project_02_data_607
Mahmud Hasan Al Raji
2022-10-09
library(readr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyr)
library(stringr)
library(ggplot2)Dataset1: This dataset is taken from discussion board (posted by Sangeetha Sashikumar). After collecting the dataset I put it my github account. With this dataset I will analyse the stuents grades based on their genders and ages.
data1<-data.frame(read.csv("https://raw.githubusercontent.com/Raji030/data607_project02/main/data607_project2.csv",sep=",", header = T))
data1## Names Age Grades Gender
## 1 Sally 6 85.00000 1
## 2 Michael "7" 89.66667 0
## 3 Elizabeth "6" 92.00000 1
## 4 Anthony 7 93.50000 0
## 5 Mary 6.111 90.00010 1
## 6 Steven 7 91.00000 0Transforming Gender column’s numeric value into characters, male and female
data1<-mutate(data1, Gender = case_when(Gender == 1 ~'girl',
Gender == 0 ~'boy'))
data1## Names Age Grades Gender
## 1 Sally 6 85.00000 girl
## 2 Michael "7" 89.66667 boy
## 3 Elizabeth "6" 92.00000 girl
## 4 Anthony 7 93.50000 boy
## 5 Mary 6.111 90.00010 girl
## 6 Steven 7 91.00000 boyRounding Grades column numeric values to 2 decimal points
data1<-data1 %>% mutate_at('Grades', round, 2)
data1## Names Age Grades Gender
## 1 Sally 6 85.00 girl
## 2 Michael "7" 89.67 boy
## 3 Elizabeth "6" 92.00 girl
## 4 Anthony 7 93.50 boy
## 5 Mary 6.111 90.00 girl
## 6 Steven 7 91.00 boyTransforming Age clomun character values to numeric values and rounding the coulmn values in 1 decimel point
data1$Age[2]<-7
data1$Age[3]<-6
data1## Names Age Grades Gender
## 1 Sally 6 85.00 girl
## 2 Michael 7 89.67 boy
## 3 Elizabeth 6 92.00 girl
## 4 Anthony 7 93.50 boy
## 5 Mary 6.111 90.00 girl
## 6 Steven 7 91.00 boydata1$Age<-as.numeric(data1$Age)
data1## Names Age Grades Gender
## 1 Sally 6.000 85.00 girl
## 2 Michael 7.000 89.67 boy
## 3 Elizabeth 6.000 92.00 girl
## 4 Anthony 7.000 93.50 boy
## 5 Mary 6.111 90.00 girl
## 6 Steven 7.000 91.00 boydata1<-data1 %>% mutate_at('Age', round, 1)
data1## Names Age Grades Gender
## 1 Sally 6.0 85.00 girl
## 2 Michael 7.0 89.67 boy
## 3 Elizabeth 6.0 92.00 girl
## 4 Anthony 7.0 93.50 boy
## 5 Mary 6.1 90.00 girl
## 6 Steven 7.0 91.00 boyAnalyzing the dataset to see gender wise highest grades, names and ages
data2<-data1%>%arrange(desc(Grades))
data2## Names Age Grades Gender
## 1 Anthony 7.0 93.50 boy
## 2 Elizabeth 6.0 92.00 girl
## 3 Steven 7.0 91.00 boy
## 4 Mary 6.1 90.00 girl
## 5 Michael 7.0 89.67 boy
## 6 Sally 6.0 85.00 girldata3<-data1%>%summarise(average=mean(Grades))
data3## average
## 1 90.195data4<-data1[order(data1$Gender,-data1$Grades),]
data4## Names Age Grades Gender
## 4 Anthony 7.0 93.50 boy
## 6 Steven 7.0 91.00 boy
## 2 Michael 7.0 89.67 boy
## 3 Elizabeth 6.0 92.00 girl
## 5 Mary 6.1 90.00 girl
## 1 Sally 6.0 85.00 girldata1%>%ggplot(aes(x=" ",y=Grades,fill=Names))+geom_bar(stat="identity",position="dodge")+ xlab("Names") + ylab("Grades")
From the above tables, it is seen that the highest grade 93.50 is received by Anthony who is a 7 years boy. Elizabeth got the second highest grade, 92 and her grade is also the highest among all the girls’ grades and she is 6. Also, Sally got the lowest grade 85 and the average grade is 90.195. The plot is also reflecting the grades by names where it is clearly found that Anthony has got the highest grade, Elizabeth has received the seocnd highest grade and Sally has received the lowest grade.
