1 VERITAS dataset description

Unlike the Eligibility or Health questionnaires, which can mostly be encoded as a flat table, the VERITAS questionnaire implicitly records a series of entities and their relationships:

  • Places: list of geocoded locations visited by participants, along with the following characteristics: category, name, visit frequency, transportation mode
  • Social contacts: people and/or groups frequented by participants
  • Relationships: between social contacts (who knows who / who belongs to which group) as well as between locations and social contacts (places visited along with whom)

The diagram below illustrates the various entities collected throught the VERITAS questionnaire:

VERITAS entities
VERITAS entities

2 Basic descriptive statistics

2.1 Section 1: Residence and Neighbourhood

2.1.1 Now, let’s start with your home. What is your address?

home_location <- locations[locations$location_category == 1, ]

## version ggmap
vic_aoi <- st_bbox(home_location)
names(vic_aoi) <- c("left", "bottom", "right", "top")
vic_aoi[["left"]] <- vic_aoi[["left"]] - .07
vic_aoi[["right"]] <- vic_aoi[["right"]] + .07
vic_aoi[["top"]] <- vic_aoi[["top"]] + .01
vic_aoi[["bottom"]] <- vic_aoi[["bottom"]] - .01

bm <- get_stadiamap(vic_aoi, zoom = 11, maptype = "stamen_toner_lite") %>%
  ggmap(extent = "device")
bm + geom_sf(data = st_jitter(home_location, .008), inherit.aes = FALSE, color = "blue", size = 1.8, alpha = .3) # see https://github.com/r-spatial/sf/issues/336

NB: Home locations have been randomly shifted from their original position to protect privacy.

# Number of participants by municipalites
home_by_municipalites <- st_join(home_location, municipalities["NAME"])
home_by_mun_cnt <- home_by_municipalites %>%
  group_by(NAME) %>%
  dplyr::count() %>%
  arrange(desc(n), NAME)
home_by_mun_cnt$geom <- NULL
kable(home_by_mun_cnt, caption = "Number of participants by municipalities") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Number of participants by municipalities
NAME n
Victoria 92
Saanich 54
Esquimalt 9
Oak Bay 6
Langford 3
View Royal 2
Colwood 1

2.1.2 When did you move to your current address?

# N of addresses by date of move
year_of_move <- veritas_main[c("interact_id", "home_move_date")]
year_of_move$home_move_date <- year(ymd(year_of_move$home_move_date))
ggplot(data = year_of_move) +
  geom_histogram(aes(x = home_move_date))

# recode date of move
year_of_move$home_move_date_recode <- as.character(year_of_move$home_move_date)
year_of_move$home_move_date_recode[year_of_move$home_move_date <= 2005] <- "2005 - 2001"
year_of_move$home_move_date_recode[year_of_move$home_move_date <= 2000] <- "2000 - 1991"
year_of_move$home_move_date_recode[year_of_move$home_move_date <= 1990] <- paste("1990 -", min(year_of_move$home_move_date))

year_of_move_cnt <- year_of_move %>%
  group_by(home_move_date_recode) %>%
  dplyr::count() %>%
  arrange(desc(home_move_date_recode))
kable(year_of_move_cnt, caption = "Year of move to current address") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Year of move to current address
home_move_date_recode n
2017 31
2016 30
2015 19
2014 4
2013 8
2012 6
2011 8
2010 8
2009 6
2008 5
2007 5
2006 5
2005 - 2001 14
2000 - 1991 9
1990 - 1980 9

2.1.3 If you were asked to draw the boundaries of your neighbourhood, what would they be?

prn <- poly_geom[poly_geom$area_type == "neighborhood", ] %>%
  st_make_valid()

## version ggmap
bm + geom_sf(data = prn, inherit.aes = FALSE, fill = alpha("blue", 0.05), color = alpha("blue", 0.3))

# Min, max, median & mean area of PRN
prn$area_m2 <- st_area(prn$geom)
kable(t(as.matrix(summary(prn$area_m2))),
  caption = "Area (in square meters) of the perceived residential neighborhood",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Area (in square meters) of the perceived residential neighborhood
Min. 1st Qu. Median Mean 3rd Qu. Max.
20506.3 648252.3 1238329 2275925 2322473 31896607

NB only 154 valid neighborhoods were collected, as many participants struggled to draw polygons on the map.

2.1.4 How attached are you to your neighbourhood?

# extract and recode
.ngh_att <- veritas_main[veritas_main$neighbourhood_attach != 99, c("interact_id", "neighbourhood_attach")] %>% dplyr::rename(neighbourhood_attach_code = neighbourhood_attach)
.ngh_att$neighbourhood_attach <- factor(ifelse(.ngh_att$neighbourhood_attach_code == 1, "1 [Not attached at all]",
  ifelse(.ngh_att$neighbourhood_attach_code == 6, "6 [Very attached]",
    .ngh_att$neighbourhood_attach_code
  )
))

# histogram of attachment
ggplot(data = .ngh_att) +
  geom_histogram(aes(x = neighbourhood_attach), stat = "count") +
  scale_x_discrete(labels = function(lbl) str_wrap(lbl, width = 20)) +
  labs(x = "neighbourhood_attach")

.ngh_att_cnt <- .ngh_att %>%
  group_by(neighbourhood_attach) %>%
  dplyr::count() %>%
  arrange(neighbourhood_attach)
kable(.ngh_att_cnt, caption = "Neigbourhood attachment") %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Neigbourhood attachment
neighbourhood_attach n
1 [Not attached at all] 7
2 8
3 18
4 41
5 56
6 [Very attached] 36

2.1.5 On average, how many hours per day do you spend outside of your home?

# histogram of n hours out
ggplot(data = veritas_main) +
  geom_histogram(aes(x = hours_out))

# Min, max, median & mean hours/day out
kable(t(as.matrix(summary(veritas_main$hours_out))),
  caption = "Hours/day outside home",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Hours/day outside home
Min. 1st Qu. Median Mean 3rd Qu. Max.
1 8 10 8.8 10 24

2.1.6 Of this time spent outside your home, on average how many hours do you spend outside your neighbourhood?

# histogram of n hours out
ggplot(data = veritas_main) +
  geom_histogram(aes(x = hours_out_neighb))

# Min, max, median & mean hours/day out of neighborhood
kable(t(as.matrix(summary(veritas_main$hours_out_neighb))),
  caption = "Hours/day outside neighbourhood",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Hours/day outside neighbourhood
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 5 8 7 9 20

2.1.7 Are there one or more areas close to where you live that you tend to avoid because you do not feel safe there (for any reason)?

