Our data are usually stored as a .csv file and after loading a .csv file into RStudio, we will have a “data frame”. A data frame can be considered a special case of matrix where each column represents a measurement or variable of interest for each observation which correspond to the rows of the dataset. After loading the tidyverse suite of packages, we use the read_csv() function to load the heart_disease dataset from yesterday:
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
Rows: 788 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Gender
dbl (9): Cost, Age, Interventions, Drugs, ERVisit, Complications, Comorbidit...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
By default, read_csv() reads in the dataset as a tbl (aka tibble) object instead of a data.frame object. You can read about the differences here, but it’s not that meaningful for purposes.
We can use the functions slice_head() and slice_tail() to view a sample of the data. Use the slice_head() function to view the first 6 rows, then use the slice_tail() function to view the last 3 rows:
# INSERT CODE HERE
View the dimensions of the data with dim():
# INSERT CODE HERE
Quickly view summary statistics for all variables with the summary() function:
# Uncomment the following code by deleting the # at the front# summary(heart_disease)
View the data structure types with str():
# str(heart_disease)
What’s the difference between the output from the two functions?
An easier way to manipulate the data frame is through the dplyr package, which is in the tidyverse suite of packages. The operations we can do include: selecting specific columns, filtering for rows, re-ordering rows, adding new columns and summarizing data. The “split-apply-combine” concept can be achieved by dplyr.
Selecting columns with select()
The function select() can be use to select certain column with the column names. First create a new table called heart_disease_ad that only contains the Age and Drugs columns:
# INSERT CODE HERE
To select all columns except a specific column, use the - (subtraction) operator. For example, view the output from uncommenting the following line of code:
# select(heart_disease, -Interventions)
To select a range of columns by name (that are in consecutive order), use the : (colon) operator. For example, view the output from uncommenting the following line of code:
# select(heart_disease, Drugs:Duration)
To select all columns that start with certain character strings, use the function starts_with(). Other matching options are:
ends_with(): select columns that end with a character string
contains(): select columns that contain a character string
matches(): select columns that match a regular expression
one_of(): select columns names that are from a group of names
# Uncomment the following lines of code# select(heart_disease, starts_with("Com"))# select(heart_disease, contains("er"))
Extracting rows using filter()
We can also extract the rows/observations that satisfy certain criteria. Try extracting the rows with Cost of more than 400:
# INSERT CODE HERE
We can also filter on multiple criteria. Subset the rows with Age above 60 and the gender is “Male”:
# INSERT CODE HERE
Arranging rows using arrange()
To arrange the data frame by a specific order we need to use the function arrange(). The default is by increasing order and the desc() will provide the decreasing order. First arrange the heart_disease table by Duration in ascending order:
# INSERT CODE HERE
Next by descending order:
# INSERT CODE HERE
Try combining a pipeline of select(), filter(), and arrange() steps together with the |> operator by:
Selecting the Age, Cost, ERVisit, and Duration columns,
Filter to select only rows with Age above 60,
Sort by Duration in descending order
# INSERT CODE HERE
Creating new columns using mutate()
Sometimes the data does not include the variable that we are interested in and we need to manipulate the current variables to add new variables into the data frame. Create a new column cost_per_day by taking the Cost and dividing by Duration (reassign this output to the heart_disease table following the commented code chunk so this column is added to the table):
To create summary statistics for a given column in the data frame, we can use summarize() function. Compute the mean, min, and max amount of Cost:
# INSERT CODE HERE
The advantage of summarize() is more obvious if we combine it with group_by(), the group operators. Try to group_by() the Gender column first and then compute the same summary statistics for Cost: