将数据从.txt文件导入R

Question

I am very new in R, and trying to transpose a .txt file to R, Please, see the example below. It is much appreciated, if I can get a solution of transferring the data.

Data in .txt file:

user_14:beneficiary_649,beneficiary_1312,beneficiary_1983,beneficiary_726,beneficiary_759,beneficiary_229,beneficiary_673,
user_13:beneficiary_1928,beneficiary_553,beneficiary_483,beneficiary_1127,beneficiary_2887
user_11:beneficiary_2158,beneficiary_871,beneficiary_1969,beneficiary_1120,beneficiary_185,beneficiary_2180

Expecting output in R as:

user_14                 user_13                  user_11
beneficiary_649     beneficiary_1928         beneficiary_2158
beneficiary_1312    beneficiary_553          beneficiary_871
beneficiary_1983    beneficiary_483          beneficiary_1969
beneficiary_726     beneficiary_1127         beneficiary_1120
beneficiary_759     beneficiary_2887         beneficiary_185
beneficiary_229     beneficiary_2180
beneficiary_673

Answer 1

A possible solution:

Use readLines to read the data. Tidyverse functions to fix put everything in its place.

input <- readLines("text_file.txt") # read the data from the text file

df <- data.frame(input = input, stringsAsFactors = F) # store it in a data.frame

library(tidyr)
library(dplyr)

df %>% 
  separate(input, into = c("users", "data"), sep = ":") %>%  # split users and rest
  separate_rows(data, sep = ",") %>%  # build rows from data
  group_by(users) %>% # group by needed for creating row numbers per user
  mutate(rowid = row_number()) %>% # add row numbers
  spread(users, data, fill = "") # put data under the users, empty values as "" instead of NA

# A tibble: 7 x 4
  rowid user_11          user_13          user_14         
  <int> <chr>            <chr>            <chr>           
1     1 beneficiary_2158 beneficiary_1928 beneficiary_649 
2     2 beneficiary_871  beneficiary_553  beneficiary_1312
3     3 beneficiary_1969 beneficiary_483  beneficiary_1983
4     4 beneficiary_1120 beneficiary_1127 beneficiary_726 
5     5 beneficiary_185  beneficiary_2887 beneficiary_759 
6     6 beneficiary_2180 ""               beneficiary_229 
7     7 ""               ""               beneficiary_673 

Answer 2

In base you can use strsplit to split on : and , , then look how long the longest char vector is and fill up all others with NA using sapply which also transposes the vectors.

tt <- readLines(con=textConnection("user_14:beneficiary_649,beneficiary_1312,beneficiary_1983,beneficiary_726,beneficiary_759,beneficiary_229,beneficiary_673,
user_13:beneficiary_1928,beneficiary_553,beneficiary_483,beneficiary_1127,beneficiary_2887
user_11:beneficiary_2158,beneficiary_871,beneficiary_1969,beneficiary_1120,beneficiary_185,beneficiary_2180"))

tt <- strsplit(tt, ":|,")  #Split on : or ,
ttn <- max(sapply(tt, length))  #Get longest vector
tt <- sapply(tt, function(x) x[seq_len(ttn)]) #Fill up with NA and give per col
colnames(tt)  <- tt[1,] #Set colnames from first line
tt <- tt[-1,]  #Remove first line
tt
#     user_14            user_13            user_11           
#[1,] "beneficiary_649"  "beneficiary_1928" "beneficiary_2158"
#[2,] "beneficiary_1312" "beneficiary_553"  "beneficiary_871" 
#[3,] "beneficiary_1983" "beneficiary_483"  "beneficiary_1969"
#[4,] "beneficiary_726"  "beneficiary_1127" "beneficiary_1120"
#[5,] "beneficiary_759"  "beneficiary_2887" "beneficiary_185" 
#[6,] "beneficiary_229"  NA                 "beneficiary_2180"
#[7,] "beneficiary_673"  NA                 NA                

Answer 3

You can use strsplit() to get a list:

DF <- read.table(stringsAsFactors = FALSE, sep=':', text=
"user_14:beneficiary_649,beneficiary_1312,beneficiary_1983,beneficiary_726,beneficiary_759,beneficiary_229,beneficiary_673,
user_13:beneficiary_1928,beneficiary_553,beneficiary_483,beneficiary_1127,beneficiary_2887
user_11:beneficiary_2158,beneficiary_871,beneficiary_1969,beneficiary_1120,beneficiary_185,beneficiary_2180")
L <- strsplit(DF$V2, ',')
names(L) <- DF$V1

Answer 4

All answers above are correct, but here is what I find a simpler alternative using base R:

