Data Presentation

Install Packages

#install.packages("tidyverse")
library(tidyverse)

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Warning: package 'dplyr' was built under R version 4.4.3

Warning: package 'forcats' was built under R version 4.4.3

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.2     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

#install.packages("ggplot2")
library(ggplot2)
library(here)

here() starts at C:/Users/mn27712/MADA_NEW/muhammadnasir-mada2025-portofolio

#install.packages("patchwork")  # This package is to redefine "/" operator for plot arrangement
library(patchwork)

Warning: package 'patchwork' was built under R version 4.4.3

#install.packages("writexl")
library(writexl)
library(haven)
#install.packages("ggforce")
library(ggforce)

Warning: package 'ggforce' was built under R version 4.4.3

#install.packages("dplyr")
library(dplyr)
library(tidyverse)
library(lubridate)
#install.packages("ggridges") 
library(ggridges)  #

Warning: package 'ggridges' was built under R version 4.4.3

library(forcats)
#install.packages("gt")
library(gt)
#install.packages("gtExtras", dependencies = TRUE)
library(gtExtras)

Warning: package 'gtExtras' was built under R version 4.4.3

About the dataset

In this exercise, I use US Weather History Dataset which contains weather summary of several cities. In this exercise, I only use Philadelphia as example, because the chart are same across the cities. I got the dataset and chart from fivethirtyeight. Link: https://fivethirtyeight.com/features/what-12-months-of-record-setting-temperatures-looks-like-across-the-u-s/

Dataset link: https://data.fivethirtyeight.com/

Figure 1 is the original graph.

#| label: fig-temperature
#| fig-cap: "Temperature Record in Philadelphia."
#| echo: FALSE
knitr::include_graphics(here("presentation-exercise","results","figures","phila-ori.png"))

Read the dataset

# Load the dataset
data_loc <- here("presentation-exercise", "data", "KPHL.csv") 
data <- read_csv(data_loc)

Rows: 365 Columns: 13
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (1): date
dbl (12): actual_mean_temp, actual_min_temp, actual_max_temp, average_min_te...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

data$date <- as.Date(data$date, format="%Y-%m-%d") # convert variable into date format 
head(data)

# A tibble: 6 × 13
  date       actual_mean_temp actual_min_temp actual_max_temp average_min_temp
  <date>                <dbl>           <dbl>           <dbl>            <dbl>
1 2014-07-01               83              72              93               68
2 2014-07-02               86              75              96               68
3 2014-07-03               83              74              92               68
4 2014-07-04               73              68              78               68
5 2014-07-05               74              64              83               69
6 2014-07-06               75              64              86               69
# ℹ 8 more variables: average_max_temp <dbl>, record_min_temp <dbl>,
#   record_max_temp <dbl>, record_min_temp_year <dbl>,
#   record_max_temp_year <dbl>, actual_precipitation <dbl>,
#   average_precipitation <dbl>, record_precipitation <dbl>

summary(data)

      date            actual_mean_temp actual_min_temp actual_max_temp
 Min.   :2014-07-01   Min.   :10.00    Min.   : 2.00   Min.   :17.00  
 1st Qu.:2014-09-30   1st Qu.:39.00    1st Qu.:33.00   1st Qu.:46.00  
 Median :2014-12-30   Median :59.00    Median :50.00   Median :69.00  
 Mean   :2014-12-30   Mean   :55.88    Mean   :47.27   Mean   :63.98  
 3rd Qu.:2015-03-31   3rd Qu.:73.00    3rd Qu.:64.00   3rd Qu.:82.00  
 Max.   :2015-06-30   Max.   :86.00    Max.   :77.00   Max.   :96.00  
 average_min_temp average_max_temp record_min_temp  record_max_temp 
 Min.   :25.00    Min.   :40.00    Min.   :-11.00   Min.   : 61.00  
 1st Qu.:32.00    1st Qu.:49.00    1st Qu.:  9.00   1st Qu.: 73.00  
 Median :46.00    Median :66.00    Median : 29.00   Median : 88.00  
 Mean   :47.22    Mean   :64.72    Mean   : 28.03   Mean   : 84.99  
 3rd Qu.:63.00    3rd Qu.:81.00    3rd Qu.: 46.00   3rd Qu.: 97.00  
 Max.   :70.00    Max.   :87.00    Max.   : 59.00   Max.   :106.00  
 record_min_temp_year record_max_temp_year actual_precipitation
 Min.   :1872         Min.   :1874         Min.   :0.0000      
 1st Qu.:1930         1st Qu.:1931         1st Qu.:0.0000      
 Median :1960         Median :1957         Median :0.0000      
 Mean   :1947         Mean   :1958         Mean   :0.1245      
 3rd Qu.:1969         3rd Qu.:1990         3rd Qu.:0.0500      
 Max.   :2014         Max.   :2014         Max.   :2.0100      
 average_precipitation record_precipitation
 Min.   :0.0800        Min.   :0.850       
 1st Qu.:0.1000        1st Qu.:1.550       
 Median :0.1100        Median :1.880       
 Mean   :0.1138        Mean   :2.135       
 3rd Qu.:0.1200        3rd Qu.:2.490       
 Max.   :0.1500        Max.   :8.020       