Dataset2: This dataset is taken from discussion board (posted by Jhalak Das). After collecting the dataset I put it on my github account. This dataset will be analyzed to get average test scores of students.
dataset2<-read.csv("https://raw.githubusercontent.com/Raji030/data_for_project2_607/main/student_results.csv",sep=",",header=T)
dataset2## id name phone sex.and.age test.number term.1 term.2 term.3
## 1 1 Mike 134 m_12 test 1 76 84 87
## 2 2 Linda 270 f_13 test 1 88 90 73
## 3 3 Sam 210 m_11 test 1 78 74 80
## 4 4 Esther 617 f_12 test 1 68 75 74
## 5 5 Mary 114 f_14 test 1 65 67 64
## 6 1 Mike 134 m_12 test 2 85 80 90
## 7 2 Linda 270 f_13 test 2 87 82 94
## 8 3 Sam 210 m_11 test 2 80 87 80
## 9 4 Esther 617 f_12 test 2 70 75 78
## 10 5 Mary 114 f_14 test 2 68 70 63Separating sex.and.age column into two columns, sex and age
dataset02<-dataset2%>%separate(sex.and.age,into=c("sex","age"))
dataset02## id name phone sex age test.number term.1 term.2 term.3
## 1 1 Mike 134 m 12 test 1 76 84 87
## 2 2 Linda 270 f 13 test 1 88 90 73
## 3 3 Sam 210 m 11 test 1 78 74 80
## 4 4 Esther 617 f 12 test 1 68 75 74
## 5 5 Mary 114 f 14 test 1 65 67 64
## 6 1 Mike 134 m 12 test 2 85 80 90
## 7 2 Linda 270 f 13 test 2 87 82 94
## 8 3 Sam 210 m 11 test 2 80 87 80
## 9 4 Esther 617 f 12 test 2 70 75 78
## 10 5 Mary 114 f 14 test 2 68 70 63Renaming column by removing dot character
new_col_names <-
colnames(dataset02) %>%
str_replace_all("\\.", '_')
colnames(dataset02) <- new_col_names
head(dataset02,5)## id name phone sex age test_number term_1 term_2 term_3
## 1 1 Mike 134 m 12 test 1 76 84 87
## 2 2 Linda 270 f 13 test 1 88 90 73
## 3 3 Sam 210 m 11 test 1 78 74 80
## 4 4 Esther 617 f 12 test 1 68 75 74
## 5 5 Mary 114 f 14 test 1 65 67 64Pivoting three term columns into a single term column
dataset03<-dataset02%>%pivot_longer(c("term_1","term_2","term_3"),names_to="term", values_to ="score" )
dataset03## # A tibble: 30 × 8
## id name phone sex age test_number term score
## <int> <chr> <int> <chr> <chr> <chr> <chr> <int>
## 1 1 Mike 134 m 12 test 1 term_1 76
## 2 1 Mike 134 m 12 test 1 term_2 84
## 3 1 Mike 134 m 12 test 1 term_3 87
## 4 2 Linda 270 f 13 test 1 term_1 88
## 5 2 Linda 270 f 13 test 1 term_2 90
## 6 2 Linda 270 f 13 test 1 term_3 73
## 7 3 Sam 210 m 11 test 1 term_1 78
## 8 3 Sam 210 m 11 test 1 term_2 74
## 9 3 Sam 210 m 11 test 1 term_3 80
## 10 4 Esther 617 f 12 test 1 term_1 68
## # … with 20 more rowsRemoving extra terms from column valuses
dataset03$term <- str_extract(dataset03$term, "\\d")
dataset03$test_number<-str_extract(dataset03$test_number,"\\d" )
dataset03## # A tibble: 30 × 8
## id name phone sex age test_number term score
## <int> <chr> <int> <chr> <chr> <chr> <chr> <int>
## 1 1 Mike 134 m 12 1 1 76
## 2 1 Mike 134 m 12 1 2 84
## 3 1 Mike 134 m 12 1 3 87
## 4 2 Linda 270 f 13 1 1 88
## 5 2 Linda 270 f 13 1 2 90
## 6 2 Linda 270 f 13 1 3 73
## 7 3 Sam 210 m 11 1 1 78
## 8 3 Sam 210 m 11 1 2 74
## 9 3 Sam 210 m 11 1 3 80
## 10 4 Esther 617 f 12 1 1 68
## # … with 20 more rowsFinding students’ average scores in different terms
student_avg_scores <-
dataset03%>%group_by(name,term)%>%
summarise(avg_score = mean(score))## `summarise()` has grouped output by 'name'. You can override using the
