Stat412 Recitation 2

Previously on STAT 412:

Descriptive Statistics
Visualization Techniques
- Bar-plot and Stacked-bar-plot
- Histogram and Density-plot
- Box-plot
- Scatter-plot
- Spine-plot
- Mosaic-plot
- Correlation-plot
Additional exercise for difference btw box-plot and histogram & density-plot
Requirements for plots
Color Customization Info

Introduction to the Dataset

Loading the data

This week, we will use the data “m111survey” from the package “tigerstats”. Firstly, we have to install the package, then call it via library().

library(tigerstats)
data("m111survey")

We may prefer to work with data frames, therefore we have to check class of the data.

class(m111survey)

## [1] "data.frame"

dim(m111survey)

## [1] 71 12

It is a data-frame with 71 observations and 12 attributes. Now, let’s examine the class of each attribute. We have 5 numerical values, 1 integer and 6 factors with 2 or 3 levels.

str(m111survey)

## 'data.frame':    71 obs. of  12 variables:
##  $ height         : num  76 74 64 62 72 70.8 70 79 59 67 ...
##  $ ideal_ht       : num  78 76 NA 65 72 NA 72 76 61 67 ...
##  $ sleep          : num  9.5 7 9 7 8 10 4 6 7 7 ...
##  $ fastest        : int  119 110 85 100 95 100 85 160 90 90 ...
##  $ weight_feel    : Factor w/ 3 levels "1_underweight",..: 1 2 2 1 1 3 2 2 2 3 ...
##  $ love_first     : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
##  $ extra_life     : Factor w/ 2 levels "no","yes": 2 2 1 1 2 1 2 2 2 1 ...
##  $ seat           : Factor w/ 3 levels "1_front","2_middle",..: 1 2 2 1 3 1 1 3 3 2 ...
##  $ GPA            : num  3.56 2.5 3.8 3.5 3.2 3.1 3.68 2.7 2.8 NA ...
##  $ enough_Sleep   : Factor w/ 2 levels "no","yes": 1 1 1 1 1 2 1 2 1 2 ...
##  $ sex            : Factor w/ 2 levels "female","male": 2 2 1 1 2 2 2 2 1 1 ...
##  $ diff.ideal.act.: num  2 2 NA 3 0 NA 2 -3 2 0 ...

head(m111survey,10)

height	ideal_ht	sleep	fastest	weight_feel	love_first	extra_life	seat	GPA	enough_Sleep	sex	diff.ideal.act.
76.0	78	9.5	119	1_underweight	no	yes	1_front	3.56	no	male	2
74.0	76	7.0	110	2_about_right	no	yes	2_middle	2.50	no	male	2
64.0	NA	9.0	85	2_about_right	no	no	2_middle	3.80	no	female	NA
62.0	65	7.0	100	1_underweight	no	no	1_front	3.50	no	female	3
72.0	72	8.0	95	1_underweight	no	yes	3_back	3.20	no	male	0
70.8	NA	10.0	100	3_overweight	no	no	1_front	3.10	yes	male	NA
70.0	72	4.0	85	2_about_right	no	yes	1_front	3.68	no	male	2
79.0	76	6.0	160	2_about_right	no	yes	3_back	2.70	yes	male	-3
59.0	61	7.0	90	2_about_right	no	yes	3_back	2.80	no	female	2
67.0	67	7.0	90	3_overweight	no	no	2_middle	NA	yes	female	0

sum(is.na(m111survey))

## [1] 5

m111survey=na.omit(m111survey)
sum(is.na(m111survey))

## [1] 0

dim(m111survey)

## [1] 68 12

However, the data includes 5 missing observations. To have an effective visualization for this recitation, we prefer to delete (with the purpose of exemplifying) the rows including missing values. Hence, we have 68 rows with fully observed data.

Data dictionary are given below:

Data Info: Results of a survey of MAT 111 students at Georgetown College.

height: How tall are you, in inches?
ideal_ht: A numeric vector How tall would you LIKE to be, in inches?
sleep: How much sleep did you get last night?
fastest: What is the highest speed at which you have ever driven a car?
weight_feel: How do you feel about your weight?
love_first: Do you believe in love at first sight?
extra_life: Do you believe in extraterrestrial life?
seat: When you have a choice, where do you prefer to sit in a classroom?
GPA: What is your college GPA?
enough_Sleep: Do you think you get enough sleep?
sex: What sex are you?
diff.ideal.act.: Your ideal height minus your actual height.

