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I am trying to read-in a dataset from this zip file link: https://s3.amazonaws.com/hubway-data/201901-bluebikes-tripdata.zip within an R markdown. Firstly, I used the code called "code1" below, but the console spits out an error message:"

line 1 appears to contain embedded nulls
Error in read.table("https://s3.amazonaws.com/hubway-data/201901-bluebikes-tripdata.zip", : more columns than column names".

Then I made some adjustment, the other code is called "code2" as shown below, but the console still spits out an error message:

invalid input found on input connection
'https://s3.amazonaws.com/hubway-data/201901-bluebikes-tripdata.zip'incomplete final line found by readTableHeader on 'https://s3.amazonaws.com/hubway-data/201901-bluebikes-tripdata.zip'"

I have looked through all the possible solutions online and tried many other ways, but still could not make it to work. Could someone tell me a solution? Really appreciate it!

code1 <- read.table("https://s3.amazonaws.com/hubway-data/201901-bluebikes-tripdata.zip", header = TRUE, sep = ",")   
code2 <- read.table("https://s3.amazonaws.com/hubway-data/201901-bluebikes-tripdata.zip", header = TRUE, sep = ",", fileEncoding = "utf-8", skipNul = TRUE)
Rui Barradas
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Zhao Jianting
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1 Answers1

1

You can wrap all it in one function

library(tidyverse)

read_zip <- function(path_down, file_name = NULL){

  if(is.null(file_name)) stop("please provide a file name")

  download.file(path_down,
                destfile = paste0(file_name, ".zip"))

  unzip(paste0(file_name, ".zip"))    
  return(read_csv(paste0(file_name, ".csv")))
}

data <- read_zip(path_down = "https://s3.amazonaws.com/hubway-data/201901-bluebikes-tripdata.zip",
                 file_name = "201901-bluebikes-tripdata")

data

## A tibble: 69,872 x 15
#   tripduration starttime           stoptime           
#          <dbl> <dttm>              <dttm>             
# 1          371 2019-01-01 00:09:13 2019-01-01 00:15:25
# 2          264 2019-01-01 00:33:56 2019-01-01 00:38:20
# 3          458 2019-01-01 00:41:54 2019-01-01 00:49:33
# 4          364 2019-01-01 00:43:32 2019-01-01 00:49:37
# 5          681 2019-01-01 00:49:56 2019-01-01 01:01:17
# 6          549 2019-01-01 00:50:01 2019-01-01 00:59:10
# 7          304 2019-01-01 00:54:48 2019-01-01 00:59:53
# 8          425 2019-01-01 01:00:48 2019-01-01 01:07:53
# 9         1353 2019-01-01 01:03:34 2019-01-01 01:26:07
#10          454 2019-01-01 01:08:56 2019-01-01 01:16:30
## ... with 69,862 more rows, and 12 more variables: `start
##   station id` <dbl>, `start station name` <chr>, `start
##   station latitude` <dbl>, `start station longitude` <dbl>,
##   `end station id` <dbl>, `end station name` <chr>, `end
##   station latitude` <dbl>, `end station longitude` <dbl>,
##   bikeid <dbl>, usertype <chr>, `birth year` <dbl>,
##   gender <dbl>
patL
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