Basic commands for Data Analysis in R
Here we will design a basic data analysis program in R using R Studio by utilizing the features of R Studio to create some visual representation of data. We need to perform the following tasks in order
1. Importing and understanding data in R
2. Pre-processing the data
3. Basic data analysis using statistical averages
4. Plotting data distributionLet’s understand the code step by step in order to achieve our goal!
1. Importing and understanding Data
acs <- read.csv(url("http://stat511.cwick.co.nz/homeworks/acs_or.csv")) #reads data from internet in local R variable "acs"
View(acs) #view whole dataset with all rows and all columns
head(acs,10) # view top 10 rows of dataset## household age_husband age_wife income_husband income_wife bedrooms
## 1 48 64 62 11000 29200 1
## 2 218 63 64 100000 3100 4
## 3 279 56 51 31000 0 2
## 4 612 71 68 51700 8800 3
## 5 947 37 33 16600 26000 3
## 6 1373 86 91 77500 30000 4
## 7 1733 67 67 8400 4800 4
## 8 1858 70 74 73670 11000 0
## 9 1947 33 31 55050 600 1
## 10 1962 41 47 42000 36000 3
## electricity gas number_children internet mode
## 1 90 3 0 Yes followup
## 2 230 30 0 Yes mail
## 3 200 40 0 No followup
## 4 170 3 0 Yes internet
## 5 260 3 2 Yes internet
## 6 20 30 0 Yes internet
## 7 70 150 0 Yes internet
## 8 180 80 0 Yes mail
## 9 20 30 0 Yes internet
## 10 80 200 2 Yes followup
## own language decade_built
## 1 Owned with mortgage or loan English only 1940
## 2 Owned with mortgage or loan English only 1990
## 3 Rented English only 1950
## 4 Owned free and clear English only 1950
## 5 Owned with mortgage or loan English only 1990
## 6 Owned free and clear English only 1980
## 7 Owned free and clear Other 1980
## 8 Owned free and clear English only 2000
## 9 Rented English only 1930
## 10 Owned with mortgage or loan English only 2000tail(acs) # view last rows of dataset(default is 6 rows)## household age_husband age_wife income_husband income_wife bedrooms
## 7806 1491153 41 37 42000 27000 2
## 7807 1491548 64 62 75700 13900 3
## 7808 1491592 71 63 21700 36000 3
## 7809 1492112 40 40 65000 4800 3
## 7810 1492158 64 65 90000 85030 4
## 7811 1492278 38 36 150000 28000 4
## electricity gas number_children internet mode
## 7806 200 3 0 Yes followup
## 7807 80 3 0 Yes internet
## 7808 90 30 0 Yes mail
## 7809 180 120 0 Yes internet
## 7810 60 3 0 Yes followup
## 7811 70 60 2 Yes mail
## own language decade_built
## 7806 Owned with mortgage or loan English only 1950
## 7807 Owned free and clear English only 1930
## 7808 Owned with mortgage or loan English only 1990
## 7809 Owned with mortgage or loan English only 1940
## 7810 Owned free and clear English only 1940
## 7811 Rented Other 2000summary(acs$age_husband) #get the statistical summary of the dataset by just running on either a column or the complete dataset## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 17.00 42.00 55.00 54.32 66.00 95.00summary(acs[,5]) #summary of 5th column## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -6300 6225 18110 28990 39600 421000str(acs) # structure of dataset will column details## 'data.frame': 7811 obs. of 14 variables:
## $ household : int 48 218 279 612 947 1373 1733 1858 1947 1962 ...
## $ age_husband : int 64 63 56 71 37 86 67 70 33 41 ...
## $ age_wife : int 62 64 51 68 33 91 67 74 31 47 ...
## $ income_husband : int 11000 100000 31000 51700 16600 77500 8400 73670 55050 42000 ...
## $ income_wife : int 29200 3100 0 8800 26000 30000 4800 11000 600 36000 ...
## $ bedrooms : int 1 4 2 3 3 4 4 0 1 3 ...
## $ electricity : int 90 230 200 170 260 20 70 180 20 80 ...
## $ gas : int 3 30 40 3 3 30 150 80 30 200 ...
## $ number_children: int 0 0 0 0 2 0 0 0 0 2 ...
## $ internet : Factor w/ 2 levels "No","Yes": 2 2 1 2 2 2 2 2 2 2 ...
## $ mode : Factor w/ 3 levels "followup","internet",..: 1 3 1 2 2 2 2 3 2 1 ...
## $ own : Factor w/ 4 levels "Occupied without payment of rent",..: 3 3 4 2 3 2 2 2 4 3 ...
## $ language : Factor w/ 3 levels "English only",..: 1 1 1 1 1 1 2 1 1 1 ...
## $ decade_built : int 1940 1990 1950 1950 1990 1980 1980 2000 1930 2000 ...table(acs$bedrooms) # give data frequency distribution##
## 0 1 2 3 4 5 10
## 24 198 1475 4040 1652 350 72table(acs$bedrooms, acs$number_children) # gives cross tabulation##
## 0 1 2 3 4 5 6 7 8 10 12
## 0 15 1 5 3 0 0 0 0 0 0 0
## 1 175 16 6 1 0 0 0 0 0 0 0
## 2 1103 178 140 42 10 2 0 0 0 0 0
## 3 2750 504 526 202 39 13 6 0 0 0 0
## 4 892 241 322 130 39 20 5 1 1 1 0
## 5 174 37 66 48 14 5 2 2 1 0 1
## 10 42 11 10 2 5 1 1 0 0 0 0class(acs) # gives class/ data type of argument## [1] "data.frame"class(acs$language) # gives class/ data type of particular column of dataset## [1] "factor"2. Pre-processing the Data
#Subsetting the data: Find rows from the dataset in which the age_husband is greater than age_wife
a <- subset(acs , age_husband > age_wife) # assign the subset of rows in variable "a"
a[1:5,c(2,3)] # display first 6 rows and column 2 and 3 from "a" to verify subset data## age_husband age_wife
## 1 64 62
## 3 56 51
## 4 71 68
## 5 37 33
## 9 33 31#Adding a new column to dataset
acs$newcol<- rnorm(nrow(acs)) # find number of rows using "rnum(acs)" and assign that many values to new column "newcol" in "acs"
names(acs) # view column names of "acs" dataset## [1] "household" "age_husband" "age_wife"
## [4] "income_husband" "income_wife" "bedrooms"
## [7] "electricity" "gas" "number_children"
## [10] "internet" "mode" "own"
## [13] "language" "decade_built" "newcol"#removing a column from dataset
acs$newcol <- NULL
names(acs)## [1] "household" "age_husband" "age_wife"
## [4] "income_husband" "income_wife" "bedrooms"
## [7] "electricity" "gas" "number_children"
## [10] "internet" "mode" "own"
## [13] "language" "decade_built"3. Getting Statistical Averages from data
#For mean of any column :
mean(acs$age_husband)## [1] 54.31776#Median:
median(acs$age_husband)## [1] 55#Quantile:
quantile(acs$age_wife)## 0% 25% 50% 75% 100%
## 19 40 53 63 95#Variance:
var(acs$age_wife)## [1] 220.527#Standard Deviation:
sd(acs$age_wife)## [1] 14.850154. Plotting Data
#create a scatter plot of a data set
plot(x = acs$age_husband , y = acs$age_wife, type = 'p', col="red")
#create a histogram
hist(acs$electricity, col="blue")
#create a boxplot
boxplot(acs$age_husband~acs$internet, col="blue")
title(main="Husband Age Vs Internet Availability",xlab = "Internet Availability", ylab = "Age of Husband")
Congratulations for your first Data Analysis!! Remember its just a beginning and there are lots of other data analysis functionalities that you need to try. As we progress, the complexity of data increases and therefore we require a lot of pre-processing on the dataset.
You may also wish to try out Data Classification, Clustering or Linear Regression from following links:
k-NN Classification for beginners
Using Airquality Datasetk-means Clustering for beginners
Using Airquality DatasetLinear Regression for beginners
Good luck! :)