1) The document discusses merging multiple CSV files containing air pollution data from 332 different monitors into a single dataframe in R. Each file has data from a single monitor and the ID is in the file name (e.g. data for monitor 200 is in "200.csv"). 2) It provides information on relevant functions in R like rbind() and lists the steps to bind all 332 files into a single dataframe. First, all file paths are stored in a list using list.files(). Then, the files are read and row bound (rbind()) into a growing data object. 3) It also discusses how to handle missing values (NAs) in R and provides an example function to calculate the

1. Merge Multiple files into single
dataframe using R
Yogesh Khandelwal

2. Problem Description
• The zip file contains 332 comma-separated-value (CSV) files
containing pollution monitoring data for fine particulate
matter (PM) air pollution at 332 locations in the United States.
Each file contains data from a single monitor and the ID
number for each monitor is contained in the file name. For
example, data for monitor 200 is contained in the file
"200.csv".
• Data Source: http://spark-
public.s3.amazonaws.com/compdata/data/specdata.zip

4. Variable Name

5. Variables in file
• Date: the date of observation in YYYY-MM-DD format
(year-month-day) ,Datatype:factor
• sulfate: the level of sulfate PM in the air on that date
(measured in micrograms per cubic
meter),Datatype:num
• nitrate: the level of nitrate PM in the air on that date
(measured in micrograms per cubic
meter),Datatype:num
• Id:location id,Datatype:int

6. Before we start we should know
• Functions in R
• How to merge data files

7. Functions in R

8. Functions in R
Functions are created using the function() directive and are
stored as R objects just like anything else. In particular, they are R
objects of class “function”.
f <- function(<arguments>) {
## Do something interesting
}
• Functions in R are “first class objects”, which means that they can
be treated much like any other R object. Importantly,
• Functions can be passed as arguments to other functions.
• Functions can be nested, so that you can define a function
inside of another function
• The return value of a function is the last expression in the function
• body to be evaluated.

9. Function contd..
• For ex:
Function name
Function defination
Function call

10. Our objective
• How we can merge no. of files into single data
frame?
• How to apply same function to different files
in efficient way?

11. How to merge two different files?

12. • No.of options available like
1. Use merge() function
2. Use rbind(),cbind() etc.

13. How to merge no.of files as a single
data frame
• Approach 1
files<-list.files("specdata",full.names = TRUE)
dat<-NULL
for(i in 1:332)
{
dat<-rbind(dat,read.csv(files[i]))
}
• Further we can run various command on merged file object as per our need some are like:
1. Str(dat)
2. Head(dat)
3. Tail(dat) etc.
Notes:full.names= a logical value. If TRUE, the directory path is prepended to the file names to give a relative file path. If FALSE,
the file names (rather than paths) are returned.

14. How to handle missing value in R ?

15. contd.
• In R, NA is used to represent any value that is 'not available' or 'missing' (in
the | statistical sense)
• Missing values play an important role in statistics and data analysis. Often,
missing values must not be ignored, but rather they should be carefully
studied to see if there's an underlying pattern or cause for their
missingness.
• For ex:
• X<-c(1,2,NA,4)
• Y<-c(NA,2,3,1)
• >x+y
• [1] NA 4 NA 5
• Multiple options are available in R to handle NA values like
• Is.NA()
• Set na.rm=TRUE as a function argument
> mean(X) [1] NA
> mean(X,na.rm = TRUE) [1] 2.333333

16. Apply what we learn to our dataset
Function defination

17. Function call
pollutantmean('specdata','nitrate',1:10)
[1] 0.7976266

18. Thank You!!