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<div id="anonymising-the-data" class="section level1 tab-content">
<h1>Anonymising the data</h1>
<div id="load-data" class="section level2">
<h2>Load data</h2>
<pre class="r"><code>library(knitr)
opts_chunk$set(fig.width = 12, fig.height = 12, cache = F, warning = F, message = F)</code></pre>
<pre class="r"><code>source("0_helpers.R")
load("full_data.rdata")</code></pre>
</div>
<div id="set-some-defaults" class="section level2">
<h2>set some defaults</h2>
<pre class="r"><code>diary = diary %>%
mutate(
included = included_all,
fertile = if_else(is.na(prc_stirn_b_squished), prc_stirn_b_backward_inferred, prc_stirn_b_squished),
contraceptive_methods = factor(contraceptive_method, levels =
c("barrier_or_abstinence", "fertility_awareness", "none", "hormonal")),
relationship_status_clean = factor(relationship_status_clean),
cohabitation = factor(cohabitation),
certainty_menstruation = as.numeric(as.character(certainty_menstruation))
) %>% group_by(person) %>%
mutate(
fertile_mean = mean(fertile, na.rm = T)
)</code></pre>
</div>
<div id="cleaning" class="section level2">
<h2>cleaning</h2>
<p>These fields were stored as text, but can be simplified.</p>
<pre class="r"><code>diary = diary %>% mutate(
want_more_info = as.integer(want_more_info),
trying_to_get_pregnant = if_else(trying_to_get_pregnant == 'yes', 1, 0),
had_sex_with_partner_yet = if_else(had_sex_with_partner_yet == 'yes', 1, 0),
breast_feeding_in_last_3_months = if_else(breast_feeding_in_last_3_months == 'yes', 1, 0),
hormonal_medication_in_last_3_months = if_else(hormonal_medication_in_last_3_months == 'yes', 1, 0),
pill_in_last_3_months = if_else(pill_in_last_3_months == 'yes', 1, 0)
)</code></pre>
</div>
<div id="remove-free-text-fields" class="section level2">
<h2>remove free-text fields</h2>
<p>Free text fields often have unique values and can contain things like IDs, addresses, free-text responses to questions, etc.</p>
<pre class="r"><code># first I look only at character columns (they tend to have most unique values)
diary %>% select_if(is.character) %>%
gather(variable, value) %>% # putting this in long format allows me to easily
group_by(variable) %>%
summarise(n = n_nonmissing(value), n_dist = n_distinct(value)) %>% # summarise frequencies of values
pander() # and display them</code></pre>
<table style="width:67%;">
<colgroup>
<col width="47%" />
<col width="8%" />
<col width="11%" />
</colgroup>
<thead>
<tr class="header">
<th align="center">variable</th>
<th align="center">n</th>
<th align="center">n_dist</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center">change_contraception_2</td>
<td align="center">1057</td>
<td align="center">25</td>
</tr>
<tr class="even">
<td align="center">children</td>
<td align="center">30956</td>
<td align="center">10</td>
</tr>
<tr class="odd">
<td align="center">children_broad_categories</td>
<td align="center">30956</td>
<td align="center">2</td>
</tr>
<tr class="even">
<td align="center">children_narrow_categories</td>
<td align="center">30956</td>
<td align="center">5</td>
</tr>
<tr class="odd">
<td align="center">contraception</td>
<td align="center">30952</td>
<td align="center">73</td>
</tr>
<tr class="even">
<td align="center">contraceptive_method</td>
<td align="center">30956</td>
<td align="center">5</td>
</tr>
<tr class="odd">
<td align="center">contraceptive_method_by_pearl</td>
<td align="center">30956</td>
<td align="center">5</td>
</tr>
<tr class="even">
<td align="center">contraceptives_broad_categories</td>
<td align="center">30952</td>
<td align="center">11</td>
</tr>
<tr class="odd">
<td align="center">contraceptives_categories_natural</td>
<td align="center">29911</td>