# extract and recode
.unsafe <- veritas_main[c("interact_id", "unsafe_area")] %>% dplyr::rename(unsafe_area_code = unsafe_area)
.unsafe$unsafe_area <- factor(ifelse(.unsafe$unsafe_area_code == 1, "1 [Yes]",
  ifelse(.unsafe$unsafe_area_code == 2, "2 [No]", "N/A")
))

# histogram of answers
ggplot(data = .unsafe) +
  geom_histogram(aes(x = unsafe_area), stat = "count") +
  scale_x_discrete(labels = function(lbl) str_wrap(lbl, width = 20)) +
  labs(x = "unsafe_area")

.unsafe_cnt <- .unsafe %>%
  group_by(unsafe_area) %>%
  dplyr::count() %>%
  arrange(unsafe_area)
kable(.unsafe_cnt, caption = "unsafe_area") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
unsafe_area
unsafe_area n
1 [Yes] 21
2 [No] 146
# map
unsafe <- poly_geom[poly_geom$area_type == "unsafe area", ]

## version ggmap
bm + geom_sf(data = unsafe, inherit.aes = FALSE, fill = alpha("blue", 0.3), color = alpha("blue", 0.5))

# Min, max, median & mean area of PRN
unsafe$area_m2 <- st_area(unsafe$geom)
kable(t(as.matrix(summary(unsafe$area_m2))),
  caption = "Area (in square meters) of the perceived unsafe area",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Area (in square meters) of the perceived unsafe area
Min. 1st Qu. Median Mean 3rd Qu. Max.
803.7 26305.5 80057.5 219865.3 292833.6 1099054

2.1.8 Do you spend the night somewhere other than your home at least once per week?

# extract and recode
.o_res <- veritas_main[c("interact_id", "other_resid")] %>% dplyr::rename(other_resid_code = other_resid)
.o_res$other_resid <- factor(ifelse(.o_res$other_resid_code == 1, "1 [Yes]",
  ifelse(.o_res$other_resid_code == 2, "2 [No]", "N/A")
))

# histogram of answers
ggplot(data = .o_res) +
  geom_histogram(aes(x = other_resid), stat = "count") +
  scale_x_discrete(labels = function(lbl) str_wrap(lbl, width = 20)) +
  labs(x = "other_resid")

.o_res_cnt <- .o_res %>%
  group_by(other_resid) %>%
  dplyr::count() %>%
  arrange(other_resid)
kable(.o_res_cnt, caption = "Other residence") %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Other residence
other_resid n
1 [Yes] 9
2 [No] 158

2.2 Section 2: Occupation

2.2.1 Are you currently working?

# extract and recode
.work <- veritas_main[c("interact_id", "working")] %>% dplyr::rename(working_code = working)
.work$working <- factor(ifelse(.work$working_code == 1, "1 [Yes]",
  ifelse(.work$working_code == 2, "2 [No]", "N/A")
))

# histogram of answers
ggplot(data = .work) +
  geom_histogram(aes(x = working), stat = "count") +
  scale_x_discrete(labels = function(lbl) str_wrap(lbl, width = 20)) +
  labs(x = "working")

.work_cnt <- .work %>%
  group_by(working) %>%
  dplyr::count() %>%
  arrange(working)
kable(.work_cnt, caption = "Currently working") %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Currently working
working n
1 [Yes] 142
2 [No] 25

2.2.2 Where do you work?

work_location <- locations[locations$location_category == 3, ]

bm + geom_sf(data = work_location, inherit.aes = FALSE, color = "blue", size = 1.8, alpha = .3)

2.2.3 On average, how many hours per week do you work?

# histogram of n hours out
ggplot(data = veritas_main[veritas_main$working == 1, ]) +
  geom_histogram(aes(x = work_hours))

# Min, max, median & mean hours/day out
kable(t(as.matrix(summary(veritas_main$work_hours[veritas_main$working == 1]))),
  caption = "Work hours/week",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Work hours/week
Min. 1st Qu. Median Mean 3rd Qu. Max.
2 35 36.5 36.3 40 90

2.2.4 Which of the following categories best describes the amount of physical activity required for your job?

ATTENTION: a bug in Treksoft survey has prevented the collection of work_pa variable in Victoria.

2.2.5 Are you currently a registered student?

# extract and recode
.study <- veritas_main[c("interact_id", "studying")] %>% dplyr::rename(studying_code = studying)
.study$studying <- factor(ifelse(.study$studying_code == 1, "1 [Yes]",
  ifelse(.study$studying_code == 2, "2 [No]", "N/A")
))

# histogram of answers
ggplot(data = .study) +
  geom_histogram(aes(x = studying), stat = "count") +
  scale_x_discrete(labels = function(lbl) str_wrap(lbl, width = 20)) +
  labs(x = "Studying")

.study_cnt <- .study %>%
  group_by(studying) %>%
  dplyr::count() %>%
  arrange(studying)
kable(.study_cnt, caption = "Currently studying") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Currently studying
studying n
1 [Yes] 16
2 [No] 151

2.2.6 Where do you study?

study_location <- locations[locations$location_category == 4, ]

bm + geom_sf(data = study_location, inherit.aes = FALSE, color = "blue", size = 1.8, alpha = .3)

2.2.7 On average, how many hours per week do you study?

# histogram of n hours out
ggplot(data = veritas_main[veritas_main$studying == 1, ]) +
  geom_histogram(aes(x = study_hours))

# Min, max, median & mean hours/day out
kable(t(as.matrix(summary(veritas_main$study_hours[veritas_main$studying == 1]))),
  caption = "study hours/week",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
study hours/week
Min. 1st Qu. Median Mean 3rd Qu. Max.
2 10 17.5 17.7 26.2 40

2.3 Section 3: Shopping activities

The following questions are used to generate the locations grouped into this section:

