Example of R code to perform a paired t-test on blood pressure data for a group of patients before and after a treatment:
```R
# Create a sample dataset with blood pressure measurements
before_treatment <- c(120, 130, 140, 115, 135)
after_treatment <- c(110, 125, 130, 112, 130)
# Combine the data into a data frame
blood_pressure_data <- data.frame(Before = before_treatment, After = after_treatment)
# Conduct a paired t-test
result <- t.test(blood_pressure_data$Before, blood_pressure_data$After, paired = TRUE)
# Print the t-test results
cat("Paired t-test results:\n")
cat("t-statistic =", result$statistic, "\n")
cat("p-value =", result$p.value, "\n")
cat("Degrees of freedom =", result$parameter, "\n")
```
In this example, we first create two vectors `before_treatment` and `after_treatment` containing blood pressure measurements before and after the treatment, respectively. Then, we combine the data into a data frame `blood_pressure_data`.
Next, we conduct a paired t-test using the `t.test` function with the `paired = TRUE` argument, which specifies that the data are paired (before and after measurements from the same patients). The function returns a list of results, including the t-statistic, p-value, and degrees of freedom.
Finally, we print out the t-test results using the `cat` function.
Keep in mind that this is a simple example with synthetic data. In real-world scenarios, you would replace the `before_treatment` and `after_treatment` vectors with your actual data from your health project to perform the t-test on your dataset. Also, consider performing data preparation and validation steps before conducting statistical analyses on actual health data.
/* Read the data into SAS */
data health_data;
input Patient_ID Blood_Pressure_Before Blood_Pressure_After;
datalines;
1 120 110
2 130 125
3 140 130
4 115 112
5 135 130
;
run;
/* Descriptive Statistics */
proc means data=health_data n mean std;
var Blood_Pressure_Before Blood_Pressure_After;
run;
/* Paired t-test */
proc ttest data=health_data;
paired Blood_Pressure_Before*Blood_Pressure_After;
run;
To provide you with a sample R code for a health data project related to blood pressure and nutritional needs, I'll assume we want to analyze the relationship between blood pressure measurements and nutritional intake for a group of individuals. In this example, we'll generate some synthetic data to demonstrate the process.
Let's create a dataset with columns for "Blood_Pressure," "Calories_Intake," and "Sodium_Intake." We'll then perform some basic analyses, including scatter plots and correlation calculations.
```R
# Load necessary libraries
library(ggplot2)
# Create a sample dataset
set.seed(42) # Setting seed for reproducibility
# Number of individuals in the dataset
n <- 100
# Generating synthetic data for blood pressure, calories intake, and sodium intake
blood_pressure <- rnorm(n, mean = 120, sd = 10)
calories_intake <- rnorm(n, mean = 2000, sd = 500)
sodium_intake <- rnorm(n, mean = 2000, sd = 500)
# Creating a data frame
health_data <- data.frame(Blood_Pressure = blood_pressure,
Calories_Intake = calories_intake,
Sodium_Intake = sodium_intake)
# Scatter plot of Blood Pressure vs. Calories Intake
ggplot(health_data, aes(x = Calories_Intake, y = Blood_Pressure)) +
geom_point() +
labs(x = "Calories Intake", y = "Blood Pressure",
title = "Scatter Plot of Blood Pressure vs. Calories Intake")
# Scatter plot of Blood Pressure vs. Sodium Intake
ggplot(health_data, aes(x = Sodium_Intake, y = Blood_Pressure)) +
geom_point() +
labs(x = "Sodium Intake", y = "Blood Pressure",
title = "Scatter Plot of Blood Pressure vs. Sodium Intake")
# Correlation analysis
correlation_matrix <- cor(health_data[, c("Blood_Pressure", "Calories_Intake", "Sodium_Intake")])
print(correlation_matrix)
```
In this example, we generate synthetic data for "Blood_Pressure," "Calories_Intake," and "Sodium_Intake" using the `rnorm` function, which creates random samples from a normal distribution. We then create a data frame `health_data` to store this data.
We then create two scatter plots using the `ggplot2` library to visualize the relationship between "Blood_Pressure" and "Calories_Intake" and "Sodium_Intake," respectively.
Finally, we calculate the correlation matrix using the `cor` function to examine the correlation between the variables.
Note that this is a simple demonstration using synthetic data. In a real-world health data project, you would use actual data collected from individuals, and you might need to perform more advanced statistical analyses, such as regression modeling or hypothesis testing, based on the specific research question and objectives of the project.
