Application of R Programming Language in Learning Statistics

Liliia V. Pavlenko

1 a

, Maksym P. Pavlenko

1 b

, Vitalii H. Khomenko

1 c

and Vitaliy I. Mezhuyev

2 d

Berdyansk State Pedagogical University, 4 Schmidta Str., Berdyansk, 71100, Ukraine

FH JOANNEUM University of Applied Sciences, Institute of Industrial Management, Werk-VI-Strasse 46, 8605

Kapfenberg, Austria

Keywords:

R Programming Language, Statistics, Statistical Data Analysis, Teaching Statistics.

Abstract:

The study examines the problem of teaching statistics to future programmers. The theoretical content of

teaching statistics has undergone signiﬁcant development and requires a change in its focus on the practical

ﬁeld, even while studying at a higher education institution. It is determined that the improvement of teaching

statistics to students requires moving from theoretical teaching methods to the practical solution of applied

problems and shifting of emphasis from the process of statistical calculations to the analysis and results in-

terpretation. The research allowed establishing that the training of statistics of future programmers should be

based on the use of applied system of tasks developed with the help of real data sets obtained as a result of

statistical research. Such tasks allow increasing the educational motivation of students in comparison with

synthetic examples, which are usually used in the study of statistics. The research has analyzed the software

for statistical data analysis as well as identiﬁed features of its application in the learning process. It is offered

to use a specialized programming language R as the main learning tool.

1 INTRODUCTION

During the “information explosion” there is a problem

in the statistical education of the society. The study

of statistics is an important component of the educa-

tional programs for training specialists in the ﬁeld of

IT. While teaching courses on statistics teachers face

a signiﬁcant number of problems such as: different

levels of knowledge, low level of motivation, lack of

understanding by students of the necessity to study

statistics for application in the future profession (Zi-

efﬂer et al., 2008).

There have been serious concerns about the future

of statistics as a discipline recently. Cox (Cox, 1997),

Moore (Moore, 1997), Smith and Staetsky (Smith and

Staetsky, 2007) raise many questions about the need

to improve the goals, content, methods and forms of

teaching statistics.

Many scientists have conducted researches that

have studied the problems of teaching statistics. Rec-

ommendations for teaching statistics in the educa-

tional institutions of various types are given in (Ben-

https://orcid.org/0000-0001-7823-7399

https://orcid.org/0000-0003-0091-696X

https://orcid.org/0000-0002-7361-2169

https://orcid.org/0000-0002-9335-6131

Zvi and Garﬁeld, 2004; Biehler et al., 2018; Bishop

et al., 1996; Garﬁeld and Ben-Zvi, 2008; Langrall

et al., 2017; Shaughnessy, 1992, 2007). In the stud-

ies (Garﬁeld and Ben-Zvi, 2008; Watson et al., 2013)

it was offered to move from the theoretical learning to

the application of statistical methods in practice dur-

ing learning.

Nicholl (Nicholl, 2001) notes that over the past 50

years, the theoretical content of teaching statistics has

developed signiﬁcantly, but this process has been un-

controlled, by introducing new concepts into the con-

tent. As a result, the content of teaching statistics as a

discipline has provided teachers with a large number

of theoretical concepts aimed at improving the the-

oretical training of students. All this does not con-

tribute to the development of students’ motivation and

interest to study statistics. Rumsey (Rumsey, 2002),

Gal and Garﬁeld (Gal and Garﬁeld, 1997) draw atten-

tion to the problems of teaching statistics, and propose

to change the training paradigm and direct its focus

on the practical ﬁeld, even during training in higher

educational institutions.

Investments in education are seen as investments

in production, where human capital is created. The

World Economic Forum predicts that over the next

ﬁve years, there will be a nearly six-fold increase in

Pavlenko, L., Pavlenko, M., Khomenko, V. and Mezhuyev, V.

Application of R Programming Language in Learning Statistics.

DOI: 10.5220/0010928500003364

In Proceedings of the 1st Symposium on Advances in Educational Technology (AET 2020) - Volume 2, pages 62-72

ISBN: 978-989-758-558-6

global demand for statistical data analysts.

According to the Modis survey (Modis, 2018),

97.44% of respondents (representatives of banks and

industry) consider data analysis as a prospect for suc-

cessful development in sales and marketing. How-

ever, the interest in interpreting the data is higher than

in the calculations. 42% of respondents complain

about the lack of qualiﬁed professionals who have

methods of statistic data analysis in the labor market.

55% of respondents say that it is difﬁcult to ﬁnd spe-

cialists who can calculate and interpret the results.

Every day in the world large amounts of various

data are generated, which are constantly increasing

(Khomenko et al., 2020). Therefore, the demand in

the labor market for data analysts and data researchers

is constantly growing. Varian (Varian, 2017) notes

that data analyst will become the most popular pro-

fession in the future.

Thus, improving the education of students in

statistics requires moving from the theoretical teach-

ing methods to the practical solution of applied prob-

lems and the shift of emphasis from the process of

statistical calculations to the analysis and results in-

terpretation.

To begin preparing an intellectually active, en-

riched with the knowledge and skills specialist, ed-

ucation must move from the reproductive to the inno-

vative learning. Innovative learning is a creative com-

bination of the traditional and new teaching methods,

their choice for each discipline, based on its theoret-

ical content and practical orientation (Kaminskaya,

2011). At the same time, it should be considered

that while teaching educational material to students,

it is important not only to form certain professional

competencies, but also to adjust them to today’s mod-

ern requirements. It means that the future specialists

must be able to express their thoughts and concepts

in words, understand the language of symbols, signs

and schemes. This is not just the ability to think cre-

atively, but also the ability to make extraordinary de-

cisions and actions.

