In this assignment, data frame - an useful R oject will be discussed. A data frame has two dimensional structure where each component has equal length. It is designed to store tabular data similar like the table in Excel spreadsheet with rows representing the records of observation and the columns representing different variables. Data frame is an extension to list and more general than a matrix as it can hold element of different data types in different columns which then combined into one object. Commonly, datasets in R are stored in data frames before using for data analysis.
It is common to get external data into R from various sources such as CSV, delimited file, excel, SAS, SPSS and etc. While importing, R read the data and store it in an organized table called data frame which can be easily used for further analysis and processing. Below are some sample functions used to import data from different sources:
# Read CSV file
file_path="c:/sample.csv"
read.csv(file_path, header=TRUE)
# Read tab-delimited file
file_path="c:/mydata.txt"
read.table(file_path, header=TRUE)
# Read Excel file
install.packages("readxl")
library(readxl)
file_path="sample.xls"
read_excel(file_path, sheet = "data")
# Read SAS dataset
install.packages("haven")
library("haven")
read_sas("c:/sample.sas7bdat")
# Read SPSS dataset
install.packages("haven")
library("haven")
read_spss("c:/sample.sav")
Data frame can be created by passing vectors with the same length to data.frame() function. The vectors can made up of different types, for example characters, numeric, factors etc. However, you have to set argument stringAsFactors to FALSE, otherwise all strings are setted as factors.
# Create vectors
name = c("Susan","Amy","Patrick","Hanson","Kris")
age = c(21,34,28,27,30)
gender = c("Female","Female","Male","Male","Male")
# Create data frame
df= data.frame(name,age,gender,stringsAsFactors = FALSE)
df## name age gender
## 1 Susan 21 Female
## 2 Amy 34 Female
## 3 Patrick 28 Male
## 4 Hanson 27 Male
## 5 Kris 30 MaleNote that the columns are unnamed, thus variable names are shown above the columns. You can assign name when passing the vectors created as arguments to the data.frame() function. Otherwise, you can also reassign using names() or colnames(). Both give the same output.
# Assign name in data.frame()
df = data.frame("Name"=name,"Age"=age,"Gender"=gender,stringsAsFactors = FALSE)
# Use names()
df = data.frame(name,age,gender,stringsAsFactors = FALSE)
names(df)=c("Name","Age","Gender")
# Use colnames()
df = data.frame(name,age,gender,stringsAsFactors = FALSE)
colnames(df)=c("Name","Age","Gender")
df## Name Age Gender
## 1 Susan 21 Female
## 2 Amy 34 Female
## 3 Patrick 28 Male
## 4 Hanson 27 Male
## 5 Kris 30 MaleYou can also change the name of each row of observations using rownames().
# Use rownames() to label the row
rownames(df)=c("Person 1","Person 2","Person 3","Person 4","Person 5")
df## Name Age Gender
## Person 1 Susan 21 Female
## Person 2 Amy 34 Female
## Person 3 Patrick 28 Male
## Person 4 Hanson 27 Male
## Person 5 Kris 30 MaleYou can access dataset from the library and load it. In fact, the loaded dataset is a data frame. The function class() and is.data.frame() help us to check whether the dataset is data frame.
dslabs library is installed and the dataset gapminder is loaded. This dataset will be used to demonstrate for more examples in the following code chunks.
# Load gapminder library
library(gapminder)
data(gapminder)
# Check whether the dataset is data frame
class(gapminder)
## [1] "tbl_df" "tbl" "data.frame"
is.data.frame(gapminder)
## [1] TRUEYou can check the structure of the data frame using the functions as follows:
ncol() returns the number of columns in data framenrow() returns the number of rows in data framedim() returns the dimension. For example, 3 x 2 shows that there is 3 rows and 2 columns in the data frame.attributes() which returns the class of object, colume names and row names in data frame# Number of columns
ncol(gapminder)## [1] 6# Number of rows
nrow(gapminder)## [1] 1704# Dimension
dim(gapminder)## [1] 1704 6# Class of object, column and row names
attributes(gapminder)## $names
## [1] "country" "continent" "year" "lifeExp"
## [5] "pop" "gdpPercap"
##
## $class
## [1] "tbl_df" "tbl" "data.frame"
##
## $row.names
## [1] 1 2 3 4 5 6 7 8 9 10
## [11] 11 12 13 14 15 16 17 18 19 20
## [21] 21 22 23 24 25 26 27 28 29 30
## [31] 31 32 33 34 35 36 37 38 39 40
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## [1181] 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190
## [1191] 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
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## [1331] 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340
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## [1361] 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370
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## [1601] 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610
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## [1631] 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640
## [1641] 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650
## [1651] 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660
## [1661] 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670
## [1671] 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
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## [1691] 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700
## [1701] 1701 1702 1703 1704Alternatively, you could use function str() to display the structure of the data frame as it provides detailed information on the dimension of the data frame, data types and peek for first few records for each variable.
