Chapter 7 Manipulating data frames.

Most of the time data does not come us ready to analyze and we must make manipulations to the data frame. Base R has many useful functions that will allow us to do this.

7.1 Slicing a data frame

We may intersted in looking at just 1 column or 1 row. We can do this with by specifying the indicies for the rows and columns.

• full data frame

# dataframe[rows, columns]
hits_data_frame

##   PlayerID Hits AtBats
## 1 Player01    3      4
## 2 Player02    1      4
## 3 Player03    0      3
## 4 Player04    2      5
## 5 Player05    4      4

# dataframe[rows, columns]
hits_data_frame[,]

##   PlayerID Hits AtBats
## 1 Player01    3      4
## 2 Player02    1      4
## 3 Player03    0      3
## 4 Player04    2      5
## 5 Player05    4      4

Note: If we dont specify which rows or columns we want we get the whole data frame back.

• Second row

# dataframe[rows, columns]
hits_data_frame[2,]

##   PlayerID Hits AtBats
## 2 Player02    1      4

• second column

hits_data_frame[,2]

## [1] 3 1 0 2 4

• Second row of the second column

hits_data_frame[2,2]

## [1] 1

We can also use column names to slice data:

First lets get the names of the columns in the data frame.

names(hits_data_frame)

## [1] "PlayerID" "Hits"     "AtBats"

Now we can use these column names to select the hits columns.

hits_data_frame[,c("Hits")]

## [1] 3 1 0 2 4

We can pass a vector of column names if we want more than 1 column.

hits_data_frame[,c("Hits","PlayerID")]

##   Hits PlayerID
## 1    3 Player01
## 2    1 Player02
## 3    0 Player03
## 4    2 Player04
## 5    4 Player05

You can also use the $ sign to specify columns

# get player ID column
hits_data_frame$PlayerID

## [1] "Player01" "Player02" "Player03" "Player04" "Player05"

In the case the column is treated as a vector which can be manipulated.

## select 4th player
hits_data_frame$PlayerID[4]

## [1] "Player04"

We can also assign a new value here.

## Changing value of a sliced data frame. 
hits_data_frame$PlayerID[4] <- "PlayerNew"
hits_data_frame$PlayerID[4]

## [1] "PlayerNew"

Operations can also be also be completed in the brackets to help filter data

## Filtering with which()
which(hits_data_frame$PlayerID == "PlayerNew")

## [1] 4

what this means is player new is the 4th observation. We can filter the whole data frame with.

# Hits filtered
hits_data_frame[which(hits_data_frame$PlayerID == "PlayerNew"),]

##    PlayerID Hits AtBats
## 4 PlayerNew    2      5

7.2 Creating new columns

you can create new columns with mathematical operations like \(+\),\(-\),\(/\) and \(*\)

### Create new columns with / dividing
hits_data_frame$batting_average <- hits_data_frame$Hits / hits_data_frame$AtBats
### Create new columns with - subtracting
hits_data_frame$outs <- hits_data_frame$AtBats - hits_data_frame$Hits
hits_data_frame

##    PlayerID Hits AtBats batting_average outs
## 1  Player01    3      4            0.75    1
## 2  Player02    1      4            0.25    3
## 3  Player03    0      3            0.00    3
## 4 PlayerNew    2      5            0.40    3
## 5  Player05    4      4            1.00    0