To organize innovative training of statistics in ac-

cordance with modern requirements, it is advisable to

use special software for statistical data analysis. How-

ever, today there are specialized programming lan-

guages and environments that can be used to quickly

and efﬁciently analyze data, interpret results and pre-

pare conclusions as well as reports in various formats.

Thus, there is a contradiction between traditional

approaches to teaching statistics and society’s re-

quirements for the level of modern IT specialists

training in the ﬁeld of statistical data analysis, as well

as between the theoretical orientation of the content

of statistics training and the need to train a special-

ist with applied tools and methods of statistical data

analysis.

The article aims to justify the use of R program-

ming language as a teaching method in learning statis-

tics.

2 RESULTS

The following main methods were used in the re-

search process: content analysis of scientiﬁc and me-

thodical literature, generalization and systematization

to clarify the state of the problem development; ques-

tionnaire of those getting higher education and initial

statistical processing of the obtained results to clar-

ify the current state of the researched problem; gen-

eralization of theoretical and practical data to justify

the introduction of innovative approaches to the study

of statistics by students based on the use of program-

ming language R.

The process of teaching statistics to the students

is associated with certain difﬁculties: the study ma-

terial in this course contains a large number of def-

initions and formulas. At the same time, students

need not only to reproduce them, but also to under-

stand the meaning and be able to apply in practice.

However, with the traditional organization of the ed-

ucational process, practical tasks are far from the real

economic, social and other processes that occur in real

life. The analyzed data are generalized and do not al-

low to fully form students’ understanding of the need

and expediency of studying this discipline and the op-

portunity to implement the acquired competencies in

their further professional activities.

Therefore, most students learn statistics in frag-

ments, and do not form systemic knowledge as a re-

sult. In addition, mainly verbal presentation of infor-

mation increases fatigue, resulting reducing produc-

tivity of the learning process (Fitsula, 2000).

The number of statistically educated people is de-

creasing. It is difﬁcult for potential employers to

ﬁnd a specialist who will be able to perform statis-

tical calculations without prior training and explana-

tion. Therefore, there is a need to improve the content

of teaching this discipline through the introduction of

practical tasks.

Improving the content of the statistics course re-

quires the introduction of changes in the methods and

means of its teaching using innovative technologies.

Scientiﬁc innovations that promote scientiﬁc

progress cover all areas of knowledge. There are

socio-economic, organizational and managerial, tech-

nical and technological innovations. One of the types

of social innovations is pedagogical innovation.

Application of R Programming Language in Learning Statistics

Pedagogical innovation is an innovation in the

ﬁeld of pedagogy, purposeful progressive changes

that make stable elements (innovations) in the educa-

tional environment that improve the characteristics of

both – its individual components and the educational

system on the whole (Rapatsevych, 2006).

Pedagogical innovations can be carried out both

with the application of the educational system’s

own resources (intensive way of development) and

with the involvement of additional capacities (invest-

ments) – new means, equipment, technologies, capital

investments, etc. (extensive way of development).

Kazakov (Kazakov, 2006) notes that the combi-

nation of intensive and extensive ways of pedagogi-

cal systems development allows to carry out so-called

“integrated innovations”, which are built at the junc-

tion of various, multilevel pedagogical subsystems

and their components.

The main ways and objects of innovative transfor-

mations in the teaching of statistics are:

• making concepts and strategies for the develop-

ment of statistical education (Tishkovskaya and

Lancaster, 2012);

• updating the content of statistics training;

• change and development of new learning tech-

nologies;

• improving the training of IT specialists in the ﬁeld

of statistical data analysis;

• designing new models of the educational process

for teaching statistics;

• improving the monitoring of the educational pro-

cess and student learning;

• new generation electronic teaching aids develop-

ment.

Innovation can take place at different levels. The

highest level includes innovations that affect the entire

pedagogical system.

Kulinenko (Kulinenko, 2013) notes that while or-

ganizing the innovation, it should be considered that:

• innovative ideas must be clear, convincing and ad-

equate to the real educational needs of man and

society, they must be transformed into speciﬁc

goals, objectives and technologies;

• innovation activity should be morally and materi-

ally stimulated, legal support of innovation activ-

ity is necessary;

• not only results are important in pedagogical ac-

tivity, but also ways, means, methods of their

achievement.

The current problems of teaching statistics in

modern higher educational institutions include the re-

view of experience associated with the intensiﬁcation

of learning. One of the main teacher’s tasks is to teach

students to obtain the necessary information indepen-

dently, to teach them to consciously process the ob-

tained information (Pavlenko and Pavlenko, 2021). In

order for them to be able to study the teaching mate-

rials on their own, the materials need to be designed

primarily for students and not for teachers.

Possibilities of “Statistics” discipline for experts

in the ﬁeld of IT consists ﬁrst of all of that know-

ing mathematical language and modeling that will al-

low the student to be better guided in forecasting of

economic, social, technical and other processes; sec-

ondly, that statistics by its internal nature has rich op-

portunities for the formation of students algorithmic

thinking.

