Amadou Kone
Reputation: 956
Weird output when writing a data.frame to a text file
I keep getting weird output when writing a data frame to a text file using R. I've had the same problem using write.csv(), write.csv2() and write.table().
I have a data.frame, which I created by binding multiple data.frames together using rbind() that were previously subset from a larger data.frame. It is called "extract", and I am trying to write every column but the first (a large text string) to a txt file.
> dim(extract)
[1] 1001 16
> extract[1:5,2:16]
latitude longitude WordCount English Common Tagalog Bikol Cebuano Hiligaynon Ilocano Kapampangan Pangasinense Waray Zam Spanish
20 20.45164941 121.9671742 7 1 0 0 3 0 0 0 0 0 0 0 0
77 20.45164941 121.9671742 7 1 0 0 3 0 0 0 0 0 0 0 0
106 20.45164941 121.9671742 7 1 0 0 3 0 0 0 0 0 0 0 0
48670 20.45164941 121.9671742 7 1 0 0 3 0 0 0 0 0 0 0 0
94831 8.19183664 124.17909375 13 8 2 0 3 0 0 0 0 0 0 0 0
When I do
> write.csv(extract[,2:16], 'extract 6-30.txt', row.names=FALSE)
My table treats the first two items latitude and longitude as columns, and then has concatenated lists of every item in a column for each individual items in the table. So, for example:
> write.csv(extract[1:5,2:16], 'extract 6-29.txt', row.names=FALSE)
yields the following text file:
"latitude","longitude","WordCount","English","Common","Tagalog","Bikol","Cebuano","Hiligaynon","Ilocano","Kapampangan","Pangasinense","Waray","Zam","Spanish"
"20.45164941","121.9671742",c(7, 7, 7, 7, 13),c(1, 1, 1, 1, 8),c(0, 0, 0, 0, 2),c(0, 0, 0, 0, 0),c(3, 3, 3, 3, 3),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0)
"20.45164941","121.9671742",c(7, 7, 7, 7, 13),c(1, 1, 1, 1, 8),c(0, 0, 0, 0, 2),c(0, 0, 0, 0, 0),c(3, 3, 3, 3, 3),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0)
"20.45164941","121.9671742",c(7, 7, 7, 7, 13),c(1, 1, 1, 1, 8),c(0, 0, 0, 0, 2),c(0, 0, 0, 0, 0),c(3, 3, 3, 3, 3),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0)
"20.45164941","121.9671742",c(7, 7, 7, 7, 13),c(1, 1, 1, 1, 8),c(0, 0, 0, 0, 2),c(0, 0, 0, 0, 0),c(3, 3, 3, 3, 3),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0)
"8.19183664","124.17909375",c(7, 7, 7, 7, 13),c(1, 1, 1, 1, 8),c(0, 0, 0, 0, 2),c(0, 0, 0, 0, 0),c(3, 3, 3, 3, 3),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0),c(0, 0, 0, 0, 0)
Is there some argument I am missing? I have tried with and without row.names, as well as using write.table with a variety of different separators. I appreciate any help, let me know if you have more questions. I believe it might have something to do with how the dataframe was created. Sorry if this has been asked already, but this specific problem I am having is pretty difficult to search for.
Edit: So using dput yields:
> dput(extract[1:5,2:16])
structure(list(latitude = c("20.45164941", "20.45164941", "20.45164941",
"20.45164941", "8.19183664"), longitude = c("121.9671742", "121.9671742",
"121.9671742", "121.9671742", "124.17909375"), WordCount = structure(list(
WordCount = c(7L, 7L, 7L, 7L, 13L)), .Names = "WordCount", row.names = c("20",
"77", "106", "48670", "94831"), class = "data.frame"), English = structure(list(
English = c(1L, 1L, 1L, 1L, 8L)), .Names = "English", row.names = c("20",
"77", "106", "48670", "94831"), class = "data.frame"), Common = structure(list(
Common = c(0L, 0L, 0L, 0L, 2L)), .Names = "Common", row.names = c("20",
"77", "106", "48670", "94831"), class = "data.frame"), Tagalog = structure(list(
Tagalog = c(0L, 0L, 0L, 0L, 0L)), .Names = "Tagalog", row.names = c("20",
"77", "106", "48670", "94831"), class = "data.frame"), Bikol = structure(list(
