Download data used in this tutorial.
Install packages and call libraries:
install.packages(c('psych', 'Hmisc', 'janitor'))
# Call libraries:
library(psych)
library(Hmisc)
library(janitor)
Read csv data:
d <- read.csv('D://R4Researchers//columbus.csv')
d
Create data frame:
dt<- data.frame(x=c(1,3,5), y=c(2,4,6))
Clean names:
d<-d %>%
clean_names(case = "title") # Only the first letter of names will be in capital letter
d<- clean_names(d) # to convert names to lowercase and clean them.
View(d)
Common descriptive functions
range(d$crime)
# 221.718 328.100
median(d$crime)
# 301.494
min(d$crime)
# 221.718
max(d$crime)
# 328.1
mean(d$evi) # evi includes the missing value (NA)
# NA
mean(d$evi, na.rm=T) # to ignore the missing (NA) values
# 0.2056042
# standard deviation
sd(dt$x)
# 2
sd(d$crime, na.rm=T)
# 21.09107
quantile(d$crime, probs=0.025)
# 2.5%
#263.051
quantile(d$crime, probs=0.25)
# 25%
#293.723
# Confidence intervals
mean(d$crime)+1.96*sd(d$crime)/sqrt(200)
# 300.9643
mean(d$crime)-1.96*sd(d$crime)/sqrt(200)
# 295.1182
Table () function
table(d$evi)
table(d$evi>0.3)
#FALSE TRUE
#36 12
tb1 <-table(d$evi)
addmargins(tb1)
prop.table(tb1)
table(d$crime>290, d$open>1.5)
The Chi-Square test
chisq.test(dt) # chisq.test performs chi-squared contingency table tests and goodness-of-fit tests.
#Pearson's Chi-squared test
#data: dt
#X-squared = 0.14141, df = 2, p-value = 0.9317 # df = degrees of freedom
#Warning message:
#In stats::chisq.test(x, y, ...) :
# Chi-squared approximation may be incorrect
# the total Chi-square statistic is 0.14141
# In our example, the row and the column variables are not significantly associated (p-value > 0.05).
Describe
psych::describe(dt) #using describe from psych package
#vars n mean sd median trimmed mad min max range skew
#x 1 3 3 2 3 3 2.97 1 5 4 0
#y 2 3 4 2 4 4 2.97 2 6 4 0
#kurtosis se
#x -2.33 1.15
#y -2.33 1.15
Hmisc::describe(dt) # using describe from Hmisc package
#dt
#2 Variables 3 Observations
#---------------------------------------------------------------
# x
#n missing distinct Info Mean Gmd
#3 0 3 1 3 2.667
#Value 1 3 5
#Frequency 1 1 1
#Proportion 0.333 0.333 0.333
#---------------------------------------------------------------
# y
#n missing distinct Info Mean Gmd
#3 0 3 1 4 2.667
#Value 2 4 6
#Frequency 1 1 1
#Proportion 0.333 0.333 0.333
#---------------------------------------------------------------
Using tapply () function for calculating descriptive statistics
tapply(d$crime, d$open, mean)
tapply( d$crime, list( d$open, d$evi < 0.3), mean, na.rm=T )
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