Downloaded the historical loan files from Lending Club as zipped csv files.

First unzip all the files

files <- dir(pattern='*.zip');
for (f in files) {
  unzip(zipfile=f, exdir ='.');
}

Now load each file into R and append to a data frame

Also add a column to indicate which file it came from.

library(data.table);
data.table 1.10.0
  The fastest way to learn (by data.table authors): https://www.datacamp.com/courses/data-analysis-the-data-table-way
  Documentation: ?data.table, example(data.table) and browseVignettes("data.table")
  Release notes, videos and slides: http://r-datatable.com
library(tibble);
files <- dir(pattern='*.csv$');
n <- 1
d <- tibble()
for (f in files) {
  a <- as_tibble(fread(file = f,showProgress = TRUE, skip=1));
  a[, "file"] <- f
  d <- rbind(d, a);
}

Read 52.3% of 133891 rows
Read 89.6% of 133891 rows
Read 133887 rows and 111 (of 111) columns from 0.098 GB file in 00:00:04

Read 80.7% of 99124 rows
Read 99120 rows and 111 (of 111) columns from 0.072 GB file in 00:00:03

Read 69.1% of 188185 rows
Read 188181 rows and 111 (of 111) columns from 0.146 GB file in 00:00:04

Read 42.4% of 235633 rows
Read 76.4% of 235633 rows
Read 235629 rows and 111 (of 111) columns from 0.170 GB file in 00:00:05

Read 0.0% of 421099 rows
Read 19.0% of 421099 rows
Read 38.0% of 421099 rows
Read 54.6% of 421099 rows
Read 71.2% of 421099 rows
Read 87.9% of 421099 rows
Read 421095 rows and 111 (of 111) columns from 0.300 GB file in 00:00:10

Let’s double check that the data frame has all the data from each file:

library(ggplot2)
table(d[,"file"])

LoanStats_2016Q1.csv LoanStats_2016Q2.csv LoanStats_2016Q3.csv      LoanStats3a.csv      LoanStats3b.csv 
              133887                97854                99120                39786               188181 
     LoanStats3c.csv      LoanStats3d.csv 
              235629               421095

