library(readr)
education <- read_csv("C:/Users/KimCS/Downloads/education.csv")
## Rows: 5000 Columns: 20
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Student_ID, Gender, Field_of_Study, Current_Job_Level, Entrepreneu...
## dbl (15): Age, High_School_GPA, SAT_Score, University_Ranking, University_GP...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
View(education)
# Task: Print the structure of your dataset
str(education)
## spc_tbl_ [5,000 × 20] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Student_ID : chr [1:5000] "S00001" "S00002" "S00003" "S00004" ...
## $ Age : num [1:5000] 24 21 28 25 22 24 27 20 24 28 ...
## $ Gender : chr [1:5000] "Male" "Other" "Female" "Male" ...
## $ High_School_GPA : num [1:5000] 3.58 2.52 3.42 2.43 2.08 2.4 2.36 2.68 2.84 3.02 ...
## $ SAT_Score : num [1:5000] 1052 1211 1193 1497 1012 ...
## $ University_Ranking : num [1:5000] 291 112 715 170 599 631 610 240 337 138 ...
## $ University_GPA : num [1:5000] 3.96 3.63 2.63 2.81 2.48 3.78 3.83 2.84 3.31 2.33 ...
## $ Field_of_Study : chr [1:5000] "Arts" "Law" "Medicine" "Computer Science" ...
## $ Internships_Completed: num [1:5000] 3 4 4 3 4 2 0 1 2 1 ...
## $ Projects_Completed : num [1:5000] 7 7 8 9 6 3 1 5 3 5 ...
## $ Certifications : num [1:5000] 2 3 1 1 4 2 3 5 0 3 ...
## $ Soft_Skills_Score : num [1:5000] 9 8 1 10 10 2 3 5 5 10 ...
## $ Networking_Score : num [1:5000] 8 1 9 6 9 2 3 1 5 2 ...
## $ Job_Offers : num [1:5000] 5 4 0 1 4 1 2 2 2 0 ...
## $ Starting_Salary : num [1:5000] 27200 25000 42400 57400 47600 68400 55500 38000 68900 58900 ...
## $ Career_Satisfaction : num [1:5000] 4 1 9 7 9 9 7 2 2 4 ...
## $ Years_to_Promotion : num [1:5000] 5 1 3 5 5 2 4 3 2 2 ...
## $ Current_Job_Level : chr [1:5000] "Entry" "Mid" "Entry" "Mid" ...
## $ Work_Life_Balance : num [1:5000] 7 7 7 5 2 8 3 3 2 2 ...
## $ Entrepreneurship : chr [1:5000] "No" "No" "No" "No" ...
## - attr(*, "spec")=
## .. cols(
## .. Student_ID = col_character(),
## .. Age = col_double(),
## .. Gender = col_character(),
## .. High_School_GPA = col_double(),
## .. SAT_Score = col_double(),
## .. University_Ranking = col_double(),
## .. University_GPA = col_double(),
## .. Field_of_Study = col_character(),
## .. Internships_Completed = col_double(),
## .. Projects_Completed = col_double(),
## .. Certifications = col_double(),
## .. Soft_Skills_Score = col_double(),
## .. Networking_Score = col_double(),
## .. Job_Offers = col_double(),
## .. Starting_Salary = col_double(),
## .. Career_Satisfaction = col_double(),
## .. Years_to_Promotion = col_double(),
## .. Current_Job_Level = col_character(),
## .. Work_Life_Balance = col_double(),
## .. Entrepreneurship = col_character()
## .. )
## - attr(*, "problems")=<externalptr>
# Task: List the variables in your dataset
names(education)
## [1] "Student_ID" "Age" "Gender"
## [4] "High_School_GPA" "SAT_Score" "University_Ranking"
## [7] "University_GPA" "Field_of_Study" "Internships_Completed"
## [10] "Projects_Completed" "Certifications" "Soft_Skills_Score"
## [13] "Networking_Score" "Job_Offers" "Starting_Salary"
## [16] "Career_Satisfaction" "Years_to_Promotion" "Current_Job_Level"
## [19] "Work_Life_Balance" "Entrepreneurship"
# Task: Print the top 15 rows of your dataset
head(education, n=15)
## # A tibble: 15 × 20
## Student_ID Age Gender High_School_GPA SAT_Score University_Ranking
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 S00001 24 Male 3.58 1052 291
## 2 S00002 21 Other 2.52 1211 112
## 3 S00003 28 Female 3.42 1193 715
## 4 S00004 25 Male 2.43 1497 170
## 5 S00005 22 Male 2.08 1012 599
## 6 S00006 24 Male 2.4 1600 631
## 7 S00007 27 Male 2.36 1011 610
## 8 S00008 20 Male 2.68 1074 240
## 9 S00009 24 Male 2.84 1201 337
## 10 S00010 28 Male 3.02 1415 138
## 11 S00011 28 Female 2.95 1120 594
## 12 S00012 25 Female 2.54 1070 236
## 13 S00013 22 Female 2.06 1217 648
## 14 S00014 21 Male 3.21 1112 794
## 15 S00015 25 Male 2.79 1152 3
## # ℹ 14 more variables: University_GPA <dbl>, Field_of_Study <chr>,
## # Internships_Completed <dbl>, Projects_Completed <dbl>,
## # Certifications <dbl>, Soft_Skills_Score <dbl>, Networking_Score <dbl>,
## # Job_Offers <dbl>, Starting_Salary <dbl>, Career_Satisfaction <dbl>,
## # Years_to_Promotion <dbl>, Current_Job_Level <chr>, Work_Life_Balance <dbl>,
## # Entrepreneurship <chr>
# Task: Write a user defined function using any of the variables from the data set
# defining the terms first:
pass_term <-as.character("Pass")
fail_term <-as.character("Fail")
# function using the above predefined terms
pass_or_fail <- function(gpa) {
if (gpa >= 2.5) {
return(pass_term)
} else {
return(fail_term)
}
}
# Task: use data manipulation techniques and filter rows based on any logical criteria that exist in your dataset.
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ purrr 1.0.2
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ── 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
pass_or_fail(education$University_GPA[1])
## [1] "Pass"
# Task: Identify the dependent & independent variables and use reshaping techniques and create a new data frame by joining those variables from your dataset.
library(dplyr)
library(tidyr)
# Select relevant columns
df_selected <-education %>% select(Student_ID, University_GPA, Internships_Completed, Job_Offers)
# Reshape from wide to long format (this format is useful for visualization and statistical modeling)
df_long <-df_selected %>%
pivot_longer(cols = c(University_GPA, Internships_Completed),
names_to = "Variable",
values_to = "Value")
# whereas this format is used for machine learning and regression models
df_wide <- df_long %>%
pivot_wider(names_from = Variable, values_from = Value)
# Task: Remove missing values in your dataset
#
colSums(is.na(education))
## Student_ID Age Gender
## 0 0 0
## High_School_GPA SAT_Score University_Ranking
## 0 0 0
## University_GPA Field_of_Study Internships_Completed
## 0 0 0
## Projects_Completed Certifications Soft_Skills_Score
## 0 0 0
## Networking_Score Job_Offers Starting_Salary
## 0 0 0
## Career_Satisfaction Years_to_Promotion Current_Job_Level
## 0 0 0
## Work_Life_Balance Entrepreneurship
## 0 0
education_clean <- education %>% drop_na(University_GPA, Job_Offers)
# to verify that missing values are removed
colSums(is.na(education_clean))
