Chapter 14 R&D expenditures
Employees 16-64 years by region of work, occupation (3-digit SSYK 2012) and sex. Year 2014 - 2017 214 Engineering professionals
Population 16-74 years of age by region, highest level of education, age and sex. Year 1985 - 2018 total, 16-74 years
Regional distribution of enterprises R&D. Every other year 2007 - 2017 Intramural R&D, SEK million
Total R&D person years performed by employees of a higher education institution by region, sex and field of science. Every second year 2013 - 2017 Total R&D person years County men, women All fields of science and technology
tb <- readfile("000000NL_1.csv") %>%
group_by (`region`, year) %>%
mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
mutate (sum_ing = sum(salary))
tb <- readfile("UF0506A1_2.csv") %>%
group_by(`level of education`, region, year, sex) %>%
mutate(utbregno = sum(salary)) %>%
group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
group_by(`level of education`, region, year) %>%
mutate (sum_edu = sum(utbregno)) %>%
filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
right_join(tb, by = c("region", "year", "sex")) %>%
mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
right_join(tb, by = c("region", "year"))
tb1 <- tb %>% select(
salary.x.x,
salary.y,
salary.x,
year2.x,
utbregno,
perc_edu,
sum_edu,
salary.y.y,
perc_women,
sum_ing,
perc_eng)
chart.Correlation(tb1, histogram=TRUE, pch=19)
tb <- readfile("000000NL_1.csv") %>%
group_by (`region`, year) %>%
mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
mutate (sum_ing = sum(salary))
tb <- readfile("UF0506A1_2.csv") %>%
group_by(`level of education`, region, year, sex) %>%
mutate(utbregno = sum(salary)) %>%
group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
group_by(`level of education`, region, year) %>%
mutate (sum_edu = sum(utbregno)) %>%
filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
right_join(tb, by = c("region", "year", "sex")) %>%
mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
right_join(tb, by = c("region", "year"))
tb1 <- tb %>% select(
salary.x.x,
salary.y,
salary.x,
year2.x,
utbregno,
perc_edu,
sum_edu,
salary.y.y,
perc_women,
sum_ing,
perc_eng)
get_clust_tendency(
tb1,
n = 50,
gradient = list(low = "steelblue", high = "white"))
## $hopkins_stat
## [1] 0.04789467
##
## $plot
tb <- readfile("000000NL_1.csv") %>%
group_by (`region`, year) %>%
mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
mutate (sum_ing = sum(salary))
tb <- readfile("UF0506A1_2.csv") %>%
group_by(`level of education`, region, year, sex) %>%
mutate(utbregno = sum(salary)) %>%
group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
group_by(`level of education`, region, year) %>%
mutate (sum_edu = sum(utbregno)) %>%
filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
right_join(tb, by = c("region", "year", "sex")) %>%
mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
right_join(tb, by = c("region", "year"))
tb1 <- tb %>% select(
salary.x.x,
salary.y,
salary.x,
year2.x,
utbregno,
perc_edu,
sum_edu,
salary.y.y,
perc_women,
sum_ing,
perc_eng)
tb2 <- tb1 %>% na.omit() %>% scale()
intern <- clValid(tb2, nClust = 2:6,
clMethods = c("hierarchical","kmeans","pam"),
validation = "internal")
## Warning in clValid(tb2, nClust = 2:6, clMethods = c("hierarchical",
## "kmeans", : rownames for data not specified, using 1:nrow(data)
summary(intern) %>%
tidy() %>%
knitr::kable(
booktabs = TRUE,
caption = 'Clustering validation measures')
##
## Clustering Methods:
## hierarchical kmeans pam
##
## Cluster sizes:
## 2 3 4 5 6
##
## Validation Measures:
## 2 3 4 5 6
##
## hierarchical Connectivity 5.6464 10.0151 13.9048 20.1786 22.8548
## Dunn 0.4585 0.5304 0.5388 0.4915 0.4915
## Silhouette 0.6346 0.5555 0.3919 0.4039 0.4050
## kmeans Connectivity 5.6464 10.1651 18.7151 20.1786 22.8548
## Dunn 0.4585 0.5385 0.4235 0.4915 0.4915
## Silhouette 0.6346 0.4999 0.4037 0.4039 0.4050
## pam Connectivity 5.6464 16.8294 23.2329 33.0845 36.8401
## Dunn 0.4585 0.2863 0.1590 0.1590 0.1579
## Silhouette 0.6346 0.2690 0.2254 0.2326 0.2533
##
## Optimal Scores:
##
## Score Method Clusters
## Connectivity 5.6464 hierarchical 2
## Dunn 0.5388 hierarchical 4
## Silhouette 0.6346 hierarchical 2
## Warning in kable_markdown(x, padding = padding, ...): The table should have
## a header (column names)
Table: (#tab:rndexpenditures3)Clustering validation measures
|| || || ||
tb <- readfile("000000NL_1.csv") %>%
group_by (`region`, year) %>%
mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
mutate (sum_ing = sum(salary))
tb <- readfile("UF0506A1_2.csv") %>%
group_by(`level of education`, region, year, sex) %>%
mutate(utbregno = sum(salary)) %>%
group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
group_by(`level of education`, region, year) %>%
mutate (sum_edu = sum(utbregno)) %>%
filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
right_join(tb, by = c("region", "year", "sex")) %>%
mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
right_join(tb, by = c("region", "year"))
tb1 <- tb %>% select(
salary.x.x,
salary.y,
salary.x,
year2.x,
utbregno,
perc_edu,
sum_edu,
salary.y.y,
perc_women,
sum_ing,
perc_eng)
tb2 <- tb1 %>% na.omit() %>% scale()
d <- dist(tb2, method = "euclidean")
res.hc <- hclust(d, method = "ward.D2" )
grp <- cutree(res.hc, k = 2)
plot(res.hc, cex = 0.6) # plot tree
rect.hclust(res.hc, k = 2, border = 2:5)
14.1 Correlation between R&D person years performed by employees of a higher education institution by region and per cent of engineers who are women
tb <- readfile("000000NL_1.csv") %>%
group_by (`region`, year) %>%
mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
mutate (sum_ing = sum(salary))
tb <- readfile("UF0506A1_2.csv") %>%
group_by(`level of education`, region, year, sex) %>%
mutate(utbregno = sum(salary)) %>%
group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
group_by(`level of education`, region, year) %>%
mutate (sum_edu = sum(utbregno)) %>%
filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
right_join(tb, by = c("region", "year", "sex")) %>%
mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
right_join(tb, by = c("region", "year"))
tb1 <- tb %>% select(
salary.x.x,
salary.y,
salary.x,
year2.x,
utbregno,
perc_edu,
sum_edu,
salary.y.y,
perc_women,
sum_ing,
perc_eng)
tb2 <- tb1
tb3 <- tb2 %>% scale()
d <- dist(tb3, method = "euclidean")
res.hc <- hclust(d, method = "ward.D2" )
grp <- cutree(res.hc, k = 2)
tb4 <- tb2 %>% mutate(grp)
model1 <- lm(perc_women ~ ., data = filter(tb4, grp == 1))
summary(model1) %>%
tidy() %>%
knitr::kable(
booktabs = TRUE,
caption = 'Summary of model data for group 1, number of population with 3 years or more post-secondary education > 193000')
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -3232.4031087 | 0 | -3.956486e+12 | 0.0000000 |
| salary.x.x | 0.0058631 | 0 | 2.877303e+12 | 0.0000000 |
| salary.y | -0.0001395 | 0 | -2.654362e+12 | 0.0000000 |
| salary.x | 0.0000000 | 0 | 8.429982e-01 | 0.4611338 |
| year2.x | 1.6116179 | 0 | 3.981184e+12 | 0.0000000 |
| perc_edu | 0.0000000 | 0 | -1.035823e+00 | 0.3764532 |
| sum_edu | -0.0000488 | 0 | -1.887907e+12 | 0.0000000 |
| salary.y.y | 0.0000000 | 0 | 1.588736e-01 | 0.8838614 |
| sum_ing | 0.0001593 | 0 | 1.705022e+12 | 0.0000000 |
model2 <- lm(perc_women ~ ., data = filter(tb4, grp == 2))
summary(model2) %>%
tidy() %>%
knitr::kable(
booktabs = TRUE,
caption = 'Women engineers and population with 3 years or more post-secondary education')
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -405.3202427 |<PHONE_NUMBER> | -0.4859143 | 0.6288587 |
| salary.x.x | 0.0033701 | 0.0011007 | 3.0617573 | 0.0033322 |
| salary.y | -0.0002970 | 0.0007141 | -0.4159109 | 0.6790101 |
| salary.x | -0.0002938 | 0.0003383 | -0.8686163 | 0.3886372 |
| year2.x | 0.2109100 | 0.4144858 | 0.5088473 | 0.6127896 |
| perc_edu | 25.8430631 | 25.0172384 | 1.0330102 | 0.3058874 |
| sum_edu | 0.0000128 | 0.0001935 | 0.0661902 | 0.9474541 |
| salary.y.y | -0.0001661 | 0.0011022 | -0.1506891 | 0.8807438 |
| sum_ing | 0.0021199 | 0.0017251 | 1.2288449 | 0.2240926 |
| perc_eng | -93.1069852 | 49.9236663 | -1.8649869 | 0.0672449 |
Anova(model2, type=2) %>%
tidy() %>%
knitr::kable(
booktabs = TRUE,
caption = 'Anova report from linear model fit')
## Note: model has aliased coefficients
## sums of squares computed by model comparison
| term | sumsq | df | statistic | p.value |
|---|---|---|---|---|
| salary.x.x | 95.2758135 | 1 | 9.3743575 | 0.0033322 |
| salary.y | 1.7580924 | 1 | 0.1729819 | 0.6790101 |
| salary.x | NA | 0 | NA | NA |
| year2.x | 2.6315775 | 1 | 0.2589256 | 0.6127896 |
| utbregno | NA | 0 | NA | NA |
| perc_edu | 10.8455204 | 1 | 1.0671101 | 0.3058874 |
| sum_edu | 0.0445275 | 1 | 0.0043811 | 0.9474541 |
| salary.y.y | 0.2307836 | 1 | 0.0227072 | 0.8807438 |
| sum_ing | 15.3474169 | 1 | 1.5100598 | 0.2240926 |
| perc_eng | 35.3502704 | 1 | 3.4781762 | 0.0672449 |
| grp | NA | 0 | NA | NA |
| Residuals |<PHONE_NUMBER> | 58 | NA | NA |
tb %>% mutate(grp) %>%
ggplot(aes(x = salary.x.x, y = perc_women, colour = region, size = year2.x)) +
geom_point() +
theme(legend.position="bottom") +
facet_grid(. ~ grp) +
labs(
x = "Total R&D person years performed by employees of a higher education institution by region",
y = "Per cent of engineers who are women"
)
