India GST Collection Analysis

Interactive Dashboard — Financial Years 2017–18 to 2024–25

gst_data <- fread("/home/pawan/tax/gst.csv")

num_cols <- c("CGST", "SGST", "IGST", "CESS", "TOTAL")
gst_data[, (num_cols) := lapply(.SD, function(x) as.numeric(gsub(",", "", x))),
         .SDcols = num_cols]

latest_year  <- gst_data[!is.na(TOTAL) & TOTAL > 0, max(Year)]
exclude_rows <- c("Domestic GST Collection- All India",
                  "Gross Revenue",
                  "IGST and CESS on Import - All India")

Overview

This interactive report explores India’s Goods and Services Tax (GST) collection data across states and financial years. All monetary values are expressed in ₹ Thousands Crores (K Cr).

Tip: Hover over any chart element to see detailed figures. Use the toolbar icon to download charts as PNG.

---

1 · All India Domestic GST Trends

Year-wise breakdown of Central (CGST), State (SGST), Integrated (IGST) and Compensation Cess (CESS) for the consolidated All India basket.

all_india_long <- melt(
  gst_data[State == "Domestic GST Collection- All India"],
  id.vars       = c("State", "Year"),
  measure.vars  = c("CGST", "SGST", "IGST", "CESS"),
  variable.name = "Tax_Type",
  value.name    = "Amount"
)
all_india_long[, tooltip := paste0(
  "<b>", Tax_Type, "</b><br>Year: ", Year,
  "<br>Amount: ", fmt_cr(Amount)
)]

p1 <- ggplot(all_india_long,
             aes(x = Year, y = Amount / 1000,
                 fill = Tax_Type, group = Tax_Type)) +
  geom_bar_interactive(
    stat = "identity",
    aes(tooltip = tooltip, data_id = paste(Year, Tax_Type)),
    width = 0.7, alpha = 0.88
  ) +
  scale_fill_manual(values = gst_colors) +
  scale_y_continuous(labels = label_comma(suffix = " K")) +
  labs(title    = "All India Domestic GST Collection Trends",
       subtitle = "Year-wise breakdown by tax type  (₹ Thousands Crores)",
       x = "Financial Year", y = "Amount (₹ Thousands Crores)",
       fill = "Tax Type",
       caption = "Source: GST Council / Ministry of Finance") +
  theme_gst()

girafe(ggobj = p1, options = girafe_opts("#3B9EE0"))

Stacked bars — hover to inspect individual tax components.

---

2 · GST Collection by State

Top 16 states ranked by cumulative GST, broken down by tax type.

states_dt   <- gst_data[!State %in% exclude_rows]
top16_names <- states_dt[TOTAL > 0,
                          .(S = sum(TOTAL, na.rm = TRUE)), by = State
                         ][order(-S)][1:16, State]

states_long <- melt(
  states_dt[State %in% top16_names],
  id.vars       = c("State", "Year", "TOTAL"),
  measure.vars  = c("CGST", "SGST", "IGST", "CESS"),
  variable.name = "Tax_Type",
  value.name    = "Amount"
)
states_long[, tooltip := paste0(
  "<b>", State, "</b><br>Year: ", Year,
  "<br><i>", Tax_Type, "</i>: ", fmt_cr(Amount),
  "<br>Total: ", fmt_cr(TOTAL)
)]

p2 <- ggplot(states_long,
             aes(x = Year, y = Amount / 1000, fill = Tax_Type)) +
  geom_bar_interactive(
    stat = "identity",
    aes(tooltip = tooltip, data_id = paste(State, Year, Tax_Type)),
    alpha = 0.88
  ) +
  scale_fill_manual(values = gst_colors) +
  scale_y_continuous(labels = label_comma()) +
  facet_wrap(~State, scales = "free_y", ncol = 4) +
  labs(title    = "GST Collection by State",
       subtitle = "Top 16 states — stacked by tax type  (₹ Thousands Crores)",
       x = "Financial Year", y = "Amount (₹ Thousands Crores)",
       fill = "Tax Type", caption = "Source: GST Council") +
  theme_gst() +
  theme(axis.text.x = element_text(size = 7))

girafe(ggobj = p2, width_svg = 14, height_svg = 10,
       options = girafe_opts("#2DBE8E"))

Stacked bars per state — hover to see component amounts.

