Graduate training on R at the Guangdong Provincial CDC was my first serious programming course.

R Training Notes Summary

Overview

The training lasted three days and covered five main topics:

Introduction to R language and basic concepts, operations
Using R to draw statistical graphs (graph & ggplot2)
Introduction to time series analysis and the use of Generalized Linear Models (GLM) and Generalized Additive Models (GAM)
Use of Distributed Lag Nonlinear Models
Meta-analysis in R

Pros: Free, rich features, fast updates, process visualization, beautiful plots
Cons: Difficult to master, inconsistent, requires time to learn, slow (can be optimized with specific packages)

R and RStudio can be downloaded from the official website
Packages can be installed manually or through the software itself
Functions in R need to load the corresponding package before use, and help can only be viewed after loading.

Case-sensitive
Statements are composed of functions and assignments
All symbols must be half-width English
# is used for comments, #text#### can be indexed (script mode)
No special symbol at the end of each line; if a statement is incomplete, the program will continue to execute on the next line until it is complete
<- is the assignment operator, e.g., x<-3, = can replace it
== is the logical equality operator, & for AND, | for OR, >= greater than or equal to, <= less than or equal to
Single and double quotes are mostly interchangeable
There are two equivalent ways to express paths: C:\\R\\Data is equivalent to C:/R/Data; R has poor support for Chinese,尽量 use full English paths
$ can be used to extract variables within an object; after extraction, the variable can be assigned or modified.
[] can be used to extract subsets of a data frame
! is the negation symbol

Function basic form	Function purpose	Notes
`summary(对象)`	Displays maximum, minimum, arithmetic mean, median, quartile range for numerical variables; Counts for categorical variables; If missing values exist, displays the number of missing values.	Use `rm.na=` parameter to automatically remove missing values when calculating corresponding statistics.

Function expression	Operation effect	Notes
`names(data)<-c("1","2","3")`	Renames the first three variables of data frame data to 1, 2, 3	If there are more than three variables in the data frame, the names of the variables after the third will be lost; if too many names are assigned, it cannot be executed
`data=rename(data,c("old1"="new1", "old2"="new2"))`	Replaces specified variable names in data frame data	rename() is a function from the plyr package
`as.date(data)` `as.numeric(data)` `as.character(data)` `as.factor(data)` etc.	Converts specified variables to types, after conversion, can be assigned to an object
`subset(data, select=c(v1,v2))` `subset(data, v1==n)` `data[row,colum]`	Extracts specified rows/columns from data frame	Whether the two methods are completely equivalent is unknown; `t[]` defaults to taking rows
`data$v=data$v1+test$v2`	Performs calculations on existing data in data frame and writes it as a new variable
`substr(strData,1,3)`	Extracts a specified number of characters from a string variable
`mutate(data,)`	Simplifies code by performing multiple operations simultaneously on data frame data	The main function of mutate() is to simplify code
`join(data1, data2, by= ,type=)`	Merges different data frames according to certain standards	join() function does not require sorting of the table

Two common graph drawing packages in R are:
- graph (built-in): Basic plotting
- ggplot2 (needs installation): Advanced plotting, high customization, beautiful finished products