  1. Do you shop for groceries at a supermarket at least once per month?
  2. Do you shop at a public/farmer’s market at least once per month?
  3. Do you shop at a bakery at least once per month?
  4. Do you go to a specialty food store at least once per month? For example: a cheese shop, fruit and vegetable store, butcher’s shop, natural and health food store.
  5. Do you go to a convenience store at least once per month?
  6. Do you go to a liquor store at least once per month?
shop_location <- locations[locations$location_category %in% c(5, 6, 7, 8, 9, 10), ] %>% dplyr::rename(location_category_code = location_category)
shop_location$location_category <- factor(ifelse(shop_location$location_category_code == 5, " 5 [Supermarket]",
  ifelse(shop_location$location_category_code == 6, " 6 [Public/farmer’s market]",
    ifelse(shop_location$location_category_code == 7, " 7 [Bakery]",
      ifelse(shop_location$location_category_code == 8, " 8 [Specialty food store]",
        ifelse(shop_location$location_category_code == 9, " 9 [Convenience store/Dépanneur]", "10 [Liquor store/SAQ]")
      )
    )
  )
))

# map
bm + geom_sf(data = shop_location, inherit.aes = FALSE, aes(color = location_category), size = 1.5, alpha = .3) +
  scale_color_brewer(palette = "Accent") +
  theme(legend.position = "bottom", legend.text = element_text(size = 8), legend.title = element_blank())

# compute number of shopping locations by category
ggplot(data = shop_location) +
  geom_histogram(aes(x = location_category), stat = "count") +
  scale_x_discrete(labels = function(lbl) str_wrap(lbl, width = 20)) +
  labs(x = "Shopping locations by categories")

.location_category_cnt <- as.data.frame(shop_location[c("location_category")]) %>%
  group_by(location_category) %>%
  dplyr::count() %>%
  arrange(location_category)
kable(.location_category_cnt, caption = "Shopping locations by categories") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Shopping locations by categories
location_category n
5 [Supermarket] 501
6 [Public/farmer’s market] 59
7 [Bakery] 115
8 [Specialty food store] 94
9 [Convenience store/Dépanneur] 39
# compute statistics on shopping locations by participants and categories
# > one needs to account for participants who did not report location for some categories
.loc_iid_category_cnt <- as.data.frame(shop_location[c("interact_id", "location_category")]) %>%
  group_by(interact_id, location_category) %>%
  dplyr::count()

# (cont'd) simulate SQL JOIN TABLE ON TRUE
.dummy <- data.frame(
  interact_iid = character(),
  location_category = character()
)
for (iid in as.vector(veritas_main$interact_id)) {
  .dmy <- distinct(.loc_iid_category_cnt[c("location_category")])
  .dmy$interact_id <- as.character(iid)
  .dummy <- rbind(.dummy, .dmy)
}

# (cont'd) find iid/categ combination without match in veritas locations
.no_shop_iid <- dplyr::setdiff(.dummy, .loc_iid_category_cnt[c("location_category", "interact_id")]) %>%
  mutate(n = 0)
.loc_iid_category_cnt <- bind_rows(.loc_iid_category_cnt, .no_shop_iid)

.location_category_cnt <- .loc_iid_category_cnt %>%
  group_by(location_category) %>%
  dplyr::summarise(min = min(n), mean = round(mean(n), 2), median = median(n), max = max(n)) %>%
  arrange(location_category)
kable(.location_category_cnt, caption = "Number of shopping locations by participant and category") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Number of shopping locations by participant and category
location_category min mean median max
5 [Supermarket] 0 3.00 3 5
6 [Public/farmer’s market] 0 0.35 0 4
7 [Bakery] 0 0.69 0 5
8 [Specialty food store] 0 0.56 0 5
9 [Convenience store/Dépanneur] 0 0.23 0 3

2.4 Section 4: Services

The following questions are used to generate the locations grouped into this section:

  1. Where is the bank you go to most often located?
  2. Where is the hair salon or barber shop you go to most often?
  3. Where is the post office where you go to most often?
  4. Where is the drugstore you go to most often?
  5. If you need to visit a doctor or other healthcare provider, where do you go most often?
serv_location <- locations[locations$location_category %in% c(11, 12, 13, 14, 15), ] %>% dplyr::rename(location_category_code = location_category)
serv_location$location_category <- factor(ifelse(serv_location$location_category_code == 11, "11 [Bank]",
  ifelse(serv_location$location_category_code == 12, "12 [Hair salon/barbershop]",
    ifelse(serv_location$location_category_code == 13, "13 [Post office]",
      ifelse(serv_location$location_category_code == 14, "14 [Drugstore]", "15 Doctor/healthcare provider]")
    )
  )
))

# map
bm + geom_sf(data = serv_location, inherit.aes = FALSE, aes(color = location_category), size = 1.5, alpha = .3) +
  scale_color_brewer(palette = "Accent") +
  theme(legend.position = "bottom", legend.text = element_text(size = 8), legend.title = element_blank())

# compute number of shopping locations by category
ggplot(data = serv_location) +
  geom_histogram(aes(x = location_category), stat = "count") +
  scale_x_discrete(labels = function(lbl) str_wrap(lbl, width = 20)) +
  labs(x = "Shopping locations by categories")

.location_category_cnt <- as.data.frame(serv_location[c("location_category")]) %>%
  group_by(location_category) %>%
  dplyr::count() %>%
  arrange(location_category)
kable(.location_category_cnt, caption = "Shopping locations by categories") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Shopping locations by categories
location_category n
11 [Bank] 130
12 [Hair salon/barbershop] 116
13 [Post office] 110
14 [Drugstore] 121
15 Doctor/healthcare provider] 163
# compute statistics on shopping locations by participants and categories
# > one needs to account for participants who did not report location for some categories
.loc_iid_category_cnt <- as.data.frame(serv_location[c("interact_id", "location_category")]) %>%
  group_by(interact_id, location_category) %>%
  dplyr::count()

# (cont'd) simulate SQL JOIN TABLE ON TRUE
.dummy <- data.frame(
  interact_iid = character(),
  location_category = character()
)
for (iid in as.vector(veritas_main$interact_id)) {
  .dmy <- distinct(.loc_iid_category_cnt[c("location_category")])
  .dmy$interact_id <- as.character(iid)
  .dummy <- rbind(.dummy, .dmy)
}

# (cont'd) find iid/categ combination without match in veritas locations
.no_serv_iid <- dplyr::setdiff(.dummy, .loc_iid_category_cnt[c("location_category", "interact_id")]) %>%
  mutate(n = 0)
.loc_iid_category_cnt <- bind_rows(.loc_iid_category_cnt, .no_serv_iid)

