# DataFrames > `data.frame`, [`data.table`](https://rdatatable.gitlab.io/data.table/) and the [dplyr](https://dplyr.tidyverse.org/) package provide a set of tools for working with tabular data in R. Their design and functionality are similar to those of DataFrames.jl (in Julia) and [pandas](https://pandas.pydata.org/) (in Python), making them great general purpose data science tools. This page provides examples of using data.frame, data.table, and dplyr, demonstrating the syntax and common functions within the tools. ## Example **Installing data.frame, data.table, and dplyr in R.** The data.frame package comes preloaded into R, and the dplyr package is part of the tidyverse package (see [Packages](https://docs.bcbi.brown.edu/codiac-for-health/computing/r/packages) section for tidyverse installation instructions). To install data.table, use install.packages('data.table'). This example will take place using data.frame as it is does not require additional packages- see resources at the bottom of this page for additional information on data.table and dplyr. **Create DataFrame** ```r #Create DataFrame df <- data.frame( id = 1:5, gender = c("F", "M", "F", "M", "F"), age = c(68, 54, 49, 28, 36) ) ``` **Display DataFrame** Input: ```r #Display DataFrame df ``` Output: ```r id gender age 1 1 F 68 2 2 M 54 3 3 F 49 4 4 M 28 5 5 F 36 ``` Print first two lines of DataFrame Input: ```r #Print first two lines of DataFrame head(df, 2) ``` Output: ```r id gender age 1 1 F 68 2 2 M 54 ``` Print last two lines of DataFrame Input: ```r # Last two lines of DataFrame tail(df, 2) ``` Output: ```r id gender age 4 4 M 28 5 5 F 36 ``` **Describe DataFrame** DataFrame size: Input: ```r #DataFrame size dim(df) ``` Output: ```r #First value represents number of rows, second value represents number of columns [1] 5 3 ``` DataFrame column names: Input: ```r #DataFrame column names colnames(df) ``` Output: ```r [1] "id" "gender" "age" ``` DataFrame description: Input: ```r #Describe DataFrame summary(df) ``` Output: ```r id gender age Min. :1 Length:5 Min. :28 1st Qu.:2 Class :character 1st Qu.:36 Median :3 Mode :character Median :49 Mean :3 Mean :47 3rd Qu.:4 3rd Qu.:54 Max. :5 Max. :68 ``` **Accessing DataFrames** ***Get "age" column (different ways to call the column)*** Input: ```r #Call by column name df$age df[["age"]] #Get column by column number df[[3]] ``` Output: ```r #Call by column name [1] 68 54 49 28 36 [1] 68 54 49 28 36 #Get column by column number [1] 68 54 49 28 36 ``` ***Get row*** Input: ```r #Print row 2 df[2, ] ``` Output: ```r id gender age 2 2 M 54 ``` ***Get element*** Input: ```r #Get element in row 2, column 3 df[2,3] ``` Output: ```r 54 ``` ***Get subset (specific rows and all columns)*** Input: ```r #Print out rows 1, 3, & 5 df[c(1, 3, 5), ] ``` Output: ```r id gender age 1 1 F 68 3 3 F 49 5 5 F 36 ``` ***Get subset (all rows and specific columns)*** Input: ```r #Print out all rows and only columns 1 (id) and 3 (age) #Using column names df[, c("id", "age")] #Using column numbers df[, c(1, 3)] ``` Output: ```r #Using column names: id age 1 1 68 2 2 54 3 3 49 4 4 28 5 5 36 #Using column numbers id age 1 1 68 2 2 54 3 3 49 4 4 28 5 5 36 ``` ***Get subset (all rows meeting specified criteria - numbers)*** Input: ```r #Print all rows where age is greater than 50 df[df$age > 50, ] ``` Output: ```r id gender age 1 1 F 68 2 2 M 54 ``` ***Get subset (all rows meeting specified criteria - strings)*** Input: ```r #Print all rows where gender is female ("F") df[df$gender == "F", ] ``` Output: ```r id gender age 1 1 F 68 3 3 F 49 5 5 F 36 ``` ***Get subset (all rows meeting specified criteria)*** Input: ```r #Print all rows where gender is female ("F") and age is between 25-50 df[df$gender == "F" & df$age > 25 & df$age < 50, ] ``` Output: ```r id gender age 3 3 F 49 5 5 F 36 ``` **Add Column** ***New columns with specified values*** Input: ```r #Add a column for height df$height <- c(62, 60, 61, 63, 64) #Add a column for weight df$weight <- c(100, 120, 150, 175, 300) #Print DataFrame to see changes df #Describe DataFrame to see column names and summary summary(df) ``` Output: ```r id gender age height weight 1 1 F 68 62 100 2 2 M 54 60 120 3 3 F 49 61 150 4 4 M 28 63 175 5 5 F 36 64 300 #Describe dataframe to see column names and summary: id gender age height weight Min. :1 Length:5 Min. :28 Min. :60 Min. :100 1st Qu.:2 Class :character 1st Qu.:36 1st Qu.:61 1st Qu.:120 Median :3 Mode :character Median :49 Median :62 Median :150 Mean :3 Mean :47 Mean :62 Mean :169 3rd Qu.:4 3rd Qu.:54 3rd Qu.:63 3rd Qu.:175 Max. :5 Max. :68 Max. :64 Max. :300 ``` ***New column with calculated value*** Input: ```r # add a column with calculated BMI df$bmi <- (df$weight / (df$height^2)) * 703 #Print DataFrame to see changes df #Describe DataFrame to see column names and summary summary(df) ``` Output: 
#Updated DataFrame
