Learners' Reference

Last updated on 2022-08-09 | Edit this page

Cheat sheet of functions used in the lessons

Lesson 1 – Introduction to R

download.file() # download files from the internet to your computer
read_csv() # load CSV file into R memory
head() # shows the first 6 rows
view() # invoke a spreadsheet-style data viewer
read_delim() # load a file in table format into R memory
str() # check structure of the object and information about the class, length and content of each column
dim() # check dimension of data frame
nrow() # returns the number of rows
ncol() # returns the number of columns
tail() # shows the last 6 rows
names() # returns the column names (synonym of colnames() for data frame objects)
rownames() # returns the row names
summary() # summary statistics for each column
factor() # create factors
levels() # check levels of a factor
nlevels() # check number of levels of a factor
as.character() # convert an object to a character vector
as.numeric() # convert an object to a numeric vector
as.numeric(as.character(x)) # convert factors where the levels appear as characters to a numeric vector
as.numeric(levels(x))[x] # convert factors where the levels appear as numbers to a numeric vector
plot() # plot an object
addNA() # convert NA into a factor level
data.frame() # create a data.frame object
ymd() # convert a vector representing year, month, and day to a Date vector
paste() # concatenate vectors after converting to character

str() # check structure of the object and information about the class, length and content of each column
view() # invoke a spreadsheet-style data viewer
select() # select columns of a data frame
filter() # allows you to select a subset of rows in a data frame
%>% # pipes to select and filter at the same time
mutate() # create new columns based on the values in existing columns
head() # shows the first 6 rows
group_by() # split the data into groups, apply some analysis to each group, and then combine the results.
summarize() # collapses each group into a single-row summary of that group
mean() # calculate the mean value of a vector
!is.na() # test if there are no missing values
print() # print values to the console
min() # return the minimum value of a vector
arrange() # arrange rows by variables
desc() # transform a vector into a format that will be sorted in descending order
count() # counts the total number of records for each category
spread() # reshape a data frame by a key-value pair across multiple columns
gather() # reshape a data frame by collapsing into a key-value pair
n_distinct() # get a count of unique values
write_csv() # save to a csv formatted file

read_csv() # load a csv formatted file into R memory
ggplot2(data= , aes(x= , y= )) + geom_point( ) + facet_wrap () + theme_bw() + theme()
aes() # by selecting the variables to be plotted and the variables to define the presentation such as plotting size, shape color, etc.
geom_ # graphical representation of the data in the plot (points, lines, bars). To add a geom to the plot use + operator
facet_wrap() # allows to split one plot into multiple plots based on a factor included in the dataset
labs() # set labels to plot
theme_bw() # set the background to white
theme() # used to locally modify one or more theme elements in a specific ggplot object
+ # arrange ggplots horizontally
/ # arrange ggplots vertically
plot_layout() # set width and height of individual plots in a patchwork of plots
ggsave() # save a ggplot