.location_category_cnt <- .loc_iid_category_cnt %>%
  group_by(location_category) %>%
  dplyr::summarise(min = min(n), mean = round(mean(n), 2), median = median(n), max = max(n)) %>%
  arrange(location_category)
kable(.location_category_cnt, caption = "Number of shopping locations by participant and category") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Number of shopping locations by participant and category
location_category min mean median max
11 [Bank] 0 0.78 1 1
12 [Hair salon/barbershop] 0 0.69 1 1
13 [Post office] 0 0.66 1 1
14 [Drugstore] 0 0.72 1 1
15 Doctor/healthcare provider] 0 0.98 1 5

2.5 Section 5: Transportation

2.5.1 Do you use public transit from your home?

# extract and recode
.transp <- veritas_main[c("interact_id", "public_transit")] %>% dplyr::rename(public_transit_code = public_transit)
.transp$public_transit <- factor(ifelse(.transp$public_transit_code == 1, "1 [Yes]",
  ifelse(.transp$public_transit_code == 2, "2 [No]", "N/A")
))

# histogram of answers
ggplot(data = .transp) +
  geom_histogram(aes(x = public_transit), stat = "count") +
  scale_x_discrete(labels = function(lbl) str_wrap(lbl, width = 20)) +
  labs(x = "public_transit")

.transp_cnt <- .transp %>%
  group_by(public_transit) %>%
  dplyr::count() %>%
  arrange(public_transit)
kable(.transp_cnt, caption = "Use public transit") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Use public transit
public_transit n
1 [Yes] 88
2 [No] 79

2.5.2 Where are the public transit stops that you access from your home?

transp_location <- locations[locations$location_category == 16, ]

bm + geom_sf(data = transp_location, inherit.aes = FALSE, color = "blue", size = 1.8, alpha = .3)

2.6 Section 6: Leisure activities

The following questions are used to generate the locations grouped into this section:

  1. Do you participate in any (individual or group) sports or leisure-time physical activities at least once per month?
  2. Do you visit a park at least once per month?
  3. Do you participate in or attend as a spectator a cultural or non-sport leisure activity at least once per month? For example: singing or drawing lessons, book or poker club, concert or play.
  4. Do you volunteer at least once per month?
  5. Do you engage in any religious or spiritual activities at least once per month?
  6. Do you go to a restaurant, café, bar or other food and drink establishment at least once per month?
  7. Do you get take-out food at least once per month?
  8. Do you regularly go for walks?
leisure_location <- locations[locations$location_category %in% c(17, 18, 19, 20, 21, 22, 23, 24), ] %>% dplyr::rename(location_category_code = location_category)
leisure_location$location_category <- factor(ifelse(leisure_location$location_category_code == 17, "17 [Leisure-time physical activity]",
  ifelse(leisure_location$location_category_code == 18, "18 [Park]",
    ifelse(leisure_location$location_category_code == 19, "19 [Cultural activity]",
      ifelse(leisure_location$location_category_code == 20, "20 [Volunteering place]",
        ifelse(leisure_location$location_category_code == 21, "21 [Religious or spiritual activity]",
          ifelse(leisure_location$location_category_code == 22, "22 [Restaurant, café, bar, etc. ]",
            ifelse(leisure_location$location_category_code == 23, "23 [Take-out]", "24 [Walk]")
          )
        )
      )
    )
  )
))

# map
bm + geom_sf(data = leisure_location, inherit.aes = FALSE, aes(color = location_category), size = 1.5, alpha = .3) +
  scale_color_brewer(palette = "Accent") +
  theme(legend.position = "bottom", legend.text = element_text(size = 8), legend.title = element_blank())

# compute number of shopping locations by category
ggplot(data = leisure_location) +
  geom_histogram(aes(x = location_category), stat = "count") +
  scale_x_discrete(labels = function(lbl) str_wrap(lbl, width = 20)) +
  labs(x = "Leisure locations by categories")

.location_category_cnt <- as.data.frame(leisure_location[c("location_category")]) %>%
  group_by(location_category) %>%
  dplyr::count() %>%
  arrange(location_category)
kable(.location_category_cnt, caption = "Shopping locations by categories") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Shopping locations by categories
location_category n
17 [Leisure-time physical activity] 266
18 [Park] 349
19 [Cultural activity] 126
20 [Volunteering place] 81
21 [Religious or spiritual activity] 18
22 [Restaurant, café, bar, etc. ] 403
23 [Take-out] 138
24 [Walk] 313
# compute statistics on shopping locations by participants and categories
# > one needs to account for participants who did not report location for some categories
.loc_iid_category_cnt <- as.data.frame(leisure_location[c("interact_id", "location_category")]) %>%
  group_by(interact_id, location_category) %>%
  dplyr::count()

# (cont'd) simulate SQL JOIN TABLE ON TRUE
.dummy <- data.frame(
  interact_iid = character(),
  location_category = character()
)
for (iid in as.vector(veritas_main$interact_id)) {
  .dmy <- distinct(.loc_iid_category_cnt[c("location_category")])
  .dmy$interact_id <- as.character(iid)
  .dummy <- rbind(.dummy, .dmy)
}

# (cont'd) find iid/categ combination without match in veritas locations
.no_leisure_iid <- dplyr::setdiff(.dummy, .loc_iid_category_cnt[c("location_category", "interact_id")]) %>%
  mutate(n = 0)

.loc_iid_category_cnt <- bind_rows(.loc_iid_category_cnt, .no_leisure_iid)

.location_category_cnt <- .loc_iid_category_cnt %>%
  group_by(location_category) %>%
  dplyr::summarise(min = min(n), mean = round(mean(n), 2), median = median(n), max = max(n)) %>%
  arrange(location_category)
kable(.location_category_cnt, caption = "Number of leisure locations by participant and category") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Number of leisure locations by participant and category
location_category min mean median max
17 [Leisure-time physical activity] 0 1.59 1 5
18 [Park] 0 2.09 2 5
19 [Cultural activity] 0 0.75 0 5
20 [Volunteering place] 0 0.49 0 4
21 [Religious or spiritual activity] 0 0.11 0 2
22 [Restaurant, café, bar, etc. ] 0 2.41 2 5
23 [Take-out] 0 0.83 0 5
24 [Walk] 0 1.87 1 5

2.7 Section 7: Other places/activities

2.7.1 Are there other places that you go to at least once per month that we have not mentioned? For example: a mall, a daycare, a hardware store, or a community center.

