Set the working directory:

  1. Download “StudentSurvey.csv” to your computer.
  2. Set Working directory to the folder you saved your file in.
  3. read the file using read.csv command.
#If your assignment does not render, you might need to install.packages("htmltools")

Instructions:

Read the StudentSurvey into this markdown and answers the following questions

#read the StudentSurvey.csv in here

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.1     ✔ stringr   1.5.2
## ✔ ggplot2   4.0.0     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── 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
setwd("C:/Users/Mulut/Desktop/Classes/Data101")
student_survey <- read_csv("StudentSurvey.csv")
## Rows: 79 Columns: 17
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): Year, Sex, Smoke, Award, HigherSAT
## dbl (12): Exercise, TV, Height, Weight, Siblings, BirthOrder, VerbalSAT, Mat...
## 
## 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.

Check the data structure:

#check the head of the data set

head(student_survey)
## # A tibble: 6 × 17
##   Year      Sex   Smoke Award   HigherSAT Exercise    TV Height Weight Siblings
##   <chr>     <chr> <chr> <chr>   <chr>        <dbl> <dbl>  <dbl>  <dbl>    <dbl>
## 1 Senior    M     No    Olympic Math            10     1     71    180        4
## 2 Sophomore F     Yes   Academy Math             4     7     66    120        2
## 3 FirstYear M     No    Nobel   Math            14     5     72    208        2
## 4 Junior    M     No    Nobel   Math             3     1     63    110        1
## 5 Sophomore F     No    Nobel   Verbal           3     3     65    150        1
## 6 Sophomore F     No    Nobel   Verbal           5     4     65    114        2
## # ℹ 7 more variables: BirthOrder <dbl>, VerbalSAT <dbl>, MathSAT <dbl>,
## #   SAT <dbl>, GPA <dbl>, Pulse <dbl>, Piercings <dbl>
#check the dimensions

dim(student_survey)
## [1] 79 17
#create a table of students'sex and "HigherSAT"

table(student_survey$Sex, student_survey$HigherSAT)
##    
##     Math Verbal
##   F   25     15
##   M   24     15
# Display summary statistics for VerbalSAT

summary(student_survey$VerbalSAT)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   420.0   550.0   580.0   583.2   630.0   720.0
#Find the average GPA of students

mean(student_survey$GPA)
## [1] 3.169114
#Create a new dataframe, call it "column_df". This new dataframe should contain students' weight and number of hours the exercise 

column_df <- data.frame(student_survey$Weight,student_survey$Exercise)
column_df
##    student_survey.Weight student_survey.Exercise
## 1                    180                      10
## 2                    120                       4
## 3                    208                      14
## 4                    110                       3
## 5                    150                       3
## 6                    114                       5
## 7                    128                      10
## 8                    235                      13
## 9                    115                      12
## 10                   140                      12
## 11                   135                       6
## 12                   110                      10
## 13                    99                       3
## 14                   165                       7
## 15                   120                       2
## 16                   154                      14
## 17                   110                      10
## 18                   145                      14
## 19                   195                      20
## 20                   200                       7
## 21                   167                      12
## 22                   175                      10
## 23                   155                       6
## 24                   185                      14
## 25                   190                      12
## 26                   165                      10
## 27                   175                       8
## 28                   126                       0
## 29                   187                      10
## 30                   170                       6
## 31                   158                       5
## 32                   119                      24
## 33                   205                       2
## 34                   129                      10
## 35                   145                       6
## 36                   130                       5
## 37                   215                       5
## 38                   135                      12
## 39                   145                       2
## 40                    98                       7
## 41                   150                      15
## 42                   159                       5
## 43                   174                       7
## 44                   160                      15
## 45                   165                       8
## 46                   161                      14
## 47                   130                       4
## 48                   175                      15
## 49                   255                       4
## 50                   160                      15
## 51                   160                       3
## 52                    95                       3
## 53                   115                      15
## 54                   120                      20
## 55                   135                       3
## 56                   180                       6
## 57                   155                      12
## 58                   110                       4
## 59                   215                      20
## 60                   140                      10
## 61                   195                      10
## 62                   185                       4
## 63                   185                       9
## 64                   209                      12
## 65                   145                       2
## 66                   180                       2
## 67                   170                       5
## 68                   135                       5
## 69                   165                       6
## 70                   137                      10
## 71                   147                       4
## 72                   150                       5
## 73                   155                      17
## 74                   160                       7
## 75                   130                       2
## 76                   180                       8
## 77                   150                       1
## 78                   205                      14
## 79                   115                      12
#Access the fourth element in the first column from the StudentSurvey's dataset.

student_survey[4,1]
## # A tibble: 1 × 1
##   Year  
##   <chr> 
## 1 Junior