This seems relatively straightforward and possibly doable with scale_x_datetime or scale_x_discrete.
I have a column with time increments like on a stopwatch:
[1] 0:00:01 0:00:02 0:00:03 0:00:04 0:00:05 0:00:06
1800 Levels: 0:00:01 0:00:02 0:00:03 0:00:04 0:00:05 0:00:06 0:00:07 ... 0:30:00
Using ggplot how can I set the x-axis labels for 30 second intervals?
Reproducible data:
structure(list(`somedata$Time` = structure(1:4, .Label = c("0:00:01",
"0:00:02", "0:00:03", "0:00:04", "0:00:05", "0:00:06", "0:00:07",
"0:00:08", "0:00:09", "0:00:10", "0:00:11", "0:00:12", "0:00:13",
"0:00:14", "0:00:15", "0:00:16", "0:00:17", "0:00:18", "0:00:19",
"0:00:20", "0:00:21", "0:00:22", "0:00:23", "0:00:24", "0:00:25",
"0:00:26", "0:00:27", "0:00:28", "0:00:29", "0:00:30", "0:00:31",
"0:00:32", "0:00:33", "0:00:34", "0:00:35", "0:00:36", "0:00:37",
"0:00:38", "0:00:39", "0:00:40", "0:00:41", "0:00:42", "0:00:43",
"0:00:44", "0:00:45", "0:00:46", "0:00:47", "0:00:48", "0:00:49",
"0:00:50", "0:00:51", "0:00:52", "0:00:53", "0:00:54", "0:00:55",
"0:00:56", "0:00:57", "0:00:58", "0:00:59", "0:01:00", "0:01:01",
"0:01:02", "0:01:03", "0:01:04", "0:01:05", "0:01:06", "0:01:07",
"0:01:08", "0:01:09", "0:01:10", "0:01:11", "0:01:12", "0:01:13",
"0:01:14", "0:01:15", "0:01:16", "0:01:17", "0:01:18", "0:01:19",
"0:01:20", "0:01:21", "0:01:22", "0:01:23", "0:01:24", "0:01:25",
"0:01:26", "0:01:27", "0:01:28", "0:01:29", "0:01:30", "0:01:31",
"0:01:32", "0:01:33", "0:01:34", "0:01:35", "0:01:36", "0:01:37",
"0:01:38", "0:01:39", "0:01:40", "0:01:41", "0:01:42", "0:01:43",
"0:01:44", "0:01:45", "0:01:46", "0:01:47", "0:01:48", "0:01:49",
"0:01:50", "0:01:51", "0:01:52", "0:01:53", "0:01:54", "0:01:55",
"0:01:56", "0:01:57", "0:01:58", "0:01:59", "0:02:00", "0:02:01",
"0:02:02", "0:02:03", "0:02:04", "0:02:05", "0:02:06", "0:02:07",
"0:02:08", "0:02:09", "0:02:10", "0:02:11", "0:02:12", "0:02:13",
"0:02:14", "0:02:15", "0:02:16", "0:02:17", "0:02:18", "0:02:19",
"0:02:20", "0:02:21", "0:02:22", "0:02:23", "0:02:24", "0:02:25",
"0:02:26", "0:02:27", "0:02:28", "0:02:29", "0:02:30", "0:02:31",
"0:02:32", "0:02:33", "0:02:34", "0:02:35", "0:02:36", "0:02:37",
"0:02:38", "0:02:39", "0:02:40", "0:02:41", "0:02:42", "0:02:43",
"0:02:44", "0:02:45", "0:02:46", "0:02:47", "0:02:48", "0:02:49",
"0:02:50", "0:02:51", "0:02:52", "0:02:53", "0:02:54", "0:02:55",
"0:02:56", "0:02:57", "0:02:58", "0:02:59", "0:03:00", "0:03:01",
"0:03:02", "0:03:03", "0:03:04", "0:03:05", "0:03:06", "0:03:07",
"0:03:08", "0:03:09", "0:03:10", "0:03:11", "0:03:12", "0:03:13",
"0:03:14", "0:03:15", "0:03:16", "0:03:17", "0:03:18", "0:03:19",
"0:03:20", "0:03:21", "0:03:22", "0:03:23", "0:03:24", "0:03:25",
"0:03:26", "0:03:27", "0:03:28", "0:03:29", "0:03:30", "0:03:31",
"0:03:32", "0:03:33", "0:03:34", "0:03:35", "0:03:36", "0:03:37",
"0:03:38", "0:03:39", "0:03:40", "0:03:41", "0:03:42", "0:03:43",
"0:03:44", "0:03:45", "0:03:46", "0:03:47", "0:03:48", "0:03:49",
"0:03:50", "0:03:51", "0:03:52", "0:03:53", "0:03:54", "0:03:55",
"0:03:56", "0:03:57", "0:03:58", "0:03:59", "0:04:00", "0:04:01",
"0:04:02", "0:04:03", "0:04:04", "0:04:05", "0:04:06", "0:04:07",
"0:04:08", "0:04:09", "0:04:10", "0:04:11", "0:04:12", "0:04:13",
"0:04:14", "0:04:15", "0:04:16", "0:04:17", "0:04:18", "0:04:19",
"0:04:20", "0:04:21", "0:04:22", "0:04:23", "0:04:24", "0:04:25",
"0:04:26", "0:04:27", "0:04:28", "0:04:29", "0:04:30", "0:04:31",
"0:04:32", "0:04:33", "0:04:34", "0:04:35", "0:04:36", "0:04:37",
"0:04:38", "0:04:39", "0:04:40", "0:04:41", "0:04:42", "0:04:43",
"0:04:44", "0:04:45", "0:04:46", "0:04:47", "0:04:48", "0:04:49",
"0:04:50", "0:04:51", "0:04:52", "0:04:53", "0:04:54", "0:04:55",
"0:04:56", "0:04:57", "0:04:58", "0:04:59", "0:05:00", "0:05:01",
"0:05:02", "0:05:03", "0:05:04", "0:05:05", "0:05:06", "0:05:07",
"0:05:08", "0:05:09", "0:05:10", "0:05:11", "0:05:12", "0:05:13",
"0:05:14", "0:05:15", "0:05:16", "0:05:17", "0:05:18", "0:05:19",
"0:05:20", "0:05:21", "0:05:22", "0:05:23", "0:05:24", "0:05:25",
"0:05:26", "0:05:27", "0:05:28", "0:05:29", "0:05:30", "0:05:31",
"0:05:32", "0:05:33", "0:05:34", "0:05:35", "0:05:36", "0:05:37",
"0:05:38", "0:05:39", "0:05:40", "0:05:41", "0:05:42", "0:05:43",
"0:05:44", "0:05:45", "0:05:46", "0:05:47", "0:05:48", "0:05:49",
"0:05:50", "0:05:51", "0:05:52", "0:05:53", "0:05:54", "0:05:55",
"0:05:56", "0:05:57", "0:05:58", "0:05:59", "0:06:00", "0:06:01",
"0:06:02", "0:06:03", "0:06:04", "0:06:05", "0:06:06", "0:06:07",
"0:06:08", "0:06:09", "0:06:10", "0:06:11", "0:06:12", "0:06:13",
"0:06:14", "0:06:15", "0:06:16", "0:06:17", "0:06:18", "0:06:19",
"0:06:20", "0:06:21", "0:06:22", "0:06:23", "0:06:24", "0:06:25",
"0:06:26", "0:06:27", "0:06:28", "0:06:29", "0:06:30", "0:06:31",
"0:06:32", "0:06:33", "0:06:34", "0:06:35", "0:06:36", "0:06:37",
"0:06:38", "0:06:39", "0:06:40", "0:06:41", "0:06:42", "0:06:43",
"0:06:44", "0:06:45", "0:06:46", "0:06:47", "0:06:48", "0:06:49",
"0:06:50", "0:06:51", "0:06:52", "0:06:53", "0:06:54", "0:06:55",
"0:06:56", "0:06:57", "0:06:58", "0:06:59", "0:07:00", "0:07:01",
"0:07:02", "0:07:03", "0:07:04", "0:07:05", "0:07:06", "0:07:07",
"0:07:08", "0:07:09", "0:07:10", "0:07:11", "0:07:12", "0:07:13",
"0:07:14", "0:07:15", "0:07:16", "0:07:17", "0:07:18", "0:07:19",
"0:07:20", "0:07:21", "0:07:22", "0:07:23", "0:07:24", "0:07:25",
"0:07:26", "0:07:27", "0:07:28", "0:07:29", "0:07:30", "0:07:31",
"0:07:32", "0:07:33", "0:07:34", "0:07:35", "0:07:36", "0:07:37",
"0:07:38", "0:07:39", "0:07:40", "0:07:41", "0:07:42", "0:07:43",
"0:07:44", "0:07:45", "0:07:46", "0:07:47", "0:07:48", "0:07:49",
"0:07:50", "0:07:51", "0:07:52", "0:07:53", "0:07:54", "0:07:55",
"0:07:56", "0:07:57", "0:07:58", "0:07:59", "0:08:00", "0:08:01",
"0:08:02", "0:08:03", "0:08:04", "0:08:05", "0:08:06", "0:08:07",
"0:08:08", "0:08:09", "0:08:10", "0:08:11", "0:08:12", "0:08:13",
"0:08:14", "0:08:15", "0:08:16", "0:08:17", "0:08:18", "0:08:19",
"0:08:20", "0:08:21", "0:08:22", "0:08:23", "0:08:24", "0:08:25",
"0:08:26", "0:08:27", "0:08:28", "0:08:29", "0:08:30", "0:08:31",
"0:08:32", "0:08:33", "0:08:34", "0:08:35", "0:08:36", "0:08:37",
"0:08:38", "0:08:39", "0:08:40", "0:08:41", "0:08:42", "0:08:43",
"0:08:44", "0:08:45", "0:08:46", "0:08:47", "0:08:48", "0:08:49",
"0:08:50", "0:08:51", "0:08:52", "0:08:53", "0:08:54", "0:08:55",
"0:08:56", "0:08:57", "0:08:58", "0:08:59", "0:09:00", "0:09:01",