Check out all these columns

glimpse(d)
Observations: 1,215,552
Variables: 112
$ id                             <chr> "75983127", "75933549", "75800404", "76022756", "76041549", "75993583"...
$ member_id                      <chr> "81463882", "81424322", "81268205", "81513504", "81532277", "81484367"...
$ loan_amnt                      <chr> "30000", "24000", "12500", "25000", "16800", "22000", "12000", "35000"...
$ funded_amnt                    <chr> "30000", "24000", "12500", "25000", "16800", "22000", "12000", "35000"...
$ funded_amnt_inv                <chr> "30000", "24000", "12500", "25000", "16800", "22000", "12000", "35000"...
$ term                           <chr> " 60 months", " 60 months", " 36 months", " 60 months", " 60 months", ...
$ int_rate                       <chr> "  9.75%", " 19.53%", " 12.99%", " 12.99%", " 17.27%", "  6.49%", " 11...
$ installment                    <chr> "633.73", "629.6", "421.12", "568.7", "419.97", "674.18", "263.74", "8...
$ grade                          <chr> "B", "D", "C", "C", "D", "A", "B", "D", "B", "B", "C", "C", "D", "B", ...
$ sub_grade                      <chr> "B3", "D5", "C2", "C2", "D2", "A2", "B5", "D3", "B4", "B3", "C4", "C5"...
$ emp_title                      <chr> "Senior Systems Administrator", "Consultant - Gemba Coach", "Modular t...
$ emp_length                     <chr> "2 years", "1 year", "5 years", "10+ years", "10+ years", "10+ years",...
$ home_ownership                 <chr> "MORTGAGE", "OWN", "RENT", "RENT", "RENT", "MORTGAGE", "RENT", "MORTGA...
$ annual_inc                     <chr> "88000", "135000", "55000", "138000", "40000", "134000", "30000", "134...
$ verification_status            <chr> "Source Verified", "Verified", "Verified", "Not Verified", "Source Ver...
$ issue_d                        <chr> "Mar-2016", "Mar-2016", "Mar-2016", "Mar-2016", "Mar-2016", "Mar-2016"...
$ loan_status                    <chr> "Current", "Current", "Current", "Current", "Current", "Current", "Cur...
$ pymnt_plan                     <chr> "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", "n", ...
$ url                            <chr> "https://lendingclub.com/browse/loanDetail.action?loan_id=75983127", "...
$ desc                           <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""...
$ purpose                        <chr> "debt_consolidation", "credit_card", "debt_consolidation", "debt_conso...
$ title                          <chr> "Debt consolidation", "Credit card refinancing", "Debt consolidation",...
$ zip_code                       <chr> "230xx", "270xx", "980xx", "078xx", "460xx", "338xx", "802xx", "605xx"...
$ addr_state                     <chr> "VA", "NC", "WA", "NJ", "IN", "FL", "CO", "IL", "TX", "CT", "OH", "NC"...
$ dti                            <chr> "22.98", "20.2", "33.71", "12.44", "36.18", "26.33", "40.76", "25.69",...
$ delinq_2yrs                    <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", ...
$ earliest_cr_line               <chr> "Jul-2003", "Sep-2002", "Oct-2007", "Aug-1995", "Jan-1998", "May-1972"...
$ inq_last_6mths                 <chr> "0", "0", "0", "1", "0", "1", "1", "0", "0", "0", "0", "1", "2", "0", ...
$ mths_since_last_delinq         <chr> "", "49", "", "", "", "", "", "", "", "20", "", "44", "", "", "67", "1...
$ mths_since_last_record         <chr> "", "", "", "64", "74", "", "", "", "", "", "", "70", "", "", "", "", ...
$ open_acc                       <chr> "15", "14", "11", "9", "14", "20", "29", "23", "20", "25", "9", "10", ...
$ pub_rec                        <chr> "0", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "1", "0", "0", ...
$ revol_bal                      <chr> "18628", "25934", "18170", "8577", "9906", "60963", "23705", "41602", ...