str(gapminder)## tibble [1,704 x 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...You can check the data frame by passing dataset gapminder to view(). A spreadsheet-style of data viewer will appear for you.
view(gapminder)Data frame can store massive records. You can obtain the first few rows of data frame using head() and use tail() to obtain the last few rows instead. By default, only 6 rows are displayed.
# By default, only the first 6 observations are displayed
head(gapminder)## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.# By default, only the last 6 observations are displayed
tail(gapminder)## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Zimbabwe Africa 1982 60.4 7636524 789.
## 2 Zimbabwe Africa 1987 62.4 9216418 706.
## 3 Zimbabwe Africa 1992 60.4 10704340 693.
## 4 Zimbabwe Africa 1997 46.8 11404948 792.
## 5 Zimbabwe Africa 2002 40.0 11926563 672.
## 6 Zimbabwe Africa 2007 43.5 12311143 470.But you can adjust the argument n in the function for desired number of rows to be shown. The code below will output top 10 and bottom 10 rows of obeservation respectively.
## # A tibble: 10 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
## 7 Afghanistan Asia 1982 39.9 12881816 978.
## 8 Afghanistan Asia 1987 40.8 13867957 852.
## 9 Afghanistan Asia 1992 41.7 16317921 649.
## 10 Afghanistan Asia 1997 41.8 22227415 635.## # A tibble: 10 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Zimbabwe Africa 1962 52.4 4277736 527.
## 2 Zimbabwe Africa 1967 54.0 4995432 570.
## 3 Zimbabwe Africa 1972 55.6 5861135 799.
## 4 Zimbabwe Africa 1977 57.7 6642107 686.
## 5 Zimbabwe Africa 1982 60.4 7636524 789.
## 6 Zimbabwe Africa 1987 62.4 9216418 706.
## 7 Zimbabwe Africa 1992 60.4 10704340 693.
## 8 Zimbabwe Africa 1997 46.8 11404948 792.
## 9 Zimbabwe Africa 2002 40.0 11926563 672.
## 10 Zimbabwe Africa 2007 43.5 12311143 470.[]We can enter the certain row and column coordinates in the single square bracket [] to retrieve data from data frame. Taking data frame, df as an example, we can slice the data by specifying rows and columns coordinates separated by comma. Take note that the order cannot be reverted.
# Taking the data frame df as example
# Obtain data from 3rd row and 2nd column
df[3,2]
## [1] 28
# Obtain data from 3rd row and column named "Age"
df[3,"Age"]
## [1] 28
# Obtain the entire 3rd row of observations
df[3,]
## Name Age Gender
## Person 3 Patrick 28 Male
# Obtain the entire row of "Age" column
df[,"Age"]
## [1] 21 34 28 27 30We can subset the data by using either the vector of index, column name or row name. List is returned. Take note that when only one column is selected, vector is returned as the elements are made up of the same type. You may include drop=FALSE to return the normal data frame.
# Taking the data frame df as an example
# Obtain data from 3rd row and 2nd column
# Vector is returned
df[c(1,2),c(2)]
## [1] 21 34
class(df[c(1,2),c(2)])
## [1] "numeric"
# Obtain data from 3rd row and 2nd column, with drop=FALSE
# Data Frame is returned
df[c(1,2),c(2),drop=FALSE]
## Age
## Person 1 21
## Person 2 34
class(df[c(1,2),c(2),drop=FALSE])
## [1] "data.frame"
# Obtain data from 3rd row and column named "Age"
# Data Frame is returned because having column of different types
df[c("Person 3"),c("Age","Gender")]
## Age Gender
## Person 3 28 Male
class(df[c("Person 3"),c("Age","Gender")])
## [1] "data.frame"
# Obtain the entire 3rd row of observations
df[2]
## Age
## Person 1 21
## Person 2 34
## Person 3 28
## Person 4 27
## Person 5 30
class(df[2])
## [1] "data.frame"
# Return the entire row of "Age" column
df["Age"]
## Age
## Person 1 21
## Person 2 34
## Person 3 28
## Person 4 27
## Person 5 30
class(df["Age"])
## [1] "data.frame"[[]] or $By using double brackets [[]] or dollar sign $, list of data frame is returned. If you are using [[]] and the names to subset the column, you need to enclose the names in double quotes or single quotes. The 3 lines of code below show the same output.