"0:09:02", "0:09:03", "0:09:04", "0:09:05", "0:09:06", "0:09:07",
"0:09:08", "0:09:09", "0:09:10", "0:09:11", "0:09:12", "0:09:13",
"0:09:14", "0:09:15", "0:09:16", "0:09:17", "0:09:18", "0:09:19",
"0:09:20", "0:09:21", "0:09:22", "0:09:23", "0:09:24", "0:09:25",
"0:09:26", "0:09:27", "0:09:28", "0:09:29", "0:09:30", "0:09:31",
"0:09:32", "0:09:33", "0:09:34", "0:09:35", "0:09:36", "0:09:37",
"0:09:38", "0:09:39", "0:09:40", "0:09:41", "0:09:42", "0:09:43",
"0:09:44", "0:09:45", "0:09:46", "0:09:47", "0:09:48", "0:09:49",
"0:09:50", "0:09:51", "0:09:52", "0:09:53", "0:09:54", "0:09:55",
"0:09:56", "0:09:57", "0:09:58", "0:09:59", "0:10:00", "0:10:01",
"0:10:02", "0:10:03", "0:10:04", "0:10:05", "0:10:06", "0:10:07",
"0:10:08", "0:10:09", "0:10:10", "0:10:11", "0:10:12", "0:10:13",
"0:10:14", "0:10:15", "0:10:16", "0:10:17", "0:10:18", "0:10:19",
"0:10:20", "0:10:21", "0:10:22", "0:10:23", "0:10:24", "0:10:25",
"0:10:26", "0:10:27", "0:10:28", "0:10:29", "0:10:30", "0:10:31",
"0:10:32", "0:10:33", "0:10:34", "0:10:35", "0:10:36", "0:10:37",
"0:10:38", "0:10:39", "0:10:40", "0:10:41", "0:10:42", "0:10:43",
"0:10:44", "0:10:45", "0:10:46", "0:10:47", "0:10:48", "0:10:49",
"0:10:50", "0:10:51", "0:10:52", "0:10:53", "0:10:54", "0:10:55",
"0:10:56", "0:10:57", "0:10:58", "0:10:59", "0:11:00", "0:11:01",
"0:11:02", "0:11:03", "0:11:04", "0:11:05", "0:11:06", "0:11:07",
"0:11:08", "0:11:09", "0:11:10", "0:11:11", "0:11:12", "0:11:13",
"0:11:14", "0:11:15", "0:11:16", "0:11:17", "0:11:18", "0:11:19",
"0:11:20", "0:11:21", "0:11:22", "0:11:23", "0:11:24", "0:11:25",
"0:11:26", "0:11:27", "0:11:28", "0:11:29", "0:11:30", "0:11:31",
"0:11:32", "0:11:33", "0:11:34", "0:11:35", "0:11:36", "0:11:37",
"0:11:38", "0:11:39", "0:11:40", "0:11:41", "0:11:42", "0:11:43",
"0:11:44", "0:11:45", "0:11:46", "0:11:47", "0:11:48", "0:11:49",
"0:11:50", "0:11:51", "0:11:52", "0:11:53", "0:11:54", "0:11:55",
"0:11:56", "0:11:57", "0:11:58", "0:11:59", "0:12:00", "0:12:01",
"0:12:02", "0:12:03", "0:12:04", "0:12:05", "0:12:06", "0:12:07",
"0:12:08", "0:12:09", "0:12:10", "0:12:11", "0:12:12", "0:12:13",
"0:12:14", "0:12:15", "0:12:16", "0:12:17", "0:12:18", "0:12:19",
"0:12:20", "0:12:21", "0:12:22", "0:12:23", "0:12:24", "0:12:25",
"0:12:26", "0:12:27", "0:12:28", "0:12:29", "0:12:30", "0:12:31",
"0:12:32", "0:12:33", "0:12:34", "0:12:35", "0:12:36", "0:12:37",
"0:12:38", "0:12:39", "0:12:40", "0:12:41", "0:12:42", "0:12:43",
"0:12:44", "0:12:45", "0:12:46", "0:12:47", "0:12:48", "0:12:49",
"0:12:50", "0:12:51", "0:12:52", "0:12:53", "0:12:54", "0:12:55",
"0:12:56", "0:12:57", "0:12:58", "0:12:59", "0:13:00", "0:13:01",
"0:13:02", "0:13:03", "0:13:04", "0:13:05", "0:13:06", "0:13:07",
"0:13:08", "0:13:09", "0:13:10", "0:13:11", "0:13:12", "0:13:13",
"0:13:14", "0:13:15", "0:13:16", "0:13:17", "0:13:18", "0:13:19",
"0:13:20", "0:13:21", "0:13:22", "0:13:23", "0:13:24", "0:13:25",
"0:13:26", "0:13:27", "0:13:28", "0:13:29", "0:13:30", "0:13:31",
"0:13:32", "0:13:33", "0:13:34", "0:13:35", "0:13:36", "0:13:37",
"0:13:38", "0:13:39", "0:13:40", "0:13:41", "0:13:42", "0:13:43",
"0:13:44", "0:13:45", "0:13:46", "0:13:47", "0:13:48", "0:13:49",
"0:13:50", "0:13:51", "0:13:52", "0:13:53", "0:13:54", "0:13:55",
"0:13:56", "0:13:57", "0:13:58", "0:13:59", "0:14:00", "0:14:01",
"0:14:02", "0:14:03", "0:14:04", "0:14:05", "0:14:06", "0:14:07",
"0:14:08", "0:14:09", "0:14:10", "0:14:11", "0:14:12", "0:14:13",
"0:14:14", "0:14:15", "0:14:16", "0:14:17", "0:14:18", "0:14:19",
"0:14:20", "0:14:21", "0:14:22", "0:14:23", "0:14:24", "0:14:25",
"0:14:26", "0:14:27", "0:14:28", "0:14:29", "0:14:30", "0:14:31",
"0:14:32", "0:14:33", "0:14:34", "0:14:35", "0:14:36", "0:14:37",
"0:14:38", "0:14:39", "0:14:40", "0:14:41", "0:14:42", "0:14:43",
"0:14:44", "0:14:45", "0:14:46", "0:14:47", "0:14:48", "0:14:49",
"0:14:50", "0:14:51", "0:14:52", "0:14:53", "0:14:54", "0:14:55",
"0:14:56", "0:14:57", "0:14:58", "0:14:59", "0:15:00", "0:15:01",
"0:15:02", "0:15:03", "0:15:04", "0:15:05", "0:15:06", "0:15:07",
"0:15:08", "0:15:09", "0:15:10", "0:15:11", "0:15:12", "0:15:13",
"0:15:14", "0:15:15", "0:15:16", "0:15:17", "0:15:18", "0:15:19",
"0:15:20", "0:15:21", "0:15:22", "0:15:23", "0:15:24", "0:15:25",
"0:15:26", "0:15:27", "0:15:28", "0:15:29", "0:15:30", "0:15:31",
"0:15:32", "0:15:33", "0:15:34", "0:15:35", "0:15:36", "0:15:37",
"0:15:38", "0:15:39", "0:15:40", "0:15:41", "0:15:42", "0:15:43",
"0:15:44", "0:15:45", "0:15:46", "0:15:47", "0:15:48", "0:15:49",
"0:15:50", "0:15:51", "0:15:52", "0:15:53", "0:15:54", "0:15:55",
"0:15:56", "0:15:57", "0:15:58", "0:15:59", "0:16:00", "0:16:01",
"0:16:02", "0:16:03", "0:16:04", "0:16:05", "0:16:06", "0:16:07",
"0:16:08", "0:16:09", "0:16:10", "0:16:11", "0:16:12", "0:16:13",
"0:16:14", "0:16:15", "0:16:16", "0:16:17", "0:16:18", "0:16:19",
"0:16:20", "0:16:21", "0:16:22", "0:16:23", "0:16:24", "0:16:25",
"0:16:26", "0:16:27", "0:16:28", "0:16:29", "0:16:30", "0:16:31",
"0:16:32", "0:16:33", "0:16:34", "0:16:35", "0:16:36", "0:16:37",
"0:16:38", "0:16:39", "0:16:40", "0:16:41", "0:16:42", "0:16:43",
"0:16:44", "0:16:45", "0:16:46", "0:16:47", "0:16:48", "0:16:49",
"0:16:50", "0:16:51", "0:16:52", "0:16:53", "0:16:54", "0:16:55",
"0:16:56", "0:16:57", "0:16:58", "0:16:59", "0:17:00", "0:17:01",
"0:17:02", "0:17:03", "0:17:04", "0:17:05", "0:17:06", "0:17:07",
"0:17:08", "0:17:09", "0:17:10", "0:17:11", "0:17:12", "0:17:13",
"0:17:14", "0:17:15", "0:17:16", "0:17:17", "0:17:18", "0:17:19",
"0:17:20", "0:17:21", "0:17:22", "0:17:23", "0:17:24", "0:17:25",
"0:17:26", "0:17:27", "0:17:28", "0:17:29", "0:17:30", "0:17:31",
"0:17:32", "0:17:33", "0:17:34", "0:17:35", "0:17:36", "0:17:37",
"0:17:38", "0:17:39", "0:17:40", "0:17:41", "0:17:42", "0:17:43",
"0:17:44", "0:17:45", "0:17:46", "0:17:47", "0:17:48", "0:17:49",
"0:17:50", "0:17:51", "0:17:52", "0:17:53", "0:17:54", "0:17:55",
"0:17:56", "0:17:57", "0:17:58", "0:17:59", "0:18:00", "0:18:01",
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"0:29:56", "0:29:57", "0:29:58", "0:29:59", "0:30:00"), class = "factor"),
student = c("bob", "bob", "bob", "bob"), somemeasure = c(0L,
0L, 1L, 1L)), .Names = c("somedata$Time", "student", "somemeasure"
), row.names = c(NA, 4L), class = "data.frame")
Assuming that your data frame is named df. First, create new column which is POSIXct by pasting together some arbitrary date and original Time column and then converting with as.POSIXct().
Then use function scale_x_datetime() to set breaks and format for labels you want to see.
df$Time2<-as.POSIXct(paste("1960-01-01 ",df$Time))
library(scales)
ggplot(df,aes(Time2,somemeasure))+geom_point()+
scale_x_datetime(breaks=date_breaks("30 sec"),labels = date_format("%M:%S"))
Related
Suppose you have a matrix of time series, where each column is a time series.
Now, suppose you take second differences of this matrix, (so the data is stationary), using this code:
var_train_data <- diff(var_train, differences = 2)
and then you estimate this model:
var_1 <- VAR(y=var_train_data, lag=9, type="const")
Recall that this function VARcomes from vars package.
How can you plot the original data and the fitted(var_1) data? Is there a function to return the fitted data from the VAR to "level"?
I want to do this but with a VAR.
Thanks in advance!!
Here is the data:
> dput(var_train)
structure(c(8.59225428063812, 8.61632381521795, 8.48941975098913,
8.42616336163893, 8.4656261753598, 8.49981580266834, 8.50658473928686,
8.54846279771184, 8.58669260125764, 8.56316032696837, 8.52323490346238,
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3.95072671083008, 4.03652777624058, 4.06630193640976, 4.08838169421096,
4.09074775372752), .Dim = c(192L, 7L), .Dimnames = list(NULL,
c("EMBI+", "M2 (pesos)", "Commodity Price index", "emae",
"gasto programas SS", "recursos tributarios", "ex_rate")), .Tsp = c(2004,
2019.91666666667, 12), class = c("mts", "ts", "matrix"))
I need to show the unique Count by Url and then show the Avg, Max, Min time that it took as different columns. Was looking at using either dplyr or sqldf.
here is what I am essentially trying to duplicate
SELECT cs-uri-stem as Url, COUNT(*) as totalRequests,
AVG(time-taken) As avgRequestDuration,
MAX(time-taken) As maxRequestDuration,
MIN(time-taken) As minRequestDuration
FROM '[LOGFILEPATH]'
GROUP BY Url
ORDER By totalRequests DESC
Head of data for reference:
> head(iislog1)
iisdate iistime csUriStem timeTaken
1 2019-05-17 03:05:39 /eACommon/SystemConfigurationService.svc/customBinding 7421
2 2019-05-17 03:07:22 /Services/2015V1/EngService.svc/customBinding 8390
3 2019-05-17 03:16:40 /eACommon/SystemConfigurationService.svc/customBinding 515
4 2019-05-17 03:17:39 /eACommon/SystemConfigurationService.svc/customBinding 505
5 2019-05-17 03:25:22 /Services/2015V1/EngService.svc/customBinding 1385
6 2019-05-17 03:31:16 /eAudIT/Services/SAPv1/EngService.svc/customBinding 1365
structure(list(iisdate = structure(c(1L, 1L, 1L, 1L, 1L, 1L), .Label = "2019-05-17", class = "factor"),
iistime = structure(1:6, .Label = c("03:05:39", "03:07:22",
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"20:39:39", "20:39:41", "20:48:52", "20:48:54", "20:48:55",
"20:49:01", "20:49:02", "20:49:03", "20:49:09"), class = "factor"),
csUriStem = c("/eacommon/systemconfigurationservice.svc/custombinding",
"/services/2015v1/engservice.svc/custombinding", "/eacommon/systemconfigurationservice.svc/custombinding",
"/eacommon/systemconfigurationservice.svc/custombinding",
"/services/2015v1/engservice.svc/custombinding", "/eaudit/services/sapv1/engservice.svc/custombinding"
), timeTaken = c(7421L, 8390L, 515L, 505L, 1385L, 1365L)), row.names = c(NA,
6L), class = "data.frame")
install.packages("sqldf")
library(sqldf)
#create subset of the original data
iislog1 <- iislog %>% select(iisdate,iistime,csUriStem,timeTaken)
#Find Count by Url and then Avg,Max,Min timeTaken for each Url
iislog6 <- sqldf(SELECT csUriStem AS iislog6$baseUrl FROM iislog1,
Count(*) as iislog6$totalRequests,
AVG(timeTaken) AS iislog6$avgRequestDuration,
MAX(timeTaken) AS iislog6$maxRequestDuration,
MIN(timeTaken) AS iislog6$minRequestDuration
GROUP BY iislog6$baseUrl
ORDER By iislog6$totalRequests DESC
)
I think this is what you were trying to do. I mainly used your SQL to translate it into R.
group_by is very powerfull in R and it is easy in use.
install.packages("dplyr")
library(dplyr)
iislog1 <- iislog %>% select(iisdate,iistime,csUriStem,timeTaken)
iislog6 <- group_by(iislog1, csUriStem) %>% summarise(totalRequests = n(),
avgRequestDuration = mean(timeTaken), maxRequestDuration = max(timeTaken),
minRequestDuration = min(timeTaken))
# order by
iislog6 <- arrange(iislog6, totalRequests)
There is something wrong with the dput output in the question so we used the data in the Note at the end.
The SQL syntax in the code in the question was incorrect -- it was a mix of SQL and R but the string must be pure SQL like this:
library(sqldf)
sqldf("SELECT
csUriStem AS baseUrl,
COUNT(*) AS totalRequests,
AVG(timeTaken) AS avgRequestDuration,
MAX(timeTaken) AS maxRequestDuration,
MIN(timeTaken) AS minRequestDuration
FROM iislog1
GROUP BY baseUrl
ORDER BY totalRequests DESC")
Note
This is the data used in reproducible form:
Lines <- "iisdate iistime csUriStem timeTaken
1 2019-05-17 03:05:39 /eACommon/SystemConfigurationService.svc/customBinding 7421
2 2019-05-17 03:07:22 /Services/2015V1/EngService.svc/customBinding 8390
3 2019-05-17 03:16:40 /eACommon/SystemConfigurationService.svc/customBinding 515
4 2019-05-17 03:17:39 /eACommon/SystemConfigurationService.svc/customBinding 505
5 2019-05-17 03:25:22 /Services/2015V1/EngService.svc/customBinding 1385
6 2019-05-17 03:31:16 /eAudIT/Services/SAPv1/EngService.svc/customBinding 1365"
iislog1 <- read.table(text = Lines, as.is = TRUE)
I have a homework assignment where I need to take a CSV file based around population data around the United States and do some data analysis on the data inside. I need to find the data that exists for my state and for starters run a Linear Regression Analysis to predict the size of the population.
I've been studying R for a few weeks now, went through a LinkedIn Learning training, as well as 2 different trainings on pluralsight about R. I have also tried searching for how to do a Linear Regression Analysis in R and I find plenty of examples for how to do it when the data is perfectly laid out in a table in just the right way to Analyze.
The CSV file is laid out so that each state is defined on a single line/row so I used the filter function to grab just the data for my State and put it into a variable.
Within that dataset the population data is defined across several columns with the most important data being the Population Estimates for each year from 2010 to 2018.
library(tidyverse)
population.data <- read_csv("nst-est2018-alldata.csv")
mn.state.data <- filter(population.data, NAME == "Minnesota")
I'm looking for some help to get headed in the right direction my thought is that I will need to create to containers of data 1 having each year from 2010 to 2018 and one that contains the population data for each of those years. And then use the xyplot function with those two containers? If you have some experience in this area please help me think this through I'm not looking for anybody to do the assignment for me just want some help trying to think it through.