Descriptive Stats

Before the visualization part, it is worthwhile to examine the descriptive stats.

summary(m111survey)

##      height         ideal_ht         sleep          fastest     
##  Min.   :51.00   Min.   :54.00   Min.   :2.000   Min.   : 60.0  
##  1st Qu.:65.00   1st Qu.:67.00   1st Qu.:5.000   1st Qu.: 92.5  
##  Median :68.00   Median :68.50   Median :6.500   Median :104.5  
##  Mean   :68.02   Mean   :69.99   Mean   :6.294   Mean   :106.5  
##  3rd Qu.:72.00   3rd Qu.:75.00   3rd Qu.:7.000   3rd Qu.:120.0  
##  Max.   :79.00   Max.   :90.00   Max.   :9.500   Max.   :190.0  
##         weight_feel love_first extra_life       seat         GPA       
##  1_underweight: 9   no :42     no :38     1_front :26   Min.   :1.900  
##  2_about_right:24   yes:26     yes:30     2_middle:30   1st Qu.:2.875  
##  3_overweight :35                         3_back  :12   Median :3.225  
##                                                         Mean   :3.188  
##                                                         3rd Qu.:3.553  
##                                                         Max.   :4.000  
##  enough_Sleep     sex     diff.ideal.act. 
##  no :45       female:38   Min.   :-4.000  
##  yes:23       male  :30   1st Qu.: 0.000  
##                           Median : 2.000  
##                           Mean   : 1.974  
##                           3rd Qu.: 3.000  
##                           Max.   :18.000

Let’s continue with the “Lattice Graphs”!

Visualization Techniques

Lattice Plots

We have mostly covered the plots to represent univariate and bivariate data. What about multivariate data? Lattice plots are commonly used to show the relation between multiple variables. They provide a variety of functions for creating different types of lattice plots such as scatter plots, density plots, and bivariate plots.

For lattice plots, you have to install the package lattice and then call it.

Histogram and Density-plot

RQ: Is there a significant difference in height distributions among students based on gender?

library(lattice)
library(mosaic)
favstats(~height,data=m111survey)  # to decide the number of breaks; if you use "height|sex" you can obtain the stats for height for each gender

	min	Q1	median	Q3	max	mean	sd	n	missing
	51	65	68	72	79	68.01838	5.379327	68	0

histogram(~height|factor(sex),data=m111survey,type="count",xlab="Height of Students in inches", ylab="Frequency",main="Histogram of Height",breaks=c(50,55,60,65,70,75,80)) #type=percent,count,density(default) #breaks=seq(from=50,to=80,by=5)

Highest frequency belongs to the range between 60-65 for females and 70-75 for males. Observations for male are mainly gathered around 65-75 but, females have wider spread.

Info: 65 inches = 165cm 72inches = 183cm

The same graphs but different looks:

histogram(~height|factor(sex),data=m111survey,type="count",xlab="Height of Students in inches", ylab="Frequency",main="Histogram of Height",breaks=c(50,55,60,65,70,75,80),layout=c(1,2)) #c(1,2): 1 column, 2 rows

RQ1: How does the distribution of students’ GPA vary when considering both gender and love at first sight?

RQ2: Are there noticeable patterns or differences in GPA frequencies when accounting for both gender and love at first sight?

histogram(~GPA|factor(sex)*love_first,data=m111survey,type="count",xlab="GPA of Students", ylab="Frequency",main="Histogram of GPA")

Those who don’t believe in love at first sight show varying GPAs for both males and females. On the other side, female students who believe in love at first sight are mostly successful, with a GPA higher than 2.5. Male students who believe in love at first sight are not many and scattered.

Density-plot

RQ1: How does the density of students’ heights vary based on their seating preferences, and is there a noticeable difference when considering gender?

RQ2: Are there specific patterns in height distribution within different seating arrangements, and do these patterns differ between male and female students?

densityplot(~ height|seat , data=m111survey, groups = sex, col=c("yellowgreen","darkblue"),par.settings = list(superpose.line = list(col=c("yellowgreen", "darkblue"))), plot.points = FALSE, auto.key = list(col=c("yellowgreen","darkblue")),lwd=2, main="Density-plot of Height by seat preferences and gender") #plot.points=FALSE: to prevent adding points at the

Boys and girls sitting in the front and middle seem to have similar heights. But, if boys sit at the back, they’re mostly tall.

Box-plot

RQ1: How does the actual sleep duration relate to both the feeling of weight and the perceived amount of sleep, as reported by students (enough_sleep)?

RQ2: Are there any noticeable trends or differences in sleep duration based on individuals’ perception of their weight and their actual sleep habits?

bwplot(sleep ~ factor(enough_Sleep) | weight_feel, data=m111survey)

table(m111survey$weight_feel,m111survey$enough_Sleep)

##                
##                 no yes
##   1_underweight  9   0
##   2_about_right 15   9
##   3_overweight  21  14

Scatter-plot

RQ: How does the relationship between actual height and ideal height vary between male and female students?

xyplot(height ~ ideal_ht | sex  , data=m111survey , pch=20 , cex=1.5 )

#xyplot(height ~ ideal_ht | sex  , data=m111survey , pch=20 , cex=1.5 )[1,] you would see the plot left-hand side closer

RQ: How does the relationship between actual height and ideal height vary across different GPA levels, taking into account seating preferences?