## Student_ID Age Gender
## 0 0 0
## High_School_GPA SAT_Score University_Ranking
## 0 0 0
## University_GPA Field_of_Study Internships_Completed
## 0 0 0
## Projects_Completed Certifications Soft_Skills_Score
## 0 0 0
## Networking_Score Job_Offers Starting_Salary
## 0 0 0
## Career_Satisfaction Years_to_Promotion Current_Job_Level
## 0 0 0
## Work_Life_Balance Entrepreneurship
## 0 0
# Task Identify and remove duplicated data from your dataset.
# to find and count duplicate rows
# a. to count duplicate rows
sum(duplicated(education))
## [1] 0
# to display duplicated rows
education[duplicated(education),]
## # A tibble: 0 × 20
## # ℹ 20 variables: Student_ID <chr>, Age <dbl>, Gender <chr>,
## # High_School_GPA <dbl>, SAT_Score <dbl>, University_Ranking <dbl>,
## # University_GPA <dbl>, Field_of_Study <chr>, Internships_Completed <dbl>,
## # Projects_Completed <dbl>, Certifications <dbl>, Soft_Skills_Score <dbl>,
## # Networking_Score <dbl>, Job_Offers <dbl>, Starting_Salary <dbl>,
## # Career_Satisfaction <dbl>, Years_to_Promotion <dbl>,
## # Current_Job_Level <chr>, Work_Life_Balance <dbl>, Entrepreneurship <chr>
# to check for duplicated values
# a. count duplicate GPA values
sum(duplicated(education$University_GPA))
## [1] 4799
# b. show duplicated GPA values
education$University_GPA[duplicated(education$University_GPA)]
## [1] 3.78 3.36 2.72 3.48 3.10 2.44 3.85 4.00 2.39 2.84 2.02 3.78 3.18 2.96
## [15] 3.96 3.69 2.39 2.70 3.44 3.50 2.17 3.14 3.51 3.50 2.33 2.14 4.00 3.45
## [29] 3.39 4.00 2.72 3.79 2.74 3.49 3.21 2.58 3.55 2.37 2.58 2.37 3.37 3.31
## [43] 2.83 3.02 2.81 3.18 2.39 2.81 2.45 3.79 3.38 3.67 2.29 2.96 3.38 2.49
## [57] 3.37 2.15 2.76 3.20 3.26 2.95 3.74 3.35 3.33 3.20 2.41 2.86 3.39 3.07
## [71] 2.04 3.69 3.85 2.91 3.67 2.76 2.13 2.82 3.44 2.41 3.37 3.20 3.37 3.96
## [85] 2.93 2.15 2.66 2.24 2.93 2.01 2.21 3.83 2.56 2.99 2.87 2.54 3.40 2.25
## [99] 2.11 3.33 2.94 2.99 2.87 3.00 3.06 3.38 2.76 2.83 3.07 2.91 3.78 3.39
## [113] 3.42 3.94 2.10 2.84 2.42 2.63 3.95 3.79 2.58 2.74 2.79 2.20 3.76 3.85
## [127] 2.05 3.42 3.04 2.78 3.04 2.65 2.19 3.24 2.04 3.11 3.16 2.85 2.14 3.79
## [141] 3.22 3.02 2.99 2.12 3.21 3.62 3.50 2.41 2.69 2.14 2.97 3.19 3.54 3.25
## [155] 2.04 3.22 2.98 2.39 3.79 2.59 3.08 2.63 3.55 3.86 2.72 3.61 3.00 2.79
## [169] 3.02 2.93 2.52 2.65 3.86 3.36 3.65 2.54 3.09 3.94 2.64 2.32 3.02 2.78
## [183] 2.32 3.78 3.98 3.14 3.55 3.72 3.40 2.09 2.32 2.14 2.02 2.78 2.48 2.13
## [197] 3.06 2.55 3.59 2.02 3.69 3.91 3.43 3.47 2.42 3.99 3.55 3.45 2.72 3.09
## [211] 3.31 3.24 2.16 2.87 2.52 2.35 2.73 2.99 2.13 3.89 2.35 3.21 3.06 3.96
## [225] 3.50 3.70 2.31 3.38 2.20 2.23 3.49 2.54 3.26 3.83 2.63 2.43 2.21 2.12
## [239] 2.86 2.64 3.41 3.74 3.06 2.20 3.42 2.73 2.17 2.68 2.44 3.74 3.80 2.68
## [253] 2.71 3.16 3.47 2.13 3.19 3.91 2.52 3.82 3.90 2.54 3.51 2.48 3.48 3.59
## [267] 2.49 3.43 3.62 2.72 2.34 2.18 3.15 3.96 3.65 2.87 3.07 2.56 2.33 2.96
## [281] 2.93 3.04 3.91 3.21 3.21 2.26 3.99 3.55 3.75 2.83 3.00 3.04 2.39 3.38
## [295] 3.81 2.33 2.17 3.48 2.55 3.25 3.39 2.64 3.63 3.10 3.74 3.53 3.74 2.04
## [309] 2.49 2.18 3.78 2.94 3.70 2.65 2.68 3.79 2.85 3.58 3.90 3.15 3.52 2.18
## [323] 2.99 3.80 3.75 2.09 2.61 2.89 2.34 3.46 2.37 2.69 3.33 2.96 3.48 2.23
## [337] 3.42 2.83 2.27 2.64 2.68 3.80 3.48 3.94 3.20 2.48 2.66 2.64 2.65 3.35
## [351] 3.06 2.77 2.04 2.44 2.21 3.57 2.22 3.45 2.77 2.15 3.63 3.16 2.47 3.79
## [365] 2.09 3.50 3.42 2.58 2.12 3.72 2.63 2.39 3.03 3.82 2.09 3.53 3.14 2.85
## [379] 3.49 3.93 2.46 2.29 3.83 3.34 2.88 3.15 3.49 3.55 2.85 2.59 2.83 3.19
## [393] 2.84 2.31 2.94 3.38 3.60 2.99 2.33 2.83 3.57 2.83 3.39 3.01 2.48 3.59
## [407] 2.17 3.05 2.42 2.13 3.01 3.00 3.41 3.49 3.46 2.82 3.26 3.15 2.84 3.19
## [421] 3.89 2.39 3.81 2.11 2.58 3.25 2.26 3.76 3.30 3.87 3.66 3.00 3.40 3.18
## [435] 2.66 2.92 2.16 2.71 3.39 3.91 3.93 3.11 3.21 2.37 3.39 2.14 2.57 3.34
## [449] 2.62 2.10 3.66 2.62 2.13 2.15 2.50 3.55 2.64 3.61 2.35 2.99 3.38 3.30
## [463] 3.20 3.85 2.46 2.99 3.77 2.16 2.11 2.47 3.27 2.52 2.92 3.23 2.50 3.23
## [477] 3.89 3.35 3.57 2.73 2.54 2.91 3.61 2.91 3.69 3.98 2.14 2.09 2.30 2.77
## [491] 2.26 3.74 2.05 2.63 2.37 3.86 3.34 3.82 3.94 2.99 2.47 2.39 2.45 3.42
## [505] 2.10 3.22 3.28 3.33 2.74 3.61 3.07 2.21 3.59 3.91 2.12 2.78 2.98 2.29
## [519] 3.74 2.57 3.57 3.34 3.20 2.38 2.44 2.09 2.48 2.69 3.67 2.73 3.51 2.88
## [533] 3.57 3.95 2.48 3.72 3.24 3.63 3.30 2.85 2.46 2.14 3.07 3.98 3.48 2.65
## [547] 3.65 3.54 2.56 2.03 2.24 3.75 3.67 2.59 2.57 3.59 3.88 3.21 2.49 3.05
## [561] 2.47 3.62 2.77 3.67 2.76 3.65 2.67 2.88 3.23 2.14 3.90 3.15 3.02 3.68
## [575] 3.73 3.04 3.85 2.13 2.23 3.19 2.71 2.93 2.66 2.81 3.65 3.19 3.99 3.57
## [589] 2.55 2.67 3.69 2.05 2.46 3.81 3.28 2.45 2.44 3.73 3.79 2.51 2.84 3.67
## [603] 3.54 3.63 3.04 3.82 2.65 2.31 3.42 3.08 2.16 3.69 2.59 3.31 2.08 3.22
## [617] 3.43 2.74 2.43 3.08 3.67 2.65 2.50 2.56 3.90 2.72 2.01 3.56 3.40 2.26
## [631] 3.97 3.22 3.00 