A typical task in data analysis is to import CSV-formatted data. CSV is nothing more than a text file with data in rectangular form; rows stand for observations (eg., persons), and columns represent variables (such as age). Columns are separed by a “separator”, often a comma. Hence the name “CSV” - “comma separeted values”. Note however that the separator can in principle anything you like (eg., “;” or tabulator or “ “).
An easy example for importing a CSV is this
d <- read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/MASS/birthwt.csv")
head(d)
## X low age lwt race smoke ptl ht ui ftv bwt
## 1 85 0 19 182 2 0 0 0 1 0 2523
## 2 86 0 33 155 3 0 0 0 0 3 2551
## 3 87 0 20 105 1 1 0 0 0 1 2557
## 4 88 0 21 108 1 1 0 0 1 2 2594
## 5 89 0 18 107 1 1 0 0 1 0 2600
## 6 91 0 21 124 3 0 0 0 0 0 2622
It comes in handy that read.csv is able to address websites out of the box!
Now at times it happens that the CSV is somehow strange. Consider this example:
dd <- read.csv("http://www.stat.ufl.edu/~winner/data/slash_survsex.dat")
head(dd)
## X1.......1.......1
## 1 1 1 1
## 2 1 1 1
## 3 1 1 1
## 4 1 1 1
## 5 1 1 1
## 6 1 1 1
Hm, how many columns do we have?
ncol(dd)
## [1] 1
One only. Something got lost in translation. We would expect three columns. Now what?
To be honest, I am not really sure what the problem exactly consists of. But that does not stop from finding a solution.
The little at X1......1... appear to indicate blanks (spaces). So let’s try to use a blank as the column separator.
dd <- read.csv("http://www.stat.ufl.edu/~winner/data/slash_survsex.dat",
sep = " ")
ncol(dd)
## [1] 22
Yosh! 22 columns, that’s too much of something good… Hm, let’s look at the dataframe.
head(dd)
## X X.1 X.2 X.3 X.4 X.5 X.6 X1 X.7 X.8 X.9 X.10 X.11 X.12 X1.1 X.13 X.14
## 1 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA 1 NA NA
## 2 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA 1 NA NA
## 3 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA 1 NA NA
## 4 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA 1 NA NA
## 5 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA 1 NA NA
## 6 NA NA NA NA NA NA NA 1 NA NA NA NA NA NA 1 NA NA
## X.15 X.16 X.17 X.18 X1.2
## 1 NA NA NA NA 1
## 2 NA NA NA NA 1
## 3 NA NA NA NA 1
## 4 NA NA NA NA 1
## 5 NA NA NA NA 1
## 6 NA NA NA NA 1
It appears that X1, X1.1, and X1.2 are the only columns which are of interest (all others only consist of NAs). So let’s select those columns and discard the rest.
library(dplyr)
dd <- select(dd, X1, X1.1, X1.2)
Worked!
Finally, let’s change the column names to our desire.
dd <- rename(dd, V1 = X1,V2 = X1.1, V3 = X1.2)