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     "href": "zero_dl_up.html",
     "title": "Quick EDA about BSL with 0 speeds Download/Upload",
     "section": "",
-    "text": "EDA to store quick notes about locations services with 0 uploads and 0 downloads.\nThe data used to provide most of the analysis was done with this 2 SQL queries. They were saved and stored in data/\n\n\nCode\nselect \n    state_abbr,\n    brand_name,\n    count(brand_name)\nfrom \n    staging.june23\nwhere\n(max_advertised_download_speed = 0 AND\n    max_advertised_upload_speed = 0) = true\ngroup by brand_name, state_abbr, technology;\n\n-- first get all 0/0 then get all the non 0/0\n\nselect \n    state_abbr,\n    brand_name,\n    count(brand_name)\nfrom \n    staging.june23\nwhere\n(max_advertised_download_speed = 0 AND\n    max_advertised_upload_speed = 0) = false\ngroup by brand_name, state_abbr, technology;\n\n\n\n\nCode\nzero_loc <- read.csv(\"data/zero_dl_up.csv\")\nnot_zero <- read.csv(\"data/not_zero_dl.csv\")\n\n\nSummary by technologies:\n\n\nCode\nagg_tech <- function(dat) {\n    aggregate(cbind(count = dat$count),\n                        list(technology = dat$technology),\n                        sum)\n}\n\nagg <- agg_tech(zero_loc) \nagg_not <- agg_tech(not_zero)\ntechnology <- merge(agg, agg_not, by.x = \"technology\", by.y = \"technology\", all.x = TRUE, all.y = TRUE) \ncolnames(technology) <- c(\"technology\",  \"cnt_zero_dl\", \"cnt_non_zero\")\ntechnology$rate_zero <- round(technology$cnt_zero_dl / (technology$cnt_zero_dl +  technology$cnt_non_zero), 4)\ntechnology\n\n\n  technology cnt_zero_dl cnt_non_zero rate_zero\n1          0          29         4060    0.0071\n2         10    17271298     40567062    0.2986\n3         40       24721    102795944    0.0002\n4         50       22942     62521744    0.0004\n5         60       36678    339792513    0.0001\n6         61          NA    114863490        NA\n7         70     2201042     45586988    0.0461\n8         71    38540018     95278280    0.2880\n9         72       72535      8554415    0.0084\n\n\n\n\nCode\nagg <- aggregate(cbind(count = zero_loc$count), \n          list(brand_name = zero_loc$brand_name),\n          FUN = sum)\nagg_not <- aggregate(cbind(count = not_zero$count), \n          list(brand_name = not_zero$brand_name),\n          FUN = sum)\nrate_zero <- merge(agg, agg_not, by.x = \"brand_name\", by.y = \"brand_name\", all.x = TRUE) \ncolnames(rate_zero) <- c(\"brand_name\", \"cnt_zero_dl\", \"cnt_non_zero\")\nrate_zero$rate_zero <- rate_zero$cnt_zero_dl / (rate_zero$cnt_zero_dl +  rate_zero$cnt_non_zero)\n\nrate_zero[order(rate_zero$cnt_zero_dl, decreasing = TRUE),] |> head(n = 20)\n\n\n                                 brand_name cnt_zero_dl cnt_non_zero\n395                             T-Mobile US    37606165     39995310\n28                                 AT&T Inc     4608318     36228564\n449                                 Verizon     4242079      9906930\n83                              CenturyLink     3739262      8454587\n61                              Brightspeed     2798237      1122730\n435                      TWN Communications     1258827        15014\n108             Consolidated Communications      529512       547949\n88                          Choice Wireless      457196       126881\n343                          Rise Broadband      383897      4711833\n95                                    Claro      278907      1035234\n102                                 Comcell      253851        35380\n515                             Ziply Fiber      222424      1081237\n453                     VTel Wireless, Inc.      130430       143176\n174                                FRONTIER       76159      7593836\n330 Plateau Telecommunications Incorporated       75939        37711\n455                            W A T C H TV       64002      1363903\n447   Valor Telecommunications of Texas, LP       62524       