Author: Zhanbii Kadyrkulov

Descriptive statistics of dataset iris

data()

mydata <- force(iris)

head(mydata)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

1 Description of data set used in the analysis:

Unit of observation: One flower

Sample size: 150 flowers

1.1 Definition of variables:

-Sepal.Length: it is the measurement of small, leaf-shaped, green-coloured and outermost part of the flower length in cm

-Sepal.Width: it is the measurement of small, leaf-shaped, green-coloured and outermost part of the flower width in cm

-Petal.Length: it is the measurement of the second whorl of the flower lenght in cm

-Petal.Width: it is the measurement of the second whorl of the flower width in cm

-Species: it is the name of the flower type

1.2 Source:

Source: Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7, Part II, 179–188.

The data were collected by Anderson, Edgar (1935). The irises of the Gaspe Peninsula, Bulletin of the American Iris Society, 59, 2–5.

2 Descriptive statistics

mydata$SpeciesF <- factor(mydata$Species,
                          levels = c("setosa", "versicolor", "virginica"),
                          labels = c("Flower1", "Flower2", "Flower3"))

Writing this code, I changed categorical variable specie to factor

#install.packages(psych)

library(psych)

describeBy(mydata$Sepal.Length , group = mydata$SpeciesF)
## 
##  Descriptive statistics by group 
## group: Flower1
##    vars  n mean   sd median trimmed mad min max range skew kurtosis   se
## X1    1 50 5.01 0.35      5       5 0.3 4.3 5.8   1.5 0.11    -0.45 0.05
## ------------------------------------------------------------ 
## group: Flower2
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 50 5.94 0.52    5.9    5.94 0.52 4.9   7   2.1  0.1    -0.69 0.07
## ------------------------------------------------------------ 
## group: Flower3
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 50 6.59 0.64    6.5    6.57 0.59 4.9 7.9     3 0.11     -0.2 0.09

We can see that using the describeBy command we can say that 50% (median) of of virginica species had sepal lenght less or equal 6.5 cm and the other half had more than 6.5.

summary(mydata[ ,c(-5, -6)])
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500

We can say that 75% (3rd quartile) of all species had sepal width equal to 3.3cm. Another thing to outline is that the maximum petal lenght of all species was 6.9cm. Also the petal width of all species was equal to 0.1cm.

boxplot(mydata[ , c(-5,-6)])

According to this boxplot we can see that the biggest sepal length of flower is 7.9cm because we can see that the highest line in the boxplot is somewhere around 8. The bold line in each box represents the median of all of variables, meaning 50% of each statistics. The circles on the sepal width mean that there are some outstanders that don’t belong to the other units. The lowest line mean the minimum of the variable. Bottom edge of these gray boxes means 1st quartile. Upper edge means 3rd quartile.