Edit: Here is the results of the
dput(head(population.data))
command:
structure(list(SUMLEV = c("010", "020", "020", "020", "020",
"040"), REGION = c("0", "1", "2", "3", "4", "3"), DIVISION = c("0",
"0", "0", "0", "0", "6"), STATE = c("00", "00", "00", "00", "00",
"01"), NAME = c("United States", "Northeast Region", "Midwest Region",
"South Region", "West Region", "Alabama"), CENSUS2010POP = c(308745538L,
55317240L, 66927001L, 114555744L, 71945553L, 4779736L), ESTIMATESBASE2010
= c(308758105L,
55318430L, 66929743L, 114563045L, 71946887L, 4780138L), POPESTIMATE2010 =
c(309326085L,
55380645L, 66974749L, 114867066L, 72103625L, 4785448L), POPESTIMATE2011 =
c(311580009L,
55600532L, 67152631L, 116039399L, 72787447L, 4798834L), POPESTIMATE2012 =
c(313874218L,
55776729L, 67336937L, 117271075L, 73489477L, 4815564L), POPESTIMATE2013 =
c(316057727L,
55907823L, 67564135L, 118393244L, 74192525L, 4830460L), POPESTIMATE2014 =
c(318386421L,
56015864L, 67752238L, 119657737L, 74960582L, 4842481L), POPESTIMATE2015 =
c(320742673L,
56047587L, 67869139L, 121037542L, 75788405L, 4853160L), POPESTIMATE2016 =
c(323071342L,
56058789L, 67996917L, 122401186L, 76614450L, 4864745L), POPESTIMATE2017 =
c(325147121L,
56072676L, 68156035L, 123598424L, 77319986L, 4875120L), POPESTIMATE2018 =
c(327167434L,
56111079L, 68308744L, 124753948L, 77993663L, 4887871L), NPOPCHG_2010 =
c(567980L,
62215L, 45006L, 304021L, 156738L, 5310L), NPOPCHG_2011 = c(2253924L,
219887L, 177882L, 1172333L, 683822L, 13386L), NPOPCHG_2012 = c(2294209L,
176197L, 184306L, 1231676L, 702030L, 16730L), NPOPCHG_2013 = c(2183509L,
131094L, 227198L, 1122169L, 703048L, 14896L), NPOPCHG_2014 = c(2328694L,
108041L, 188103L, 1264493L, 768057L, 12021L), NPOPCHG_2015 = c(2356252L,
31723L, 116901L, 1379805L, 827823L, 10679L), NPOPCHG_2016 = c(2328669L,
11202L, 127778L, 1363644L, 826045L, 11585L), NPOPCHG_2017 = c(2075779L,
13887L, 159118L, 1197238L, 705536L, 10375L), NPOPCHG_2018 = c(2020313L,
38403L, 152709L, 1155524L, 673677L, 12751L), BIRTHS2010 = c(987836L,
163454L, 212614L, 368752L, 243016L, 14227L), BIRTHS2011 = c(3973485L,
646265L, 834909L, 1509597L, 982714L, 59689L), BIRTHS2012 = c(3936976L,
637904L, 830701L, 1504936L, 963435L, 59070L), BIRTHS2013 = c(3940576L,
635741L, 830869L, 1504799L, 969167L, 57936L), BIRTHS2014 = c(3963195L,
632433L, 836505L, 1525280L, 968977L, 58907L), BIRTHS2015 = c(3992376L,
634515L, 837968L, 1545722L, 974171L, 59637L), BIRTHS2016 = c(3962654L,
628039L, 831667L, 1541342L, 961606L, 59388L), BIRTHS2017 = c(3901982L,
616552L, 816177L, 1519944L, 949309L, 58259L), BIRTHS2018 = c(3855500L,
609336L, 804431L, 1499838L, 941895L, 57216L), DEATHS2010 = c(598691L,
110848L, 140785L, 228706L, 118352L, 11073L), DEATHS2011 = c(2512442L,
470816L, 586840L, 962751L, 492035L, 48818L), DEATHS2012 = c(2501531L,
460985L, 584817L, 960575L, 495154L, 48364L), DEATHS2013 = c(2608019L,
480032L, 605188L, 1011093L, 511706L, 50847L), DEATHS2014 = c(2582448L,
470196L, 597078L, 1006057L, 509117L, 49692L), DEATHS2015 = c(2699826L,
488881L, 626494L, 1052360L, 532091L, 51820L), DEATHS2016 = c(2703215L,
480331L, 619471L, 1058173L, 545240L, 51662L), DEATHS2017 = c(2779436L,
501022L, 620556L, 1092949L, 564909L, 53033L), DEATHS2018 = c(2814013L,
506909L, 621030L, 1109152L, 576922L, 53425L), NATURALINC2010 = c(389145L,
52606L, 71829L, 140046L, 124664L, 3154L), NATURALINC2011 = c(1461043L,
175449L, 248069L, 546846L, 490679L, 10871L), NATURALINC2012 = c(1435445L,
176919L, 245884L, 544361L, 468281L, 10706L), NATURALINC2013 = c(1332557L,
155709L, 225681L, 493706L, 457461L, 7089L), NATURALINC2014 = c(1380747L,
162237L, 239427L, 519223L, 459860L, 9215L), NATURALINC2015 = c(1292550L,
145634L, 211474L, 493362L, 442080L, 7817L), NATURALINC2016 = c(1259439L,
147708L, 212196L, 483169L, 416366L, 7726L), NATURALINC2017 = c(1122546L,
115530L, 195621L, 426995L, 384400L, 5226L), NATURALINC2018 = c(1041487L,
102427L, 183401L, 390686L, 364973L, 3791L), INTERNATIONALMIG2010 =
c(178835L,
45723L, 25158L, 68742L, 39212L, 928L), INTERNATIONALMIG2011 = c(792881L,
206686L, 116948L, 285343L, 183904L, 4716L), INTERNATIONALMIG2012 =
c(858764L,
207584L, 120995L, 344198L, 185987L, 5874L), INTERNATIONALMIG2013 =
c(850952L,
194103L, 126681L, 329897L, 200271L, 5111L), INTERNATIONALMIG2014 =
c(947947L,
222685L, 134310L, 365281L, 225671L, 3753L), INTERNATIONALMIG2015 =
c(1063702L,
227275L, 142759L, 429088L, 264580L, 4685L), INTERNATIONALMIG2016 =
c(1069230L,
236718L, 144859L, 436795L, 250858L, 5950L), INTERNATIONALMIG2017 =
c(953233L,
215872L, 126013L, 404582L, 206766L, 3190L), INTERNATIONALMIG2018 =
c(978826L,
229700L, 127583L, 418418L, 203125L, 3344L), DOMESTICMIG2010 = c(0L,
-32918L, -50873L, 90679L, -6888L, 1238L), DOMESTICMIG2011 = c(0L,
-159789L, -186896L, 335757L, 10928L, -2239L), DOMESTICMIG2012 = c(0L,
-205314L, -181285L, 336615L, 49984L, 59L), DOMESTICMIG2013 = c(0L,
-216273L, -123814L, 293443L, 46644L, 2641L), DOMESTICMIG2014 = c(0L,
-274391L, -182730L, 373439L, 83682L, -755L), DOMESTICMIG2015 = c(0L,
-339996L, -234823L, 452879L, 121940L, -1553L), DOMESTICMIG2016 = c(0L,
-372953L, -228200L, 442633L, 158520L, -1977L), DOMESTICMIG2017 = c(0L,
-316879L, -161387L, 364465L, 113801L, 2065L), DOMESTICMIG2018 = c(0L,
-292928L, -157048L, 345132L, 104844L, 5718L), NETMIG2010 = c(178835L,
12805L, -25715L, 159421L, 32324L, 2166L), NETMIG2011 = c(792881L,
46897L, -69948L, 621100L, 194832L, 2477L), NETMIG2012 = c(858764L,
2270L, -60290L, 680813L, 235971L, 5933L), NETMIG2013 = c(850952L,
-22170L, 2867L, 623340L, 246915L, 7752L), NETMIG2014 = c(947947L,
-51706L, -48420L, 738720L, 309353L, 2998L), NETMIG2015 = c(1063702L,
-112721L, -92064L, 881967L, 386520L, 3132L), NETMIG2016 = c(1069230L,
-136235L, -83341L, 879428L, 409378L, 3973L), NETMIG2017 = c(953233L,
-101007L, -35374L, 769047L, 320567L, 5255L), NETMIG2018 = c(978826L,
-63228L, -29465L, 763550L, 307969L, 9062L), RESIDUAL2010 = c(0L,
-3196L, -1108L, 4554L, -250L, -10L), RESIDUAL2011 = c(0L, -2459L,
-239L, 4387L, -1689L, 38L), RESIDUAL2012 = c(0L, -2992L, -1288L,
6502L, -2222L, 91L), RESIDUAL2013 = c(0L, -2445L, -1350L, 5123L,
-1328L, 55L), RESIDUAL2014 = c(0L, -2490L, -2904L, 6550L, -1156L,
-192L), RESIDUAL2015 = c(0L, -1190L, -2509L, 4476L, -777L, -270L
), RESIDUAL2016 = c(0L, -271L, -1077L, 1047L, 301L, -114L), RESIDUAL2017 =
c(0L,
-636L, -1129L, 1196L, 569L, -106L), RESIDUAL2018 = c(0L, -796L,
-1227L, 1288L, 735L, -102L), RBIRTH2011 = c(12.79898857, 11.646389369,
12.449493906, 13.0753983, 13.564866164, 12.455601786), RBIRTH2012 =
c(12.589173852,
11.454833676, 12.353389372, 12.900715293, 13.172754439, 12.287820829
), RBIRTH2013 = c(12.511116578, 11.384582534, 12.318197145, 12.770698648,
13.1250523, 12.012410502), RBIRTH2014 = c(12.493440163, 11.301146646,
12.363692308, 12.814734, 12.993051496, 12.179749675), RBIRTH2015 =
c(12.493175596,
11.324209532, 12.357461907, 12.843808208, 12.92441189, 12.301816868
), RBIRTH2016 = c(12.309933949, 11.20434042, 12.242454436, 12.663079639,