Prompt to AI

Me: Hi Good morning, thanks for being my best friend for coding activities.

ChatGPT: Of course, I will be always be your friend, what can I help you?

Me: Today, I want to regenerate a chart. Here I provide the picture of the chart, the original link of the chart, and the dataset. Please generate the code to create the exact same chart, including the color, display, and also legend. Please look at the chart very details to be able to create the same chart. Please generate the code in R.

# Create the plot
plot_1 <- ggplot(data, aes(x = date)) +
  # Shaded area for normal range
  geom_ribbon(aes(ymin = average_min_temp, ymax = average_max_temp), fill = "tan", alpha = 0.5) +
  # Shaded area for record range
  geom_ribbon(aes(ymin = record_min_temp, ymax = record_max_temp), fill = "grey", alpha = 0.3) +
  # Lines for actual min and max temps
  geom_linerange(aes(ymin = actual_min_temp, ymax = actual_max_temp), color = "black") +
  # Points for new record highs and lows
  geom_point(data = filter(data, actual_max_temp > record_max_temp), aes(y = actual_max_temp), color = "red", size = 2) +
  geom_point(data = filter(data, actual_min_temp < record_min_temp), aes(y = actual_min_temp), color = "blue", size = 2) +
  # Labels and themes
  labs(title = "Philadelphia",
       x = NULL,
       y = "Temperature (°F)",
       caption = "Data source: wunderground.com\nAuthor: Randy Olson (randalolson.com / @randal_olson)") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"))

plot(plot_1)

Me: Hi, thank you for generating the code. However, there are some detail different with the original chart.

Here, I modify by myself with some help from ChatGPT. In this step, I try to adjust the line. I see from the original chart, it is look more bar than line.

# Create the plot
plot_2 <- ggplot(data, aes(x = date)) +
  # Shaded area for normal range
  geom_ribbon(aes(ymin = average_min_temp, ymax = average_max_temp), fill = "#9B9B9B", alpha = 1) +
  # Shaded area for record range
  geom_ribbon(aes(ymin = record_min_temp, ymax = record_max_temp), fill = "#696651", alpha = 0.3) +
  geom_linerange(aes(ymin = actual_min_temp, ymax = actual_max_temp), color = "black", size = 0.8) +
  geom_linerange(aes(ymin = actual_min_temp, ymax = actual_max_temp), color = "#4B3B47", size = 2, alpha = 0.8)+
  geom_point(data = filter(data, actual_max_temp > record_max_temp), aes(y = actual_max_temp), color = "red", size = 10) +
  geom_point(data = filter(data, actual_min_temp < record_min_temp), aes(y = actual_min_temp), color = "blue", size = 2) +
  # Labels and themes
  labs(title = "Philadelphia",
       x = NULL,
       y = "Temperature (°F)",
       caption = "Data source: wunderground.com\nAuthor: Randy Olson (randalolson.com / @randal_olson)") +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5, size = 16, face = "bold"))

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

plot(plot_2)