# extract and recode
.other <- veritas_main[c("interact_id", "other")] %>% dplyr::rename(other_code = other)
.other$other <- factor(ifelse(.other$other_code == 1, "1 [Yes]",
  ifelse(.other$other_code == 2, "2 [No]", "N/A")
))

# histogram of answers
ggplot(data = .other) +
  geom_histogram(aes(x = other), stat = "count") +
  scale_x_discrete(labels = function(lbl) str_wrap(lbl, width = 20)) +
  labs(x = "other")

.other_cnt <- .other %>%
  group_by(other) %>%
  dplyr::count() %>%
  arrange(other)
kable(.other_cnt, caption = "Other places") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Other places
other n
1 [Yes] 68
2 [No] 99

2.7.2 Can you locate this place?

other_location <- locations[locations$location_category == 25, ]

bm + geom_sf(data = other_location, inherit.aes = FALSE, color = "blue", size = 1.8, alpha = .3)

2.8 Section 9: Social contact

2.8.1 Do you visit anyone at his or her home at least once per month?

# extract and recode
.visiting <- veritas_main[c("interact_id", "visiting")] %>% dplyr::rename(visiting_code = visiting)
.visiting$visiting <- factor(ifelse(.visiting$visiting_code == 1, "1 [Yes]",
  ifelse(.visiting$visiting_code == 2, "2 [No]", "N/A")
))

# histogram of answers
ggplot(data = .visiting) +
  geom_histogram(aes(x = visiting), stat = "count") +
  scale_x_discrete(labels = function(lbl) str_wrap(lbl, width = 20)) +
  labs(x = "visiting")

.visiting_cnt <- .visiting %>%
  group_by(visiting) %>%
  dplyr::count() %>%
  arrange(visiting)
kable(.visiting_cnt, caption = "Social contact") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Social contact
visiting n
1 [Yes] 107
2 [No] 60

2.8.2 Where does this person live?

visiting_location <- locations[locations$location_category == 26, ]

bm + geom_sf(data = st_jitter(visiting_location, .008), inherit.aes = FALSE, color = "blue", size = 1.8, alpha = .3)

NB: Visiting locations have been randomly shifted from their original position to protect privacy.

2.8.3 Great, we are almost done completing this questionnaire. You have documented all your activity places on a map, and specified with whom you generally do these activities. These last few questions concern the people you documented earlier.

# compute statistics on groups / participant
# > one needs to account for participants who did not report any group
.gr_iid_cnt <- as.data.frame(group[c("interact_id")]) %>%
  group_by(interact_id) %>%
  dplyr::count()

# (cont'd) find iid combination without match in veritas group
.no_gr_iid <- anti_join(veritas_main[c("interact_id")], .gr_iid_cnt, by = "interact_id") %>%
  mutate(n = 0)
.gr_iid_cnt <- bind_rows(.gr_iid_cnt, .no_gr_iid)

kable(t(as.matrix(summary(.gr_iid_cnt$n))), caption = "Number of groups per participant") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Number of groups per participant
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 1 1.844311 3 8
# compute statistics on people / participant
# > one needs to account for participants who did not report any group
.pl_iid_cnt <- as.data.frame(people[c("interact_id")]) %>%
  group_by(interact_id) %>%
  dplyr::count()

# (cont'd) find iid combination without match in veritas group
.no_pl_iid <- anti_join(veritas_main[c("interact_id")], .pl_iid_cnt, by = "interact_id") %>%
  mutate(n = 0)
.pl_iid_cnt <- bind_rows(.pl_iid_cnt, .no_pl_iid)

kable(t(as.matrix(summary(.pl_iid_cnt$n))), caption = "Number of people per participant") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Number of people per participant
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 1 3 4.437126 5 45
# histogram
.sc_iid_cnt <- .pl_iid_cnt %>% mutate(soc_type = "people")
.sc_iid_cnt <- .gr_iid_cnt %>%
  mutate(soc_type = "group") %>%
  bind_rows(.sc_iid_cnt)

ggplot(data = .sc_iid_cnt) +
  geom_histogram(aes(x = n, y = stat(count), fill = soc_type), position = "dodge") +
  labs(x = "Social network size by element type", fill = element_blank())

2.8.3.1 Among these people, who do you discuss important matters with?

# extract number of important people / participant
.n_important <- important %>% dplyr::count(interact_id)
.n_people <- people %>% dplyr::count(interact_id)

.n_people_imp <- left_join(veritas_main[c("interact_id")], .n_people, by = "interact_id") %>%
  left_join(.n_important, by = "interact_id") %>%
  mutate_all(~ replace(., is.na(.), 0)) %>%
  dplyr::rename(n_people = n.x, n_important = n.y) %>%
  mutate(pct = 100 * n_important / n_people)

kable(t(as.matrix(summary(.n_people_imp$n_important))),
  caption = "Number of important people per participant",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Number of important people per participant
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 1 2 2.6 3 16
kable(t(as.matrix(summary(.n_people_imp$pct))),
  caption = "% of important people among social contact per participant",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
% of important people among social contact per participant
Min. 1st Qu. Median Mean 3rd Qu. Max. NA’s
0 50 75 71.7 100 300 8

NB In Victoria, 3 participants also listed groups in this catagory, hence the max value over 100%.

2.8.3.2 Among these people, who do you like to socialize with?

# extract number of important people / participant
.n_socialize <- socialize %>% dplyr::count(interact_id)
.n_people <- people %>% dplyr::count(interact_id)

.n_people_soc <- left_join(veritas_main[c("interact_id")], .n_people, by = "interact_id") %>%
  left_join(.n_socialize, by = "interact_id") %>%
  mutate_all(~ replace(., is.na(.), 0)) %>%
  dplyr::rename(n_people = n.x, n_socialize = n.y) %>%
  mutate(pct = 100 * n_socialize / n_people)

kable(t(as.matrix(summary(.n_people_soc$n_socialize))),
  caption = "Number of people with whom to socialize per participant",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Number of people with whom to socialize per participant
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 1 2 3.3 4 27
kable(t(as.matrix(summary(.n_people_soc$pct))),
  caption = "% of people with whom to  socialize among social contact per participant",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
% of people with whom to socialize among social contact per participant
Min. 1st Qu. Median Mean 3rd Qu. Max. NA’s
0 60 100 81.8 100 300 8

NB In Victoria, 6 participants also listed groups in this catagory, hence the max value over 100%.

