Cheat sheet of functions used in the lessons

## Lesson 1 – Introduction to R

• length() # how many elements are in a particular vector
• class() # the class (the type of element) of an object
• str() # an overview of the object and the elements it contains
• c() # create vector; add elements to vector
• [ ] # extract and subset vector
• %in% # to test if a value is found in a vector
• is.na() # test if there are missing values
• na.omit() # Returns the object with incomplete cases removed
• complete.cases()# elements which are complete cases

## Lesson 2 – Starting with data

• download.file() # download files from the internet to your computer
• read.csv() # load CSV file into R memory
• head() # check the top (the first 6 lines) of an object including data frames
• factor() # create factors
• levels() # check levels of a factor
• nlevels() # check number of levels of a factor
• as.numeric(levels(x))[x] # convert factors where the levels appear as numbers to a numeric vector

## Lesson 3 – Introducing data.frame

• data.frame() # create a data frame
• dim() # check dimension of data frame
• nrow() # returns the number of rows
• ncol() # returns the number of columns
• head() # shows the first 6 rows
• tail() # shows the last 6 rows
• names() # returns the column names (synonym of colnames() for data frame objects)
• rownames() # returns the row names
• str() # check structure of the object and information about the class, length and content of each column
• summary() # summary statistics for each column
• seq() # generates a sequence of numbers

## Lesson 4 – Aggregating and analyzing data with dplyr

• install.packages() # install a CRAN package in R
• library() # load installed package into the current session
• 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
• 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
• tally() # counts the total number of records for each category.
• write.csv() # save CSV file

## Lesson 5 – Data visualization with ggplot2

• 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
• theme_bw() # set the background to white
• theme() # used to locally modify one or more theme elements in a specific ggplot object
• ## Lesson 6 – R and SQL

• src_sqlite # connect dplyr to a SQLite database file
• tbl # connect to a table within a database
• collect # retrieve all the results from the database
• explain # show the SQL translation of a dplyr query
• inner_join # perform an inner join between two tables
• copy_to # copy a data frame as a table into a database