library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ 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
setwd("C:/Users/dylan/OneDrive/Desktop/Data 101")
student_sur <- read.csv("StudentSurvey.csv")
 head(student_sur)
##        Year Sex Smoke   Award HigherSAT Exercise TV Height Weight Siblings
## 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
##   BirthOrder VerbalSAT MathSAT  SAT  GPA Pulse Piercings
## 1          4       540     670 1210 3.13    54         0
## 2          2       520     630 1150 2.50    66         3
## 3          1       550     560 1110 2.55   130         0
## 4          1       490     630 1120 3.10    78         0
## 5          1       720     450 1170 2.70    40         6
## 6          2       600     550 1150 3.20    80         4
 dim(student_sur)
## [1] 79 17
SAT_table <- xtabs(~Sex + HigherSAT, data = student_sur)
 SAT_table
##    HigherSAT
## Sex Math Verbal
##   F   25     15
##   M   24     15
 summary(student_sur$VerbalSAT)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   420.0   550.0   580.0   583.2   630.0   720.0
 mean(student_sur$GPA)
## [1] 3.169114
column_df <- data.frame(weight = student_sur$Weight, Hours = student_sur$Exercise)
 column_df
##    weight Hours
## 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