2.8.3.3 Among these people, who do you meet often with but do not necessarily feel close to?

# extract number of important people / participant
.n_not_close <- not_close %>% dplyr::count(interact_id)
.n_people <- people %>% dplyr::count(interact_id)

.n_people_not_close <- left_join(veritas_main[c("interact_id")], .n_people, by = "interact_id") %>%
  left_join(.n_not_close, by = "interact_id") %>%
  mutate_all(~ replace(., is.na(.), 0)) %>%
  dplyr::rename(n_people = n.x, n_not_close = n.y) %>%
  mutate(pct = 100 * n_not_close / n_people)

kable(t(as.matrix(summary(.n_people_not_close$n_not_close))),
  caption = "Number of not so close people per participant",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Number of not so close people per participant
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 0 0.8 1 12
kable(t(as.matrix(summary(.n_people_not_close$pct))),
  caption = "% of not so close people among social contact per participant",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
% of not so close people among social contact per participant
Min. 1st Qu. Median Mean 3rd Qu. Max. NA’s
0 0 0 17.9 25 100 8

2.8.3.4 Among these people, who knows whom?

# extract number of who knows who relationships
.n_relat <- relationship %>%
  filter(relationship_type == 1) %>%
  dplyr::count(interact_id)
.n_people <- people %>% dplyr::count(interact_id)

.n_people_relat <- left_join(veritas_main[c("interact_id")], .n_people, by = "interact_id") %>%
  left_join(.n_relat, by = "interact_id") %>%
  mutate_all(~ replace(., is.na(.), 0)) %>%
  dplyr::rename(n_people = n.x, n_relat = n.y) %>%
  mutate(pct = 100 * n_relat * 2 / (n_people * (n_people - 1))) # potential number of relationships = N x (N -1) / 2

kable(t(as.matrix(summary(.n_people_relat$n_relat))),
  caption = "Number of relationships « who knows who » per participant",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Number of relationships « who knows who » per participant
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 3 9.7 9 322
kable(t(as.matrix(summary(.n_people_relat$pct))),
  caption = "% of relationships « who knows who » per participant",
  digits = 1
) %>%
  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
% of relationships « who knows who » per participant
Min. 1st Qu. Median Mean 3rd Qu. Max. NA’s
0 47.6 71.4 70.8 100 100 50

2.9 Derived metrics

2.9.1 Transportation mode preferences

Based on the answers to the question Usually, how do you go there? (Check all that apply.).

# code  en
# 1 By car and you drive
# 2 By car and someone else drives
# 3 By taxi/Uber
# 4 On foot
# 5 By bike
# 6 By bus
# 7 By subway
# 8 By train
# 99    Other

loc_labels <- data.frame(location_category = c(2:26), description = c(
  " 2 [Other residence]",
  " 3 [Work]",
  " 4 [School/College/University]",
  " 5 [Supermarket]",
  " 6 [Public/farmer’s market]",
  " 7 [Bakery]",
  " 8 [Specialty food store]",
  " 9 [Convenience store/Dépanneur]",
  "10 [Liquor store/SAQ]",
  "11 [Bank]",
  "12 [Hair salon/barbershop]",
  "13 [Post office]",
  "14 [Drugstore]",
  "15 [Doctor/healthcare provider]",
  "16 [Public transit stop]",
  "17 [Leisure-time physical activity]",
  "18 [Park]",
  "19 [Cultural activity]",
  "20 [Volunteering place]",
  "21 [Religious/spiritual activity]",
  "22 [Restaurant, café, bar, etc.]",
  "23 [Take-out]",
  "24 [Walk]",
  "25 [Other place]",
  "26 [Social contact residence]"
))

# extract and summary stats
.tm <- locations %>%
  st_set_geometry(NULL) %>%
  filter(location_category != 1) %>%
  left_join(loc_labels)

.tm_grouped <- .tm %>%
  group_by(description) %>%
  dplyr::summarise(
    N = n(), "By car (driver)" = sum(location_tmode_1),
    "By car (passenger)" = sum(location_tmode_2),
    "By taxi/Uber" = sum(location_tmode_3),
    "On foot" = sum(location_tmode_4),
    "By bike" = sum(location_tmode_5),
    "By bus" = sum(location_tmode_6),
    "By train" = sum(location_tmode_7),
    "Other" = sum(location_tmode_99)
  )

kable(.tm_grouped, caption = "Transportation mode preferences") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Transportation mode preferences
description N By car (driver) By car (passenger) By taxi/Uber On foot By bike By bus By train Other
2 [Other residence] 10 3 4 1 2 8 3 0 1
3 [Work] 180 32 16 1 32 147 30 0 13
4 [School/College/University] 16 2 1 0 1 8 3 0 5
5 [Supermarket] 501 242 67 0 170 238 7 0 10
6 [Public/farmer’s market] 59 18 5 0 22 34 2 0 1
7 [Bakery] 115 30 5 0 58 71 5 0 0
8 [Specialty food store] 94 44 14 0 36 38 2 0 2
9 [Convenience store/Dépanneur] 39 6 0 0 32 16 0 0 0
11 [Bank] 130 29 4 0 69 74 6 0 2
12 [Hair salon/barbershop] 116 33 1 0 40 73 9 0 2
13 [Post office] 110 37 4 0 61 60 2 0 2
14 [Drugstore] 121 42 5 0 70 73 7 0 0
15 [Doctor/healthcare provider] 163 64 5 0 43 101 18 0 2
16 [Public transit stop] 195 1 1 0 192 3 0 0 2
17 [Leisure-time physical activity] 266 108 33 0 62 149 5 1 15
18 [Park] 349 102 53 0 197 127 6 0 5
19 [Cultural activity] 126 44 21 0 41 58 9 0 2
20 [Volunteering place] 81 34 6 0 22 42 7 0 8
21 [Religious/spiritual activity] 18 6 1 0 5 8 0 0 2
22 [Restaurant, café, bar, etc.] 403 92 30 1 227 172 18 0 1
23 [Take-out] 138 51 25 0 66 29 4 0 6
24 [Walk] 313 66 21 0 245 54 4 0 4
25 [Other place] 115 51 7 0 33 67 5 0 1
26 [Social contact residence] 164 87 31 1 53 85 15 0 1
# graph
.tm1 <- .tm %>%
  filter(location_tmode_1 == 1) %>%
  mutate(tm = "[1] By car (driver)")
.tm2 <- .tm %>%
  filter(location_tmode_2 == 1) %>%
  mutate(tm = "[2] By car (passenger)")
.tm3 <- .tm %>%
  filter(location_tmode_3 == 1) %>%
  mutate(tm = "[3] By taxi/Uber")
.tm4 <- .tm %>%
  filter(location_tmode_4 == 1) %>%
  mutate(tm = "[4] On foot")
.tm5 <- .tm %>%
  filter(location_tmode_5 == 1) %>%
  mutate(tm = "[5] By bike")
.tm6 <- .tm %>%
  filter(location_tmode_6 == 1) %>%
  mutate(tm = "[6] By bus")
.tm7 <- .tm %>%
  filter(location_tmode_7 == 1) %>%
  mutate(tm = "[7] By train")
.tm99 <- .tm %>%
  filter(location_tmode_99 == 1) %>%
  mutate(tm = "[99] Other")
.tm <- bind_rows(.tm1, .tm2) %>%
  bind_rows(.tm3) %>%
  bind_rows(.tm4) %>%
  bind_rows(.tm5) %>%
  bind_rows(.tm6) %>%
  bind_rows(.tm7) %>%
  bind_rows(.tm99)