# Just read in your data as a comma separated data frame
df <- read.table("link to your file", header = F, sep = ',', fill = T)
# The first column will contain both the user and the first "beneficiary" as they are separated by ":", so you need to split its values by ":"
k <- sapply(df[,1], function(x){strsplit(x, split = ":")[[1]]})
# Add the corrected first column to your data frame and transpose the data frame to have one column per user
df <- t(cbind(k[2,], df[,2:ncol(df)]))
# Provide the "user" as colnames
colnames(df) <- k[1,]
# I noticed that some lines in your text have a comma at the end, which introduces NAs. To remove them: 
df[is.na(df)] <- ""

Answer 5

Thanks to All for all of the trying and suggestions:

I solve the problem and get the exact data table I expected. Sharing solution below for your review and if there is any improvement suggestions:

Here is all the data in users.txt

user_14: beneficiary_649,beneficiary_1312,beneficiary_1983,beneficiary_726,beneficiary_759,beneficiary_229,beneficiary_673,beneficiary_2322,beneficiary_2598,beneficiary_1705,beneficiary_2743,beneficiary_220,beneficiary_977,beneficiary_1098,beneficiary_2891,beneficiary_1253,beneficiary_2065,beneficiary_1492,beneficiary_268,beneficiary_1991,beneficiary_684,beneficiary_1493,beneficiary_2294,beneficiary_73,beneficiary_1524,beneficiary_2349,beneficiary_2978,beneficiary_2575,beneficiary_2506,beneficiary_3051,beneficiary_612,beneficiary_617,beneficiary_1748,beneficiary_3031,beneficiary_2431,beneficiary_948,beneficiary_46,beneficiary_469,beneficiary_2047,beneficiary_1461,beneficiary_2549,beneficiary_2539,beneficiary_412,beneficiary_1615,beneficiary_2842,beneficiary_2228,beneficiary_2634,beneficiary_2534,beneficiary_358,beneficiary_1475,beneficiary_146,beneficiary_1971,beneficiary_1411,beneficiary_2395,beneficiary_1047,beneficiary_2062,beneficiary_2373,beneficiary_2328,beneficiary_1669,benefici ary_2986,beneficiary_1040,beneficiary_248,beneficiary_1816,beneficiary_1465,beneficiary_133,beneficiary_2401,beneficiary_2626,beneficiary_1819,beneficiary_2864,beneficiary_1008,beneficiary_1101,beneficiary_2529,beneficiary_1487,beneficiary_787,beneficiary_2595,beneficiary_2947,beneficiary_2808,beneficiary_547,beneficiary_2113,beneficiary_825,beneficiary_396,beneficiary_2321,beneficiary_2512,beneficiary_72,beneficiary_90,beneficiary_957,beneficiary_1799,beneficiary_2787,beneficiary_277,beneficiary_2472,beneficiary_194,beneficiary_2521,beneficiary_760,beneficiary_558,beneficiary_2404,beneficiary_763,beneficiary_2466,beneficiary_1881,beneficiary_2483,beneficiary_107,beneficiary_1392,beneficiary_2558,beneficiary_557,beneficiary_1923,beneficiary_322,beneficiary_310,beneficiary_1655,beneficiary_226,beneficiary_527,beneficiary_2542,beneficiary_1372,beneficiary_142,beneficiary_1055,beneficiary_378,beneficiary_296,beneficiary_733,beneficiary_1755,beneficiary_1932,beneficiary_1989,beneficiary_13 79,beneficiary_2199,beneficiary_1288,beneficiary_2877,beneficiary_1045,beneficiary_2613,beneficiary_2455,beneficiary_2503,beneficiary_706,beneficiary_1562,beneficiary_1446,beneficiary_247,beneficiary_1020,beneficiary_1250,beneficiary_777,beneficiary_2645,beneficiary_1850,beneficiary_2724,beneficiary_2192,beneficiary_715,beneficiary_1321,beneficiary_201,beneficiary_961,beneficiary_2802,beneficiary_414,beneficiary_1997,beneficiary_2760,beneficiary_82,beneficiary_2746,beneficiary_918,beneficiary_2386,beneficiary_729,beneficiary_3057,beneficiary_491,beneficiary_1190,beneficiary_1561,beneficiary_2744,beneficiary_923,beneficiary_1815,beneficiary_240,beneficiary_2016,beneficiary_2479,beneficiary_1692,beneficiary_1630,beneficiary_2899,beneficiary_965,beneficiary_2675,beneficiary_34,beneficiary_2226,beneficiary_550,beneficiary_1795,beneficiary_981,beneficiary_1934,beneficiary_2579,beneficiary_3012,beneficiary_2366,beneficiary_1684,beneficiary_2107,beneficiary_1249,beneficiary_2574,beneficiary_1 447,beneficiary_1052,beneficiary_219,beneficiary_357,beneficiary_2324,beneficiary_2791,beneficiary_2528,beneficiary_1066,beneficiary_2984,beneficiary_2559,beneficiary_767,beneficiary_1031,beneficiary_271,beneficiary_2278,beneficiary_15,beneficiary_463,beneficiary_917,beneficiary_1839,beneficiary_1048,beneficiary_2435,beneficiary_2441,beneficiary_1272,beneficiary_2056,beneficiary_993,beneficiary_371,beneficiary_2582,beneficiary_1476 user_13: beneficiary_1928,beneficiary_553,beneficiary_483,beneficiary_1127,beneficiary_2887,beneficiary_2184,beneficiary_1694,beneficiary_2276,beneficiary_1961,beneficiary_2994,beneficiary_781,beneficiary_1264,beneficiary_2001,beneficiary_1657,beneficiary_1065,beneficiary_636,beneficiary_1892,beneficiary_1091,beneficiary_2237,beneficiary_205,beneficiary_1699,beneficiary_2023,beneficiary_2767,beneficiary_104,beneficiary_157,beneficiary_1199,beneficiary_493,beneficiary_375,beneficiary_2614,beneficiary_1856,beneficiary_1177,beneficiary_3024,beneficiary_1185,ben eficiary_1205,beneficiary_773,beneficiary_1508,beneficiary_2379,beneficiary_433,beneficiary_1801,beneficiary_33,beneficiary_510,beneficiary_2552,beneficiary_575,beneficiary_2492,beneficiary_2839,beneficiary_1033,beneficiary_1396,beneficiary_2281,beneficiary_41,beneficiary_677,beneficiary_2862,beneficiary_652,beneficiary_1582,beneficiary_2422,beneficiary_1599,beneficiary_2844,beneficiary_466,beneficiary_2639,beneficiary_984,beneficiary_407,beneficiary_1097,beneficiary_594,beneficiary_2073,beneficiary_2773,beneficiary_1504,beneficiary_3064,beneficiary_816,beneficiary_577,beneficiary_804,beneficiary_2148,beneficiary_949,beneficiary_2520,beneficiary_443,beneficiary_2453,beneficiary_408,beneficiary_554,beneficiary_754,beneficiary_2960,beneficiary_2344,beneficiary_1497,beneficiary_184,beneficiary_255,beneficiary_542,beneficiary_2004,beneficiary_692,beneficiary_89,beneficiary_1385,beneficiary_1814,beneficiary_2621,beneficiary_670,beneficiary_2022,beneficiary_24,beneficiary_2820,beneficiary_29 58,beneficiary_1708,beneficiary_685,beneficiary_1552,beneficiary_420,beneficiary_2168,beneficiary_2209,beneficiary_2189,beneficiary_1474,beneficiary_2253,beneficiary_1159,beneficiary_2210,beneficiary_2537,beneficiary_177,beneficiary_1355,beneficiary_2092,beneficiary_2231,beneficiary_613,beneficiary_2227,beneficiary_520,beneficiary_2139,beneficiary_2742,beneficiary_720,beneficiary_770,beneficiary_1247,beneficiary_717 user_11: beneficiary_2158,beneficiary_871,beneficiary_1969,beneficiary_1120,beneficiary_185,beneficiary_2180,beneficiary_2120,beneficiary_1832,beneficiary_1470,beneficiary_2689,beneficiary_1679,beneficiary_769,beneficiary_2380,beneficiary_2999,beneficiary_1113,beneficiary_2932,beneficiary_1763,beneficiary_391,beneficiary_2381,beneficiary_650,beneficiary_419,beneficiary_1998,beneficiary_775,beneficiary_2590,beneficiary_2593,beneficiary_2042,beneficiary_2102,beneficiary_1765,beneficiary_1201,beneficiary_332,beneficiary_26,beneficiary_1273,beneficiary_799,beneficiary_79,benefi ciary_2099,beneficiary_622,beneficiary_394,beneficiary_2830,beneficiary_934,beneficiary_1170,beneficiary_2297,beneficiary_3009,beneficiary_1278,beneficiary_1573,beneficiary_315,beneficiary_1610,beneficiary_1875,beneficiary_1899,beneficiary_88,beneficiary_560,beneficiary_508,beneficiary_1674,beneficiary_1490,beneficiary_1824,beneficiary_751,beneficiary_2122,beneficiary_936,beneficiary_132,beneficiary_2756,beneficiary_2246,beneficiary_561,beneficiary_2063,beneficiary_2600,beneficiary_2875,beneficiary_2333,beneficiary_3003,beneficiary_381,beneficiary_1528,beneficiary_1733,beneficiary_1316,beneficiary_573,beneficiary_2312,beneficiary_991,beneficiary_202,beneficiary_1858,beneficiary_17,beneficiary_2130,beneficiary_571,beneficiary_1631,beneficiary_2720,beneficiary_2132,beneficiary_1526,beneficiary_232,beneficiary_2444,beneficiary_1721,beneficiary_537,beneficiary_2408,beneficiary_1918,beneficiary_946,beneficiary_300,beneficiary_2049,beneficiary_768,beneficiary_1854,beneficiary_2028,beneficiar y_319,beneficiary_1433,beneficiary_343,beneficiary_2897,beneficiary_61,beneficiary_1803,beneficiary_2400,beneficiary_2758,beneficiary_910,beneficiary_7,beneficiary_172,beneficiary_1503,beneficiary_453,beneficiary_69,beneficiary_823,beneficiary_986,beneficiary_2123,beneficiary_802 user_27: beneficiary_1003,beneficiary_1919,beneficiary_2304,beneficiary_2597,beneficiary_2242,beneficiary_2818,beneficiary_580,beneficiary_305,beneficiary_651,beneficiary_260,beneficiary_2071,beneficiary_1703,beneficiary_3052,beneficiary_2588,beneficiary_2860,beneficiary_2943,beneficiary_1293,beneficiary_2066,beneficiary_2191,beneficiary_1135,beneficiary_2084,beneficiary_994,beneficiary_2658,beneficiary_628,beneficiary_2313,beneficiary_2355,beneficiary_2730,beneficiary_1634,beneficiary_2159,beneficiary_974,beneficiary_3016,beneficiary_678,beneficiary_2665,beneficiary_1325,beneficiary_1598,beneficiary_1985,beneficiary_416,beneficiary_274,beneficiary_369,beneficiary_1802,beneficiary_3054,beneficiary_2648,benefic iary_663,beneficiary_960,beneficiary_2190,beneficiary_476,beneficiary_405,beneficiary_1256,beneficiary_85,beneficiary_1782,beneficiary_2949,beneficiary_947,beneficiary_1384,beneficiary_401,beneficiary_1026,beneficiary_2208,beneficiary_1304,beneficiary_1455,beneficiary_2198,beneficiary_2556,beneficiary_1871,beneficiary_449,beneficiary_1566,beneficiary_52,beneficiary_811,beneficiary_1859,beneficiary_559,beneficiary_1798,beneficiary_1067,beneficiary_494,beneficiary_2908,beneficiary_16,beneficiary_1940,beneficiary_94,beneficiary_2375,beneficiary_842,beneficiary_1976,beneficiary_1424,beneficiary_2221,beneficiary_1794,beneficiary_2982,beneficiary_2640,beneficiary_353,beneficiary_1565,beneficiary_195,beneficiary_1017,beneficiary_1458,beneficiary_1004,beneficiary_820,beneficiary_1187,beneficiary_1716,beneficiary_91,beneficiary_2478,beneficiary_1596,beneficiary_632,beneficiary_2382,beneficiary_1847,beneficiary_2274,beneficiary_699