GPA_levels=equal.count(m111survey$GPA, number=3, overlap=0.5) 
GPA_levels

## 
## Data:
##  [1] 3.560 2.500 3.500 3.200 3.680 2.700 2.800 2.100 2.500 3.890 3.200 3.200
## [13] 2.200 3.500 3.550 3.750 3.500 3.400 2.770 3.000 3.167 3.200 3.413 3.700
## [25] 3.500 3.750 2.800 2.200 3.600 2.800 3.100 3.000 3.500 3.900 3.787 3.200
## [37] 3.700 2.000 3.100 3.500 3.700 2.550 3.730 3.500 3.200 3.100 3.900 3.250
## [49] 3.200 3.294 2.000 3.000 3.700 3.300 3.200 3.600 3.300 4.000 3.400 2.700
## [61] 2.900 1.900 2.800 3.500 3.300 3.700 2.914 2.700
## 
## Intervals:
##     min   max count
## 1 1.897 3.203    34
## 2 2.897 3.553    34
## 3 3.247 4.003    34
## 
## Overlap between adjacent intervals:
## [1] 17 17

xyplot(height ~ ideal_ht | GPA_levels*seat  , data=m111survey , pch=20 , cex=1.5)

ggplot Graphics

ggplot() allows you to make complex plots with just a few lines of code because it’s based on a rich underlying theory, the grammar of graphics.

Firstly, we have to call ggplot2 package.

Scatter-plot

RQ: Is there a linear trend in the relationship between students’ actual height and their perceived ideal height

library(ggplot2)
ggplot(m111survey, aes(x = height, y = ideal_ht)) + 
  geom_point()+
  geom_smooth(method="lm")

Bubble-plot

RQ: How is the relationship between actual height and ideal height, and is there any observable pattern when considering the sleep duration and seating preferences?

ggplot(m111survey, aes(x = height, y = ideal_ht,size=sleep,col=seat)) + 
  geom_point()

Scatter-plot

RQ: How does the fastest driving time relate to students’ GPA? Is there any discernible pattern or correlation between the time spent on driving and academic performance among the surveyed students?

ggplot(m111survey, aes(fastest, GPA)) + 
  geom_point() + 
  facet_wrap(~seat)

Box-plot

RQ: How does the distribution of fastest driving times vary among students with different levels of love_first?

ggplot(m111survey, aes(love_first,fastest)) + geom_boxplot()

Violin-plot

RQ: Is there a noticeable variation in students’ GPA based on their seating preferences?

ggplot(m111survey, aes(x=seat, y=GPA, fill=seat)) + geom_violin(trim=FALSE)  + geom_boxplot(width=0.2,fill="beige")

Bar-plot

RQ: How do the self-perceived weight feelings vary among the surveyed students, as indicated by the bar plot? Are there any notable differences in the frequencies of feeling underweight, about right, and overweight?

f=table(m111survey$weight_feel)
f_data=data.frame(f) #automatically write their frequencies regarding the group name
ggplot(f_data,aes(x=reorder(Var1,Freq),y=Freq,fill=Var1))+geom_bar(stat="identity")+
  labs(title="Bar Plot of Weight_feel",y="Frequencies",x="Levels")+geom_text(aes(label=c("underweight","about right", "overweight")),vjust=-0.25,fontface="bold")

Lollipop chart

f_seat=data.frame(table(m111survey$seat))
ggplot(f_seat,aes(x=Var1,y=Freq))+geom_point(size=5, color="darkblue", fill="greenyellow", alpha=0.8, shape=21, stroke=1.5) + geom_segment(aes(x=Var1, xend=Var1, y=0, yend=Freq))

Data Manipulation

“tidyverse” is a package that includes several sub-packages (e.g., dplyr, ggplot2, etc.) in it. These packages are really good at working with data. They make it easy to organize and analyze information effectively, helping users get valuable insights from their data.

library(tidyverse)

Quick reference guide:

mutate(): adds new columns or modifies current variables in the dataset
summarize(): collapses all rows and returns a one-row summary
group_by(): takes existing data and groups specific variables together for future operations
filter(): only retain specific rows of data that meet the specified requirement(s)
select(): select only the columns (variables) that you want to see
arrange(): allows you arrange values within a variable in ascending or descending order
Display the data exclusively for female students.

head(filter(m111survey,sex=="female"))

height	ideal_ht	sleep	fastest	weight_feel	love_first	extra_life	seat	GPA	enough_Sleep	sex	diff.ideal.act.
62	65	7	100	1_underweight	no	no	1_front	3.50	no	female	3
59	61	7	90	2_about_right	no	yes	3_back	2.80	no	female	2
65	69	6	100	2_about_right	no	no	1_front	2.10	yes	female	4
62	62	7	60	3_overweight	no	no	1_front	2.50	yes	female	0
59	62	5	80	2_about_right	yes	yes	1_front	3.89	no	female	3
78	75	7	80	3_overweight	no	no	2_middle	3.20	yes	female	-3