# histogram of answers
ggplot(data = .tm) +
  geom_bar(aes(x = fct_rev(description), fill = tm), position = "fill") +
  scale_fill_brewer(palette = "Set3", name = "Transport modes") +
  scale_y_continuous(labels = percent) +
  labs(y = "Proportion of transportation mode by location category", x = element_blank()) +
  coord_flip() +
  theme(legend.position = "bottom", legend.justification = c(0, 0), legend.text = element_text(size = 8)) +
  guides(fill = guide_legend(nrow = 3))

2.9.2 Visiting places alone

Based on the answers to the question Do you usually go to this place alone or with other people?.

loc_labels <- data.frame(location_category = c(2:26), description = c(
  " 2 [Other residence]",
  " 3 [Work]",
  " 4 [School/College/University]",
  " 5 [Supermarket]",
  " 6 [Public/farmer’s market]",
  " 7 [Bakery]",
  " 8 [Specialty food store]",
  " 9 [Convenience store/Dépanneur]",
  "10 [Liquor store/SAQ]",
  "11 [Bank]",
  "12 [Hair salon/barbershop]",
  "13 [Post office]",
  "14 [Drugstore]",
  "15 [Doctor/healthcare provider]",
  "16 [Public transit stop]",
  "17 [Leisure-time physical activity]",
  "18 [Park]",
  "19 [Cultural activity]",
  "20 [Volunteering place]",
  "21 [Religious/spiritual activity]",
  "22 [Restaurant, café, bar, etc.]",
  "23 [Take-out]",
  "24 [Walk]",
  "25 [Other place]",
  "26 [Social contact residence]"
))

# extract and summary stats
.alone <- locations %>%
  st_set_geometry(NULL) %>%
  filter(location_category != 1) %>%
  left_join(loc_labels) %>%
  mutate(location_alone_recode = case_when(
    location_alone == 1 ~ 1,
    location_alone == 2 ~ 0
  ))

.alone_grouped <- .alone %>%
  group_by(description) %>%
  dplyr::summarise(
    N = n(), "Visited alone" = sum(location_alone_recode),
    "Visited alone (%)" = round(sum(location_alone_recode) * 100.0 / n(), digits = 1)
  )

kable(.alone_grouped, caption = "Visiting places alone") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Visiting places alone
description N Visited alone Visited alone (%)
2 [Other residence] 10 NA NA
3 [Work] 180 42 23.3
4 [School/College/University] 16 13 81.2
5 [Supermarket] 501 337 67.3
6 [Public/farmer’s market] 59 32 54.2
7 [Bakery] 115 71 61.7
8 [Specialty food store] 94 61 64.9
9 [Convenience store/Dépanneur] 39 34 87.2
11 [Bank] 130 117 90.0
12 [Hair salon/barbershop] 116 111 95.7
13 [Post office] 110 102 92.7
14 [Drugstore] 121 100 82.6
15 [Doctor/healthcare provider] 163 142 87.1
16 [Public transit stop] 195 150 76.9
17 [Leisure-time physical activity] 266 97 36.5
18 [Park] 349 105 30.1
19 [Cultural activity] 126 22 17.5
20 [Volunteering place] 81 24 29.6
21 [Religious/spiritual activity] 18 5 27.8
22 [Restaurant, café, bar, etc.] 403 90 22.3
23 [Take-out] 138 53 38.4
24 [Walk] 313 123 39.3
25 [Other place] 115 53 46.1
26 [Social contact residence] 164 52 31.7
# histogram of answers
ggplot(data = .alone) +
  geom_bar(aes(x = fct_rev(description), fill = factor(location_alone)), position = "fill") +
  scale_fill_brewer(palette = "Set3", name = "Visiting places", labels = c("N/A", "Alone", "With someone")) +
  scale_y_continuous(labels = percent) +
  labs(y = "Proportion of places visited alone", x = element_blank()) +
  coord_flip()

2.9.3 Visit frequency

Based on the answers to the question How often do you go there?.

loc_labels <- data.frame(location_category = c(2:26), description = c(
  " 2 [Other residence]",
  " 3 [Work]",
  " 4 [School/College/University]",
  " 5 [Supermarket]",
  " 6 [Public/farmer’s market]",
  " 7 [Bakery]",
  " 8 [Specialty food store]",
  " 9 [Convenience store/Dépanneur]",
  "10 [Liquor store/SAQ]",
  "11 [Bank]",
  "12 [Hair salon/barbershop]",
  "13 [Post office]",
  "14 [Drugstore]",
  "15 [Doctor/healthcare provider]",
  "16 [Public transit stop]",
  "17 [Leisure-time physical activity]",
  "18 [Park]",
  "19 [Cultural activity]",
  "20 [Volunteering place]",
  "21 [Religious/spiritual activity]",
  "22 [Restaurant, café, bar, etc.]",
  "23 [Take-out]",
  "24 [Walk]",
  "25 [Other place]",
  "26 [Social contact residence]"
))

# extract and summary stats
.freq <- locations %>%
  st_set_geometry(NULL) %>%
  filter(location_category != 1) %>%
  left_join(loc_labels)