---

3 · Major States — Collection Trends

Line + point chart for 10 major states. Hover a point to see YoY growth.

major_states <- c("Maharashtra", "Gujarat", "Tamil Nadu", "Karnataka",
                  "Uttar Pradesh", "West Bengal", "Delhi",
                  "Rajasthan", "Madhya Pradesh", "Telangana")

state_palette <- c(
  Maharashtra = "#3B9EE0", Gujarat = "#2DBE8E",
  `Tamil Nadu` = "#F5A623", Karnataka = "#E05C5C",
  `Uttar Pradesh` = "#9B6CF5", `West Bengal` = "#F0C060",
  Delhi = "#60C0F0", Rajasthan = "#E08050",
  `Madhya Pradesh` = "#70D080", Telangana = "#C070D0"
)

major_dt <- gst_data[State %in% major_states & !is.na(TOTAL) & TOTAL > 0
                     ][order(State, Year)]
major_dt[, Growth := round((TOTAL - shift(TOTAL)) / shift(TOTAL) * 100, 1),
         by = State]
major_dt[, tooltip := paste0(
  "<b>", State, "</b><br>Year: ", Year,
  "<br>Total: ", fmt_cr(TOTAL),
  ifelse(!is.na(Growth), paste0("<br>YoY: <b>", Growth, "%</b>"), "")
)]

p3 <- ggplot(major_dt,
             aes(x = Year, y = TOTAL / 1000,
                 color = State, group = State)) +
  geom_line_interactive(linewidth = 1.3,
                        aes(tooltip = State, data_id = State)) +
  geom_point_interactive(size = 2.5,
                         aes(tooltip = tooltip,
                             data_id = paste(State, Year))) +
  scale_color_manual(values = state_palette) +
  scale_y_continuous(labels = label_comma()) +
  facet_wrap(~State, ncol = 5, scales = "free_y") +
  labs(title    = "GST Collection Trends — Major States",
       subtitle = "Hover a point to see YoY growth  (₹ Thousands Crores)",
       x = "Financial Year", y = "Total GST (₹ Thousands Crores)",
       caption  = "Source: GST Council") +
  theme_gst() +
  theme(legend.position = "none")

girafe(ggobj = p3, width_svg = 14, height_svg = 7,
       options = girafe_opts("#F5A623"))

Each panel is one state; hover points reveal YoY growth %.

---

4 · GST Composition — 2025-26

Donut showing the share of each tax head for the latest available financial year across all states.

latest_comp <- gst_data[!State %in% exclude_rows & Year == latest_year,
                          .(CGST = sum(CGST, na.rm = TRUE),
                            SGST = sum(SGST, na.rm = TRUE),
                            IGST = sum(IGST, na.rm = TRUE),
                            CESS = sum(CESS, na.rm = TRUE))]

comp_long <- melt(latest_comp,
                  measure.vars  = c("CGST", "SGST", "IGST", "CESS"),
                  variable.name = "Tax_Type",
                  value.name    = "Amount")
comp_long[, Share := round(Amount / sum(Amount) * 100, 1)]
comp_long[, tooltip := paste0(
  "<b>", Tax_Type, "</b><br>",
  fmt_cr(Amount), "<br>Share: <b>", Share, "%</b>"
)]

p4 <- ggplot(comp_long,
             aes(x = "", y = Amount / 1000, fill = Tax_Type)) +
  geom_bar_interactive(
    stat = "identity", width = 1,
    aes(tooltip = tooltip, data_id = as.character(Tax_Type)),
    color = "#0F1923", linewidth = 1.2
  ) +
  coord_polar("y", start = 0) +
  geom_text(aes(label = paste0(Tax_Type, "\n", Share, "%")),
            position = position_stack(vjust = 0.55),
            color = "white", size = 4.2, fontface = "bold") +
  scale_fill_manual(values = gst_colors) +
  labs(title    = paste0("GST Composition — All States (", latest_year, ")"),
       subtitle = "Distribution across tax types",
       fill = "Tax Type", caption = "Source: GST Council") +
  theme_gst() +
  theme(axis.title = element_blank(), axis.text = element_blank(),
        panel.grid = element_blank())

girafe(ggobj = p4, options = girafe_opts("#9B6CF5"))

Hover a segment to see the exact amount and share.

