"User-ID";"ISBN";"Book-Rating"
"276725";"034545104X";"0"
"276726";"0155061224";"5"
"276727";"0446520802";"0"
Ouput would be like:
"034545104X";"0155061224";"0446520802"
"276725" "0"
"276726" "5"
"276727" "0"
Solved it with below script in R.
loading reader library
library(readr)
reading CSV file with ';' as delimiter
BX_Book_Ratings <- read_delim("C:/Users/panch/Desktop/Lambton/Term_2_Fall_2017/2017F-T2 BDM 2013 - Data Collection Methods/project_03/dataset/BX-Book-Ratings.csv", ";", escape_double = FALSE, trim_ws = TRUE)
viewing data
View(BX_Book_Ratings)
loading reshape2 library
library(reshape2)
reading only few first rows from dataset
sample_data<-head(BX_Book_Ratings,30)
generating matrix
d <- dcast( sample_data, User_ID ~ ISBN, value.var = "Book_Rating" )
replacing NA with 0
d[is.na(d)] <- 0
loading grid library to display data in table format
library(gridExtra)
displaying table
grid.table(d)
Storing output in CSV format
write.csv(d,"C:/Users/panch/Desktop/Lambton/Term_2_Fall_2017/2017F-T2 BDM 2013 - Data Collection Methods/project_03/dataset/output.csv")
Related
I need help solving this problem:
I have a directory full of .txt files that look like this:
file1.no
file2.no
file3.no
And every file has the following structure (I only care for the first two "columns" in the .txt):
#POS SEQ SCORE QQ-INTERVAL STD MSA DATA
#The alpha parameter 0.75858
#The likelihood of the data given alpha and the tree is:
#LL=-4797.62
1 M 0.3821 [0.01331,0.5465] 0.4421 7/7
2 E 0.4508 [0.05393,0.6788] 0.5331 7/7
3 L 0.5334 [0.05393,0.6788] 0.6279 7/7
4 G 0.5339 [0.05393,0.6788] 0.624 7/7
And I want to parse all of them into one DataFrame, while also converting the columns into lists for each row (i.e., the first column should be converted into a string like this: ["MELG"]).
But now I am running into two issues:
How to read the different files and append all of them to a single DataFrame, and also making a single column out of al the rows inside said files
How to parse this files, giving that the spaces between the columns vary for almost all of them.
My output should look like this:
|File |SEQ |SCORE|
| --- | ---| --- |
|File1|MELG|0.3821,0.4508,0.5334,0.5339|
|File2|AAHG|0.5412,1,2345,0.0241,0.5901|
|File3|LLKM|0.9812,0,2145,0.4142,0.4921|
So, the first column for the first file (file1.no), the one with single letters, is now in a list, in a row with all the information from that file, and the DataFrame has one row for each file.
Any help is welcome, thanks in advance.
Here is an example code that should work for you:
using DataFrames
function parsefile(filename)
l = readlines(filename)
filter!(x -> !startswith(x, "#"), l)
sl = split.(l)
return (File=filename,
SEQ=join(getindex.(sl, 2)),
SCORE=parse.(Float64, getindex.(sl, 3)))
end
df = DataFrame()
foreach(fn -> push!(df, parsefile(fn)), ["file$i.no" for i in 1:3])
your result will be in df data frame.
I am new using dplyr package and I have been trying to read multiple files in R and then create a data frame by binding all the rows, but including the name of each file as a new column. This new column is the corresponding date which is not included in the data.
My list of files (for example):
01012019.aps
02012019.aps
I would like to have my final dataframe like this:
x y file date
1 4 01012019 01-01-2019
2 5 01012019 01-01-2019
3 6 02012019 02-01-2019
4 7 02012019 02-01-2019
I've been trying this:
path_aps<- "C:/Users/.../.../APS"
files_aps <- list.files(path_aps, pattern = "*.aps")
data_aps <- files_aps %>%
map(~ read.table(file.path(path_aps, .), sep = "\t")) %>%
map(~ mutate(filename = files_aps, .))%>%
reduce(gtools::smartbind)
But I am getting this error:
Error: Column filename must be length 288 (the number of rows) or one, not 61
I understand that the list of files in files_aps has 61 elements as this is the number of files that I have in my directory and 288 is the number of rows of each .aps file; however, I haven't been able to make it work to the extend of each .aps file. I've been reading multiple answers to similar questions but still I am not getting the expected result.
I've solved it with the help of this other answer and I've got this:
data_aps <- list.files(path_aps, pattern = "*.aps", full.names = TRUE) %>%
map_df(function(x) read.table(x, sep = "\t") %>%
mutate(filename=gsub(".aps","", basename(x))))
Whenever I use read.csv.sql I cannot select from the first column with and any output from the code places an unusual character (A(tilde)-..) at the begging of the first column's name.
So suppose I create a df.csv file in in Excel that looks something like this
df = data.frame(
a = 1,
b = 2,
c = 3,
d = 4)
Then if I use sqldf to query the csv which is in my working directory I get the following error:
> read.csv.sql("df.csv", sql = "select * from file where a == 1")
Error in result_create(conn#ptr, statement) : no such column: a
If I query a different column than the first, I get a result but with the output of the unusual characters as seen below
df <- read.csv.sql("df.csv", sql = "select * from file where b == 2")
View(df)
Any idea how to prevent these characters from being added to the first column name?
The problem is presumably that you have a file that is larger than R can handle and so only want to read a subset of rows into R and specifying the condition to filter it by involves referring to the first column whose name is messed up so you can't use it.
Here are two alternative approaches. The first one involves a bit more code but has the advantage that it is 100% R. The second one is only one statement and also uses R but additionally makes use an of an external utility.
1) skip header Read the file in skipping over the header. That will cause the columns to be labelled V1, V2, etc. and use V1 in the condition.
# write out a test file - BOD is a data frame that comes with R
write.csv(BOD, "BOD.csv", row.names = FALSE, quote = FALSE)
# read file skipping over header
DF <- read.csv.sql("BOD.csv", "select * from file where V1 < 3",
skip = 1, header = FALSE)
# read in header, assign it to DF and fix first column
hdr <- read.csv.sql("BOD.csv", "select * from file limit 0")
names(DF) <- names(hdr)
names(DF)[1] <- "TIME" # suppose we want TIME instead of Time
DF
## TIME demand
## 1 1 8.3
## 2 2 10.3
2) filter Another way to proceed is to use the filter= argument. Here we assume we know that the end of the column name is ime but there are other characters prior to that that we don't know. This assumes that sed is available and on your path. If you are on Windows install Rtools to get sed. The quoting might need to be changed depending on your shell.
When trying this on Windows I noticed that sed from Rtools changed the line endings so below we specified eol= to ensure correct processing. You may not need that.
DF <- read.csv.sql("BOD.csv", "select * from file where TIME < 3",
filter = 'sed -e "1s/.*ime,/TIME,/"' , eol = "\n")
DF
## TIME demand
## 1 1 8.3
## 2 2 10.3
So I figured it out by reading through the above comments.
I'm on a Windows 10 machine using Excel for Office 365. The special characters will go away by changing how I saved the file from a "CSV UTF-8 (Comma Delimited)" to just "CSV (Comma delimited)".
I have a very wide Excel sheet, from Column A - DIE (about 2500 columns wide), of survey data. Each column is a question, and each row is a response. I'm trying to upload the data to SQL and convert it to a more SQL-friendly format using the UNPIVOT function, but I can't even get it loaded into SQL because it exceeds the 1024-column limit.
Basically, I have an Excel sheet that looks like this:
But I want to convert it to look like this:
What options do I have to make this change, either in Excel (prior to upload) or SQL (while circumventing the 1024 column limit)?
I have had to do this quite a bit. My solution was to write a Python script that would un-crosstab a CSV file (typically exported from Excel), creating another CSV file. The Python code is here: https://pypi.python.org/pypi/un-xtab/ and the documentation is here: http://pythonhosted.org/un-xtab/. I've never run it on a file with 2500 columns, but don't know why it wouldn't work.
R has a very specific function call in one of it's libraries. You can also connect, read, and write data with R into a database. Would suggest downloading R and Rstudio.
Here is a working script to get you started that does what you need:
Sample data:
df <- data.frame(id = c(1,2,3), question_1 = c(1,0,1), question_2 = c(2,0,2))
df
Input table:
id question_1 question_2
1 1 1 2
2 2 0 0
3 3 1 2
Code to transpose the data:
df2 <- gather(df, key = id, value = values)
df2
Output:
id id values
1 1 question_1 1
2 2 question_1 0
3 3 question_1 1
4 1 question_2 2
5 2 question_2 0
6 3 question_2 2
Some helper functions for you to import and export the csv data:
# Install and load the necessary libraries
install.packages(c('tidyr','readr'))
library(tidyr)
library(readr)
# to read a csv file
df <- read_csv('[some directory][some filename].csv')
# To output the csv file
write.csv(df2, '[some directory]data.csv', row.names = FALSE)
Thanks for all the help. I ended up using Python due to limitations in both SQL (over 1024 columns wide) and Excel (well over 1 million rows in the output). I borrowed the concepts from rd_nielson's code, but that was a bit more complicated than I needed. In case it's helpful to anyone else, this is the code I used. It outputs a csv file with 3 columns and 14 million rows that I can upload to SQL.
import csv
with open('Responses.csv') as f:
reader = csv.reader(f)
headers = next(reader) # capture current field headers
newHeaders = ['ResponseID','Question','Response'] # establish new header names
with open('PythonOut.csv','w') as outputfile:
writer=csv.writer(outputfile, dialect='excel', lineterminator='\n')
writer.writerow(newHeaders) # write new headers to output
QuestionHeaders = headers[1:len(headers)] # Slice the question headers from original header list
for row in reader:
questionCount = 0 # start counter to loop through each question (column) for every response (row)
while questionCount <= len(QuestionHeaders) - 1:
newRow = [row[0], QuestionHeaders[questionCount], row[questionCount + 1]]
writer.writerow(newRow)
questionCount += 1
I have a similiar problem like this question:
selecting every Nth column in using SQLDF or read.csv.sql
I want to read some columns of large files (table of 150rows, >500,000 columns, space separated, filled with numeric data and only a 32 bit system available). This file has no header, therefore the code in the thread above didn't work and I decided to write a new post.
Do you have an idea to solve this problem?
I thought about something like that, but any results with fread or read.table are also ok:
MyConnection <- file("path/file.txt")
df<-sqldf("select column 1 100 1000 235612 from MyConnection",file.format = list(header=F,sep=" "))
You can use substr to specify the start and end position of the columns you want to read in if they are fixed width:
x <- tempfile()
cat("12345", "67890", "09876", "54321", sep = "\n", file = x)
myfile <- file(x)
sqldf("select substr(V1, 1, 1) var1, substr(V1, 3, 5) var2 from myfile")
# var1 var2
# 1 1 345
# 2 6 890
# 3 9 76
# 4 5 321
See this blog post for some more examples. The "select" statement can easily be constructed with paste if you know the details about the column starting positions and widths.