diff --git a/devstats/reports/issues/first_responders.md b/devstats/reports/issues/first_responders.md
deleted file mode 100644
index 13e4bb4..0000000
--- a/devstats/reports/issues/first_responders.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### First responders
-
-```{code-cell} ipython3
----
-tags: [hide-input]
----
-```
-
-```{code-cell} ipython3
----
-tags: [hide-input]
----
-first_commenter_tab = pd.DataFrame(
-    {
-        k: v
-        for k, v in zip(
-            ("Contributor", "# of times commented first"),
-            np.unique(first_commenters, return_counts=True),
-        )
-    }
-)
-first_commenter_tab.sort_values(
-    "# of times commented first", ascending=False
-).head(10)
-```
diff --git a/devstats/reports/issues/new_issues.md b/devstats/reports/issues/new_issues.md
deleted file mode 100644
index f63a51f..0000000
--- a/devstats/reports/issues/new_issues.md
+++ /dev/null
@@ -1,60 +0,0 @@
-### New issues
-
-%TODO: should probably use pandas for this
-
-```{code-cell} ipython3
----
-tags: [hide-input]
----
-
-newly_created = [
-    iss for iss in issues if np.datetime64(iss["createdAt"]) > query_date
-]
-new_issues_closed = [iss for iss in newly_created if iss["state"] == "CLOSED"]
-
-new_issue_lifetime = np.array(
-    [
-        np.datetime64(iss["closedAt"]) - np.datetime64(iss["createdAt"])
-        for iss in new_issues_closed
-    ],
-).astype("m8[h]")  # in hours
-
-glue("{{ project }}_num_new_issues", len(newly_created))
-glue("{{ project }}_num_new_issues_closed", percent_val(len(new_issues_closed), len(newly_created)))
-glue("{{ project }}_new_issue_median_lifetime", f"{np.median(new_issue_lifetime)}")
-```
-
-{glue:text}`{{ project }}_num_new_issues` new issues have been opened since
-{glue:text}`{{ project }}_query_date`, of which {glue:text}`{{ project }}_num_new_issues_closed` have been
-closed.
-
-The median lifetime of new issues that were created and closed in this period
-is {glue:text}`{{ project }}_new_issue_median_lifetime`.
-
-% TODO: replace with bokeh or some other live-plot
-% TODO: for any remaining static/mpl plots, set default params for things
-% like fontsize in a mplstyle file.
-
-```{code-cell} ipython3
----
-tags: [hide-input]
----
-title = (
-    f"Lifetime of issues created and closed in the last "
-    f"{(np.datetime64(datetime.datetime.now()) - query_date).astype('m8[D]')}"
-)
-h, bedges = np.histogram(
-    new_issue_lifetime.astype("m8[D]").astype(int), bins=np.arange(30)
-)
-
-p = figure(
-    width=670,
-    height=400,
-    title=title,
-    tooltips=[("lifetime", "@right days"), (r"# issues", "@top")],
-)
-p.quad(top=h, bottom=0, left=bedges[:-1], right=bedges[1:])
-p.xaxis.axis_label = "Issue lifetime (days)"
-p.yaxis.axis_label = "# Issues"
-show(p)
-```
diff --git a/devstats/reports/issues/time_to_response.md b/devstats/reports/issues/time_to_response.md
deleted file mode 100644
index cd7142e..0000000
--- a/devstats/reports/issues/time_to_response.md
+++ /dev/null
@@ -1,97 +0,0 @@
-### Time to response
-
-```{code-cell} ipython3
----
-tags: [hide-input]
----
-# Remove issues that are less than a day old for the following analysis
-newly_created_day_old = [
-    iss for iss in newly_created
-    if np.datetime64(datetime.datetime.now()) - np.datetime64(iss["createdAt"])
-    > np.timedelta64(1, "D")
-]
-
-# TODO: really need pandas here
-commented_issues = [
-    iss for iss in newly_created_day_old
-    if any(