523687\n208                          Hometown Cable       56214         5035\n20           altafiber Extended Territories       54272       113524\n13                    ALASKA COMMUNICATIONS       52802        76103\n      rate_zero\n395 0.484606317\n28  0.112846960\n449 0.299814567\n83  0.306651493\n61  0.713659921\n435 0.988213600\n108 0.491444238\n88  0.782766656\n343 0.075336998\n95  0.212235217\n102 0.877675630\n515 0.170614907\n453 0.476707382\n174 0.009929472\n330 0.668183018\n455 0.044822310\n447 0.106657842\n208 0.917794576\n20  0.323440368\n13  0.409619487\n\n\nI am bothered by those results\n\n\nCode\nst_agg <- aggregate(cbind(count = zero_loc$count),\n           list(state_abbr = zero_loc$state_abbr),\n           sum) \n\nst_agg[order(st_agg$count, decreasing = TRUE), ]\n\n\n   state_abbr   count\n46         TX 4941001\n5          CA 3646886\n40         PA 2844345\n10         FL 2770745\n29         NC 2695309\n36         NY 2681806\n37         OH 2315645\n4          AZ 1937060\n24         MI 1882225\n48         VA 1742659\n16         IL 1577140\n50         WA 1525201\n33         NJ 1517378\n11         GA 1513840\n21         MA 1366389\n45         TN 1356454\n17         IN 1355637\n25         MN 1207171\n2          AL 1092723\n51         WI 1076904\n6          CO 1063596\n34         NM  995923\n22         MD  986789\n43         SC  935817\n20         LA  908055\n39         OR  854570\n14         IA  853064\n19         KY  850097\n26         MO  846778\n38         OK  837809\n3          AR  790574\n27         MS  661016\n35         NV  628314\n18         KS  600306\n47         UT  537778\n23         ME  534610\n7          CT  462478\n15         ID  409825\n31         NE  407737\n32         NH  394023\n49         VT  376123\n28         MT  367473\n41         PR  279108\n52         WV  275874\n44         SD  236459\n9          DE  228395\n30         ND  196069\n53         WY  180125\n42         RI  177713\n1          AK  101385\n13         HI  100408\n8          DC   42776\n12         GU    1678"
+    "text": "Code\ntable_with_options <- function(x){DT::datatable(x, rownames = FALSE,\n              extensions = 'Buttons', \n                options = list(\n                          dom = 'Blfrtip',\n                          buttons = list('copy', 'print', list(\n                            extend = 'collection',\n                            buttons = c('csv', 'excel'),\n                            text = 'Download')\n                                    )\n                          )\n            )}\n# very lazish function, col should be a string \nagg_count <- function(dat, col) {\n    agg <- aggregate(cbind(count = dat$count),\n                     list(name_col = dat[[col]]),\n                      sum)\n    colnames(agg) <- c(col, \"count\")\n    return(agg)\n}\nThe goals of this page is storing a quick EDA about broadband services locations with 0 MBps uploads and 0 MBps downloads. To be concise we are going to call them 0/0 speeds.\nWe have counted every services that have been declared with 0/0 speeds and associated with their States, ISP and technology. To clarify that does not meen a location have 0/0 speeds only but that one ISP x technology is providing this kind of service in this location.\nThe data used to provide most of the analysis was done with this 2 SQL queries. They were saved and stored in data/\nCode\nSELECT \n    state_abbr,\n    brand_name,\n    count(brand_name)\nFROM\n    staging.june23\nWHERE\n(max_advertised_download_speed = 0 AND\n    max_advertised_upload_speed = 0) = true\nGROUP BY brand_name, state_abbr, technology;\n\n-- first get all 0/0 then get all the non 0/0\n\nSELECT \n    state_abbr,\n    brand_name,\n    count(brand_name)\nFROM \n    staging.june23\nWHERE\n(max_advertised_download_speed = 0 AND\n    max_advertised_upload_speed = 0) = false\nGROUP BY brand_name, state_abbr, technology;\nCode\nzero_loc <- read.csv(\"data/zero_dl_up.csv\")\nnot_zero <- read.csv(\"data/not_zero_dl.csv\")"