$ revol_util                     <chr> "43.5%", "67.9%", "73.3%", "37.1%", "41.6%", "67%", "16.7%", "63%", "5...
$ total_acc                      <chr> "36", "19", "18", "24", "35", "34", "43", "26", "43", "38", "11", "14"...
$ initial_list_status            <chr> "w", "w", "w", "w", "w", "w", "w", "w", "w", "w", "f", "w", "w", "w", ...
$ out_prncp                      <chr> "27202.69", "22243.07", "10433.19", "22844.48", "15497.51", "18050.00"...
$ out_prncp_inv                  <chr> "27202.69", "22243.07", "10433.19", "22844.48", "15497.51", "18050.00"...
$ total_pymnt                    <chr> "4403.61", "4535.58", "2929.8", "3944.82", "2907.55", "4715.29", "1830...
$ total_pymnt_inv                <chr> "4403.61", "4535.58", "2929.80", "3944.82", "2907.55", "4715.29", "183...
$ total_rec_prncp                <chr> "2797.31", "1756.93", "2066.81", "2155.52", "1302.49", "3950.00", "107...
$ total_rec_int                  <chr> "1606.30", "2778.65", "862.99", "1789.30", "1605.06", "765.29", "757.2...
$ total_rec_late_fee             <chr> "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", ...
$ recoveries                     <chr> "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", ...
$ collection_recovery_fee        <chr> "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", "0.0", ...
$ last_pymnt_d                   <chr> "Nov-2016", "Nov-2016", "Nov-2016", "Nov-2016", "Nov-2016", "Nov-2016"...
$ last_pymnt_amnt                <chr> "633.73", "629.6", "421.12", "568.7", "419.97", "674.18", "263.74", "8...
$ next_pymnt_d                   <chr> "Dec-2016", "Dec-2016", "Dec-2016", "Dec-2016", "Dec-2016", "Dec-2016"...
$ last_credit_pull_d             <chr> "Nov-2016", "Nov-2016", "Nov-2016", "Nov-2016", "Nov-2016", "Nov-2016"...
$ collections_12_mths_ex_med     <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", ...
$ mths_since_last_major_derog    <chr> "", "49", "", "", "", "", "", "108", "", "53", "", "63", "", "", "", "...
$ policy_code                    <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", ...
$ application_type               <chr> "INDIVIDUAL", "INDIVIDUAL", "INDIVIDUAL", "INDIVIDUAL", "INDIVIDUAL", ...
$ annual_inc_joint               <chr> "", "", "", "", "", "", "", "", "", "", "90000", "", "", "", "", "", "...
$ dti_joint                      <chr> "", "", "", "", "", "", "", "", "", "", "30.17", "", "", "", "", "", "...
$ verification_status_joint      <chr> "", "", "", "", "", "", "", "", "", "", "Verified", "", "", "", "", ""...
$ acc_now_delinq                 <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", ...
$ tot_coll_amt                   <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "350", "0", "95", "0", "0...
$ tot_cur_bal                    <chr> "230635", "118667", "46568", "11565", "51348", "405151", "36850", "310...
$ open_acc_6m                    <chr> "1", "2", "2", "2", "0", "2", "0", "1", "0", "2", "1", "1", "2", "1", ...
$ open_il_6m                     <chr> "5", "4", "3", "1", "4", "2", "2", "4", "13", "3", "1", "1", "1", "1",...
$ open_il_12m                    <chr> "0", "4", "2", "0", "1", "1", "0", "3", "0", "2", "1", "0", "0", "0", ...
$ open_il_24m                    <chr> "2", "5", "3", "0", "5", "1", "0", "4", "1", "2", "1", "0", "1", "1", ...
$ mths_since_rcnt_il             <chr> "17", "1", "4", "33", "8", "1", "54", "7", "19", "2", "3", "31", "18",...
$ total_bal_il                   <chr> "65991", "92733", "28398", "2988", "41442", "39179", "13145", "44024",...
$ il_util                        <chr> "56", "94", "82", "", "35", "71", "33", "82", "81", "68", "92", "48", ...
$ open_rv_12m                    <chr> "1", "5", "0", "2", "2", "0", "0", "3", "0", "1", "0", "2", "3", "3", ...
