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

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