Glossary

Key Points

Before we Start	Use RStudio to write and run R programs. Use `install.packages()` to install packages (libraries).
Introduction to R	Access individual values by location using `[]`. Access arbitrary sets of data using `[c(...)]`. Use logical operations and logical vectors to access subsets of data.
Starting with Data	Use read_csv to read tabular data in R. Use factors to represent categorical data in R.
Data Wrangling with dplyr and tidyr	Use the `dplyr` package to manipulate dataframes. Use `select()` to choose variables from a dataframe. Use `filter()` to choose data based on values. Use `group_by()` and `summarize()` to work with subsets of data. Use `mutate()` to create new variables. Use the `tidyr` package to change the layout of dataframes. Use `pivot_wider()` to go from long to wide format. Use `pivot_longer()` to go from wide to long format.
Data Visualisation with ggplot2	`ggplot2` is a flexible and useful tool for creating plots in R. The data set and coordinate system can be defined using the `ggplot` function. Additional layers, including geoms, are added using the `+` operator. Boxplots are useful for visualizing the distribution of a continuous variable. Barplots are useful for visualizing categorical data. Faceting allows you to generate multiple plots based on a categorical variable.
Processing JSON data	JSON is a popular data format for transferring data used by a great many Web based APIs The complex structure of a JSON document means that it cannot easily be ‘flattened’ into tabular data We can use R code to extract values of interest and place them in a csv file
Reports with RMarkdown	RMarkdown document change dynamically in response to changes in the data RMarkdown lets you instantiate literally programming easily

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
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

Lesson 2 – 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

Lesson 3 – 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

Lesson 4 – 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

Lesson 5 – Processing JSON data

read_json() # load json object to an R object

R for Social Scientists: Glossary

Key Points

Glossary

Lesson 1 – Introduction to R

Lesson 2 – Starting with Data

Lesson 3 – Data Wrangling with dplyr and tidyr

Lesson 4 – Data Visualization with ggplot2

Lesson 5 – Processing JSON data