# Reading data from the web

###### PUBLISHED ON APR 13, 2017 — R

In this tutorial I show how to read data from online sources, in various ways. I will also demonstrate how to tidy data using the tidyr package, in particular converting from wide to long format.

In the case where a csv or other text file is available as a direct link, we can simply read the file from the URL without having to download it. To read the current CO2 concentrations on Mauna Loa, for example, we can use the following:

url <- "ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_weekly_mlo.txt"
plot(d[,4], d[,5], type='l', xlab="Year", ylab=expression("["*CO[2]*"]"~(ppm)))

The only hitch here is that the column names are not read (they are embedded in the comments in a messy way), but we can find that year is the 4th column, and CO2 concentration the 5th.

Also note the use of na.strings to convert missing values coded as -999.99 to NA.

## Finding the URL of a datasource

In the example above, and all that follow, we have to save the URL of the data source. When you find the URL of the datafile, or the button that says ‘Click to Download’, or whatever, right-click on it and select ‘Copy Link Address’ (Windows instructions).

In the above example, we read a text file from a direct URL. Sometimes this is not possible, and other times you would also like to have a local copy of the text file for safekeeping (in case something happens to NOAA, for example).

url <- "ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_weekly_mlo.txt"
fn <- basename(url)
download.file(url, fn)

Here I use basename to extract the right-hand side of the url (co2_weekly_mlo.txt), a natural name for the file.

The next example shows how to download a zip file, containing data files, from an online source. Downloading a zip file proceeds as above, however we have to specify one extra argument.

Warning: For non-text files, on Windows, you must use the argument mode="wb"!

If you don’t follow this warning, the file will download just fine but you cannot read it (and it may take you some time to figure out why not).

The following example downloads data from the Research Data at Western Sydney University (your data ends up here if you publish your data via the HIEv).

url <- "http://research-data.westernsydney.edu.au/redbox/verNum1.8-SNAPSHOT/default/detail/094c4376cead38dc21556f59470de5a0/WTC_TEMPERATURE_SUNSHADE.zip?preview=true"

# Note the mode='wb'

# Using the built-in unzipper - it returns the filenames.
u <- unzip("data.zip")

# Find the csv files
csvfiles <- u[grep("[.]csv", u, ignore.case=TRUE)]

dat <- read.csv(csvfiles[1])

# Reading HTML tables (Option 1)

There are a few options to read HTML tables directly into R. This example shows the use of the simple readHTMLTable from the XML package. Reading data from HTML tables on the web is particularly useful for data that are updated frequently.

Here I read the water level of the Hawkesbury river at Windsor, as provided by the BOM.

library(XML)
url <- "http://www.bom.gov.au/fwo/IDN60233/IDN60233.567044.tbl.shtml"

# to open a URL with the default browser, do this:
## # browseURL(url)

names(wat) <- c("DateTime","WaterLevel")

# for 'mutate', a cleaner way to add new variables
library(dplyr)
wat <- mutate(wat,
WaterLevel = as.numeric(as.character(WaterLevel)),
DateTime = as.POSIXct(DateTime, format="%d/%m/%Y %H:%M"))

# Simple line plot of the river height
par(cex.main=0.85)
with(wat, plot(DateTime, WaterLevel, type='l', main="Hawkesbury River water level at Windsor"))

# Reading HTML table (Option 2)

The second option uses the rvest package. I will also introduce the pipe operator (%>%) for tidier coding.

library(rvest)
url <- "https://en.wikipedia.org/wiki/Demography_of_Australia"

# Many tables are found
## {xml_nodeset (6)}
## [2] <table class="wikitable" style="float:right; margin-left:8px;">\n<tr ...
## [3] <table class="navbox">\n<tr>\n<th colspan="10" class="navbox-title"> ...
## [4] <table class="wikitable sortable plainrowheaders" style="text-align: ...
## [5] <table class="wikitable plainrowheaders" style="text-align:right;">\ ...
## [6] <table class="wikitable plainrowheaders" style="text-align:right">\n ...

In the above example we do the usual nesting of expressions. First the url is read (read_html(url)), then the table nodes are found (html_nodes). Often we have multiple steps that are nested in this way, which can make for long and sometimes unreadable code. An alternate way of writing these expressions is to use the pipe operator, which allows us to write expressions in the order that they occur. Example:

# this expression:

# is equivalent to:
tb <- url %>% read_html %>% html_nodes("table")

With the pipe operator, the result of the expression to the left will be used as the first argument to the next expression. For example, url is sent to read_html which treats it as its first argument. The output from read_html is sent to html_nodes which uses that as its first argument.

