Create a gist now

Instantly share code, notes, and snippets.

What would you like to do?
Basic Statistics in R
# ------------------------------------------------------------------
# |PROGRAM NAME: R basic statistics
# |DATE: 2/20/17
# |CREATED BY: MATT BOGARD
# |PROJECT FILE:
# |----------------------------------------------------------------
# | PURPOSE: BASIC STATISTICS IN R
# |----------------------------------------------------------------
# create some toy data
GARST <- c(150,140,145,137,141,145,149,153,157,161)
PIO <- c(160,150,146,138,142,146,150,154,158,162)
MYC <- c(137,148,151,139,143,120,115,136,130,129)
DEK <- c(150,149,145,140,144,148,152,156,160,164)
PLOT <- c(1,2,3,4,5,6,7,8,9,10)
BT <- c('Y','Y','N','N','N','N','Y','N','Y','Y')
RR <- c('Y','N','Y','N','N','N','N','Y','Y','N')
yield_data <- data.frame(GARST,PIO,MYC,DEK,PLOT,BT,RR)
# create GMO trait field
yield_data$GMO <- ifelse(yield_data$BT == 'Y' & yield_data$RR == 'Y','Stacked Trait',
ifelse(yield_data$RR == "Y" , 'Single Trait ',
ifelse(yield_data$BT =="Y",'Single Trait ', 'Non-GMO ')))
#--------------------------------
# summary statistics
#--------------------------------
summary(yield_data)
#by processing
by(yield_data, yield_data$GMO, summary)
#frequencies
table(yield_data$GMO)
#----------------------------
# graphics
#----------------------------
#histogram
hist(yield_data$GARST)
#scatterplot
plot(yield_data$GARST,yield_data$PIO)
#-----------------------------------
# t-tests
#-----------------------------------
# independent samples t-test
t.test(yield_data$GARST,yield_data$PIO,var.equal=TRUE ) # difference in GARST vs PIO yields from yield_data file
#Welch's 2 sample ttest
t.test(yield_data$GARST,yield_data$PIO,var.equal=FALSE) # this is default
# run as if paired ttest
t.test(yield_data$GARST,yield_data$PIO,paired=TRUE) # not actually appropriate but notice the difference in the results from the previous t-test on # GARST and PIO
#-------------------------------
# correlations
#-------------------------------
cor(yield_data$GARST,yield_data$PIO, method="pearson")
#-------------------------------
# regression
#-------------------------------
summary(lm(GARST~PIO,data=yield_data))
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment