# List of packages
packages <- c("tidyverse", "modelsummary", "forcats", "RColorBrewer",
"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")table(ess$essround)##
## 1 2 3 4 5 6 7 8 9 10
## 42359 47537 43000 56752 52458 54673 40185 44387 49519 59685ess$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]
}sweden_data <- ess[ess$cntry == "SE", ]
Provide either a time trend of the mean for an outcome variable by
survey year. If you only have say one or two rounds, provide instead a
graph by birth year. Other options include showcasing the distribution
or frequency counts.
se_data_subset <- sweden_data %>%
filter(essround %in% c("8")) %>%
mutate(
vote = ifelse(vote %in% c(7, 8, 9), NA, vote),
prtvtbse = ifelse(prtvtbse %in% c(66, 77, 88, 99), NA, prtvtbse),
gvslvue = ifelse(gvslvue %in% c(77, 88, 99), NA, gvslvue),
hinctnta = ifelse(hinctnta %in% c(77, 88, 99), NA, hinctnta),
mbtru = ifelse(mbtru %in% c(7, 8, 9), NA, mbtru),
edlvdse = ifelse(edlvdse %in% c(5555, 7777, 8888, 9999), NA, edlvdse),
yrbrn = ifelse(yrbrn %in% c(7777, 8888, 9999), NA, yrbrn),
) %>%
select(vote, prtvtbse, gvslvue, mbtru, edlvdse, hinctnta, essround, year, yrbrn)## check and clean always
table(se_data_subset$yrbrn)##
## 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941
## 18 5 6 6 3 8 11 5 14 14 12 7 24 18 19 9
## 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957
## 21 19 20 28 33 36 36 36 18 30 34 30 28 24 30 26
## 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973
## 21 16 30 26 21 25 27 27 23 21 26 30 15 18 27 23
## 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989
## 24 31 30 26 22 16 26 18 16 27 18 24 15 19 17 17
## 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
## 22 19 20 19 12 15 21 17 14 15 5 18table(se_data_subset$gvslvue)##
## 0 1 2 3 4 5 6 7 8 9 10
## 2 5 9 34 80 214 256 326 305 116 185se_data_subset %>%
filter(essround %in% c("8")) %>%
group_by(yrbrn) %>%
summarise(avg_gvslvue = mean(gvslvue, na.rm = TRUE)) %>%
ggplot(aes(x = yrbrn, y = avg_gvslvue)) +
geom_line() +
ylim(0, 10) +
labs(title = "Average Attitudes Towards Government's Responsibility for \nStandard of Living for Unemployed by Birth Year",
x = "Birth Year", y = "Average Attitudes Government Responsibility \nfor Standard of Living Unemployed (0-10)") ## Warning: Removed 1 row containing missing values (`geom_line()`).
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Provide the same main trend graph but this time comparing it to
either the ESS baseline (all countries) or specified countries. If
choosing the latter, it must be justified. Meaning, you should just
choosing 2-5 other countries at random. Rather, focus say on countries
often compared to in the literature or inclusive of the same region
(e.g., Western Europe) or chosen to reflect one of the regions (e.g.,
one country for Western Europe, one for Eastern Europe, Scandinavia,
etc.).
ess$gvslvue[ess$gvslvue %in% c(77, 88, 99)] <- NA
avg_gvslvue_se <- se_data_subset %>%
group_by(yrbrn) %>%
summarize(mean_gvslvue_se = mean(gvslvue, na.rm = TRUE))
avg_gvslvue_all_countries <- ess %>%
filter(essround == "8") %>%
group_by(yrbrn) %>%
summarize(mean_gvslvue_all = mean(gvslvue, na.rm = TRUE))
combined_data <- left_join(avg_gvslvue_se, avg_gvslvue_all_countries, by = "yrbrn")
long_data <- combined_data %>%
pivot_longer(
cols = starts_with("mean_gvslvue"),
names_to = "Gvslvue_Type",
values_to = "Mean_Gvslvue"
)
ggplot(long_data) +
geom_line(aes(x = yrbrn, y = Mean_Gvslvue, color = Gvslvue_Type), size = 1) +
geom_point(aes(x = yrbrn, y = Mean_Gvslvue, color = Gvslvue_Type), size = 1) +
labs(title = "Average Attitudes Towards Government's Responsibility for \nStandard of Living for Unemployed in Sweden (2016)",
x = "Birth Year",
y = "Mean Attitudes Towards Government's Responsibility for \nStandard of Living for Unemployed (0-10)") +
ylim(5, 10) +
scale_color_manual(values = c("blue", "red"),
name = "Dataset",
labels = c("Sweden", "All ESS"),
breaks = c("mean_gvslvue_se", "mean_gvslvue_all")) +
theme_minimal()## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Removed 2 rows containing missing values (`geom_line()`).## Warning: Removed 2 rows containing missing values (`geom_point()`).