$ open_rv_24m                    <chr> "2", "6", "0", "6", "4", "1", "4", "9", "3", "7", "0", "2", "3", "5", ...
$ max_bal_bc                     <chr> "7784", "10614", "9810", "4350", "1196", "11746", "3595", "6819", "132...
$ all_util                       <chr> "50", "83", "78", "37", "39", "67", "20", "63", "66", "46", "68", "70"...
$ total_rev_hi_lim               <chr> "42800", "38200", "24800", "23100", "23800", "90900", "141900", "66400...
$ inq_fi                         <chr> "0", "2", "1", "1", "0", "0", "1", "0", "0", "5", "0", "0", "0", "1", ...
$ total_cu_tl                    <chr> "0", "1", "4", "0", "10", "0", "3", "0", "3", "1", "0", "0", "0", "0",...
$ inq_last_12m                   <chr> "0", "3", "2", "2", "1", "2", "1", "1", "0", "11", "1", "2", "2", "0",...
$ acc_open_past_24mths           <chr> "4", "11", "3", "6", "9", "3", "4", "13", "4", "11", "1", "2", "4", "6...
$ avg_cur_bal                    <chr> "15376", "8476", "4233", "1285", "3668", "21323", "1474", "16354", "12...
$ bc_open_to_buy                 <chr> "15887", "7068", "291", "9281", "1574", "7333", "109815", "6438", "130...
$ bc_util                        <chr> "52.7", "69.9", "97.6", "45.4", "55", "91", "15.7", "72", "95.3", "48....
$ chargeoff_within_12_mths       <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", ...
$ delinq_amnt                    <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", ...
$ mo_sin_old_il_acct             <chr> "152", "162", "55", "136", "112", "133", "147", "19", "213", "157", "9...
$ mo_sin_old_rev_tl_op           <chr> "151", "121", "101", "247", "218", "526", "241", "232", "205", "256", ...
$ mo_sin_rcnt_rev_tl_op          <chr> "5", "4", "41", "2", "8", "20", "15", "5", "19", "12", "31", "6", "1",...
$ mo_sin_rcnt_tl                 <chr> "5", "1", "4", "2", "8", "1", "15", "5", "19", "2", "3", "6", "1", "3"...
$ mort_acc                       <chr> "1", "0", "0", "0", "3", "4", "0", "2", "2", "1", "2", "2", "0", "0", ...
$ mths_since_recent_bc           <chr> "5", "4", "41", "2", "8", "20", "15", "5", "19", "14", "56", "6", "1",...
$ mths_since_recent_bc_dlq       <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "81", "", "", ...
$ mths_since_recent_inq          <chr> "20", "1", "4", "0", "10", "2", "0", "7", "19", "2", "3", "6", "1", "1...
$ mths_since_recent_revol_delinq <chr> "", "", "", "", "", "", "", "", "", "20", "", "46", "", "", "81", "", ...
$ num_accts_ever_120_pd          <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "1", "0", "0", "0", "0", ...
$ num_actv_bc_tl                 <chr> "6", "5", "3", "5", "2", "6", "16", "8", "3", "5", "3", "4", "2", "3",...
$ num_actv_rev_tl                <chr> "7", "10", "5", "8", "8", "14", "18", "13", "3", "13", "5", "8", "4", ...
$ num_bc_sats                    <chr> "7", "5", "3", "5", "2", "11", "20", "9", "3", "6", "4", "4", "2", "3"...
$ num_bc_tl                      <chr> "12", "5", "4", "11", "5", "11", "25", "9", "7", "10", "4", "4", "2", ...
$ num_il_tl                      <chr> "17", "8", "7", "3", "16", "5", "3", "4", "26", "8", "2", "2", "1", "1...
$ num_op_rev_tl                  <chr> "9", "10", "8", "8", "10", "14", "27", "13", "6", "19", "7", "8", "5",...
$ num_rev_accts                  <chr> "18", "11", "11", "20", "16", "24", "40", "20", "15", "28", "7", "10",...
$ num_rev_tl_bal_gt_0            <chr> "7", "10", "5", "8", "8", "13", "18", "10", "3", "13", "5", "8", "4", ...
$ num_sats                       <chr> "15", "14", "11", "9", "14", "19", "29", "19", "20", "25", "9", "10", ...
$ num_tl_120dpd_2m               <chr> "0", "0", "0", "0", "0", "", "0", "", "0", "0", "0", "0", "0", "0", "0...
