Fitted line

# Fitted line with scatter and beta
################################ Initial Setup #################################
# Install required packages if they are not already in your system
packages <- c( 'tidyverse', 'MASS')

lapply(packages, function(i) {if(!i %in% installed.packages() == T)
{install.packages(i, dependencies = TRUE, repos='http://cran.rstudio.com/')}})

# Loading required packages
library("tidyverse")
library("MASS")

# Setting working directory
setwd("~/Dropbox (IDinsight)/Data visualization library")

################################ Loading dataset ###############################
# Loading dummy dataset created using the Stata code
mydata <- read_csv("Data/line2.csv", show_col_types = FALSE)

################################ Data processing ###############################
# Running regression to store the beta coefficient and SE
# rlm indicates that it is a robust regression. The command is available to us
# from the MASS package
model <- rlm(y_var ~ x_var, data = mydata)
model_summary <- summary(model)

# Storing the slope
slope <- round(model_summary$coefficients[2,1], 2)

# Storing the SE
se <- round(model_summary$coefficients[2,2], 2)

########################################## Creating the graph ##########################################
plot <- mydata %>%

  # Setting aesthetic which will be inherited by other geometric objects
  ggplot(aes(x = x_var, y = y_var)) +

  # Creating the fitted line.
  # This geometric object is created before the points since we want the points
  # to appear on top of the fitted line
  geom_smooth(method = rlm, color = "maroon", formula = y ~ x,
              fill = "grey10") +

  # Creating the points
  geom_point(color = "#264D96", size = 2.5) +

  # Creating the box with the slope and standard error
  # \n indicates new line
  annotate(geom = "label", x = 0.15, y = 0.9,
           label = paste0("Slope: ", slope, "\n(", se, ")"),
           size = 4.5) +

  # Customizing the y-axis
  # Limits determine the extent of the axis
  # Breaks determine the axis ticks
  # Expand determine the gap between the axis and the data. Here, we are using
  # (0.035, 0), which puts some space below the 0 on the y-axis
  scale_y_continuous(expand = c(0.035, 0),
                     limits = c(0, 1.05),
                     breaks = c(seq(from = 0, to = 1, by = 0.2))) +

  # Customizing the x-axis
  # Limits determine the extent of the axis
  # Breaks determine the axis ticks
  # Expand determines the gap between the axis and the data. Here, we are using
  # (0, 0.002), which puts some space to the left of the 0 on the x-axis
  # Labels is used to customize the labels
  scale_x_continuous(expand = c(0, 0.002),
                     breaks = c(seq(from = 0, to = 1, by = 0.2)),
                     labels = c("0", ".2", ".4", ".6", ".8", "1")) +

  # Graph, x-axis and y-axis titles
  labs(x = "Variable 1",
       y = "Variable 2",
       title = "Graph Title")

########################################## Formatting the graph ##########################################
plot +
  theme_classic() +
  # The following visual elements have been modified:
    # Changed the font face to Inter
    # Removed the legend title
    # Changed the legend position
    # Changed the color of the legend box border to black
    # Changed the size of the axis text
    # Center aligned and changed the size of the graph title
  theme(text = element_text(family = "Inter"),
        axis.text = element_text(size = 12),
        plot.title = element_text(hjust = 0.5, size = 15))

########################################## Saving and exporting ##########################################
#indicating the export folder and the image file name
export_folder <- "R/Line graphs/Exports/"
img_name <- "scatter_fitted_line_R_reviewed.png"
ggsave(paste(export_folder, img_name,sep = ""))