+  },
+  {
+    "objectID": "zero_dl_up.html#summary-by-technologies",
+    "href": "zero_dl_up.html#summary-by-technologies",
+    "title": "Quick EDA about BSL with 0 speeds Download/Upload",
+    "section": "Summary by technologies:",
+    "text": "Summary by technologies:\n\n\nCode\nagg <- agg_count(zero_loc, \"technology\") \nagg_not <- agg_count(not_zero, \"technology\")\n\ntechnology <- merge(agg, agg_not, by.x = \"technology\", \n                    by.y = \"technology\", all.x = TRUE, all.y = TRUE) \ncolnames(technology) <- c(\"technology\",  \"cnt_zero_dl\", \"cnt_non_zero\")\ntechnology$rate_zero <- round(technology$cnt_zero_dl / \n                (technology$cnt_zero_dl +  technology$cnt_non_zero), 4)\n\ntable_with_options(technology)\n\n\n\n\n\n\n\n\nWe do not mind too much 70 (Unlicensed Terrestrial Fixed Wireless) because we are filtering it out but we are keeping 71 (Licensed Terrestrial Fixed Wireless) , 72 (Licensed-by-Rule Terrestrial Fixed Wireless)and 10 (Copper Wire).\nTo take that into account I will filter out the Unlicensed Terrestrial Fixed Wireless"
+  },
+  {
+    "objectID": "zero_dl_up.html#summary-by-isp",
+    "href": "zero_dl_up.html#summary-by-isp",
+    "title": "Quick EDA about BSL with 0 speeds Download/Upload",
+    "section": "Summary by ISP",
+    "text": "Summary by ISP\n\n\nCode\nzero_loc <- zero_loc[which(zero_loc$technology != 70), ]\nnot_zero <- not_zero[which(not_zero$technology != 70), ]\n\nagg <- agg_count(zero_loc, \"brand_name\")\nagg_not <- agg_count(not_zero, \"brand_name\")\n\nrate_zero <- merge(agg, agg_not, \n                   by.x = \"brand_name\", by.y = \"brand_name\"\n                   , all.x = TRUE) \n\ncolnames(rate_zero) <- c(\"brand_name\", \"cnt_zero_dl\", \"cnt_non_zero\")\nrate_zero$rate_zero <- round(rate_zero$cnt_zero_dl /\n                 (rate_zero$cnt_zero_dl +  rate_zero$cnt_non_zero),\n                            4)\n\ntable_with_options(rate_zero[\n                    order(rate_zero$cnt_zero_dl, decreasing = TRUE),])\n\n\n\n\n\n\n\n\n\n402 ISPs are declaring services with 0/0 MBips (We have 2902 ISPs registered in FCC NBM)"
+  },
+  {
+    "objectID": "zero_dl_up.html#sumamry-by-states",
+    "href": "zero_dl_up.html#sumamry-by-states",
+    "title": "Quick EDA about BSL with 0 speeds Download/Upload",
+    "section": "Sumamry by States",
+    "text": "Sumamry by States\n\n\nCode\nst_agg <- aggregate(cbind(count = zero_loc$count),\n           list(state_abbr = zero_loc$state_abbr),\n           sum) \n\nst_agg[order(st_agg$count, decreasing = TRUE), ]\n\n\n   state_abbr   count\n46         TX 4386312\n5          CA 3634408\n40         PA 2828074\n10         FL 2770745\n29         NC 2695144\n36         NY 2681628\n37         OH 2239266\n24         MI 1871399\n48         VA 1734018\n33         NJ 1517378\n11         GA 1513818\n16         IL 1498313\n50         WA 1464164\n21         MA 1366389\n45         TN 1352542\n4          AZ 1318026\n25         MN 1206570\n17         IN 1097195\n2          AL 1085451\n51         WI 1070819\n6          CO 1029544\n22         MD  986784\n43         SC  935805\n20         LA  906797\n39         OR  846556\n14         IA  820711\n34         NM  819808\n26         MO  818771\n38         OK  812718\n19         KY  808691\n3          AR  783883\n27         MS  661013\n35         NV  618826\n18         KS  595722\n23         ME  534541\n47         UT  522833\n7          CT  462477\n31         NE  397298\n32         NH  394023\n49         VT  374912\n28         MT  359156\n15         ID  343850\n41         PR  279108\n52         WV  275714\n44         SD  236435\n9          DE  228395\n30         ND  195666\n42         RI  177713\n53         WY  165209\n13         HI  100408\n1          AK   98741\n8          DC   42776\n12         GU    1678\n\n\n One point of concern is that services with 0/0 speeds could be generated for various reasons. One coulb be"
   }
 ]