Display the data exclusively for female students who believe in love at the first sight.

head(filter(m111survey,sex=="female" & love_first=="yes"))

height	ideal_ht	sleep	fastest	weight_feel	love_first	extra_life	seat	GPA	enough_Sleep	sex	diff.ideal.act.
59	62	5	80	2_about_right	yes	yes	1_front	3.890	no	female	3
65	70	8	120	3_overweight	yes	yes	1_front	3.750	yes	female	5
65	68	7	125	3_overweight	yes	no	3_back	3.500	no	female	3
66	66	7	120	3_overweight	yes	no	1_front	3.167	yes	female	0
68	68	4	90	3_overweight	yes	no	2_middle	3.200	no	female	0
54	54	4	130	3_overweight	yes	yes	1_front	3.413	no	female	0

Display the data for GPA in descending order.

head(arrange(m111survey, desc(GPA))) #for increased order, ignore desc()

height	ideal_ht	sleep	fastest	weight_feel	love_first	extra_life	seat	GPA	enough_Sleep	sex	diff.ideal.act.
63	68	7.5	75	3_overweight	no	no	1_front	4.000	yes	female	5
74	76	5.0	115	3_overweight	yes	no	1_front	3.900	no	male	2
65	67	6.5	90	2_about_right	no	yes	2_middle	3.900	yes	female	2
59	62	5.0	80	2_about_right	yes	yes	1_front	3.890	no	female	3
63	67	7.5	105	3_overweight	yes	no	1_front	3.787	yes	female	4
65	70	8.0	120	3_overweight	yes	yes	1_front	3.750	yes	female	5

Display only “height” and “ideal height” variables

head(select(m111survey,height,ideal_ht))

	height	ideal_ht
1	76	78
2	74	76
4	62	65
5	72	72
7	70	72
8	79	76

Display the data, including the new variable ‘ratio,’ which represents the ratio of height to ideal height.

head(mutate(m111survey, ratio = height/ideal_ht))

	height	ideal_ht	sleep	fastest	weight_feel	love_first	extra_life	seat	GPA	enough_Sleep	sex	diff.ideal.act.	ratio
1	76	78	9.5	119	1_underweight	no	yes	1_front	3.56	no	male	2	0.9743590
2	74	76	7.0	110	2_about_right	no	yes	2_middle	2.50	no	male	2	0.9736842
4	62	65	7.0	100	1_underweight	no	no	1_front	3.50	no	female	3	0.9538462
5	72	72	8.0	95	1_underweight	no	yes	3_back	3.20	no	male	0	1.0000000
7	70	72	4.0	85	2_about_right	no	yes	1_front	3.68	no	male	2	0.9722222
8	79	76	6.0	160	2_about_right	no	yes	3_back	2.70	yes	male	-3	1.0394737

Display the mean and median values of GPA.

summarise(m111survey,avg_gpa=mean(GPA,na.rm=T),med_gpa=median(GPA,na.rm=T))

avg_gpa	med_gpa
3.187574	3.225

Display the average height for each seat level.

summarise(group_by(m111survey,seat),avg_height_byseat=mean(height,na.rm=T))

seat	avg_height_byseat
1_front	65.22115
2_middle	69.73333
3_back	69.79167

Using %>% operator:

The operator helps you link different actions together. This makes your code shorter and easier to understand. It’s handy when you’re doing many things to data or working with different sets of data.

Display the mean, standard deviation and median for GPA.

m111survey %>% 
  mutate(m = mean(GPA),    
         sd = sd(GPA),      
         med = median(GPA))