This is the step where I change many things, including, put the date (months) in both sides (top and bottom, and put ’14 below Jul, and ’15 below Jan), I also put the temperature both side. In the previous chart, the grip also did not show every month. Therefore, I adjust the grip into each month and the temperature for every 10 Fahrenheit degree. I adjust the position of the caption (source and author).

plot_3 <- ggplot(data, aes(x = date)) +
  # Shaded area for normal range
  geom_ribbon(aes(ymin = average_min_temp, ymax = average_max_temp), fill = "#9B9B9B", alpha = 1) +
  # Shaded area for record range
  geom_ribbon(aes(ymin = record_min_temp, ymax = record_max_temp), fill = "#696651", alpha = 0.3) +
  # Temperature ranges
  geom_linerange(aes(ymin = actual_min_temp, ymax = actual_max_temp), color = "black", linewidth = 0.8) +
  geom_linerange(aes(ymin = actual_min_temp, ymax = actual_max_temp), color = "#4B3B47", linewidth = 2, linetype = 1) +
  # Record-breaking temperatures (Fixed)
  geom_point(data = data %>% filter(!is.na(actual_max_temp), actual_max_temp > record_max_temp),
             aes(x = date, y = actual_max_temp), color = "red", size = 3) +
  geom_point(data = data %>% filter(!is.na(actual_min_temp), actual_min_temp < record_min_temp),
             aes(x = date, y = actual_min_temp), color = "blue", size = 3) +
  # Labels and themes
  labs(title = "Philadelphia",
       x = NULL,
       y = "Temperature (°F)",
       caption = "Data source: wunderground.com\nAuthor: Randy Olson (randalolson.com / @randal_olson)") +
  # Custom theme
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
    panel.background = element_rect(fill = "white", color = NA),
    panel.grid.major = element_line(color = "grey50", linetype = "dashed"),  # Remove empty linetype value
    panel.grid.minor = element_blank(),
    # Align caption to the left
    plot.caption.position = "plot",
    plot.caption = element_text(hjust = 0, face = "italic", size = 10)
  ) +
  # Define x-axis (date) for both top and bottom
  scale_x_date(date_breaks = "1 month", date_labels = "%b",
               sec.axis = dup_axis(name = NULL)) +  # Duplicate axis at top
  # Define y-axis (temperature) for both left and right
  scale_y_continuous(breaks = seq(-10, 110, 10),
                     sec.axis = dup_axis(name = NULL),
                     limits = c(min(data$record_min_temp) - 5, max(data$record_max_temp) + 5))  # Ensure all values fit

# Plot the chart
plot(plot_3)

The chart looks much better. However, the new record for high and low temperature did not show up in the chart. Therefore, I try to create a new set then insert it into the chart.

# Identify new record highs and lows
weather_data_subset <- data
new_max_records <- weather_data_subset %>%
  filter(record_max_temp <= actual_max_temp)

new_min_records <- weather_data_subset %>%
  filter(record_min_temp >= actual_min_temp)

