Bosses are impatient. They often want you to cut to the chase, and give them an answer that's ok, but in a short amount of time. Descriptive statistics are the first place to start - they are often the 10s answer to any question about the data. 

Computing a frequency distribution using R

A histogram is a good visual summary of your data. 

Computing the Mean, Median, Mode in R

The mean, median and mode are point estimates to represent your data. IQR is a measure that explains the spread of the data.

Visualize the IQR and outliers using box and whisker plots

The standard deviation measures the spread of a dataset, and it so happens, the standard deviation is actually very profound.

Drawing inferences from data is key to being able to take decisions using data. There is a science to this, whose foundation is in random variables, probability distributions, and performing tests of statistical significance. 

Random variables are everywhere. Any data that you'll study is a random variable whose behaviour is determined by a probability distribution.

The Normal Distribution is arguably the most well-known and commonly seen probability distribution. It is characterized by its probability density function, mean and standard deviation.

Sampling is a little like fishing. Sampling is crucial to induction - drawing conclusions about something by looking at some evidence.

A sample is described by sample statistics like the sample mean. The sampling distribution is the probability distribution of sample means. 

Find a point estimate for the average weight of all football players using a sample of football players in 1 college team.

Find a point estimate for the % of voters in favor of a candidate.

A test of significance is an important step in building support for your findings and inferences. Here is the first example of a test of significance - is the population mean equal to a given value? 

Perform a test of significance to check whether the population % is equal to a certain value

Perform a test of significance to compare 2 population means. The example used is A/B Testing - which is pretty widely used in internet companies to test out product features.

Perform a test of significance to compare two population proportions

The next few sections dive deep into all the data processing, slicing and dicing ability that R provides. The wide variety of R packages available is one reason why R is popular among many data scientists. 

Let's start with the basics. What are variables and how do we assign variables in R? 

print(), show(), message(), cat() are different ways to print something to screen. 

Numbers in R are of type numeric. 

R has built-in datatypes for dates and timestamps. 

Logical is a datatype that is the result of conditional tests in R

The wide variety of built-in data structures are what makes R different from other standard programming languages. These include vectors, arrays, matrices, data frames and lists. 

The mode of a vector is the datatype of all its elements. 

Finding the sum, product, or mean of a vector

Generate sequences using the : operator, rep() and seq()

Access elements based on their position in the vector.

Access elements based on whether they pass a conditional test. 

Assign names to the elements of a vector

Creating an array can be done by using a vector and then arranging it along dimensions.

Outer products are complex operations that operate on every pair of elements from two arrays.

A Matrix is a 2 Dimensional array. But it has special meaning and can be interpreted in a bunch of different ways.

rbind() and cbind() to merge matrices.

A factor is a special type of vector used to represent categorical variables

Lists are fundamentally different from vectors, arrays and matrices - which are all homogenous data structures.

Data Frames are how R stores data read from files and databases.

Using the aggregate() and order() functions

Merge data frames based on one or more common columns