2.76 3.80 2.49 2.57 2.24 3.52 3.19 3.22 2.44 3.30 3.53
## [645] 2.72 3.60 2.70 3.52 2.07 3.10 3.94 3.96 2.56 2.88 2.13 3.75 3.14 2.77
## [659] 2.23 3.89 3.06 2.57 2.70 2.54 3.20 3.86 3.71 2.92 3.65 2.85 2.50 3.97
## [673] 2.61 2.65 3.98 2.27 3.52 3.87 3.02 2.01 2.75 2.22 2.83 3.58 2.88 2.86
## [687] 3.11 3.16 3.71 2.62 3.13 2.60 2.59 2.72 3.25 2.46 3.84 3.15 3.84 2.09
## [701] 2.69 2.05 2.54 3.72 2.07 2.87 2.24 2.83 3.68 3.25 3.47 2.70 3.89 3.42
## [715] 2.84 2.85 3.84 3.15 3.41 2.83 2.45 3.45 3.22 3.00 3.25 2.42 3.59 3.90
## [729] 3.78 2.64 2.81 3.62 3.19 3.10 3.85 3.07 3.03 2.08 2.14 2.01 3.83 2.82
## [743] 2.94 3.69 3.22 3.22 2.59 3.67 2.33 2.11 2.38 2.54 2.56 3.34 2.44 3.35
## [757] 2.18 2.29 3.02 3.84 3.72 3.94 3.30 3.35 3.25 3.25 2.29 3.09 2.77 2.89
## [771] 3.05 2.26 2.72 2.13 3.96 2.24 2.52 3.78 2.97 2.27 2.77 2.23 2.40 2.10
## [785] 2.88 3.61 3.17 3.21 2.40 2.69 3.69 2.18 3.72 2.97 3.67 2.64 2.68 3.50
## [799] 3.57 2.46 3.95 2.17 2.14 3.19 3.45 3.92 2.81 2.05 3.21 2.58 2.87 2.98
## [813] 2.13 2.97 2.85 3.43 3.29 2.37 2.41 2.50 2.36 3.31 3.99 3.30 3.21 2.82
## [827] 2.63 3.82 3.94 2.99 2.58 3.54 2.26 3.53 3.64 2.25 3.95 3.97 3.87 2.99
## [841] 2.49 3.16 2.84 2.41 2.85 3.33 2.14 2.67 2.04 3.50 3.28 2.13 3.93 2.05
## [855] 3.18 3.55 3.58 2.07 3.81 3.84 3.12 2.96 2.09 3.12 2.82 3.55 2.81 3.37
## [869] 2.20 2.27 2.28 2.09 3.08 3.59 3.22 3.15 3.36 2.68 2.72 2.63 3.42 2.32
## [883] 2.03 3.19 3.88 3.22 3.57 3.35 2.99 3.44 3.65 2.45 2.67 2.45 2.90 3.77
## [897] 2.69 2.75 3.72 3.03 3.07 3.72 2.98 3.36 2.17 2.36 2.81 2.99 3.93 3.94
## [911] 3.70 2.63 3.93 3.38 2.79 3.40 2.03 2.29 3.66 3.29 2.73 3.00 3.81 3.26
## [925] 2.80 2.24 2.59 3.96 2.87 2.94 3.00 3.77 3.21 3.39 2.78 3.86 3.90 3.90
## [939] 3.89 3.63 3.37 3.69 3.20 2.27 2.78 3.94 2.70 3.74 2.19 2.67 2.70 2.10
## [953] 2.24 2.39 3.68 2.13 2.60 2.02 3.39 3.24 3.29 2.75 2.42 3.44 3.62 2.28
## [967] 2.97 2.50 2.34 3.47 3.08 2.43 2.62 3.93 3.70 3.76 2.92 3.25 2.75 2.16
## [981] 2.80 3.19 2.57 3.70 2.30 2.73 2.25 2.81 2.08 2.55 2.80 2.96 3.09 2.55
## [995] 3.05 3.02 2.63 3.66 3.09 2.66 3.89 3.69 3.97 3.38 2.76 2.56 3.33 3.96
## [1009] 3.25 3.76 2.66 2.81 3.81 3.51 3.96 3.55 2.90 3.06 3.80 3.10 2.51 2.87
## [1023] 3.03 2.19 3.49 2.22 2.81 3.61 2.94 3.68 2.68 3.92 2.42 2.08 3.74 3.50
## [1037] 2.46 3.97 3.00 3.90 3.01 2.94 2.90 2.67 3.07 2.91 2.07 2.52 2.43 3.73
## [1051] 3.20 3.53 3.91 3.29 3.97 2.62 2.37 2.81 2.94 3.79 3.12 2.58 3.49 3.01
## [1065] 2.32 3.15 3.82 3.69 3.17 2.75 2.17 2.94 3.50 2.72 2.47 3.86 3.64 3.28
## [1079] 2.27 2.29 2.21 3.23 2.23 3.69 3.99 2.73 2.91 3.42 3.87 3.79 2.12 2.34
## [1093] 3.51 3.38 2.27 3.00 3.56 2.44 3.74 2.11 3.56 3.23 2.63 3.44 3.56 3.87
## [1107] 2.74 3.37 3.21 3.05 3.78 2.01 2.56 3.08 2.36 3.82 3.30 2.96 3.63 2.90
## [1121] 3.12 2.01 2.73 2.28 3.38 2.96 2.60 2.94 2.42 3.85 2.11 3.73 3.02 3.24
## [1135] 2.61 3.93 2.56 3.18 3.99 3.76 3.07 3.98 3.40 2.42 2.81 2.67 2.69 3.52
## [1149] 3.17 2.08 3.75 2.59 3.99 3.91 3.88 3.62 2.44 3.07 2.45 2.52 3.22 2.08
## [1163] 3.94 3.84 2.76 3.03 2.93 3.76 3.38 2.56 2.89 2.86 3.92 2.35 2.18 3.76
## [1177] 3.79 3.74 3.02 3.13 2.14 3.19 2.03 2.65 3.86 2.61 3.88 2.50 2.63 3.26
## [1191] 3.52 3.00 3.76 2.08 3.84 2.39 3.77 2.80 3.67 3.34 2.35 3.84 2.49 2.49
## [1205] 3.63 2.21 2.44 3.01 3.66 2.29 3.10 3.42 3.72 3.87 3.57 3.31 2.87 2.22
## [1219] 3.42 3.99 3.47 2.03 2.99 3.23 3.38 3.44 3.12 2.45 3.88 3.39 2.47 2.64
## [1233] 3.84 3.77 2.81 2.08 2.10 2.57 3.26 3.17 3.27 3.43 2.04 2.21 2.72 2.31
## [1247] 2.15 3.28 3.63 3.51 3.63 3.33 3.47 2.54 3.99 2.37 2.43 2.58 3.95 3.98
## [1261] 3.59 3.26 3.04 3.94 3.39 3.89 2.63 2.67 3.71 2.30 3.89 3.01 3.85 2.67
## [1275] 3.43 2.97 3.25 3.01 3.93 3.38 2.50 3.82 2.59 2.74 2.20 3.80 2.57 2.52
## [1289] 3.84 3.61 2.28 3.54 3.83 2.16 3.65 3.09 3.34 2.99 3.40 2.07 3.53 2.07
## [1303] 2.91 2.30 3.65 2.63 2.91 2.86 3.55 2.96 3.58 2.15 2.38 3.10 3.89 2.15
## [1317] 2.09 2.31 3.28 2.80 3.65 2.14 2.39 2.60 3.25 3.37 2.25 3.89 2.68 3.43
## [1331] 3.05 2.45 3.69 3.64 2.79 3.18 2.52 3.38 2.62 3.69 2.43 2.03 2.54 2.38
## [1345] 3.02 2.41 3.53 2.48 3.41 3.16 3.97 4.00 3.74 3.52 2.77 3.51 3.91 2.05
## [1359] 3.08 2.44 3.12 2.93 3.15 3.10 3.92 3.10 3.69 2.64 2.88 2.94 2.56 2.76
## [1373] 3.20 3.16 3.27 2.70 2.63 3.22 2.27 2.51 2.08 2.15 2.49 2.04 2.49 3.31
## [1387] 2.12 2.87 3.83 3.58 2.61 3.85 2.66 2.15 3.99 3.74 2.93 3.33 3.34 2.39
## [1401] 2.40 2.94 3.82 2.02 3.57 2.12 2.96 3.28 3.37 2.12 2.84 3.17 2.13 2.27
## [1415] 3.33 3.04 2.32 3.07 3.83 2.03 3.29 2.15 2.01 2.30 2.85 3.60 2.56 3.25
## [1429] 2.82 2.47 3.03 3.59 3.66 3.69 2.68 3.59 2.42 3.48 3.90 2.03 2.88 3.30
## [1443] 3.40 2.35 3.94 3.23 2.33 2.04 3.57 2.16 2.28 3.15 3.21 3.37 2.79 3.55
## [1457] 3.95 2.54 3.41 3.86 3.37 3.36 2.45 3.07 3.13 3.40 3.99 2.07 2.13 2.16
## [1471] 3.49 2.27 3.24 2.65 2.91 2.43 3.73 3.17 3.89 3.15 2.25 2.34 3.98 3.45
## [1485] 2.10 3.41 2.82 2.61 2.31 3.68 2.13 2.58 2.61 2.32 3.02 2.02 2.60 3.87