# Insert the new record into the plot 
plot1 <- ggplot(weather_data_subset, aes(x = date)) +
  # Shaded area for normal range
  geom_ribbon(aes(ymin = average_min_temp, ymax = average_max_temp), fill = "#73736D", alpha = 0.9) +
  # Shaded area for record range
  geom_ribbon(aes(ymin = record_min_temp, ymax = record_max_temp), fill =  "#9a9a77", alpha = 0.7) +
  # Temperature ranges
  geom_linerange(aes(ymin = actual_min_temp, ymax = actual_max_temp), color = "black", linewidth = 0.8) +
  geom_linerange(aes(ymin = actual_min_temp, ymax = actual_max_temp), color = "#4B3B47", linewidth = 1, alpha = 0.8) +
  # Record-breaking temperatures
  geom_point(data = new_max_records, aes(x = date, y = actual_max_temp), color = "red", size = 2) +
  geom_point(data = new_min_records, aes(x = date, y = actual_min_temp), color = "lightblue", size = 2) +
  # create the legend 
  geom_rect(aes(xmin = as.Date("2014-08-10") -4, xmax = as.Date("2014-08-11") + 4, ymin = 0, ymax = 40), fill = "#9a9a77", alpha = 0.7) + # First bar
  geom_rect(aes(xmin = as.Date("2014-08-10") -4, xmax = as.Date("2014-08-11") + 4, ymin = 10, ymax = 30), fill = "#73736D", alpha = 0.9) + # Second bar
  geom_rect(aes(xmin = as.Date("2014-08-10") -4, xmax = as.Date("2014-08-11") + 4, ymin = 15, ymax = 25), fill = "#4B3B47", alpha = 0.8) +   # Third bar
  ## Add two points inside the horizontal bar
  geom_point(aes(x = as.Date("2014-08-10"), y = 42), color = "red", size = 3) +  # Point 1
  geom_point(aes(x = as.Date("2014-08-10"), y = -2), color = "lightblue", size = 3) +  # Point 2
  geom_text(aes(x = as.Date("2014-09-10"), y = 25, label = "New \nrecord high"), family= "Serif", color = "#73736D", vjust = -1, size = 3) + # adding text 
  geom_text(aes(x = as.Date("2014-09-10"), y = -12, label = "New \nrecod low"), family = "Serif",color = "#73736D", vjust = -1, size = 3) + # Adding text 
  geom_text(aes(x = as.Date("2014-07-10"), y = 30, label = "Record high"), family ="Serif", color ="#73736D", vjust = -1, size = 3) + # adding text 
  geom_text(aes(x = as.Date("2014-07-10"), y = 2, label = "Recod low"), family ="Serif", color = "#73736D", vjust = -1, size = 3) + # Adding text 
  geom_text(aes(x = as.Date("2014-09-10"), y = 15, label = "Normal range"), family = "Serif", color ="#73736D", vjust = -1, size = 3) + # adding text 
  geom_text(aes(x = as.Date("2014-07-06"), y = 14, label = "Actual range"), family = "Serif",color = "#73736D", vjust = -1, size = 3) + # Adding text 
  # Labels and themes
  labs(title = "Philadelphia",
       x = NULL,
       y = "Temperature (°F)",
       caption = "Data source: wunderground.com\nAuthor: Randy Olson (randalolson.com / @randal_olson)") +
  # Custom theme
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
    panel.background = element_rect(fill = "white", color = NA),
    panel.grid.major = element_line(color = "grey50", linetype = "longdash", linewidth= 0.2),
    panel.grid.minor = element_blank(),
    plot.caption.position = "plot",
    plot.caption = element_text(hjust = 0, size = 10),
    axis.text.x = element_text(size = 12)  # Ensure x-axis labels are readable
  ) +
  # Custom x-axis labels
  scale_x_date(
    breaks = seq(as.Date("2014-07-01"), as.Date("2015-07-01"), by = "1 month"),
    labels = c("Jul\n'14", "Aug", "Sep", "Oct", "Nov", "Dec", "Jan\n'15", "Feb", "Mar", "Apr", "May", "Jun", "Jul"), # Jul\'14 is to place '14 under Jul, and the same for jan 15
    sec.axis = dup_axis(name = NULL)
  ) +
  # Define y-axis (temperature)
  scale_y_continuous(
    breaks = seq(-10, 110, 10),
    sec.axis = dup_axis(name = NULL),
    limits = c(min(weather_data_subset$record_min_temp) - 5, 
               max(weather_data_subset$record_max_temp) + 5),
    labels = function(x) paste0(x, "°F"))  # Add degree symbol to y-axis labels
  
# save the chart

chart_location <- here( "presentation-exercise","results", "figures", "philadelphia.png") # to set up location for the pictures created 
ggsave(filename = chart_location, plot=plot1, width = 8, height = 6, units = "in", dpi = 300) # save the pictures created 

Warning in geom_point(aes(x = as.Date("2014-08-10"), y = 42), color = "red", : All aesthetics have length 1, but the data has 365 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
  a single row.

Warning in geom_point(aes(x = as.Date("2014-08-10"), y = -2), color = "lightblue", : All aesthetics have length 1, but the data has 365 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
  a single row.