<td align="center">6</td>
</tr>
<tr class="even">
<td align="center">feedback_for_us</td>
<td align="center">6114</td>
<td align="center">176</td>
</tr>
<tr class="odd">
<td align="center">gestagen_type</td>
<td align="center">16178</td>
<td align="center">12</td>
</tr>
<tr class="even">
<td align="center">hormonal_2</td>
<td align="center">1716</td>
<td align="center">9</td>
</tr>
<tr class="odd">
<td align="center">hypothesis_guessed</td>
<td align="center">30956</td>
<td align="center">6</td>
</tr>
<tr class="even">
<td align="center">illness_2</td>
<td align="center">9284</td>
<td align="center">167</td>
</tr>
<tr class="odd">
<td align="center">income_partner</td>
<td align="center">30956</td>
<td align="center">5</td>
</tr>
<tr class="even">
<td align="center">living_situation</td>
<td align="center">30956</td>
<td align="center">4</td>
</tr>
<tr class="odd">
<td align="center">meaning_study</td>
<td align="center">14437</td>
<td align="center">414</td>
</tr>
<tr class="even">
<td align="center">medicament_2</td>
<td align="center">11349</td>
<td align="center">278</td>
</tr>
<tr class="odd">
<td align="center">method_meeting</td>
<td align="center">16257</td>
<td align="center">10</td>
</tr>
<tr class="even">
<td align="center">method_meeting_clean</td>
<td align="center">16222</td>
<td align="center">7</td>
</tr>
<tr class="odd">
<td align="center">name_hormonal</td>
<td align="center">195</td>
<td align="center">7</td>
</tr>
<tr class="even">
<td align="center">occupation</td>
<td align="center">30956</td>
<td align="center">14</td>
</tr>
<tr class="odd">
<td align="center">occupation_clean</td>
<td align="center">30850</td>
<td align="center">7</td>
</tr>
<tr class="even">
<td align="center">other_pill_name</td>
<td align="center">1531</td>
<td align="center">43</td>
</tr>
<tr class="odd">
<td align="center">pills</td>
<td align="center">16212</td>
<td align="center">72</td>
</tr>
<tr class="even">
<td align="center">postal_code</td>
<td align="center">30956</td>
<td align="center">403</td>
</tr>
<tr class="odd">
<td align="center">pregnant_in_last_3_months</td>
<td align="center">30956</td>
<td align="center">2</td>
</tr>
<tr class="even">
<td align="center">relationship_status</td>
<td align="center">30956</td>
<td align="center">15</td>
</tr>
<tr class="odd">
<td align="center">religion</td>
<td align="center">30956</td>
<td align="center">18</td>
</tr>
<tr class="even">
<td align="center">religion_clean</td>
<td align="center">30881</td>
<td align="center">6</td>
</tr>
<tr class="odd">
<td align="center">sess_day</td>
<td align="center">30924</td>
<td align="center">30925</td>
</tr>
<tr class="even">
<td align="center">session</td>
<td align="center">30956</td>
<td align="center">1208</td>
</tr>
<tr class="odd">
<td align="center">session_id</td>
<td align="center">30924</td>
<td align="center">30925</td>
</tr>
<tr class="even">
<td align="center">session_id.abschluss</td>
<td align="center">26913</td>
<td align="center">785</td>
</tr>
<tr class="odd">
<td align="center">session_id.vorab</td>
<td align="center">30956</td>
<td align="center">1208</td>
</tr>
<tr class="even">
<td align="center">sex_orientation</td>
<td align="center">30956</td>
<td align="center">5</td>
</tr>
<tr class="odd">
<td align="center">short</td>
<td align="center">30956</td>
<td align="center">1208</td>
</tr>
<tr class="even">
<td align="center">short_session</td>
<td align="center">30924</td>
<td align="center">1177</td>
</tr>
<tr class="odd">
<td align="center">special_days</td>
<td align="center">16985</td>
<td align="center">403</td>
</tr>
<tr class="even">
<td align="center">special_events</td>
<td align="center">2836</td>
