# Summary or Descriptive statistics in R

Descriptive Statistics of the dataframe in R can be calculated by 3 different methods. Let’s see how to calculate summary statistics of each column of dataframe  in R with an example for each method.

• Descriptive statistics with summary function in R
• summary statistics in R using stat.desc() function from “pastecs” package
• Descriptive statistics with describe() function from “Hmisc” package

Let’s first create the dataframe.

```### Create Data Frame
df1 = data.frame(Name = c('George','Andrea', 'Micheal','Maggie','Ravi','Xien','Jalpa'),
Mathematics1_score=c(45,78,44,89,66,49,72),
Science_score=c(56,52,45,88,33,90,47))
df1
```

So the resultant dataframe will be #### Descriptive statistics in R (Method 1):

Descriptive statistics in R with simple summary function calculates

• minimum value of each column
• maximum value of each column
• mean value of each column
• median value of each column
• 1st quartile  of each column
• 3rd quartile of each column

as shown below

```# Summary statistics of dataframe in R

summary(df1)
```

summary statistics is #### Summary / Descriptive statistics in R (Method 2):

Descriptive statistics in R with pastecs package does bit more than simple describe () function. It also Calculates

• number of missing values and null of each column in R
• number of non missing values of each column
• sum , range ,variance and standard deviation etc for each column
```# descripive statistics of dataframe in R

install.packages("pastecs")
library(pastecs)
stat.desc(df1)
```

summary statistics is #### Summary statistics in R (Method 3):

Descriptive statistics in R with Hmisc package calculates the  distinct value of each column, frequency of each value and proportion of that value in that column. as shown below

```# Summary statistics of dataframe in R

install.packages("Hmisc")
library(Hmisc)
describe(df1)
```

summary statistics is 