12.619264908, 12.222387438), RBIRTH2017 = c(12.039095529, 10.996948983,
11.989119413, 12.357287884, 12.333939366, 11.962999487), RBIRTH2018 =
c(11.820984126,
10.863177115, 11.789576855, 12.078306222, 12.128940451, 11.720998206
), RDEATH2011 = c(8.0928244199, 8.4846099623, 8.7504877826, 8.3388830191,
6.7917918366, 10.187095914), RDEATH2012 = c(7.9990857588, 8.2779015368,
8.6968381072, 8.2343067033, 6.7700904074, 10.060744313), RDEATH2013 =
c(8.2803198685,
8.5962112289, 8.9723230665, 8.5807898649, 6.9298356343, 10.542582104
), RDEATH2014 = c(8.1408206164, 8.4020820365, 8.8249187702, 8.4524499397,
6.8267702932, 10.274434632), RDEATH2015 = c(8.4484528254, 8.7250748685,
9.2388679994, 8.7443343664, 7.0592978512, 10.689339673), RDEATH2016 =
c(8.3975028099,
8.5692003816, 9.1188486402, 8.6935469035, 7.1552465339, 10.632332792
), RDEATH2017 = c(8.5756150392, 8.9363320099, 9.1155717285, 8.8857783149,
7.3396052849, 10.889883997), RDEATH2018 = c(8.6277792774, 9.0371195009,
9.1016891619, 8.9320830002, 7.4291216994, 10.944391939), RNATURALINC2011 =
c(4.7061641498,
3.161779407, 3.6990061239, 4.7365152812, 6.7730743272, 2.2685058724
), RNATURALINC2012 = c(4.5900880929, 3.1769321388, 3.656551265,
4.66640859, 6.402664032, 2.2270765159), RNATURALINC2013 = c(4.2307967093,
2.7883713049, 3.3458740787, 4.1899087829, 6.1952166656, 1.4698283977
), RNATURALINC2014 = c(4.3526195469, 2.89906461, 3.5387735378,
4.3622840605, 6.1662812026, 1.9053150433), RNATURALINC2015 =
c(4.0447227708,
2.5991346635, 3.1185939072, 4.0994738414, 5.8651140389, 1.6124771946
), RNATURALINC2016 = c(3.912431139, 2.6351400388, 3.123605796,
3.969532736, 5.4640183742, 1.5900546466), RNATURALINC2017 =
c(3.4634804902,
2.0606169731, 2.8735476848, 3.4715095687, 4.9943340813, 1.0731154898
), RNATURALINC2018 = c(3.1932048488, 1.8260576141, 2.687887693,
3.1462232219, 4.6998187519, 0.7766062675), RINTERNATIONALMIG2011 =
c(2.5539481982,
3.7247036946, 1.7438348531, 2.4715029092, 2.5385138982, 0.9841112772
), RINTERNATIONALMIG2012 = c(2.7460490726, 3.7275831375, 1.7993217139,
2.9505576333, 2.5429438207, 1.2219173785), RINTERNATIONALMIG2013 =
c(2.7017267715,
3.4759149144, 1.8781318506, 2.7997195452, 2.7121923767, 1.0597112344
), RINTERNATIONALMIG2014 = c(2.988275652, 3.9792291689, 1.9851256285,
3.0689308523, 3.0260314993, 0.7759790947), RINTERNATIONALMIG2015 =
c(3.3285982753,
4.0561842059, 2.1052580818, 3.5654043717, 3.5102060089, 0.9664136698
), RINTERNATIONALMIG2016 = c(3.3215493142, 4.2230961065, 2.1323795548,
3.5885415898, 3.2920380658, 1.2245437674), RINTERNATIONALMIG2017 =
c(2.9410856198,
3.8503376372, 1.8510505744, 3.2892897676, 2.6864164429, 0.6550398799
), RINTERNATIONALMIG2018 = c(3.0010858795, 4.0950670621, 1.8698304564,
3.3695510667, 2.6156748143, 0.685035969), RDOMESTICMIG2011 = c(0,
-2.879569389, -2.786843372, 2.9081645678, 0.1508443529, -0.467223314
), RDOMESTICMIG2012 = c(0, -3.686820778, -2.69589683, 2.8855541222,
0.6834160664, 0.0122732593), RDOMESTICMIG2013 = c(0, -3.872925953,
-1.835626629, 2.4903472978, 0.6316815776, 0.5475831286), RDOMESTICMIG2014
= c(0,
-4.903180146, -2.700781819, 3.1374707924, 1.1220952977, -0.156105573
), RDOMESTICMIG2015 = c(0, -6.067919504, -3.462920156, 3.7630900106,
1.6177886489, -0.320350145), RDOMESTICMIG2016 = c(0, -6.653555548,
-3.359190761, 3.6365043774, 2.0802759896, -0.40687782), RDOMESTICMIG2017 =
c(0,
-5.651919379, -2.370672066, 2.963134779, 1.4785645494, 0.4240305179
), RDOMESTICMIG2018 = c(0, -5.222289092, -2.301663494, 2.7793734944,
1.350093835, 1.1713623417), RNETMIG2011 = c(2.5539481982, 0.845134306,
-1.043008519, 5.379667477, 2.6893582511, 0.516887963), RNETMIG2012 =
c(2.7460490726,
0.0407623599, -0.896575116, 5.8361117555, 3.2263598871, 1.2341906378
), RNETMIG2013 = c(2.7017267715, -0.397011039, 0.0425052219,
5.2900668429, 3.3438739543, 1.6072943629), RNETMIG2014 = c(2.988275652,
-0.923950977, -0.71565619, 6.2064016447, 4.148126797, 0.6198735214
), RNETMIG2015 = c(3.3285982753, -2.011735298, -1.357662074,
7.3284943823, 5.1279946578, 0.6460635248), RNETMIG2016 = c(3.3215493142,
-2.430459441, -1.226811206, 7.2250459672, 5.3723140554, 0.8176659475
), RNETMIG2017 = c(2.9410856198, -1.801581742, -0.519621492,
6.2524245465, 4.1649809923, 1.0790703978), RNETMIG2018 = c(3.0010858795,
-1.12722203, -0.431833037, 6.1489245611, 3.9657686492, 1.8563983107
)), .Names = c("SUMLEV", "REGION", "DIVISION", "STATE", "NAME",
"CENSUS2010POP", "ESTIMATESBASE2010", "POPESTIMATE2010",
"POPESTIMATE2011",
"POPESTIMATE2012", "POPESTIMATE2013", "POPESTIMATE2014",
"POPESTIMATE2015",
"POPESTIMATE2016", "POPESTIMATE2017", "POPESTIMATE2018", "NPOPCHG_2010",
"NPOPCHG_2011", "NPOPCHG_2012", "NPOPCHG_2013", "NPOPCHG_2014",
"NPOPCHG_2015", "NPOPCHG_2016", "NPOPCHG_2017", "NPOPCHG_2018",
"BIRTHS2010", "BIRTHS2011", "BIRTHS2012", "BIRTHS2013", "BIRTHS2014",
"BIRTHS2015", "BIRTHS2016", "BIRTHS2017", "BIRTHS2018", "DEATHS2010",
"DEATHS2011", "DEATHS2012", "DEATHS2013", "DEATHS2014", "DEATHS2015",
"DEATHS2016", "DEATHS2017", "DEATHS2018", "NATURALINC2010",
"NATURALINC2011",
"NATURALINC2012", "NATURALINC2013", "NATURALINC2014", "NATURALINC2015",
"NATURALINC2016", "NATURALINC2017", "NATURALINC2018",
"INTERNATIONALMIG2010",
"INTERNATIONALMIG2011", "INTERNATIONALMIG2012", "INTERNATIONALMIG2013",
"INTERNATIONALMIG2014", "INTERNATIONALMIG2015", "INTERNATIONALMIG2016",
"INTERNATIONALMIG2017", "INTERNATIONALMIG2018", "DOMESTICMIG2010",
"DOMESTICMIG2011", "DOMESTICMIG2012", "DOMESTICMIG2013",
"DOMESTICMIG2014",
"DOMESTICMIG2015", "DOMESTICMIG2016", "DOMESTICMIG2017",
"DOMESTICMIG2018",
"NETMIG2010", "NETMIG2011", "NETMIG2012", "NETMIG2013", "NETMIG2014",
"NETMIG2015", "NETMIG2016", "NETMIG2017", "NETMIG2018", "RESIDUAL2010",
"RESIDUAL2011", "RESIDUAL2012", "RESIDUAL2013", "RESIDUAL2014",
"RESIDUAL2015", "RESIDUAL2016", "RESIDUAL2017", "RESIDUAL2018",
"RBIRTH2011", "RBIRTH2012", "RBIRTH2013", "RBIRTH2014", "RBIRTH2015",
"RBIRTH2016", "RBIRTH2017", "RBIRTH2018", "RDEATH2011", "RDEATH2012",
"RDEATH2013", "RDEATH2014", "RDEATH2015", "RDEATH2016", "RDEATH2017",
"RDEATH2018", "RNATURALINC2011", "RNATURALINC2012", "RNATURALINC2013",
"RNATURALINC2014", "RNATURALINC2015", "RNATURALINC2016",
"RNATURALINC2017",
"RNATURALINC2018", "RINTERNATIONALMIG2011", "RINTERNATIONALMIG2012",
"RINTERNATIONALMIG2013", "RINTERNATIONALMIG2014", "RINTERNATIONALMIG2015",
"RINTERNATIONALMIG2016", "RINTERNATIONALMIG2017", "RINTERNATIONALMIG2018",
"RDOMESTICMIG2011", "RDOMESTICMIG2012", "RDOMESTICMIG2013",
"RDOMESTICMIG2014",
"RDOMESTICMIG2015", "RDOMESTICMIG2016", "RDOMESTICMIG2017",
"RDOMESTICMIG2018",
"RNETMIG2011", "RNETMIG2012", "RNETMIG2013", "RNETMIG2014", "RNETMIG2015",
"RNETMIG2016", "RNETMIG2017", "RNETMIG2018"), row.names = c(NA,
-6L), class = c("tbl_df", "tbl", "data.frame"))
In order to help you out, an example data using dput(head(population.data)) would be helpful. Based on your comments, your data is in what is called 'wide' format, meaning each observation is contained in a column, rather than a row (pupulation 2010, population 2011 etc.).
