While I aim to introduce data structures: vectors, dataframes, lists, matrices, however our key focus is on dataframes
To understand data types: vectors, dataframes, lists, matrices
To do basic analysis
Remember objects we created in the previous section)? Those were all vectors. A vector is the basic data structure used to hold values of the same type. Similar to the previous section, a vector can be:
numeric
character
logical
Although we are repeating stuff from previous section, but it worth it.
Let us create a character vector of countries in Southern Africa:
southern_africa <- c("Angola", "Botswana", "Lesotho", "Malawi", "Mozambique", "Namibia", "South Africa", "Swaziland", "Zambia", "Zimbabwe")
## print southern_africa
southern_africa [1] "Angola" "Botswana" "Lesotho" "Malawi" "Mozambique"
[6] "Namibia" "South Africa" "Swaziland" "Zambia" "Zimbabwe" We have created vector that named southern_africa, and it has the countries in the Southern African Region. Let us use basic functions to examine our southern_africa vector. We can get the type of vector by using class() function:
It is character vector. Remember from the previous section what is the character data type.
We can examine the length by using length() function:
We have 10 elements in the southern_africa vector
Let us create a numeric vector, that we name life_expectancy, that has the average life expectancy of the countries of Southern Africa:
life_expectancy <- c(61.6, 61.1, 57.1, 53.1, 62.9, 59.3, 59.3, 62.3, 61.2, 59.3)
## print the life_expectancy vector
life_expectancy [1] 61.6 61.1 57.1 53.1 62.9 59.3 59.3 62.3 61.2 59.3We can confirm the type of vector we have created by using the class() function:
Indeed, the life_expectancy vector is a numeric vector.
Let us do basic analyses of this vector. We can get the mean by using mean() functions:
We can get the median and standard deviation of life_expectancy vector using median() and sd() functions, respectively:
You can get an element of vector by using [] function. Let us get the first element in life_expectancy vector:
To get the 1st, 5th, 8th elements within a vector, you would do the following:
You can also extract the vector elements by using the colon (:):
Here, we wanted to get all the elements starting from the 3rd position to the 6th position.
Key lesson: a vector holds items of a similar type: as we have seen in the southern_countries and life_exepctancy vectors.
Dataframes will be the key focus throughout the course, so I will just briefly explain what is a dataframe. A dataframe is tabular data format, consisting of columns and rows. Let us use an example by creating a dataframe in R:
# Create a character vector
country_names <- c("Angola", "Botswana", "Lesotho", "Malawi", "Mozambique", "Namibia", "South Africa", "Swaziland", "Zambia", "Zimbabwe")
country_names [1] "Angola" "Botswana" "Lesotho" "Malawi" "Mozambique"
[6] "Namibia" "South Africa" "Swaziland" "Zambia" "Zimbabwe" ## Create a numeric vector
life_expectancy <- c(61.6, 61.1, 57.1, 53.1, 62.9, 59.3, 59.3, 62.3, 61.2, 59.3)
life_expectancy [1] 61.6 61.1 57.1 53.1 62.9 59.3 59.3 62.3 61.2 59.3Because we have 2 vectors of equal length, we can create a dataframe, using a data.frame() function:
africa_df <- data.frame(country_names, life_expectancy) #combine two vectors to create a dataframe
africa_df country_names life_expectancy
1 Angola 61.6
2 Botswana 61.1
3 Lesotho 57.1
4 Malawi 53.1
5 Mozambique 62.9
6 Namibia 59.3
7 South Africa 59.3
8 Swaziland 62.3
9 Zambia 61.2
10 Zimbabwe 59.3We have created africa_df dataframe, with columns and rows. Let us examine it. How many columns and rows are in the dataframe. We can use the str() function:
'data.frame': 10 obs. of 2 variables:
$ country_names : chr "Angola" "Botswana" "Lesotho" "Malawi" ...
$ life_expectancy: num 61.6 61.1 57.1 53.1 62.9 59.3 59.3 62.3 61.2 59.3R tells us that there are 10 observations (rows) and 2 columns
Let us view the first few observations (rows) using the head() function:
country_names life_expectancy
1 Angola 61.6
2 Botswana 61.1
3 Lesotho 57.1
4 Malawi 53.1
5 Mozambique 62.9
6 Namibia 59.3View the last few observations using the tail()
country_names life_expectancy
5 Mozambique 62.9
6 Namibia 59.3
7 South Africa 59.3
8 Swaziland 62.3
9 Zambia 61.2
10 Zimbabwe 59.3In the following sections, we will be working with dataframes a lot, and other non-exhastive functions to manipulate and transofrm dataframes.
You will learn more about matrix objects when you advance in your data science career.
lists are another data structure that are used to hold objects of different type. For example, they can hold both vector and dataframe:
We have objects: africa_df (dataframe) and names (vector) and ages (vector) objects. From these objects, we can create a lists:
[[1]]
country_names life_expectancy
1 Angola 61.6
2 Botswana 61.1
3 Lesotho 57.1
4 Malawi 53.1
5 Mozambique 62.9
6 Namibia 59.3
7 South Africa 59.3
8 Swaziland 62.3
9 Zambia 61.2
10 Zimbabwe 59.3
[[2]]
[1] "Aubrey" "Sphethu" "Peter"
[[3]]
[1] 32 7 19Did you see that Jimmy? We actually printed the list. As you advance in your programming with R, you will see why lists are important and how everything is a lit.