Mission (D) Provide either a time trend of the mean for an outcome
variable by survey year. If you only have say one or two rounds, provide
instead a graph by birth year. Other options include showcasing the
distribution or frequency counts. Describe your graph, explaining what
it shows.Note: if you want to, you can make alterations to the graph to
‘zoom in’ or highlight/annotate certain aspects as well – but do make
sure to provide the full scale (e.g., 0 to 10 or 0 to 1) first.Create
tables between ess and variables of interest. Take note of what you need
to clean (i.e., the 7, 8, 9, and 77, 88, 99).
print("Sample, variables, and main trend ")## [1] "Sample, variables, and main trend "setwd("C:/Users/Owner/Downloads/All Downloads/Stepping 2 Workspace")
getwd()## [1] "C:/Users/Owner/Downloads/All Downloads/Stepping 2 Workspace"# List of packages
packages <- c("tidyverse", "modelsummary", "forcats", "RColorBrewer", "ggplot2",
"fst", "viridis", "knitr", "kableExtra", "rmarkdown", "ggridges", "viridis", "questionr") # 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
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## [26] "methods" "base"ess <- read_fst("All-ESS-Data.fst")
#This command saves workspace or global environment
save.image()
#Cleaning Explanatory and Outcome of Interests variables, and filtering by Country
finland_data <- ess %>%
filter(cntry == "FI") %>%
select(wrkorg, trstplc, pplhlp, medcrgvc, cntry, trstlgl)
finland_data_cleaned <- ess %>%
filter(cntry == "FI") %>%
select(wrkorg, trstplc, pplhlp, medcrgvc, cntry, trstlgl) %>%
filter(cntry == "FI") %>%
mutate(wrkorg_recode = ifelse(wrkorg %in% c(7, 8, 9), NA, wrkorg)) %>%
filter(cntry == "FI") %>%
mutate(trstplc_recode = ifelse(trstplc %in% c(77, 88, 99), NA, trstplc)) %>%
filter(cntry == "FI") %>%
mutate(pplhlp_recode = ifelse(pplhlp %in% c(77, 88, 99), NA, pplhlp)) %>%
filter(cntry == "FI") %>%
mutate(medcrgvc_recode = ifelse( medcrgvc %in% c(77, 88, 99), NA, medcrgvc)) %>%
mutate(trstlgl_recode = ifelse( trstlgl %in% c(77, 88, 99), NA, trstlgl))
#Cleaning ess rounds into years.
survey_year_averages <-ess$year <- NA
replacements <- c(2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, 2020)
for(i in 1:10){
ess$year[ess$essround == i] <- replacements[i]
}
#Cleaning average trust in police for Finland and filtering answers with refusal, don't know, or No answer.
survey_averages <- ess %>%
filter(cntry == "FI") %>%
filter(!(trstplc %in% c(77, 88, 99))) %>%
group_by(year) %>%
summarize(avg_trstplc = mean(trstplc, na.rm = TRUE))
#Generating Survey Years and Average Trust in Police
plot <- ggplot(survey_averages, aes(x = year, y = avg_trstplc)) +
geom_point(aes(size = avg_trstplc, color = avg_trstplc), alpha = 0.6) + # Set point size and color based on avg_clsprty
geom_line(aes(group = 1), color = "violet", linetype = "dashed") + # Connecting points with a dashed line
labs(title = "Average of Feeling close to the Police in Finland (2002-2020)",
x = "Survey Year",
y = "Trstplc Average") +
scale_color_gradient(low = "violet", high = "blue") +
geom_line(aes(group = 1), color = "blue", linetype = "dashed")
theme_minimal() +
theme(
axis.text.x = element_blank(),
axis.title.x = element_text(face="bold", size=14))+
theme_minimal() +
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Mission (e) Provide the same main trend graph but this time
comparing it to either the ESS baseline (all countries) or specified
countries. If choosing the latter, it must be justified. Meaning, you
should just choosing 2-5 other countries at random. Rather, focus say on
countries often compared to in the literature or inclusive of the same
region (e.g., Western Europe) or chosen to reflect one of the regions
(e.g., one country for Western Europe, one for Eastern Europe,
Scandinavia, etc.).