As i hinted in my comment, a sub-goal within statistical modelling is always to clean and reshape data to a proper format, that will work for running models. In this case the problem is that your format is in an incorrect shape. The most common is likely melting to long format via the reshape2 or data.table package as explained in this link. I personally prefer the data.table package, as it seems to have better large scale performance. Their usage however is identical.
Lets say you have a column 'NAME' for states and 9 columns for population estimates (2010 population estimates, 2011 population estimates and so on), we could then convert these columns into a long format, using melt from either of the two suggested packages (They are identical in use)
require(data.table)
value_columns <- paste(2010:2018, "Population Estimates")
population.data_long <- melt(population.data, id.vars = "NAME",
measure.vars = value_columns, #Columns containing values we (that are grouped by their column names)
variable.name = 'Year (Population Estimate)', #Name of the column which tells us [(Year) Population Estimate]
value.name = 'Population Estimate') #Name of the column with values
population.data_long$year <- as.integer(substr(population.data_long$`Year (Population Estimate)`, 1, 4)) #Create a year column in a bit of a hacky way
Note i have ignored any additional columns, and these should be included in your melt statement. From here on a linear regression should follow any standard example that you have found.
We used the R library forecast to make predictions for the next 24 hours. We have the following:
fore_cast=forecast.tbats(model,h=24,level=90)
fore_cast
Point Forecast Lo 90 Hi 90
5.380952 6270.778 5389.089 7296.643
5.386905 5458.096 4557.375 6536.743
5.392857 5219.995 4248.967 6412.814
5.398810 5187.102 4126.390 6520.328
Now we have 2 problems:
We need 'time' (in hour e.g. 01,23,19 etc) instead of 'point'.
We wish to plot the trendline against time showing the actual observed
values against these predicted values. We have loaded actual observed
values from a CSV file.
We tried:
actual_data = read.csv('actualdata.csv')
plot(actual_data,fore_cast)
Doesn't work, and using plot(actual_data) just shows some points in a straight line instead of curved trendline.
EDIT:
Sample output of fore_cast from dput:
structure(list(model = structure(list(lambda = 0.000438881055939422,
alpha = 0.65694875480321, beta = -0.0983972877836753, damping.parameter = 0.800419363290521,
gamma.one.values = c(-0.00150031474145603, -0.00124696854910294
), gamma.two.values = c(0.0023600487982342, -0.002465549595849
), ar.coefficients = NULL, ma.coefficients = NULL, likelihood = 13202.294346586,
optim.return.code = 0L, variance = 0.00855092137349485, AIC = 13258.294346586,
parameters = structure(list(vect = c(0.000438881055939422,
0.65694875480321, 0.800419363290521, -0.0983972877836753,
-0.00150031474145603, -0.00124696854910294, 0.0023600487982342,
-0.002465549595849), control = structure(list(use.beta = TRUE,
use.box.cox = TRUE, use.damping = TRUE, length.gamma = 4L,
p = 0, q = 0), .Names = c("use.beta", "use.box.cox",
"use.damping", "length.gamma", "p", "q"))), .Names = c("vect",
"control")), seed.states = structure(c(7.44188559667267,
0.00357069100887873, -0.0664300680553579, 0.0229067500159256,
0.00460111570469819, -0.00772324725408007, -0.000610110386029883,
0.00568378752162509, -0.0084050648066819, -0.0324093004247092,
-0.000720936399990958, -0.00705790547321605, -0.00738992950838566,
0.00180424326179638, -0.00107745502434416, 0.00242014705705761,
-0.01824679745657, 0.0123019701003545, -0.0245935735677402,
0.0181321397860132), .Dim = c(20L, 1L)), fitted.values = structure(c(1598.57443298879,
1435.74973092922, 1397.92464316794, 1296.90202189518, 1440.3201303663,
1544.11695101118, 1777.97079874181, 1766.50571671645, 1925.27360388028,
1863.26963233038, 1773.08363764691, 1887.26580055295, 1887.48006609474,
1841.66200850472, 1991.90290660363, 2233.04775631848, 2081.30246965768,
1872.12639817609, 1899.38583561568, 2213.43437455052, 2214.00832820531,
1745.36311914995, 1678.67975050944, 1502.35472259274, 1512.27350460399,
1456.14165844166, 1464.3803467642, 1517.99443293857, 1484.54280422369,
1382.37041287489, 1452.43700910726, 1545.16934543365, 1440.50974319508,
1475.59742668699, 1544.88546424501, 1790.95280713647, 1916.4267023671,
1928.72804180587, 1819.15839770808, 1916.43079357329, 1836.80043977753,
1720.25638746452, 1730.03629161412, 1614.6048115754, 1599.23641723244,
1635.86950932066, 1543.46360784778, 1641.35066985679, 1608.60556151299,
1651.47649465456, 1475.15006990464, 1403.67294742438, 1507.58932406857,
1666.3170708439, 1696.06132797576, 1543.32187293056, 1704.58043626911,
1914.72424191575, 2109.33624862625, 2092.98934458578, 2222.13355258602,
2084.68677709368, 1962.9230489947, 2045.61547393981, 2140.30565941261,
2097.46130996426, 2126.07936955385, 2226.18935508502, 2269.54492801286,
2300.37314952852, 2398.48786829541, 2303.31270702723, 2332.74139979969,
2146.51487558643, 2101.27480789243, 2111.61910899422, 2053.57840714969,
2046.56606362537, 2073.82870990658, 2094.88831798868, 2334.85185938782,
2541.72156227893, 2502.36031483721, 2398.12240784327, 2266.35832277135,
2151.05248890962, 2266.88803633019, 2366.19453856405, 2399.97570044332,
2341.74959623409, 2144.33465155869, 2102.91952061083, 2214.48622101851,
2179.48115699957, 2288.28092735955, 2224.55218736155, 2195.1506809087,
2163.94619334319, 2161.41843642149, 2134.75060670667, 2138.77895768654,
2142.84680080931, 2258.55072549978, 2297.90237035988, 2314.94197015208,
2300.99928929609, 2277.39754662665, 2291.06980363364, 2487.04257346235,
2381.05768214413, 2509.40078456481, 2657.61336243367, 2528.65026804303,
2434.2722174014, 2366.04811963942, 2270.6647135766, 2231.33965004538,
2376.51043520344, 2249.42861599343, 2193.98771109322, 2252.12327312365,
2210.76969838623, 2180.50451255189, 2221.92898123682, 2537.84678083006,
2329.57350097532, 2252.82349908982, 2143.92033677754, 2092.3142840022,
2084.70304624685, 2111.18929138546, 2160.05383108999, 2280.94409931504,
2118.22029344747, 2214.65738250204, 2269.05911898631, 2084.26658709038,
2016.04764576402, 2095.57091797435, 2161.07354463394, 2427.77607700887,
2333.91103594967, 2234.23838054763, 2250.71557301013, 2186.97925802073,
2129.51096829218, 2115.40228652934, 2094.89231085691, 2086.41044567131,
2180.94542608489, 2105.38187642016, 2459.45788915933, 2292.36325639374,
2410.75372754831, 2375.56640249604, 2491.11938114866, 2470.51372278037,
2464.95765202085, 2600.85929020727, 2709.48518695182, 2779.77558137814,
2518.29927341458, 2344.06621605191, 2391.56719713269, 2368.68842788795,
2199.93530349068, 2113.92970206565, 2458.96718445444, 3121.97852988865,
2559.40932439262, 2331.12829078836, 2238.54586985577, 2241.91440620202,
2225.29804576634, 2154.14147781021, 2060.57980596908, 2037.30100544426,
2215.93410789353, 2364.42668160056, 2518.72871618042, 2537.34279365294,
2473.76096855791, 2623.63387707374, 2589.08335304697, 2577.0563838788,
2349.53279218826, 2305.52193868551, 2232.63712180453, 2167.50003597208,
2320.23187534213, 2281.86365949586, 2281.21119271599, 2323.2014703372,
2185.94404743238, 2140.21863271207, 2011.67723856012, 1966.52063119589,
2002.67344212857, 1952.41101080662, 1988.37461163105, 2126.75137749373,
2239.14722292367, 2320.98046489603, 2444.91847853015, 2431.69548763034,
2514.73820659393, 2505.85249387343, 2888.19773974179, 2853.20690693738,
2502.20865871069, 2524.56894781003, 2659.52271740553, 2615.9025930681,
2923.69327019152, 2754.76074569658, 2784.59488335761, 2874.24378479002,
2683.41908597168, 2733.83011888159, 2774.1325162997, 2906.41593326865,
2726.06821502751, 2460.21579967528, 2450.8035097605, 2547.39389733175,
2625.60323572861, 2827.94083526683, 2971.92012845614, 3042.90981987278,
2835.00811374845, 2846.98066660519, 2871.21876763166, 2901.99696373824,
2627.47532996657, 2583.75084300313, 2602.68041642846, 2632.8054092953,
2667.85374690972, 2639.10586730146, 2466.95799545022, 2381.06823502402,
2531.32611053776, 2407.14812148706, 2342.75701798463, 2401.73791085847,
2365.50645844524, 2404.50408575777, 2452.57343738519, 2613.15332739214,
2665.50965844576, 2723.8237337447, 2915.09266385617, 2890.17498445896,
2853.6278331055, 2868.1228183545, 2917.07803535669, 2876.59409770233,
2577.82035337979, 2581.91435020803, 2520.20342021937, 2603.37973251208,
2536.03988578365, 2510.83398648802, 2472.80606784857, 2425.51212342113,
2442.02863541673, 2465.73405821711, 2384.42988766816, 2555.51500549788,
2737.77091706275, 2425.00224845814, 2460.17325671183, 2639.16650619329,
2816.37024420397, 2755.69999167982, 2802.64991688288, 2685.12803367301,
2521.77568128564, 2500.99980614696, 2620.41659854805, 2529.25134423133,
2590.14804885984, 2318.80485234464, 2341.88940012276, 2460.21008281205,
2513.70688167177, 2437.71670675479, 2383.29782281743, 2499.36244454453,
2472.98602901478, 2491.10649022417, 2350.1405559119, 2362.78308814045,
2431.3911847573, 2321.15216823049, 2355.74203614213, 2429.60523843166,
2355.61947983433, 2346.3751018515, 2453.82214513707, 2542.98125962684,
2342.43364707529, 2302.17741211575, 2388.93541944219, 2435.41878657221, ....
