Experiment 2

1. Create an excel sheet ( Or CSV format) with following columns and import it into R. This will be a data frame in R. “Sl.No” ,“Reg.No”, “Name”, “SSC %”, “Inter %”," B.Tech %“,”Cap/Non CAP“.

df1<-read.csv("C:/Users/pradeep/OneDrive/dm,cns,cp and JKC/data mining/dm files 2020 passouts/lab/sheet1.csv")
head(df1)
##   Sl.No Reg.No Name SSC.. Inter.. B.Tech.. Cap.Non.CAP
## 1     1    RAJ   16    90      88       75        TRUE
## 2     2  RAKHI   13    98      90       85       FALSE
## 3     3    RAM   23    80      80       65       FALSE
## 4     4    SAI   34    85      85       80        TRUE

2. Create another excel sheet ( Or CSV format) same as above with following columns and import it into R. This will be a data frame in R. “Sl.No” ,“Reg.No”, “Name”, “SSC %”, “Inter %”," B.Tech %“,”Cap/Non CAP“.

df2<-read.csv("C:/Users/pradeep/OneDrive/dm,cns,cp and JKC/data mining/dm files 2020 passouts/lab/sheet2.csv")
head(df2)
##   Sl.No   Reg.No         Name SSC.. Inter.. B.Tech.. Cap.Non.CAP
## 1     1 PREETHAM          5B4    93      91       75        TRUE
## 2     2    VARMA          5B5    98      97       81        TRUE
## 3     3  KARTHIK          5B6    98      98       85        TRUE

3a. Now, after creating the two data frames (above), merge them and create another data frame.

df<-merge(df1,df2,all=TRUE)
df
##   Sl.No   Reg.No         Name SSC.. Inter.. B.Tech.. Cap.Non.CAP
## 1     1      RAJ           16    90      88       75        TRUE
## 2     1 PREETHAM          5B4    93      91       75        TRUE
## 3     2    RAKHI           13    98      90       85       FALSE
## 4     2    VARMA          5B5    98      97       81        TRUE
## 5     3      RAM           23    80      80       65       FALSE
## 6     3  KARTHIK          5B6    98      98       85        TRUE
## 7     4      SAI           34    85      85       80        TRUE

3b. Display its top 2 rows of the merged data frame.

# here, in c(1,2)  c stands for concatenate. And is used for combing more than 1 entity
df[c(1,2),]
##   Sl.No   Reg.No   Name SSC.. Inter.. B.Tech.. Cap.Non.CAP
## 1     1      RAJ     16    90      88       75        TRUE
## 2     1 PREETHAM    5B4    93      91       75        TRUE
# You can also use head command: head(df,2)

4. Now export the above data frame into csv format.

write.csv(df,“df.csv”)

# To write the file use command:  write.csv(df,"df.csv")

5. Consider a dataset below (df3) . Predict the intermediate percentage of 11PA1A0503 by taking mean of other students and substitute in the place of the NA.

df3<-read.csv("C:/Users/pradeep/OneDrive/dm,cns,cp and JKC/data mining/dm files 2020 passouts/lab/sheet3.csv")
df3
##   Sl.No    Roll.No      Name  SSC inter B.Tech.perc CAP..NOT.CAP
## 1     1 11PA1A0501   AVINASH 87.3  65.3       40.00          CAP
## 2     2 11PA1A0503 KEERTHANA 89.0    NA       71.45      NOT_CAP
## 3     3 11PA1A0504 PRAVALIKA 67.0  68.0       45.26      NOT_CAP
## 4     4 11PA1A0505  SAI BABA   NA  67.0          NA          CAP
## 5     5 11PA1A0506  SRINIVAS 67.0  65.5       42.52          CAP

Now replace the missing values in Inter percentage

# Here, na.rm=TRUE because we want to calculate mean without considering NA. If this is not used, then we get an error.
df3$inter[is.na(df3$inter)]<-mean(df3$inter,na.rm=TRUE)
df3
##   Sl.No    Roll.No      Name  SSC inter B.Tech.perc CAP..NOT.CAP
## 1     1 11PA1A0501   AVINASH 87.3 65.30       40.00          CAP
## 2     2 11PA1A0503 KEERTHANA 89.0 66.45       71.45      NOT_CAP
## 3     3 11PA1A0504 PRAVALIKA 67.0 68.00       45.26      NOT_CAP
## 4     4 11PA1A0505  SAI BABA   NA 67.00          NA          CAP
## 5     5 11PA1A0506  SRINIVAS 67.0 65.50       42.52          CAP

6) Notice that in the above table(df3), 4th row is having more NA’s. It’s a good choice to remove the 4th row and having a new dataframe without 4th row. Remove the 4th row.

# Use prefix minus ( - ) before the row number to remove that row
df3[-4,]
##   Sl.No    Roll.No      Name  SSC inter B.Tech.perc CAP..NOT.CAP
## 1     1 11PA1A0501   AVINASH 87.3 65.30       40.00          CAP
## 2     2 11PA1A0503 KEERTHANA 89.0 66.45       71.45      NOT_CAP
## 3     3 11PA1A0504 PRAVALIKA 67.0 68.00       45.26      NOT_CAP
## 5     5 11PA1A0506  SRINIVAS 67.0 65.50       42.52          CAP