#Cleaning average trust in police by summarizing averages, grouping by al countries, filtering answers with refusal, don't know, and No answer.
country_averages <- ess %>%
filter(!(trstplc %in% c(77, 88, 99))) %>%
group_by(cntry) %>%
summarize(avg_trstplc = mean(trstplc, na.rm = TRUE))
# Reorder the factor levels of 'cntry' based on 'avg_trstplc'
country_averages$cntry <- reorder(country_averages$cntry, country_averages$avg_trstplc)
# Plotting the factor levels of 'cntry' based on 'avg_trstplc.'
Main_Trend_of_Country_Feeling_Close_to_Trust_in_Police <- ggplot(country_averages, aes(x = cntry, y = avg_trstplc, label = cntry)) +
geom_point(aes(color = avg_trstplc), size = 3) +
geom_text(nudge_y = 0.2, size = 3) +
labs(
x = "Country",
y = "Average Trust in Police",
title = "Main Trend of Average Trust in Police by Country"
) +
scale_color_gradient(low = "violet", high = "blue") +
geom_line(aes(group = 1), color = "blue", linetype = "dashed")
theme_minimal() +
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## - attr(*, "complete")= logi TRUE
## - attr(*, "validate")= logi TRUEprint(Main_Trend_of_Country_Feeling_Close_to_Trust_in_Police)
Mission (f) Summarize two variables related to your outcome of
interest, along with three to five other variables that are related to
potential explanatory variables. These latter variables can be tied to
socio-demographics, but they do not have to. Decisions on the
explanatory variables should be anchored in the literature (i.e., which
ones to focus on and why). Note: use datasummary_skim from the
modelsummary package. You can alter aesthetics if you wish. See details
here: https://modelsummary.com/articles/datasummary.html .
Discuss your table in terms of what is showcases (mean, SD, min, max,
etc.) and describe some of what is contained in the table (not
all).
datasummary_skim(finland_data_cleaned %>% select(trstplc_recode, trstlgl_recode, wrkorg_recode, pplhlp_recode, medcrgvc_recode))
Unique (#)
Missing (%)
Mean
SD
Min
Median
Max
trstplc_recode
12
0
8.1
1.7
0.0
8.0
10.0
trstlgl_recode
12
1
7.0
2.1
0.0
8.0
10.0
wrkorg_recode
3
8
1.6
0.5
1.0
2.0
2.0
pplhlp_recode
12
0
5.9
1.9
0.0
6.0
10.0
medcrgvc_recode
12
81
8.2
1.7
0.0
9.0
10.0
Mission (g) 1.Provide column percentages (representing conditional
probabilities if divided by 100) for your 3-5 explanatory variables
relative to a central outcome variable. 2.Along with these, provide the
mean and standard deviation (e.g., average male, average female for
outcome variable). Note: use cprop() from the questionr package for
column percentages. For the mean and std, you can use, e.g., group_by()
and summarise() by piping with tidyverse.
print("Bivariate statistics and visualizations")## [1] "Bivariate statistics and visualizations"#Finland_data_wrkorg
finland_data <- ess %>%
mutate(wrkorg_recode = case_when(
wrkorg == 1 ~ 'yes',
wrkorg == 2 ~ 'no', # to retain other values as they are (optional)
))
# check the recoded variable table(finland_data$wrkorg_recode)
finland_data <- finland_data %>%
mutate(trstplc_finland = recode(as.character(trstplc),
'0' = "No Trust at all",
'1' = "1",
'2' = "2",
'3' = "3",
'4' = "4",
'5' = "5",
'6' = "6",
'7' = "7",
'8' = "8",
'9' = "9",
'10' = "Complete trust",
'77' = NA_character_,
'88' = NA_character_,
'99' = NA_character_))
# check table(finland_data$trstplc_finland)
finland_data <- finland_data %>%
mutate(pplhlp_recode = recode(as.character(pplhlp),
'0' = "People mostly look out for themselves",
'1' = "1",
'2' = "2",
'3' = "3",
'4' = "4",
'5' = "5",
'6' = "6",
'7' = "7",
'8' = "8",
'9' = "9",
'10' = "People mostly try to be helpful",
'77' = NA_character_,
'88' = NA_character_,
