Update reference.md to match each chapter

reference.md

@@ -1,8 +1,10 @@
 Cheat sheet of functions used in the lessons
 
-
 ## Lesson 1 -- Introduction to R
 
+  * `sqrt()`    # calculate the square root
+  * `round()`   # round a number
+  * `args()`    # find what arguments a function takes
   * `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
@@ -15,57 +17,80 @@ Cheat sheet of functions used in the lessons
 
 ## 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
+  * `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.table()`  # 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
-  * `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
+  * `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
+  * `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
 
-## Lesson 4 -- Aggregating and analyzing data with dplyr
+## Lesson 3 -- Manipulating, analyzing and exporting data with tidyverse
 
-  * `install.packages()` # install a CRAN package in R
-  * `library() `         # load installed package into the current session
+  * `read_csv()` # load a csv formatted file into R memory
+  * `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
-  * `tally()`            # counts the total number of records for each category.
-  * `write.csv() `       # save CSV file
+  * `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
 
-## Lesson 5 -- Data visualization with ggplot2
+## Lesson 4 -- Data visualization with ggplot2
 
-  * `ggplot2(data= , aes(x= , y= )) + geom_point( ) + facet_wrap () +
-    theme_bw() + theme() `
+  * `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
-  *
-## Lesson 6 -- R and SQL
+  * `grid.arrange()` # combine and arrange multiple ggplots into a single figure
+  * `ggsave()` # save a ggplot
+
+## Lesson 5 -- SQL databases and R
 
-  * `src_sqlite`  # connect dplyr to a SQLite database file
+  * `dir.create()` # create a directory
+  * `download.file() ` # download files from the internet to your computer
+  * `dbConnect()` # create a connection to a database
+  * `SQLite()` # connect to a SQLite database
+  * `src_dbi()` # connect dplyr to a DBI-compatible 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
+  * `sql()` # combine character vectors into a single SQL expression 
+  * `show_query()` # show which SQL commands are sent to the database
+  * `collect()`     # retrieve all the results from the database
+  * `inner_join()`  # perform an inner join between two tables
+  * `src_sqlite()` # connect dplyr to a SQLite database file
+  * `copy_to()`     # copy a data frame as a table into a database