R

2. Starting with a plot

3. Adding labels with xlab and ylab

Text and symbol size are highly tunable. Here are a series of commands based on the argument cex and its many variations. In all cases, you will have to use a value X which is a magnification factor by which the original size of the object will be multiplied. If X […]

4. Tuning text and symbol size with cex

pch col — The default symbol for plotting data elements is an open circle, but you are not obliged to stick to this. The argument pch= (plotting character) allows you to choose the symbol from a list. Check in the list below which symbol you like best and indicate the corresponding number in the […]

5. Modifying symbols

par mar order — When you start playing with the size of the labels and titles, you quickly realize that the frame of the plot becomes too small. It is time to introduce the argument par(mar=c(B,L,T,R)) where B, L, T and R stand for bottom, left, top and right, respectively. Of course you will […]

6. Adjusting margins

coloured titles — Unsurprisingly, the function that adds a title to your plot is called… title(). This function can be “decorated” with several arguments to set the different titles or labels of the chart. Among these are main="text", sub="text", ylab="text", xlab="text", which purposes are to display the main title, a subtitle (secondary title), […]

7. Adding titles and labels

8. Tuning axes

9. Adding legends

10. Adding text annotations

Reference lines are useful to visualize a threshold on a chart, or to represent or point at specific values such as mean and median. The function abline( ) draws such a line directly in your chart. abline() needs to know whether the line is horizontal (h=) or vertical (v=) and […]

11. Adding reference lines

type n — The function lines() draws a line connecting the symbols on the chart. It does not produce a line chart by itself, nor does it build the axes… but it only adds lines to an existing plot. The main parameter in lines() is type= and the options for using type= are […]

12. Adding lines linking the data points

clipboard — Once you’ve done your magic, you may be interested in keeping a memory of your masterpiece. This is where the Save as menu becomes useful. Note that the graphic window (the one containing your graph) must be active when saving; if you aren’t sure about it, click once on the […]

13. Saving a graph

14. Combining graphs

rep() — Here you will find useful functions to create series of numbers to populate your tables and objects. These functions are also quite useful when you want to make a simulation or try functions on a random data set or on a test sample. Note that some functions return integers (numbers […]

1. Creating series

class() — Here are a few functions which are useful to handle and manage objects. ls() lists all the vectors and variables currently stored in memory. [code language=”r”] ls() [/code] Note that the function objects() does exactly the same. rm() erases from the memory the vector or variable which is named […]

2. Managing objects

dataset[] — head(test) returns by default the first sixth entries in the vector “test”; head(test, 3) returns only the first three entries in the vector “test”. Check the example below: [code language=”r”] test <- c(4,7,9,6,89,45,3,5,78,23,45,0,2,12) test head(test) head(test, 3) [/code] tail(test) returns the last sixth entries in the vector “test”; tail(test, […]

3. Diverse functions

The function rnorm() generates automatically a series of random values which are normally distributed. This comes quite handy when you want to test functions, statistical analyses or scripts on a random dataset, and you need to be sure that the content is normally distributed beforehand. rnorm() needs the following information: […]

4. Generating random series

NOT a dataframe — A dataframe looks like a two-dimensional matrix. However, unlike a matrix, a dataframe can contain more than numerical values. You can also fill in columns with text values and boolean/logical values (TRUE and FALSE). In a dataframe, the variables, dependent and independent, are placed in columns, while the rows represent the […]

1. Structure and technical properties of a dataframe

in R / R -4- Dataframes

attach() — Most of the time you will load the dataframe from an external file, most likely a text file, an excel file or a CSV file (Comma-Separated Values). In that case, you may follow this procedure. In brief, you have to use the command read.xls(), read.table() or read.csv() to open a file and transfer its […]

2. Loading a dataframe

in R / R -4- Dataframes

dim dataframe — From here, you can use summary() to get a quick overview of your imported dataframe. summary() offers a reduced, but in fact very informative list of descriptive statistics for each numerical variable, including mean, median, quartiles and minimum/maximum. As for the text and logical variables, a count of each level […]

