diff --git a/reference.md b/reference.md index 7fafacdf..c8b9a886 100644 --- a/reference.md +++ b/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