## [1499] 2.62 2.72 2.95 3.12 3.62 2.38 3.74 3.52 3.45 2.17 2.04 3.19 3.73 3.17
## [1513] 2.43 2.18 3.40 3.44 2.35 2.06 2.82 3.17 3.41 3.62 3.22 2.22 3.37 3.97
## [1527] 3.81 3.74 2.60 3.50 3.70 3.36 2.50 3.91 2.90 2.26 2.22 3.30 2.88 2.19
## [1541] 3.48 3.60 3.45 2.40 3.38 2.02 2.61 2.80 3.67 2.78 2.02 2.70 3.00 3.90
## [1555] 3.88 2.36 2.37 2.33 3.58 2.01 2.25 3.08 2.50 2.18 2.72 2.55 3.02 3.96
## [1569] 2.25 2.75 3.86 3.75 2.79 3.40 2.18 3.43 3.95 3.07 3.00 2.37 3.27 3.18
## [1583] 3.30 2.05 3.10 3.19 2.42 2.42 2.55 3.42 3.21 3.69 2.59 3.82 3.49 2.79
## [1597] 2.19 2.51 3.91 2.62 2.66 2.38 3.61 2.55 2.66 2.83 2.87 2.82 3.01 3.63
## [1611] 3.64 3.60 2.20 3.60 2.24 2.25 3.90 3.64 3.10 3.75 3.98 3.22 3.23 3.51
## [1625] 2.46 2.33 3.52 2.69 2.27 2.63 2.72 3.93 3.40 3.06 3.94 3.81 3.02 3.81
## [1639] 2.81 2.46 2.47 3.32 3.00 3.32 3.37 2.04 2.11 3.07 2.38 3.27 3.10 3.17
## [1653] 2.60 3.28 3.66 4.00 3.68 3.11 2.39 3.36 3.88 2.42 2.51 2.30 3.47 2.81
## [1667] 2.13 3.75 3.12 2.66 2.69 2.58 2.91 2.67 3.10 3.55 3.35 2.59 3.06 3.68
## [1681] 2.14 2.16 3.11 3.28 2.42 3.49 3.29 3.85 2.34 3.50 2.17 3.13 3.10 2.78
## [1695] 3.69 2.01 3.04 2.43 3.49 2.09 3.66 3.68 3.67 2.31 3.26 2.03 3.70 3.45
## [1709] 3.06 3.46 2.19 3.06 2.58 3.95 3.71 3.47 3.87 2.69 3.80 2.65 3.47 3.47
## [1723] 2.04 2.72 2.04 3.99 2.24 3.09 3.58 2.40 2.97 3.21 2.32 3.56 2.60 2.42
## [1737] 3.12 2.82 3.84 3.76 2.97 2.72 2.65 2.54 3.09 3.61 2.62 2.38 2.95 2.14
## [1751] 3.40 3.04 2.99 2.60 3.81 2.18 3.74 2.75 3.76 3.46 2.21 3.66 3.36 2.06
## [1765] 2.27 3.27 3.33 3.49 3.44 3.32 3.53 3.15 2.52 3.82 2.04 2.73 3.46 2.85
## [1779] 2.05 2.13 3.30 3.93 3.10 3.01 2.64 2.51 2.04 2.55 2.90 2.40 2.17 3.44
## [1793] 3.79 3.87 3.24 2.11 3.48 2.22 3.34 3.56 3.45 3.90 3.48 3.39 2.52 3.85
## [1807] 2.36 3.81 2.63 2.26 3.65 2.00 3.66 2.52 3.77 3.38 3.33 2.73 2.12 3.66
## [1821] 2.34 2.74 3.20 2.96 2.40 2.28 2.25 3.73 2.06 3.95 2.40 2.22 3.15 2.16
## [1835] 3.20 3.70 2.10 3.60 2.79 2.32 2.35 3.84 2.22 3.73 3.09 2.08 2.93 3.63
## [1849] 3.45 2.98 3.72 2.24 3.82 3.69 3.42 3.64 3.09 3.35 3.12 2.71 3.14 2.51
## [1863] 2.88 2.56 2.25 2.64 3.70 2.76 3.50 2.35 2.85 2.23 2.28 3.23 3.21 2.43
## [1877] 2.95 3.73 2.35 2.04 3.30 2.11 3.96 3.22 2.94 2.86 3.15 2.18 3.86 3.25
## [1891] 2.37 3.13 2.27 2.04 3.26 3.30 3.17 2.37 3.21 2.11 3.22 3.11 3.31 3.79
## [1905] 3.70 3.96 3.95 3.54 3.91 3.73 3.46 2.15 2.10 3.68 2.98 3.63 2.63 2.80
## [1919] 3.40 3.83 2.18 3.72 3.59 2.63 2.71 2.09 2.03 2.95 2.19 2.39 3.69 3.14
## [1933] 3.27 2.37 3.66 3.74 3.05 3.83 3.69 3.46 3.47 2.21 2.54 2.13 2.02 2.32
## [1947] 3.76 2.48 3.94 3.19 2.47 2.99 2.44 3.97 3.69 2.05 3.90 3.44 2.16 3.34
## [1961] 3.29 2.44 3.52 2.17 2.98 2.02 3.94 3.50 2.32 2.68 2.32 3.53 2.12 3.68
## [1975] 3.88 3.35 2.43 2.72 2.95 3.21 3.34 3.11 2.70 3.78 2.16 2.93 3.55 2.12
## [1989] 3.41 3.64 3.32 2.03 2.36 2.68 2.47 3.93 3.30 2.59 3.86 3.48 2.57 2.76
## [2003] 2.40 3.94 3.03 3.79 2.69 3.72 2.11 3.96 3.82 2.73 3.55 3.50 3.80 2.61
## [2017] 2.93 3.73 3.56 2.79 3.69 2.09 3.56 2.41 2.57 2.99 2.90 3.04 3.09 2.66
## [2031] 2.35 3.54 2.80 3.01 3.29 3.82 3.42 2.09 3.68 3.98 2.94 3.94 3.09 2.92
## [2045] 2.66 3.32 2.46 2.21 2.05 2.58 2.79 3.10 2.59 3.97 2.58 2.88 2.21 2.52
## [2059] 2.10 3.12 3.50 2.39 2.74 2.03 3.68 3.56 2.13 2.73 3.91 2.78 3.05 2.71
## [2073] 3.19 3.96 4.00 3.55 2.81 2.99 2.71 3.97 3.42 2.90 2.02 3.04 3.97 3.05
## [2087] 2.36 2.04 2.71 3.84 3.99 3.89 3.46 3.12 3.18 3.08 3.41 2.84 3.34 2.24
## [2101] 2.53 3.67 3.81 3.08 2.06 3.85 3.40 2.83 3.20 2.86 2.93 3.77 2.32 2.21
## [2115] 2.01 2.63 2.29 2.44 2.02 2.45 2.67 2.06 3.22 3.14 2.19 3.19 4.00 3.89
## [2129] 2.01 3.59 2.71 2.13 2.59 2.64 3.05 3.32 3.50 2.49 2.64 2.62 2.73 4.00
## [2143] 3.56 3.95 3.39 2.81 2.78 2.98 2.83 3.66 2.93 2.59 3.55 3.71 2.84 3.34
## [2157] 2.12 3.06 2.09 2.06 3.23 2.61 3.49 3.79 2.28 3.45 3.03 2.22 3.30 3.22
## [2171] 3.03 3.85 3.04 3.98 3.02 2.23 2.94 2.36 3.19 3.39 2.94 2.20 2.94 2.16
## [2185] 3.36 3.58 3.92 2.30 3.06 3.14 3.84 3.25 2.29 2.87 3.31 3.40 3.73 2.36
## [2199] 3.90 2.12 3.97 3.72 3.32 3.47 2.15 2.08 2.20 2.17 3.09 2.43 3.72 2.89
## [2213] 3.39 3.31 2.64 2.49 2.68 3.59 3.17 2.51 3.64 2.11 3.40 2.88 3.46 3.85
## [2227] 3.50 2.88 2.30 3.61 2.92 2.15 3.44 2.58 2.10 3.81 2.97 2.56 3.43 3.77
## [2241] 2.00 3.38 3.53 2.42 2.98 2.18 3.49 3.69 2.22 3.63 3.54 2.69 3.38 2.00
## [2255] 3.86 3.10 3.24 3.33 3.12 3.71 3.22 3.82 3.16 2.34 3.83 3.36 3.40 3.97
## [2269] 2.15 2.86 2.48 3.26 3.61 3.93 2.91 3.57 3.83 3.37 2.49 3.22 3.94 3.98
## [2283] 3.28 3.77 2.40 2.49 3.91 2.27 2.38 2.84 3.18 3.21 2.58 3.84 3.83 2.78
## [2297] 2.87 3.40 3.33 2.76 2.83 2.70 2.47 2.92 3.37 3.48 3.56 2.75 3.53 2.01
## [2311] 2.95 3.59 3.26 2.95 3.29 3.81 3.13 3.60 3.96 3.41 3.15 2.07 2.75 3.13
## [2325] 3.59 2.88 3.92 2.54 3.49 2.84 2.97 3.61 2.59 2.19 3.04 3.06 3.59 3.94
## [2339] 2.11 3.18 3.36 3.97 3.89 3.42 3.04 2.32 2.77 3.17 3.45 3.27 3.43 2.86
## [2353] 3.46 2.29 2.33 2.33 3.78 2.15 