-        e["node"]["__typename"] == "IssueComment" for e in iss["timelineItems"]["edges"]
-    )
-]
-first_commenters, time_to_first_comment = [], []
-for iss in commented_issues:
-    for e in iss["timelineItems"]["edges"]:
-        if e["node"]["__typename"] == "IssueComment":
-            try:
-                user = e["node"]["author"]["login"]
-            except TypeError as err:
-                # This can happen e.g. when a user deletes their GH acct
-                user = "UNKNOWN"
-            first_commenters.append(user)
-            dt = np.datetime64(e["node"]["createdAt"]) - np.datetime64(iss["createdAt"])
-            time_to_first_comment.append(dt.astype("m8[m]"))
-            break  # Only want the first commenter
-time_to_first_comment = np.array(time_to_first_comment)  # in minutes
-
-median_time_til_first_response = np.median(time_to_first_comment.astype(int) / 60)
-
-cutoffs = [
-    np.timedelta64(1, "h"),
-    np.timedelta64(12, "h"),
-    np.timedelta64(24, "h"),
-    np.timedelta64(3, "D"),
-    np.timedelta64(7, "D"),
-    np.timedelta64(14, "D"),
-]
-num_issues_commented_by_cutoff = np.array(
-    [
-        np.sum(time_to_first_comment < cutoff) for cutoff in cutoffs
-    ]
-)
-
-# TODO: Update IssueComment query to include:
-#  - whether the commenter is a maintainer
-#  - datetime of comment
-# This will allow analysis of what fraction of issues are addressed by
-# maintainers vs. non-maintainer, and the distribution of how long an issue
-# usually sits before it's at least commented on
-
-glue(
-    "{{ project }}_num_new_issues_responded",
-    percent_val(len(commented_issues), len(newly_created_day_old))
-)
-
-glue("{{ project }}_new_issues_at_least_1_day_old", len(newly_created_day_old))
-glue("{{ project }}_median_response_time", f"{median_time_til_first_response:1.0f}")
-```
-
-Of the {glue:text}`{{ project }}_new_issues_at_least_1_day_old` issues that are at least 24
-hours old, {glue:text}`{{ project }}_num_new_issues_responded` of them have been commented
-on.
-The median time until an issue is first responded to is
-{glue:text}`{{ project }}_median_response_time` hours.
-
-```{code-cell} ipython3
----
-tags: [hide-input]
----
-from bokeh.transform import dodge
-
-title = f"Percentage of issues opened since {query_date} that are commented on within..."
-
-x = [str(c) for c in cutoffs]
-y = 100 * num_issues_commented_by_cutoff / len(newly_created_day_old)
-
-p = figure(
-    x_range=x,
-    y_range=(0, 100),
-    width=670,
-    height=400,
-    title=title,
-    tooltips=[(r"%", "@top")],
-)
-p.vbar(x=x, top=y, width=0.8)
-p.xaxis.axis_label = "Time interval"
-p.yaxis.axis_label = "Percentage of issues commented on within interval"
-show(p)
-```
diff --git a/devstats/reports/pull_requests/mergeability_of_open_prs.md b/devstats/reports/pull_requests/mergeability_of_open_prs.md
deleted file mode 100644
index f6ab545..0000000
--- a/devstats/reports/pull_requests/mergeability_of_open_prs.md
+++ /dev/null
@@ -1,37 +0,0 @@
-### Mergeability of Open PRs
-
-```{code-cell} ipython3
----
-tags: [hide-input]
----
-open_prs = [pr for pr in prs if pr["state"] == "OPEN"]
-
-# The GraphQL query does not reliably return information on PR mergeability.
-# Warn if there are problems
-if any([pr["mergeable"] == "UNKNOWN" for pr in open_prs]):
-    warnings.warn(
-        (
-            "\n\nThe data contains PRs with unknown merge status.\n"
-            "Please re-download the data to get accurate info about PR mergeability."