Future IT professionals must not only know the

theoretical foundations, but also be able to apply the

means of automating statistical analysis. Such tools

include specialized statistical software packages and

programming languages.

Statistical packages on the basis of functionality

can be divided into 3 main groups.

1. Universal statistical packages Statistica, SPSS,

Statgraphics, STATA, Stadia, SYSTAT, S-PLUS

and MS Excel. These packages are not targeted at

a speciﬁc subject area and can be used to analyze

data from different industries. Typically, they of-

fer a wide range of statistical methods and have a

relatively simple interface.

It is recommended to work with such packages for

starter users who have only basic knowledge in

the ﬁeld of statistics, as well as experienced users

in the initial stages of working with data, when

statistical methods that will be used to address a

particular issue are not clearly deﬁned yet. The

versatility of the universal package allows hold-

ing a pilot analysis of different data types using a

wide range of statistical methods. The vast major-

ity of existing universal packages has much com-

mon functionality and is similar in the composi-

tion of the built-in statistical procedures.

2. Professional statistical packages such as SAS or

BMDP. Professional packages, in the contrast to

the universal ones, allow you to work with ex-

tremely large amounts of data, apply highly spe-

cialized methods of analysis and create your own

data processing system. As a rule, such packages

are complex and should not be used in the educa-

tional process.

3. Specialized statistical packages BioStat, Datas-

AET 2020 - Symposium on Advances in Educational Technology

tream, Datascope, etc. were designed for statis-

tical analysis in speciﬁc areas of activity, which

use special methods of statistical analysis, usually

not presented in the universal packages.

Specialized packages allow analysis using a lim-

ited number of specialized statistical methods or are

used in a specialized subject area. As a rule, such

statistical packages are handled by specialists who

are well acquainted with data analysis methods in the

ﬁeld to which the package is focused. For example,

the BioStat statistical package was created to analyze

data in the ﬁeld of biology and medicine.

Most of the existing statistical packages have a

ﬂexible modular structure that can be supplemented

and expanded owing to the custom modules that are

optionally purchased or freely available on the Inter-

net. Such ﬂexibility allows you to adapt packages to

the needs of a particular user.

Statistical packages are just the tools for an experi-

enced professional. If the specialist does not have suf-

ﬁcient knowledge and competencies, then, even the

most advanced software product will not allow hold-

ing quality data analysis. However, the wrong soft-

ware, which does not contain the required set of sta-

tistical procedures, can make the work of even an ex-

perienced specialist more difﬁcult.

Therefore, during the training of IT specialists it

is necessary to acquaint those who get higher educa-

tion with the available statistical packages and their

characteristics, but the application of specialized pro-

gramming languages is closer and more understand-

able for the students while conducting statistical data

analysis.

For statistical data analysis it is possible and ap-

propriate to use R and Python programming lan-

guages.

We will consider the features of the programming

language R. The language R is a powerful high-level

object-oriented programming language and environ-

ment for statistical calculations and visualization of

source and calculation data, which allows you to solve

many problems in the ﬁeld of data processing. It’s a

free open source program under GNU GPL designed

to run common operating systems (Windows, macOS,

Linux).

Tens of thousands of specialized modules and util-

ities have been developed for this language. One of

the most important features of the programming lan-

guage R is the efﬁcient implementation of vector op-

erations, which allows the application of compact no-

tation while processing large amounts of data. All this

makes R an effective tool for obtaining useful infor-

mation from large amounts of various statistics, in-

cluding Big Data.

The R language is a convenient and effective tool

for teaching statistical analysis, data processing and

visualization.

It is also possible to use the Python program-

ming language in the ﬁeld of data analysis and in-

teractive research calculations with results visualiza-

tion. Python is an open source object-oriented pro-

gramming language. The relatively recent advent of

improved libraries for Python (primarily pandas) has

made it a serious competitor to the R language for sta-

tistical data analysis. Combining with the beneﬁts of

Python as a universal programming language makes

it an excellent choice for creating data processing ap-

plications.

So, the use of a specialized programming lan-

guage as a learning tool contributes to the develop-

ment of statistical data analysis skills as well as the

development of algorithmic thinking of future IT pro-

fessionals.

In order to study the relevance of the problem

of scientiﬁc research, a ascertaining experiment was

conducted among students of IT specialties. The is-

sues that allow ﬁnding out the opinion of higher edu-

cation students on the problem of improving the meth-

ods of teaching statistics to future IT professionals

were studied.

The results of the ascertaining experiment are pre-

sented in percentages and indicate the number of pos-

itive answers to the questions. The survey was orga-

nized using GoogleForms. 83 students majoring in

015 Professional Education (Digital Technology) and

015 Professional Education (Computer Technology)

took part in ascertaining experiment.

2.1 Declared Interest of Students in

Studying the Course of Statistics

In this block students were asked two questions. You

can see the results of the answers to the ﬁrst question

of the survey in ﬁgure 1. The analysis of answers

allows establishing the level of awareness of students

in the demand for specialists in the labor market who

know how to analyze data.

Figure 1: Results of answers to the question regarding stu-

dents’ awareness in the demand for the specialists on data

analysis in the labor market.

Analysis of students’ answers allows us to con-

clude that the majority of respondents, 42.17% be-

Application of R Programming Language in Learning Statistics

lieve that a data analysis specialist is in demand in the

labor market. This conﬁrms the relevance and need to

study the course of statistics for IT professionals.