<td align="center">2010</td>
</tr>
</tbody>
</table>
<pre class="r"><code># of course manual inspection (for me, using View() ) is important too
# same thing for factors shows no rare unique values
diary %>% select_if(is.factor) %>%
gather(variable, value) %>%
group_by(variable) %>%
summarise(n = n_nonmissing(value), n_dist = n_distinct(value)) %>%
arrange(n_dist) %>%
pander()</code></pre>
<table style="width:65%;">
<colgroup>
<col width="45%" />
<col width="8%" />
<col width="11%" />
</colgroup>
<thead>
<tr class="header">
<th align="center">variable</th>
<th align="center">n</th>
<th align="center">n_dist</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center">weekend</td>
<td align="center">30956</td>
<td align="center">2</td>
</tr>
<tr class="even">
<td align="center">cohabitation</td>
<td align="center">30956</td>
<td align="center">3</td>
</tr>
<tr class="odd">
<td align="center">cycle_regularity</td>
<td align="center">30956</td>
<td align="center">3</td>
</tr>
<tr class="even">
<td align="center">days_with_partner</td>
<td align="center">30956</td>
<td align="center">3</td>
</tr>
<tr class="odd">
<td align="center">fertile_window</td>
<td align="center">11682</td>
<td align="center">3</td>
</tr>
<tr class="even">
<td align="center">fertile_window_backward_inferred</td>
<td align="center">17013</td>
<td align="center">3</td>
</tr>
<tr class="odd">
<td align="center">fertile_window_forward_counted</td>
<td align="center">16201</td>
<td align="center">3</td>
</tr>
<tr class="even">
<td align="center">fertile_window_squished</td>
<td align="center">11877</td>
<td align="center">3</td>
</tr>
<tr class="odd">
<td align="center">included</td>
<td align="center">28493</td>
<td align="center">3</td>
</tr>
<tr class="even">
<td align="center">included_all</td>
<td align="center">28493</td>
<td align="center">3</td>
</tr>
<tr class="odd">
<td align="center">included_conservative</td>
<td align="center">14147</td>
<td align="center">3</td>
</tr>
<tr class="even">
<td align="center">included_lax</td>
<td align="center">17960</td>
<td align="center">3</td>
</tr>
<tr class="odd">
<td align="center">included_strict</td>
<td align="center">7682</td>
<td align="center">3</td>
</tr>
<tr class="even">
<td align="center">menstruation</td>
<td align="center">30956</td>
<td align="center">3</td>
</tr>
<tr class="odd">
<td align="center">nights_with_partner</td>
<td align="center">30956</td>
<td align="center">3</td>
</tr>
<tr class="even">
<td align="center">relationship_status_clean</td>
<td align="center">30956</td>
<td align="center">3</td>
</tr>
<tr class="odd">
<td align="center">cycle_length_groups</td>
<td align="center">30956</td>
<td align="center">4</td>
</tr>
<tr class="even">
<td align="center">estrogen_categories</td>
<td align="center">26785</td>
<td align="center">4</td>
</tr>
<tr class="odd">
<td align="center">menstruation_strength</td>
<td align="center">30956</td>
<td align="center">4</td>
</tr>
<tr class="even">
<td align="center">contraceptive_methods</td>
<td align="center">30357</td>
<td align="center">5</td>
</tr>
<tr class="odd">
<td align="center">included_levels</td>
<td align="center">28493</td>
<td align="center">5</td>
</tr>
<tr class="even">
<td align="center">age_group</td>
<td align="center">30956</td>
<td align="center">6</td>
</tr>
<tr class="odd">
<td align="center">days_with_partner_per_month</td>
<td align="center">9538</td>
<td align="center">6</td>
</tr>
<tr class="even">
<td align="center">income</td>
<td align="center">13229</td>
<td align="center">6</td>
</tr>
<tr class="odd">
<td align="center">week_number</td>
<td align="center">30924</td>
<td align="center">7</td>
</tr>
<tr class="even">