Warning in geom_text(aes(x = as.Date("2014-09-10"), y = 25, label = "New \nrecord high"), : All aesthetics have length 1, but the data has 365 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
  a single row.

Warning in geom_text(aes(x = as.Date("2014-09-10"), y = -12, label = "New \nrecod low"), : All aesthetics have length 1, but the data has 365 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
  a single row.

Warning in geom_text(aes(x = as.Date("2014-07-10"), y = 30, label = "Record high"), : All aesthetics have length 1, but the data has 365 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
  a single row.

Warning in geom_text(aes(x = as.Date("2014-07-10"), y = 2, label = "Recod low"), : All aesthetics have length 1, but the data has 365 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
  a single row.

Warning in geom_text(aes(x = as.Date("2014-09-10"), y = 15, label = "Normal range"), : All aesthetics have length 1, but the data has 365 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
  a single row.

Warning in geom_text(aes(x = as.Date("2014-07-06"), y = 14, label = "Actual range"), : All aesthetics have length 1, but the data has 365 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
  a single row.

The most challenging part in this chart is the legend. I spent hours to work on it. Finally I made it, but not exactly same.

Figure 1: Temperature Record in Philadelphia (imitation)

Part 2: Creating a table

I create a table containing weather summary of Philadelphia in each season. To create this table, I got help from ChatGPT, I added multiple prompt step by step. I went through multiple steps.

Before asking AI to generate the code to create a table, I create subsets for each seasons.

#extract data
weather_data <- data


# Extract month for filtering
weather_data$Month <- format(data$date, "%m")

# Create subsets
summer <- filter(weather_data, Month %in% c("06", "07", "08"))  # June - August
fall <- filter(weather_data, Month %in% c("09", "10", "11"))    # September - November
winter <- filter(weather_data, Month %in% c("12", "01", "02"))  # December - February
spring <- filter(weather_data, Month %in% c("03", "04", "05"))  # March - May

summary(summer)

      date            actual_mean_temp actual_min_temp actual_max_temp
 Min.   :2014-07-01   Min.   :56.00    Min.   :53.00   Min.   :58.00  
 1st Qu.:2014-07-23   1st Qu.:73.00    1st Qu.:64.00   1st Qu.:81.00  
 Median :2014-08-15   Median :76.00    Median :68.00   Median :84.50  
 Mean   :2014-11-12   Mean   :76.14    Mean   :67.30   Mean   :84.35  
 3rd Qu.:2015-06-07   3rd Qu.:80.00    3rd Qu.:70.25   3rd Qu.:89.00  
 Max.   :2015-06-30   Max.   :86.00    Max.   :77.00   Max.   :96.00  
 average_min_temp average_max_temp record_min_temp record_max_temp 
 Min.   :59.00    Min.   :79.00    Min.   :44.00   Min.   : 93.00  
 1st Qu.:66.00    1st Qu.:84.00    1st Qu.:48.75   1st Qu.: 98.00  
 Median :68.00    Median :86.00    Median :52.00   Median : 99.00  
 Mean   :67.09    Mean   :85.04    Mean   :51.68   Mean   : 99.16  
 3rd Qu.:69.00    3rd Qu.:87.00    3rd Qu.:55.00   3rd Qu.:100.25  
 Max.   :70.00    Max.   :87.00    Max.   :59.00   Max.   :106.00  
 record_min_temp_year record_max_temp_year actual_precipitation
 Min.   :1884         Min.   :1888         Min.   :0.0000      
 1st Qu.:1952         1st Qu.:1930         1st Qu.:0.0000      
 Median :1963         Median :1957         Median :0.0000      
 Mean   :1960         Mean   :1961         Mean   :0.1818      
 3rd Qu.:1972         3rd Qu.:1994         3rd Qu.:0.1950      
 Max.   :1999         Max.   :2012         Max.   :1.9600      
 average_precipitation record_precipitation    Month          
 Min.   :0.0900        Min.   :1.040        Length:92         
 1st Qu.:0.1100        1st Qu.:1.958        Class :character  
 Median :0.1200        Median :2.440        Mode  :character  
 Mean   :0.1226        Mean   :2.689                          
 3rd Qu.:0.1400        3rd Qu.:3.050                          
 Max.   :0.1500        Max.   :8.020                          

