Mike Spencer 25/11/2021
library(tidyverse)
library(haven)
library(knitr)theme_temp = function(){
theme_bw() +
theme(text = element_text(size = 15))
}This is an RMarkdown document, summarising variables of interest. I’m considering the relationship between income and mode of travel. For example, is cycling the preserve of the affluent middle class?
Understanding Society variable guide: https://www.understandingsociety.ac.uk/documentation/mainstage/dataset-documentation?search_api_views_fulltext=salary.
df = read_dta("~/Cloud/personal/gofcoe/understanding_society/6614stata_B17CC6790677EF32F72CE50881AE98E1B9FC1F79133B07B63B353396D3AB917A_V1/UKDA-6614-stata/stata/stata13_se/ukhls_w10/j_indresp.dta")df %>%
count(j_pdvage) %>%
ggplot(aes(j_pdvage, n)) +
geom_col() +
labs(title = "Age of respondents",
x = "Age",
y = "Respondents") +
theme_temp()
df %>%
count(j_sex)## # A tibble: 2 x 2
## j_sex n
## <dbl+lbl> <int>
## 1 1 [male] 15383
## 2 2 [female] 18935
df %>%
count(j_wktrvfar)## # A tibble: 15 x 2
## j_wktrvfar n
## <dbl+lbl> <int>
## 1 -8 [inapplicable] 31914
## 2 -7 [proxy] 804
## 3 -2 [refusal] 7
## 4 -1 [don't know] 18
## 5 1 [Drive myself by car or van] 434
## 6 2 [Get a lift with someone from household] 106
## 7 3 [ Get a lift with someone outside the household] 58
## 8 4 [Motorcycle/moped/scooter] 14
## 9 5 [Taxi/minicab] 18
## 10 6 [Bus/coach] 205
## 11 7 [Train] 411
## 12 8 [Underground/Metro/Tram/Light railway] 173
## 13 9 [Cycle] 58
## 14 10 [Walk] 90
## 15 97 [Other] 8
Variables:
- wktrv1 Drive myself by car or van
- wktrv2 Get a lift with someone from household
- wktrv3 Get a lift with someone outside the household
- wktrv4 Motorcycle/moped/scooter
- wktrv5 Taxi/minicab
- wktrv6 Bus/coach
- wktrv7 Train
- wktrv8 Underground/Metro/Tram/Light railway
- wktrv9 Cycle
- wktrv10 Walk
- wktrv97 Other
Response options:
- Value = 0.0 Label = Not mentioned
- Value = 1.0 Label = Yes mentioned
- Value = -1.0 Label = don’t know
- Value = -9.0 Label = missing
- Value = -8.0 Label = inapplicable
- Value = -7.0 Label = proxy
- Value = -2.0 Label = refusal
Employed:
df %>%
select(pidp, contains("j_wktrv"), -j_wktrvfar) %>%
pivot_longer(cols = !pidp) %>%
count(name, value) %>%
pivot_wider(names_from = name, values_from = n)## # A tibble: 6 x 12
## value j_wktrv1 j_wktrv10 j_wktrv2 j_wktrv3 j_wktrv4 j_wktrv5 j_wktrv6
## <dbl+lbl> <int> <int> <int> <int> <int> <int> <int>
## 1 -8 [inapplica… 17995 17995 17995 17995 17995 17995 17995
## 2 -7 [proxy] 804 804 804 804 804 804 804
## 3 -2 [refusal] 5 5 5 5 5 5 5
## 4 -1 [don't kno… 10 10 10 10 10 10 10
## 5 0 [Not menti… 5389 13147 14674 15097 15389 15384 14130
## 6 1 [Yes menti… 10115 2357 830 407 115 120 1374
## # … with 4 more variables: j_wktrv7 <int>, j_wktrv8 <int>, j_wktrv9 <int>,
## # j_wktrv97 <int>
Self employed:
df %>%
select(pidp, contains("j_jswktrv"), -j_jswktrvfar) %>%
pivot_longer(cols = !pidp) %>%
count(name, value) %>%
pivot_wider(names_from = name, values_from = n)## # A tibble: 6 x 12
## value j_jswktrv1 j_jswktrv10 j_jswktrv2 j_jswktrv3 j_jswktrv4 j_jswktrv5
## <dbl+lbl> <int> <int> <int> <int> <int> <int>
## 1 -8 [inappl… 31852 31852 31852 31852 31852 31852
## 2 -7 [proxy] 804 804 804 804 804 804
## 3 -2 [refusa… 3 3 3 3 3 3
## 4 -1 [don't … 4 4 4 4 4 4
## 5 0 [Not me… 352 1499 1622 1627 1647 1637
## 6 1 [Yes me… 1303 156 33 28 8 18
## # … with 5 more variables: j_jswktrv6 <int>, j_jswktrv7 <int>,
## # j_jswktrv8 <int>, j_jswktrv9 <int>, j_jswktrv97 <int>
Larissa Pople notes we can add together travel from employed and self employed categories.
