kidney-stone-treatment-analysis-r.Rmd

---
title: "R Notebook"
output: html_notebook
---

```{r}
# Load the readr and dplyr packages
library(readr)
library(dplyr)

# Read datasets kidney_stone_data.csv into data
data <- read_csv("C:/Users/User/Documents/Sandbox/simpsons-paradox/datasets/kidney_stone_data.csv")

# Take a look at the first few rows of the dataset
head(data)
```

```{r}
# Calculate the number and frequency of success and failure of each treatment 
data %>% 
  group_by(treatment, success) %>%
  summarise(N = n()) %>% 
  mutate(Freq = round(N/sum(N), 3))
```
```{r}
# Calculate number and frequency of success and failure by stone size for each treatment
sum_data <- 
  data %>% 
  group_by(treatment, stone_size, success ) %>%
  summarise(N = n()) %>%
  mutate(Freq = round(N/sum(N),3))

# Print out the data frame we just created
print(sum_data)
```
```{r}
# Load ggplot2
library(ggplot2)

# Create a bar plot to show stone size count within each treatment
sum_data %>%
# x-axis as "treatment" and the y-axis as "N" (the count)

  ggplot(aes(x = treatment, y = N)) + 
  geom_bar(aes(fill = stone_size), stat="identity") 
```
```{r}
# Load the broom package 
library(broom)

# Run a Chi-squared test
trt_ss <- chisq.test(data$treatment, data$stone_size)

# Print out the result in tidy format 
tidy(trt_ss)
```
```{r}
# Run a multiple logistic regression
m <- glm(data = data, data$success ~ data$treatment + data$stone_size, family = "binomial")

# Print out model coefficient table in tidy format
tidy(m)
```

```{r}
# Save the tidy model output into an object
tidy_m <- tidy(m)

# Plot the coefficient estimates with 95% CI for each term in the model
tidy_m %>%
  ggplot(aes(x = term, y = estimate)) + 
  geom_pointrange(aes(ymin = estimate - 1.96 * std.error, 
                      ymax = estimate + 1.96 * std.error)) +
  geom_hline(yintercept = 0)
```