3.60 2.47 2.94 3.83 3.17 2.42 2.57 3.84
## [2367] 2.85 3.17 2.92 2.76 2.45 3.37 3.44 2.55 3.58 3.71 3.84 2.32 2.61 3.72
## [2381] 2.40 2.01 3.74 2.65 2.83 2.89 3.70 2.68 3.78 3.23 3.05 2.65 2.49 3.25
## [2395] 2.89 3.82 2.98 3.02 2.83 3.58 2.85 2.54 3.72 3.19 3.18 3.22 3.44 2.63
## [2409] 2.07 3.62 2.26 3.44 2.16 2.75 2.13 3.04 3.31 2.34 2.74 3.86 2.88 3.61
## [2423] 3.85 3.95 3.80 2.63 3.93 3.62 3.67 2.03 3.91 3.56 3.54 2.16 2.47 3.92
## [2437] 3.00 2.80 3.68 2.17 2.83 2.75 2.70 2.62 2.42 2.35 3.28 3.84 3.02 2.60
## [2451] 2.32 3.66 3.94 2.70 2.58 3.20 3.55 2.91 3.26 3.43 3.92 2.33 3.99 2.78
## [2465] 3.45 3.14 2.97 3.40 2.79 2.25 2.98 3.04 2.40 3.87 2.45 2.82 2.31 3.07
## [2479] 2.62 2.98 2.45 2.11 3.56 3.28 2.68 2.09 3.73 2.34 3.86 2.96 2.68 3.48
## [2493] 3.75 2.74 2.81 2.87 3.84 3.39 2.50 2.37 2.46 2.53 2.46 3.32 2.23 2.77
## [2507] 3.25 2.58 2.13 2.16 2.25 3.67 3.32 2.13 2.78 2.56 3.16 3.05 2.96 2.63
## [2521] 2.28 3.48 3.76 3.89 2.12 3.48 3.45 3.89 2.51 3.48 2.11 3.26 2.70 3.54
## [2535] 3.73 2.73 3.05 2.23 2.83 2.72 2.37 3.79 2.30 3.27 3.78 3.82 3.59 2.35
## [2549] 3.63 2.55 3.84 3.42 3.57 2.17 3.17 3.64 3.72 2.06 3.88 2.09 3.23 3.39
## [2563] 2.21 2.10 3.57 2.77 2.21 3.53 3.70 3.17 2.32 2.03 3.40 3.10 3.30 3.63
## [2577] 2.87 2.25 3.88 3.09 3.23 3.87 3.86 3.14 2.60 3.99 3.36 2.82 3.12 2.83
## [2591] 3.78 2.96 3.65 3.68 3.65 3.44 3.71 2.77 2.23 3.68 3.00 2.25 2.23 2.32
## [2605] 3.50 3.42 2.41 3.69 2.56 3.59 2.98 3.46 2.76 2.69 3.68 2.07 3.95 3.30
## [2619] 2.86 3.26 2.25 3.03 3.97 2.47 3.44 2.87 3.30 2.41 2.94 3.96 2.39 3.27
## [2633] 3.15 3.13 3.23 3.00 2.03 3.80 3.65 2.35 2.83 3.54 3.96 3.90 3.80 2.32
## [2647] 3.83 3.76 2.75 2.79 2.92 3.31 2.52 2.05 2.28 2.88 2.56 3.53 2.21 3.83
## [2661] 3.79 3.82 3.48 2.21 2.88 2.26 3.98 2.67 3.17 3.56 3.84 3.51 3.35 2.93
## [2675] 3.21 2.19 3.77 3.09 2.08 3.84 2.98 3.74 3.71 3.63 2.12 2.49 3.75 3.80
## [2689] 2.92 2.88 2.35 3.94 2.43 2.38 3.32 2.49 3.88 2.02 2.01 3.01 2.98 2.33
## [2703] 2.99 2.82 3.78 2.02 3.95 3.25 2.30 2.09 3.08 2.47 2.60 2.49 3.30 3.11
## [2717] 2.16 3.05 3.58 2.16 3.78 3.16 3.90 2.30 3.99 3.29 3.13 3.03 2.05 3.88
## [2731] 3.29 2.18 2.04 2.10 3.22 3.23 3.00 2.61 3.06 3.03 2.04 2.56 2.11 3.80
## [2745] 3.45 3.99 2.60 3.76 3.21 2.91 2.45 3.48 3.81 2.47 2.35 3.39 3.95 3.50
## [2759] 2.57 2.84 2.77 3.70 3.54 3.18 2.55 3.88 3.31 3.94 3.16 3.64 3.63 2.67
## [2773] 3.93 2.04 3.54 3.93 2.68 3.30 3.12 3.52 3.73 3.21 3.63 2.62 2.46 2.86
## [2787] 3.36 2.37 3.36 2.79 3.59 2.07 3.34 3.30 3.88 3.43 3.59 3.81 3.35 2.58
## [2801] 3.74 3.50 2.85 3.42 3.74 3.95 3.90 2.65 2.84 2.22 2.50 3.76 2.72 3.24
## [2815] 3.33 3.56 2.37 2.61 2.90 2.73 3.97 3.67 2.96 3.76 2.70 3.57 2.45 3.54
## [2829] 2.91 2.21 2.55 2.80 3.50 2.15 3.32 3.43 3.31 3.52 2.36 3.17 3.88 3.22
## [2843] 3.22 3.47 2.67 3.40 3.83 2.02 3.74 2.44 2.91 3.70 3.65 3.78 3.88 2.89
## [2857] 3.31 2.78 2.70 2.86 3.49 3.00 3.63 3.49 2.04 3.26 2.87 2.91 3.64 3.10
## [2871] 3.77 3.43 3.11 3.51 2.08 2.05 2.17 2.88 2.47 3.97 3.08 3.32 2.56 2.64
## [2885] 2.67 3.40 2.95 3.04 3.18 3.54 2.18 3.55 3.13 3.34 2.66 2.82 2.46 3.96
## [2899] 2.99 3.99 2.98 2.44 3.01 2.08 3.81 3.64 3.45 3.22 3.16 3.99 2.56 3.56
## [2913] 3.16 3.95 3.66 4.00 3.18 2.41 3.01 3.42 3.55 2.00 3.01 2.47 3.75 2.31
## [2927] 3.33 2.53 2.71 3.88 2.48 2.19 2.14 3.01 3.28 2.65 3.38 2.12 3.71 3.63
## [2941] 3.97 3.87 2.71 2.80 3.49 2.27 2.63 2.24 2.91 3.10 2.42 2.05 3.44 2.04
## [2955] 3.95 2.44 3.33 2.87 2.12 2.94 3.03 3.71 3.46 2.16 2.42 2.62 2.23 2.91
## [2969] 3.97 3.03 3.48 3.37 3.70 2.43 3.51 3.76 2.34 2.69 3.94 3.56 2.57 3.13
## [2983] 2.81 2.98 2.71 2.56 3.35 3.56 3.75 2.31 3.65 2.51 2.85 3.80 2.91 2.39
## [2997] 2.32 2.95 3.53 3.99 3.57 3.75 3.50 3.79 2.15 3.86 2.79 3.84 2.15 3.43
## [3011] 3.47 3.99 3.54 2.77 3.11 2.73 2.39 2.50 2.44 3.10 3.81 3.83 2.11 2.16
## [3025] 2.49 2.51 2.19 2.78 3.18 3.16 3.09 2.68 3.41 3.65 2.69 2.17 2.59 2.27
## [3039] 2.93 2.31 2.93 2.44 3.48 2.06 3.11 2.34 2.00 3.74 3.34 2.20 3.33 3.22
## [3053] 2.95 2.84 3.77 2.49 2.80 2.66 3.54 3.83 3.70 3.60 3.97 3.39 3.50 3.97
## [3067] 2.01 3.27 3.48 3.84 3.77 3.68 3.89 3.07 2.01 2.02 3.09 2.87 2.90 2.80
## [3081] 3.84 2.72 3.71 3.81 3.00 2.89 3.52 3.42 2.66 2.21 2.53 2.26 2.59 2.52
## [3095] 3.28 3.91 3.37 3.85 3.57 3.46 2.83 2.65 3.69 3.81 2.90 2.02 3.59 2.57
## [3109] 2.02 3.61 3.03 3.37 3.83 3.08 3.01 2.58 2.61 3.59 2.90 2.33 2.19 2.95
## [3123] 2.93 3.31 2.94 2.29 3.02 3.81 2.32 3.38 3.19 3.50 2.30 3.88 3.24 3.69
## [3137] 2.49 3.48 3.56 2.79 2.77 3.32 2.19 3.47 2.09 2.38 3.33 2.20 3.94 3.74
## [3151] 3.25 3.64 3.78 2.52 3.70 2.39 2.83 3.12 3.43 2.06 2.58 3.22 2.71 3.55
## [3165] 3.30 2.47 2.29 3.26 2.90 2.34 2.65 2.61 2.22 3.09 3.53 3.15 3.65 2.26
## [3179] 2.87 3.22 3.35 3.39 3.46 3.30 2.19 3.39 3.45 3.99 3.39 3.37 3.03 3.74
## [3193] 3.99 3.56 3.23 2.16 3.45 3.42 3.55 2.25 3.31 2.59 3.52 2.48 3.37 2.59
## [3207] 2.02 3.27 2.23 3.32 3.85 3.94 2.47 3.62 2.71 3.80 3.57 2.15 2.81 