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(se_data_subset$vote)
Unique (#)
Missing (%)
Mean
SD
Min
Median
Max
data
4
0
1.2
0.5
1.0
1.0
3.0
datasummary_skim(se_data_subset$prtvtbse)
Unique (#)
Missing (%)
Mean
SD
Min
Median
Max
data
12
19
5.4
2.1
1.0
6.0
11.0
datasummary_skim(se_data_subset$gvslvue)
Unique (#)
Missing (%)
Mean
SD
Min
Median
Max
data
12
1
7.0
1.9
0.0
7.0
10.0
datasummary_skim(se_data_subset$mbtru)
Unique (#)
Missing (%)
Mean
SD
Min
Median
Max
data
4
0
1.7
0.8
1.0
2.0
3.0
datasummary_skim(se_data_subset$edlvdse)
Unique (#)
Missing (%)
Mean
SD
Min
Median
Max
data
21
1
8.8
5.0
1.0
8.0
20.0
datasummary_skim(se_data_subset$hinctnta)
Unique (#)
Missing (%)
Mean
SD
Min
Median
Max
data
11
7
6.3
2.9
1.0
7.0
10.0
Provide column percentages (representing conditional probabilities
if divided by 100) for your 3-5 explanatory variables relative to a
central outcome variable. Along with these, provide the mean and
standard deviation (e.g., average male, average female for outcome
variable).
Note: use cprop() from the questionr package for column percentages.
For the mean and std, you can use, e.g., group_by() and summarise() by
piping with tidyverse.
Highlight a few of the generated statistics (not all) from the tables
that stand out to you.
# mbtru
table(se_data_subset$mbtru, se_data_subset$gvslvue) %>%
cprop()##
## 0 1 2 3 4 5 6 7 8 9 10 All
## 1 50.0 60.0 44.4 38.2 48.1 45.3 48.4 50.6 52.8 50.0 48.1 49.2
## 2 50.0 20.0 44.4 38.2 31.6 29.4 28.7 27.9 27.9 35.3 29.2 29.5
## 3 0.0 20.0 11.1 23.5 20.3 25.2 22.8 21.5 19.3 14.7 22.7 21.3
## Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0sd_mbtru <- sd(se_data_subset$mbtru, na.rm = TRUE)
sd_mbtru## [1] 0.7947318mean_mbtru <- se_data_subset %>%
summarise(avg_mbtru = mean(mbtru, na.rm = TRUE))
mean_mbtru## avg_mbtru
## 1 1.726214# edlvdse
table(se_data_subset$edlvdse, se_data_subset$gvslvue) %>%
cprop()##
## 0 1 2 3 4 5 6 7 8 9 10 All
## 1 0.0 0.0 0.0 0.0 2.5 2.3 2.0 0.3 1.6 1.7 2.2 1.6
## 2 0.0 0.0 0.0 5.9 8.9 10.3 4.7 9.3 5.9 3.5 7.6 7.1
## 3 0.0 20.0 11.1 8.8 5.1 11.7 7.1 11.4 8.9 10.4 8.2 9.4
## 4 0.0 0.0 0.0 5.9 2.5 6.1 7.5 6.2 6.2 8.7 6.5 6.4
## 5 50.0 20.0 11.1 2.9 11.4 7.9 5.9 9.9 11.5 8.7 10.3 9.2
## 6 50.0 20.0 0.0 2.9 5.1 1.9 4.3 4.0 5.6 6.1 5.4 4.5
## 7 0.0 40.0 0.0 20.6 7.6 10.7 12.9 10.8 8.2 7.8 15.8 11.1
## 8 0.0 0.0 33.3 5.9 5.1 2.8 4.7 4.0 3.3 4.3 2.2 3.9
## 9 0.0 0.0 0.0 2.9 6.3 3.7 2.4 3.7 3.0 1.7 2.7 3.1
## 10 0.0 0.0 22.2 2.9 11.4 7.0 5.1 3.7 6.6 7.0 6.0 6.0
## 11 0.0 0.0 11.1 0.0 3.8 7.5 6.3 7.7 7.2 5.2 4.3 6.4
## 12 0.0 0.0 0.0 11.8 6.3 5.1 6.3 4.0 5.3 0.9 3.8 4.8
## 13 0.0 0.0 11.1 17.6 8.9 6.5 7.5 5.9 7.6 5.2 9.2 7.3
## 14 0.0 0.0 0.0 0.0 3.8 2.8 4.7 4.9 4.6 7.8 1.6 4.1
## 15 0.0 0.0 0.0 0.0 1.3 1.9 2.7 4.9 5.3 6.1 5.4 4.0
## 16 0.0 0.0 0.0 0.0 0.0 1.4 1.2 0.6 0.7 0.0 1.1 0.8
## 17 0.0 0.0 0.0 2.9 2.5 3.7 4.7 3.4 2.3 7.8 3.8 3.7
## 18 0.0 0.0 0.0 2.9 7.6 4.7 6.3 3.7 3.3 5.2 3.8 4.5
## 19 0.0 0.0 0.0 0.0 0.0 0.9 0.0 0.0 0.3 0.0 0.0 0.2
## 20 0.0 0.0 0.0 5.9 0.0 0.9 3.9 1.5 2.6 1.7 0.0 1.9
## Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0sd_edlvdse <- sd(se_data_subset$edlvdse, na.rm = TRUE)
sd_edlvdse## [1] 5.008842mean_edlvdse <- se_data_subset %>%
summarise(avg_edlvdse = mean(edlvdse, na.rm = TRUE))