'99' = NA_character_))
finland_data <- finland_data %>%
mutate(medcrgvc_recode = recode(as.character(medcrgvc),
'0' = "Does not apply at all",
'1' = "1",
'2' = "2",
'3' = "3",
'4' = "4",
'5' = "5",
'6' = "6",
'7' = "7",
'8' = "8",
'9' = "9",
'10' = "Applies completely",
'77' = NA_character_,
'88' = NA_character_,
'99' = NA_character_))
#Check table(finland_data$medcrgvc_recode )
print("1.Below is the Provided column percentages (representing conditional probabilities if divided by 100) for your 3-5 explanatory variables relative to a central outcome variable.") ## [1] "1.Below is the Provided column percentages (representing conditional probabilities if divided by 100) for your 3-5 explanatory variables relative to a central outcome variable."#Cprop of explanatory wrkorg_recode and outcome of interest trstplc
table(finland_data$trstplc_finland, finland_data$wrkorg_recode) %>%
cprop()##
## no yes All
## 1 3.1 1.4 2.9
## 2 4.7 2.6 4.4
## 3 6.5 4.3 6.2
## 4 7.2 5.1 6.9
## 5 15.1 10.5 14.4
## 6 10.9 11.2 10.9
## 7 15.4 18.9 15.9
## 8 16.7 23.2 17.6
## 9 8.5 14.1 9.3
## Complete trust 6.3 6.8 6.4
## No Trust at all 5.6 1.9 5.1
## Total 100.0 100.0 100.0#Cprop of explanatory medcrgvc_recode and outcome of interest trstplc
table(finland_data$trstplc_finland, finland_data$medcrgvc_recode ) %>%
cprop()##
## 1 2 3 4 5 6 7 8 9
## 1 11.0 7.0 5.1 4.3 3.7 2.7 1.9 1.8 1.7
## 2 9.9 9.5 8.1 6.6 5.6 4.9 3.6 3.2 2.8
## 3 12.2 11.8 10.7 9.2 7.9 6.7 5.6 4.7 4.2
## 4 9.0 9.0 9.6 10.1 8.3 8.0 6.9 5.7 4.8
## 5 13.8 15.3 15.2 15.4 19.3 15.1 13.1 12.5 10.0
## 6 7.7 8.8 10.6 13.4 11.4 14.8 13.3 11.4 10.0
## 7 8.5 10.4 13.6 13.3 13.3 17.2 19.1 19.2 17.3
## 8 8.3 9.7 10.8 12.5 12.9 15.4 19.2 22.1 23.1
## 9 3.2 4.6 5.3 5.6 5.7 7.1 9.1 11.6 17.1
## Complete trust 3.0 4.2 3.2 3.1 5.5 4.3 4.8 5.4 6.3
## No Trust at all 13.4 9.7 7.7 6.5 6.5 3.8 3.3 2.7 2.6
## Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
##
## Applies completely Does not apply at all All
## 1 1.7 6.1 2.8
## 2 3.0 8.7 4.5
## 3 4.1 9.3 6.2
## 4 4.3 6.0 6.5
## 5 11.7 14.8 13.5
## 6 8.7 5.3 10.8
## 7 14.5 6.7 15.6
## 8 20.3 7.0 17.7
## 9 13.8 2.7 10.2
## Complete trust 13.0 6.7 6.8
## No Trust at all 4.8 26.7 5.5
## Total 100.0 100.0 100.0#Cprop of explanatory pplhlp_recode and outcome of interest trstplc
table(finland_data$trstplc_finland, finland_data$pplhlp_recode) %>%
cprop()##
## 1 2 3 4 5 6 7 8 9
## 1 8.8 5.6 3.8 2.7 2.2 1.5 1.2 1.3 1.9
## 2 8.5 8.4 6.4 4.8 4.0 2.8 2.3 2.3 2.5
## 3 9.0 9.4 9.1 7.3 6.1 4.6 3.9 3.4 3.3
## 4 7.8 8.6 9.2 9.4 6.9 6.4 4.8 3.9 3.9
## 5 16.5 16.8 16.1 15.2 17.3 12.2 10.9 9.9 9.2
## 6 7.5 9.7 11.5 13.5 11.6 13.9 11.2 9.1 7.0
## 7 9.2 11.8 14.6 16.8 16.5 20.3 20.2 16.3 12.7
## 8 9.9 11.9 13.9 15.5 17.6 20.8 23.9 25.5 21.4
## 9 5.0 5.7 6.5 7.3 8.0 10.6 13.5 16.6 20.4
## Complete trust 5.3 4.3 4.0 3.9 5.7 4.9 6.2 9.7 15.1
## No Trust at all 12.4 7.7 4.9 3.4 3.9 2.1 1.9 2.0 2.8
## Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
##
## People mostly look out for themselves
## 1 4.6
## 2 6.6
## 3 7.1
## 4 5.9
## 5 17.1
## 6 6.0
## 7 7.2
## 8 8.3
## 9 4.0
## Complete trust 10.7
## No Trust at all 22.5
## Total 100.0
##
## People mostly try to be helpful All
## 1 2.0 2.8
## 2 2.9 4.5
## 3 3.5 6.2
## 4 3.4 6.8
## 5 11.9 14.4
## 6 5.2 10.9
## 7 8.0 15.8
## 8 14.8 17.7
## 9 10.2 9.5
## Complete trust 31.0 6.5
## No Trust at all 7.1 5.0
## Total 100.0 100.0print("2. Along with these, below is the provided mean and standard deviation for my three explanatory variables medcrgvc, wrkorg, and pplhlp relative to trstplc.")## [1] "2. Along with these, below is the provided mean and standard deviation for my three explanatory variables medcrgvc, wrkorg, and pplhlp relative to trstplc."#mean and standard deviation: medcrgvc_recode
finland_data %>%