2.67
## [3221] 3.57 2.82 3.37 3.20 3.95 3.88 2.86 3.45 3.66 3.80 2.14 2.60 2.64 2.87
## [3235] 2.34 2.99 3.33 2.99 3.33 2.13 3.75 3.88 3.24 3.76 2.26 3.59 2.24 2.82
## [3249] 2.65 3.17 2.60 3.27 2.73 3.72 3.27 2.43 2.67 3.69 2.77 3.07 3.30 2.70
## [3263] 2.59 2.95 2.43 2.57 2.12 2.61 2.64 3.92 3.18 3.57 2.09 2.21 3.63 3.25
## [3277] 2.38 2.81 3.08 3.69 2.12 3.19 3.43 3.96 3.61 2.55 3.42 3.53 3.46 3.64
## [3291] 3.81 2.20 2.21 3.59 2.50 2.12 2.80 3.64 2.25 3.34 2.01 2.22 2.70 2.17
## [3305] 3.69 3.73 3.51 2.48 3.05 2.37 2.56 3.03 3.47 2.19 2.59 3.85 2.86 3.88
## [3319] 3.98 3.05 2.58 3.58 2.32 2.50 3.57 2.87 2.40 3.30 3.47 2.25 2.55 2.44
## [3333] 2.76 3.53 3.43 3.33 2.01 2.28 2.45 2.54 2.29 2.82 2.15 3.50 3.58 3.15
## [3347] 2.18 3.78 3.93 2.40 3.41 3.68 3.08 2.65 2.96 3.02 2.80 3.86 3.49 2.48
## [3361] 3.17 3.38 2.28 2.72 3.81 3.13 2.43 2.25 3.04 3.10 2.67 3.26 2.14 2.29
## [3375] 3.09 2.12 3.76 2.28 3.35 2.78 2.80 3.53 3.00 3.45 3.01 3.19 3.52 3.21
## [3389] 3.23 3.58 3.90 2.75 3.15 2.17 2.72 2.82 3.67 3.03 3.46 2.04 2.93 3.80
## [3403] 2.45 2.06 2.87 3.41 2.91 2.92 3.34 2.51 3.50 2.31 2.79 3.31 3.66 3.58
## [3417] 3.44 2.72 2.24 2.43 3.73 3.71 3.90 2.15 3.07 3.30 3.42 3.29 3.54 2.72
## [3431] 2.36 2.41 2.02 2.70 2.18 2.64 3.26 3.79 3.68 2.51 2.27 2.39 3.94 3.10
## [3445] 2.37 3.68 2.79 3.80 2.52 2.79 3.01 3.79 3.74 3.15 2.44 2.81 3.95 3.22
## [3459] 3.56 2.90 3.06 2.86 3.41 3.70 3.01 2.96 2.68 2.35 3.88 2.63 3.60 3.43
## [3473] 3.58 3.87 3.25 3.80 2.34 3.25 3.54 2.54 3.48 3.56 3.66 2.63 3.36 2.61
## [3487] 3.48 2.93 3.20 2.83 2.32 2.36 2.16 3.24 3.70 2.04 2.13 2.91 3.48 2.32
## [3501] 2.54 2.46 2.80 2.45 2.71 3.46 3.22 2.16 3.28 2.95 3.87 2.58 3.45 3.46
## [3515] 2.44 3.81 2.98 3.36 2.29 2.52 3.17 2.64 3.00 2.75 3.28 2.97 2.01 3.30
## [3529] 2.84 2.68 2.19 2.44 2.45 3.87 2.87 2.08 2.28 2.16 3.18 2.37 3.00 2.74
## [3543] 3.61 2.64 2.92 3.44 2.87 3.59 3.32 2.82 3.01 3.93 3.32 3.81 2.36 2.97
## [3557] 3.82 2.65 3.94 2.19 3.81 2.89 2.15 3.76 2.50 2.04 2.27 3.32 3.96 2.22
## [3571] 2.92 2.36 3.49 2.24 2.48 2.39 3.11 3.75 3.02 2.12 3.13 3.60 2.26 2.15
## [3585] 2.38 3.49 3.18 3.08 2.50 2.66 3.93 2.48 3.80 3.16 2.25 2.93 3.90 2.84
## [3599] 3.73 2.51 3.14 3.69 3.84 2.97 3.28 2.01 2.51 2.56 2.19 2.56 2.64 3.94
## [3613] 3.34 2.85 3.98 2.31 2.95 3.29 2.11 3.43 2.20 2.28 2.32 3.86 3.37 3.31
## [3627] 3.11 2.59 3.24 2.82 2.29 3.38 2.26 2.38 3.64 3.32 3.26 2.84 2.79 3.38
## [3641] 3.48 3.02 3.94 3.91 2.82 3.64 3.09 3.02 2.11 3.15 3.80 3.56 3.57 2.32
## [3655] 2.72 2.47 2.63 3.16 2.73 3.12 2.88 2.57 2.19 3.78 2.02 2.00 3.65 3.99
## [3669] 3.45 2.03 2.45 3.94 3.62 3.13 2.47 2.03 2.04 3.22 2.04 3.99 2.37 3.42
## [3683] 2.31 3.08 2.16 3.53 3.17 2.17 3.70 3.09 3.84 3.83 3.84 2.16 2.24 3.98
## [3697] 2.98 2.48 3.54 2.53 3.45 3.67 3.57 2.55 2.80 2.83 2.98 2.33 2.90 3.02
## [3711] 3.06 3.64 3.03 2.46 3.33 3.59 2.64 3.29 2.29 2.68 3.82 3.82 3.15 3.40
## [3725] 3.71 3.92 2.98 3.12 2.83 2.14 2.85 3.58 3.61 3.78 2.10 3.05 2.21 2.40
## [3739] 3.80 2.99 3.52 2.13 2.83 3.35 2.27 2.65 2.13 3.30 3.94 3.65 3.95 3.69
## [3753] 3.71 3.15 3.04 2.81 3.64 2.36 2.11 3.90 2.01 3.95 3.46 2.66 3.40 3.64
## [3767] 2.49 3.54 3.61 2.50 2.08 2.41 2.40 2.13 3.45 2.76 3.77 2.50 2.91 3.28
## [3781] 3.75 3.05 2.75 3.73 2.62 2.16 2.89 3.57 2.06 3.17 3.19 3.86 2.57 2.56
## [3795] 2.88 2.71 3.47 3.28 3.52 3.64 3.85 2.86 3.97 2.43 3.43 3.07 3.63 2.95
## [3809] 3.57 3.86 3.46 3.55 2.13 2.41 3.95 3.39 3.12 2.73 2.09 2.74 3.00 2.42
## [3823] 2.35 3.02 3.17 2.48 3.17 3.80 2.95 3.71 3.85 2.18 2.19 2.95 3.18 2.85
## [3837] 2.65 3.28 2.00 3.94 2.46 3.83 2.22 2.48 2.47 3.99 2.01 3.28 3.74 3.34
## [3851] 2.73 3.92 3.79 2.87 3.15 3.94 3.97 2.90 3.69 2.60 2.74 2.33 2.42 3.31
## [3865] 3.02 2.46 2.81 2.75 3.97 3.71 2.78 3.83 2.69 3.34 3.93 2.78 3.36 2.59
## [3879] 2.53 3.15 3.96 2.91 3.88 3.83 2.51 2.80 3.29 3.04 2.19 3.99 3.51 2.88
## [3893] 3.24 2.18 2.99 3.86 3.79 2.67 3.06 3.39 2.68 2.13 3.01 3.20 2.01 2.19
## [3907] 2.41 2.70 3.08 2.59 2.55 3.74 2.29 2.99 3.91 3.25 3.43 2.86 2.04 3.29
## [3921] 3.26 2.22 2.92 3.83 2.41 2.92 2.92 3.62 3.65 3.52 2.73 2.14 2.70 3.36
## [3935] 3.41 3.93 2.91 3.26 3.08 2.30 2.92 3.09 3.90 2.73 3.96 3.08 2.48 2.38
## [3949] 2.28 2.91 2.04 2.89 3.98 3.34 3.52 2.24 2.95 2.57 3.79 2.36 2.92 3.18
## [3963] 3.03 3.66 2.46 2.75 3.58 3.06 2.80 2.06 3.51 3.94 3.24 3.71 3.03 2.46
## [3977] 2.03 3.31 2.99 3.56 3.39 3.98 3.02 2.85 2.57 2.95 3.75 2.08 3.96 3.99
## [3991] 3.20 2.66 2.45 2.55 2.10 2.95 2.11 3.44 2.74 2.40 3.09 3.95 3.39 2.98
## [4005] 3.55 3.47 3.78 2.41 3.72 2.60 3.88 2.56 3.85 3.93 2.29 3.95 2.17 2.44
## [4019] 2.89 3.99 2.98 3.10 3.39 3.94 3.57 2.45 2.61 2.20 3.29 2.36 2.51 3.78
## [4033] 2.65 3.13 2.80 2.15 2.73 2.82 3.79 2.65 2.76 3.57 3.52 3.60 3.91 3.59
## [4047] 2.82 3.64 3.02 2.56 2.61 2.45 2.29 2.37 2.13 3.78 3.86 3.17 3.68 2.37
## [4061] 3.80 3.01 2.13 2.28 2.62 2.09 2.62 2.12 2.47 2.53 3.99 2.47 2.48 3.37
## [4075] 3.72 2.77 3.75 2.68 