<td align="center">distance_to_partner_hours</td>
<td align="center">9538</td>
<td align="center">8</td>
</tr>
<tr class="odd">
<td align="center">weekday</td>
<td align="center">30924</td>
<td align="center">8</td>
</tr>
<tr class="even">
<td align="center">gestagens</td>
<td align="center">1497</td>
<td align="center">10</td>
</tr>
</tbody>
</table>
<pre class="r"><code># diary %>% select_if(is.character) %>% names() %>% cat(sep=", -")
# here I remove all columns that have text fields which might betray someone's identity
diary = diary %>% select(-session, -session_id, -session_id.vorab, -short, -sess_day, -session_id.abschluss, -short_session, -vpn, -session_id.nachbe_other_hormonal,
-pills,-other_pill_name, -religion, -relationship_status, -postal_code, -children, -method_meeting, -meaning_study, -medicament_2, -special_days, -illness_2, -change_contraception_2, -feedback_for_us, -name_hormonal, -hypothesis_guessed, -children_narrow_categories, -special_events)</code></pre>
</div>
<div id="dates-and-times" class="section level2">
<h2>Dates and times</h2>
<pre class="r"><code>diary %>% select_if(is.instant) %>%
gather(variable, value) %>%
group_by(variable) %>%
summarise(n = n_nonmissing(value), n_dist = n_distinct(value)) %>%
arrange(n_dist) %>%
pander()</code></pre>
<table style="width:62%;">
<colgroup>
<col width="43%" />
<col width="8%" />
<col width="11%" />
</colgroup>
<thead>
<tr class="header">
<th align="center">variable</th>
<th align="center">n</th>
<th align="center">n_dist</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center">ended.nachbe_other_hormonal</td>
<td align="center">1716</td>
<td align="center">52</td>
</tr>
<tr class="even">
<td align="center">created.nachbe_other_hormonal</td>
<td align="center">1804</td>
<td align="center">55</td>
</tr>
<tr class="odd">
<td align="center">modified.nachbe_other_hormonal</td>
<td align="center">1804</td>
<td align="center">55</td>
</tr>
<tr class="even">
<td align="center">first_day</td>
<td align="center">30924</td>
<td align="center">209</td>
</tr>
<tr class="odd">
<td align="center">first_diary_date</td>
<td align="center">30924</td>
<td align="center">209</td>
</tr>
<tr class="even">
<td align="center">cyklus_1</td>
<td align="center">30956</td>
<td align="center">306</td>
</tr>
<tr class="odd">
<td align="center">last_menstruation</td>
<td align="center">27362</td>
<td align="center">308</td>
</tr>
<tr class="even">
<td align="center">next_menstrual_onset</td>
<td align="center">18614</td>
<td align="center">330</td>
</tr>
<tr class="odd">
<td align="center">menstrual_onset_date_inferred</td>
<td align="center">4068</td>
<td align="center">363</td>
</tr>
<tr class="even">
<td align="center">last_diary_date</td>
<td align="center">30924</td>
<td align="center">371</td>
</tr>
<tr class="odd">
<td align="center">menstruation_dates</td>
<td align="center">4805</td>
<td align="center">437</td>
</tr>
<tr class="even">
<td align="center">next_menstrual_onset_inferred</td>
<td align="center">30924</td>
<td align="center">450</td>
</tr>
<tr class="odd">
<td align="center">last_menstrual_onset</td>
<td align="center">30924</td>
<td align="center">453</td>
</tr>
<tr class="even">
<td align="center">created_date</td>
<td align="center">30924</td>
<td align="center">532</td>
</tr>
<tr class="odd">
<td align="center">ended.abschluss</td>
<td align="center">26432</td>
<td align="center">770</td>
</tr>
<tr class="even">
<td align="center">created.abschluss</td>
<td align="center">26913</td>
<td align="center">785</td>
</tr>
<tr class="odd">
<td align="center">modified.abschluss</td>
<td align="center">26913</td>
<td align="center">785</td>
</tr>
<tr class="even">
<td align="center">created.vorab</td>