## `.groups` argument.head(student_avg_scores)## # A tibble: 6 × 3
## # Groups: name [2]
## name term avg_score
## <chr> <chr> <dbl>
## 1 Esther 1 69
## 2 Esther 2 75
## 3 Esther 3 76
## 4 Linda 1 87.5
## 5 Linda 2 86
## 6 Linda 3 83.5student_avg_scores$name[which.max(student_avg_scores$avg_score)]## [1] "Mike"student_avg_scores$term[which.max(student_avg_scores$avg_score)]## [1] "3"student_avg_scores$name[which.min(student_avg_scores$avg_score)]## [1] "Mary"student_avg_scores$term[which.min(student_avg_scores$avg_score)]## [1] "3"From the above average score table it is found that in term 3, Mike’s average score is maximum whereas Mary scored minimum in the same term. Students average scores are also depicted in the following plot.
student_avg_scores%>%ggplot(aes(x=" ",y= avg_score,fill=name))+geom_bar(stat="identity",position="dodge")+ xlab("Names") + ylab("avg_scores")
Dataset3: This dataset reflects the students performances in three different exams. Here, I examine students academic performances based on their gender, ethnicity, test preparation, parental level of education and type of lunch they are receiving. After collecting the dataset from kaggle.com, i put it in my github account.
students<-data.frame(read.csv("https://raw.githubusercontent.com/Raji030/project_02data/main/StudentsPerformance.csv", sep=",", header=TRUE, stringsAsFactors=FALSE))
head(students)## gender race.ethnicity parental.level.of.education lunch
## 1 female group B bachelor's degree standard
## 2 female group C some college standard
## 3 female group B master's degree standard
## 4 male group A associate's degree free/reduced
## 5 male group C some college standard
## 6 female group B associate's degree standard
## test.preparation.course math.score reading.score writing.score
## 1 none 72 72 74
## 2 completed 69 90 88
## 3 none 90 95 93
## 4 none 47 57 44
## 5 none 76 78 75
## 6 none 71 83 78Renaming few columns
names(students)[2] <- "race_ethnicity"
names(students)[3] <- "parental_edu"
names(students)[5]<- "test_prep"
names(students)[6] <- "math_score"
names(students)[7] <- "reading_score"
names(students)[8] <- "writing_score"
head(students)## gender race_ethnicity parental_edu lunch test_prep math_score
## 1 female group B bachelor's degree standard none 72
## 2 female group C some college standard completed 69
## 3 female group B master's degree standard none 90
## 4 male group A associate's degree free/reduced none 47
## 5 male group C some college standard none 76
## 6 female group B associate's degree standard none 71
## reading_score writing_score
## 1 72 74
## 2 90 88
## 3 95 93
## 4 57 44
## 5 78 75
## 6 83 78Sorting out students’ math,writing and reading scores
students_math<-students[order(students$math_score, decreasing = TRUE),]
head(students_math)## gender race_ethnicity parental_edu lunch test_prep math_score
## 150 male group E associate's degree free/reduced completed 100
## 452 female group E some college standard none 100
## 459 female group E bachelor's degree standard none 100
## 624 male group A some college standard completed 100
## 626 male group D some college standard completed 100
## 917 male group E bachelor's degree standard completed 100
## reading_score writing_score
## 150 100 93
## 452 92 97
## 459 100 100
## 624 96 86
## 626 97 99
## 917 100 100By sorting out math scores, it is found that group E students have possessed the top three positions where two of them are female. It is also found that student in 6th position has received 100 percent in all three exams.