summary(fall)

      date            actual_mean_temp actual_min_temp actual_max_temp
 Min.   :2014-09-01   Min.   :28.00    Min.   :20.00   Min.   :36.00  
 1st Qu.:2014-09-23   1st Qu.:50.50    1st Qu.:42.00   1st Qu.:57.00  
 Median :2014-10-16   Median :61.00    Median :52.00   Median :70.00  
 Mean   :2014-10-16   Mean   :58.79    Mean   :50.54   Mean   :66.55  
 3rd Qu.:2014-11-07   3rd Qu.:67.00    3rd Qu.:59.00   3rd Qu.:75.00  
 Max.   :2014-11-30   Max.   :84.00    Max.   :74.00   Max.   :93.00  
 average_min_temp average_max_temp record_min_temp record_max_temp 
 Min.   :35.00    Min.   :50.00    Min.   : 8.00   Min.   : 70.00  
 1st Qu.:41.50    1st Qu.:59.00    1st Qu.:25.00   1st Qu.: 78.00  
 Median :48.00    Median :67.00    Median :31.00   Median : 87.00  
 Mean   :49.34    Mean   :66.95    Mean   :31.53   Mean   : 85.23  
 3rd Qu.:57.50    3rd Qu.:75.00    3rd Qu.:39.00   3rd Qu.: 92.00  
 Max.   :65.00    Max.   :83.00    Max.   :53.00   Max.   :102.00  
 record_min_temp_year record_max_temp_year actual_precipitation
 Min.   :1875         Min.   :1879         Min.   :0.0000      
 1st Qu.:1944         1st Qu.:1927         1st Qu.:0.0000      
 Median :1961         Median :1949         Median :0.0000      
 Mean   :1954         Mean   :1949         Mean   :0.0911      
 3rd Qu.:1969         3rd Qu.:1975         3rd Qu.:0.0100      
 Max.   :2013         Max.   :2014         Max.   :1.0500      
 average_precipitation record_precipitation    Month          
 Min.   :0.0800        Min.   :0.900        Length:91         
 1st Qu.:0.1000        1st Qu.:1.600        Class :character  
 Median :0.1100        Median :1.950        Mode  :character  
 Mean   :0.1093        Mean   :2.276                          
 3rd Qu.:0.1200        3rd Qu.:2.725                          
 Max.   :0.1400        Max.   :6.630                          

summary(winter)

      date            actual_mean_temp actual_min_temp actual_max_temp
 Min.   :2014-12-01   Min.   :10.00    Min.   : 2.00   Min.   :17.00  
 1st Qu.:2014-12-23   1st Qu.:27.25    1st Qu.:20.00   1st Qu.:34.00  
 Median :2015-01-14   Median :34.00    Median :27.00   Median :41.00  
 Mean   :2015-01-14   Mean   :33.06    Mean   :25.74   Mean   :39.87  
 3rd Qu.:2015-02-05   3rd Qu.:39.75    3rd Qu.:33.00   3rd Qu.:45.75  
 Max.   :2015-02-28   Max.   :56.00    Max.   :47.00   Max.   :65.00  
 average_min_temp average_max_temp record_min_temp   record_max_temp
 Min.   :25.00    Min.   :40.00    Min.   :-11.000   Min.   :61.00  
 1st Qu.:26.00    1st Qu.:41.00    1st Qu.:  0.000   1st Qu.:65.00  
 Median :27.00    Median :42.00    Median :  3.000   Median :68.00  
 Mean   :27.84    Mean   :42.94    Mean   :  2.778   Mean   :68.03  
 3rd Qu.:29.00    3rd Qu.:45.00    3rd Qu.:  6.750   3rd Qu.:70.00  
 Max.   :34.00    Max.   :50.00    Max.   : 15.000   Max.   :79.00  
 record_min_temp_year record_max_temp_year actual_precipitation
 Min.   :1875         Min.   :1874         Min.   :0.0000      
 1st Qu.:1897         1st Qu.:1941         1st Qu.:0.0000      
 Median :1942         Median :1968         Median :0.0000      
 Mean   :1937         Mean   :1963         Mean   :0.1128      
 3rd Qu.:1977         3rd Qu.:1996         3rd Qu.:0.0475      
 Max.   :2014         Max.   :2013         Max.   :1.8400      
 average_precipitation record_precipitation    Month          
 Min.   :0.0900        Min.   :0.900        Length:90         
 1st Qu.:0.0900        1st Qu.:1.343        Class :character  
 Median :0.1000        Median :1.585        Mode  :character  
 Mean   :0.1027        Mean   :1.687                          
 3rd Qu.:0.1100        3rd Qu.:1.847                          
 Max.   :0.1300        Max.   :3.860                          