#Get data file from .txt
usersDbase <- read.csv("C:/Users/users.txt", header=FALSE)

#naming column Automatically with a prefix and auto number
colnames(usersDbase) <- paste0("Ben", 1:ncol(usersDbase))

#Create AutoID as per row number
id <- rownames(usersDbase)
dwithID <- cbind(id=id, usersDbase)


#Split one column into two columns
df1 <- setNames(data.frame(do.call("rbind",strsplit(gsub("\\(|\\)|,","",dwithID$Ben1),split=" "))),c("User","Ben1"))

#pick specific column
library(dplyr)
dfc1<-select(df1,'User')

#remove Special Character

library(dplyr)
dfc11<-dfc1 %>%
  mutate_all(funs(gsub("[[:punct:]]", "", .)))

#Create AutoID as per rownumber
id <- rownames(dfc11)
dwithIDS <- cbind(id=id, dfc11)


#pick specific column
library(dplyr)
dfben1<-select(df1,'Ben1')


#Create AutoID as per rownumber
id <- rownames(dfben1)
dbenwithIDS <- cbind(id=id, dfben1)

# Merge
mergeUb1<-merge(dwithIDS, dbenwithIDS, dwithIDS = "id",dbenwithIDS= "id")

#pick all except column 1

library(dplyr)
dwithIDAll<-select(dwithID,-"Ben1")


#merge 

mergeall<-merge(mergeUb1, dwithIDAll, mergeUb1 = "id",dwithIDAll= "id")

#pick all except column 1

library(dplyr)
UsersClean<-select(mergeall,-"id") 

# Transpose data where first column will be taken as header

Finaldf = setNames(data.frame(t(UsersClean[,-1])), UsersClean[,1])

# Remove rowname
rownames(Finaldf) <- c()

#Remove all temporary data frame and values

rm(dbenwithIDS, df1, dfben1,dfc1,dfc11,dwithID,dwithIDAll,dwithIDS,mergeall,mergeUb1,UsersClean,usersDbase, "id")