2.39 2.66 3.22 2.73 3.35 2.47 3.40 2.69 2.91 3.12
## [4089] 3.37 2.48 3.67 3.44 2.47 3.95 3.33 2.90 2.32 3.07 2.33 2.86 2.82 3.44
## [4103] 2.38 3.64 2.65 2.89 3.54 3.25 2.62 2.77 2.61 3.19 2.67 3.45 3.16 2.29
## [4117] 2.62 2.44 2.78 3.03 3.01 3.51 2.38 3.89 2.69 2.60 3.37 3.61 2.76 3.49
## [4131] 3.77 3.53 2.07 3.65 3.55 3.34 3.52 3.96 2.86 2.13 2.92 2.09 2.59 3.17
## [4145] 2.62 3.37 3.64 2.84 3.80 2.16 3.68 3.66 2.64 2.60 2.61 3.62 3.66 2.66
## [4159] 3.19 2.28 2.94 4.00 2.32 3.90 3.65 3.94 2.36 2.43 2.03 3.45 3.29 2.36
## [4173] 3.58 2.60 2.27 2.85 2.18 2.53 2.74 3.70 3.59 2.89 3.45 3.14 2.68 3.45
## [4187] 3.37 3.24 3.24 3.37 2.77 3.74 2.32 3.77 2.68 2.25 3.20 2.75 3.63 2.86
## [4201] 3.34 2.62 2.42 2.78 3.21 3.31 3.16 3.98 3.38 3.86 2.24 2.12 2.62 2.27
## [4215] 3.05 3.04 3.15 3.49 3.94 3.76 2.24 2.68 2.51 2.35 3.09 3.11 2.45 3.42
## [4229] 3.47 2.04 2.79 3.78 3.04 2.70 2.51 2.49 2.20 3.63 2.17 2.58 2.09 3.56
## [4243] 2.08 2.62 3.15 2.82 2.56 2.55 3.60 3.95 2.70 2.08 2.07 3.52 3.10 3.57
## [4257] 3.39 3.82 3.77 3.58 2.95 2.62 3.01 3.00 3.22 2.37 3.31 2.91 3.24 2.48
## [4271] 2.87 2.42 3.75 2.98 2.45 2.08 3.35 2.43 3.98 3.07 2.58 3.32 2.55 3.64
## [4285] 2.62 3.98 3.21 3.69 2.15 2.70 3.47 3.31 2.39 2.71 2.53 3.23 2.69 2.62
## [4299] 2.41 2.82 2.39 3.93 2.31 3.83 2.86 2.76 2.46 3.27 3.37 2.94 2.11 3.84
## [4313] 3.96 2.86 3.23 2.90 2.64 2.15 2.91 3.64 2.03 3.16 3.76 2.44 2.37 3.54
## [4327] 2.51 2.38 2.76 2.79 3.75 3.24 2.81 3.07 3.91 2.23 2.42 3.93 3.91 2.35
## [4341] 2.29 2.84 2.10 3.31 2.15 2.12 2.97 3.92 2.85 2.71 4.00 3.06 2.75 2.77
## [4355] 2.39 3.02 2.88 2.54 2.32 2.62 3.02 2.12 2.31 3.90 3.09 2.13 3.39 2.12
## [4369] 3.48 3.50 3.30 3.46 2.18 3.45 2.27 2.68 3.89 3.01 3.93 2.18 2.96 2.12
## [4383] 3.26 2.16 2.35 2.52 3.50 2.33 3.69 2.26 2.07 2.79 3.96 2.22 2.20 3.50
## [4397] 2.62 3.43 2.40 2.92 2.53 2.90 3.54 3.52 3.03 3.86 2.53 2.48 3.23 3.24
## [4411] 2.09 3.22 2.75 3.49 3.62 3.10 2.00 3.27 3.50 2.50 2.93 3.93 2.60 2.45
## [4425] 2.42 3.12 3.90 3.22 2.61 3.28 3.25 3.19 2.32 3.27 2.86 2.04 3.08 3.98
## [4439] 2.70 3.93 2.19 3.46 2.23 2.55 3.14 3.19 3.55 3.23 3.19 3.82 2.90 2.45
## [4453] 2.32 2.80 3.43 2.09 2.26 2.21 3.17 2.49 2.33 3.19 3.52 2.66 3.16 2.20
## [4467] 3.87 2.67 4.00 3.96 3.56 2.04 3.09 2.17 3.79 3.58 3.94 3.22 3.04 3.04
## [4481] 2.77 2.94 3.27 3.87 3.19 3.71 3.73 3.04 2.69 3.28 2.89 2.16 2.76 3.99
## [4495] 2.10 3.39 3.68 3.30 3.14 3.42 2.86 2.18 3.92 2.86 3.33 3.06 2.11 3.91
## [4509] 3.59 2.30 3.96 3.47 2.94 3.22 2.21 3.12 2.15 3.09 3.11 3.75 3.45 2.50
## [4523] 3.78 3.15 3.21 2.34 3.72 3.86 3.55 3.88 2.60 3.72 2.98 2.33 3.34 3.37
## [4537] 2.57 2.05 2.40 2.80 2.34 2.39 2.78 3.92 3.53 2.64 3.90 3.29 2.91 2.94
## [4551] 2.82 2.47 2.77 3.06 3.06 3.52 2.72 3.43 3.16 3.92 2.77 3.80 3.64 3.28
## [4565] 3.31 2.17 3.90 2.21 3.13 2.66 2.86 3.22 3.82 2.92 3.98 3.19 2.32 2.38
## [4579] 3.43 2.10 2.96 2.30 2.87 3.50 2.92 2.33 2.50 2.74 2.14 3.17 3.22 2.12
## [4593] 2.80 2.83 2.67 3.12 2.69 2.83 3.60 2.32 3.24 3.01 2.61 3.46 3.10 2.95
## [4607] 3.34 3.60 3.95 3.26 3.75 2.00 3.06 2.43 2.19 3.06 3.49 2.82 3.34 2.98
## [4621] 2.87 2.58 2.77 2.70 3.29 2.23 2.19 3.32 3.09 2.86 2.30 2.82 3.58 3.78
## [4635] 3.41 2.12 3.34 2.48 2.63 2.39 3.55 3.30 3.22 3.34 2.47 2.89 3.27 3.27
## [4649] 3.58 2.57 3.54 2.88 3.34 2.91 3.90 2.36 3.35 3.15 2.35 2.13 3.60 2.31
## [4663] 2.37 3.96 3.43 2.50 3.18 3.07 3.69 3.46 3.66 2.42 3.28 2.08 2.14 3.43
## [4677] 2.05 3.16 3.68 2.76 2.09 3.43 3.86 3.43 3.16 2.46 2.03 3.39 2.46 3.72
## [4691] 2.93 3.06 3.51 3.56 3.29 3.88 3.96 3.89 3.65 3.41 3.33 2.21 2.54 2.01
## [4705] 2.27 2.88 2.29 3.80 3.61 2.14 2.50 3.64 3.61 3.89 4.00 3.95 2.82 2.50
## [4719] 3.52 2.60 3.61 3.07 3.59 2.55 3.02 3.13 3.89 2.53 3.87 3.61 3.46 3.57
## [4733] 3.21 2.49 2.38 3.73 3.45 2.43 3.23 2.90 2.32 3.06 2.56 2.19 2.93 3.18
## [4747] 2.09 2.80 2.52 2.06 2.25 2.28 2.37 3.61 2.88 2.29 2.20 2.60 3.60 2.73
## [4761] 2.32 2.81 3.11 2.06 3.26 2.79 2.02 3.35 3.42 3.26 2.68 3.06 2.77 3.40
## [4775] 3.17 2.16 3.29 3.37 2.74 3.75 3.69 2.35 3.81 2.88 3.45 3.63 3.01 2.80
## [4789] 3.83 2.46 2.51 2.72 3.72 3.88 2.44 3.73 2.52 3.94 3.19
# Task reorder multiple rows in descending order
library(dplyr)
# reorder by University_GPA and Job_Offers in descending order
education_sorted <- education %>% arrange(desc(University_GPA), desc(Job_Offers))
# view the sorted dataset
head(education_sorted)
## # A tibble: 6 × 20
## Student_ID Age Gender High_School_GPA SAT_Score University_Ranking
## <chr> <dbl> <chr> <dbl> <dbl> <dbl>
## 1 S00114 20 Male 2.9 963 819
## 2 S03117 20 Male 3.85 1243 281
## 3 S01553 26 Female 2.11 1322 322
## 4 S02276 23 Male 3.8 1238 644
## 5 S04363 24 Female 3.34 1117 644
## 6 S04670 18 Female 2.48 1051 244
## # ℹ 14 more variables: University_GPA <dbl>, Field_of_Study <chr>,
## # Internships_Completed <dbl>, Projects_Completed <dbl>,
## # Certifications <dbl>, Soft_Skills_Score <dbl>, Networking_Score <dbl>,
## # Job_Offers <dbl>, Starting_Salary <dbl>, Career_Satisfaction <dbl>,
## # Years_to_Promotion <dbl>, Current_Job_Level <chr>, Work_Life_Balance <dbl>,
## # Entrepreneurship <chr>
# Task: rename some of the columns in your dataset
library(dplyr)