$ num_tl_30dpd                   <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", ...
$ num_tl_90g_dpd_24m             <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", ...
$ num_tl_op_past_12m             <chr> "1", "9", "2", "2", "3", "2", "0", "6", "0", "5", "1", "2", "3", "3", ...
$ pct_tl_nvr_dlq                 <chr> "100", "94.7", "100", "100", "100", "100", "100", "100", "100", "94.6"...
$ percent_bc_gt_75               <chr> "0", "20", "100", "20", "0", "100", "0", "50", "100", "16.7", "75", "7...
$ pub_rec_bankruptcies           <chr> "0", "0", "0", "1", "1", "0", "0", "0", "0", "0", "0", "0", "0", "0", ...
$ tax_liens                      <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", ...
$ tot_hi_cred_lim                <chr> "290300", "134314", "59392", "58967", "79107", "456923", "182147", "35...
$ total_bal_ex_mort              <chr> "84619", "118667", "46568", "11565", "51348", "101287", "36850", "8562...
$ total_bc_limit                 <chr> "33600", "23500", "11900", "17000", "3500", "44000", "130300", "51500"...
$ total_il_high_credit_limit     <chr> "89607", "96114", "34592", "35867", "55307", "55537", "40247", "53811"...
$ file                           <chr> "LoanStats_2016Q1.csv", "LoanStats_2016Q1.csv", "LoanStats_2016Q1.csv"...
library(knitr)
kable(table(d[,c("purpose","loan_status")]))
Charged Off Current Default Fully Paid In Grace Period Late (16-30 days) Late (31-120 days)
car 717 6739 0 4760 59 36 140
credit_card 15526 177293 39 77950 1683 820 3622
debt_consolidation 54490 425137 114 211844 5794 2544 12382
educational 56 0 0 270 0 0 0
home_improvement 4515 46251 12 22634 519 274 1198
house 494 2465 0 2072 38 17 105
major_purchase 1613 14331 5 8478 166 100 398
medical 1038 7360 5 3719 80 42 210
moving 751 4309 3 2517 63 28 156
other 5107 37718 11 18965 471 248 1039
renewable_energy 87 365 0 303 7 1 16
small_business 1985 6598 3 4724 109 78 294
vacation 535 4227 1 2229 41 19 98
wedding 276 59 0 1955 2 0 0 
library(dplyr)
kable(table(d[,c("grade", "loan_status")]))
Charged Off Current Default Fully Paid In Grace Period Late (16-30 days) Late (31-120 days)
AK 212 1741 1 984 26 12 41
AL 1306 9035 3 4339 122 64 332
AR 697 5635 0 2509 70 34 199
AZ 2068 16620 8 8859 183 84 462
CA 13322 96486 27 59334 1117 582 2617
CO 1538 14437 0 8833 166 63 325
CT 1151 11766 1 5228 149 59 268
DC 160 1704 0 1208 21 8 53
DE 231 2150 0 964 19 12 55
FL 6649 50949 13 24668 661 283 1432
GA 2590 24667 7 11720 347 158 543
HI 493 3378 1 2026 54 30 96
IA 1 0 0 6 0 0 0
ID 5 868 1 67 9 6 10
IL 3020 31029 6 13585 302 147 672
IN 1342 13046 4 4636 145 80 324
KS 622 6647 1 3130 64 35 118
KY 826 7356 0 3203 92 44 159
LA 1182 8616 0 4159 102 54 258
MA 1888 16763 2 8663 200 90 426
MD 2091 17214 4 8408 238 102 489
ME 11 1447 0 136 15 7 17
MI 2250 19648 4 8923 243 106 490
MN 1615 13099 1 6440 134 73 328
MO 1440 11891 6 5555 124 61 321
MS 336 4529 2 912 56 31 147
MT 207 2050 0 1120 19 10 56
NC 2567 20512 8 9944 284 165 611
ND 22 1130 0 103 11 7 19
NE 75 2319 2 267 16 14 60
NH 276 3758 2 1727 50 12 66
NJ 3361 27394 5 13216 364 171 784
NM 523 3955 1 1990 49 21 137
NV 1573 9785 4 5230 121 60 273
NY 7872 60776 19 29156 955 418 1883
OH 2914 25838 6 11073 297 159 703
OK 918 6853 2 3058 80 39 240
OR 945 8275 0 4989 87 36 153
PA 3064 25686 13 12126 299 142 753
RI 355 3268 0 1529 25 18 93
SC 868 9389 2 4217 110 37 195
SD 214 1445 0 768 15 4 39
TN 1291 12255 4 4301 172 56 322
TX 6330 61186 18 28752 774 331 1726
UT 663 4398 2 3104 47 19 114
VA 2742 20615 5 10858 266 124 576
VT 116 1674 0 668 20 4 24
WA 1753 14948 5 8560 154 73 324
WI 995 9998 1 4599 101 34 221
WV 329 3028 1 1679 35 11 63
WY 171 1596 1 891 22 17 41
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