R Programming Assignment Help | Analysis of Life In The Good Place Using EDA

In this blog we will cover visualization concept Using R programming, here we have analyse "Life In The Good Place Using EDA".

Data

Data you can download from Realcode4you GitHub link:

Click here to download Dataset

First Importing All Related Libraries

library(tidyverse) 
library(forcats)     # For factors
library(scales)      # For nicer scales

Creating Dataset example_data

```
set.seed(1234)  # Make all random draws the same
example_data <- data_frame(x1 = rnorm(10000),
                           x2 = rnorm(10000),
                           y1 = sample(1:100, 10000, replace = TRUE),
                           y2 = sample(LETTERS[1:4], 10000, replace = TRUE),
                           y3 = sample(LETTERS[10:11], 10000, replace = TRUE),
                           year = sample(2010:2017, 10000, replace = TRUE)) %>%
  arrange(y2, year)

# write_csv(example_data, "data/example_data.csv")

To make life easier, I created a custom ggplot theme that I can use in all my figures:

```
my_beautiful_fancy_theme <- theme_minimal(base_family = "Source Sans Pro") +
  theme(legend.position = "bottom",
        panel.background = element_rect(fill = "transparent", colour = NA),
        plot.background = element_rect(fill = "transparent", colour = NA),
        axis.title.x = element_text(margin = margin(t = 15)),
        axis.title.y = element_text(margin = margin(r = 15)),
        strip.text = element_text(family = "Source Sans Pro", face = "bold", size = rel(1.3)))
```

Lollipop chart

```{r lollipop, fig.height=4, fig.width=6} # add this line if you use R markdown

example_data_summarized <- example_data %>%
  group_by(y2, y3) %>%
  summarize(n = n())

figure1 <- ggplot(example_data_summarized, aes(x = n, y = fct_rev(y2), color = y3)) +
  geom_pointrange(aes(xmin = 0, xmax = n), position = position_dodge(width = 0.5),
                  size = 1, fatten = 5) +
  labs(x = "Total number of things", y = NULL) +
  guides(color = guide_legend(title = NULL)) +
  scale_color_manual(values = c("#FF4266", "#82B09C")) +
  my_beautiful_fancy_theme + 
  theme(panel.grid.minor = element_blank(),
        panel.grid.major.y = element_blank())

figure1

#Use ggsave to save output graphics
ggsave(figure1, filename = "output/figure1.pdf", device = cairo_pdf,
       width = 6, height = 4, units = "in", bg = "transparent")

``` # add this to close if you use R markdown

Output Result

Changes over time(Time Plot)

Next, I wanted to see how things have changed over time.

```{r change-over-time, fig.width=10, fig.height=3}

example_data_time <- example_data %>%
  pivot_longer(cols = c(x1, x2), names_to = "x_names", values_to = "value") %>%
  group_by(x_names, year, y2) %>%
  summarize(x_avg = mean(value),
            error = sd(value) / sqrt(length(value))) %>%
  ungroup() %>%
  mutate(upper = x_avg + (1.96 * error),
         lower = x_avg - (1.96 * error)) %>%
  mutate(x_names = recode(x_names, 
                          x1 = "X1 (average)",
                          x2 = "X2 (average)"))

figure2 <- ggplot(example_data_time, aes(x = year, y = x_avg, color = x_names)) +
  geom_hline(yintercept = 0, size = 0.75, color = "#CC3340", linetype = "dotted") +
  geom_ribbon(aes(ymin = lower, ymax = upper, fill = x_names, color = NULL), alpha = 0.2) +
  geom_line(size = 1) + 
  scale_color_manual(values = c("#FA6900", "#69D1E8")) + 
  scale_y_continuous(labels = percent) +
  guides(color = guide_legend(title = NULL), fill = "none") +
  labs(x = NULL, y = "Whatever this is measuring") +
  facet_wrap(~ y2, nrow = 1) + 
  my_beautiful_fancy_theme + 
  theme(panel.grid.minor = element_blank())

figure2

ggsave(figure2, filename = "output/figure2.pdf", device = cairo_pdf,
       width = 16, height = 3, units = "in", bg = "transparent")
       
```

Output Result

Relationships Graph

This graph use to show relationship between two graphs

```{r relationships, fig.width=6, fig.height=4}

# There are a lot of points here and they're all random and pointless, so I
# simplify this graphic by just taking a subset of them
example_data_subset <- example_data %>%
  sample_n(500)
  
figure3 <- ggplot(example_data_subset, aes(x = y1, y = x2, color = y2)) +
  geom_point(size = 1, alpha = 0.75) + 
  geom_smooth(method = "lm", color = "#85144A", size = 2) +
  labs(x = "Some variable", y = "Some other variable") +
  guides(color = guide_legend(title = NULL)) +
  scale_color_manual(values = c("#188146", "#004259", "#B00DC9", "#FFE01C")) +
  facet_wrap(~ y3) +
  my_beautiful_fancy_theme + 
  theme(panel.grid.minor = element_blank())

figure3

ggsave(figure3, filename = "output/figure3.pdf", device = cairo_pdf,
       width = 6, height = 4, units = "in", bg = "transparent")

```

Output Result

Final Fancy Graph After combining all three graph using "Adobe Illustrator"

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