.freq_grouped <- .freq %>%
  group_by(description) %>%
  dplyr::summarise(
    N = n(), min = min(location_freq_visit),
    max = max(location_freq_visit),
    mean = mean(location_freq_visit),
    median = median(location_freq_visit),
    sd = sd(location_freq_visit)
  )

kable(.freq_grouped, caption = "Visit frequency (expressed in times/year)") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
Visit frequency (expressed in times/year)
description N min max mean median sd
2 [Other residence] 10 24 364 142.000000 104 106.920531
3 [Work] 180 0 365 208.488889 260 94.130748
4 [School/College/University] 16 0 260 153.500000 208 108.771320
5 [Supermarket] 501 NA NA NA NA NA
6 [Public/farmer’s market] 59 NA NA NA NA NA
7 [Bakery] 115 3 312 39.017391 24 50.551690
8 [Specialty food store] 94 2 260 31.861702 24 39.998818
9 [Convenience store/Dépanneur] 39 NA NA NA NA NA
11 [Bank] 130 NA NA NA NA NA
12 [Hair salon/barbershop] 116 1 24 6.646552 5 4.727817
13 [Post office] 110 2 208 13.800000 6 23.464125
14 [Drugstore] 121 2 156 25.371901 12 25.754654
15 [Doctor/healthcare provider] 163 1 52 5.828221 3 8.963985
16 [Public transit stop] 195 1 260 41.225641 12 69.894105
17 [Leisure-time physical activity] 266 2 1040 82.962406 52 97.917864
18 [Park] 349 NA NA NA NA NA
19 [Cultural activity] 126 NA NA NA NA NA
20 [Volunteering place] 81 NA NA NA NA NA
21 [Religious/spiritual activity] 18 4 364 100.833333 52 102.930159
22 [Restaurant, café, bar, etc.] 403 1 520 30.575682 12 47.793774
23 [Take-out] 138 NA NA NA NA NA
24 [Walk] 313 NA NA NA NA NA
25 [Other place] 115 2 624 60.556522 36 87.438017
26 [Social contact residence] 164 NA NA NA NA NA
# graph
ggplot(data = .freq) +
  geom_boxplot(aes(x = fct_rev(description), y = location_freq_visit)) +
  scale_y_continuous(limits = c(0, 365)) +
  labs(y = "Visits/year (Frequency over 1 visit/day not shown)", x = element_blank()) +
  coord_flip()

2.9.4 Spatial indicators: Camille Perchoux’s toolbox

Below is a list of indicators proposed by Camille Perchoux in her paper Assessing patterns of spatial behavior in health studies: Their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study).

-- Reading Camille tbx indics from Essence table
SELECT interact_id,
  n_acti_places, n_weekly_vst, n_acti_types,
  cvx_perimeter, cvx_surface,
  min_length, max_length, median_length, 
  pct_visits_neighb, 
  n_acti_prn, pct_visits_prn, prn_area_km2
FROM essence_table.essence_perchoux_tbx
WHERE city_id = 'Victoria' AND wave_id = 1

2.9.5 Social indicators: Alexandre Naud’s toolbox

See Alex’s document for a more comprehensive presentation of the social indicators.

-- Reading Alex tbx indics from Essence table
SELECT interact_id,
  people_degree, 
  socialize_size, socialize_meet, socialize_chat,
  important_size, group_degree, simmelian
FROM essence_table.essence_naud_social
WHERE city_id = 'Victoria' AND wave_id = 1

2.9.5.1 Number of people in the network (people_degree)

ggplot(ess.tab.alex) +
  geom_histogram(aes(x = people_degree))

kable(t(as.matrix(summary(ess.tab.alex$people_degree))), caption = "people_degree") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
people_degree
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 1 3 4.437126 5 45

2.9.5.2 Simmelian Brokerage (simmelian)

ggplot(ess.tab.alex) +
  geom_histogram(aes(x = simmelian))

kable(t(as.matrix(summary(ess.tab.alex$simmelian))), caption = "simmelian") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
simmelian
Min. 1st Qu. Median Mean 3rd Qu. Max. NA’s
1 2.842949 8.17803 10.51054 14.93557 71 15

2.9.5.3 Number of people with whom the participant like to socialize (socialize_size)

ggplot(ess.tab.alex) +
  geom_histogram(aes(x = socialize_size))

kable(t(as.matrix(summary(ess.tab.alex$socialize_size))), caption = "socialize_size") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
socialize_size
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 1 2 3.203593 4 27

2.9.5.4 Weekly face-to-face interactions among people with whom the participant like to socialize (socialize_meet)

ggplot(filter(ess.tab.alex, socialize_meet < 100)) +
  geom_histogram(aes(x = socialize_meet)) +
  annotate(geom = "text", x = 75, y = 100, label = "X-axis: values over 100 not displayed", alpha = .5)

kable(t(as.matrix(summary(ess.tab.alex$socialize_meet))), caption = "socialize_meet") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
socialize_meet
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 272 416 1293.988 832 36400

2.9.5.5 Weekly ICT interactions among people with whom the participant like to socialize (socialize_chat)

ggplot(filter(ess.tab.alex, socialize_chat < 100)) +
  geom_histogram(aes(x = socialize_chat)) +
  annotate(geom = "text", x = 55, y = 100, label = "X-axis: values over 100 not displayed", alpha = .5)

kable(t(as.matrix(summary(ess.tab.alex$socialize_chat))), caption = "socialize_chat") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
socialize_chat
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 345 388 4098.593 702 520000

2.9.5.6 Number of people with whom the participant discuss important matters (important_size)

ggplot(ess.tab.alex) +
  geom_histogram(aes(x = important_size))

kable(t(as.matrix(summary(ess.tab.alex$important_size))), caption = "important_size") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
important_size
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 1 2 2.586826 3 16

2.9.5.7 Number of people in all groups (group_degree)

ggplot(filter(ess.tab.alex, group_degree < 100)) +
  geom_histogram(aes(x = group_degree)) +
  annotate(geom = "text", x = 20, y = 100, label = "X-axis: values over 100 not displayed", alpha = .5)

kable(t(as.matrix(summary(ess.tab.alex$group_degree))), caption = "group_degree") %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")
group_degree
Min. 1st Qu. Median Mean 3rd Qu. Max.
0 0 8 9.682635 14 82