The second question clariﬁed which specialties in

data analysis students consider the most relevant to-

day. The results of the student survey are shown in

ﬁgure 2.

Figure 2: The results of questionnaire regarding students’

awareness about modern professions on data analysis in the

labor market.

The most famous profession among the future stu-

dents programmers is the profession of data analysts

(65.06%), in second place is the profession of data

scientists (51.81%). These professions are known

to more than 50% of students, which indicates their

awareness and interest in this ﬁeld.

So, based on the results of studying the answers to

the questions of this block, we can draw the follow-

ing conclusion. Training statistics of future IT pro-

fessionals is relevant, because students are aware of

the existence of professions in the ﬁeld of data analy-

sis and believe that they will need statistics in future

professional activities.

2.2 Students’ Opinion about the Need to

Fill the Content with Tasks of an

Applied Nature

Students were asked to answer open-ended questions:

“Which subject area data analysis you are interested

conducting in?” The students’ answers showed that

the most popular data for processing are data from

sociology, medicine, engineering, economics and bi-

ology.

Also, the idea of what data students are interested

in working with in practice was studied. The results

of answers to the questions are shown in ﬁgure 3.

Figure 3: Students’ opinion on data origin for practical

tasks.

Among the surveyed respondents, 71.08% believe

that data obtained as a result of practical research and

having an applied nature are most attractive for them.

This indicates the need to develop practical and labo-

ratory work based on real data obtained from statisti-

cal studies.

2.3 Students’ Interest in using

Programming Languages and

Software for Statistical Data

Analysis

The purpose of the third block of questions was to

study the opinion of respondents about the need and

feasibility of using software and programming lan-

guages for statistical data analysis.

Students were asked the following questions: “Do

you know programming languages with which it

is possible to perform statistical data analysis (en-

ter)?”, “Which software product interface is more

user friendly for you?”, “Are you more interested in

data analysis using special software or using a pro-

gramming language?”

According to the ﬁrst question, the opinions of the

respondents were divided as follows: 55.42% indi-

cated the programming language R, 28.92% indicated

the Python programming language. Programming

languages such as C++ (9.64%) and Java (6.02%)

were also indicated (ﬁgure 4).

Figure 4: Respondents’ answers to the question on conve-

nience of program packages interface.

The obtained results allow us to state that the R

language is the best known as a mean of statistical

data analysis. So, we will use this programming lan-

guage to solve application problems.

In choosing the convenience of the software

package interface, respondents preferred MS Excel

(56.63%), followed by Statistica software package

(28.92%), followed by SPSS (14.46%) (ﬁgure 5).

Figure 5: Choosing program packages for statistical data

analysis.

AET 2020 - Symposium on Advances in Educational Technology

So, the students will be asked to use MS Excel and

Statistica for practical calculations.

According to the results of students’ answers to

the third question of this block, the programming lan-

guage (57.83%) was chosen by the students as the

main tool for organizing the training of statistical data

analysis (ﬁgure 6).

Figure 6: Respondents’ answers regarding choosing the

mean of solving the tasks of statistical data analysis.

So, students in the class will be asked to use the

programming language R as the main tool for practi-

cal calculations. MS Excel and Statistica will be used

as aids in statistical analysis.

3 USING APPLIED TASKS FOR

TEACHING STATISTICS

Taking into account and summarizing the results of

the study, in our opinion, it is advisable to build the

content and structure of the course considering the

wishes of students. In practical classes, tasks that are

of a real applied nature and based on real statistics

should be considered. One of the main teaching meth-

ods should be a practical method of learning based on

programming. The means of statistical data analysis

in practical classes can be both software tools for data

analysis (MS Excel and Statistica) and the language

and programming environment R.

A system of tasks has been developed for the

course. Let’s consider an example for training of the

statistical analysis in the R environment. For carrying

out the analysis we will take data from the website

https://abit-poisk.org.ua, namely data concerning en-

trants for 2017. This site contains large amounts of

data, for our example we will take only entrants who

entered the Faculty of Physical and Mathematical

Computer and Technological Education of Berdyansk

State Pedagogical University in the specialty “Profes-

sional Education (Computer Technology)” and “Pro-

fessional Education)”, the level of “bachelor”.

A total of 31 applications were submitted for these

specialties. We will analyze these data, using descrip-

tive statistics in R and present the results using the

most common graphs in R when analyzing this data.

Step 1. We set the name, specialty, id, total

score of the external evaluation, status (budget /

contract), then enter the data into the table. We will

set the value in the form of vectors with the command

c(‘‘vector_value1’’,‘‘vector_value2’’,...).

