1) How to read a csv file in R ?

data<-read.csv(filename,header=TRUE)

2) How to display the first n lines of the file ?

head(data,n) : The default value of n is 6.

3) How to display the last n lines of the file ?

tail(data,n)

4) Calculate missing values in all the columns in the data set ?

colSums(data)

Other functions that can be used for this purpose are sapply and apply.

5) Calculate the mean of a column without the missing values ?

colMeans(data,na.rm=TRUE) Ozone Solar.R Wind Temp Month Day 42.129310 185.931507 9.957516 77.882353 6.993464 15.803922 colMeans(data) Ozone Solar.R Wind Temp Month Day NA NA 9.957516 77.882353 6.993464 15.803922 colMeans(data["Ozone"],na.rm=TRUE) Ozone 42.12931

6) Extract the subset of rows of the data frame where Ozone values are above 31 and Temp values are above 90. What is the mean of Solar.R in this subset?

colMeans(subset(data,(Ozone>31 & Temp>90))) Ozone Solar.R Wind Temp Month Day 89.5 212.8 5.6 93.4 8.2 14.5

7) Find the mean temperature in the Month of n ?

colMeans(subset(data,Month==n)) Ozone Solar.R Wind Temp Month Day NA 190.16667 10.26667 79.10000 6.00000 15.50000

Additional Resources :

1) Filling in nas with column medians in R

2) Apply function and its variants