Visualisasi Data

Memvisualisasikan Data

Salah satu buku visualisasi untuk ilmu sosial adalah Healy (2018).

Persiapan

data_wvs <- read_csv(here("datasets", "WVS_Cross-National_Wave_7_csv_v5_0.csv"))

wvs <- data_wvs |> 
  rename(negara               = B_COUNTRY_ALPHA,
         kota_desa            = H_URBRURAL,
         jenis_kelamin        = Q260,
         usia                 = Q262,
         status_pernikahan    = Q273,
         pendidikan_terakhir  = Q275)

head(wvs)

# A tibble: 6 × 606
  version     doi   A_WAVE A_YEAR A_STUDY B_COUNTRY negara C_COW_NUM C_COW_ALPHA
  <chr>       <chr>  <dbl>  <dbl>   <dbl>     <dbl> <chr>      <dbl> <chr>      
1 5-0-0 (202… doi.…      7   2018       2        20 AND          232 AND        
2 5-0-0 (202… doi.…      7   2018       2        20 AND          232 AND        
3 5-0-0 (202… doi.…      7   2018       2        20 AND          232 AND        
4 5-0-0 (202… doi.…      7   2018       2        20 AND          232 AND        
5 5-0-0 (202… doi.…      7   2018       2        20 AND          232 AND        
6 5-0-0 (202… doi.…      7   2018       2        20 AND          232 AND        
# ℹ 597 more variables: D_INTERVIEW <dbl>, S007 <dbl>, J_INTDATE <dbl>,
#   FW_END <dbl>, FW_START <dbl>, K_TIME_START <dbl>, K_TIME_END <dbl>,
#   K_DURATION <dbl>, Q_MODE <dbl>, N_REGION_ISO <dbl>, N_REGION_WVS <dbl>,
#   N_REGION_NUTS2 <dbl>, reg_nuts1 <dbl>, N_TOWN <dbl>, G_TOWNSIZE <dbl>,
#   G_TOWNSIZE2 <dbl>, H_SETTLEMENT <dbl>, kota_desa <dbl>,
#   L_INTERVIEWER_NUMBER <dbl>, I_PSU <dbl>, O1_LONGITUDE <dbl>,
#   O2_LATITUDE <dbl>, S_INTLANGUAGE <dbl>, LNGE_ISO <chr>, E_RESPINT <dbl>, …

wvs <- wvs |> 
  mutate(kota_desa = recode(kota_desa,
                            `1` = "Perkotaan",
                            `2` = "Pedesaan"),
         jenis_kelamin = recode(jenis_kelamin, 
                                       `1` = "Laki-laki", 
                                       `2` = "Perempuan"),
         status_pernikahan = recode(status_pernikahan, 
                                            `1` = "Menikah",
                                            `2` = "Tinggal bersama",
                                            `3` = "Cerai hidup",
                                            `4` = "Pisah",
                                            `5` = "Cerai mati",
                                            `6` = "Belum menikah"))

head(wvs)

# A tibble: 6 × 606
  version     doi   A_WAVE A_YEAR A_STUDY B_COUNTRY negara C_COW_NUM C_COW_ALPHA
  <chr>       <chr>  <dbl>  <dbl>   <dbl>     <dbl> <chr>      <dbl> <chr>      
1 5-0-0 (202… doi.…      7   2018       2        20 AND          232 AND        
2 5-0-0 (202… doi.…      7   2018       2        20 AND          232 AND        
3 5-0-0 (202… doi.…      7   2018       2        20 AND          232 AND        
4 5-0-0 (202… doi.…      7   2018       2        20 AND          232 AND        
5 5-0-0 (202… doi.…      7   2018       2        20 AND          232 AND        
6 5-0-0 (202… doi.…      7   2018       2        20 AND          232 AND        
# ℹ 597 more variables: D_INTERVIEW <dbl>, S007 <dbl>, J_INTDATE <dbl>,
#   FW_END <dbl>, FW_START <dbl>, K_TIME_START <dbl>, K_TIME_END <dbl>,
#   K_DURATION <dbl>, Q_MODE <dbl>, N_REGION_ISO <dbl>, N_REGION_WVS <dbl>,
#   N_REGION_NUTS2 <dbl>, reg_nuts1 <dbl>, N_TOWN <dbl>, G_TOWNSIZE <dbl>,
#   G_TOWNSIZE2 <dbl>, H_SETTLEMENT <dbl>, kota_desa <chr>,
#   L_INTERVIEWER_NUMBER <dbl>, I_PSU <dbl>, O1_LONGITUDE <dbl>,
#   O2_LATITUDE <dbl>, S_INTLANGUAGE <dbl>, LNGE_ISO <chr>, E_RESPINT <dbl>, …

Bar plot

library(ggplot2)
ggplot(wvs, aes(x = status_pernikahan)) +
  geom_bar()

Box plot

ggplot(wvs, aes(x = kota_desa,
                y = pendidikan_terakhir)) +
  geom_boxplot()

Histogram

ggplot(wvs, aes(x = usia)) +
  geom_histogram(bins = 30)

Daftar Bacaan Lanjutan

Healy, Kieran. 2018. Data Visualization: A Practical Introduction. Princeton University Press.