Bikol = c(3L, 3L, 3L, 3L, 3L)), .Names = "Bikol", row.names = c("20",
"77", "106", "48670", "94831"), class = "data.frame"), Cebuano = structure(list(
Cebuano = c(0L, 0L, 0L, 0L, 0L)), .Names = "Cebuano", row.names = c("20",
"77", "106", "48670", "94831"), class = "data.frame"), Hiligaynon = structure(list(
Hiligaynon = c(0L, 0L, 0L, 0L, 0L)), .Names = "Hiligaynon", row.names = c("20",
"77", "106", "48670", "94831"), class = "data.frame"), Ilocano = structure(list(
Ilocano = c(0L, 0L, 0L, 0L, 0L)), .Names = "Ilocano", row.names = c("20",
"77", "106", "48670", "94831"), class = "data.frame"), Kapampangan = structure(list(
Kapampangan = c(0L, 0L, 0L, 0L, 0L)), .Names = "Kapampangan", row.names = c("20",
"77", "106", "48670", "94831"), class = "data.frame"), Pangasinense = structure(list(
Pangasinense = c(0L, 0L, 0L, 0L, 0L)), .Names = "Pangasinense", row.names = c("20",
"77", "106", "48670", "94831"), class = "data.frame"), Waray = structure(list(
Waray = c(0L, 0L, 0L, 0L, 0L)), .Names = "Waray", row.names = c("20",
"77", "106", "48670", "94831"), class = "data.frame"), Zam = structure(list(
Zam = c(0L, 0L, 0L, 0L, 0L)), .Names = "Zam", row.names = c("20",
"77", "106", "48670", "94831"), class = "data.frame"), Spanish = structure(list(
Spanish = c(0L, 0L, 0L, 0L, 0L)), .Names = "Spanish", row.names = c("20",
"77", "106", "48670", "94831"), class = "data.frame")), .Names = c("latitude",
"longitude", "WordCount", "English", "Common", "Tagalog", "Bikol",
"Cebuano", "Hiligaynon", "Ilocano", "Kapampangan", "Pangasinense",
"Waray", "Zam", "Spanish"), row.names = c(20L, 77L, 106L, 48670L,
94831L), class = "data.frame")
And a summary:
> summary(extract)
alltweets.text latitude
Grabe. Kasanok masungbatan nu ana nakaturog nak idin. 1AM kano ngamin idin. Haha. \xed\xa0\xbd\xed\xb1\x8a\xed\xa0\xbd\xed\xb1\x8a : 9 Length:1001
@MitchyyWong mejo mejo lang pud hahaha ikaw ang hinfi talaga ba haha labyu : 8 Class :character
#EvictLoisa nakaka inis panoorin sa tv screen di na maganda tingnan palayasin nayan ngaung saturday! #PBBStopAndStare : 8 Mode :character
Lionel Messi for breakfast! \xed\xa0\xbd\xed\xb8\x9c Happy Monday.. #ARG vs #BIH #WorldCup2014 : 8
ngano lami man kaayo ang kan.on oy? ilabi nag parisan ug bulad ug ginamos. ginoo ko tabang! : 8
"@IndayBanat: Ang taong pinakamahirap tanggalin sa isipan mo, ay yong taong kahit hindi kayo pero pinapasaya ka at binubuo ang araw mo." \xed\xa0\xbd\xed\xb8\xad: 7
(Other) :953
longitude WordCount.WordCount English.English Common.Common Tagalog.Tagalog Bikol.Bikol Cebuano.Cebuano Hiligaynon.Hiligaynon Ilocano.Ilocano
Length:1001 Min. : 3.000000 Min. : 0.000000 Min. : 0.000000 Min. :0.000000 Min. :0.00000 Min. :0.000000 Min. :0.000000 Min. :0.000000
Class :character 1st Qu.: 9.000000 1st Qu.: 1.000000 1st Qu.: 2.000000 1st Qu.:0.000000 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0.000000 1st Qu.:0.000000
Mode :character Median :12.000000 Median : 2.000000 Median : 4.000000 Median :1.000000 Median :0.00000 Median :0.000000 Median :0.000000 Median :0.000000
Mean :12.384615 Mean : 2.298701 Mean : 4.074925 Mean :1.599401 Mean :0.07992 Mean :0.418581 Mean :0.140859 Mean :0.305694
3rd Qu.:15.000000 3rd Qu.: 3.000000 3rd Qu.: 6.000000 3rd Qu.:3.000000 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0.000000 3rd Qu.:0.000000
Max. :22.000000 Max. :15.000000 Max. :13.000000 Max. :7.000000 Max. :4.00000 Max. :5.000000 Max. :4.000000 Max. :4.000000
Kapampangan.Kapampangan Pangasinense.Pangasinense Waray.Waray Zam.Zam Spanish.Spanish
Min. :0.00000 Min. :0.000000 Min. : 0.000000 Min. :0 Min. :0.000000