summary(spring)

      date            actual_mean_temp actual_min_temp actual_max_temp
 Min.   :2015-03-01   Min.   :19.00    Min.   :11.00   Min.   :26.00  
 1st Qu.:2015-03-23   1st Qu.:44.00    1st Qu.:34.00   1st Qu.:51.75  
 Median :2015-04-15   Median :56.00    Median :46.00   Median :66.50  
 Mean   :2015-04-15   Mean   :55.09    Mean   :45.04   Mean   :64.67  
 3rd Qu.:2015-05-08   3rd Qu.:65.50    3rd Qu.:54.00   3rd Qu.:77.00  
 Max.   :2015-05-31   Max.   :81.00    Max.   :71.00   Max.   :92.00  
 average_min_temp average_max_temp record_min_temp record_max_temp
 Min.   :30.00    Min.   :48.00    Min.   : 5.00   Min.   :71.00  
 1st Qu.:36.75    1st Qu.:55.75    1st Qu.:14.00   1st Qu.:82.00  
 Median :44.00    Median :64.00    Median :27.00   Median :90.00  
 Mean   :44.22    Mean   :63.51    Mean   :25.62   Mean   :87.16  
 3rd Qu.:51.25    3rd Qu.:71.25    3rd Qu.:35.25   3rd Qu.:92.25  
 Max.   :59.00    Max.   :78.00    Max.   :43.00   Max.   :97.00  
 record_min_temp_year record_max_temp_year actual_precipitation
 Min.   :1872         Min.   :1880         Min.   :0.0000      
 1st Qu.:1894         1st Qu.:1936         1st Qu.:0.0000      
 Median :1956         Median :1962         Median :0.0000      
 Mean   :1938         Mean   :1958         Mean   :0.1118      
 3rd Qu.:1966         3rd Qu.:1990         3rd Qu.:0.0300      
 Max.   :2002         Max.   :2013         Max.   :2.0100      
 average_precipitation record_precipitation    Month          
 Min.   :0.1000        Min.   :0.850        Length:92         
 1st Qu.:0.1100        1st Qu.:1.478        Class :character  
 Median :0.1200        Median :1.765        Mode  :character  
 Mean   :0.1202        Mean   :1.882                          
 3rd Qu.:0.1300        3rd Qu.:2.192                          
 Max.   :0.1400        Max.   :4.420                          

In this step, I used ChatGPT to create table. Here is the prompt that I used:

Now I have 4 subsets: summer, fall,, winter, and spring. Those subsets have actual_mean_temp, average_min_temp, average_max_temp, average_precipitation. I want to create a beautiful and easy read table. Please give me idea and step by step, including code.

The first table generated did not look good. Therefore, I add more prompt in each feature that I want. For example, I want to add icons representing the season, I want to arrange indicators in the column, and seasons as rows. I actually added more prompt.