Sample output from dput for actual observed values:
structure(list(index12 = c(6297.416944, 5406.865556, 4718.355556,
5304.729167, 4968.014722, 5081.130833, 5544.955, 4655.009444,
4269.023056, 4346.588333, 4511.455833, 5102.57, 4818.673333,
4862.343056, 4785.176667, 5385.005278, 6469.080833, 7166.025278,
7010.708333, 511.114167)), .Names = "index12", class = "data.frame", row.names = c(NA,
-20L))
The value of Point is unusual in spite of hour unit data. I think you failed to make a model.
Here is my example:
actual_data <- structure(list(index12 = c(6297.416944, 5406.865556, 4718.355556,
5304.729167, 4968.014722, 5081.130833, 5544.955, 4655.009444,
4269.023056, 4346.588333, 4511.455833, 5102.57, 4818.673333,
4862.343056, 4785.176667, 5385.005278, 6469.080833, 7166.025278,
7010.708333, 511.114167)),
.Names = "index12", class = "data.frame", row.names = c(NA, -20L))
# I suppose that actual_data was taken per hour.
num_actual <- as.numeric(actual_data[,1])
model <- bats(num_actual)
fore_cast <- forecast(model, h=24, level=90)
fore_cast # Point is from 21 to 44 because of length(actual_data)=20 and demanding predictions for the next 24 hours
# Point Forecast Lo 90 Hi 90
# 21 5063.207 2902.187 7224.226
# 22 5108.114 2946.988 7269.241
# :
# 44 5108.114 2944.629 7271.600
# plot() has forecast method. It draws actual_data and prediction, and paints Lo90-Hi90.
plot(fore_cast, main="")
I have a large time series data set, which I've used xts to summarize in 30 second periods. Not sure how to make this set easily reproducible but it looks like this
> str(taonedf)
'data.frame': 480 obs. of 2 variables:
$ time : POSIXct, format: "2013-01-06 13:00:29" "2013-01-06 13:00:59" "2013-01-06 13:01:29" ...
$ count: int 20763 12030 22188 12183 21112 11628 21543 12609 20095 12992 ...
> head(taonedf)
time count
1 2013-01-06 13:00:29 20763
2 2013-01-06 13:00:59 12030
3 2013-01-06 13:01:29 22188
4 2013-01-06 13:01:59 12183
5 2013-01-06 13:02:29 21112
6 2013-01-06 13:02:59 11628
I've plotted a normal line plot of this and it works fine.
ggplot(data=taonedf, aes(x=time, y=count/30)) + #
geom_line(color="#009E73") +
scale_y_continuous(name="requests per second", labels = format_format(scientific=FALSE, big.mark=",")) +
scale_x_datetime(name="",labels = date_format("%b %d\n%H:%M") ) +
labs(title=paste("Requests per Second - All Requests",count,sep="\n")) +
theme(legend.position = "none")
I want to add some vline annotations. I've created a second dataframe called EV, it looks like this:
> str(ev)
'data.frame': 10 obs. of 2 variables:
$ dt : POSIXct, format: "2013-01-06 13:45:00" "2013-01-06 14:18:00" "2013-01-06 14:49:00" ...
$ event: Factor w/ 9 levels "Event 1",..: 7 8 3 2 5 6 1 4 2 9
> head(ev)
dt event
1 2013-01-06 13:45:00 Event 1
Now, when I add the vline option I get odd results. I'm using the same date time format between the two so the scale should align.
ggplot(data=taonedf, aes(x=time, y=count/30)) +
geom_line(color="#009E73") +
geom_vline(data=ev,aes(xtintercept=dt))+
scale_y_continuous(name="requests per second", labels = format_format(scientific=FALSE, big.mark=",")) +
scale_x_datetime(name="",labels = date_format("%b %d\n%H:%M") ) +
labs(title=paste("Requests per Second - All Requests",count,sep="\n")) +
theme(legend.position = "none")
What am I missing? This doesn't appear to be that hard. All of the documentation and examples show simple numeric X axis so I'm assuming there is some issue with dates in the X axis but I can't pinpoint it. Any help would be appreciated.
> dput(taonedf)
structure(list(time = structure(c(1357506029.996, 1357506059.999,
1357506089.997, 1357506119.998, 1357506149.998, 1357506179.996,
1357506209.996, 1357506239.993, 1357506269.999, 1357506299.996,
1357506329.998, 1357506359.998, 1357506389.999, 1357506419.998,
1357506449.986, 1357506479.996, 1357506509.99, 1357506539.988,
1357506569.996, 1357506599.999, 1357506629.991, 1357506659.998,
1357506689.999, 1357506719.995, 1357506749.996, 1357506779.998,
1357506809.998, 1357506839.997, 1357506869.996, 1357506899.996,
1357506929.997, 1357506959.994, 1357506989.998, 1357507019.999,
1357507049.999, 1357507079.998, 1357507109.998, 1357507139.999,
1357507169.998, 1357507199.99, 1357507229.999, 1357507259.999,
1357507289.999, 1357507319.998, 1357507349.997, 1357507379.997,
1357507409.999, 1357507439.998, 1357507469.994, 1357507499.996,
1357507529.996, 1357507559.996, 1357507589.995, 1357507619.988,
1357507649.999, 1357507679.994, 1357507709.996, 1357507739.996,
1357507769.994, 1357507799.991, 1357507829.999, 1357507859.999,
1357507889.999, 1357507919.999, 1357507949.999, 1357507979.999,
1357508009.999, 1357508039.999, 1357508069.998, 1357508099.999,
1357508129.999, 1357508159.999, 1357508189.999, 1357508219.998,
1357508249.999, 1357508279.999, 1357508309.999, 1357508339.999,
1357508369.999, 1357508399.999, 1357508429.998, 1357508459.999,
1357508489.999, 1357508519.999, 1357508549.999, 1357508579.999,
1357508609.999, 1357508639.999, 1357508669.999, 1357508699.999,
1357508729.999, 1357508759.998, 1357508789.999, 1357508819.998,
1357508849.999, 1357508879.998, 1357508909.999, 1357508939.996,
1357508969.999, 1357508999.999, 1357509029.999, 1357509059.999,
1357509089.999, 1357509119.999, 1357509149.999, 1357509179.999,
1357509209.999, 1357509239.999, 1357509269.999, 1357509299.999,
1357509329.999, 1357509359.999, 1357509389.999, 1357509419.999,
1357509449.999, 1357509479.999, 1357509509.999, 1357509539.999,
1357509569.976, 1357509599.999, 1357509629.999, 1357509659.999,
1357509689.999, 1357509719.999, 1357509749.996, 1357509779.999,
1357509809.999, 1357509839.999, 1357509869.999, 1357509899.999,
1357509929.999, 1357509959.996, 1357509989.999, 1357510019.997,
1357510049.998, 1357510079.997, 1357510109.999, 1357510139.999,
1357510169.999, 1357510199.999, 1357510229.999, 1357510259.999,
1357510289.999, 1357510319.999, 1357510349.999, 1357510379.999,
1357510409.999, 1357510439.999, 1357510469.999, 1357510499.999,
1357510529.999, 1357510559.999, 1357510589.999, 1357510619.999,
1357510649.999, 1357510679.999, 1357510709.999, 1357510739.983,
1357510769.999, 1357510799.999, 1357510829.999, 1357510859.999,
1357510889.999, 1357510919.999, 1357510949.999, 1357510979.999,
1357511009.997, 1357511039.999, 1357511069.999, 1357511099.999,
1357511129.999, 1357511159.999, 1357511189.999, 1357511219.999,
1357511249.999, 1357511279.999, 1357511309.999, 1357511339.999,
1357511369.999, 1357511399.999, 1357511429.999, 1357511459.999,
1357511489.999, 1357511519.999, 1357511549.999, 1357511579.999,
1357511609.999, 1357511639.999, 1357511669.999, 1357511699.999,
1357511729.999, 1357511759.999, 1357511789.996, 1357511819.999,
1357511849.999, 1357511879.999, 1357511909.999, 1357511939.993,
1357511969.999, 1357511999.998, 1357512029.999, 1357512059.999,
1357512089.999, 1357512119.999, 1357512149.999, 1357512179.998,
1357512209.999, 1357512239.999, 1357512269.999, 1357512299.999,
1357512329.997, 1357512359.993, 1357512389.997, 1357512419.999,
1357512449.999, 1357512479.998, 1357512509.999, 1357512539.999,
1357512569.999, 1357512599.999, 1357512629.999, 1357512659.995,
1357512689.999, 1357512719.999, 1357512749.999, 1357512779.995,
