sokarob

## Create Summary with Aggregate Function.r
df2 <- aggregate(df$points, by=list(df$team, df$position), FUN=mean)

#credit to https://www.statology.org/aggregate-r/

## Convert Month Number to Month Name.r
# Use month.name to change the index of months to its corresponding name
month.name[v1]


# Use month.abb for 3 character abbreviation for month
month.abb[v1]

# credit to: https://stackoverflow.com/questions/50607659/convert-months-number-to-month-name

# Example using YYYYMM format to make Month + Year:

## Find a String in Another String (Contains).R
# Simple Condition
str_detect(`Column`,'STRING')==T

# Condition with case handling
str_detect(toupper(`Column`),'STRING')==T

# Also works for excluding records
str_detect(`Column`,'STRING')==F

## Turn Dataframe Columns Into Vectors.r
#use $ operator
new_vector <- df$column_name

	#Example for geographic x and y coordinates:
	x <- location$longitude
	y <- location$latitude


#use indexing
new_vector <- df[['column_name']]

## Import SAS Data (.sas7bdat).r
library(haven)
df <- read_sas("data.sas7bdat")

## Import Excel Files with readxl and openxlsx.r

library(readxl)  # or tidyverse
df <- readxl::read_excel('C:\\R\\Project Folder\\Spreadsheet.xlsx',sheet=NULL,range=NULL)
df <- readxl::read_excel('Spreadsheet.xlsx',sheet="Sheet1",range="C3:D5")  #If working directory is set
df <- readxl::read_excel("Spreadsheet.xlsx", na="NA", col_names=FALSE)  #If there is no header


library(openxlsx)
df <- read.xlsx("Spreadsheet.xlsx", sheet = 3, skipEmptyRows = TRUE)
df <- read.xlsx(string_variable, sheet = "Sheet1", skipEmptyRows = FALSE)

## Set Working Directory.r
#Set Working Directory between "" (use forward slashes instead of back slashes or use double back slashes):
wd <- "C:\\R\\Project Folder"

## Use Regular Expressions to Remove Characters.r
# Remove from strings that should consist only of numerals
df<-df %>% mutate("Column2"=gsub("\\D","",Column1))
	# Use condition to replace fields that would be blank after removing the non-numeric characters with NA.
	df<-df %>% mutate("Column2"=ifelse(gsub("\\D","",Column1)=="",NA,gsub("\\D","",Column1)))

# Remove everything that isn't alpha-numeric
df<-df %>% mutate("Column2"=gsub("[^a-zA-Z0-9]","",Column1))
	# Use condition to replace fields that would be blank after removing the non-numeric characters with NA.
	df<-df %>% mutate("Column2"=ifelse(gsub("[^a-zA-Z0-9]","",Column1)=="",NA,gsub("[^a-zA-Z0-9]","",Column1)))


## Prompt for User Input In Console With readline() Command From devtools.R
library(devtools)

# ***************************************************
# *********** ENTER RESPONSE INTO CONSOLE! **********
# ***************************************************

x = readline(prompt = "Enter x:")  # creates variable that can be used like any other string variable

# Credit to: https://www.geeksforgeeks.org/taking-input-from-user-in-r-programming/
	# Note: There are validation tips and more details in this web page.

## Filter in Base R.r
# Character filter
df2 <- df1[df1$'column'=="string",]

# Numeric filter
df2 <- df1[df1$'column'>=1234,]

# Filter on date and additional condition:
df2 <- df1[df1$'date' >= "2023-12-01" & df1$'column'=="string",]
	df2 <- aggregate(df$points, by=list(df$team, df$position), FUN=mean)

	#credit to https://www.statology.org/aggregate-r/
	# Use month.name to change the index of months to its corresponding name
	month.name[v1]


	# Use month.abb for 3 character abbreviation for month
	month.abb[v1]

	# credit to: https://stackoverflow.com/questions/50607659/convert-months-number-to-month-name

	# Example using YYYYMM format to make Month + Year:
	# Simple Condition
	str_detect(`Column`,'STRING')==T

	# Condition with case handling
	str_detect(toupper(`Column`),'STRING')==T

	# Also works for excluding records
	str_detect(`Column`,'STRING')==F
	#use $ operator
	new_vector <- df$column_name

	#Example for geographic x and y coordinates:
	x <- location$longitude
	y <- location$latitude


	#use indexing
	new_vector <- df[['column_name']]

	library(readxl) # or tidyverse
	df <- readxl::read_excel('C:\\R\\Project Folder\\Spreadsheet.xlsx',sheet=NULL,range=NULL)
	df <- readxl::read_excel('Spreadsheet.xlsx',sheet="Sheet1",range="C3:D5") #If working directory is set
	df <- readxl::read_excel("Spreadsheet.xlsx", na="NA", col_names=FALSE) #If there is no header


	library(openxlsx)
	df <- read.xlsx("Spreadsheet.xlsx", sheet = 3, skipEmptyRows = TRUE)
	df <- read.xlsx(string_variable, sheet = "Sheet1", skipEmptyRows = FALSE)
	#Set Working Directory between "" (use forward slashes instead of back slashes or use double back slashes):
	wd <- "C:\\R\\Project Folder"
	library(devtools)

	# ***************************************************
	# ********* ENTER RESPONSE INTO CONSOLE! ********
	# ***************************************************

	x = readline(prompt = "Enter x:") # creates variable that can be used like any other string variable

	# Credit to: https://www.geeksforgeeks.org/taking-input-from-user-in-r-programming/
	# Note: There are validation tips and more details in this web page.
	# Character filter
	df2 <- df1[df1$'column'=="string",]

	# Numeric filter
	df2 <- df1[df1$'column'>=1234,]

	# Filter on date and additional condition:
	df2 <- df1[df1$'date' >= "2023-12-01" & df1$'column'=="string",]