df$Name
## [1] "Susan" "Amy" "Patrick" "Hanson" "Kris"
df[["Name"]]
## [1] "Susan" "Amy" "Patrick" "Hanson" "Kris"
df[[1]]
## [1] "Susan" "Amy" "Patrick" "Hanson" "Kris"The usage of $ or [[]] to subset data frame can be used together with logical operators. Let’s say, you would like to know who is 30 years old and above. The condition is df$Age>=30 which return logical vectors with TRUE for each person who are in 30s. To see who they are, we can create index of logicals. To get the total number of count which meet the condition, we can use sum() as logical value implicityly coerced to numeric where TRUE is coerced to 1 and FALSE is coerced to 0.
df[df$Age>=30,]
## Name Age Gender
## Person 2 Amy 34 Female
## Person 5 Kris 30 Male
sum(df$Age>=30)
## [1] 2
df[df$Age>=30 & df$Gender=="Male",]
## Name Age Gender
## Person 5 Kris 30 Male
sum(df$Age>=30 & df$Gender=="Male")
## [1] 1In addition, you can also use:
which() that show which entries of logical vector is TRUEmatch() that tells which indexes of second subsetted data frame match with the element in first vector.# Function which()
cond=which(df$Age>=30)
df[cond,]
## Name Age Gender
## Person 2 Amy 34 Female
## Person 5 Kris 30 Male
# Function match()
cond=match(c("Female"),df$Gender)
df[cond,]
## Name Age Gender
## Person 1 Susan 21 FemaleIn fact, we can also subset the observations with largest and smallest value from a variable using which.max() and which.min() respectively.
# Who is eldest
index_max = which.max(df$Age)
df[index_max,]## Name Age Gender
## Person 2 Amy 34 Female# Who is youngest
index_min = which.max(df$Age)
df[index_max,]## Name Age Gender
## Person 2 Amy 34 FemaleWe can expand the data frame by adding new columns to the existing data frame. You can add the new column by assigning a vector to the new column with the $ or [[]]. Another way is by using function cbind() to bind the columns from the existing data frame with the new column and form new data frame.
job=c("teacher","professor","doctor","engineer","solicitor")
df[["Job"]]=job # or df$job = job
salary=c(3000,4000,3500,3500,3200)
df=cbind(df,salary)
df## Name Age Gender Job salary
## Person 1 Susan 21 Female teacher 3000
## Person 2 Amy 34 Female professor 4000
## Person 3 Patrick 28 Male doctor 3500
## Person 4 Hanson 27 Male engineer 3500
## Person 5 Kris 30 Male solicitor 3200New rows can be appended to existing data frame using rbind() function.
# Add list of observation
add_row = list(Name="Ben",Age=35,Gender="Male",Job="Scientist",salary=5000)
new_df = rbind(df,add_row)
new_df
## Name Age Gender Job salary
## Person 1 Susan 21 Female teacher 3000
## Person 2 Amy 34 Female professor 4000
## Person 3 Patrick 28 Male doctor 3500
## Person 4 Hanson 27 Male engineer 3500
## Person 5 Kris 30 Male solicitor 3200
## 6 Ben 35 Male Scientist 5000
# Add new data frame
add_row = data.frame(Name="Ben",Age=35,Gender="Male",Job="Scientist",salary=5000)
new_df = rbind(df,add_row)
new_df
## Name Age Gender Job salary
## Person 1 Susan 21 Female teacher 3000
## Person 2 Amy 34 Female professor 4000
## Person 3 Patrick 28 Male doctor 3500
## Person 4 Hanson 27 Male engineer 3500
## Person 5 Kris 30 Male solicitor 3200
## 1 Ben 35 Male Scientist 5000The function order() provide outputs of the indexes that sorts the input vector. We can use the indexes to sort the data frame by specific variable. By setting the argument decreasing=TRUE, you are ordering the value from largest to smallest and vice versa.