1357512809.999, 1357512839.999, 1357512869.999, 1357512899.999,
1357512929.999, 1357512959.999, 1357512989.997, 1357513019.996,
1357513049.999, 1357513079.999, 1357513109.999, 1357513139.999,
1357513169.999, 1357513199.993, 1357513229.999, 1357513259.999,
1357513289.999, 1357513319.999, 1357513349.998, 1357513379.999,
1357513409.999, 1357513439.999, 1357513469.999, 1357513499.999,
1357513529.999, 1357513559.999, 1357513589.999, 1357513619.999,
1357513649.999, 1357513679.999, 1357513709.999, 1357513739.999,
1357513769.999, 1357513799.998, 1357513829.997, 1357513859.999,
1357513889.999, 1357513919.999, 1357513949.999, 1357513979.998,
1357514009.999, 1357514039.996, 1357514069.999, 1357514099.999,
1357514129.999, 1357514159.999, 1357514189.999, 1357514219.999,
1357514249.999, 1357514279.999, 1357514309.999, 1357514339.993,
1357514369.999, 1357514399.999, 1357514429.999, 1357514459.999,
1357514489.999, 1357514519.999, 1357514549.988, 1357514579.997,
1357514609.999, 1357514639.998, 1357514669.984, 1357514699.999,
1357514729.999, 1357514759.999, 1357514789.999, 1357514819.999,
1357514849.999, 1357514879.999, 1357514909.999, 1357514939.996,
1357514969.999, 1357514999.999, 1357515029.999, 1357515059.998,
1357515089.999, 1357515119.97, 1357515149.998, 1357515179.999,
1357515209.999, 1357515239.999, 1357515269.999, 1357515299.999,
1357515329.999, 1357515359.999, 1357515389.999, 1357515419.999,
1357515449.999, 1357515479.999, 1357515509.999, 1357515539.999,
1357515569.999, 1357515599.999, 1357515629.995, 1357515659.999,
1357515689.999, 1357515719.999, 1357515749.999, 1357515779.999,
1357515809.995, 1357515839.999, 1357515869.999, 1357515899.999,
1357515929.999, 1357515959.999, 1357515989.999, 1357516019.999,
1357516049.999, 1357516079.999, 1357516109.999, 1357516139.999,
1357516169.999, 1357516199.999, 1357516229.999, 1357516259.998,
1357516289.998, 1357516319.999, 1357516349.999, 1357516379.999,
1357516409.999, 1357516439.999, 1357516469.999, 1357516499.999,
1357516529.999, 1357516559.999, 1357516589.999, 1357516619.999,
1357516649.999, 1357516679.999, 1357516709.999, 1357516739.999,
1357516769.999, 1357516799.999, 1357516829.999, 1357516859.999,
1357516889.999, 1357516919.999, 1357516949.999, 1357516979.999,
1357517009.999, 1357517039.999, 1357517069.999, 1357517099.999,
1357517129.999, 1357517159.998, 1357517189.999, 1357517219.999,
1357517249.999, 1357517279.999, 1357517309.999, 1357517339.999,
1357517369.999, 1357517399.998, 1357517429.999, 1357517459.999,
1357517489.999, 1357517519.999, 1357517549.999, 1357517579.999,
1357517609.999, 1357517639.999, 1357517669.999, 1357517699.999,
1357517729.999, 1357517759.999, 1357517789.999, 1357517819.999,
1357517849.999, 1357517879.999, 1357517909.999, 1357517939.999,
1357517969.999, 1357517999.999, 1357518029.999, 1357518059.976,
1357518089.999, 1357518119.998, 1357518149.998, 1357518179.999,
1357518209.987, 1357518239.999, 1357518269.998, 1357518299.991,
1357518329.998, 1357518359.999, 1357518389.994, 1357518419.994,
1357518449.995, 1357518479.999, 1357518509.999, 1357518539.998,
1357518569.983, 1357518599.999, 1357518629.998, 1357518659.994,
1357518689.999, 1357518719.988, 1357518749.999, 1357518779.999,
1357518809.999, 1357518839.999, 1357518869.999, 1357518899.999,
1357518929.999, 1357518959.999, 1357518989.999, 1357519019.999,
1357519049.999, 1357519079.998, 1357519109.999, 1357519139.999,
1357519169.999, 1357519199.999, 1357519229.999, 1357519259.999,
1357519289.999, 1357519319.999, 1357519349.999, 1357519379.999,
1357519409.999, 1357519439.999, 1357519469.999, 1357519499.999,
1357519529.999, 1357519559.999, 1357519589.999, 1357519619.999,
1357519649.999, 1357519679.999, 1357519709.999, 1357519739.999,
1357519769.999, 1357519799.999, 1357519829.997, 1357519859.999,
1357519889.999, 1357519919.999, 1357519949.999, 1357519979.999,
1357520009.999, 1357520039.999, 1357520069.999, 1357520099.999,
1357520129.999, 1357520159.999, 1357520189.999, 1357520219.999,
1357520249.999, 1357520279.999, 1357520309.999, 1357520339.999,
1357520369.999, 1357520399.999), tzone = "", tclass = c("POSIXct",
"POSIXt"), class = c("POSIXct", "POSIXt")), count = c(20763L,
12030L, 22188L, 12183L, 21112L, 11628L, 21543L, 12609L, 20095L,
12992L, 21552L, 12447L, 21113L, 12236L, 21705L, 12018L, 21140L,
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75830L, 104609L, 51063L, 67046L, 66977L, 82513L, 87228L, 107474L,
141878L, 127290L, 70953L, 98879L, 87814L, 117309L, 113463L, 150979L,
198271L, 170456L, 108325L, 119583L, 111803L, 117067L, 186768L,
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152180L, 168528L, 131228L, 140622L, 145363L, 93070L, 58613L,
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77676L, 262966L, 227355L, 256477L, 238905L, 241260L, 206168L,
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15267L, 8946L, 14189L, 9067L, 14359L, 9776L, 167922L, 337364L,
350941L, 362928L, 364922L, 319641L, 348687L, 321356L, 400161L,
334171L, 332829L, 323842L, 397809L, 375694L, 384432L, 356825L,
350846L, 395942L, 359471L, 296926L, 418481L, 322144L, 335658L,
347212L, 334421L, 375769L, 364300L, 317370L, 373192L, 346713L,
356341L, 327225L, 305538L, 347815L, 276914L, 322149L, 303627L,
292363L, 284724L, 305082L, 373363L, 304386L, 438592L, 403579L,
430549L, 450536L, 432445L, 389779L, 434888L, 375010L, 456096L,
577393L, 451122L, 432354L, 425547L, 417729L)), .Names = c("time",
"count"), row.names = c(NA, -480L), class = "data.frame")
> dput(ev)
structure(list(dt = structure(c(1357508700, 1357510680, 1357512540,
1357515360, 1357517220, 1357517700, 1357518000, 1357518000, 1357519140,
1357519140), class = c("POSIXct", "POSIXt"), tzone = ""), event = structure(c(7L,
8L, 3L, 2L, 5L, 6L, 1L, 4L, 2L, 9L), .Label = c("Event 1",
"Event 2", "Event 3",
"Event 4", "Event 5",
"Event 6", "Event 7",
"Event 8", "Event 9"
), class = "factor")), .Names = c("dt", "event"), row.names = c(NA,
-10L), class = "data.frame")
Library Versions:
> sessionInfo()
R version 2.15.1 (2012-06-22)
Platform: x86_64-redhat-linux-gnu (64-bit)
attached base packages:
[1] grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] reshape2_1.2.2 xts_0.9-1 zoo_1.7-9 gdata_2.12.0 data.table_1.8.6 caTools_1.14
[7] scales_0.2.3 ggplot2_0.9.3
Simplied code - this still doesnt work
library(scales)
library(ggplot2)
taonedf<-dget("taonedf") #in this thread
ev<-dget("ev") #in this thread
ggplot(data=taonedf, aes(x=time, y=count/30)) +
geom_line() +
geom_vline(data=ev,aes(xtintercept=as.numeric(dt)))
To get geom_vline() display lines as intended, first, library scales should be loaded. Then use as.numeric() in geom_vline().
library(scales)
+ geom_vline(data=ev,aes(xintercept=as.numeric(dt)))
Two things
You need to wrap the datetimes for the vline in as.numeric
You misspelled xintercept
Fixing those:
library("ggplot2")
library("scales")
ggplot(data=taonedf, aes(x=time, y=count/30)) +
geom_line(color="#009E73") +
geom_vline(data=ev,aes(xintercept=as.numeric(dt)))+
scale_y_continuous(name="requests per second", labels = format_format(scientific=FALSE, big.mark=",")) +
scale_x_datetime(name="",labels = date_format("%b %d\n%H:%M") ) +
labs(title=paste("Requests per Second - All Requests")) +
theme(legend.position = "none")