students_reading<-students[order(students$reading_score, decreasing = TRUE),]
head(students_reading)## gender race_ethnicity parental_edu lunch test_prep math_score
## 107 female group D master's degree standard none 87
## 115 female group E bachelor's degree standard completed 99
## 150 male group E associate's degree free/reduced completed 100
## 166 female group C bachelor's degree standard completed 96
## 180 female group D some high school standard completed 97
## 382 male group C associate's degree standard completed 87
## reading_score writing_score
## 107 100 100
## 115 100 100
## 150 100 93
## 166 100 100
## 180 100 100
## 382 100 95By sorting out reading scores, it is seen that top two positions are held by female students from different ethnicity groups. It is also seen that among the 6 listed data, four positions are held by female students. Moreover, student at the top most position did not take any test preparation and her parental education level is masters.
students_writing<-students[order(students$writing_score, decreasing = TRUE),]
head(students_writing)## gender race_ethnicity parental_edu lunch test_prep math_score
## 107 female group D master's degree standard none 87
## 115 female group E bachelor's degree standard completed 99
## 166 female group C bachelor's degree standard completed 96
## 180 female group D some high school standard completed 97
## 378 female group D master's degree free/reduced completed 85
## 404 female group D high school standard completed 88
## reading_score writing_score
## 107 100 100
## 115 100 100
## 166 100 100
## 180 100 100
## 378 95 100
## 404 99 100By sorting out writing scores, it is seen that the top six spots are held by female students.Like reading section, the student at top most position in writing did not have test preparation and her parents have master level of education too.From the three academic performances, it is found that the students from ethnicity groups D,E,and C are doing much better than other ethnicity groups.
Creating average score column and attaching it to the data frame
students <- students %>%
mutate(avg_score= (math_score+ reading_score + writing_score)/3)
head(students)## gender race_ethnicity parental_edu lunch test_prep math_score
## 1 female group B bachelor's degree standard none 72
## 2 female group C some college standard completed 69
## 3 female group B master's degree standard none 90
## 4 male group A associate's degree free/reduced none 47
## 5 male group C some college standard none 76
## 6 female group B associate's degree standard none 71
## reading_score writing_score avg_score
## 1 72 74 72.66667
## 2 90 88 82.33333
## 3 95 93 92.66667
## 4 57 44 49.33333
## 5 78 75 76.33333
## 6 83 78 77.33333subsetting dataframe
students_avg<-select(students, gender, lunch, parental_edu, avg_score)
head(students_avg)## gender lunch parental_edu avg_score
## 1 female standard bachelor's degree 72.66667
## 2 female standard some college 82.33333
## 3 female standard master's degree 92.66667
## 4 male free/reduced associate's degree 49.33333
## 5 male standard some college 76.33333
## 6 female standard associate's degree 77.33333Ordering avg score
students_avg<-students_avg[order(students_avg$avg_score, decreasing = TRUE),]
head(students_avg)## gender lunch parental_edu avg_score
## 459 female standard bachelor's degree 100.00000
## 917 male standard bachelor's degree 100.00000
## 963 female standard associate's degree 100.00000
## 115 female standard bachelor's degree 99.66667
## 180 female standard some high school 99.00000
## 713 female standard some college 99.00000Polotting average sccore against parental education level
ggplot(students_avg, aes(x = "", y = avg_score, fill = parental_edu)) + geom_bar(stat="identity",position="dodge") +
xlab("parental_edducation") + ylab("average_score")
ggplot(students_avg, aes(x = "", y = avg_score, fill =lunch)) + geom_bar(stat="identity",position="dodge") +
xlab("lunch_type") + ylab("average_score")
By analyzing the above tables and plots, it is seen that students
average academic performance vary about their parental level of
education and type of lunch they are receiving. Students average scores
are higher when their parents have associate and bachelor level of
education. On the other hand, students who are receiving standard lunch
are doing higher academic performances compared to those receiving
free/reduced lunch.