-        ),
-        UserWarning,
-        stacklevel=2,
-    )
-
-conflicting_prs = [isoparse(pr["createdAt"]) for pr in open_prs if pr["mergeable"] == "CONFLICTING"]
-mergeable_prs = [isoparse(pr["createdAt"]) for pr in open_prs if pr["mergeable"] == "MERGEABLE"]
-
-fig, ax = plt.subplots(figsize=(6, 4))
-ax.hist(
-    [conflicting_prs, mergeable_prs],
-    bins="auto",
-    histtype="bar",
-    label=("conflicting", "mergeable"),
-    color=("tab:red", "tab:blue"),
-)
-ax.legend()
-ax.set_xlabel("Date of PR creation")
-ax.set_ylabel(r"# of conflicting PRs")
-fig.autofmt_xdate()
-fig.tight_layout();
-```
diff --git a/devstats/reports/pull_requests/merged_prs_over_time.md b/devstats/reports/pull_requests/merged_prs_over_time.md
deleted file mode 100644
index cf2a88a..0000000
--- a/devstats/reports/pull_requests/merged_prs_over_time.md
+++ /dev/null
@@ -1,68 +0,0 @@
-### Merged PRs over time
-
-A look at merged PRs over time.
-
-```{code-cell} ipython3
----
-tags: [hide-input]
----
-
-# All contributors
-merged_prs = [pr for pr in prs if pr['state'] == 'MERGED']
-merge_dates = np.array([pr['mergedAt'] for pr in merged_prs], dtype=np.datetime64)
-binsize = np.timedelta64(30, "D")
-date_bins = np.arange(merge_dates[0], merge_dates[-1], binsize)
-h_all, bedges = np.histogram(merge_dates, date_bins)
-bcenters = bedges[:-1] + binsize / 2
-smoothing_interval = 4  # in units of bin-width
-
-# First-time contributors
-first_time_contributor = []
-prev_contrib = set()
-for record in merged_prs:
-    try:
-        author = record['author']['login']
-    except TypeError:  # Author no longer has GitHub account
-        first_time_contributor.append(None)
-        continue
-    if author not in prev_contrib:
-        first_time_contributor.append(True)
-        prev_contrib.add(author)
-    else:
-        first_time_contributor.append(False)
-# Object dtype for handling None
-first_time_contributor = np.array(first_time_contributor, dtype=object)
-# Focus on first time contributors
-ftc_mask = first_time_contributor == True
-ftc_dates = merge_dates[ftc_mask]
-
-h_ftc, bedges = np.histogram(ftc_dates, date_bins)
-
-fig, axes = plt.subplots(1, 2, figsize=(16, 8))
-for ax, h, whom in zip(
-    axes.ravel(), (h_all, h_ftc), ("all contributors", "first-time contributors")
-):
-    ax.bar(bcenters, h, width=binsize, label="Raw")
-    ax.plot(
-        bcenters,
-        np.convolve(h, np.ones(smoothing_interval), 'same') / smoothing_interval,
-        label=f"{binsize * smoothing_interval} moving average",
-        color='tab:orange',
-        linewidth=2.0,
-    )
-
-    ax.set_title(f'{whom}')
-    ax.legend()
-
-fig.suptitle("Merged PRs from:")
-axes[0].set_xlabel('Time')
-axes[0].set_ylabel(f'# Merged PRs / {binsize} interval')
-axes[1].set_ylim(axes[0].get_ylim())
-fig.autofmt_xdate()
-
-# TODO: Replace this with `glue` once the glue:figure directive supports
-# alt-text
-import os
-os.makedirs("thumbs", exist_ok=True)
-plt.savefig("thumbs/{{ project }}.png", bbox_inches="tight")
-```
diff --git a/devstats/reports/pull_requests/number_of_pr_participants.md b/devstats/reports/pull_requests/number_of_pr_participants.md
deleted file mode 100644
index 3e8c6ff..0000000
--- a/devstats/reports/pull_requests/number_of_pr_participants.md
+++ /dev/null
@@ -1,25 +0,0 @@
-### Number of PR participants
-
-% TODO: Similar analysis, but replace num participants with some metrics of
-% how "big" the PR is; num LOC changed, etc.
