In dbturner/c4bi: Coding for broader impact

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r report's Bird and Tree Report

This title is individualized by running through a vector to print each name.

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Here we would put some information about who surveyed, how they surveyed, and when they surveyed.

• This brief introduction should refresh the stakeholder's memory about the project's purpose and relevance.

If we had pictures from these fictitious surveys in this dataset, then we could could use the knitr package to print them.

knitr::include_graphics("path_to_picutre_file"))

• We advise using the knitr package when printing pictures since if/else statements can be given if you wish to print certain pictures for certain stakeholders.

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Next, you can include graphs that tell a clear story about the findings:

Here is a plot that shows r report's bird counts compared to the average across towns.

ggplot(bird_sum[c(i, length(bird_sum$town)),], aes(x = town, y = round(as.numeric(mean_birds), digits = 0))) +
    geom_bar(stat = "identity", aes(fill = town)) +
    labs(y = "Average number of birds") +
    theme_classic() +
    theme(axis.ticks.x = element_blank(),
          axis.text.x = element_blank(),
          axis.title.x = element_blank(),
          legend.title = element_blank(),
          axis.text.y = element_text(color = "grey20", size = 12, angle = 0, hjust = .5, vjust = .5, face = "plain"),
          axis.title = element_text(size = 13.6),
          legend.text = element_text(size = 13)) +
    scale_fill_brewer(palette = "Dark2")

After printing the graph, you can give a short explanation, if necessary. Additional plots can be included as requested by the stakeholder or if they help clarify the overall "story" of the report.

Here is a plot that shows r report's tree counts compared to the average across towns.

ggplot(tree_sum[c(i, length(tree_sum$town)),], aes(x = town, y = round(as.numeric(mean_trees), digits = 0))) +
    geom_bar(stat = "identity", aes(fill = town)) +
    labs(y = "Average number of trees") +
    theme_classic() +
    theme(axis.ticks.x = element_blank(),
          axis.text.x = element_blank(),
          axis.title.x = element_blank(),
          legend.title = element_blank(),
          axis.text.y = element_text(color = "grey20", size = 12, angle = 0, hjust = .5, vjust = .5, face = "plain"),
          axis.title = element_text(size = 13.6),
          legend.text = element_text(size = 13)) +
    scale_fill_brewer(palette = "Dark2")

Here is a plot that shows how bird observations correlates with the number of trees in a park.

ggplot(data = town, aes(x = num_trees, y = num_birds)) +
    geom_point() +
    geom_smooth(method = "lm", se = FALSE, color = "blue4") +
    labs(x = "Number of trees",
         y = "Number of birds") +
    theme_classic() +
    theme(axis.text = element_text(size = 13.5),
          axis.title = element_text(size = 13.5))

Each point represents a the number of birds observed each day by the number of trees in a park. We've added a trendline to show the positive relationship between the number of trees in a park and the number of birds observed in the park.

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Here we may give a brief summary and additional information to learn more.

Finally, we will link to a feedback form or provide instructions with contact information for how to provide feedback as well as any other unanswered stakeholder questions.

dbturner/c4bi documentation built on March 23, 2022, 6:36 p.m.