<td align="center">30956</td>
<td align="center">1206</td>
</tr>
<tr class="odd">
<td align="center">modified.vorab</td>
<td align="center">30956</td>
<td align="center">1206</td>
</tr>
<tr class="even">
<td align="center">ended.vorab</td>
<td align="center">30956</td>
<td align="center">1207</td>
</tr>
<tr class="odd">
<td align="center">ended</td>
<td align="center">30924</td>
<td align="center">30582</td>
</tr>
<tr class="even">
<td align="center">modified</td>
<td align="center">30924</td>
<td align="center">30795</td>
</tr>
<tr class="odd">
<td align="center">created</td>
<td align="center">30924</td>
<td align="center">30797</td>
</tr>
</tbody>
</table>
<pre class="r"><code>diary = diary %>%
# remove all datetime we don't need
select(-starts_with("created")) %>%
select(-starts_with("modified")) %>%
select(-starts_with("expired")) %>%
select(-first_day, -first_diary_date, -cyklus_1, -last_menstruation, -next_menstrual_onset, -menstrual_onset_date_inferred, -last_diary_date, -menstruation_dates, -next_menstrual_onset_inferred, -last_menstrual_onset) %>%
mutate( # we do want to know whether they finished a survey, but can't know when
ended = !is.na(ended),
ended.vorab = !is.na(ended.vorab),
ended.abschluss = !is.na(ended.abschluss),
ended.nachbe_other_hormonal = !is.na(ended.nachbe_other_hormonal)
)</code></pre>
<p>a similar problem exists for intervals, but we might need these and I can easily round them. Here I unfortunately didn’t store them as type interval, so I have to look through all numeric types</p>
<pre class="r"><code># diary %>% select_if(is.interval) %>%
# gather(variable, value) %>%
# group_by(variable) %>%
# summarise(n = n_nonmissing(value), n_dist = n_distinct(value)) %>%
# arrange(n_dist) %>%
# pander()</code></pre>
<p>The <code>Hmisc::cut2</code> function allows me to group variables into <code>g</code> groups and try to make no group contain fewer than <code>m</code> values.</p>
<pre class="r"><code>diary = diary %>%
mutate(
time_for_response = Hmisc::cut2(time_for_response, g = 10, m = 30),
time_of_response = Hmisc::cut2(time_of_response, g = 10, m = 30),
time_since_last_response = Hmisc::cut2(time_since_last_response, cuts = 10, m = 30),
biggest_diff = Hmisc::cut2(biggest_diff, g = 10, m = 30),
avg_diff = Hmisc::cut2(avg_diff, g = 10, m = 300),
days_responded_percentage = Hmisc::cut2(days_responded_percentage, g = 10, m = 300)
)</code></pre>
</div>
<div id="finding-rare-values" class="section level2">
<h2>finding rare values</h2>
<p>Although rare values won’t allow anyone to identify many participants, it is important to protect privacy for all participants. In our case especially, a potential attacker might know that e.g. his girlfriend uses a pill with a rare gestagen and identify her like this.</p>
<p>To this end, I wrote the function below. It gives (depending on the last argument) the rarest value and how frequent it is. I included the group variable, because a lot of demographic data is duplicated in my diary data.</p>
<pre class="r"><code>commonness_rarest_value = function(x, group, give_value = FALSE) {
df = data.frame(x = x, group = group) %>% unique()
counts = table(df$x)
ret = counts[which.min(counts)]
if (give_value) names(ret) else ret
}
diary %>% gather(variable, value, -person) %>%
group_by(variable) %>%
summarise(n = n_nonmissing(value), n_dist = n_distinct(value), lc_val = commonness_rarest_value(value, person, T), lc_freq = commonness_rarest_value(value, person, F)) %>%
arrange(desc(lc_freq/(n_dist+lc_freq))) %>%
pander()</code></pre>
<table>
<colgroup>
<col width="40%" />
<col width="6%" />
<col width="9%" />
<col width="33%" />
<col width="9%" />
</colgroup>
<thead>