# Summarize the data for each season
summary_table <- bind_rows(
  summer %>% summarise(Season = "Summer", 
                       `Avg Min Temp (°F)` = mean(average_min_temp, na.rm = TRUE),
                       `Avg Mean Temp (°F)` = mean(actual_mean_temp, na.rm = TRUE),
                       `Avg Max Temp (°F)` = mean(average_max_temp, na.rm = TRUE),
                       `Avg Precipitation (inches)` = mean(average_precipitation, na.rm = TRUE)),
  fall %>% summarise(Season = "Fall", 
                     `Avg Min Temp (°F)` = mean(average_min_temp, na.rm = TRUE),
                     `Avg Mean Temp (°F)` = mean(actual_mean_temp, na.rm = TRUE),
                     `Avg Max Temp (°F)` = mean(average_max_temp, na.rm = TRUE),
                     `Avg Precipitation (inches)` = mean(average_precipitation, na.rm = TRUE)),
  winter %>% summarise(Season = "Winter", 
                       `Avg Min Temp (°F)` = mean(average_min_temp, na.rm = TRUE),
                       `Avg Mean Temp (°F)` = mean(actual_mean_temp, na.rm = TRUE),
                       `Avg Max Temp (°F)` = mean(average_max_temp, na.rm = TRUE),
                       `Avg Precipitation (inches)` = mean(average_precipitation, na.rm = TRUE)),
  spring %>% summarise(Season = "Spring", 
                       `Avg Min Temp (°F)` = mean(average_min_temp, na.rm = TRUE),
                       `Avg Mean Temp (°F)` = mean(actual_mean_temp, na.rm = TRUE),
                       `Avg Max Temp (°F)` = mean(average_max_temp, na.rm = TRUE),
                       `Avg Precipitation (inches)` = mean(average_precipitation, na.rm = TRUE))
)

# Add seasonal icons in a separate column
summary_table <- summary_table %>%
  mutate(` ` = c("☀️", "🍂", "❄️", "🌸"))  # Adding icons for Summer, Fall, Winter, Spring

# Create a styled gt table
styled_table <- summary_table %>%
  gt() %>%
  tab_header(
    title = md("**Philadelphia Seasonal Weather Summary**"),
    subtitle = md("*Weather data from July 2014 - June 2015*")
  ) %>%
  tab_spanner(label = "Temperature (°F)", columns = c(`Avg Min Temp (°F)`, `Avg Mean Temp (°F)`, `Avg Max Temp (°F)`)) %>%
  tab_spanner(label = "Precipitation", columns = `Avg Precipitation (inches)`) %>%
  fmt_number(
    columns = where(is.numeric),
    decimals = 1
  ) %>%
  cols_move_to_start(columns = "Season") %>%
  cols_move(columns = " ", after = "Season") %>%  # Moves icon column after season
  cols_align(
    align = "left",
    columns = "Season"  # Keep Season column left-aligned
  ) %>%
  cols_align(
    align = "center",
    columns = c(` `, `Avg Min Temp (°F)`, `Avg Mean Temp (°F)`, `Avg Max Temp (°F)`, `Avg Precipitation (inches)`)  # Center all other columns
  ) %>%
  tab_source_note(md("**Source:** https://www.wunderground.com/, July 2014 - June 2015.")) %>%
  tab_footnote(footnote = "Precipitation measured in inches", locations = cells_column_labels(columns = `Avg Precipitation (inches)`))%>%
  data_color(
    columns = c(`Avg Min Temp (°F)`, `Avg Max Temp (°F)`),
    colors = scales::col_numeric(
      palette = c("blue", "white", "red"),
      domain = range(summary_table$`Avg Min Temp (°F)`, summary_table$`Avg Max Temp (°F)`)
    )
  )

Warning: Since gt v0.9.0, the `colors` argument has been deprecated.
• Please use the `fn` argument instead.
This warning is displayed once every 8 hours.

table_file1 = here("presentation-exercise","results", "tables", "weather.rds")
saveRDS(styled_table, file = table_file1)

# Print the table
styled_table

Philadelphia Seasonal Weather Summary
Weather data from July 2014 - June 2015
Season		Temperature (°F)			Precipitation
		Avg Min Temp (°F)	Avg Mean Temp (°F)	Avg Max Temp (°F)	Avg Precipitation (inches)1
Summer	☀️	67.1	76.1	85.0	0.1
Fall	🍂	49.3	58.8	66.9	0.1
Winter	❄️	27.8	33.1	42.9	0.1
Spring	🌸	44.2	55.1	63.5	0.1
Source: https://www.wunderground.com/, July 2014 - June 2015.
1 Precipitation measured in inches