	height	ideal_ht	sleep	fastest	weight_feel	love_first	extra_life	seat	GPA	enough_Sleep	sex	diff.ideal.act.	m	sd	med
1	76.00	78.0	9.5	119	1_underweight	no	yes	1_front	3.560	no	male	2.00	3.187574	0.5086646	3.225
2	74.00	76.0	7.0	110	2_about_right	no	yes	2_middle	2.500	no	male	2.00	3.187574	0.5086646	3.225
4	62.00	65.0	7.0	100	1_underweight	no	no	1_front	3.500	no	female	3.00	3.187574	0.5086646	3.225
5	72.00	72.0	8.0	95	1_underweight	no	yes	3_back	3.200	no	male	0.00	3.187574	0.5086646	3.225
7	70.00	72.0	4.0	85	2_about_right	no	yes	1_front	3.680	no	male	2.00	3.187574	0.5086646	3.225
8	79.00	76.0	6.0	160	2_about_right	no	yes	3_back	2.700	yes	male	-3.00	3.187574	0.5086646	3.225
9	59.00	61.0	7.0	90	2_about_right	no	yes	3_back	2.800	no	female	2.00	3.187574	0.5086646	3.225
11	65.00	69.0	6.0	100	2_about_right	no	no	1_front	2.100	yes	female	4.00	3.187574	0.5086646	3.225
12	62.00	62.0	7.0	60	3_overweight	no	no	1_front	2.500	yes	female	0.00	3.187574	0.5086646	3.225
13	59.00	62.0	5.0	80	2_about_right	yes	yes	1_front	3.890	no	female	3.00	3.187574	0.5086646	3.225
14	78.00	75.0	7.0	80	3_overweight	no	no	2_middle	3.200	yes	female	-3.00	3.187574	0.5086646	3.225
15	69.00	72.0	7.0	125	1_underweight	no	no	2_middle	3.200	no	male	3.00	3.187574	0.5086646	3.225
16	68.00	68.0	4.5	100	1_underweight	yes	yes	2_middle	2.200	no	male	0.00	3.187574	0.5086646	3.225
17	73.00	77.0	6.0	110	2_about_right	yes	yes	3_back	3.500	no	male	4.00	3.187574	0.5086646	3.225
18	73.00	75.0	8.0	120	2_about_right	no	yes	2_middle	3.550	yes	male	2.00	3.187574	0.5086646	3.225
19	65.00	70.0	8.0	120	3_overweight	yes	yes	1_front	3.750	yes	female	5.00	3.187574	0.5086646	3.225
20	65.00	68.0	7.0	125	3_overweight	yes	no	3_back	3.500	no	female	3.00	3.187574	0.5086646	3.225
21	66.00	68.0	7.0	75	3_overweight	no	no	1_front	3.400	yes	female	2.00	3.187574	0.5086646	3.225
22	67.75	70.0	7.0	90	3_overweight	no	no	1_front	2.770	no	male	2.25	3.187574	0.5086646	3.225
23	63.00	67.0	8.5	90	3_overweight	no	yes	1_front	3.000	yes	female	4.00	3.187574	0.5086646	3.225
24	66.00	66.0	7.0	120	3_overweight	yes	no	1_front	3.167	yes	female	0.00	3.187574	0.5086646	3.225
25	68.00	68.0	4.0	90	3_overweight	yes	no	2_middle	3.200	no	female	0.00	3.187574	0.5086646	3.225
26	54.00	54.0	4.0	130	3_overweight	yes	yes	1_front	3.413	no	female	0.00	3.187574	0.5086646	3.225
27	74.00	75.0	5.0	119	2_about_right	yes	yes	1_front	3.700	no	male	1.00	3.187574	0.5086646	3.225
28	68.00	66.0	4.5	112	2_about_right	yes	no	1_front	3.500	no	female	-2.00	3.187574	0.5086646	3.225
29	68.00	68.0	6.0	93	2_about_right	yes	yes	1_front	3.750	no	female	0.00	3.187574	0.5086646	3.225
30	69.00	67.0	6.0	145	3_overweight	no	no	3_back	2.800	no	female	-2.00	3.187574	0.5086646	3.225
31	72.00	90.0	9.0	125	3_overweight	no	yes	3_back	2.200	no	male	18.00	3.187574	0.5086646	3.225
32	70.50	73.0	7.0	190	2_about_right	no	yes	3_back	3.600	no	male	2.50	3.187574	0.5086646	3.225
33	70.00	75.0	7.5	90	2_about_right	yes	yes	2_middle	2.800	yes	male	5.00	3.187574	0.5086646	3.225
34	75.00	78.0	7.0	143	3_overweight	yes	no	2_middle	3.100	yes	male	3.00	3.187574	0.5086646	3.225
35	72.00	75.0	7.0	120	2_about_right	yes	no	2_middle	3.000	no	male	3.00	3.187574	0.5086646	3.225
36	62.00	62.0	4.5	95	3_overweight	no	no	2_middle	3.500	no	female	0.00	3.187574	0.5086646	3.225
