9  Simple Descriptive Statistics

Calculating the Mean and Standard Deviation

Let’s load the swiss dataset.

data("swiss")

Let’s print the first few rows

head(swiss)

             Fertility Agriculture Examination Education Catholic
Courtelary        80.2        17.0          15        12     9.96
Delemont          83.1        45.1           6         9    84.84
Franches-Mnt      92.5        39.7           5         5    93.40
Moutier           85.8        36.5          12         7    33.77
Neuveville        76.9        43.5          17        15     5.16
Porrentruy        76.1        35.3           9         7    90.57
             Infant.Mortality
Courtelary               22.2
Delemont                 22.2
Franches-Mnt             20.2
Moutier                  20.3
Neuveville               20.6
Porrentruy               26.6

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Mean

We can calculate simple average in R using the function mean()

Let’s calculate the average Agriculture in the swiss dataset (i.e. % of males involved in agriculture as occupation)

mean(swiss$Agriculture)

[1] 50.65957

We can see further distributional measures with the function summary() (e.g. Minimum, Median, Maximum)

summary(swiss$Agriculture)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.20   35.90   54.10   50.66   67.65   89.70 

We can further have a sense of the variation in fertility by looking at the histogram

hist(swiss$Agriculture)

Standard Deviation

The standard deviation is a measure that quantifies the amount of variability or dispersion. It tells us how spread out the values are from the mean (average) of the data set.

The standard deviation can be easily computed with the function sd()

sd(swiss$Agriculture)

[1] 22.71122