-
-```{code-cell} ipython3
----
-tags: [hide-input]
----
-# Get the lifetimes and number of participants for merged PRs
-lifetimes = np.array(
-    [isoparse(pr["mergedAt"]) - isoparse(pr["createdAt"]) for pr in merged_prs],
-    dtype="m8[h]",
-)
-num_participants = np.array([pr["participants"]["totalCount"] for pr in merged_prs])
-
-title = "Distribution of lifetimes for merged PRs based on the number of participants"
-
-p = figure(width=600, height=300, title=title)
-p.xgrid.grid_line_color = None
-p.xaxis.ticker = sorted(np.unique(num_participants))
-p.yaxis.axis_label = "PR lifetime (hours)"
-p.scatter(x=num_participants, y=lifetimes.astype(int), size=9, alpha=0.4)
-show(p)
-```
diff --git a/devstats/reports/pull_requests/pony_factor.md b/devstats/reports/pull_requests/pony_factor.md
deleted file mode 100644
index a5d50a9..0000000
--- a/devstats/reports/pull_requests/pony_factor.md
+++ /dev/null
@@ -1,58 +0,0 @@
-### Pony factor
-
-Another way to look at these data is in terms of the
-[pony factor](https://ke4qqq.wordpress.com/2015/02/08/pony-factor-math/),
-described as:
-
-> The minimum number of contributors whose total contribution constitutes a
-> majority of the contributions.
-
-For this analysis, we will consider merged PRs as the metric for contribution.
-Considering all merged PRs over the lifetime of the project, the pony factor
-is: {glue:text}`{{ project }}_pony_factor`.
-
-% TODO: pandas-ify to improve sorting
-
-```{code-cell} ipython3
----
-tags: [hide-input]
----
-# Sort by number of merged PRs in descending order
-num_merged_prs_per_author.sort()
-num_merged_prs_per_author = num_merged_prs_per_author[::-1]
-
-num_merged_prs = num_merged_prs_per_author.sum()
-pf_thresh = 0.5
-pony_factor = np.searchsorted(
-    np.cumsum(num_merged_prs_per_author), num_merged_prs * pf_thresh
-) + 1
-
-fig, ax = plt.subplots()
-ax.plot(
-    np.arange(len(num_merged_prs_per_author)) + 1,
-    np.cumsum(num_merged_prs_per_author),
-    "."
-)
-ax.set_title(f"How the pony factor is calculated")
-ax.set_xlabel("# unique contributors")
-ax.set_xscale("log")
-ax.set_ylabel("Cumulative sum of merged PRs / contributor")
-ax.hlines(
-    xmin=0,
-    xmax=len(contributions_by_author),
-    y=num_merged_prs * pf_thresh,
-    color="tab:green",
-    label=f"Pony factor threshold = {100 * pf_thresh:1.0f}%",
-)
-ax.legend();
-
-glue("{{ project }}_pony_factor", pony_factor)
-```
-
-% TODO: Add:
-% - Augmented pony factor (only consider contributors active in a time window)
-% - pony factor over time, e.g yearly bins
-
-[^master_to_main]: i.e. `master` or `main`.
-
-[^only_active]: This only includes PRs from users with an active GitHub account.
diff --git a/devstats/reports/pull_requests/pr_lifetime.md b/devstats/reports/pull_requests/pr_lifetime.md
deleted file mode 100644
index b1032d6..0000000
--- a/devstats/reports/pull_requests/pr_lifetime.md
+++ /dev/null
@@ -1,71 +0,0 @@
-### PR lifetime
-
-The following plot shows the "survival" of PRs over time.
-That means, the plot shows how many PRs are open for at least these many days.
-This is separated into PRs that are merged and those that are still open
-(closed but unmerged PRs are not included currently).
-
-```{code-cell} ipython3
----
-tags: [hide-input]
----
-
-merged_prs = [pr for pr in prs if pr['state'] == 'MERGED']
-lifetimes_merged = np.array(
-    [isoparse(pr["mergedAt"]) - isoparse(pr["createdAt"]) for pr in merged_prs],
-    dtype="m8[m]").view("int64") / (60 * 24)  # days
-lifetimes_merged.sort()
-
-#closed_prs = [pr for pr in prs if pr['state'] == 'CLOSED']
-#lifetimes_closed = np.array(
-#    [isoparse(pr["mergedAt"]) - isoparse(pr["createdAt"]) for pr in closed_prs],
-#    dtype="m8[m]").view("int64") / (60 * 24)  # days
-#lifetimes_closed.sort()
-
-
-# Use the newest issue to guess a time when the data was generated.
-# Can this logic be improved?
