## # A tibble: 1 × 4
##   Name  Gender   Age Test_Score
##   <chr> <fct>  <int>      <int>
## 1 Alice Female    18         65

98. Calculate the percentage of students who are 20 years old and scored 70 or above

data %>% filter(Age == 20, Test_Score >= 70) %>% summarize(Percentage = n() / nrow(data) * 100)

## # A tibble: 1 × 1
##   Percentage
##        <dbl>
## 1          0

99. Find the student with the highest test score in the age group of 25

data %>% filter(Age == 25) %>% arrange(desc(Test_Score)) %>% head(1)

## # A tibble: 0 × 4
## # ℹ 4 variables: Name <chr>, Gender <fct>, Age <int>, Test_Score <int>

100. Calculate the average age of students who scored 60 or below

data %>% filter(Test_Score <= 60) %>% summarize(Avg_Age = mean(Age, na.rm = TRUE))

## # A tibble: 1 × 1
##   Avg_Age
##     <dbl>
## 1     NaN

Exploring Student Data: A Comprehensive Dplyr Tutorial

Syed Hamed Raza

2023-11-05

1. Display the first 5 rows of the dataset

2. Display basic statistics of the numeric columns

3. Count the number of rows and columns

4. Display the unique ages in the dataset

5. Calculate the average test score

6. Find the student with the highest test score

7. Filter students who are older than 21

8. Group students by age and calculate the average test score for each age group

9. Sort the dataset by test score in descending order

10. Count the number of students in each age group

11. Calculate the standard deviation of test scores

12. Find the student(s) with the lowest test score

13. Filter students with test scores above 90

14. Calculate the median age of the students

15. Calculate the correlation between age and test scores

16. Display the last 5 rows of the dataset

17. Filter students who scored below 70

18. Group students by test score range and count the number of students in each range

19. Calculate the total test score of all students

20. Calculate the percentage of students who passed (scored 70 or above)

21. Find the student with the longest name

22. Calculate the range of test scores (max - min)

23. Find the student with the highest age

24. Calculate the average age of male and female students (assuming a gender column)

25. Calculate the total number of male and female students (assuming a gender column)

26. Find the student with the closest age to the median age

27. Calculate the median test score

28. Filter students who are 20 years old and scored above 80

29. Find the student with the highest test score for each age group

30. Calculate the correlation between age and test scores

31. Calculate the number of students in each age group, grouped in 5-year intervals

32. Calculate the average age of students with test scores above 90

33. Find the student with the lowest test score in the age group of 22

34. Calculate the percentage of students who are 25 years old

35. Group students by age and gender and calculate the average test score for each group

36. Calculate the median test score for students aged 21 or younger

37. Find the student with the closest test score to the median test score

38. Calculate the average age of students who scored below 70

39. Calculate the total number of students for each unique age

40. Calculate the variance of test scores

41. Calculate the percentage of students who are 24 years old and scored between 70 and 80

42. Find the student with the highest test score among students aged 23

43. Calculate the percentage of male and female students (assuming a gender column)

44. Calculate the interquartile range (IQR) of test scores

45. Find the student with the lowest test score for each age group

46. Calculate the coefficient of variation (CV) for test scores

47. Find the student with the highest age and test score

48. Calculate the average test score for male and female students (assuming a gender column)

49. Calculate the number of students who are 22 years old and scored above 75

50. Find the student with the highest test score among students aged 21 or younger

51. Calculate the percentage of students who are 23 years old and scored 60 or below

52. Find the student with the highest age and the lowest test score

53. Calculate the median age of students who scored above 80

54. Calculate the average test score for each unique age and gender combination

55. Calculate the range of ages (max - min)

56. Find the student with the lowest age

57. Calculate the percentage of students who are 19 years old and scored 70 or above

58. Find the student with the lowest test score in the age group of 24

59. Calculate the standard error of the mean (SEM) for test scores

60. Calculate the percentage of students who are 20 years old and scored 75 or above

61. Calculate the percentage of students who are 21 years old and scored below 60

62. Find the student with the highest age in the age group of 24

63. Calculate the average test score for students who are 22 years old or older

64. Find the student with the highest test score in the age group of 20

65. Calculate the percentage of students who are 18 years old and scored 80 or above

66. Find the student with the lowest test score for each gender

67. Calculate the number of students who are 21 years old and scored 65 or above

68. Find the student with the highest age and the lowest test score in the age group of 22

69. Calculate the median test score for male and female students (assuming a gender column)

70. Find the student with the highest age and the highest test score

71. Calculate the range of test scores for male and female students (assuming a gender column)

72. Find the student with the highest test score for each gender

73. Calculate the percentage of students who are 25 years old and scored below 75

74. Find the student with the highest test score in the age group of 23

75. Calculate the average age of students who scored 75 or above

76. Calculate the percentage of students who are 19 years old and scored below 70

77. Find the student with the highest test score for each unique age and gender combination