group_by(finland_data$medcrgvc_recode) %>%
summarise(
mean_trstplc = mean(trstplc, na.rm = TRUE),
std_trstplc = sd(trstplc, na.rm = TRUE)
)## # A tibble: 12 × 3
## `finland_data$medcrgvc_recode` mean_trstplc std_trstplc
## <chr> <dbl> <dbl>
## 1 1 5.17 10.2
## 2 2 5.39 8.61
## 3 3 5.94 9.63
## 4 4 5.81 7.74
## 5 5 6.31 9.20
## 6 6 6.27 6.74
## 7 7 6.79 7.64
## 8 8 7.02 7.31
## 9 9 7.26 7.08
## 10 Applies completely 7.40 8.30
## 11 Does not apply at all 4.57 9.00
## 12 <NA> 7.08 9.85#mean and standard deviation: wrkorg_recode
finland_data %>%
group_by(finland_data$wrkorg_recode) %>%
summarise(
mean_trstplc = mean(trstplc, na.rm = TRUE),
std_trstplc = sd(trstplc, na.rm = TRUE)
)## # A tibble: 3 × 3
## `finland_data$wrkorg_recode` mean_trstplc std_trstplc
## <chr> <dbl> <dbl>
## 1 no 6.95 9.92
## 2 yes 6.96 4.90
## 3 <NA> 7.19 10.1#mean and standard deviation: pplhlp_recode
finland_data %>%
group_by(finland_data$pplhlp_recode) %>%
summarise(
mean_trstplc = mean(trstplc, na.rm = TRUE),
std_trstplc = sd(trstplc, na.rm = TRUE)
)## # A tibble: 12 × 3
## `finland_data$pplhlp_recode` mean_trstplc std_trstplc
## <chr> <dbl> <dbl>
## 1 1 6.08 11.7
## 2 2 6.10 9.88
## 3 3 6.31 8.95
## 4 4 6.52 8.24
## 5 5 6.96 9.27
## 6 6 7.02 7.26
## 7 7 7.39 7.60
## 8 8 7.72 7.98
## 9 9 8.24 9.72
## 10 People mostly look out for themselves 6.27 12.7
## 11 People mostly try to be helpful 9.05 13.3
## 12 <NA> 21.2 33.1
Mission (h) Provide a visualization for one of the explanatory
variable relative to your main outcome.
#cleaning trstplc_finland
finland_data_cleaned <- ess %>%
mutate(trstplc_recode = recode(as.character(trstplc),
'0' = "No Trust at all",
'1' = "1",
'2' = "2",
'3' = "3",
'4' = "4",
'5' = "5",
'6' = "6",
'7' = "7",
'8' = "8",
'9' = "9",
'10' = "Complete trust",
'77' = NA_character_,
'88' = NA_character_,
'99' = NA_character_)) %>%
mutate(wrkorg_recode = case_when(
wrkorg == 1 ~ 'yes',
wrkorg == 2 ~ 'no', # to retain other values as they are (optional)
))
filtered_data_cleaned <- finland_data_cleaned %>% filter(!is.na(trstplc_recode), !is.na(wrkorg_recode))
# Compute percentages
percentages <- filtered_data_cleaned %>%
group_by(wrkorg_recode, trstplc_recode) %>%
summarise(n = n()) %>%
mutate(percentage = n/sum(n)*100)## `summarise()` has grouped output by 'wrkorg_recode'. You can override using the
## `.groups` argument.Percentage_of_Attitudes_Towards_Police_by_Joining_Finland_Civil_Organization <- ggplot(percentages, aes(x = as.factor(wrkorg_recode), y = percentage, fill = trstplc_recode)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.9), width = 0.7) +
geom_text(aes(label = paste0(round(percentage, 1), "%"), group = trstplc_recode),
position = position_dodge(width = 0.9), vjust = -0.5, size = 3) + # Add rounded percentage labels
labs(title = "Percentage of Attitudes Towards Police by Joining Finland's Civil Organization",
x = "Attitude towards Police",
y = "Percentage (%)") +
scale_x_discrete(breaks = c("no", "yes"), labels = c("No", "Yes")) +
scale_fill_manual(values = c("Complete trust" = "darkblue", "8" = "orange", "9" = "orange", "7" = "yellow", "6" = "yellow", "5" = "yellow", "4" = "yellow", "3" = "yellow", "2" = "yellow", "1" = "yellow", "No Trust at all" = "Red"),
name = "Trust in Police Levels") +
theme_minimal()
#Check
unique(finland_data_cleaned$wrkorg_recode)## [1] "no" "yes" NAunique(finland_data_cleaned$trstplc_recode)## [1] "Complete trust" "5" "8" "9"
## [5] "4" "6" "7" "3"
## [9] "2" "No Trust at all" "1" NA#Plot
print(Percentage_of_Attitudes_Towards_Police_by_Joining_Finland_Civil_Organization)