1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.: 0.000000 1st Qu.:0 1st Qu.:0.000000
Median :0.00000 Median :0.000000 Median : 0.000000 Median :0 Median :0.000000
Mean :0.26973 Mean :0.076923 Mean : 0.818182 Mean :0 Mean :0.367632
3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.: 1.000000 3rd Qu.:0 3rd Qu.:1.000000
Max. :8.00000 Max. :5.000000 Max. :10.000000 Max. :0 Max. :5.000000
And a sample of the string column:
> extract$alltweets.text[1:10]
[1] Helo #Payi!! #lobster!! #happyfiesta diptan!! \xed\xa0\xbc\xed\xbe\x89\xed\xa0\xbc\xed\xbe\x8a\xed\xa0\xbd\xed\xb8\x8d\xed\xa0\xbd\xed\xb1\x8c @ Diptan, Basco,Batanes http://t.co/rZy4QlIwxW
[2] Helo #Payi!! #lobster!! #happyfiesta diptan!! \xed\xa0\xbc\xed\xbe\x89\xed\xa0\xbc\xed\xbe\x8a\xed\xa0\xbd\xed\xb8\x8d\xed\xa0\xbd\xed\xb1\x8c @ Diptan, Basco,Batanes http://t.co/rZy4QlIwxW
[3] Helo #Payi!! #lobster!! #happyfiesta diptan!! \xed\xa0\xbc\xed\xbe\x89\xed\xa0\xbc\xed\xbe\x8a\xed\xa0\xbd\xed\xb8\x8d\xed\xa0\xbd\xed\xb1\x8c @ Diptan, Basco,Batanes http://t.co/rZy4QlIwxW
[4] Helo #Payi!! #lobster!! #happyfiesta diptan!! \xed\xa0\xbc\xed\xbe\x89\xed\xa0\xbc\xed\xbe\x8a\xed\xa0\xbd\xed\xb8\x8d\xed\xa0\xbd\xed\xb1\x8c @ Diptan, Basco,Batanes http://t.co/rZy4QlIwxW
[5] Here is what's cooking at Gloria's Lechon in Iligan City! Who wants lechon manok? @ Gloria's Lechon http://t.co/9gxv4GxgIW
[6] Here is what's cooking at Gloria's Lechon in Iligan City! Who wants lechon manok? @ Gloria's Lechon http://t.co/9gxv4GxgIW
[7] "@PBBabscbn: Pindot, pindot lang! Pindot, pindot lang! #PBBStopAndStare"
[8] "@PBBabscbn: Pindot, pindot lang! Pindot, pindot lang! #PBBStopAndStare"
[9] @eyyyorange dae pwede duman. Crowded na duman ta dakulon na boarders. Dae ngani ko duman makaadal tultol ta maribok. Inda matxt na lang ako
[10] Pindot pindot lang..pindot pindot pindot lang xD hahahahahaha
92162 Levels: ⃣ phones are the best \xed��\xed�\u008a _beown eyes tells his soul\nxo xo http://t.co/vzbMmzgdk7 ...
Upvotes: 2
Views: 371
Answers (1)
A5C1D2H2I1M1N2O1R2T1
Reputation: 193497
As indicated in the comments, your problem is due to the structure of your data.frame. If you can't fix it at the source of the problem, you should be able to fix it with:
do.call(data.frame, extract) ## extract is the name of your original data frame
Example:
str(do.call(data.frame, extract))
# 'data.frame': 5 obs. of 15 variables:
# $ latitude : Factor w/ 2 levels "20.45164941",..: 1 1 1 1 2
# $ longitude : Factor w/ 2 levels "121.9671742",..: 1 1 1 1 2
# $ WordCount : int 7 7 7 7 13
# $ English : int 1 1 1 1 8
# $ Common : int 0 0 0 0 2
# $ Tagalog : int 0 0 0 0 0
# $ Bikol : int 3 3 3 3 3
# $ Cebuano : int 0 0 0 0 0
# $ Hiligaynon : int 0 0 0 0 0
# $ Ilocano : int 0 0 0 0 0
# $ Kapampangan : int 0 0 0 0 0
# $ Pangasinense: int 0 0 0 0 0
# $ Waray : int 0 0 0 0 0
# $ Zam : int 0 0 0 0 0
# $ Spanish : int 0 0 0 0 0
Here's what it should look like:
A <- tempfile()
write.csv(do.call(data.frame, extract), A)
readLines(A)
# [1] "\"\",\"latitude\",\"longitude\",\"WordCount\",\"English\",\"Common\",\"Tagalog\",\"Bikol\",\"Cebuano\",\"Hiligaynon\",\"Ilocano\",\"Kapampangan\",\"Pangasinense\",\"Waray\",\"Zam\",\"Spanish\""
# [2] "\"20\",\"20.45164941\",\"121.9671742\",7,1,0,0,3,0,0,0,0,0,0,0,0"
# [3] "\"77\",\"20.45164941\",\"121.9671742\",7,1,0,0,3,0,0,0,0,0,0,0,0"
# [4] "\"106\",\"20.45164941\",\"121.9671742\",7,1,0,0,3,0,0,0,0,0,0,0,0"
# [5] "\"48670\",\"20.45164941\",\"121.9671742\",7,1,0,0,3,0,0,0,0,0,0,0,0"
# [6] "\"94831\",\"8.19183664\",\"124.17909375\",13,8,2,0,3,0,0,0,0,0,0,0,0"
Upvotes: 1
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