---

5 · Heatmap — State × Year

heatmap_dt <- gst_data[
  !State %in% c(exclude_rows, "OIDAR", "Other Territory") & TOTAL > 0,
  .(State, Year, TOTAL)
]
top20 <- heatmap_dt[, .(S = sum(TOTAL, na.rm = TRUE)), by = State
                    ][order(-S)][1:20, State]

heatmap_dt <- heatmap_dt[State %in% top20]
heatmap_dt[, tooltip := paste0(
  "<b>", State, "</b><br>Year: ", Year, "<br>Total: ", fmt_cr(TOTAL)
)]

p5 <- ggplot(heatmap_dt,
             aes(x = Year,
                 y = reorder(State, TOTAL, FUN = sum),
                 fill = TOTAL / 1000)) +
  geom_tile_interactive(
    aes(tooltip = tooltip, data_id = paste(State, Year)),
    color = "#0F1923", linewidth = 0.4
  ) +
  scale_fill_gradient2(
    low = "#0D2137", mid = "#1E6FAD", high = "#FFD166",
    midpoint = median(heatmap_dt$TOTAL / 1000, na.rm = TRUE),
    name = "₹ K Crores", labels = label_comma()
  ) +
  labs(title    = "Heatmap — GST Collections by State & Year",
       subtitle = "Top 20 States/UTs  (₹ Thousands Crores)",
       x = "Financial Year", y = NULL,
       caption  = "Source: GST Council") +
  theme_gst() +
  theme(legend.position = "right",
        axis.text.y = element_text(size = 9))

girafe(ggobj = p5, width_svg = 12, height_svg = 7,
       options = girafe_opts("#FFD166"))

Top 20 states by cumulative GST; brighter = higher collection.

---

6 · YoY Growth Rate — All India

growth_dt <- gst_data[
  State == "Domestic GST Collection- All India" & !is.na(TOTAL) & TOTAL > 0
][order(Year)]
growth_dt[, Growth_Rate := round((TOTAL - shift(TOTAL)) / shift(TOTAL) * 100, 1)]
growth_dt[, tooltip := paste0(
  "<b>", Year, "</b><br>Growth: <b>",
  ifelse(!is.na(Growth_Rate), paste0(Growth_Rate, "%"), "N/A"),
  "</b><br>Total: ", fmt_cr(TOTAL)
)]
growth_dt[, bar_fill := ifelse(Growth_Rate >= 0, "Positive", "Negative")]

p6 <- ggplot(growth_dt[!is.na(Growth_Rate)],
             aes(x = Year, y = Growth_Rate, fill = bar_fill)) +
  geom_bar_interactive(
    stat = "identity", width = 0.65, alpha = 0.88,
    aes(tooltip = tooltip, data_id = Year)
  ) +
  geom_hline(yintercept = 0, color = "#8FA8C8", linewidth = 0.6) +
  geom_text(
    aes(label = paste0(Growth_Rate, "%"),
        vjust = ifelse(Growth_Rate >= 0, -0.4, 1.4)),
    color = "#C8DCEF", size = 3.5, fontface = "bold"
  ) +
  scale_fill_manual(values = c(Positive = "#2DBE8E", Negative = "#E05C5C"),
                    guide = "none") +
  scale_y_continuous(labels = label_percent(scale = 1)) +
  labs(title    = "All India Domestic GST — YoY Growth Rate",
       subtitle = "Percentage change from previous financial year",
       x = "Financial Year", y = "Growth Rate (%)",
       caption  = "Source: GST Council") +
  theme_gst()

girafe(ggobj = p6, options = girafe_opts("#2DBE8E"))

Green = positive growth; red = contraction.