## Warning: Removed 92 rows containing missing values (geom_point).
14.2 Correlation between total R&D person years performed by employees of a higher education institution by region and number of the population who are engineers
tb <- readfile("000000NL_1.csv") %>%
group_by (`region`, year) %>%
mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
mutate (sum_ing = sum(salary))
tb <- readfile("UF0506A1_2.csv") %>%
group_by(`level of education`, region, year, sex) %>%
mutate(utbregno = sum(salary)) %>%
group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
group_by(`level of education`, region, year) %>%
mutate (sum_edu = sum(utbregno)) %>%
filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
right_join(tb, by = c("region", "year", "sex")) %>%
mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
right_join(tb, by = c("region", "year"))
tb1 <- tb %>% select(
salary.x.x,
salary.y,
salary.x,
year2.x,
utbregno,
perc_edu,
sum_edu,
salary.y.y,
perc_women,
sum_ing,
perc_eng)
tb2 <- tb1
tb3 <- tb2 %>% scale()
d <- dist(tb3, method = "euclidean")
res.hc <- hclust(d, method = "ward.D2" )
grp <- cutree(res.hc, k = 2)
tb4 <- tb2 %>% mutate(grp)
model1 <- lm(sum_edu ~ ., data = filter(tb4, grp == 1))
summary(model1) %>%
tidy() %>%
knitr::kable(
booktabs = TRUE,
caption = 'Summary of model data for group 1, number of population with 3 years or more post-secondary education > 193000')
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -6.620101e+07 | 2.06e-05 | -3.207153e+12 | 0.0000000 |
| salary.x.x | 1.200785e+02 | 0.00e+00 | 4.127007e+12 | 0.0000000 |
| salary.y | -2.857963e+00 | 0.00e+00 | -2.686799e+12 | 0.0000000 |
| salary.x | 0.000000e+00 | 0.00e+00 | 8.426335e-01 | 0.4613091 |
| year2.x | 3.300663e+04 | 0.00e+00 | 3.196360e+12 | 0.0000000 |
| perc_edu | -2.000000e-07 | 2.00e-07 | -1.035078e+00 | 0.3767507 |
| salary.y.y | 0.000000e+00 | 0.00e+00 | 1.588732e-01 | 0.8838617 |
| perc_women | -2.048043e+04 | 0.00e+00 | -1.887715e+12 | 0.0000000 |
| sum_ing | 3.263440e+00 | 0.00e+00 | 1.834379e+12 | 0.0000000 |
model2 <- lm(sum_edu ~ ., data = filter(tb4, grp == 2))
summary(model2) %>%
tidy() %>%
knitr::kable(
booktabs = TRUE,
caption = 'Summary of model data for group 2, number of population with 3 years or more post-secondary education < 193000')
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -1.039565e+06 | 5.503698e+05 | -1.8888490 | 0.0639155 |
| salary.x.x | 3.840863e+00 | 6.272259e-01 | 6.1235728 | 0.0000001 |
| salary.y | -5.649046e-01 | 4.794746e-01 | -1.1781742 | 0.2435386 |
| salary.x | 1.570842e+00 | 1.039662e-01 | 15.1091537 | 0.0000000 |
| year2.x | 5.265551e+02 | 2.732082e+02 | 1.9273035 | 0.0588444 |
| perc_edu | -9.880334e+04 | 1.118214e+04 | -8.8358203 | 0.0000000 |
| salary.y.y | 2.899929e+00 | 6.437333e-01 | 4.5048608 | 0.0000327 |
| perc_women | 5.896028e+00 | 8.907710e+01 | 0.0661902 | 0.9474541 |
| sum_ing | 1.640474e+00 | 1.165766e+00 | 1.4072073 | 0.1647024 |
| perc_eng | -8.237740e+04 | 3.314969e+04 | -2.4850124 | 0.0158610 |
Anova(model2, type=2) %>%
tidy() %>%
knitr::kable(
booktabs = TRUE,
caption = 'Anova report from linear model fit')
## Note: model has aliased coefficients
## sums of squares computed by model comparison
| term | sumsq | df | statistic | p.value |
|---|---|---|---|---|
| salary.x.x | 175405754.82 | 1 | 37.4981435 | 0.0000001 |
| salary.y | 6493114.71 | 1 | 1.3880944 | 0.2435386 |
| salary.x | NA | 0 | NA | NA |
| year2.x | 17375378.31 | 1 | 3.7144986 | 0.0588444 |
| utbregno | NA | 0 | NA | NA |
| perc_edu | 365197516.66 | 1 | 78.0717195 | 0.0000000 |
| salary.y.y | 94928546.25 | 1 | 20.2937712 | 0.0000327 |
| perc_women | 20493.73 | 1 | 0.0043811 | 0.9474541 |
| sum_ing | 9262969.01 | 1 | 1.9802323 | 0.1647024 |
| perc_eng | 28886250.67 | 1 | 6.1752864 | 0.0158610 |
| grp | NA | 0 | NA | NA |
| Residuals | 271307665.61 | 58 | NA | NA |
tb %>% mutate(grp) %>%
ggplot(aes(x = salary.x.x, y = sum_edu, colour = region, size = year2.x)) +
geom_point() +
theme(legend.position="bottom") +
facet_grid(. ~ grp) +
labs(
x = "Total R&D person years performed by employees of a higher education institution by region",
y = "Number of the population with 3 years or more post-secondary education"
)
## Warning: Removed 92 rows containing missing values (geom_point).
14.3 Correlation between regional distribution of enterprises R&D and number of the population who are engineers
tb <- readfile("000000NL_1.csv") %>%
group_by (`region`, year) %>%
mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
mutate (sum_ing = sum(salary))
tb <- readfile("UF0506A1_2.csv") %>%
group_by(`level of education`, region, year, sex) %>%
mutate(utbregno = sum(salary)) %>%
group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
group_by(`level of education`, region, year) %>%
mutate (sum_edu = sum(utbregno)) %>%
filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
right_join(tb, by = c("region", "year", "sex")) %>%
mutate (perc_eng = sum_ing / sum_edu)
tb <- readfile("000002O5.csv") %>%
right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
right_join(tb, by = c("region", "year"))
tb1 <- tb %>% select(
salary.x.x,
salary.y,
salary.x,
year2.x,
utbregno,
perc_edu,
sum_edu,
salary.y.y,
perc_women,
sum_ing,
perc_eng)
tb2 <- tb1
tb3 <- tb2 %>% scale()
d <- dist(tb3, method = "euclidean")
res.hc <- hclust(d, method = "ward.D2" )
grp <- cutree(res.hc, k = 2)
tb4 <- tb2 %>% mutate(grp)
model1 <- lm(sum_ing ~ ., data = filter(tb4, grp == 1))
summary(model1) %>%
tidy() %>%
knitr::kable(
booktabs = TRUE,
caption = 'Summary of model data for group 1, number of population with 3 years or more post-secondary education > 193000')
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.028566e+07 | 8.7e-06 | 2.328515e+12 | 0.0000000 |
| salary.x.x | -3.679508e+01 | 0.0e+00 | -2.518763e+12 | 0.0000000 |
| salary.y | 8.757519e-01 | 0.0e+00 | 3.309505e+12 | 0.0000000 |
| salary.x | 0.000000e+00 | 0.0e+00 | -8.432274e-01 | 0.4610237 |
| year2.x | -1.011406e+04 | 0.0e+00 | -2.323401e+12 | 0.0000000 |
| perc_edu | 1.000000e-07 | 1.0e-07 | 1.035901e+00 | 0.3764222 |
| sum_edu | 3.064252e-01 | 0.0e+00 | 1.834501e+12 | 0.0000000 |
| salary.y.y | 0.000000e+00 | 0.0e+00 | -1.591774e-01 | 0.8836417 |
| perc_women | 6.275719e+03 | 0.0e+00 | 1.704962e+12 | 0.0000000 |
model2 <- lm(sum_ing ~ ., data = filter(tb4, grp == 2))
summary(model2) %>%
tidy() %>%
knitr::kable(
booktabs = TRUE,
caption = 'Summary of model data for group 2, number of population with 3 years or more post-secondary education < 193000')
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 21196.0979826 | 6.274442e+04 | 0.3378164 | 0.7367212 |
| salary.x.x | -0.1087238 | 8.799350e-02 | -1.2355896 | 0.2215925 |
| salary.y | 0.3015347 | 3.633450e-02 | 8.2988643 | 0.0000000 |
| salary.x | 0.0278309 | 2.532140e-02 | 1.0991060 | 0.2762633 |
| year2.x | -11.1555186 | 3.118023e+01 | -0.3577753 | 0.7218102 |
| perc_edu | -3.9728911 | 1.897053e+03 | -0.0020942 | 0.9983362 |
| sum_edu | 0.0201251 | 1.430150e-02 | 1.4072073 | 0.1647024 |
| salary.y.y | 0.1947262 | 7.879540e-02 | 2.4712903 | 0.0164209 |
| perc_women | 11.9696948 | 9.740607e+00 | 1.2288449 | 0.2240926 |
| perc_eng | 23016.3036434 | 2.404771e+03 | 9.5711002 | 0.0000000 |
Anova(model2, type=2) %>%
tidy() %>%
knitr::kable(
booktabs = TRUE,
caption = 'Anova report from linear model fit')
## Note: model has aliased coefficients
## sums of squares computed by model comparison
| term | sumsq | df | statistic | p.value |
|---|---|---|---|---|
| salary.x.x | 8.760967e+04 | 1 | 1.5266817 | 0.2215925 |
| salary.y | 3.952218e+06 | 1 | 68.8711485 | 0.0000000 |
| salary.x | NA | 0 | NA | NA |
| year2.x | 7.345550e+03 | 1 | 0.1280032 | 0.7218102 |
| utbregno | NA | 0 | NA | NA |
| perc_edu | 2.516853e-01 | 1 | 0.0000044 | 0.9983362 |
| sum_edu | 1.136370e+05 | 1 | 1.9802323 | 0.1647024 |
| salary.y.y | 3.504702e+05 | 1 | 6.1072758 | 0.0164209 |
| perc_women | 8.665581e+04 | 1 | 1.5100598 | 0.2240926 |
| perc_eng | 5.256871e+06 | 1 | 91.6059592 | 0.0000000 |
| grp | NA | 0 | NA | NA |
| Residuals | 3.328370e+06 | 58 | NA | NA |
tb %>% mutate(grp) %>%
ggplot(aes(x = salary.y, y = sum_ing, colour = region, size = year2.x)) +
geom_point() +
theme(legend.position="bottom") +
facet_grid(. ~ grp) +
labs(
x = "Regional distribution of enterprises R&D",
y = "Number of the population who are engineers"
)