# By default, it is arranged by ascending order
rank = order(df$salary)
df[rank,]## Name Age Gender Job salary
## Person 1 Susan 21 Female teacher 3000
## Person 5 Kris 30 Male solicitor 3200
## Person 3 Patrick 28 Male doctor 3500
## Person 4 Hanson 27 Male engineer 3500
## Person 2 Amy 34 Female professor 4000# By descending order
rank = order(df$salary, decreasing =TRUE)
df[rank,]## Name Age Gender Job salary
## Person 2 Amy 34 Female professor 4000
## Person 3 Patrick 28 Male doctor 3500
## Person 4 Hanson 27 Male engineer 3500
## Person 5 Kris 30 Male solicitor 3200
## Person 1 Susan 21 Female teacher 3000The function summary() is a generally used to produce descriptive statistics measure such as mean, median, Q1, Q3, minimun and maximum of numerical variable in data frame, whereby for strings and factor, the summary table will display the frequency of occurence of the elements. You can observe the difference from the example below.
summary(gapminder)## country continent year
## Afghanistan: 12 Africa :624 Min. :1952
## Albania : 12 Americas:300 1st Qu.:1966
## Algeria : 12 Asia :396 Median :1980
## Angola : 12 Europe :360 Mean :1980
## Argentina : 12 Oceania : 24 3rd Qu.:1993
## Australia : 12 Max. :2007
## (Other) :1632
## lifeExp pop gdpPercap
## Min. :23.60 Min. :6.001e+04 Min. : 241.2
## 1st Qu.:48.20 1st Qu.:2.794e+06 1st Qu.: 1202.1
## Median :60.71 Median :7.024e+06 Median : 3531.8
## Mean :59.47 Mean :2.960e+07 Mean : 7215.3
## 3rd Qu.:70.85 3rd Qu.:1.959e+07 3rd Qu.: 9325.5
## Max. :82.60 Max. :1.319e+09 Max. :113523.1
## Tibble is the brand new and modern data frame which is commonly use along with tidyverse package to replace the older outdated data frame especially while working with tidy data. Hadley Wickham created Tidyverse package and it is an useful packages widely used by data scientists for manipulating with the data frames.
There are some operations which are useful when working with data frames. The dplyr package from tidyverse has many handy functions such as:
filter() for subsetting the data frame.mutate() for adding new column.select() for selecting certain columnsgroup_by() for grouppig or spliting the data into groups and it is frequently use with summarize()summarize() for computing of summary statistics such as mean, median, interquatile ranges and etc.Although the dplyr package will be discussed in the coming lecture, it is worth to briefly show you some functions that work with data frames. With dplyr, we can perform a series of steps. We can use %>% to send the output of one function to another without having an intermediate steps. Let’s start with the simple one, passing data frame to the functions. All these functions are aware of the column names and you can omit the quotes when specifying the column names.
Function filter() can be used to keep only specific rows of data frame.
filter_df = df %>% filter(Gender == "Female")
# same: filter_df = filter(df,Gender == "Female")
filter_df## Name Age Gender Job salary
## Person 1 Susan 21 Female teacher 3000
## Person 2 Amy 34 Female professor 4000Function mutate() can be used to add columns to the data frame.
mutate_df = df %>% mutate(Year_Income = 12*salary)
# same: mutate_df = mutate(df, Year_Income = 12*salary)
mutate_df## Name Age Gender Job salary Year_Income
## 1 Susan 21 Female teacher 3000 36000
## 2 Amy 34 Female professor 4000 48000
## 3 Patrick 28 Male doctor 3500 42000
## 4 Hanson 27 Male engineer 3500 42000
## 5 Kris 30 Male solicitor 3200 38400Funtion select() can be used to subset data with selected columns
select_df= df %>% select(Name, Job, salary)
# same: select_df= select(df, Name, Job, salary)
select_df## Name Job salary
## Person 1 Susan teacher 3000
## Person 2 Amy professor 4000
## Person 3 Patrick doctor 3500
## Person 4 Hanson engineer 3500
## Person 5 Kris solicitor 3200Function group() is to split data into groups and summarize() will summarize the data for computation by the grouped data frame.
mean_salary_by_gender_df = df %>% group_by(Gender) %>% summarize(average_salary=mean(salary))
## `summarise()` ungrouping output (override with `.groups` argument)
# same:
# salary_by_gender_df = group_by(df,Gender)
# mean_salary_by_gender = summarize(salary_by_gender_df,average_salary=mean(salary))
mean_salary_by_gender_df
## # A tibble: 2 x 2
## Gender average_salary
## <chr> <dbl>
## 1 Female 3500
## 2 Male 3400Let’s recap what have been shared in this assignment. In summary, we have learned about data frame and how to create data frame for the use of analysis in future. A few functions that are useful to understand the structure of the data frame are discussed and we learned different ways on how to slice and access values from data frame. There are more advanced library available in tidyverse package that are handy for data frame manipulation expecially the functions in dplyr package that can make your works efficient with data frame. We have just showed a few essential here. I hope you find these useful. Enjoy learning R!