df %>%
select(pidp, contains("wktrv"), -j_wktrvfar, -j_jswktrvfar) %>%
pivot_longer(cols = !pidp) %>%
mutate(name = str_replace(name, "j_js", "j_")) %>%
filter(value == 1) %>%
count(pidp, name = "modes") %>%
count(modes, name = "respondents")## # A tibble: 7 x 2
## modes respondents
## <int> <int>
## 1 1 15444
## 2 2 1159
## 3 3 440
## 4 4 96
## 5 5 17
## 6 6 2
## 7 7 1
df %>%
count(j_workdis)## # A tibble: 133 x 2
## j_workdis n
## <dbl> <int>
## 1 -8 16190
## 2 -7 804
## 3 -2 91
## 4 -1 777
## 5 0 611
## 6 1 1989
## 7 2 1622
## 8 3 1571
## 9 4 1013
## 10 5 1304
## # … with 123 more rows
df %>%
filter(j_workdis >= 0) %>%
ggplot(aes(j_workdis)) +
geom_histogram() +
scale_x_log10() +
labs(title = "How far do respondents live from work?",
x = "Distance (miles, log scale)",
y = "Respondents")
df %>%
filter(! j_fimnnet_dv %in% c(-9, -8, -2, -1)) %>%
transmute(j_fimnnet_dv = as.numeric(j_fimnnet_dv)) %>%
ggplot(aes(j_fimnnet_dv)) +
geom_histogram() +
scale_x_log10() +
labs(title = "Total net personal income",
x = "Income (£, log scale)",
y = "Respondents") +
theme_temp()
df %>%
count(j_benbase1)## # A tibble: 5 x 2
## j_benbase1 n
## <dbl+lbl> <int>
## 1 -7 [proxy] 804
## 2 -2 [refusal] 161
## 3 -1 [don't know] 219
## 4 0 [Not mentioned] 32457
## 5 1 [Mentioned] 677
df %>%
count(j_benbase2)## # A tibble: 5 x 2
## j_benbase2 n
## <dbl+lbl> <int>
## 1 -7 [proxy] 804
## 2 -2 [refusal] 161
## 3 -1 [don't know] 219
## 4 0 [Not mentioned] 32885
## 5 1 [Mentioned] 249
df %>%
count(j_benbase4)## # A tibble: 5 x 2
## j_benbase4 n
## <dbl+lbl> <int>
## 1 -7 [proxy] 804
## 2 -2 [refusal] 161
## 3 -1 [don't know] 219
## 4 0 [Not mentioned] 32377
## 5 1 [Mentioned] 757
How many respondents claim one or multiple benefits, from income support, jobs seekers or universal credit.
df %>%
select(pidp, j_benbase1, j_benbase2, j_benbase4) %>%
pivot_longer(!pidp) %>%
mutate(value = replace(value, value < 0, 0)) %>%
group_by(pidp) %>%
summarise(benefits = sum(value)) %>%
count(benefits)## # A tibble: 4 x 2
## benefits n
## <dbl> <int>
## 1 0 32668
## 2 1 1618
## 3 2 31
## 4 3 1