Creating a Line Plot with ggplot2: A Step-by-Step Guide to Addressing Common Issues in R's Geom-Line Function.

Understanding the Problem: A Deep Dive into ggplot 2 geom_line by Year

The problem at hand involves creating a line plot using R’s ggplot2 package, where the lines are colored based on the month and the y-axis represents the mean temperature (tmean) over time. However, when attempting to create this plot with real-world data, unexpected results occur.

Step 1: Filtering Data

The first step in addressing this issue is to understand that the problem may stem from having multiple values for a single year-month combination, as indicated by the presence of different Variable values (tmax, tmean, and tmin) in the original dataset. This means we need to filter out the rows where Variable is not “tmean.”

library(tidyverse)

# Sample data with multiple values for each year-month
sample_df = data.frame(
  month = rep(1:6, each = 30),
  year = rep(1980:2009, 6),
  so2 = rnorm(180)
)

# Filter out rows where Variable is not "tmean"
filtered_sample_df = sample_df %>% filter(Variable == "tmean")

print(filtered_sample_df)

Step 2: Aggregation

After filtering, it becomes apparent that the data contains multiple values for each year-month combination. This indicates that we need to aggregate this data in some way to obtain a single value per year-month.

# Group by year and month, then summarize with mean(tmean)
aggregated_sample_df = filtered_sample_df %>% 
  group_by(year, month) %>% 
  summarise(mean_value = mean(tmean))

print(aggregated_sample_df)

Step 3: Creating the Plot

With the aggregated data in hand, we can now proceed to create the line plot as desired. This involves creating a ggplot object with aes() mapping year to x-axis and tmean to y-axis, while using the month variable for color.

# Create the ggplot
ggplot(aggregated_sample_df, aes(x = year, y = mean_value, color = as.factor(month))) +
  geom_line()

Step 4: Identifying Potential Issues

To further understand potential issues with this approach, let’s examine some key characteristics of the dataset:

Number of observations: With approximately 88,000 rows and 505 distinct year-month combinations, the data is vast.

# Counting nrow(df)
nrow(df)

# Observing unique values for each variable using dput()
df %>% summarize(across(c(year, month, Variable, tmean), n_distinct))

Distinct values: The presence of multiple values per year-month indicates that aggregation may be necessary.

# Exploring the distribution of observations by filtering Variable == "tmax"
df %>% filter(Variable == "tmax") %>% 
  count(year, month, name = "yearmo_obs")

Step 5: Troubleshooting

To further troubleshoot potential issues, let’s examine how sorting data can affect our results:

# Sorting the data by year and month
df %>% arrange(year, month)

# Examining the resulting data structure for consistency
head(df)

By breaking down the problem step-by-step and using appropriate R functions to handle aggregation and filtering, we can successfully create a line plot with ggplot2 that meets our requirements.

Example Use Case

Here is an example of how to apply these steps to real-world data:

library(tidyverse)

# Load your dataset into df
df = read.csv("path")

# Filter out rows where Variable is not "tmean"
filtered_df = df %>% filter(Variable == "tmean")

# Group by year and month, then summarize with mean(tmean)
aggregated_df = filtered_df %>% 
  group_by(year, month) %>% 
  summarise(mean_value = mean(tmean))

# Create the ggplot
ggplot(aggregated_df, aes(x = year, y = mean_value, color = as.factor(month))) +
  geom_line()

Last modified on 2024-09-30