We build the table from the received vectors by means

of the command > studentdata. Commands for a

table creation with the information about applicants:

> last_name <-c(’’Shvachko’’, ‘‘Dybiaga’’,

‘‘Kartashov’’, ‘‘Sytosenko’’,‘‘Filipenko’’,

‘‘Klimenko’’, ‘‘Veretelnik’’, ‘‘Diakov’’,

‘‘Salionov’’, ‘‘Bagnuk’’, ‘‘Kombarov’’,

‘‘Baranovsky’’, ‘‘Kiseliov’’, ‘‘Sakun’’,

‘‘Bova’’, ‘‘Potapova’’, ‘‘Kobzar’’,

‘‘Sementsov’’, ‘‘Cybulka’’, ‘‘Teplov’’,

‘‘Mitushkin’’, ‘‘Kartinik’’, ‘‘Gavrylenko’’,

‘‘Trotsenko’’, ‘‘Panchukov’’, ‘‘Kyslynsky’’,

‘‘Sagirov’’, ‘‘Korobov’’, ‘‘Shatalina’’,

‘‘Tichovod’’, ‘‘Popov’’)

> specialty <-c(1,1,1,1,1,1,1,1,1,1,1,1,

1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2)

> id <-c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,

15,16,17,18,19,20,21,22,23,24,25,26,27,28,

29,30,31)

> rating <-c(186,184,180,179,173,173,170,

168,167,166,163,162,160,156,148,145,145,

142,142,140,140,139,135,131,129,123,147,

146,140,136,128)

> status <-c(1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,

0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0)

> studentdata <- data.frame(id, last_name,

rating, status)

> studentdata

id last_name rating status

1 1 Shvachko 186 1

2 2 Dybiaga 184 1

3 3 Kartashov 180 1

4 4 Sytosenko 179 1

5 5 Filipenko 173 1

6 6 Klimenko 173 1

7 7 Veretelnik 170 1

8 8 Diakov 168 1

9 9 Salionov 167 1

10 10 Bagnuk 166 1

11 11 Kombarov 163 1

12 12 Baranovsky 162 1

13 13 Kiseliov 160 0

14 14 Sakun 156 0

15 15 Bova 148 0

16 16 Potapova 145 0

17 17 Kobzar 145 0

18 18 Sementsov 142 0

19 19 Cybulka 142 0

20 20 Teplov 140 0

21 21 Mitushkin 140 0

22 22 Kartinik 139 0

23 23 Gavrylenko 135 0

24 24 Trotsenko 131 0

25 25 Panchukov 129 0

26 26 Kyslynsky 123 0

27 27 Sagirov 147 1

28 28 Korobov 146 1

29 29 Shatalina 140 1

Application of R Programming Language in Learning Statistics

30 30 Tichovod 136 1

31 31 Popov 128 0

Step 2. We will calculate the main statistical val-

ues: average, median, standard deviation, minimum

and maximum value. The results of the main statisti-

cal values calculation:

> y <- mean(rating)

> y

[1] 153

> sd <-sd(rating)

> sd

[1] 18.03145

> var <-var(rating)

> var

[1] 325.1333

> mad <-mad(rating)

> mad

[1] 22.239

> min <-min(rating)

> min

[1] 123

> max <-max(rating)

> max

[1] 186

According to the results of the calculations, the

following data were obtained: the average score of

entrants with external evaluation is 153, the average

difference between the scores of different entrants is

22 points, the lowest result (min) – 123 points, the

best result (max) – 186 points.

Step 3. Let’s construct a histogram of frequen-

cies for external evaluation points using the command

> barplot (ﬁgure 7):

> counts <- table(studentdata$rating)

> barplot(counts,

main=’’Frequency diagram’’,

xlab=’’Rating’’, ylab=’’Frequency’’)

Figure 7: The histogram of frequencies for external evalua-

tion points.

The histogram of frequencies shows that the

largest number of entrants has a score from 139 to 142

points, as well as the fact that the vast majority has a

unique score with EIT, which is no longer repeated.

Step 4. We construct histograms of points / fre-

quencies with a normal distribution curve. With this

purpose we use the command > box. We will build:

on the x-axis – the parameter rating, and on the y-axis

– the frequency of the score in the table (ﬁgure 8):

> box()

> library(plotrix)

> x <-studentdata$rating

> h <-hist(x, breaks=12, col=’’darkblue’’,

xlab=’’ZNO score’’, main=’’Frequencies

histogram with the curve of distribution‘‘)

> xfit <-seq(min(x), max(x), length=40)

> yfit <-dnorm(xfit,mean=mean(x),sd=sd(x))

> yfit <-yfit*diff(h$mids[1:2]*length(x))

> lines(xfit, yfit, col=’’red’’, lwd=3)

Figure 8: Frequencies histogram with the curve of distribu-

tion.

The distribution histogram shows that the data on

the scores of applicants are not the subject to the nor-

mal law of distribution. We have a lot of “average”

entrants, i.e. those who passed the external exami-

nation from 135 to 145 points. There are also those

who passed 165 points, i.e. entrants with a “sufﬁ-

cient” level. There are very few who scored more than

180 points.

Step 5. We construct a diagram of the nuclear esti-

mation of the density of values for external evaluation

points using the command > box (ﬁgure 9):

> box()

> par(mfrow=c(2,1))

> d <- density(studentdata$rating)

> plot(d)

The nuclear density estimation diagram shows

that the highest density is observed in the range from

130 to 155 points. That is, in this interval, based on

the graph, the values differ by 25 points, then, if you

take the full table, they differ by 22 (see standard de-

viation).

AET 2020 - Symposium on Advances in Educational Technology

Figure 9: The diagram of nuclear density estimation.

As a result of solving applied problems using the-

oretical knowledge from different sections of statis-

tics, students will not only master the skills of using

statistical methods, but also develop the ability to in-

terpret the results and predict the studied processes.

It should be emphasized that the use of programming

as a practical teaching method will allow students to

improve their knowledge and skills in the ﬁeld of pro-

gramming as well as the use of algorithms and design

patterns.

Using real data for statistical analysis, students

will be able to understand the need and feasibility of

statistical research in future professional activities.

