Gather Rows and Plot a Stacked Bar Plot in dplyr

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Introduction

The task at hand is to gather rows from multiple dataframes, each representing a year, and then plot a stacked bar chart using the sentiment values for each year. We’ll explore how to accomplish this using the popular R package dplyr, which provides a powerful and flexible way to manipulate and transform data.

Problem Statement

We have a collection of dataframes, each representing a year from 2010 to 2019. Each dataframe contains columns labeled with sentiment values (e.g., anger, anticipation, disgust, etc.). The task is to combine these dataframes into a single dataframe, where each row represents a year and its corresponding sentiment values. We then want to create a stacked bar chart using the combined dataframe.

Solution Overview

To solve this problem, we’ll use the dplyr package to perform the following steps:

1. Create a list of dataframes from the available data.
2. Bind the rows together while creating a ‘year’ column from the names of the dataframes in the list.
3. Reshape the resulting dataframe into long format using pivot_longer.
4. Plot a stacked bar chart using ggplot.

Step 1: Load Required Libraries

Before we begin, let’s load the required libraries:

library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)

Step 2: Create a List of Dataframes

We can create a list of dataframes using mget, which retrieves all objects matching a specified pattern.

# Get the list of dataframes
dataframes <- mget(ls(pattern = '^nrc\\d{4}$'))

This will return a list containing all dataframes with names matching the pattern.

Step 3: Bind Rows Together

Now, we’ll use bind_rows to combine the rows from each dataframe into a single dataframe while creating a ‘year’ column based on the names of the dataframes in the list:

# Bind rows together and create a 'year' column
combined_df <- dataframes %>% 
  bind_rows(.id = 'year') %>% 
  mutate(year = str_remove(year, '^nrc'))

This will produce a new dataframe with a ‘year’ column containing the year values from the original dataframes.

Step 4: Reshape into Long Format

Next, we’ll use pivot_longer to reshape the combined dataframe into long format:

# Pivot rows into long format
long_df <- combined_df %>% 
  pivot_longer(cols = -year, names_to = 'sentiment', values_to = 'count')

This will result in a new dataframe with a ‘sentiment’ column and a ‘count’ column containing the corresponding values.

Step 5: Arrange Data by Count

To ensure that the plot is ordered correctly, we’ll sort the long dataframe by the count values in descending order:

# Sort data by count in descending order
long_df <- long_df %>% 
  arrange(desc(count))

This will position the years with higher count values at the top of the plot.

Step 6: Create Stacked Bar Chart

Finally, we can use ggplot to create a stacked bar chart:

# Plot stacked bar chart
ggplot(long_df, aes(x = year, y = count, fill = sentiment)) +
  geom_bar(colour = 'black', stat = 'identity')

This will produce the desired stacked bar chart.

Conclusion

In this article, we demonstrated how to gather rows from multiple dataframes and plot a stacked bar chart using dplyr. By breaking down the problem into manageable steps and leveraging the power of dplyr and ggplot, we were able to create an effective solution for visualizing sentiment values across different years.

Last modified on 2024-01-12