3. Summary of the dataframe

in R / R -4- Dataframes

combination — R gives you the possibility to extract parts of your dataframe. This can be useful to isolate data elements, columns of variables, subsets in your sample… Here are a few commands that create subsets from the dataframe. extracting single data elements Type the name of the object followed by […]

4. Subscripts of a dataframe

in R / R -4- Dataframes

rev() — It is not because you have imported your data from an external source that the order of the observations has to stay as it originally was. You can sort the observations based on ascending or descending values in a column. To do so, you will use the function object.name[order(object.name[,X]),a:b] where X […]

5. Sorting data in the dataframe

in R / R -4- Dataframes

When working with big (or huge) dataframes, it might be convenient to create a new dataframe that contains parts of the original one which are selected based on variable names, specific columns or rows, values or ranges… In this post, we will use the following “pseudo-huge” dataframe as an example: […]

6. Working on a subset of the dataframe

in R / R -4- Dataframes

It is fairly easy to build a dataframe from scratch in R. All you need is a series of vectors and/or series containing your data and a good recipe! And very often, a good recipe is a simple one… First you have to decide how many columns this dataframe will have, […]

7. Building a dataframe from vectors/series

in R / R -4- Dataframes

warning — Let’s see first how you can create a file in MS Excel that can be read by R and which will keep the data structure intact. Here is an example of a simple data table in MS Excel: This table contains 4 columns and 13 rows. This first row contains […]

1. How to export data from MS Excel

in R / R -5- Importing data into R

There are several ways and commands to import data from a file into R. Providing that your data are in an Excel file (.xls, .xlsx), in a text file (.txt), in a comma-separated values file (.csv), you will have to choose the appropriate function. Before going further, check the function […]

2. How to import data into R

in R / R -5- Importing data into R

With the gsheet R package you can download Google Sheets using a sharing link. You can download the data from the google sheet as a data frame or plain text. Installation [code language=”r”] install.packages(‘gsheet’) [/code] Getting started To download a google sheet, use the following code. The link can be […]

3. How to read data from Google sheets

in R / R -5- Importing data into R

norwegian names — You may use objects to store single values (integers, letters, words… see vectors) but you can store in fact much more than that. Using the function concatenate, you will be able to store a series of value or words under the same variable. Here is an example where we store […]

1. Objects that contain a series of integers or words

The name that you choose for your vector may be a single character (such as X) or a word (such as data) but you are allowed/encouraged to be more creative. It may be a more or less abstract sequence of letters and numbers (dataEXT2), a combination of words separated by […]

1. Choosing a name for your vector

in R / R -2- Creating vectors

matrix coordinates — Objects can also store series of data arranged in the form of a two-dimensional matrix, i.e. an arrangement of data in rows and columns. The function matrix() creates this type of object, but requires a few arguments to properly “draw” the matrix and dispatch the data. First, let’s agree that […]

2. Objects that contain a matrix

long.box array — An array is nothing more than a matrix with more than two dimensions. It is basically a series of two-dimensional matrices “on top of each other”; in other words, a matrix is an array with only two dimensions, or only one layer. To build an array, there is a function […]

3. Objects that contain an array

concatenate — In R, storing data under a specific name is called assignment. There are at least 4 ways to assign data in R: a. with the operator = b. with the operator <- c. with the operator -> d. with the function assign("name,..."). Here is a practical example where single values […]

2. Assigning data elements to a vector

in R / R -2- Creating vectors

names in list — If you need to work on several objects at the same time or need for any reason to gather objects in a single collection, you may create a list object. And this is done using the function list(). In the following example, we’ll make a list that contains the new […]

4. Objects that contain a list of objects or vectors

table random list — The table() function in R is used to create… a table. So far, so good. In fact, this is a rather simple function that gathers data and creates a contingency table or frequency table, in other words, a table which counts the number of occurrences of any unique value in […]

5. Objects that contain a table