mean_edlvdse## avg_edlvdse
## 1 8.768332# hinctnta
table(se_data_subset$hinctnta, se_data_subset$gvslvue) %>%
cprop()##
## 0 1 2 3 4 5 6 7 8 9 10 All
## 1 0.0 25.0 22.2 6.1 10.8 9.2 5.8 5.0 5.9 2.7 9.9 6.8
## 2 0.0 0.0 22.2 9.1 6.8 9.7 6.2 6.9 9.4 10.6 11.7 8.6
## 3 50.0 0.0 0.0 6.1 6.8 9.2 5.3 9.9 5.2 6.2 5.8 7.0
## 4 0.0 0.0 11.1 9.1 9.5 5.1 6.6 8.9 7.0 12.4 11.1 8.2
## 5 0.0 0.0 0.0 3.0 4.1 10.7 7.0 6.6 10.5 7.1 6.4 7.7
## 6 50.0 0.0 11.1 12.1 4.1 8.2 8.6 7.6 8.4 8.8 11.1 8.5
## 7 0.0 0.0 0.0 21.2 6.8 9.7 6.2 9.9 13.6 12.4 7.0 9.8
## 8 0.0 25.0 11.1 21.2 8.1 9.7 18.1 14.9 9.8 10.6 13.5 13.0
## 9 0.0 50.0 0.0 0.0 16.2 12.2 16.9 14.2 14.6 14.2 13.5 14.1
## 10 0.0 0.0 22.2 12.1 27.0 16.3 19.3 16.2 15.7 15.0 9.9 16.2
## Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0sd_hinctnta <- sd(se_data_subset$hinctnta, na.rm = TRUE)
sd_hinctnta## [1] 2.917055mean_hinctnta <- se_data_subset %>%
summarise(avg_hinctnta = mean(hinctnta, na.rm = TRUE))
mean_hinctnta## avg_hinctnta
## 1 6.298197
Provide a visualization for one of the explanatory variable relative
to your main outcome.
sweden_data <- ess[ess$cntry == "SE", ]sweden_variables_cleaned <- sweden_data %>%
filter(cntry == "SE", essround == 8) sweden_variables_cleaned <- sweden_variables_cleaned %>%
filter(cntry == "SE") %>%
mutate(
prtvtbse = ifelse(prtvtbse %in% c(66, 77, 88, 99), NA, prtvtbse),
mbtru = ifelse(mbtru %in% c(7, 8, 9), NA, mbtru))%>%
select(prtvtbse, mbtru)datasummary_skim(sweden_variables_cleaned)
Unique (#)
Missing (%)
Mean
SD
Min
Median
Max
prtvtbse
12
19
5.4
2.1
1.0
6.0
11.0
mbtru
4
0
1.7
0.8
1.0
2.0
3.0
table(sweden_variables_cleaned)## mbtru
## prtvtbse 1 2 3
## 1 41 27 12
## 2 34 32 12
## 3 23 8 13
## 4 67 15 14
## 5 134 115 80
## 6 248 128 43
## 7 49 28 3
## 8 20 3 5
## 9 3 0 0
## 10 43 38 13
## 11 2 1 6plot_df<- sweden_variables_cleaned %>%
group_by(mbtru, prtvtbse)%>%
summarise(count = n())## `summarise()` has grouped output by 'mbtru'. You can override using the
## `.groups` argument.#remove NA
plot_df <- drop_na(plot_df)
#make sure the variable you want to color by is factor
plot_df$mbtru <- as.factor(plot_df$mbtru)
plot_df$prtvtbse <- as.factor(plot_df$prtvtbse)ggplot(plot_df, aes(x = prtvtbse, y= count, fill = mbtru)) +
geom_bar(stat="identity", position = "dodge2") +
# Customize fill colors and add labels to the legend
scale_fill_manual(values = c("1" = "blue", "2" = "lightblue", "3" = "purple"),
labels = c("1" = "Yes, Currently",
"2" = "Yes, Previously",
"3" = "No")) +
scale_x_discrete(labels = c("1" = "Centern", "2" = "Folkpartiet liberalerna", "3" = "Kristdemokraterna", "4" = "Miljöpartiet de gröna", "5" = "Moderata samlingspartiet", "6" = "Socialdemokraterna", "7" = "Vänsterpartiet", "8" = "FI (Feministiskt initiativ)", "9" = "Piratpartiet", "10" = "Sverigedemokraterna", "11" = "Annat parti")) +
# Customize other plot elements
labs(title = "Comparison of Party Vote Choice by Union Membership",
x = "Party Vote",
y = "Count",
color= "Union Membership",
fill = "Union Membership") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
# Display the plot
print(plot)## function (x, y, ...)
## UseMethod("plot")
## <bytecode: 0x7ff6eae62718>
## <environment: namespace:base>