<tr class="header">
<th align="center">variable</th>
<th align="center">n</th>
<th align="center">n_dist</th>
<th align="center">lc_val</th>
<th align="center">lc_freq</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="center">ended.vorab</td>
<td align="center">30956</td>
<td align="center">1</td>
<td align="center">TRUE</td>
<td align="center">1208</td>
</tr>
<tr class="even">
<td align="center">gender</td>
<td align="center">30956</td>
<td align="center">1</td>
<td align="center">1</td>
<td align="center">1208</td>
</tr>
<tr class="odd">
<td align="center">hetero_relationship</td>
<td align="center">30956</td>
<td align="center">1</td>
<td align="center">1</td>
<td align="center">1208</td>
</tr>
<tr class="even">
<td align="center">weekend</td>
<td align="center">30956</td>
<td align="center">2</td>
<td align="center">TRUE</td>
<td align="center">1122</td>
</tr>
<tr class="odd">
<td align="center">time_since_last_response</td>
<td align="center">23275</td>
<td align="center">2</td>
<td align="center">[10,31]</td>
<td align="center">1097</td>
</tr>
<tr class="even">
<td align="center">menstruated_at_all</td>
<td align="center">30956</td>
<td align="center">2</td>
<td align="center">TRUE</td>
<td align="center">910</td>
</tr>
<tr class="odd">
<td align="center">in_pair_public_intimacy</td>
<td align="center">29902</td>
<td align="center">3</td>
<td align="center">0</td>
<td align="center">1081</td>
</tr>
<tr class="even">
<td align="center">mate_retention_1</td>
<td align="center">29902</td>
<td align="center">3</td>
<td align="center">2</td>
<td align="center">960</td>
</tr>
<tr class="odd">
<td align="center">spent_night_with_partner</td>
<td align="center">29902</td>
<td align="center">3</td>
<td align="center">0</td>
<td align="center">960</td>
</tr>
<tr class="even">
<td align="center">fertile_broad_backward_inferred</td>
<td align="center">17013</td>
<td align="center">3</td>
<td align="center">0.0533333333333333</td>
<td align="center">959</td>
</tr>
<tr class="odd">
<td align="center">had_sexual_intercourse</td>
<td align="center">29902</td>
<td align="center">3</td>
<td align="center">1</td>
<td align="center">945</td>
</tr>
<tr class="even">
<td align="center">premenstrual_phase_backward_inferred</td>
<td align="center">27143</td>
<td align="center">3</td>
<td align="center">TRUE</td>
<td align="center">929</td>
</tr>
<tr class="odd">
<td align="center">premenstrual_phase_fab</td>
<td align="center">27143</td>
<td align="center">3</td>
<td align="center">TRUE</td>
<td align="center">929</td>
</tr>
<tr class="even">
<td align="center">fertile_broad_forward_counted</td>
<td align="center">16201</td>
<td align="center">3</td>
<td align="center">0.0533333333333333</td>
<td align="center">928</td>
</tr>
<tr class="odd">
<td align="center">fertile_window_forward_counted</td>
<td align="center">16201</td>
<td align="center">3</td>
<td align="center">broad</td>
<td align="center">919</td>
</tr>
<tr class="even">
<td align="center">fertile_window_backward_inferred</td>
<td align="center">17013</td>
<td align="center">3</td>
<td align="center">broad</td>
<td align="center">915</td>
</tr>
<tr class="odd">
<td align="center">menstruation_1</td>
<td align="center">29901</td>
<td align="center">3</td>
<td align="center">1</td>
<td align="center">910</td>
</tr>
<tr class="even">
<td align="center">fertile_narrow_forward_counted</td>
<td align="center">12407</td>
<td align="center">3</td>
<td align="center">0.51</td>
<td align="center">908</td>
</tr>
<tr class="odd">
<td align="center">fertile_narrow_backward_inferred</td>
<td align="center">13345</td>
<td align="center">3</td>
<td align="center">0.51</td>
<td align="center">905</td>
</tr>