After digging in the results a bit, we find we want the third table. That returns a list of length one, so we take just the first element (pop[[1]]).

pop <- html_table(tb[4], fill=TRUE)

# Also delete first row, contains headers (again), keep only first two columnss.
pop <- pop[[1]][-1,1:2] 

The resulting table is far from pretty. First we fix the column names, then format the total population column.

names(pop) <- c("Region","population")

# Remove commas by substituting them with nothing ("")
pop$population <- as.numeric(gsub(",","", pop$population))

# And a simple barplot
par(mar=c(10,4,1,1), las=3, cex.axis=0.8)
pop <- pop[order(pop$population),] with(pop, barplot(population * 10^-6, col="cornflowerblue", names.arg=Region, ylab="Population (millions)")) # Reading and tidying a complex Excel workbook In some cases data are stored as an Excel workbook, with data organized in tabs. I usually prefer to convert all tabs to CSV files and work with those. However there are two distinct advantages to reading data from the Excel workbook directly (although of course I do not advocate you use Excel to begin with; the point is others do and we have to work with that). Advantages are: 1. Reading from Excel directly avoids a manual step (saving as CSV), making your workflow more reproducible. 2. If cells are formatted in Excel (for example, large numbers may have commas in it), reading the data from Excel will ignore the formatting and just read the actual data in the sheets. This also applies to cells formatted as Dates (which will be read as the number of days since 1900-1-1, and can be converted with as.Date(num, origin="1900-1-1")). ## Getting the data In this example I use data form agricultural commodities (crops, livestock), available on the Australian Bureau of Statistics (http://www.abs.gov.au/AUSSTATS). Again the URL could be found by ‘Copy Link Address’ after right-clicking the Download button. url <- "http://www.abs.gov.au/AUSSTATS/subscriber.nsf/log?openagent&7124%20data%20cube.xls&7124.0&Data%20Cubes&EF15C557DF98A5F9CA257B2500137D3B&0&2010-11&06.03.2013&Latest" download.file(url, "tmp.xls", mode="wb") ## Simple timeseries We use read_excel from the readxl package, which is pretty quick and easy to use. It does have some issues, for example it spits out a long message a lot of the time (in hexadecimal code), which is quite useless and annoying. I have suppressed the messages below, but you will see many on the screen! library(readxl) # for read_excel library(tidyr) # for gather, spread As a first example we will read the ‘Horses’ tab, showing the number of horses in the country since 1885. The data are in wide format (every column is a year), and we can use gather from tidyr to quickly make this into long format. The first argument is the name of the new variable in long format that will contain the names of the columns that used to be in wide format (here, year). The second argument is the new name of the variable that will contain the data (I here call it count). Also note the use of the %>% operator, and the indexing (square brackets) to keep only the first row, and to delete the first column ([1,-1]). # Look at the 'horses' tab, we just want to plot the number of horses over time. horses <- read_excel("tmp.xls", sheet=7, skip=4)[1,-1] %>% gather(year,count) with(horses, plot(year, count, type='l')) Clearly number of horses has declined steadily since the introduction of the automobile - but note the pause in decline during the Great Depression. Now let’s write a simple function that takes the name of the sheet, and plots the first row of data against year. The trick here is to store the names of the sheets first (as sh). # Read sheet names, store as vector # library(XLConnect) # sh <- getSheets(loadWorkbook("tmp.xls")) # Or with readxl sh <- excel_sheets("tmp.xls") plot_row1 <- function(sheetname){ # Look up which sheet has that name i <- which(sh == sheetname) # Read that sheet. # Keep only first row, delete first column ([1,-1]) # Make into long format, with year as first variable, new variable # 'count' as variable that stores the data (gather). data <- read_excel("tmp.xls", sheet=i, skip=4)[1,-1] %>% gather(year,count) # Simple line plot with(data, plot(year, count, main=sheetname, type='l')) } plot_row1("Sheep") ## More complex example The final example is more complex. If you inspect the Barley tab in the Excel workbook, you see that we want to reshape two variables, representing area, and total yield. I want to plot yield per area over time (productivity), so we need to keep track of both variables. This example shows the use of tidyr to accomplish that. library(readxl) library(tidyr) library(dplyr) # Read second sheet, Barley. # Keep first and second row, delete first column aus_barley <- read_excel("tmp.xls", sheet=2, skip=4)[1:2,-1] # Rename first column, reset 'Variable' to more useful names. names(aus_barley)[1] <- "Variable" aus_barley$Variable <- c("Area","Yield")

# Make long format, but ignore the 'Variable' column when doing so.
aus_barley_long <- gather(aus_barley, year, value, -Variable)

# Now we have area and yield for every year, as rows following each other:

# Now we go back to wide format, 'splitting' by the 'Variable' column,
# so that we have separate columns for area and yield, using spread() from tidyr.