education_renamed <- education %>%
rename(
GPA = University_GPA,
Internships = Internships_Completed,
Offers = Job_Offers
)
# to view the updated column names
colnames(education_renamed)
## [1] "Student_ID" "Age" "Gender"
## [4] "High_School_GPA" "SAT_Score" "University_Ranking"
## [7] "GPA" "Field_of_Study" "Internships"
## [10] "Projects_Completed" "Certifications" "Soft_Skills_Score"
## [13] "Networking_Score" "Offers" "Starting_Salary"
## [16] "Career_Satisfaction" "Years_to_Promotion" "Current_Job_Level"
## [19] "Work_Life_Balance" "Entrepreneurship"
# Task add new variables in your data frame by using a mathematical function
# creates a new column new_variable with the values of column_name multiplied by 2
library (dplyr)
education_career_success <- education_clean %>% mutate(Double_Salary = Starting_Salary * 2)
# Task Create a training set using a random number generator engine.
# using set.seed()
library (dplyr)
# setting the seed to a specific number
set.seed (123)
# randomly sample 5 numbers, ensures that random numbers are generated by sample () are reproducible
random_numbers <- sample(1:10, 5)
print(random_numbers)
## [1] 3 10 2 8 6
# Task Print the summary statistics of your dataset.
summary(education)
## Student_ID Age Gender High_School_GPA
## Length:5000 Min. :18.00 Length:5000 Min. :2.000
## Class :character 1st Qu.:20.00 Class :character 1st Qu.:2.500
## Mode :character Median :23.00 Mode :character Median :2.990
## Mean :23.44 Mean :2.997
## 3rd Qu.:26.00 3rd Qu.:3.500
## Max. :29.00 Max. :4.000
## SAT_Score University_Ranking University_GPA Field_of_Study
## Min. : 900 Min. : 1.0 Min. :2.00 Length:5000
## 1st Qu.:1076 1st Qu.: 256.0 1st Qu.:2.52 Class :character
## Median :1257 Median : 501.5 Median :3.03 Mode :character
## Mean :1254 Mean : 504.3 Mean :3.02
## 3rd Qu.:1432 3rd Qu.: 759.0 3rd Qu.:3.51
## Max. :1600 Max. :1000.0 Max. :4.00
## Internships_Completed Projects_Completed Certifications Soft_Skills_Score
## Min. :0.000 Min. :0.000 Min. :0.000 Min. : 1.000
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.: 3.000
## Median :2.000 Median :5.000 Median :3.000 Median : 6.000
## Mean :1.982 Mean :4.563 Mean :2.512 Mean : 5.546
## 3rd Qu.:3.000 3rd Qu.:7.000 3rd Qu.:4.000 3rd Qu.: 8.000
## Max. :4.000 Max. :9.000 Max. :5.000 Max. :10.000
## Networking_Score Job_Offers Starting_Salary Career_Satisfaction
## Min. : 1.000 Min. :0.000 Min. : 25000 Min. : 1.000
## 1st Qu.: 3.000 1st Qu.:1.000 1st Qu.: 40200 1st Qu.: 3.000
## Median : 6.000 Median :2.000 Median : 50300 Median : 6.000
## Mean : 5.538 Mean :2.489 Mean : 50564 Mean : 5.578
## 3rd Qu.: 8.000 3rd Qu.:4.000 3rd Qu.: 60500 3rd Qu.: 8.000
## Max. :10.000 Max. :5.000 Max. :101000 Max. :10.000
## Years_to_Promotion Current_Job_Level Work_Life_Balance Entrepreneurship
## Min. :1.000 Length:5000 Min. : 1.000 Length:5000
## 1st Qu.:2.000 Class :character 1st Qu.: 3.000 Class :character
## Median :3.000 Mode :character Median : 6.000 Mode :character
## Mean :3.016 Mean : 5.482
## 3rd Qu.:4.000 3rd Qu.: 8.000
## Max. :5.000 Max. :10.000
# Use any of the numerical variables from the dataset and perform the following statistical functions
# Median
gpa <- c(3.5, 3.8, 4.0, 2.9, 3.6, 3.2, 3.9, 2.8)
median_gpa <- median(gpa)
print(median_gpa)
## [1] 3.55
# Use any of the numerical variables from the dataset and perform the following statistical functions
# Mode
data <- c(1, 2, 2, 3, 3, 4)
mode_data <- as.numeric(names(sort(table(data), decreasing = TRUE)))
mode_data <- mode_data[table(data) == max(table(data))]
print(mode_data)
## [1] 3 1
# Use any of the numerical variables from the dataset and perform the following statistical functions
# Range
gpa <- c(3.5, 3.8, 4.0, 2.9, 3.6)
range_gpa <- range(gpa)
print(range_gpa)
## [1] 2.9 4.0
# Task: Plot a scatter plot for any 2 variables in your dataset.
library (ggplot2)
ggplot(education_clean,aes(x=University_Ranking,y=Starting_Salary))+geom_point(size = 1,color = "purple",shape = 10,alpha = 0.3) +geom_point(size = 1,color = "orange",shape = 10,alpha = 0.3)
# Task Plot a bar plot for any 2 variables in your dataset
library (ggplot2)
ggplot(data = education_clean, aes(x = Projects_Completed)) +
geom_bar() +
labs(title = "Projects Completed Frequency",
x = "Projects Completed",
y = "Frequency")
# Task Find the correlation between any 2 variables by applying least square linear regression model.
library("knitr")
# Compute using Pearson Correlation Coefficient
Education_Career_Success_Coefficient <- cor(education_clean$Starting_Salary, education_clean$University_GPA, method = "pearson")
# Print Correlation Coefficient
kable(head(Education_Career_Success_Coefficient))