37	74.00	76.0	5.0	115	3_overweight	yes	no	1_front	3.900	no	male	2.00	3.187574	0.5086646	3.225
38	63.00	67.0	7.5	105	3_overweight	yes	no	1_front	3.787	yes	female	4.00	3.187574	0.5086646	3.225
39	69.00	66.0	6.0	100	3_overweight	no	no	2_middle	3.200	no	female	-3.00	3.187574	0.5086646	3.225
40	60.00	66.0	5.0	95	3_overweight	yes	no	2_middle	3.700	no	female	6.00	3.187574	0.5086646	3.225
41	68.00	69.5	9.0	95	3_overweight	no	no	2_middle	2.000	yes	female	1.50	3.187574	0.5086646	3.225
42	73.00	76.0	8.0	110	3_overweight	no	yes	2_middle	3.100	yes	male	3.00	3.187574	0.5086646	3.225
43	66.00	68.0	9.0	91	3_overweight	yes	no	1_front	3.500	no	female	2.00	3.187574	0.5086646	3.225
44	70.00	67.0	5.5	85	3_overweight	yes	no	2_middle	3.700	no	female	-3.00	3.187574	0.5086646	3.225
45	51.00	54.0	7.0	130	2_about_right	no	no	1_front	2.550	no	female	3.00	3.187574	0.5086646	3.225
46	67.00	68.0	7.0	104	3_overweight	yes	no	2_middle	3.730	no	female	1.00	3.187574	0.5086646	3.225
47	69.00	70.0	5.0	95	3_overweight	no	no	2_middle	3.500	no	female	1.00	3.187574	0.5086646	3.225
48	71.00	67.0	3.0	105	3_overweight	no	no	2_middle	3.200	no	female	-4.00	3.187574	0.5086646	3.225
49	74.00	74.0	5.0	90	3_overweight	no	no	2_middle	3.100	no	male	0.00	3.187574	0.5086646	3.225
50	65.00	67.0	6.5	90	2_about_right	no	yes	2_middle	3.900	yes	female	2.00	3.187574	0.5086646	3.225
51	63.50	65.0	6.5	105	2_about_right	no	no	2_middle	3.250	no	female	1.50	3.187574	0.5086646	3.225
52	66.00	68.0	4.5	95	3_overweight	yes	yes	2_middle	3.200	no	female	2.00	3.187574	0.5086646	3.225
53	69.00	65.0	8.0	110	3_overweight	yes	yes	3_back	3.294	no	female	-4.00	3.187574	0.5086646	3.225
54	75.00	77.0	7.0	105	3_overweight	no	no	2_middle	2.000	yes	male	2.00	3.187574	0.5086646	3.225
55	65.00	75.0	6.0	130	2_about_right	no	yes	1_front	3.000	no	male	10.00	3.187574	0.5086646	3.225
56	74.00	76.0	7.0	95	2_about_right	no	yes	3_back	3.700	yes	male	2.00	3.187574	0.5086646	3.225
57	64.00	66.0	6.0	95	3_overweight	yes	no	1_front	3.300	no	female	2.00	3.187574	0.5086646	3.225
58	76.00	77.0	7.0	100	2_about_right	no	yes	2_middle	3.200	yes	male	1.00	3.187574	0.5086646	3.225
59	64.00	68.0	6.0	110	3_overweight	no	yes	2_middle	3.600	yes	female	4.00	3.187574	0.5086646	3.225
60	71.50	74.0	6.0	108	2_about_right	no	no	2_middle	3.300	no	male	2.50	3.187574	0.5086646	3.225
61	63.00	68.0	7.5	75	3_overweight	no	no	1_front	4.000	yes	female	5.00	3.187574	0.5086646	3.225
62	64.00	68.0	7.5	102	3_overweight	no	no	1_front	3.400	no	female	4.00	3.187574	0.5086646	3.225
63	68.00	72.0	6.5	105	3_overweight	no	yes	2_middle	2.700	yes	male	4.00	3.187574	0.5086646	3.225
64	70.00	72.0	4.0	98	1_underweight	yes	yes	3_back	2.900	no	male	2.00	3.187574	0.5086646	3.225
65	68.00	72.0	4.0	135	1_underweight	no	yes	2_middle	1.900	no	male	4.00	3.187574	0.5086646	3.225
66	75.00	75.0	6.0	130	2_about_right	no	yes	2_middle	2.800	yes	male	0.00	3.187574	0.5086646	3.225
67	69.00	67.0	2.0	85	3_overweight	yes	no	1_front	3.500	no	female	-2.00	3.187574	0.5086646	3.225
68	70.00	72.0	5.0	85	1_underweight	no	no	2_middle	3.300	no	male	2.00	3.187574	0.5086646	3.225
69	61.00	68.0	5.0	130	2_about_right	no	no	1_front	3.700	no	female	7.00	3.187574	0.5086646	3.225
70	65.00	66.0	8.0	120	2_about_right	yes	no	3_back	2.914	yes	female	1.00	3.187574	0.5086646	3.225
71	70.00	73.0	5.0	110	1_underweight	no	no	1_front	2.700	no	male	3.00	3.187574	0.5086646	3.225

