First download the data into your data directory url is the internet URL the destfile defines where the files will go in my working directory *data/portal_data_joined.csv will load the file into our data folder
download.file(url="https://ndownloader.figshare.com/files/2292169",
destfile = "data/portal_data_joined.csv")Click on your data folder to check if its downloaded
Next read the data into R, we can do this through a number of different functions, read.csv() if it is a .csv file, or read.table() specifying the separation with the sep= argument (whether tsv; tab separated or csv; comma separated). Check the filetype you have downloaded and use appropriate function.
surveys = read.csv(file = "data/portal_data_joined.csv")
# the read.table fucntion would also work with the separation argument as ,
surveys = read.table(file="data/portal_data_joined.csv", sep=",", header=TRUE)
There are lots of ways we can inspect the data once we have loaded it into R. We can check the whole dataset, this is often not advised because it can print off many thousands or rows (or columns). Instead we can check the size of the dataframe, the first 10 or last 10 rows. We can also look at the column and row names and summarize the entire table.
# Checking the data
surveys # the whole datase (won't print it all)
# View(surveys) # opens a new tab with the dataset
# Checking the size of the data
dim(surveys) #find the dimensions of your data frame
nrow(surveys) #find the number of rows
ncol(surveys) # find the number of columns
# Checking the content of the data
head(surveys) # view the first few rows only
tail(surveys) # view the last six rows
# Checking the names
names(surveys) # returns the column names (synonym of colnames() for data.frame objects)
rownames(surveys) # returns the row names
# Summarising the data
str(surveys) #get the structure of your data
summary(surveys) # summary statistics for each column
# To get the class of every column you could use apply
apply(surveys, 2, class)
Numeric indexing of a dataframe
# numeric index = data[rows, columns]
surveys[1, ] #first row
surveys[ ,1] #first column
surveys[1,1] #first column and first row
surveys[1:10, 3] #rows 1 to 10 in column 3
# take a subset
surveys[c(1,5,7), c(1,4,8)] #takes three rows and three columns
surveys[1:5, -7] #remove the 7th columnName indexing of a dataframe
# Name indexing
surveys[1:10 , "weight"]
# a shortcut to select an entire column
surveys[, "taxa"]
# Use the dollar sign $ to select a column
surveys$taxa
# subset by character
surveys[5:13, c("record_id", "genus")] # subset rows 5 to 13 and the named columnsLogical indexing of a dataframe
genus_neotoma = surveys[surveys$genus == "Neotoma", ]
nrow(genus_neotoma)
species_albigula = surveys[surveys$species == "albigula",]
# To create a data.frame containing only the data in row 200 of the surveys dataset you can do this:
surveys_200 = surveys[200, ]
surveys_200
# nrow() gives you the number of rows in a data.frame. You can use that number to pull out just the last row in the data frame
nrow(surveys)
surveys_last_row = surveys[34786, ]
surveys_last_row
# Using the tail function we can see if this is really the last row
surveys_last_row
tail(surveys)
# You can nest the functions to select the last row using nrow() instead of the row number.
surveys_last_row = surveys[nrow(surveys), ]
# Create a new data frame (surveys_last) from that last row.
surveys_last = surveys[nrow(surveys), ]
surveys_last
# Use nrow() to extract the row that is in the middle of the data frame.
# Store the content of this row in an object named surveys_middle
n_row = nrow(surveys)
surveys_middle =surveys[n_row/2, ]
# Combine nrow() with the - notation above to reproduce the behavior of head(surveys),
# keeping just the first through 6th rows of the surveys dataset.
head(surveys)
surveys[-(7:n_row), ]