DSS Social Sciences Workshop Resources

This is an introduction to R designed for participants with no programming experience.

In this section, you will learn:

• basic information about R syntax, the RStudio interface
• how to import CSV files
• the structure of data frames
• how to deal with factors
• how to add/remove rows and columns
• how to calculate summary statistics from a dataframe brief introduction to plotting

R and RStudio are separate downloads and installations. R is the underlying statistical computing environment, but using R alone is no fun. RStudio is a graphical integrated development environment (IDE) that makes using R much easier and more interactive. 

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
• typeof # determines the (R internal) type or storage mode of any object
• 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

Starting with Data

• 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
• glimpse # like str() applied to a data frame but tries to show as much data as possible
• 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

Data Wrangling with dplyr and tidyr

• 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
• pivot_wider() # reshape a data frame by a key-value pair across multiple columns
• pivot_longer() # reshape a data frame by collapsing into a key-value pair
• replace_na() # Replace NAs with specified values
• n_distinct() # get a count of unique values
• write_csv() # save to a csv formatted file

Data Visualization with ggplot2

• read_csv() # load a csv formatted file into R memory
• ggplot2(data= , aes(x= , y= )) + geom_point( ) + facet_wrap () + theme_bw() + theme() # skeleton for creating plot layers
• 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

Processing JSON data

• read_json() # load json object to an R object