One of the problems of using application tasks

with real data is the selection and use of data sets.

Much of the datasets are closed and inaccessible for

free research and use. However, there are organiza-

tions that provide free access to data:

• World Bank Open Data (https://data.worldbank.

org/) provides more than 3,000 sets of economic

and social data on various indicators. Data can be

downloaded in csv and xml formats. The service

supports API access, which allows you to auto-

mate data downloads using the programming lan-

guage R.

• The uniﬁed state web portal of open data (https:

//data.gov.ua/) contains 15 categories of data sets

that are constantly updated. Datasets are available

for download in Excel, csv, json and xml formats.

All data are available from Creative Commons At-

tribution 4.0 International license.

• The ofﬁcial page of the All-Ukrainian Popu-

lation Census (http://database.ukrcensus.gov.ua)

provides access to information on the population

living in the country, socio-economic characteris-

tics, and demographic indicators, level of educa-

tion, national composition and language charac-

teristics. Datasets can be downloaded in txt, csv,

html formats.

• Open World Health Organization data repository

(https://www.who.int/data/gho/). The site pro-

vides datasets on the health status of citizens

of World Health Organization member states.

Datasets are divided into over 100 categories.

Data can be downloaded in Excel format or use

the API for direct access to data.

• UNICEF Dataset (https://data.unicef.org/) col-

lected relevant data on education, child labor,

child disability, infant mortality, maternal mortal-

ity, water and sanitation, pneumonia, malaria and

more. Datasets are available in Excel and csv for-

mats.

• Registry of Open Data on AWS (RODA) (https:

//registry.opendata.aws/) contains data located on

AWS servers. The service offers access to over

200 datasets. There is a page with additional in-

formation, usage examples, license information,

and more for each data set. Using the wide range

of computing products offered by AWS (Amazon

EC2, AmazonAthena, AWS Lambda and Amazon

EMR), it is possible to share data in the cloud.

This allows users to spend more time analyzing

data rather than collecting data. When using data

sets hosted on AWS, it is necessary to consider the

type of license of each speciﬁc data set, as they

belong to different agencies, government organi-

zations, researchers, businesses and individuals.

• Data.gov (https://www.data.gov/) provides open

data sets of the US government. The resource

contains more than 200,000 data sets from various

sources: federal agencies, states, counties, cities,

etc. Data can be obtained in various formats, in-

cluding Excel, csv, json, xml.

• The GroupLens Research (https://grouplens.org/)

provides several sets of movie ratings data pro-

vided by MovieLens users. The kits contain

movie ratings, movie metadata (genre and release

year), and user demographics (age, gender, and

occupation). Such data can be used to develop a

recommendation system based on regression anal-

ysis.

• Open data sets Yelp (https://www.yelp.com/

dataset) is a subset of our businesses, reviews,

and user data for application in personal, educa-

tional, and academic purposes. Available as JSON

ﬁles, use it to teach students about databases, to

learn NLP, or for sample production data while

you learn statistics.

• Kaggle (https://www.kaggle.com/datasets) a so-

cial network for researchers, which provides ac-

cess to various data sets for analysis and research.

The convenience of Kaggle is that it is not just a

data warehouse. Each data set brings together a

community of researchers in which data are dis-

Application of R Programming Language in Learning Statistics

cussed and approaches to data processing are elu-

cidated.

• Google Public Data Explorer (https://www.

google.com/publicdata/directory) provides access

to more than 130 datasets submitted by World

Bank, U. S. Bureau of Labor Statistics, OECD,

IMF and other organizations.

All considered services provide access to open

data sets. This allows you to ﬁll the content of teach-

ing statistics for future programmers with the tasks of

applied direction.

4 EXPERIMENTAL

VERIFICATION OF THE

EFFECTIVENESS IN THE USE

OF APPLIED TASKS TO TEACH

STATISTICS TO THE FUTURE

PROGRAMMERS

Using programming language R and tasks of applied

direction while training statistics with future IT spe-

cialists.

The main purpose of the pedagogical experiment

is to test the hypothesis that the use of programming

language R and applied problems in teaching statistics

to the future IT professionals will help increase the

educational motivation of students.

According to the hypothesis of the study, the ex-

periment involved checking the level of motivation of

students of IT specialties in the ﬁeld of statistics based

on the results of implementation of applied problems

and programming language R. The experiment was

conducted on the basis of Berdyansk State Pedagogi-

cal University. Students majoring in 015 Professional

Education (Digital Technology) and 015 Professional

Education (Computer Technology) took part in it.

Control and experimental groups were organized.

In the control group, the educational process was car-

ried out according to the traditional methods. This

technique involved the use of specialized software

(Microsoft Excel, Statistica, etc.) and synthetic tasks,

the content of which did not take into account the

speciﬁcs of future professional activities of students

of IT specialties. The control group (CG) consisted

of 42 students. The experimental group used appli-

cation problems and the programming language R to

solve them. The experimental group (EG) included

32 students.

During the formation of control and experimen-

tal groups, their alignment was carried out taking into

account the initial level of educational motivation of

students.

The success of the pedagogical research was en-

sured by the application of the standardized methods.

This guaranteed the reliability of the results.

Experimental methods of teaching statistics of fu-

ture programmers using professional tasks and pro-

gramming language R was based on their application

at all stages of learning: in learning new material as a

motivating task, at the stage of consolidation, in inde-

pendent work of students as a professionally oriented

project.

