Introduction to R - DataFrames
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Introduction to R DataFrames
- Data frames are combinations of vectors of the same length, but can be of different types.
- It is a special type of list.
- Data frames are what is used for standard rectangular (record by field) datasets, similar to a spreadsheet
- Data frames are a functionality that both sets R aside from some languages (e.g., Matlab) and provides functionality similar to some statistical packages (e.g., Stata, SAS) and Python’s Pandas Packages.
frame=read.csv(file="../../input/iris.csv", header=TRUE,sep=",")
class(frame)
head(frame) #The first few rows.
tail(frame) #The last few rows.
str(frame) #The Structure.
dim(frame) #Results in rows x columns
nrow(frame) #The number of Rows
names(frame) #Provides the names
length(frame) #The number of columns
summary(frame) #Provides summary statistics.
is.matrix(frame) #Yields False because it has different types.
is.list(frame) #Yields True
class(frame$sepal_length)
class(frame$species)
levels(frame$species)
frame[c("species","sepal_width")]
frame['petals']<-0
frame$petals2<-0
head(frame)
mean.sepalLenth.setosa<-mean(frame[,'sepal_length'])
Slicing a Dataframe by Column
- Remember the syntax of
df[rows,columns] - Using
dataframe$columnprovides one way of selecting a column. - We can also specify the index position:
dataframe[,columnIndex] - We can also specify the column name:
dataframe[,columnsName]
sepal_length1<-frame$sepal_length #Using Dollar Sign and the column name.
sepal_length2<- frame[,1] #Using the Index Location
sepal_length3<- frame[,'sepal_length']
sepal_length4<- frame[,c('sepal_length','sepal_width')]
sepal_length1[1:5] #Print the first 5
sepal_length2[1:5]
sepal_length3[1:5]
Selecting Rows
- We can select rows from a dataframe using index position:
dataframe[rowIndex,columnIndex]. - Use
c(row1, row2, row3)to select out specific rows.
frame2<-frame[1:20,]
frame3<-frame[c(1,5,6),] #This selects out specific rows
nrow(frame2)
nrow(frame3)
frame3
Conditional Statements and Dataframes with Subset
- We can select subsets of a dataframe by putting an equality in the row or subset.
- Subset is also a dataframe.
- Can optionally select columns with the
select = c(col1, col2)
setosa.df <- subset(frame, species == 'setosa')
head(setosa.df)
class(setosa.df)
nrow(setosa.df)
mean.sepalLenth.setosa<-mean(setosa.df$sepal_length) #This creates a new vector
mean.sepalLenth.setosa
setosa.df.highseptalLength <- subset(setosa.df, sepal_length > mean.sepalLenth.setosa)
nrow(setosa.df.highseptalLength)
head(setosa.df.highseptalLength)
setosa.dfhighseptalLength2 <- subset(setosa.df, sepal_length > mean.sepalLenth.setosa, select = c(sepal_length, species))
head(setosa.dfhighseptalLength2)
Subsetting Rows Using Indices
- Just like pandas, we are using conditional statements to specify specific rows.
- See here for good coverage and examples.
setosa.df <- frame[frame$species == "setosa",]
head(setosa.df)
class(setosa.df)
nrow(setosa.df)
mean.sepalLenth.setosa<-mean(setosa.df$sepal_length) #This creates a new vector
mean.sepalLenth.setosa
setosa.df.highseptalLength <- setosa.df[setosa.df$sepal_length > mean.sepalLenth.setosa,]
nrow(setosa.df.highseptalLength)
head(setosa.df.highseptalLength)
specific.df <- frame[frame$sepal_length %in% c(5.1,5.8),]
head(specific.df)
Basics
- Load the Titanic train.csv data into an R data frame.
- Calculate the number of rows in the data frame.
- Calcuated general descriptive statistics for the data frame.
- Slice the data frame into 2 parts, selecting the first half of the rows.
- Select just the columns passangerID and whether they survivied or not.
CREDITS
Copyright AnalyticsDojo 2016 This work is licensed under the Creative Commons Attribution 4.0 International license agreement. Adopted from Berkley R Bootcamp.