Introduction

The Election Commission of India collects voter level data that is disaggregated by gender ( Male, Female and Third Gender) for each constituency. This data can help us in figuring out how men and women from each parliamentary constituency participated in the election. Other than the voter level data, the ECI also maintains data for each candidate that participated in the election for every constituency.

The visualisations below will help us explore the following data points:

For Voters

  1. Constituencies where the female participation in terms of number of votes was more than the male votes.
  2. Constituencies where the percentage of eligible female vote was more than the percentage of male female votes. The eligible vote percentage is calculated as a ratio of Total votes/Total electorate.

Analysis - Gender wise participation in polls

Where more women voted than men overall

Vote percentage within gender categories

For Candidates

  1. Constituencies with at-least one women candidate
  2. Constituencies where the ratio of women to men candidates was at-least 30 percent. For example, If there are 10 candidates in a constituency, then at-least 3 of them are women.

Analysis - Analysis of candidates

Constituencies with at-least one women candidate

Higher participation of women candidates


sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)

Matrix products: default

locale:
[1] LC_COLLATE=English_India.1252  LC_CTYPE=English_India.1252   
[3] LC_MONETARY=English_India.1252 LC_NUMERIC=C                  
[5] LC_TIME=English_India.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] kableExtra_1.3.4 knitr_1.33       DT_0.16          scales_1.1.1    
 [5] paletteer_1.4.0  leaflet_2.0.4.1  geojsonsf_2.0.1  sf_1.0-0        
 [9] readxl_1.3.1     forcats_0.5.0    stringr_1.4.0    dplyr_1.0.7     
[13] purrr_0.3.4      readr_1.4.0      tidyr_1.1.2      tibble_3.0.4    
[17] ggplot2_3.3.2    tidyverse_1.3.0  workflowr_1.7.0 

loaded via a namespace (and not attached):
 [1] fs_1.5.0           lubridate_1.7.9.2  webshot_0.5.2      httr_1.4.2        
 [5] rprojroot_2.0.2    tools_4.0.3        backports_1.2.1    bslib_0.2.4       
 [9] utf8_1.1.4         R6_2.5.0           KernSmooth_2.23-17 DBI_1.1.0         
[13] colorspace_2.0-0   withr_2.3.0        tidyselect_1.1.0   processx_3.5.2    
[17] compiler_4.0.3     git2r_0.27.1       cli_3.0.0          rvest_0.3.6       
[21] xml2_1.3.2         prismatic_1.1.0    sass_0.3.1         classInt_0.4-3    
[25] callr_3.7.0        proxy_0.4-26       systemfonts_1.0.1  digest_0.6.27     
[29] rmarkdown_2.9      svglite_2.0.0      pkgconfig_2.0.3    htmltools_0.5.1.1 
[33] dbplyr_2.0.0       htmlwidgets_1.5.2  rlang_0.4.11       rstudioapi_0.13   
[37] farver_2.0.3       jquerylib_0.1.3    generics_0.1.0     jsonlite_1.7.2    
[41] crosstalk_1.1.0.1  magrittr_2.0.1     Rcpp_1.0.7         munsell_0.5.0     
[45] fansi_0.4.1        lifecycle_1.0.0    stringi_1.6.2      whisker_0.4       
[49] yaml_2.2.1         grid_4.0.3         promises_1.2.0.1   crayon_1.4.1      
[53] haven_2.3.1        hms_1.1.0          ps_1.6.0           pillar_1.6.2      
[57] reprex_0.3.0       glue_1.4.2         evaluate_0.14      getPass_0.2-2     
[61] modelr_0.1.8       vctrs_0.3.8        httpuv_1.6.1       cellranger_1.1.0  
[65] gtable_0.3.0       rematch2_2.1.2     assertthat_0.2.1   xfun_0.28         
[69] broom_0.7.9        e1071_1.7-7        later_1.2.0        class_7.3-17      
[73] viridisLite_0.4.0  units_0.7-2        ellipsis_0.3.2