---

7 · CGST vs SGST — Selected States

comp_data  <- gst_data[State %in% c("Maharashtra", "Gujarat",
                                     "Tamil Nadu", "Karnataka") &
                          Year >= "2019-20"]
comp_long2 <- melt(comp_data,
                   id.vars       = c("State", "Year"),
                   measure.vars  = c("CGST", "SGST"),
                   variable.name = "Tax_Type",
                   value.name    = "Amount")
comp_long2[, tooltip := paste0(
  "<b>", State, "</b><br>Year: ", Year,
  "<br>", Tax_Type, ": ", fmt_cr(Amount)
)]

p7 <- ggplot(comp_long2,
             aes(x = Year, y = Amount / 1000, fill = Tax_Type)) +
  geom_bar_interactive(
    stat = "identity", position = "dodge", width = 0.7, alpha = 0.88,
    aes(tooltip = tooltip, data_id = paste(State, Year, Tax_Type))
  ) +
  scale_fill_manual(values = c(CGST = "#3B9EE0", SGST = "#2DBE8E")) +
  scale_y_continuous(labels = label_comma()) +
  facet_wrap(~State, scales = "free_y") +
  labs(title    = "CGST vs SGST — Major States",
       subtitle = "Central vs State GST since 2019-20  (₹ Thousands Crores)",
       x = "Financial Year", y = "Amount (₹ Thousands Crores)",
       fill = "Tax Type", caption = "Source: GST Council") +
  theme_gst()

girafe(ggobj = p7, width_svg = 11, height_svg = 7,
       options = girafe_opts("#3B9EE0"))

Dodged bars — CGST and SGST compared side by side.

---

8 · CESS Contribution Trend

cess_dt <- gst_data[!State %in% exclude_rows & TOTAL > 0,
                     .(CESS_Share = round(
                       sum(CESS, na.rm = TRUE) /
                         sum(TOTAL, na.rm = TRUE) * 100, 2)),
                     by = Year][order(Year)]
cess_dt[, tooltip := paste0(
  "<b>", Year, "</b><br>CESS Share: <b>", CESS_Share, "%</b>"
)]

p8 <- ggplot(cess_dt, aes(x = Year, y = CESS_Share, group = 1)) +
  geom_line_interactive(
    color = "#F5A623", linewidth = 1.4,
    aes(tooltip = "CESS trend", data_id = "line")
  ) +
  geom_point_interactive(
    color = "#E05C5C", size = 4, alpha = 0.9,
    aes(tooltip = tooltip, data_id = Year)
  ) +
  geom_text(aes(label = paste0(CESS_Share, "%")),
            vjust = -1, color = "#C8DCEF", size = 3.5) +
  scale_y_continuous(
    labels = label_percent(scale = 1),
    limits = c(0, max(cess_dt$CESS_Share, na.rm = TRUE) * 1.3)
  ) +
  labs(title    = "CESS Contribution to Total GST",
       subtitle = "Percentage share of CESS across all states, by year",
       x = "Financial Year", y = "CESS Share (%)",
       caption  = "Source: GST Council") +
  theme_gst()

girafe(ggobj = p8, options = girafe_opts("#F5A623"))

Hover points to see exact CESS share per year.

---

9 · Statistical Summary Tables

Top 15 States by Average GST Collection

state_avg <- gst_data[
  !State %in% c(exclude_rows, "OIDAR", "Other Territory") & TOTAL > 0,
  .(
    `Avg (₹ K Cr)` = round(mean(TOTAL, na.rm = TRUE) / 1000, 2),
    `Max (₹ K Cr)` = round(max(TOTAL,  na.rm = TRUE) / 1000, 2),
    `Min (₹ K Cr)` = round(min(TOTAL,  na.rm = TRUE) / 1000, 2)
  ),
  by = State
][order(-`Avg (₹ K Cr)`)][1:15]

knitr::kable(state_avg, format.args = list(big.mark = ","))