## Warning: Removed 96 rows containing missing values (geom_point).
14.4 F-value for total R&D person years performed by employees of a higher education institution by region compared to the per cent in the ssyk who are women
tb <- readfile("000000NL_10.csv") %>%
filter(salary > 0) %>%
group_by (`region`, year, `occupation (SSYK 2012)`) %>%
mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
mutate (sum_pop = sum(salary))
tb <- readfile("UF0506A1_2.csv") %>%
group_by(`level of education`, region, year, sex) %>%
mutate(utbregno = sum(salary)) %>%
group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
group_by(`level of education`, region, year) %>%
mutate (sum_edu = sum(utbregno)) %>%
filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
right_join(tb, by = c("region", "year", "sex"))
tb <- readfile("000002O5.csv") %>%
right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
right_join(tb, by = c("region", "year"))
summary_table = 0
anova_table = 0
for (i in unique(tb$`occupation (SSYK 2012)`)){
temp <- filter(tb, `occupation (SSYK 2012)` == i)
if (dim(temp)[1] > 50){
temp1 <- temp %>% select(
salary.x.x,
salary.y,
salary.x,
year2.x,
utbregno,
perc_edu,
sum_edu,
salary.y.y,
perc_women,
sum_pop)
model <- lm(perc_women ~ ., data = temp1)
summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i))
anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
}
}
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merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>%
filter (term.y == "salary.x.x") %>%
filter (term.x == "salary.x.x") %>%
ggplot () +
geom_point (mapping = aes(x = estimate, y = statistic.y))
merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>%
filter (term.y == "salary.x.x") %>%
filter (term.x == "salary.x.x") %>%
select (ssyk, estimate, statistic.y, p.value.x) %>%
arrange (desc (statistic.y)) %>%
knitr::kable(
booktabs = TRUE,
caption = 'Correlation F-value for total R&D person years performed by employees of a higher education institution by region compared to the per cent in the ssyk who are women')
| ssyk | estimate | statistic.y | p.value.x |
|---|---|---|---|
| 223 Nursing professionals (cont.) | -0.0021847 | 42.9874337 | 0.0000000 |
| 133 Research and development managers | 0.0083902 | 42.1282180 | 0.0000000 |
| 911 Cleaners and helpers | -0.0031481 | 30.4949545 | 0.0000005 |
| 214 Engineering professionals | 0.0037136 | 29.4918323 | 0.0000007 |
| 532 Personal care workers in health services | -0.0015852 | 29.3433060 | 0.0000008 |
| 819 Process control technicians | 0.0038908 | 28.2396997 | 0.0000012 |
| 816 Machine operators, food and related products | -0.0065585 | 25.5003602 | 0.0000033 |
| 831 Train operators and related workers | 0.0051004 | 22.8343195 | 0.0000096 |
| 441 Library and filing clerks | -0.0053517 | 18.2666386 | 0.0000587 |
| 533 Health care assistants | -0.0025602 | 17.4669370 | 0.0000821 |
| 742 Electronics and telecommunications installers and repairers | 0.0014082 | 16.3741388 | 0.0001307 |
| 341 Social work and religious associate professionals | -0.0035096 | 14.9875992 | 0.0002380 |
| 331 Financial and accounting associate professionals | -0.0021209 | 14.9509488 | 0.0002419 |
| 217 Designers | -0.0056023 | 14.7517188 | 0.0002639 |
| 612 Animal breeders and keepers | 0.0066739 | 13.9349503 | 0.0003785 |
| 741 Electrical equipment installers and repairers | 0.0005040 | 13.2134939 | 0.0005224 |
| 266 Social work and counselling professionals | -0.0016673 | 12.4220161 | 0.0007471 |
| 335 Tax and related government associate professionals | 0.0020394 | 12.3131587 | 0.0007851 |
| 129 Administration and service managers not elsewhere classified | 0.0025508 | 11.4164758 | 0.0011853 |
| 136 Production managers in construction and mining | 0.0012363 | 11.2564630 | 0.0012766 |
| 262 Museum curators and librarians and related professionals | -0.0035065 | 11.2463194 | 0.0012826 |
| 534 Attendants, personal assistants and related workers | -0.0019943 | 10.3956401 | 0.0019098 |
| 315 Ship and aircraft controllers and technicians | -0.0045320 | 10.1634542 | 0.0021313 |
| 342 Athletes, fitness instructors and recreational workers | 0.0029032 | 9.9933679 | 0.0023105 |
| 137 Production managers in manufacturing | 0.0016759 | 9.9039930 | 0.0024108 |
| 442 Postmen and postal facility workers | -0.0030138 | 9.6760633 | 0.0026878 |
| 222 Nursing professionals | -0.0008717 | 9.6301397 | 0.0027475 |
| 961 Recycling collectors | 0.0020107 | 8.7335737 | 0.0042366 |
| 141 Primary and secondary schools and adult education managers | 0.0025721 | 8.6905331 | 0.0043264 |
| 173 Retail and wholesale trade managers | 0.0025886 | 8.0036341 | 0.0060645 |
| 226 Dentists | 0.0023061 | 7.5457303 | 0.0076178 |
| 522 Shop staff | -0.0009155 | 7.2177187 | 0.0089835 |
| 324 Veterinary assistants | -0.0027451 | 6.9426985 | 0.0103838 |
| 941 Fast-food workers, food preparation assistants | -0.0020659 | 6.7648528 | 0.0113053 |
| 179 Other services managers not elsewhere classified | 0.0024200 | 6.1110480 | 0.0158325 |
| 962 Newspaper distributors, janitors and other service workers | -0.0014595 | 5.7758223 | 0.0188628 |
| 011 Commissioned armed forces officers | 0.0018557 | 5.7606943 | 0.0225819 |
| 151 Health care managers | -0.0014428 | 5.6771270 | 0.0198676 |
| 761 Butchers, bakers and food processors | -0.0048643 | 5.5296151 | 0.0214763 |
| 933 Dockers and ground personnel | -0.0027279 | 5.5139056 | 0.0217382 |
| 216 Architects and surveyors | 0.0026707 | 5.3769744 | 0.0232872 |
| 524 Event seller and telemarketers | -0.0041018 | 5.2568266 | 0.0248265 |
| 171 Hotel and conference managers | 0.0039124 | 5.0297831 | 0.0280383 |
| 516 Other service related workers | 0.0034150 | 4.6830041 | 0.0338279 |
| 227 Naprapaths, physiotherapists, occupational therapists | -0.0009828 | 4.6796386 | 0.0338900 |
| 422 Client information clerks | -0.0030092 | 4.4994022 | 0.0373994 |
| 512 Cooks and cold-buffet managers | -0.0019104 | 4.3182771 | 0.0413216 |
| 233 Secondary education teachers | 0.0010123 | 4.0168391 | 0.0488636 |
| 622 Aquaculture and fishery workers | -0.0097019 | 3.9164394 | 0.0536916 |
| 336 Police officers | 0.0010844 | 3.8947723 | 0.0523293 |
| 267 Religious professionals and deacons | 0.0023399 | 3.7759601 | 0.0559592 |
| 513 Waiters and bartenders | -0.0014844 | 3.6328609 | 0.0606978 |
| 134 Architectural and engineering managers | 0.0016000 | 3.5744014 | 0.0627576 |
| 241 Accountants, financial analysts and fund managers | 0.0015864 | 3.4010994 | 0.0693251 |
| 814 Machine operators, rubber, plastic and paper products | -0.0030154 | 3.3694448 | 0.0706039 |
| 243 Marketing and public relations professionals | 0.0016505 | 3.3132277 | 0.0729391 |
| 125 Sales and marketing managers | 0.0017105 | 3.3018629 | 0.0734214 |
| 242 Organisation analysts, policy administrators and human resource specialists | 0.0008063 | 3.2978973 | 0.0735905 |
| 511 Cabin crew, guides and related workers | -0.0031202 | 3.2821751 | 0.0742651 |
| 154 Managers and leaders within religious bodies | 0.0050036 | 3.2727535 | 0.0746727 |
| 261 Legal professionals | -0.0017166 | 3.2698937 | 0.0747969 |
| 833 Heavy truck and bus drivers | 0.0005226 | 3.1911286 | 0.0783082 |
| 251 ICT architects, systems analysts and test managers | -0.0007897 | 3.1387507 | 0.0807433 |
| 323 Complementary medicine therapists and associate professionals | 0.0046128 | 3.0515323 | 0.0929376 |
| 621 Forestry and related workers | -0.0024541 | 2.8220385 | 0.0973736 |
| 311 Physical and engineering science technicians | 0.0005470 | 2.7194903 | 0.1035477 |
| 235 Teaching professionals not elsewhere classified | 0.0011338 | 2.5639599 | 0.1137648 |
| 234 Primary- and pre-school teachers | -0.0004935 | 2.4977831 | 0.1184517 |
| 111 Legislators and senior officials | 0.0020444 | 2.4860681 | 0.1193039 |
| 135 Real estate and head of administration manager | 0.0019923 | 2.4650672 | 0.1208489 |
| 312 Construction and manufacturing supervisors | -0.0006152 | 2.4463760 | 0.1222431 |
| 711 Carpenters, bricklayers and construction workers | -0.0002231 | 2.4155741 | 0.1245804 |
| 344 Driving instructors and other instructors | 0.0017355 | 2.2698551 | 0.1363488 |
| 142 Preschool managers | 0.0008262 | 2.2660249 | 0.1366747 |
| 535 Dental nurses | 0.0002232 | 2.2049343 | 0.1442464 |
| 211 Physicists and chemists | 0.0069629 | 2.1522227 | 0.1467794 |
| 221 Medical doctors | 0.0005451 | 2.0392607 | 0.1576682 |
| 731 Precision-instrument makers and handicraft workers | -0.0027707 | 1.9570165 | 0.1661861 |
| 265 Creative and performing artists | -0.0020493 | 1.9103658 | 0.1712556 |
| 523 Cashiers and related clerks | 0.0013473 | 1.8953772 | 0.1729226 |
| 732 Printing trades workers | -0.0016976 | 1.8435992 | 0.1788294 |
| 832 Car, van and motorcycle drivers | -0.0006866 | 1.7109754 | 0.1950781 |
| 912 Washers, window cleaners and other cleaning workers | -0.0021987 | 1.6002302 | 0.2100068 |
| 218 Specialists within environmental and health protection | 0.0011416 | 1.5283769 | 0.2204305 |
| 721 Sheet and structural metal workers, moulders and welders, and related workers | -0.0003508 | 1.5270235 | 0.2206327 |
| 174 Sports, leisure and wellness managers | 0.0027791 | 1.4404413 | 0.2340573 |
| 352 Broadcasting and audio-visual technicians | 0.0024915 | 1.4295443 | 0.2358169 |
| 332 Insurance advisers, sales and purchasing agents | -0.0008288 | 1.3393333 | 0.2510302 |
