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setwd("C:/Users/aiden/Desktop/SOC202 RStudio")
getwd()## [1] "C:/Users/aiden/Desktop/SOC202 RStudio"ess <- read_fst("All-ESS-Data.fst")Extract the coefficients and intercept using trust in the European Parliament (as the outcome) and worked in a political party (wrkprty, as the explanatory), for the country of Belgium. Write the regression equation and interpret.
belgium <- ess %>%
filter(cntry == "BE") %>%
mutate(trstep = ifelse(trstep %in% c(77, 88, 99), NA, trstep))
unique(belgium$trstep)## [1] 0 7 8 5 6 NA 4 9 3 1 10 2belgium <- belgium %>% filter(!is.na(trstep))belgium <- belgium %>%
mutate(wrkprty = case_when(
wrkprty == 1 ~ "Yes",
wrkprty == 2 ~ "No",
wrkprty %in% c(7, 8, 9) ~ NA_character_,
TRUE ~ as.character(wrkprty)
))
table(belgium$wrkprty)##
## No Yes
## 14871 727unique(belgium$wrkprty)## [1] "No" "Yes" NAbelgium <- belgium %>% filter(!is.na(wrkprty))
unique(belgium$wrkprty)## [1] "No" "Yes"tsk1mdl <- lm(trstep ~ wrkprty, data = belgium)
coefficients <- coef(tsk1mdl)
print(coefficients)## (Intercept) wrkprtyYes
## 4.9405554 0.4308338Interpretation:
(Intercept) 4.94: This means that the value of not having worked in a political party and trusting in the European Union is ~4.94, which is close to 5, the median value between no trust at all and complete trust
wrkprtyYes 0.43: Having worked in a political party is associated with an increase of ~0.43 in the predicted value of trust in the european union, compared to having a not worked in a political party.
In summary: if you’ve worked in a political party, you’re more likely to trust the european union
Produce a model summary output using either tidy() or summary() (your choice). Use stfdem (satisfaction with democracy) as the outcome and born in country as a predictor (which we recoded/renamed as ‘native’ in the tutorial), for the country of Bulgaria. What is the expected average satisfaction with democracy for respondents that were not born in Bulgaria?
bulgaria <- ess %>%
filter(cntry == "BG") %>%
mutate(stfdem = ifelse(stfdem %in% c(77, 88, 99), NA, stfdem))
unique(bulgaria$stfdem)## [1] 0 1 NA 2 3 5 4 6 8 7 10 9bulgaria <- bulgaria %>% filter(!is.na(stfdem))
unique(bulgaria$stfdem)## [1] 0 1 2 3 5 4 6 8 7 10 9bulgaria <- bulgaria %>%
mutate(native = recode(brncntr,
`1` = "Yes",
`2` = "No",
`7` = NA_character_,
`8` = NA_character_,
`9` = NA_character_))
table(bulgaria$native)##
## No Yes
## 97 12239unique(bulgaria$native)## [1] "Yes" "No" NAbulgaria <- bulgaria %>% filter(!is.na(native))
unique(bulgaria$native)## [1] "Yes" "No"bulgariamdl <- lm(stfdem ~ native, data = bulgaria)
coefficients2 <- coef(bulgariamdl)
print(coefficients2)## (Intercept) nativeYes
## 2.8041237 0.1189909tidy(bulgariamdl)## # A tibble: 2 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 2.80 0.226 12.4 5.30e-35
## 2 nativeYes 0.119 0.227 0.523 6.01e- 1The expected average value for satisfaction with democracy of people not born in Bulgaria is ~2.804, as noted by the intercept. For people born in Bulgaria, it is ~0.119 higher, so around 2.923. Using the markers from ESS, both are are dissatsified.
Based on your own outcome and country of interest, as well as one of the predictors recoded in the tutorial, produce an equatiomatic regression equation. Interpret the regression equation.
Research Question: How does the state of the economy in Bulgaria affect the levels of happiness? happy: How happy are you (https://ess-search.nsd.no/en/variable/a5c95af9-6037-4e52-9156-2ceac708eb3f)
bulgaria_data <- ess %>%
filter(cntry == "BG") %>%
mutate(happy = ifelse(happy %in% c(77, 88, 99), NA, happy))
unique(bulgaria_data$happy)## [1] 6 0 10 5 7 9 8 4 1 3 2 NAbulgaria_data <- bulgaria_data %>% filter(!is.na(happy))bulgaria_data <- bulgaria_data %>% filter(!is.na(happy))
unique(bulgaria_data$happy)## [1] 6 0 10 5 7 9 8 4 1 3 2bulgaria_data <- bulgaria_data %>%
mutate(geo = recode(as.character(domicil),
'1' = "Urban",
'2' = "Peri-Urban",
'3' = "Rural",
'4' = "Rural",
'5' = "Rural",
'7' = NA_character_,
'8' = NA_character_,
'9' = NA_character_))
table(bulgaria_data$geo)##
## Peri-Urban Rural Urban
## 322 7447 5320unique(bulgaria_data$geo)## [1] "Rural" "Urban" "Peri-Urban" NAbulgaria_data <- bulgaria_data %>% filter(!is.na(geo))
unique(bulgaria_data$geo)## [1] "Rural" "Urban" "Peri-Urban"bulgariamodel <- lm(happy ~ geo, data = bulgaria_data)
coefficients3 <- coef(bulgariamodel)
print(coefficients3)## (Intercept) geoRural geoUrban
## 5.36024845 -0.04428229 0.51042824This is difficult to interpret since it’s a multinomial variable. However, we think that the intercept (~5.36) represents the expected value of being happy in a peri-urban location. The expected value for those in rural areas is 0.044 less than the intercept, while the expected value for rural areas is 0.51 more than the intercept.
tidy(bulgariamodel)## # A tibble: 3 × 5
## term estimate std.error statistic p.value
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 5.36 0.143 37.5 1.76e-292
## 2 geoRural -0.0443 0.146 -0.303 7.62e- 1
## 3 geoUrban 0.510 0.147 3.47 5.26e- 4remotes::install_github("datalorax/equatiomatic")## Downloading GitHub repo datalorax/equatiomatic@HEAD## xfun (0.40 -> 0.41 ) [CRAN]
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## (as 'lib' is unspecified)equatiomatic::extract_eq(bulgariamodel, use_coefs = TRUE)\[ \operatorname{\widehat{happy}} = 5.36 - 0.04(\operatorname{geo}_{\operatorname{Rural}}) + 0.51(\operatorname{geo}_{\operatorname{Urban}}) \]
y(hat) = intercept + B(hat)0 + B(hat)1 * x1 + epsilon happy(hat) = 5.36 - 0.04 (Rural) + 0.51 (Urban) + error term