-current_time = isoparse(max(iss["createdAt"] for iss in issues))
-
-open_prs = [pr for pr in prs if pr['state'] == 'OPEN']
-age_open = np.array(
-    [current_time - isoparse(pr["createdAt"]) for pr in open_prs],
-    dtype="m8[m]").view("int64") / (60 * 24)  # days
-age_open.sort()
-
-fig, ax = plt.subplots(figsize=(6, 4))
-number_merged = np.arange(1, len(lifetimes_merged)+1)[::-1]
-ax.step(lifetimes_merged, number_merged, label="Merged")
-
-#ax.step(lifetimes_closed, np.arange(1, len(lifetimes_closed)+1)[::-1])
-
-number_open = np.arange(1, len(age_open)+1)[::-1]
-ax.step(age_open, number_open, label="Open")
-
-# Find the first point where closed have a bigger survival than open PRs:
-all_lifetimes = np.concatenate([lifetimes_merged, age_open])
-all_lifetimes.sort()
-
-number_merged_all_t = np.interp(all_lifetimes, lifetimes_merged, number_merged)
-number_open_all_t = np.interp(all_lifetimes, age_open, number_open)
-
-first_idx = np.argmax(number_merged_all_t < number_open_all_t)
-first_time = all_lifetimes[first_idx]
-
-ax.vlines(
-        [first_time], 0, 1, transform=ax.get_xaxis_transform(), colors='k',
-        zorder=0, linestyle="--")
-
-ax.annotate(
-    f"{round(first_time)} days",
-    xy=(first_time, number_open_all_t[first_idx]),
-    xytext=(5, 5), textcoords="offset points",
-    va="bottom", ha="left")
-
-ax.legend()
-ax.set_xlabel("Time until merged or time open [days]")
-ax.set_ylabel(r"# of PRs open this long or longer")
-ax.set_xscale("log")
-fig.autofmt_xdate()
-fig.tight_layout();
-```
diff --git a/devstats/reports/pull_requests/where_contributions_come_from.md b/devstats/reports/pull_requests/where_contributions_come_from.md
deleted file mode 100644
index bb6d06e..0000000
--- a/devstats/reports/pull_requests/where_contributions_come_from.md
+++ /dev/null
@@ -1,53 +0,0 @@
-### Where contributions come from
-
-There have been a total of {glue:text}`{{ project }}_num_merged_prs_with_known_authors`
-merged PRs[^only_active] submitted by {glue:text}`{{ project }}_num_unique_authors_of_merged_prs`
-unique authors. {glue:text}`{{ project }}_num_flyby` of these are "fly-by" PRs, i.e.
-PRs from users who have contributed to the project once (to-date).
-
-```{code-cell} ipython3
----
-tags: [hide-input]
----
-
-# Remap PRs by author
-contributions_by_author = defaultdict(list)
-for pr in merged_prs:
-    author = pr["author"]["login"]
-    contributions_by_author[author].append(pr)
-
-num_merged_prs_per_author = np.array(
-    [len(prs) for prs in contributions_by_author.values()]
-)
-
-num_flybys = np.sum(num_merged_prs_per_author == 1)
-
-glue("{{ project }}_num_merged_prs_with_known_authors", len(merged_prs))
-glue("{{ project }}_num_unique_authors_of_merged_prs", len(contributions_by_author))
-glue("{{ project }}_num_flyby", percent_val(num_flybys, len(num_merged_prs_per_author)))
-```
-
-```{code-cell} ipython3
----
-tags: [hide-input]
----
-
-title = "Distribution of number of merged PRs per contributor"
-
-x = ["1", "2", "3", "4", "5", "6 - 10", "10 - 20", "20 - 50", "> 50"]
-bedges = np.array([0, 1, 2, 3, 4, 5, 10, 20, 50, sum(num_merged_prs_per_author)]) + 0.5
-y, _ = np.histogram(num_merged_prs_per_author, bins=bedges)
-
-p = figure(
-    x_range=x,
-    y_range=(0, 1.05 * y.max()),
-    width=670,
-    height=400,
-    title=title,
-    tooltips=[(r"# PRs merged", "@x"), ("# contributors", f"@top")],
-)
-p.vbar(x=x, top=y, width=0.8)
-p.xaxis.axis_label = "# Merged PRs per user"
-p.yaxis.axis_label = "# of unique contributors with N PRs merged"
-show(p)
-```