| 931 Construction labourers | 0.0006596 | 1.2716008 | 0.2633726 |
| 161 Financial and insurance managers | -0.0012599 | 1.1884196 | 0.2793346 |
| 159 Other social services managers | 0.0010474 | 1.1752419 | 0.2819944 |
| 264 Authors, journalists and linguists | 0.0010905 | 1.1700774 | 0.2830457 |
| 122 Human resource managers | 0.0011227 | 1.1166814 | 0.2942165 |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 0.0037044 | 1.0984348 | 0.2981646 |
| 812 Metal processing and finishing plant operators | 0.0007560 | 0.9926111 | 0.3226897 |
| 817 Wood processing and papermaking plant operators | -0.0006418 | 0.9574332 | 0.3312527 |
| 815 Machine operators, textile, fur and leather products | -0.0021553 | 0.9166440 | 0.3416065 |
| 421 Croupiers, debt collectors and related workers | -0.0026206 | 0.8565411 | 0.3579341 |
| 112 Managing directors and chief executives | 0.0008931 | 0.8513428 | 0.3592977 |
| 921 Berry pickers and planters | -0.0017895 | 0.8406439 | 0.3623166 |
| 834 Mobile plant operators | -0.0003834 | 0.8402559 | 0.3624267 |
| 432 Stores and transport clerks | 0.0005341 | 0.8314583 | 0.3649369 |
| 952 Street and market vendors | 0.0085325 | 0.7679452 | 0.3865076 |
| 411 Office assistants and other secretaries | 0.0002730 | 0.7184723 | 0.3994939 |
| 333 Business services agents | -0.0005225 | 0.7134311 | 0.4011443 |
| 515 Building caretakers and related workers | -0.0006190 | 0.6727421 | 0.4148424 |
| 443 Elected representatives | 0.0024228 | 0.6691477 | 0.4161649 |
| 138 Forestry and agricultural production managers | -0.0014463 | 0.6431126 | 0.4256006 |
| 224 Psychologists and psychotherapists | 0.0005386 | 0.6199679 | 0.4336799 |
| 0002 occupations unidentifiable | -0.0002714 | 0.5472013 | 0.4619024 |
| 228 Specialists in health care not elsewhere classified | -0.0004884 | 0.5202476 | 0.4731046 |
| 152 Managers in social and curative care | -0.0009334 | 0.4856045 | 0.4881700 |
| 031 Armed forces occupations, other ranks | -0.0005560 | 0.4666810 | 0.4971920 |
| 752 Wood treaters, cabinet-makers and related trades workers | -0.0004295 | 0.4578213 | 0.5008424 |
| 811 Mining and mineral processing plant operators | 0.0003087 | 0.4435441 | 0.5077736 |
| 149 Education managers not elsewhere classified | -0.0016034 | 0.3970769 | 0.5306841 |
| 613 Mixed crop and animal breeders | -0.0006563 | 0.3717801 | 0.5439821 |
| 124 Information, communication and public relations managers | 0.0009398 | 0.3445555 | 0.5590734 |
| 225 Veterinarians | -0.0007481 | 0.3217750 | 0.5723321 |
| 132 Supply, logistics and transport managers | 0.0004625 | 0.3005788 | 0.5852395 |
| 835 Ships deck crews and related workers | 0.0012289 | 0.2918848 | 0.5916792 |
| 723 Machinery mechanics and fitters | -0.0002402 | 0.2378631 | 0.6272564 |
| 541 Other surveillance and security workers | 0.0004745 | 0.2360714 | 0.6285542 |
| 818 Other stationary plant and machine operators | -0.0007873 | 0.2130011 | 0.6458361 |
| 712 Roofers, floor layers, plumbers and pipefitters | 0.0000576 | 0.1841870 | 0.6690989 |
| 722 Blacksmiths, toolmakers and related trades workers | 0.0002775 | 0.1718315 | 0.6797390 |
| 345 Culinary associate professionals | -0.0006867 | 0.1482087 | 0.7014040 |
| 153 Elderly care managers | 0.0002252 | 0.1473160 | 0.7022606 |
| 351 ICT operations and user support technicians | 0.0002231 | 0.1352132 | 0.7141808 |
| 932 Manufacturing labourers | -0.0004441 | 0.1278619 | 0.7217200 |
| 531 Child care workers and teachers aides | -0.0001199 | 0.0905656 | 0.7643389 |
| 232 Vocational education teachers | 0.0002095 | 0.0651001 | 0.7993470 |
| 821 Assemblers | 0.0002757 | 0.0642040 | 0.8007029 |
| 813 Machine operators, chemical and pharmaceutical products | -0.0007226 | 0.0623108 | 0.8036439 |
| 131 Information and communications technology service managers | -0.0002405 | 0.0616734 | 0.8045872 |
| 753 Tailors, upholsterers and leather craftsmen | 0.0006227 | 0.0535214 | 0.8177107 |
| 121 Finance managers | -0.0001982 | 0.0414551 | 0.8392444 |
| 514 Hairdressers, beauty and body therapists | 0.0001204 | 0.0384115 | 0.8451791 |
| 713 Painters, Lacquerers, Chimney-sweepers and related trades workers | -0.0001074 | 0.0307946 | 0.8611989 |
| 325 Dental hygienists | -0.0000642 | 0.0264478 | 0.8713159 |
| 611 Market gardeners and crop growers | 0.0001257 | 0.0248903 | 0.8750886 |
| 212 Mathematicians, actuaries and statisticians | 0.0005591 | 0.0246993 | 0.8756058 |
| 172 Restaurant managers | -0.0001507 | 0.0180974 | 0.8933667 |
| 343 Photographers, interior decorators and entertainers | -0.0001898 | 0.0137057 | 0.9071341 |
| 321 Medical and pharmaceutical technicians | 0.0001454 | 0.0095114 | 0.9225832 |
| 123 Administration and planning managers | 0.0001391 | 0.0062321 | 0.9372996 |
| 021 Non-commissioned armed forces officers | -0.0001006 | 0.0029503 | 0.9569599 |
| 334 Administrative and specialized secretaries | 0.0000210 | 0.0002274 | 0.9880116 |
| 231 University and higher education teachers | 0.0000435 | 0.0001790 | 0.9893644 |
14.5 F-value for regional distribution of enterprises R&D compared to the per cent in the ssyk who are women
tb <- readfile("000000NL_10.csv") %>%
filter(salary > 0) %>%
group_by (`region`, year, `occupation (SSYK 2012)`) %>%
mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
mutate (sum_pop = sum(salary))
tb <- readfile("UF0506A1_2.csv") %>%
group_by(`level of education`, region, year, sex) %>%
mutate(utbregno = sum(salary)) %>%
group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
group_by(`level of education`, region, year) %>%
mutate (sum_edu = sum(utbregno)) %>%
filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
right_join(tb, by = c("region", "year", "sex"))
tb <- readfile("000002O5.csv") %>%
right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
right_join(tb, by = c("region", "year"))
summary_table = 0
anova_table = 0
for (i in unique(tb$`occupation (SSYK 2012)`)){
temp <- filter(tb, `occupation (SSYK 2012)` == i)
if (dim(temp)[1] > 50){
temp1 <- temp %>% select(
salary.x.x,
salary.y,
salary.x,
year2.x,
utbregno,
perc_edu,
sum_edu,
salary.y.y,
perc_women,
sum_pop)
model <- lm(perc_women ~ ., data = temp1)
summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i))
anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
}
}
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merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>%
filter (term.y == "salary.y") %>%
filter (term.x == "salary.y") %>%
ggplot () +
geom_point (mapping = aes(x = estimate, y = statistic.y))
merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>%
filter (term.y == "salary.y") %>%
filter (term.x == "salary.y") %>%
select (ssyk, estimate, statistic.y, p.value.x) %>%
arrange (desc (statistic.y)) %>%
knitr::kable(
booktabs = TRUE,
caption = 'F-value for regional distribution of enterprises R&D compared to the per cent in the ssyk who are women')
| ssyk | estimate | statistic.y | p.value.x |
|---|---|---|---|
| 932 Manufacturing labourers | -0.0006457 | 7.8674667 | 0.0064883 |
| 621 Forestry and related workers | -0.0005947 | 6.8164730 | 0.0110114 |
| 0002 occupations unidentifiable | -0.0001579 | 6.2446815 | 0.0147719 |
| 731 Precision-instrument makers and handicraft workers | 0.0013486 | 6.1740680 | 0.0153227 |
| 138 Forestry and agricultural production managers | -0.0006651 | 5.5593064 | 0.0215007 |
| 962 Newspaper distributors, janitors and other service workers | 0.0002656 | 5.4134525 | 0.0228402 |
| 812 Metal processing and finishing plant operators | -0.0002946 | 5.2690891 | 0.0248445 |
| 611 Market gardeners and crop growers | -0.0003365 | 5.1078794 | 0.0268863 |
| 161 Financial and insurance managers | 0.0004603 | 4.7476685 | 0.0326584 |
| 124 Information, communication and public relations managers | 0.0006090 | 4.5956230 | 0.0354799 |
| 243 Marketing and public relations professionals | -0.0003370 | 4.4046033 | 0.0393999 |
| 811 Mining and mineral processing plant operators | -0.0001685 | 4.2831471 | 0.0424713 |
| 225 Veterinarians | -0.0003855 | 4.1685923 | 0.0448967 |
| 622 Aquaculture and fishery workers | 0.0014431 | 4.1407094 | 0.0475260 |
| 136 Production managers in construction and mining | 0.0001504 | 3.7806116 | 0.0558121 |
| 834 Mobile plant operators | 0.0001575 | 3.6064765 | 0.0616182 |
| 315 Ship and aircraft controllers and technicians | -0.0004471 | 3.3671367 | 0.0706981 |
| 335 Tax and related government associate professionals | -0.0002298 | 3.3401053 | 0.0718122 |
| 351 ICT operations and user support technicians | 0.0001968 | 3.3268733 | 0.0723646 |
| 821 Assemblers | -0.0004344 | 3.3020157 | 0.0734149 |
| 132 Supply, logistics and transport managers | 0.0003189 | 3.2846847 | 0.0741570 |
| 173 Retail and wholesale trade managers | -0.0002465 | 2.9120508 | 0.0922907 |
| 323 Complementary medicine therapists and associate professionals | 0.0006325 | 2.8737549 | 0.1024563 |
| 723 Machinery mechanics and fitters | 0.0001751 | 2.8455679 | 0.0960156 |
| 333 Business services agents | -0.0001954 | 2.4998182 | 0.1183044 |
| 512 Cooks and cold-buffet managers | -0.0003148 | 2.4604203 | 0.1211939 |
| 262 Museum curators and librarians and related professionals | 0.0002545 | 2.4324609 | 0.1232928 |
| 921 Berry pickers and planters | 0.0006166 | 2.3615517 | 0.1288032 |
| 815 Machine operators, textile, fur and leather products | 0.0006943 | 2.3454131 | 0.1300960 |
| 421 Croupiers, debt collectors and related workers | -0.0010400 | 2.3131308 | 0.1328551 |
| 931 Construction labourers | -0.0001629 | 2.2504645 | 0.1381364 |
| 218 Specialists within environmental and health protection | 0.0002906 | 2.1906384 | 0.1432756 |
| 111 Legislators and senior officials | 0.0003404 | 2.1245424 | 0.1493651 |
| 722 Blacksmiths, toolmakers and related trades workers | -0.0002308 | 1.9985660 | 0.1618178 |
| 129 Administration and service managers not elsewhere classified | 0.0001815 | 1.9790145 | 0.1638565 |
| 819 Process control technicians | -0.0001879 | 1.9684353 | 0.1649721 |
| 217 Designers | 0.0003557 | 1.9121452 | 0.1710589 |
| 336 Police officers | 0.0001246 | 1.8613448 | 0.1767787 |
| 233 Secondary education teachers | 0.0001264 | 1.8172563 | 0.1819255 |
| 134 Architectural and engineering managers | 0.0002561 | 1.8170276 | 0.1819527 |
| 713 Painters, Lacquerers, Chimney-sweepers and related trades workers | -0.0001725 | 1.8033889 | 0.1835807 |
| 933 Dockers and ground personnel | 0.0002727 | 1.7849490 | 0.1859325 |
| 123 Administration and planning managers | -0.0004179 | 1.6888772 | 0.1979529 |
| 121 Finance managers | 0.0002150 | 1.6793019 | 0.1992142 |
| 912 Washers, window cleaners and other cleaning workers | 0.0004237 | 1.6503922 | 0.2030812 |
| 174 Sports, leisure and wellness managers | 0.0005197 | 1.6177940 | 0.2075500 |
| 833 Heavy truck and bus drivers | 0.0000728 | 1.5477745 | 0.2175559 |
| 151 Health care managers | 0.0001466 | 1.5471463 | 0.2176483 |
| 816 Machine operators, food and related products | 0.0002940 | 1.4607410 | 0.2308221 |
| 242 Organisation analysts, policy administrators and human resource specialists | 0.0000952 | 1.4329401 | 0.2352668 |
| 753 Tailors, upholsterers and leather craftsmen | 0.0006041 | 1.3448186 | 0.2500710 |
| 432 Stores and transport clerks | 0.0001284 | 1.2955273 | 0.2588581 |
| 531 Child care workers and teachers aides | 0.0000775 | 1.2605842 | 0.2653229 |
| 171 Hotel and conference managers | 0.0003330 | 1.1982262 | 0.2773761 |
| 533 Health care assistants | 0.0001185 | 1.1454670 | 0.2881250 |
| 524 Event seller and telemarketers | -0.0003307 | 1.1302900 | 0.2913162 |
| 343 Photographers, interior decorators and entertainers | -0.0002936 | 1.1215825 | 0.2931677 |
| 952 Street and market vendors | 0.0006296 | 1.1028561 | 0.3004513 |
| 761 Butchers, bakers and food processors | 0.0003954 | 1.0896234 | 0.3000958 |
| 341 Social work and religious associate professionals | 0.0001781 | 1.0386322 | 0.3116000 |
| 226 Dentists | 0.0001967 | 1.0232630 | 0.3151812 |
| 441 Library and filing clerks | 0.0002325 | 0.9321916 | 0.3375713 |
| 011 Commissioned armed forces officers | -0.0001262 | 0.9208536 | 0.3446761 |
| 442 Postmen and postal facility workers | 0.0001607 | 0.8966173 | 0.3469020 |
| 211 Physicists and chemists | 0.0004656 | 0.8888187 | 0.3489946 |
| 135 Real estate and head of administration manager | -0.0002082 | 0.8887805 | 0.3490049 |
| 231 University and higher education teachers | -0.0002251 | 0.8545843 | 0.3583900 |
| 264 Authors, journalists and linguists | -0.0001682 | 0.8186704 | 0.3686293 |
| 541 Other surveillance and security workers | -0.0001442 | 0.7956533 | 0.3754093 |
| 752 Wood treaters, cabinet-makers and related trades workers | -0.0001048 | 0.7726790 | 0.3823549 |
| 742 Electronics and telecommunications installers and repairers | -0.0000558 | 0.7406660 | 0.3923446 |
| 721 Sheet and structural metal workers, moulders and welders, and related workers | -0.0000462 | 0.7370372 | 0.3935007 |
| 817 Wood processing and papermaking plant operators | 0.0000886 | 0.6793195 | 0.4126612 |
| 612 Animal breeders and keepers | -0.0002631 | 0.6712576 | 0.4153552 |
| 732 Printing trades workers | -0.0001864 | 0.6707332 | 0.4155366 |
| 137 Production managers in manufacturing | 0.0000850 | 0.6411173 | 0.4259778 |
| 266 Social work and counselling professionals | -0.0000727 | 0.6200694 | 0.4336424 |
| 814 Machine operators, rubber, plastic and paper products | -0.0002487 | 0.6167562 | 0.4348681 |
| 228 Specialists in health care not elsewhere classified | 0.0000981 | 0.5908623 | 0.4446353 |
| 711 Carpenters, bricklayers and construction workers | 0.0000215 | 0.5306474 | 0.4687307 |
| 234 Primary- and pre-school teachers | 0.0000435 | 0.5079513 | 0.4783629 |
| 523 Cashiers and related clerks | -0.0001238 | 0.4893701 | 0.4864940 |
| 232 Vocational education teachers | -0.0001063 | 0.4848386 | 0.4885121 |
| 324 Veterinary assistants | -0.0001322 | 0.4820243 | 0.4898379 |
| 813 Machine operators, chemical and pharmaceutical products | 0.0004432 | 0.4403635 | 0.5092241 |
| 422 Client information clerks | 0.0001506 | 0.4120946 | 0.5229756 |
| 214 Engineering professionals | 0.0001653 | 0.4107554 | 0.5236497 |
| 818 Other stationary plant and machine operators | -0.0002147 | 0.3804577 | 0.5393313 |
| 515 Building caretakers and related workers | 0.0000906 | 0.3579344 | 0.5515595 |
| 832 Car, van and motorcycle drivers | 0.0000565 | 0.3571153 | 0.5520140 |
| 443 Elected representatives | 0.0003277 | 0.3507870 | 0.5556039 |
| 532 Personal care workers in health services | -0.0000325 | 0.3324210 | 0.5660604 |
| 179 Other services managers not elsewhere classified | 0.0001052 | 0.3204798 | 0.5731045 |
| 021 Non-commissioned armed forces officers | 0.0001772 | 0.2991253 | 0.5875473 |
| 216 Architects and surveyors | 0.0001041 | 0.2893135 | 0.5923429 |
| 031 Armed forces occupations, other ranks | -0.0000769 | 0.2884937 | 0.5932074 |
| 514 Hairdressers, beauty and body therapists | 0.0000578 | 0.2729764 | 0.6029686 |
| 511 Cabin crew, guides and related workers | 0.0001527 | 0.2698108 | 0.6050743 |
| 154 Managers and leaders within religious bodies | -0.0002664 | 0.2653019 | 0.6081008 |
| 332 Insurance advisers, sales and purchasing agents | 0.0000535 | 0.2549145 | 0.6151992 |
| 222 Nursing professionals | -0.0000275 | 0.2511228 | 0.6178357 |
| 224 Psychologists and psychotherapists | -0.0000643 | 0.2379413 | 0.6271999 |
| 153 Elderly care managers | 0.0000544 | 0.2311075 | 0.6321820 |
| 261 Legal professionals | -0.0000782 | 0.2174508 | 0.6424167 |
| 312 Construction and manufacturing supervisors | -0.0000359 | 0.1906820 | 0.6636748 |
| 149 Education managers not elsewhere classified | -0.0001966 | 0.1829755 | 0.6701606 |
| 223 Nursing professionals (cont.) | 0.0000270 | 0.1810968 | 0.6717195 |
| 221 Medical doctors | -0.0000259 | 0.1710916 | 0.6803902 |
| 241 Accountants, financial analysts and fund managers | 0.0000619 | 0.1637486 | 0.6869450 |
| 334 Administrative and specialized secretaries | -0.0000959 | 0.1603409 | 0.6900452 |
| 122 Human resource managers | -0.0000645 | 0.1282636 | 0.7213019 |
| 516 Other service related workers | 0.0001031 | 0.1217120 | 0.7282175 |
| 961 Recycling collectors | 0.0000425 | 0.1182514 | 0.7319551 |
| 342 Athletes, fitness instructors and recreational workers | 0.0000608 | 0.1147586 | 0.7357900 |
| 911 Cleaners and helpers | -0.0000359 | 0.1122787 | 0.7385526 |
| 352 Broadcasting and audio-visual technicians | -0.0001250 | 0.1113099 | 0.7396411 |
| 345 Culinary associate professionals | 0.0000869 | 0.1054971 | 0.7462851 |
| 831 Train operators and related workers | 0.0000639 | 0.1041019 | 0.7479373 |
| 522 Shop staff | -0.0000201 | 0.1010053 | 0.7515594 |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 0.0001097 | 0.0993153 | 0.7535772 |
| 235 Teaching professionals not elsewhere classified | 0.0000380 | 0.0855566 | 0.7707570 |
| 159 Other social services managers | -0.0000491 | 0.0847614 | 0.7717945 |
| 941 Fast-food workers, food preparation assistants | 0.0000427 | 0.0799479 | 0.7781907 |
| 411 Office assistants and other secretaries | -0.0000162 | 0.0794263 | 0.7788962 |
| 133 Research and development managers | 0.0001401 | 0.0680307 | 0.7950260 |
| 835 Ships deck crews and related workers | -0.0001794 | 0.0601924 | 0.8073076 |
| 712 Roofers, floor layers, plumbers and pipefitters | -0.0000061 | 0.0566234 | 0.8126000 |
| 325 Dental hygienists | -0.0000176 | 0.0544556 | 0.8162173 |
| 141 Primary and secondary schools and adult education managers | 0.0000366 | 0.0532732 | 0.8181264 |
| 311 Physical and engineering science technicians | 0.0000198 | 0.0510731 | 0.8218558 |
| 613 Mixed crop and animal breeders | -0.0000450 | 0.0504070 | 0.8230014 |
| 112 Managing directors and chief executives | -0.0000344 | 0.0490575 | 0.8253474 |
| 131 Information and communications technology service managers | 0.0000323 | 0.0373463 | 0.8473135 |
| 534 Attendants, personal assistants and related workers | -0.0000222 | 0.0358542 | 0.8503572 |
| 172 Restaurant managers | -0.0000327 | 0.0311408 | 0.8604290 |
| 251 ICT architects, systems analysts and test managers | 0.0000131 | 0.0293397 | 0.8644842 |
| 142 Preschool managers | 0.0000158 | 0.0233120 | 0.8790816 |
| 321 Medical and pharmaceutical technicians | -0.0000328 | 0.0220096 | 0.8824820 |
| 152 Managers in social and curative care | -0.0000345 | 0.0208776 | 0.8855222 |
| 513 Waiters and bartenders | -0.0000189 | 0.0206471 | 0.8861515 |
| 227 Naprapaths, physiotherapists, occupational therapists | -0.0000124 | 0.0199742 | 0.8880095 |
| 535 Dental nurses | -0.0000036 | 0.0168930 | 0.8971423 |
| 125 Sales and marketing managers | 0.0000147 | 0.0090545 | 0.9244596 |
| 331 Financial and accounting associate professionals | 0.0000072 | 0.0059991 | 0.9384805 |
| 267 Religious professionals and deacons | -0.0000119 | 0.0023085 | 0.9618142 |
| 212 Mathematicians, actuaries and statisticians | -0.0000249 | 0.0016135 | 0.9680824 |
| 344 Driving instructors and other instructors | 0.0000078 | 0.0009629 | 0.9753328 |
| 265 Creative and performing artists | 0.0000052 | 0.0004044 | 0.9840117 |
| 741 Electrical equipment installers and repairers | 0.0000003 | 0.0001314 | 0.9908852 |
14.6 F-value for total R&D person years performed by employees of a higher education institution by region and number of the population in the ssyk
tb <- readfile("000000NL_10.csv") %>%
filter(salary > 0) %>%
group_by (`region`, year, `occupation (SSYK 2012)`) %>%
mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
mutate (sum_pop = sum(salary))
tb <- readfile("UF0506A1_2.csv") %>%
group_by(`level of education`, region, year, sex) %>%
mutate(utbregno = sum(salary)) %>%
group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
group_by(`level of education`, region, year) %>%
mutate (sum_edu = sum(utbregno)) %>%
filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
right_join(tb, by = c("region", "year", "sex"))
tb <- readfile("000002O5.csv") %>%
right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
right_join(tb, by = c("region", "year"))
summary_table = 0
anova_table = 0
for (i in unique(tb$`occupation (SSYK 2012)`)){
temp <- filter(tb, `occupation (SSYK 2012)` == i)
if (dim(temp)[1] > 50){
temp1 <- temp %>% select(
salary.x.x,
salary.y,
salary.x,
year2.x,
utbregno,
perc_edu,
sum_edu,
salary.y.y,
perc_women,
sum_pop)
model <- lm(sum_pop ~ ., data = temp1)
summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i))
anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
}
}
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merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>%
filter (term.y == "salary.x.x") %>%
filter (term.x == "salary.x.x") %>%
ggplot () +
geom_point (mapping = aes(x = estimate, y = statistic.y))
merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>%
filter (term.y == "salary.x.x") %>%
filter (term.x == "salary.x.x") %>%
select (ssyk, estimate, statistic.y, p.value.x) %>%
arrange (desc (statistic.y)) %>%
knitr::kable(
booktabs = TRUE,
caption = 'F-value for total R&D person years performed by employees of a higher education institution by region and number of the population in the ssyk')
| ssyk | estimate | statistic.y | p.value.x |
|---|---|---|---|
| 211 Physicists and chemists | 0.1463558 | 58.0362031 | 0.0000000 |
| 411 Office assistants and other secretaries | -0.7418784 | 33.9587364 | 0.0000002 |
| 422 Client information clerks | -0.4022829 | 22.3770565 | 0.0000111 |
| 345 Culinary associate professionals | -0.0198665 | 19.1732781 | 0.0000403 |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | 0.0740779 | 15.6583774 | 0.0001778 |
| 122 Human resource managers | -0.0806716 | 14.3185096 | 0.0003193 |
| 511 Cabin crew, guides and related workers | -0.0275086 | 14.1021568 | 0.0003514 |
| 621 Forestry and related workers | -0.0516183 | 13.4380725 | 0.0004723 |
| 812 Metal processing and finishing plant operators | -0.2192918 | 13.3306195 | 0.0005128 |
| 231 University and higher education teachers | 0.2117384 | 12.8144288 | 0.0006253 |
| 225 Veterinarians | 0.0202851 | 12.7088438 | 0.0006559 |
| 334 Administrative and specialized secretaries | -0.1507574 | 12.4085159 | 0.0007517 |
| 133 Research and development managers | 0.0465105 | 12.3546361 | 0.0007938 |
| 331 Financial and accounting associate professionals | -0.2324933 | 9.8898466 | 0.0024271 |
| 216 Architects and surveyors | -0.0297791 | 9.6219620 | 0.0027583 |
| 261 Legal professionals | -0.0976863 | 9.1598261 | 0.0034448 |
| 344 Driving instructors and other instructors | -0.0146249 | 9.0040863 | 0.0037145 |
| 343 Photographers, interior decorators and entertainers | -0.0176099 | 8.8024704 | 0.0040967 |
| 531 Child care workers and teachers aides | -0.2996087 | 8.2569096 | 0.0053513 |
| 232 Vocational education teachers | -0.0436771 | 7.7179267 | 0.0069898 |
| 817 Wood processing and papermaking plant operators | -0.1994336 | 7.4406100 | 0.0080805 |
| 262 Museum curators and librarians and related professionals | 0.0264295 | 6.8038803 | 0.0110823 |
| 742 Electronics and telecommunications installers and repairers | -0.0450196 | 6.2047048 | 0.0150811 |
| 534 Attendants, personal assistants and related workers | 0.3433275 | 6.1292707 | 0.0156833 |
| 124 Information, communication and public relations managers | -0.0186241 | 5.9783154 | 0.0169658 |
| 831 Train operators and related workers | -0.0402474 | 5.7522148 | 0.0191763 |
| 243 Marketing and public relations professionals | -0.2371783 | 5.7483964 | 0.0191365 |
| 141 Primary and secondary schools and adult education managers | -0.0240571 | 5.6421717 | 0.0202369 |
| 171 Hotel and conference managers | -0.0060783 | 5.6243691 | 0.0204278 |
| 031 Armed forces occupations, other ranks | 0.0834141 | 5.6177319 | 0.0210675 |
| 532 Personal care workers in health services | 1.2202192 | 5.2938515 | 0.0243410 |
| 514 Hairdressers, beauty and body therapists | -0.0337248 | 4.5475585 | 0.0364252 |
| 941 Fast-food workers, food preparation assistants | -0.1765814 | 4.4958363 | 0.0374727 |
| 335 Tax and related government associate professionals | -0.0977686 | 4.4595177 | 0.0382275 |
| 523 Cashiers and related clerks | -0.0353361 | 4.0807251 | 0.0471494 |
| 264 Authors, journalists and linguists | -0.0511786 | 4.0400609 | 0.0482329 |
| 342 Athletes, fitness instructors and recreational workers | -0.0392351 | 3.9455483 | 0.0508564 |
| 179 Other services managers not elsewhere classified | -0.0099504 | 3.9147611 | 0.0517440 |
| 153 Elderly care managers | 0.0357532 | 3.8588155 | 0.0534001 |
| 214 Engineering professionals | 0.3662656 | 3.5939076 | 0.0620620 |
| 212 Mathematicians, actuaries and statisticians | 0.0114794 | 3.4928962 | 0.0661394 |
| 111 Legislators and senior officials | -0.0088274 | 3.4241806 | 0.0684086 |
| 266 Social work and counselling professionals | -0.0616859 | 3.3121156 | 0.0729861 |
| 336 Police officers | -0.0226565 | 3.2996371 | 0.0735162 |
| 721 Sheet and structural metal workers, moulders and welders, and related workers | -0.1536410 | 3.2888635 | 0.0739773 |
| 722 Blacksmiths, toolmakers and related trades workers | -0.3582090 | 3.0124220 | 0.0869671 |
| 441 Library and filing clerks | 0.0085926 | 2.9690978 | 0.0892221 |
| 221 Medical doctors | -0.0437246 | 2.8247567 | 0.0972157 |
| 834 Mobile plant operators | -0.1731126 | 2.7713912 | 0.1003701 |
| 132 Supply, logistics and transport managers | 0.0221364 | 2.7713222 | 0.1003742 |
| 932 Manufacturing labourers | 0.0178137 | 2.6899696 | 0.1054052 |
| 541 Other surveillance and security workers | -0.0927008 | 2.6671869 | 0.1068642 |
| 242 Organisation analysts, policy administrators and human resource specialists | -0.1727573 | 2.6239929 | 0.1096929 |
| 421 Croupiers, debt collectors and related workers | -0.0056013 | 2.5698599 | 0.1134856 |
| 112 Managing directors and chief executives | -0.0715940 | 2.5351362 | 0.1157800 |
| 159 Other social services managers | -0.0163546 | 2.4373636 | 0.1229218 |
| 911 Cleaners and helpers | 0.1361728 | 2.3144913 | 0.1326146 |
| 753 Tailors, upholsterers and leather craftsmen | -0.0093093 | 2.2781682 | 0.1356443 |
| 952 Street and market vendors | 0.0029578 | 2.2756346 | 0.1399138 |
| 152 Managers in social and curative care | -0.0100460 | 2.1356902 | 0.1483175 |
| 516 Other service related workers | -0.0087183 | 2.1148072 | 0.1502870 |
| 174 Sports, leisure and wellness managers | -0.0030682 | 2.0916549 | 0.1525058 |
| 813 Machine operators, chemical and pharmaceutical products | 0.0198509 | 2.0334205 | 0.1585168 |
| 142 Preschool managers | -0.0073834 | 2.0108663 | 0.1605504 |
| 233 Secondary education teachers | 0.0148162 | 1.9776662 | 0.1639982 |
| 315 Ship and aircraft controllers and technicians | -0.0320213 | 1.9299456 | 0.1691062 |
| 622 Aquaculture and fishery workers | 0.0171978 | 1.6964738 | 0.1990962 |
| 613 Mixed crop and animal breeders | -0.0175992 | 1.6872578 | 0.1981655 |
| 224 Psychologists and psychotherapists | 0.0115318 | 1.4934392 | 0.2257253 |
| 533 Health care assistants | 0.1191213 | 1.4877238 | 0.2266062 |
| 962 Newspaper distributors, janitors and other service workers | 0.0529368 | 1.4755997 | 0.2284888 |
| 138 Forestry and agricultural production managers | -0.0026924 | 1.2888059 | 0.2605688 |
| 333 Business services agents | -0.0176886 | 1.2808529 | 0.2615485 |
| 811 Mining and mineral processing plant operators | -0.0408799 | 1.2334579 | 0.2708272 |
| 921 Berry pickers and planters | -0.0047961 | 1.1956664 | 0.2778857 |
| 723 Machinery mechanics and fitters | -0.1785306 | 1.1354512 | 0.2902259 |
| 241 Accountants, financial analysts and fund managers | 0.0611027 | 1.0926428 | 0.2994322 |
| 149 Education managers not elsewhere classified | -0.0015607 | 1.0682708 | 0.3049454 |
| 833 Heavy truck and bus drivers | -0.0980038 | 0.9266111 | 0.3390121 |
| 325 Dental hygienists | 0.0072419 | 0.9042218 | 0.3451747 |
| 443 Elected representatives | -0.0013574 | 0.9023337 | 0.3454721 |
| 515 Building caretakers and related workers | -0.0476197 | 0.7949414 | 0.3756218 |
| 265 Creative and performing artists | 0.0099944 | 0.7761485 | 0.3812943 |
| 136 Production managers in construction and mining | 0.0171352 | 0.7716266 | 0.3826774 |
| 267 Religious professionals and deacons | 0.0033847 | 0.7441644 | 0.3912346 |
| 819 Process control technicians | -0.0757045 | 0.7158311 | 0.4003573 |
| 172 Restaurant managers | -0.0078559 | 0.6287919 | 0.4304407 |
| 226 Dentists | -0.0050437 | 0.6141053 | 0.4358527 |
| 522 Shop staff | -0.0683203 | 0.5594339 | 0.4569587 |
| 933 Dockers and ground personnel | -0.0137935 | 0.5177664 | 0.4742262 |
| 234 Primary- and pre-school teachers | 0.1638680 | 0.4596416 | 0.4999951 |
| 251 ICT architects, systems analysts and test managers | 0.3146785 | 0.4507707 | 0.5041478 |
| 323 Complementary medicine therapists and associate professionals | 0.0005862 | 0.4300329 | 0.5179629 |
| 161 Financial and insurance managers | 0.0157011 | 0.4288542 | 0.5146678 |
| 121 Finance managers | -0.0074248 | 0.4219395 | 0.5180669 |
| 442 Postmen and postal facility workers | -0.0192643 | 0.4193823 | 0.5193340 |
| 752 Wood treaters, cabinet-makers and related trades workers | -0.0307698 | 0.4072236 | 0.5254350 |
| 612 Animal breeders and keepers | -0.0113570 | 0.3997059 | 0.5292719 |
| 218 Specialists within environmental and health protection | -0.0038586 | 0.3909445 | 0.5338079 |
| 931 Construction labourers | -0.0082315 | 0.3868301 | 0.5360201 |
| 228 Specialists in health care not elsewhere classified | 0.0075191 | 0.3624893 | 0.5490449 |
| 312 Construction and manufacturing supervisors | -0.0155602 | 0.3409234 | 0.5611472 |
| 351 ICT operations and user support technicians | -0.0739311 | 0.3295452 | 0.5677412 |
| 961 Recycling collectors | 0.0073579 | 0.3224956 | 0.5719032 |
| 611 Market gardeners and crop growers | 0.0170079 | 0.3213459 | 0.5725878 |
| 821 Assemblers | -0.1199391 | 0.2933950 | 0.5897489 |
| 912 Washers, window cleaners and other cleaning workers | 0.0051673 | 0.2652329 | 0.6081474 |
| 235 Teaching professionals not elsewhere classified | -0.0167530 | 0.2514236 | 0.6176256 |
| 137 Production managers in manufacturing | -0.0228467 | 0.2495196 | 0.6189580 |
| 123 Administration and planning managers | -0.0074163 | 0.2407100 | 0.6252067 |
| 352 Broadcasting and audio-visual technicians | 0.0101539 | 0.2390439 | 0.6264044 |
| 217 Designers | 0.0078311 | 0.2239504 | 0.6374980 |
| 818 Other stationary plant and machine operators | -0.0153782 | 0.2237146 | 0.6376749 |
| 223 Nursing professionals (cont.) | 0.0318760 | 0.2228286 | 0.6383407 |
| 816 Machine operators, food and related products | 0.0166026 | 0.2136065 | 0.6453684 |
| 513 Waiters and bartenders | -0.0057509 | 0.1904920 | 0.6638318 |
| 832 Car, van and motorcycle drivers | -0.0195279 | 0.1858496 | 0.6676997 |
| 524 Event seller and telemarketers | -0.0059479 | 0.1687607 | 0.6824528 |
| 311 Physical and engineering science technicians | 0.0530238 | 0.1575890 | 0.6925768 |
| 135 Real estate and head of administration manager | -0.0021021 | 0.1347974 | 0.7146009 |
| 021 Non-commissioned armed forces officers | 0.0033013 | 0.1314770 | 0.7188633 |
| 0002 occupations unidentifiable | -0.0589769 | 0.1209906 | 0.7289917 |
| 432 Stores and transport clerks | 0.0536965 | 0.1051802 | 0.7466531 |
| 125 Sales and marketing managers | 0.0238198 | 0.1041027 | 0.7479089 |
| 134 Architectural and engineering managers | 0.0045784 | 0.0964988 | 0.7569825 |
| 324 Veterinary assistants | -0.0018865 | 0.0886965 | 0.7667358 |
| 731 Precision-instrument makers and handicraft workers | 0.0016801 | 0.0816069 | 0.7759633 |
| 713 Painters, Lacquerers, Chimney-sweepers and related trades workers | 0.0082431 | 0.0726258 | 0.7883330 |
| 332 Insurance advisers, sales and purchasing agents | 0.0389329 | 0.0717852 | 0.7895319 |
| 814 Machine operators, rubber, plastic and paper products | -0.0172823 | 0.0599319 | 0.8073098 |
| 173 Retail and wholesale trade managers | 0.0017316 | 0.0485386 | 0.8262583 |
| 712 Roofers, floor layers, plumbers and pipefitters | 0.0088647 | 0.0470323 | 0.8289322 |
| 741 Electrical equipment installers and repairers | -0.0123605 | 0.0460158 | 0.8307620 |
| 711 Carpenters, bricklayers and construction workers | -0.0227412 | 0.0450086 | 0.8325961 |
| 131 Information and communications technology service managers | 0.0079229 | 0.0270484 | 0.8698332 |
| 011 Commissioned armed forces officers | -0.0024839 | 0.0238994 | 0.8781434 |
| 129 Administration and service managers not elsewhere classified | 0.0021438 | 0.0228879 | 0.8801781 |
| 222 Nursing professionals | 0.0105296 | 0.0194864 | 0.8893761 |
| 732 Printing trades workers | 0.0021334 | 0.0178872 | 0.8939842 |
| 512 Cooks and cold-buffet managers | 0.0057852 | 0.0163700 | 0.8985540 |
| 835 Ships deck crews and related workers | -0.0003782 | 0.0065494 | 0.9358580 |
| 154 Managers and leaders within religious bodies | 0.0001459 | 0.0059884 | 0.9385351 |
| 815 Machine operators, textile, fur and leather products | 0.0008588 | 0.0049907 | 0.9438789 |
| 535 Dental nurses | 0.0013194 | 0.0043521 | 0.9476813 |
| 341 Social work and religious associate professionals | -0.0018668 | 0.0037401 | 0.9514066 |
| 321 Medical and pharmaceutical technicians | 0.0021093 | 0.0036609 | 0.9519228 |
| 761 Butchers, bakers and food processors | 0.0018300 | 0.0036546 | 0.9519644 |
| 151 Health care managers | -0.0006433 | 0.0026781 | 0.9588732 |
| 227 Naprapaths, physiotherapists, occupational therapists | 0.0001902 | 0.0000911 | 0.9924113 |
14.7 F-value for regional distribution of enterprises R&D compared to the number of the population in the ssyk
tb <- readfile("000000NL_10.csv") %>%
filter(salary > 0) %>%
group_by (`region`, year, `occupation (SSYK 2012)`) %>%
mutate (perc_women = as.numeric (sub ("%", "", perc_women (salary)))) %>%
mutate (sum_pop = sum(salary))
tb <- readfile("UF0506A1_2.csv") %>%
group_by(`level of education`, region, year, sex) %>%
mutate(utbregno = sum(salary)) %>%
group_by(region, year, sex) %>% mutate(perc_edu = utbregno / sum(utbregno)) %>%
group_by(`level of education`, region, year) %>%
mutate (sum_edu = sum(utbregno)) %>%
filter (`level of education` == "post-secondary education 3 years or more (ISCED97 5A)") %>%
right_join(tb, by = c("region", "year", "sex"))
tb <- readfile("000002O5.csv") %>%
right_join(tb, by = c("region", "year"))
tb <- readfile("000002LR.csv") %>%
right_join(tb, by = c("region", "year"))
summary_table = 0
anova_table = 0
for (i in unique(tb$`occupation (SSYK 2012)`)){
temp <- filter(tb, `occupation (SSYK 2012)` == i)
if (dim(temp)[1] > 50){
temp1 <- temp %>% select(
salary.x.x,
salary.y,
salary.x,
year2.x,
utbregno,
perc_edu,
sum_edu,
salary.y.y,
perc_women,
sum_pop)
model <- lm(sum_pop ~ ., data = temp1)
summary_table <- rbind (summary_table, mutate (tidy (summary (model)), ssyk = i))
anova_table <- rbind (anova_table, mutate (tidy (Anova (model, type = 2)), ssyk = i))
}
}
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merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>%
filter (term.y == "salary.y") %>%
filter (term.x == "salary.y") %>%
ggplot () +
geom_point (mapping = aes(x = estimate, y = statistic.y))
merge(summary_table, anova_table, by = "ssyk", all = TRUE) %>%
filter (term.y == "salary.y") %>%
filter (term.x == "salary.y") %>%
select (ssyk, estimate, statistic.y, p.value.x) %>%
arrange (desc (statistic.y)) %>%
knitr::kable(
booktabs = TRUE,
caption = 'F-value for regional distribution of enterprises R&D compared to the number of the population in the ssyk')
| ssyk | estimate | statistic.y | p.value.x |
|---|---|---|---|
| 133 Research and development managers | 0.0288360 | 86.5766200 | 0.0000000 |
| 731 Precision-instrument makers and handicraft workers | 0.0093522 | 57.5823738 | 0.0000000 |
| 214 Engineering professionals | 0.3468839 | 53.0273935 | 0.0000000 |
| 835 Ships deck crews and related workers | 0.0071463 | 46.0241635 | 0.0000000 |
| 622 Aquaculture and fishery workers | 0.0090426 | 39.7498695 | 0.0000001 |
| 512 Cooks and cold-buffet managers | 0.0460682 | 32.5713459 | 0.0000002 |
| 224 Psychologists and psychotherapists | 0.0074879 | 21.8187489 | 0.0000138 |
| 411 Office assistants and other secretaries | 0.1121404 | 21.7044739 | 0.0000145 |
| 335 Tax and related government associate professionals | 0.0365611 | 17.5140675 | 0.0000805 |
| 722 Blacksmiths, toolmakers and related trades workers | 0.1946837 | 17.4296331 | 0.0000834 |
| 136 Production managers in construction and mining | 0.0144288 | 16.7004996 | 0.0001137 |
| 211 Physicists and chemists | 0.0095241 | 15.4773228 | 0.0001923 |
| 344 Driving instructors and other instructors | 0.0038660 | 14.4772864 | 0.0002977 |
| 941 Fast-food workers, food preparation assistants | 0.0540376 | 14.4405037 | 0.0003026 |
| 311 Physical and engineering science technicians | 0.1136168 | 12.6588107 | 0.0006709 |
| 134 Architectural and engineering managers | 0.0099713 | 10.6873560 | 0.0016649 |
| 813 Machine operators, chemical and pharmaceutical products | -0.0097243 | 10.1934751 | 0.0021475 |
| 228 Specialists in health care not elsewhere classified | 0.0067824 | 9.3376485 | 0.0031617 |
| 342 Athletes, fitness instructors and recreational workers | 0.0105651 | 9.1049243 | 0.0035375 |
| 421 Croupiers, debt collectors and related workers | 0.0023281 | 8.0259161 | 0.0060407 |
| 831 Train operators and related workers | -0.0071873 | 7.2206450 | 0.0090238 |
| 821 Assemblers | 0.1238140 | 6.7585852 | 0.0113415 |