Display the data of “sleep”, “GPA” and “sex” specifically for students who believe in love at first sight and extra life.

m111survey %>% filter(love_first == "yes", extra_life=="yes") %>% select(sleep, GPA, sex)

sleep	GPA	sex
5.0	3.890	female
4.5	2.200	male
6.0	3.500	male
8.0	3.750	female
4.0	3.413	female
5.0	3.700	male
6.0	3.750	female
7.5	2.800	male
4.5	3.200	female
8.0	3.294	female
4.0	2.900	male

Display the frequency distribution of students based on their thoughts about weight.

m111survey %>% group_by(weight_feel) %>% tally()

weight_feel	n
1_underweight	9
2_about_right	24
3_overweight	35

Display the dataset that includes a variable named “gender”, where individuals identified as female are labeled as ‘girl’ and those identified as male are labeled as ‘boy,’ based on the variable ‘sex’.

m111survey %>% 
  mutate(gender = recode(sex,
                          "female" = "girl",
                          "male" = "boy"))

	height	ideal_ht	sleep	fastest	weight_feel	love_first	extra_life	seat	GPA	enough_Sleep	sex	diff.ideal.act.	gender
1	76.00	78.0	9.5	119	1_underweight	no	yes	1_front	3.560	no	male	2.00	boy
2	74.00	76.0	7.0	110	2_about_right	no	yes	2_middle	2.500	no	male	2.00	boy
4	62.00	65.0	7.0	100	1_underweight	no	no	1_front	3.500	no	female	3.00	girl
5	72.00	72.0	8.0	95	1_underweight	no	yes	3_back	3.200	no	male	0.00	boy
7	70.00	72.0	4.0	85	2_about_right	no	yes	1_front	3.680	no	male	2.00	boy
8	79.00	76.0	6.0	160	2_about_right	no	yes	3_back	2.700	yes	male	-3.00	boy
9	59.00	61.0	7.0	90	2_about_right	no	yes	3_back	2.800	no	female	2.00	girl
11	65.00	69.0	6.0	100	2_about_right	no	no	1_front	2.100	yes	female	4.00	girl
12	62.00	62.0	7.0	60	3_overweight	no	no	1_front	2.500	yes	female	0.00	girl
13	59.00	62.0	5.0	80	2_about_right	yes	yes	1_front	3.890	no	female	3.00	girl
14	78.00	75.0	7.0	80	3_overweight	no	no	2_middle	3.200	yes	female	-3.00	girl
15	69.00	72.0	7.0	125	1_underweight	no	no	2_middle	3.200	no	male	3.00	boy
16	68.00	68.0	4.5	100	1_underweight	yes	yes	2_middle	2.200	no	male	0.00	boy
17	73.00	77.0	6.0	110	2_about_right	yes	yes	3_back	3.500	no	male	4.00	boy
18	73.00	75.0	8.0	120	2_about_right	no	yes	2_middle	3.550	yes	male	2.00	boy
19	65.00	70.0	8.0	120	3_overweight	yes	yes	1_front	3.750	yes	female	5.00	girl
20	65.00	68.0	7.0	125	3_overweight	yes	no	3_back	3.500	no	female	3.00	girl
21	66.00	68.0	7.0	75	3_overweight	no	no	1_front	3.400	yes	female	2.00	girl
22	67.75	70.0	7.0	90	3_overweight	no	no	1_front	2.770	no	male	2.25	boy
23	63.00	67.0	8.5	90	3_overweight	no	yes	1_front	3.000	yes	female	4.00	girl
24	66.00	66.0	7.0	120	3_overweight	yes	no	1_front	3.167	yes	female	0.00	girl
25	68.00	68.0	4.0	90	3_overweight	yes	no	2_middle	3.200	no	female	0.00	girl
26	54.00	54.0	4.0	130	3_overweight	yes	yes	1_front	3.413	no	female	0.00	girl
27	74.00	75.0	5.0	119	2_about_right	yes	yes	1_front	3.700	no	male	1.00	boy
28	68.00	66.0	4.5	112	2_about_right	yes	no	1_front	3.500	no	female	-2.00	girl
29	68.00	68.0	6.0	93	2_about_right	yes	yes	1_front	3.750	no	female	0.00	girl
30	69.00	67.0	6.0	145	3_overweight	no	no	3_back	2.800	no	female	-2.00	girl
31	72.00	90.0	9.0	125	3_overweight	no	yes	3_back	2.200	no	male	18.00	boy
32	70.50	73.0	7.0	190	2_about_right	no	yes	3_back	3.600	no	male	2.50	boy
33	70.00	75.0	7.5	90	2_about_right	yes	yes	2_middle	2.800	yes	male	5.00	boy
34	75.00	78.0	7.0	143	3_overweight	yes	no	2_middle	3.100	yes	male	3.00	boy
35	72.00	75.0	7.0	120	2_about_right	yes	no	2_middle	3.000	no	male	3.00	boy
