Homework_8_Di_Wu
2023-11-20
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Task 1
# List of packages
packages <- c("tidyverse", "infer", "fst", "modelsummary", "broom") # add any you need here
# Install packages if they aren't installed already
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)
# Load the packages
lapply(packages, library, character.only = TRUE)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.3 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors## [[1]]
## [1] "lubridate" "forcats" "stringr" "dplyr" "purrr" "readr"
## [7] "tidyr" "tibble" "ggplot2" "tidyverse" "stats" "graphics"
## [13] "grDevices" "utils" "datasets" "methods" "base"
##
## [[2]]
## [1] "infer" "lubridate" "forcats" "stringr" "dplyr" "purrr"
## [7] "readr" "tidyr" "tibble" "ggplot2" "tidyverse" "stats"
## [13] "graphics" "grDevices" "utils" "datasets" "methods" "base"
##
## [[3]]
## [1] "fst" "infer" "lubridate" "forcats" "stringr" "dplyr"
## [7] "purrr" "readr" "tidyr" "tibble" "ggplot2" "tidyverse"
## [13] "stats" "graphics" "grDevices" "utils" "datasets" "methods"
## [19] "base"
##
## [[4]]
## [1] "modelsummary" "fst" "infer" "lubridate" "forcats"
## [6] "stringr" "dplyr" "purrr" "readr" "tidyr"
## [11] "tibble" "ggplot2" "tidyverse" "stats" "graphics"
## [16] "grDevices" "utils" "datasets" "methods" "base"
##
## [[5]]
## [1] "broom" "modelsummary" "fst" "infer" "lubridate"
## [6] "forcats" "stringr" "dplyr" "purrr" "readr"
## [11] "tidyr" "tibble" "ggplot2" "tidyverse" "stats"
## [16] "graphics" "grDevices" "utils" "datasets" "methods"
## [21] "base"setwd("~/Desktop/SOC_202_YAY/")
getwd() ## [1] "/Users/apple/Desktop/SOC_202_YAY"ess <- read_fst("All-ESS-Data.fst")belgium_data <- ess %>%
filter(cntry == "BE") %>%
mutate(trstep = ifelse(trstep %in% c(77, 88, 99), NA, trstep), ## Example of a trust variable
)unique(belgium_data$trstep)## [1] 0 7 8 5 6 NA 4 9 3 1 10 2belgium_data <- belgium_data %>% filter(!is.na(trstep))belgium_data <- ess %>%
filter(cntry == "BE") %>%
mutate(
wrkprty = case_when(
wrkprty == 1 ~ "Yes", # Recode 1 to "Yes"
wrkprty == 2 ~ "No", # Recode 2 to "No"
wrkprty %in% c(7, 8, 9) ~ NA_character_, # Handle other specific cases where you want to set it as NA
TRUE ~ as.character(wrkprty) # Keep other values as-is but ensure they are characters
),
trstep_recode = case_when(
trstep == 0 ~ "No trust at all",
trstep == 1 ~ "1",
trstep == 2 ~ "2",
trstep == 3 ~ "3",
trstep == 4 ~ "4",
trstep == 5 ~ "5",
trstep == 6 ~ "6",
trstep == 7 ~ "7",
trstep == 8 ~ "8",
trstep == 9 ~ "9",
trstep == 10 ~ "Complete trust",
trstep %in% c(77, 88, 99) ~ NA_character_,
TRUE ~ as.character(trstep) # Keep other values as characters
)
)model1 <- lm(trstep ~ wrkprty, data = belgium_data)coefficients <- coef(model1)
print(coefficients)## (Intercept) wrkprtyYes
## 7.5491077 -0.9610318Interpretation:
(Intercept) 7.55:
The intercept represents the predicted value of the outcome variable (in this case, Trust in Politicians, 0-10) when the explanatory variable is zero or, in the case of categorical variables, at their reference level.
Put differently, when looking at the category that’s NOT “No BA” in educ_level (thus, holding a “BA”), the expected value of trstplt is approximately 7.55.
educ_levelNo BA -0.96:
This coefficient represents the difference in the predicted value of trust in politicians between the “No BA” category and the reference category (“BA”) for the educ_level variable.
Specifically, having “No BA” is associated with a decrease of approximately 0.96 in the predicted value of trust in politicians, compared to having a “BA”.
In summary:
If a person has a “BA” (assuming that’s the reference category), their predicted average trust in politicians is 7.55. If a person does not have a “BA” (i.e., “No BA”), their predicted average trust in politicians is 6.59 (given 7.55 - 0.96)
Task 2
bulgaria_data <- ess %>%
filter(cntry == "BG") %>%
mutate(stfdem = ifelse(stfdem %in% c(77, 88, 99), NA, stfdem), ## Example of a trust variable
)unique(bulgaria_data$stfdem)## [1] 0 1 NA 2 3 5 4 6 8 7 10 9bulgaria_data <- bulgaria_data %>% filter(!is.na(stfdem))bulgaria_data <- ess %>%
filter(cntry == "BE") %>%
mutate(
native = recode(brncntr,
`1` = "Yes",
`2` = "No",
`7` = NA_character_,
`8` = NA_character_,
`9` = NA_character_),
stfdem_recode = case_when(
stfdem == 0 ~ "Extremely dissatisfied",
stfdem == 1 ~ "1",
stfdem == 2 ~ "2",
stfdem == 3 ~ "3",
stfdem == 4 ~ "4",
stfdem == 5 ~ "5",
stfdem == 6 ~ "6",
stfdem == 7 ~ "7",
stfdem == 8 ~ "8",
stfdem == 9 ~ "9",
stfdem == 10 ~ "Extremely satisfied",
stfdem %in% c(77, 88, 99) ~ NA_character_,
TRUE ~ as.character(stfdem) # Keep other values as characters
)
)model2 <- lm(stfdem ~ native, data = bulgaria_data)coefficients <- coef(model2)
print(coefficients)## (Intercept) nativeYes
## 7.610496 -1.043938summary(model2)##
## Call:
## lm(formula = stfdem ~ native, data = bulgaria_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.610 -2.567 -0.610 0.433 92.433
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.6105 0.2253 33.777 < 2e-16 ***
## nativeYes -1.0439 0.2401 -4.349 1.38e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.27 on 17445 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.001083, Adjusted R-squared: 0.001026
## F-statistic: 18.91 on 1 and 17445 DF, p-value: 1.378e-05Task 3
remotes::install_github("datalorax/equatiomatic")## Skipping install of 'equatiomatic' from a github remote, the SHA1 (29ff168f) has not changed since last install.
## Use `force = TRUE` to force installation# remotes::install_github("datalorax/equatiomatic"\[ \operatorname{\widehat{stfdem}} = 7.61 - 1.04(\operatorname{native}_{\operatorname{Yes}}) \] y(hat) = intercept + B(hat)0 + B(hat)1 * x1 + epsilon
trstplt(hat) = 7.61 - 1.04 (No BA) + error term