Dataframe

In this notebook, we will be dealing with loading the dataframe. Datasets are usually stored in csv files, and it can be loaded to R by using the following code:

read.csv("Path where your CSV file is located on your computer\\File Name.csv")

We will be loading the NHANES.csv file, which is located in the Data folder:

df <- read.csv('Data//NHANES.csv')

df

ABCDEFGHIJ0123456789

ID <int>	SurveyYr <fct>	Gender <fct>	Age <int>	AgeDecade <fct>	AgeMonths <int>	Race1 <fct>	Education <fct>
51624	2009_10	male	34	30-39	409	White	High School
51624	2009_10	male	34	30-39	409	White	High School
51624	2009_10	male	34	30-39	409	White	High School
51625	2009_10	male	4	0-9	49	Other
51630	2009_10	female	49	40-49	596	White	Some College
51638	2009_10	male	9	0-9	115	White
51646	2009_10	male	8	0-9	101	White
51647	2009_10	female	45	40-49	541	White	College Grad
51647	2009_10	female	45	40-49	541	White	College Grad
51647	2009_10	female	45	40-49	541	White	College Grad

The NHANES dataframe comes from the R package NHANES:https://cran.r-project.org/web/packages/NHANES/NHANES.pdf

Since the dataframe is long, we often use head() to get the first 6 rows:

head(df)

ABCDEFGHIJ0123456789

	ID <int>	SurveyYr <fct>	Gender <fct>	Age <int>	AgeDecade <fct>	AgeMonths <int>	Race1 <fct>	Education <fct>
1	51624	2009_10	male	34	30-39	409	White	High School
2	51624	2009_10	male	34	30-39	409	White	High School
3	51624	2009_10	male	34	30-39	409	White	High School
4	51625	2009_10	male	4	0-9	49	Other
5	51630	2009_10	female	49	40-49	596	White	Some College
6	51638	2009_10	male	9	0-9	115	White

We can use:

dataframe_name$column_name

to access the column in the dataframe. For example:

head(df$Age)

## [1] 34 34 34  4 49  9

We can also change the column name by running:

dataframe_name$newColumn_name <- dataframe$column_name

df$number <- df$ID

head(df$number)

## [1] 51624 51624 51624 51625 51630 51638

Frequency table

Frequency tables can easily be obtained in R programming by using the following code:

table(dataframe$column)

For example:

table(df$Gender)

## 
## female   male 
##   5020   4980

Get data based on condition

Sometimes we are interested in getting a subset of the dataframe based on the column value. For example, instead of analyzing everyone in the NHANES dataset, we might be interested in analyzing people who are older than 18 years of age. In these cases, we can select a subset of the dataframe by using the following code:

df <- df[conditional_statement,]

where df is the name of the dataframe. For example:

# Get people who are older than 18 years of age
df <- df[df$Age >= 18,]
head(df)

ABCDEFGHIJ0123456789

	ID <int>	SurveyYr <fct>	Gender <fct>	Age <int>	AgeDecade <fct>	AgeMonths <int>	Race1 <fct>	Education <fct>
1	51624	2009_10	male	34	30-39	409	White	High School
2	51624	2009_10	male	34	30-39	409	White	High School
3	51624	2009_10	male	34	30-39	409	White	High School
5	51630	2009_10	female	49	40-49	596	White	Some College
8	51647	2009_10	female	45	40-49	541	White	College Grad
9	51647	2009_10	female	45	40-49	541	White	College Grad

# Get male
df <- df[df$Gender == 'male',]
head(df)

ABCDEFGHIJ0123456789

	ID <int>	SurveyYr <fct>	Gender <fct>	Age <int>	AgeDecade <fct>	AgeMonths <int>	Race1 <fct>	Education <fct>
1	51624	2009_10	male	34	30-39	409	White	High School
2	51624	2009_10	male	34	30-39	409	White	High School
3	51624	2009_10	male	34	30-39	409	White	High School
11	51654	2009_10	male	66	60-69	795	White	Some College
12	51656	2009_10	male	58	50-59	707	White	College Grad
13	51657	2009_10	male	54	50-59	654	White	9 - 11th Grade