An electronic learning tool has been developed

for students programmers to provide information and

methodological support for the statistics course. The

development of an electronic tool takes into account

students age and preparation level. The developed

learning tool contains theoretical materials, tasks for

practical implementation, visual materials with ex-

amples of the application of the programming lan-

guage R, a guide to the commands of the R lan-

guage and a list of recommended reading. The e-

learning tool is available on the Internet at the link

https://r.ktuni.bdpu.org/.

In order to test the effectiveness of the imple-

mented experimental training, the level of educational

motivation was chosen as a criterion. To assess the

dynamics of changes in motivation to study statis-

tics, future IT specialists used the method of Rean and

Yakunin (Il’in, 1998) aimed at diagnosing educational

motivation in general in order to identify the predom-

inant types of motives for learning. The technique al-

lows identifying the predominant type of motives and

to trace the dynamics of changes in the structure of ed-

ucational motivation. The methodology is standard-

ized and involves the study of 16 types of educational

motives of students.

Positive motivation for learning ensures the suc-

cessful formation of knowledge and skills. High posi-

tive motivation can compensate for insufﬁciently high

abilities of students. With the right choice of means of

motivation for learning, there is a positive pedagogi-

cal inﬂuence. Focusing only on “negative” motives

(avoidance, fear of failure, fear) is always less effec-

tive than “positive” ones. In our study, we will deter-

mine the impact of the developed system of tasks on

the level of educational motivation of students.

Table 1 presents the results of calculating the aver-

age scores for each type of educational motives on the

scale of Rean and Yakunin (Il’in, 1998). Comparative

analysis of table 1 allows us to conclude that before

the experiment the levels of educational motives of

students in the control and experimental groups did

not differ. After the experiment in the experimental

AET 2020 - Symposium on Advances in Educational Technology

Table 1: The results of students’ questionnaire according to the methods of Rean and Yakunin (Il’in, 1998).

Educational motivation

Before the experiment After the experiment

CG EG CG EG

1. To become a qualiﬁed specialist 6.6 6.6 6.7 6.8

2. To get the diploma 6.7 6.6 6.2 6.8

3. To continue successful studies at further

courses 5.6 6.3 6.0 6.2

4. To study successfully, to pass exams

for “good” and “excellent” marks 6.0 5.3 4.5 6.2

5. To get constant scholarship 5.5 5.2 4.9 5.5

6. To gain deep and profound knowledge 6.0 6.3 6.3 6.8

7. To be always ready for classes 4.5 4.5 5.0 5.2

8. Not to give up learning the subjects of the

educational cycle 5.5 5.6 5.5 6.5

9. Not to lag behind the classmates 6.0 5.6 5.5 5.8

10. To provide future successful professional

activity 6.8 6.6 6.5 6.9

11. To execute pedagogical requirements 5.0 4.7 5.2 5.5

12. To get teachers’ respect 4.8 5.2 3.6 4.9

13. To be an example for the classmates 3.2 4.7 3.5 4.3

14. To gain parents’ and relatives’ respect 4.5 4.8 5.0 6.6

15. To avoid condemnation and punishment

for bad studying 4.1 4.9 5.0 4.5

16. To get intellectual satisfaction 4.9 4.91 4.5 6.6

Table 2: Statistical comparison of the students of control and experimental groups educational motivation levels before and

after the experiment.

Before the experiment After the experiment

emp

crit

Taken hypothesis W

emp

crit

Taken hypothesis

0.1508 1.96 H

2.186 1.96 H

group there is an increase in the levels of the internal

educational motives of students. In general, the level

of educational motivation in the experimental group

is higher than in the control group, except for the mo-

tives of avoiding failure and punishment.

Table 2 shows the results of statistical compar-

ison of the control and experimental groups before

and after the experiment. The following statements

were formulated as working hypotheses: H

– levels

of learning motivation in the compared groups do not

differ; H

– levels of motivation to learn in the com-

pared groups differ. The Mann-Whitney U-test was

used to determine the difference between the samples.

This is a non-parametric statistical criterion used to

estimate the difference between two samples at the

level of any qualitatively measured trait. It allows you

to detect differences in the value of the parameter be-

tween small samples.

Statistical analysis allows us to conclude that at

the level of signiﬁcance α = 0.05 the initial states of

the experimental and control groups (before the ex-

periment) coincide. At the end of the experiment, the

levels of educational motivation differ.

So, the results of the study indicate that the hy-

pothesis of the study was conﬁrmed, namely the intro-

duction of statistics of the R programming language

and applied problems in the learning process helps to

increase the level of educational motivation of future

IT professionals.

5 CONCLUSIONS

During the research the introduction of innovative ap-

proaches to the study of statistics has been theoreti-

cally substantiated. The conducted research allowed

establishing that the training of statistics of future pro-

grammers should be based on the use of applied tasks

developed with the help of real data sets obtained as a

result of statistical research. Such tasks allow increas-

ing the educational motivation of students in compar-

ison with synthetic examples, which are usually used

in the study of statistics.

Datasets for statistical analysis are a source of ap-

Application of R Programming Language in Learning Statistics

plied tasks. Access to data sets on the Internet is free.

Therefore, the development of practical and labora-

tory work for future IT professionals should include

tasks that will contain real data from the following

subject areas: sociology, medicine, engineering, eco-

nomics and biology.