36	62.00	62.0	4.5	95	3_overweight	no	no	2_middle	3.500	no	female	0.00	girl
37	74.00	76.0	5.0	115	3_overweight	yes	no	1_front	3.900	no	male	2.00	boy
38	63.00	67.0	7.5	105	3_overweight	yes	no	1_front	3.787	yes	female	4.00	girl
39	69.00	66.0	6.0	100	3_overweight	no	no	2_middle	3.200	no	female	-3.00	girl
40	60.00	66.0	5.0	95	3_overweight	yes	no	2_middle	3.700	no	female	6.00	girl
41	68.00	69.5	9.0	95	3_overweight	no	no	2_middle	2.000	yes	female	1.50	girl
42	73.00	76.0	8.0	110	3_overweight	no	yes	2_middle	3.100	yes	male	3.00	boy
43	66.00	68.0	9.0	91	3_overweight	yes	no	1_front	3.500	no	female	2.00	girl
44	70.00	67.0	5.5	85	3_overweight	yes	no	2_middle	3.700	no	female	-3.00	girl
45	51.00	54.0	7.0	130	2_about_right	no	no	1_front	2.550	no	female	3.00	girl
46	67.00	68.0	7.0	104	3_overweight	yes	no	2_middle	3.730	no	female	1.00	girl
47	69.00	70.0	5.0	95	3_overweight	no	no	2_middle	3.500	no	female	1.00	girl
48	71.00	67.0	3.0	105	3_overweight	no	no	2_middle	3.200	no	female	-4.00	girl
49	74.00	74.0	5.0	90	3_overweight	no	no	2_middle	3.100	no	male	0.00	boy
50	65.00	67.0	6.5	90	2_about_right	no	yes	2_middle	3.900	yes	female	2.00	girl
51	63.50	65.0	6.5	105	2_about_right	no	no	2_middle	3.250	no	female	1.50	girl
52	66.00	68.0	4.5	95	3_overweight	yes	yes	2_middle	3.200	no	female	2.00	girl
53	69.00	65.0	8.0	110	3_overweight	yes	yes	3_back	3.294	no	female	-4.00	girl
54	75.00	77.0	7.0	105	3_overweight	no	no	2_middle	2.000	yes	male	2.00	boy
55	65.00	75.0	6.0	130	2_about_right	no	yes	1_front	3.000	no	male	10.00	boy
56	74.00	76.0	7.0	95	2_about_right	no	yes	3_back	3.700	yes	male	2.00	boy
57	64.00	66.0	6.0	95	3_overweight	yes	no	1_front	3.300	no	female	2.00	girl
58	76.00	77.0	7.0	100	2_about_right	no	yes	2_middle	3.200	yes	male	1.00	boy
59	64.00	68.0	6.0	110	3_overweight	no	yes	2_middle	3.600	yes	female	4.00	girl
60	71.50	74.0	6.0	108	2_about_right	no	no	2_middle	3.300	no	male	2.50	boy
61	63.00	68.0	7.5	75	3_overweight	no	no	1_front	4.000	yes	female	5.00	girl
62	64.00	68.0	7.5	102	3_overweight	no	no	1_front	3.400	no	female	4.00	girl
63	68.00	72.0	6.5	105	3_overweight	no	yes	2_middle	2.700	yes	male	4.00	boy
64	70.00	72.0	4.0	98	1_underweight	yes	yes	3_back	2.900	no	male	2.00	boy
65	68.00	72.0	4.0	135	1_underweight	no	yes	2_middle	1.900	no	male	4.00	boy
66	75.00	75.0	6.0	130	2_about_right	no	yes	2_middle	2.800	yes	male	0.00	boy
67	69.00	67.0	2.0	85	3_overweight	yes	no	1_front	3.500	no	female	-2.00	girl
68	70.00	72.0	5.0	85	1_underweight	no	no	2_middle	3.300	no	male	2.00	boy
69	61.00	68.0	5.0	130	2_about_right	no	no	1_front	3.700	no	female	7.00	girl
70	65.00	66.0	8.0	120	2_about_right	yes	no	3_back	2.914	yes	female	1.00	girl
71	70.00	73.0	5.0	110	1_underweight	no	no	1_front	2.700	no	male	3.00	boy

References:

https://ggplot2-book.org/getting-started

https://ggplot2.tidyverse.org/reference/geom_point.html

Cohen, Y., & Cohen, J. Y. (2008). Statistics and Data with R: An applied approach through examples. John Wiley & Sons.

Mount, J., & Zumel, N. (2019). Practical data science with R. Simon and Schuster.

Yozgatlıgil,C. (2024). Stat 412-Lecture Notes.

Stat412 Recitation 2

Koca Guler, B.

March 7, 2024

Introduction to the Dataset

Descriptive Stats

Visualization Techniques

Lattice Plots

Histogram and Density-plot

Density-plot

Box-plot

Scatter-plot

ggplot Graphics

Scatter-plot

Bubble-plot

Scatter-plot

Box-plot

Violin-plot

Bar-plot

Lollipop chart

Data Manipulation