1. Explanation
1.1 brief
“hi welcome to my Rmd :) in this LBB i will use previous data which is loan_kiva.csv”
1.2 Data’s Point of View
Kiva.org is a non-profit online crowdfunding platform that allows individuals to borrow funds for business purposes. Its mission is to improve the welfare of marginalized citizens (especially low-income entrepreneurs and students) in several countries. Crowdfunding (crowdfunding) is the activity of funds from several individuals to finance new business ventures.
Note : Detail explanatory will be given ata the of content
2. Data Preparation
2. Input Data
loan_kiva = read.csv("loan_kiva.csv")2.1 Data Inspection
head(loan_kiva)## id funded_amount loan_amount activity sector country
## 1 653051 300 300 Fruits & Vegetables Food Pakistan
## 2 653053 575 575 Rickshaw Transportation Pakistan
## 3 653068 150 150 Transportation Transportation India
## 4 653063 200 200 Embroidery Arts Pakistan
## 5 653084 400 400 Milk Sales Food Pakistan
## 6 653067 200 200 Dairy Agriculture India
## region currency partner_id posted_time funded_time
## 1 Lahore PKR 247 2014-01-01 06:12:39 2014-01-02 10:06:32
## 2 Lahore PKR 247 2014-01-01 06:51:08 2014-01-02 09:17:23
## 3 Maynaguri INR 334 2014-01-01 09:58:07 2014-01-01 16:01:36
## 4 Lahore PKR 247 2014-01-01 08:03:11 2014-01-01 13:00:00
## 5 Abdul Hakeem PKR 245 2014-01-01 11:53:19 2014-01-01 19:18:51
## 6 Maynaguri INR 334 2014-01-01 09:51:02 2014-01-01 17:18:09
## term_in_months lender_count repayment_interval
## 1 12 12 irregular
## 2 11 14 irregular
## 3 43 6 bullet
## 4 11 8 irregular
## 5 14 16 monthly
## 6 43 8 bullettail(loan_kiva)## id funded_amount loan_amount activity sector
## 323274 1002658 225 225 Sewing Services
## 323275 1002602 1500 1500 Personal Housing Expenses Housing
## 323276 1002761 1500 1500 Farming Agriculture
## 323277 1002668 725 725 Beauty Salon Services
## 323278 1002832 550 550 Food Production/Sales Food
## 323279 1002773 500 500 Grocery Store Food
## country region currency
## 323274 Tajikistan Tursun-zoda TJS
## 323275 Cambodia Kampong Speu KHR
## 323276 Cambodia Kampong Cham province, Ponhea Krek district KHR
## 323277 Pakistan Lahore PKR
## 323278 El Salvador La Unión USD
## 323279 Kenya Voi KES
## partner_id posted_time funded_time term_in_months
## 323274 63 2015-12-31 05:33:47 2015-12-31 19:47:21 14
## 323275 106 2015-12-31 01:57:56 2015-12-31 21:00:58 26
## 323276 204 2015-12-31 10:54:52 2016-01-03 03:48:36 13
## 323277 247 2015-12-31 06:18:49 2016-01-27 17:52:55 13
## 323278 199 2015-12-31 15:26:08 2016-01-05 00:28:49 20
## 323279 164 2015-12-31 11:01:43 2015-12-31 22:08:27 13
## lender_count repayment_interval
## 323274 8 monthly
## 323275 56 monthly
## 323276 54 monthly
## 323277 28 monthly
## 323278 21 monthly
## 323279 12 irregularnames(loan_kiva)## [1] "id" "funded_amount" "loan_amount"
## [4] "activity" "sector" "country"
## [7] "region" "currency" "partner_id"
## [10] "posted_time" "funded_time" "term_in_months"
## [13] "lender_count" "repayment_interval"From out inspection we can conclude : loan kiva data contain 323279 rows and 14 coloumns each of coloumn : id : Unique ID for (loan ID) funded_amount : the amount disbursed by Kiva to the agent (USD) loan_amount : Amount distributed by agents to borrowers (USD) activity : A more specific category than sectors sector : Category of loan country : The full country name, where the loan is disbursed region : The full region name of the country currency : Currency partner_id : ID for the partner organization posted_time : Loan time is posted on Kiva by an agent funded_time : The time the loan has been fully financed by the lender term_in_months : Duration of loan disbursement (in months) lender_count : The number of borrowers who contributed repayment_interval: How to pay off the loan
2.2 Data Cleansing & Coertions
check data type for each colomn
str(loan_kiva)## 'data.frame': 323279 obs. of 14 variables:
## $ id : int 653051 653053 653068 653063 653084 653067 653078 653082 653048 653060 ...
## $ funded_amount : num 300 575 150 200 400 200 400 475 625 200 ...
## $ loan_amount : num 300 575 150 200 400 200 400 475 625 200 ...
## $ activity : chr "Fruits & Vegetables" "Rickshaw" "Transportation" "Embroidery" ...
## $ sector : chr "Food" "Transportation" "Transportation" "Arts" ...
## $ country : chr "Pakistan" "Pakistan" "India" "Pakistan" ...
## $ region : chr "Lahore" "Lahore" "Maynaguri" "Lahore" ...
## $ currency : chr "PKR" "PKR" "INR" "PKR" ...
## $ partner_id : int 247 247 334 247 245 334 245 245 247 247 ...
## $ posted_time : chr "2014-01-01 06:12:39" "2014-01-01 06:51:08" "2014-01-01 09:58:07" "2014-01-01 08:03:11" ...
## $ funded_time : chr "2014-01-02 10:06:32" "2014-01-02 09:17:23" "2014-01-01 16:01:36" "2014-01-01 13:00:00" ...
## $ term_in_months : int 12 11 43 11 14 43 14 14 11 11 ...
## $ lender_count : int 12 14 6 8 16 8 8 19 24 3 ...
## $ repayment_interval: chr "irregular" "irregular" "bullet" "irregular" ...for this result , we find some of data type not in the correct type, we need to convert it into corect type (data coertion)
loan_kiva[,c("activity","sector","country","region","currency","repayment_interval")] =lapply(loan_kiva[,c("activity","sector","country","region","currency","repayment_interval")],as.factor)
loan_kiva$posted_time <- as.Date(loan_kiva$posted_time,"%Y-%m-%d %H:%M:%S")
loan_kiva$funded_time <- as.Date(loan_kiva$funded_time,"%Y-%m-%d %H:%M:%S")
str(loan_kiva)## 'data.frame': 323279 obs. of 14 variables:
## $ id : int 653051 653053 653068 653063 653084 653067 653078 653082 653048 653060 ...
## $ funded_amount : num 300 575 150 200 400 200 400 475 625 200 ...
## $ loan_amount : num 300 575 150 200 400 200 400 475 625 200 ...
## $ activity : Factor w/ 154 levels "Adult Care","Agriculture",..: 62 127 140 50 90 42 11 87 60 127 ...
## $ sector : Factor w/ 15 levels "Agriculture",..: 7 14 14 2 7 1 13 10 7 14 ...
## $ country : Factor w/ 82 levels "Afghanistan",..: 52 52 27 52 52 27 52 52 52 52 ...
## $ region : Factor w/ 9204 levels "","\"\"The first May\"\" village",..: 4377 4377 5172 4377 115 5172 2645 4377 4377 4377 ...
## $ currency : Factor w/ 66 levels "ALL","AMD","AZN",..: 44 44 22 44 44 22 44 44 44 44 ...
## $ partner_id : int 247 247 334 247 245 334 245 245 247 247 ...
## $ posted_time : Date, format: "2014-01-01" "2014-01-01" ...
## $ funded_time : Date, format: "2014-01-02" "2014-01-02" ...
## $ term_in_months : int 12 11 43 11 14 43 14 14 11 11 ...
## $ lender_count : int 12 14 6 8 16 8 8 19 24 3 ...
## $ repayment_interval: Factor w/ 3 levels "bullet","irregular",..: 2 2 1 2 3 1 3 3 2 2 ...Each of colomn already changed ito desired data type
cek for missing value
colSums(is.na(loan_kiva))## id funded_amount loan_amount activity
## 0 0 0 0
## sector country region currency
## 0 0 0 0
## partner_id posted_time funded_time term_in_months
## 0 0 0 0
## lender_count repayment_interval
## 0 0anyNA(loan_kiva)## [1] FALSEGreat! No missing value
3. Data Eplanation
Brief explantaion
summary(loan_kiva)## id funded_amount loan_amount
## Min. : 653047 Min. : 25.0 Min. : 25.0
## 1st Qu.: 737420 1st Qu.: 275.0 1st Qu.: 275.0
## Median : 827056 Median : 500.0 Median : 500.0
## Mean : 826774 Mean : 828.8 Mean : 828.8
## 3rd Qu.: 915291 3rd Qu.: 1000.0 3rd Qu.: 1000.0
## Max. :1002884 Max. :100000.0 Max. :100000.0
##
## activity sector country
## Farming : 33610 Agriculture:86509 Philippines: 81199
## General Store : 31087 Food :68752 Kenya : 31947
## Personal Housing Expenses: 15616 Retail :62118 El Salvador: 20543
## Agriculture : 14309 Services :20550 Cambodia : 13402
## Food Production/Sales : 13950 Housing :16318 Peru : 12799
## Retail : 13728 Clothing :15840 Uganda : 11832
## (Other) :200979 (Other) :53192 (Other) :151557
## region currency partner_id posted_time
## : 26253 PHP : 81199 Min. : 9.0 Min. :2014-01-01
## Kaduna : 5466 USD : 52751 1st Qu.:125.0 1st Qu.:2014-07-11
## Lahore : 4322 KES : 31467 Median :145.0 Median :2015-01-12
## Kisii : 3324 PEN : 12225 Mean :166.7 Mean :2015-01-08
## Cusco : 3013 UGX : 11772 3rd Qu.:199.0 3rd Qu.:2015-07-09
## Thanh Hoá: 2099 PKR : 11647 Max. :469.0 Max. :2015-12-31
## (Other) :278802 (Other):122218
## funded_time term_in_months lender_count repayment_interval
## Min. :2014-01-01 Min. : 2.0 Min. : 1.00 bullet : 32653
## 1st Qu.:2014-07-24 1st Qu.: 8.0 1st Qu.: 8.00 irregular:130580
## Median :2015-01-24 Median : 13.0 Median : 15.00 monthly :160046
## Mean :2015-01-23 Mean : 13.9 Mean : 22.85
## 3rd Qu.:2015-07-24 3rd Qu.: 14.0 3rd Qu.: 28.00
## Max. :2016-02-25 Max. :158.0 Max. :2986.00
## Summary :
- First loan occured in january,1 2014 2 Farming was the most popular for Activity
- Agriculture was the most popular sector for loan
- Philipin was the most country loan amount
- Kiva Loan gained funded amount average at 828,8 USD ; with max funded amount at 100000 USD
- mean of lender count at 22.85 human ; with max lender count is 2896 human
- the mos popular of repayment interval is monthly
- mean of term in months is 13.9 with max term in month is 158