Using the programming language R to teach

statistics to the future programmers allows you to use

the method of practical training based on program-

ming. This approach involves students in familiar to

them practical activities and programming. There-

fore, we propose to use the R language and program-

ming environment as the main learning tool. MS Ex-

cel and Statistica software packages should be used as

teaching aids.

In further research it is planned to develop a

methodology for the implementation and application

of programming languages R and Python for statisti-

cal data analysis.

REFERENCES

Ben-Zvi, D. and Garﬁeld, J. B. (2004). The challenge of

developing statistical literacy, reasoning and thinking.

Springer.

Biehler, R., Frischemeier, D., Reading, C., and Shaugh-

nessy, J. M. (2018). Reasoning about data. In Inter-

national handbook of research in statistics education,

pages 139–192. Springer.

Bishop, A. J., Clements, M. A. K., Clements, K., Keitel,

C., Kilpatrick, J., and Laborde, C. (1996). Interna-

tional Handbook of Mathematics Education. Springer

Science & Business Media.

Cox, D. R. (1997). The current position of statistics:

a personal view. International statistical review,

65(3):261–276.

Fitsula, M. M. (2000). Pedagogy.

Gal, I. and Garﬁeld, J. (1997). Curricular goals and assess-

ment challenges in statistics education. The assess-

ment challenge in statistics education, pages 1–13.

Garﬁeld, J. and Ben-Zvi, D. (2008). Developing students’

statistical reasoning: Connecting research and teach-

ing practice. Springer Science & Business Media.

Il’in, E. P. (1998). Human motives: the-

ory and methods of study. High school.

https://www.elibrary.ru/item.asp?id=21748410.

Kaminskaya, A. V. (2011). Forming of readiness of future

teachers to innovative activity in higher educational

establishment. Scientiﬁc Bulletin of Donbass, (1).

http://nvd.luguniv.edu.ua/archiv/NN13/11kavvnz.pdf.

Kazakov, V. H. (2006). New times - new technologies of

professional training. Professional education, (1):12.

Khomenko, V. H., Pavlenko, L. V., Pavlenko, M. P., and

Khomenko, S. V. (2020). Cloud technologies in in-

formational and methodological support of univer-

sity students’ independent study. Information Tech-

nologies and Learning Tools, 77(3):223–239. https:

//journal.iitta.gov.ua/index.php/itlt/article/view/2941.

Kulinenko, L. (2013). Technologies of innovative ed-

ucational space. Naukovyi chasopys Natsional-

noho pedahohichnoho universytetu imeni M. P. Dra-

homanova. Seriia 07. Relihiieznavstvo. Kulturolohiia.

Filosoﬁia, (29 (41)). http://enpuir.npu.edu.ua/handle/

123456789/12492.

Langrall, C. W., Makar, K., Nilsson, P., and Shaughnessy,

J. M. (2017). Teaching and learning probability and

statistics: An integrated perspective.

Modis (2018). STEM IQ Survey Results 2018.

https://www.modis.com/en-us/resources/employers/

stem-iq-survey-2018/.

Moore, D. S. (1997). New pedagogy and new content:

The case of statistics. International statistical review,

65(2):123–137.

Nicholl, D. F. (2001). Future directions for the teaching and

learning of statistics at the tertiary level. International

Statistical Review, 69(1):11–15.

Pavlenko, M. and Pavlenko, L. (2021). Formation of

communication and teamwork skills of future IT-

specialists using project technology. Journal of

Physics: Conference Series, 1840(1):012031.

Rapatsevych, E. S. (2006). Psychological and pedagogical

dictionary. Minsk.

Rumsey, D. J. (2002). Statistical literacy as a goal for in-

troductory statistics courses. Journal of Statistics Ed-

ucation, 10(3).

Shaughnessy, J. M. (1992). Research in Probability and

Statistics : Reﬂections and Directions. In Handbook

on Research in Mathematics Education, pages 465–

494. https://ci.nii.ac.jp/naid/10029959707/.

Shaughnessy, J. M. (2007). Research on Statis-

tics’ Reasoning and Learning. pages 957–1009.

https://ci.nii.ac.jp/naid/10029959708/.

Smith, T. M. F. and Staetsky, L. (2007). The teaching of

statistics in UK universities. Journal of the Royal

Statistical Society: Series A (Statistics in Society),

170(3):581–622.

Tishkovskaya, S. and Lancaster, G. A. (2012). Statistical

Education in the 21

Century: A Review of Chal-

lenges, Teaching Innovations and Strategies for Re-

form. Journal of Statistics Education, 20(2):4.

Varian, H. R. (2017). Nel 2020 il data an-

alyst sar

a la professione pi

u ricercata.

https://www.giornaledibrescia.it/rubriche/impresa-4-

0/nel-2020-il-data-analyst-sar

Watson, J. M., Fitzallen, N. E., and Carter, P.

(2013). Top drawer teachers: Statistics.

http://ecite.utas.edu.au/87993.

Ziefﬂer, A., Garﬁeld, J., Alt, S., Dupuis, D., Holleque,

K., and Chang, B. (2008). What Does Research

Suggest About the Teaching and Learning of In-

troductory Statistics at the College Level? A Re-

view of the Literature. Journal of Statistics Educa-

tion, 16(2):8. https://www.tandfonline.com/doi/full/

10.1080/10691898.2008.11889566.

AET 2020 - Symposium on Advances in Educational Technology