| 713 Painters, Lacquerers, Chimney-sweepers and related trades workers | 0.0159076 | 6.5312493 | 0.0127421 |
| 151 Health care managers | -0.0055369 | 6.0031620 | 0.0167473 |
| 226 Dentists | 0.0033322 | 5.8488952 | 0.0181536 |
| 179 Other services managers not elsewhere classified | 0.0021802 | 5.7851798 | 0.0187704 |
| 931 Construction labourers | 0.0057264 | 5.7761637 | 0.0189371 |
| 742 Electronics and telecommunications installers and repairers | 0.0072694 | 5.6302928 | 0.0203640 |
| 723 Machinery mechanics and fitters | 0.0825639 | 5.5954821 | 0.0207416 |
| 721 Sheet and structural metal workers, moulders and welders, and related workers | 0.0365659 | 5.3911909 | 0.0231119 |
| 441 Library and filing clerks | -0.0019345 | 5.2024844 | 0.0255579 |
| 142 Preschool managers | -0.0021390 | 5.1165637 | 0.0267614 |
| 137 Production managers in manufacturing | 0.0185958 | 5.0117363 | 0.0283120 |
| 312 Construction and manufacturing supervisors | 0.0117376 | 4.8582539 | 0.0307573 |
| 818 Other stationary plant and machine operators | -0.0140275 | 4.7414708 | 0.0327686 |
| 336 Police officers | -0.0044063 | 4.7323619 | 0.0329313 |
| 933 Dockers and ground personnel | 0.0069166 | 4.7062109 | 0.0335014 |
| 741 Electrical equipment installers and repairers | 0.0229156 | 4.6643992 | 0.0341726 |
| 811 Mining and mineral processing plant operators | 0.0134146 | 4.2522831 | 0.0431994 |
| 218 Specialists within environmental and health protection | 0.0025719 | 3.9990073 | 0.0493541 |
| 911 Cleaners and helpers | 0.0266583 | 3.6556016 | 0.0599165 |
| 812 Metal processing and finishing plant operators | 0.0205694 | 3.3799495 | 0.0704268 |
| 952 Street and market vendors | 0.0002171 | 3.2864816 | 0.0779721 |
| 132 Supply, logistics and transport managers | 0.0050782 | 3.2389740 | 0.0761542 |
| 541 Other surveillance and security workers | 0.0160347 | 2.8988953 | 0.0930148 |
| 532 Personal care workers in health services | 0.1439794 | 2.7076463 | 0.1042885 |
| 524 Event seller and telemarketers | -0.0038354 | 2.5348984 | 0.1157968 |
| 819 Process control technicians | 0.0220138 | 2.5213688 | 0.1167568 |
| 712 Roofers, floor layers, plumbers and pipefitters | 0.0119500 | 2.4248608 | 0.1238704 |
| 711 Carpenters, bricklayers and construction workers | 0.0322593 | 2.2702921 | 0.1363116 |
| 516 Other service related workers | 0.0016233 | 2.2289965 | 0.1398724 |
| 021 Non-commissioned armed forces officers | 0.0022965 | 2.1718683 | 0.1485818 |
| 834 Mobile plant operators | 0.0310669 | 2.1690198 | 0.1452354 |
| 535 Dental nurses | 0.0051430 | 2.0914876 | 0.1547579 |
| 752 Wood treaters, cabinet-makers and related trades workers | 0.0128855 | 2.0622667 | 0.1553769 |
| 153 Elderly care managers | 0.0050779 | 2.0399807 | 0.1575959 |
| 832 Car, van and motorcycle drivers | -0.0113870 | 2.0368134 | 0.1579143 |
| 351 ICT operations and user support technicians | 0.0327500 | 2.0031284 | 0.1613464 |
| 912 Washers, window cleaners and other cleaning workers | 0.0026571 | 1.9933215 | 0.1623618 |
| 223 Nursing professionals (cont.) | 0.0140486 | 1.9523609 | 0.1666841 |
| 325 Dental hygienists | 0.0020148 | 1.9359896 | 0.1688486 |
| 232 Vocational education teachers | 0.0042122 | 1.9140758 | 0.1708459 |
| 523 Cashiers and related clerks | 0.0043954 | 1.9105586 | 0.1712342 |
| 514 Hairdressers, beauty and body therapists | 0.0040006 | 1.8990590 | 0.1725113 |
| 833 Heavy truck and bus drivers | 0.0273845 | 1.8757354 | 0.1751359 |
| 011 Commissioned armed forces officers | 0.0033562 | 1.8373543 | 0.1850555 |
| 352 Broadcasting and audio-visual technicians | -0.0049245 | 1.8103728 | 0.1827449 |
| 345 Culinary associate professionals | -0.0010181 | 1.8087789 | 0.1829353 |
| 031 Armed forces occupations, other ranks | 0.0083822 | 1.7282458 | 0.1937226 |
| 422 Client information clerks | 0.0199932 | 1.6625489 | 0.2014442 |
| 213 Biologists, pharmacologists and specialists in agriculture and forestry | -0.0025708 | 1.6540879 | 0.2025819 |
| 231 University and higher education teachers | 0.0061336 | 1.6401569 | 0.2044718 |
| 515 Building caretakers and related workers | 0.0132252 | 1.5466976 | 0.2177143 |
| 534 Attendants, personal assistants and related workers | 0.0310932 | 1.5047835 | 0.2239893 |
| 753 Tailors, upholsterers and leather craftsmen | 0.0014844 | 1.5027277 | 0.2243027 |
| 814 Machine operators, rubber, plastic and paper products | 0.0161551 | 1.4924592 | 0.2258761 |
| 613 Mixed crop and animal breeders | 0.0030563 | 1.4729629 | 0.2289007 |
| 161 Financial and insurance managers | -0.0052858 | 1.4055083 | 0.2397562 |
| 267 Religious professionals and deacons | 0.0009268 | 1.4000808 | 0.2406569 |
| 533 Health care assistants | 0.0188154 | 1.3914583 | 0.2420965 |
| 222 Nursing professionals | 0.0159570 | 1.3476573 | 0.2495764 |
| 135 Real estate and head of administration manager | 0.0011096 | 1.2879930 | 0.2602351 |
| 315 Ship and aircraft controllers and technicians | -0.0041387 | 1.1852662 | 0.2799682 |
| 138 Forestry and agricultural production managers | -0.0004167 | 1.1690386 | 0.2837204 |
| 152 Managers in social and curative care | 0.0013286 | 1.1645932 | 0.2841675 |
| 154 Managers and leaders within religious bodies | 0.0003646 | 1.1325556 | 0.2908369 |
| 141 Primary and secondary schools and adult education managers | 0.0019010 | 1.1246890 | 0.2925054 |
| 234 Primary- and pre-school teachers | 0.0491506 | 1.1236326 | 0.2927304 |
| 815 Machine operators, textile, fur and leather products | 0.0025770 | 1.1032847 | 0.2971085 |
| 221 Medical doctors | 0.0044678 | 1.0952462 | 0.2988615 |
| 334 Administrative and specialized secretaries | -0.0081430 | 1.0555935 | 0.3077100 |
| 125 Sales and marketing managers | -0.0111966 | 0.9053781 | 0.3445717 |
| 333 Business services agents | 0.0029231 | 0.8503344 | 0.3595807 |
| 266 Social work and counselling professionals | 0.0057216 | 0.8423190 | 0.3618416 |
| 522 Shop staff | 0.0145573 | 0.8147077 | 0.3697841 |
| 227 Naprapaths, physiotherapists, occupational therapists | 0.0032989 | 0.7916877 | 0.3765952 |
| 817 Wood processing and papermaking plant operators | 0.0101785 | 0.6597192 | 0.4194525 |
| 174 Sports, leisure and wellness managers | 0.0003034 | 0.6424601 | 0.4254958 |
| 122 Human resource managers | -0.0031279 | 0.6384414 | 0.4269407 |
| 321 Medical and pharmaceutical technicians | 0.0040967 | 0.6343873 | 0.4284058 |
| 432 Stores and transport clerks | 0.0248619 | 0.6080341 | 0.4381206 |
| 443 Elected representatives | -0.0001984 | 0.5522305 | 0.4599305 |
| 921 Berry pickers and planters | -0.0006515 | 0.5063660 | 0.4790478 |
| 341 Social work and religious associate professionals | 0.0037216 | 0.4804464 | 0.4904814 |
| 732 Printing trades workers | -0.0018385 | 0.4095441 | 0.5242608 |
| 131 Information and communications technology service managers | -0.0052744 | 0.4053263 | 0.5263986 |
| 173 Retail and wholesale trade managers | 0.0006960 | 0.3376285 | 0.5630414 |
| 251 ICT architects, systems analysts and test managers | -0.0450284 | 0.3248615 | 0.5704998 |
| 611 Market gardeners and crop growers | -0.0032925 | 0.3217809 | 0.5723286 |
| 962 Newspaper distributors, janitors and other service workers | 0.0046302 | 0.3158434 | 0.5758863 |
| 265 Creative and performing artists | -0.0010876 | 0.3123452 | 0.5780032 |
| 111 Legislators and senior officials | -0.0004090 | 0.2179765 | 0.6420156 |
| 129 Administration and service managers not elsewhere classified | -0.0010255 | 0.2029696 | 0.6537073 |
| 761 Butchers, bakers and food processors | 0.0022963 | 0.1826299 | 0.6704161 |
| 264 Authors, journalists and linguists | -0.0020383 | 0.1796760 | 0.6729333 |
| 212 Mathematicians, actuaries and statisticians | -0.0004593 | 0.1759297 | 0.6762778 |
| 0002 occupations unidentifiable | -0.0126771 | 0.1747811 | 0.6771589 |
| 123 Administration and planning managers | -0.0011076 | 0.1572826 | 0.6928603 |
| 241 Accountants, financial analysts and fund managers | -0.0039146 | 0.1462714 | 0.7032668 |
| 159 Other social services managers | -0.0007028 | 0.1451539 | 0.7043478 |
| 261 Legal professionals | -0.0020287 | 0.1170421 | 0.7332756 |
| 124 Information, communication and public relations managers | -0.0004947 | 0.1168495 | 0.7334867 |
| 531 Child care workers and teachers aides | -0.0062484 | 0.1055742 | 0.7461957 |
| 332 Insurance advisers, sales and purchasing agents | 0.0067977 | 0.1013902 | 0.7511024 |
| 323 Complementary medicine therapists and associate professionals | 0.0000388 | 0.0935605 | 0.7622313 |
| 243 Marketing and public relations professionals | -0.0048685 | 0.0705013 | 0.7913777 |
| 233 Secondary education teachers | 0.0004656 | 0.0568840 | 0.8121775 |
| 816 Machine operators, food and related products | 0.0013640 | 0.0546090 | 0.8159014 |
| 216 Architects and surveyors | -0.0003675 | 0.0489906 | 0.8254644 |
| 932 Manufacturing labourers | 0.0004533 | 0.0440839 | 0.8342990 |
| 331 Financial and accounting associate professionals | 0.0024876 | 0.0416569 | 0.8388592 |
| 442 Postmen and postal facility workers | 0.0009841 | 0.0398276 | 0.8423886 |
| 961 Recycling collectors | -0.0004322 | 0.0376276 | 0.8467468 |
| 343 Photographers, interior decorators and entertainers | 0.0002024 | 0.0348484 | 0.8524461 |
| 171 Hotel and conference managers | -0.0000772 | 0.0291067 | 0.8650182 |
| 217 Designers | 0.0003867 | 0.0205982 | 0.8862855 |