    id gender age height weight      bmi
1  1      F  68     62    100 18.28824
2  2      M  54     60    120 23.43333
3  3      F  49     61    150 28.33916
4  4      M  28     63    175 30.99647
5  5      F  36     64    300 51.48926

Describe dataframe to see new bmi column and summary:
       id       gender               age         height       weight   
 Min.   :1   Length:5           Min.   :28   Min.   :60   Min.   :100  
 1st Qu.:2   Class :character   1st Qu.:36   1st Qu.:61   1st Qu.:120  
 Median :3   Mode  :character   Median :49   Median :62   Median :150  
 Mean   :3                      Mean   :47   Mean   :62   Mean   :169  
 3rd Qu.:4                      3rd Qu.:54   3rd Qu.:63   3rd Qu.:175  
 Max.   :5                      Max.   :68   Max.   :64   Max.   :300  
      bmi       
 Min.   :18.29  
 1st Qu.:23.43  
 Median :28.34  
 Mean   :30.51  
 3rd Qu.:31.00  
 Max.   :51.49
***Get counts/frequency*** Input: ```r #Get counts of males and females in the dataframe gender_counts <- table(df$gender) gender_counts ``` Output: ```r F M 3 2 ``` **Transform DataFrame** ***sort*** Input: ```r #Sort the dataframe by gender then age, in reverse order for age (oldest to youngest) df_sorted <- df[order(df$gender, -df$age), ] df_sorted ``` Output: ```r id gender age height weight bmi 1 1 F 68 62 100 18.28824 3 3 F 49 61 150 28.33916 5 5 F 36 64 300 51.48926 2 2 M 54 60 120 23.43333 4 4 M 28 63 175 30.99647 ``` ***stack (reshape from wide to long format)*** Input: ```r #Reshape from wide to long format (exclude id column) long_df <- reshape(df, varying = c("gender", "age", "weight", "height", "bmi"), v.names = "value", timevar = "variable", times = c("gender", "age", "weight", "height", "bmi"), direction = "long") long_df ``` Output: ```r id variable value 1.gender 1 gender F 2.gender 2 gender M 3.gender 3 gender F 4.gender 4 gender M 5.gender 5 gender F 1.age 1 age 68 2.age 2 age 54 3.age 3 age 49 4.age 4 age 28 5.age 5 age 36 1.weight 1 weight 100 2.weight 2 weight 120 3.weight 3 weight 150 4.weight 4 weight 175 5.weight 5 weight 300 1.height 1 height 62 2.height 2 height 60 3.height 3 height 61 4.height 4 height 63 5.height 5 height 64 1.bmi 1 bmi 18.2882414151925 2.bmi 2 bmi 23.4333333333333 3.bmi 3 bmi 28.3391561408224 4.bmi 4 bmi 30.9964726631393 5.bmi 5 bmi 51.4892578125 ``` ***unstack (reshape from long to wide format)*** Input: ```r #Unstack dataframe to return to wide format based off "id" wide_df <- reshape(long_df, idvar = "id", timevar = "variable", direction = "wide") wide_df ``` Output: ```r id value.gender value.age value.weight value.height value.bmi 1.gender 1 F 68 100 62 18.2882414151925 2.gender 2 M 54 120 60 23.4333333333333 3.gender 3 F 49 150 61 28.3391561408224 4.gender 4 M 28 175 63 30.9964726631393 5.gender 5 F 36 300 64 51.4892578125 ``` **Traversing DataFrame (for loops)** ***sort*** Input: ```r #Size of dataframe = size(df) #Set number of rows to nrows and number of columns to ncols nrows <- nrow(df) ncols <- ncol(df) cat("(nrows, ncols) = ", nrows, ncols, "\n") #Use nested for loop to get information from DataFrame by row and column for (row in 1:nrows) { for (col in 1:ncols) { cat("value for row", row, "and col", col, "is", df[row, col], "\n") } } ``` Output: ```r (nrows, ncols) = 5 6 value for row 1 and col 1 is 1 value for row 1 and col 2 is F value for row 1 and col 3 is 68 value for row 1 and col 4 is 62 value for row 1 and col 5 is 100 value for row 1 and col 6 is 18.28824 value for row 2 and col 1 is 2 value for row 2 and col 2 is M value for row 2 and col 3 is 54 value for row 2 and col 4 is 60 value for row 2 and col 5 is 120 value for row 2 and col 6 is 23.43333 value for row 3 and col 1 is 3 value for row 3 and col 2 is F value for row 3 and col 3 is 49 value for row 3 and col 4 is 61 value for row 3 and col 5 is 150 value for row 3 and col 6 is 28.33916 value for row 4 and col 1 is 4 value for row 4 and col 2 is M value for row 4 and col 3 is 28 value for row 4 and col 4 is 63 value for row 4 and col 5 is 175 value for row 4 and col 6 is 30.99647 value for row 5 and col 1 is 5 value for row 5 and col 2 is F value for row 5 and col 3 is 36 value for row 5 and col 4 is 64 value for row 5 and col 5 is 300 value for row 5 and col 6 is 51.48926 ``` ## Notes: When performing functions such as sorting or transformation, using a package like data.table or dplyr will typically be easier than using base R (data.table), as those packages include commands designed for DataFrame manipulation. This guide uses base R for the sake of continuity. ## Resources * R Documentation: [data.table](https://www.rdocumentation.org/packages/data.table/versions/1.16.2) * Tidyverse: [dplyr](https://dplyr.tidyverse.org)