E0410 Fundamentals of Statistics for Scientific Data Using R
by Daria Sapunova, PhD student, RECETOX
daria.sapunova@recetox.muni.cz
Bohunice, D29, room 123

Theoretical part

Mentimeter.com

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The data is only for visual demonstration!

     Height (cm)



R for Data Science - Grolemund, Garrett - Megaknihy.cz
https://r4ds.had.co.nz/








Trying to apply a fancy new R library to my real world data : r/rstatsmemes



How your data should not look before importing into R
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How your data should look before importing into R



How your data should look before importing into R
ID
Age
Gender
Height
Weight
Education
Freq.of fish consumption (times/month)
Source of fish (majority)
Chronical disease
Last med.exam. (years ago)
Person 1
45
Male
170.4
76.4
PhD
21
Market
Diabetes
10
Person 2
26
Male
168.3
65.3
BS
5
Self-fishing
NA
NA
Person 3
54
Female
168.6
75.3
MS
23
Grocery store
Bladder infection
10
Person 4
65
Male
156.6
44.2
MS
10
Market
Diabetes
5
Person 5
21
Female
170.1
69.9
HS
8
Market
No
NA
Person 6
…
44
Male
176.4
84.3
MS
43
Self-fishing
No
2
Person 400
6
Male
121.2
21.9
NA
15
Market
NA
1

Data preparation –
cleaning, harmonizing and structuring
üChoose variables you are going to work with.
üUpload your data.
üInspect the variables (do descriptive statistics, check unique values, check data type, check
amount of missing data).
üHarmonize your variables (esp. categorical variables).
üStructure your data.

Preliminary analysis
üBuild histograms in case of numerical variables.
üBuild box plots in case of categorical variables.
üBuild scatterplots to see associations between numerical variables.
üCheck data distributions in case of numerical variables (normality tests).
üTransform the data if necessary.
You may proceed to the analysis

Task!
Portrait of an excited young girl with laptop computer and celebrating success


Data preparation –
cleaning, harmonizing and structuring
üChoose variables you are going to work with.
üUpload your data.
üInspect the variables (do descriptive statistics, check unique values, check data type, check
amount of missing data).
üHarmonize your variables (esp. categorical variables).
üStructure your data.

Preliminary analysis
üBuild histograms in case of numerical variables.
üBuild box plots in case of categorical variables.
üBuild scatterplots to see associations between numerical variables.
üCheck data distributions in case of numerical variables (normality tests).
üTransform the data if necessary.
You may proceed to the analysis

Practical part