State	Avg (₹ K Cr)	Max (₹ K Cr)	Min (₹ K Cr)
Maharashtra	235.99	359.86	105.19
Karnataka	106.03	159.56	48.14
Gujarat	96.57	136.75	45.91
Tamil Nadu	91.22	131.12	45.32
Haryana	77.04	119.36	36.81
Uttar Pradesh	76.61	112.21	36.86
Delhi	51.85	77.00	26.44
West Bengal	49.02	66.89	23.33
Telangana	45.42	62.99	21.35
Odisha	39.68	60.93	14.85
Rajasthan	39.07	54.78	18.46
Andhra Pradesh	32.62	44.83	14.33
Madhya Pradesh	31.85	45.07	15.54
Chhattisgarh	27.27	37.18	13.01
Jharkhand	26.62	36.84	14.16

Top 10 Growth Leaders (2023-24 → 2024-25)

growth_by_state <- gst_data[
  !State %in% c(exclude_rows, "OIDAR", "Other Territory") &
    Year %in% c("2023-24", "2024-25") & TOTAL > 0
]
growth_wide <- dcast(growth_by_state, State ~ Year, value.var = "TOTAL")
growth_wide[, `Growth (%)` := round((`2024-25` - `2023-24`) / `2023-24` * 100, 2)]

knitr::kable(
  growth_wide[order(-`Growth (%)`)][1:10,
    .(State, `2023-24 (₹ Cr)` = `2023-24`,
             `2024-25 (₹ Cr)` = `2024-25`,
             `Growth (%)`)],
  format.args = list(big.mark = ",")
)

State	2023-24 (₹ Cr)	2024-25 (₹ Cr)	Growth (%)
Haryana	102,914	119,362	15.98
Delhi	66,445	77,002	15.89
Ladakh	481	546	13.51
Tripura	1,053	1,193	13.30
Andaman and Nicobar Island	428	482	12.62
Maharashtra	320,117	359,855	12.41
Bihar	18,021	20,208	12.14
Assam	15,602	17,415	11.62
Odisha	54,748	60,928	11.29
Punjab	24,061	26,721	11.06

---

Session Info

sessionInfo()

#> R version 4.5.3 (2026-03-11)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Manjaro Linux
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/libblas.so.3.12.0 
#> LAPACK: /usr/lib/liblapack.so.3.12.0  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8    LC_NUMERIC=C            LC_TIME=en_IN          
#>  [4] LC_COLLATE=en_US.UTF-8  LC_MONETARY=en_IN       LC_MESSAGES=en_US.UTF-8
#>  [7] LC_PAPER=en_IN          LC_NAME=C               LC_ADDRESS=C           
#> [10] LC_TELEPHONE=C          LC_MEASUREMENT=en_IN    LC_IDENTIFICATION=C    
#> 
#> time zone: Asia/Kolkata
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] scales_1.4.0        ggiraph_0.9.2       ggplot2_4.0.1      
#> [4] data.table_1.18.2.1
#> 
#> loaded via a namespace (and not attached):
#>  [1] gtable_0.3.6            jsonlite_2.0.0          dplyr_1.1.4            
#>  [4] compiler_4.5.3          tidyselect_1.2.1        Rcpp_1.1.1             
#>  [7] dichromat_2.0-0.1       fontquiver_0.2.1        systemfonts_1.3.1      
#> [10] yaml_2.3.12             fastmap_1.2.0           R6_2.6.1               
#> [13] labeling_0.4.3          gdtools_0.4.4           generics_0.1.4         
#> [16] knitr_1.50              htmlwidgets_1.6.4       tibble_3.3.1           
#> [19] pillar_1.11.1           RColorBrewer_1.1-3      rlang_1.2.0            
#> [22] xfun_0.55               S7_0.2.1                cli_3.6.6              
#> [25] withr_3.0.2             magrittr_2.0.5          digest_0.6.39          
#> [28] grid_4.5.3              lifecycle_1.0.5         vctrs_0.7.3            
#> [31] evaluate_1.0.5          glue_1.8.0              farver_2.1.2           
#> [34] fontLiberation_0.1.0    fontBitstreamVera_0.1.1 purrr_1.2.0            
#> [37] rmarkdown_2.30          tools_4.5.3             pkgconfig_2.0.3        
#> [40] htmltools_0.5.9