From: aaronshaw Date: Wed, 17 Apr 2019 02:29:27 +0000 (-0500) Subject: adding a bunch of new files around week 4 X-Git-Url: https://code.communitydata.science/stats_class_2019.git/commitdiff_plain/f21e8d18685e0ed07df48b7eb5050a616ee316dd?ds=inline;hp=bd9eba025b1137bb6497a56ad3f5634ff411058e adding a bunch of new files around week 4 --- diff --git a/data/week_04/COS-Statistics-Gov-Domains-Only.csv b/data/week_04/COS-Statistics-Gov-Domains-Only.csv new file mode 100644 index 0000000..1bb1e97 --- /dev/null +++ b/data/week_04/COS-Statistics-Gov-Domains-Only.csv @@ -0,0 +1,1243 @@ +domain,pageviews,unique-pageviews,average-time-on-page,bounce-rate,exit-percent,month +www.seattle.gov/,3525737,2689843,0:01:19,50.86,36.53,04/01/2015 12:00:00 AM +www2.seattle.gov/,2158182,125984,0:01:12,41.69,4.53,04/01/2015 12:00:00 AM +web6.seattle.gov/,367871,204803,0:01:18,40.66,23.23,04/01/2015 12:00:00 AM +spdblotter.seattle.gov/,117645,91076,0:01:14,69.29,46.42,04/01/2015 12:00:00 AM 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12:00:00 AM +(not set),419,87.11,365,74.22,1.37,0:01:09,08/01/2016 12:00:00 AM +Windows,18990,38.62,7334,47.08,4.2,0:03:46,08/01/2016 12:00:00 AM +Nintendo 3DS,7,100,7,100,1,0:00:00,08/01/2016 12:00:00 AM +BlackBerry,520,76.54,398,68.85,1.68,0:01:06,08/01/2016 12:00:00 AM +SymbianOS,20,100,20,100,1,0:00:00,08/01/2016 12:00:00 AM +LG,20,70,14,35,1.7,0:01:56,08/01/2016 12:00:00 AM +Windows Phone,5267,41.41,2181,63.2,2.26,0:01:42,08/01/2016 12:00:00 AM +Android,214448,46.3,99279,59.8,3.82,0:05:00,08/01/2016 12:00:00 AM diff --git a/problem_sets/week_04/ps4-worked_solution.Rmd b/problem_sets/week_04/ps4-worked_solution.Rmd new file mode 100644 index 0000000..cc3c81f --- /dev/null +++ b/problem_sets/week_04/ps4-worked_solution.Rmd @@ -0,0 +1,130 @@ +--- +title: 'Week 4 Problem set: Worked solutions' +subtitle: "Statistics and statistical programming \nNorthwestern University \nMTS 525" +author: "Aaron Shaw" +date: "April 25, 2019" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +# Programming challenges + + +## PC2 + +You may need to edit these first lines to work on your own machine. Note that for working with .Rmd files interactively in Rstudio you may find it easier to do this using the drop down menus: "Session" → "Set Working Directory" → "To Source File Location" + +```{r} +## setwd("~/Documents/Teaching/2019/stats/") +## list.files("data/week_04") + +mobile <- read.csv("data/week_04/COS-Statistics-Mobile_Sessions.csv") +total <- read.csv("data/week_04/COS-Statistics-Gov-Domains-Only.csv") +``` + +I'll write a little function to help inspect the data. Make sure you understand what the last line of the function is doing. +```{r} +summary.df <- function (d) { + print(nrow(d)) + print(ncol(d)) + print(head(d)) + print(d[sample(seq(1, nrow(d)), 5),]) +} +``` + +Then I can run these two lines a few times to look at some samples +```{r} +summary.df(mobile) + +summary.df(total) +``` +I can check for missing values and summarize the different columns using `lapply`: + +```{r} +lapply(total, summary) + +lapply(mobile, summary) +``` + +## PC3 + +First let's create a table/array using `tapply` that sums pageviews per month across all the sites: +```{r} +total.views.bymonth.tbl <- tapply(total$pageviews, total$month, sum) +total.views.bymonth.tbl +``` +If you run `class` on `total.views.bymonth.tbl` you'll notice it's not a data frame yet. We can change that: +```{r} +total.views <- data.frame(months=names(total.views.bymonth.tbl), + total=total.views.bymonth.tbl) + +head(total.views) +``` +Let's cleanup the rownames (this would all work the same if i didn't do this part). + +```{r} +rownames(total.views) <- NULL + +head(total.views) +``` +## PC4 +Onwards to the mobile dataset! + +Here we have a challenge because we have to estimate total pageviews (it's not given in the raw dataset). I'll do this by multiplying sessions by pages-per-session. This assumes that the original pages-per-session calculation is precise, but I'm not sure what else we could do under the circumstances. +```{r} +mobile$total.pages <- mobile$Sessions * mobile$PagesPerSession +``` +Then, making the views-per-month array is more or less copy/pasted from above: +```{r} +mobile.views.bymonth.tbl <- tapply(mobile$total.pages, mobile$Month, sum) +mobile.views.bymonth.tbl + +mobile.views <- data.frame(months=names(mobile.views.bymonth.tbl), + mobile=mobile.views.bymonth.tbl) +rownames(mobile.views) <- NULL +``` +## PC5 +Now we merge the two datasets. Notice that I have created the `months` column in both datasets with *exactly* the same name. +```{r} +views <- merge(mobile.views, total.views, all.x=TRUE, all.y=TRUE, by="months") +``` + +These are sorted in strange ways and will be difficult to graph because the dates are stored as characters. Let's convert them into Date objects. Then I can use `sort.list` to sort everything. + +```{r} +views$months <- as.Date(views$months, format="%m/%d/%Y %H:%M:%S") + +views <- views[sort.list(views$months),] +``` + +Take a look at the data. Some rows are missing observations. We can drop those rows using `complete.cases`: +```{r} +lapply(views, summary) + +views[rowSums(is.na(views)) > 0,] + +views.complete <- views[complete.cases(views),] +``` + +## PC6 + +For my proportion measure, I'll take the mobile views divided by the total views. + +```{r} +views.complete$prop.mobile <- views.complete$mobile / views.complete$total + +``` +## PC7. + +```{r} +library(ggplot2) +ggplot(data=views.complete) + aes(x=months, y=prop.mobile) + geom_point() + geom_line() + scale_y_continuous(limits=c(0, 1)) + +``` + +# Statistical questions + +# Empirical paper questions \ No newline at end of file diff --git a/problem_sets/week_04/ps4-worked_solution.html b/problem_sets/week_04/ps4-worked_solution.html new file mode 100644 index 0000000..cbb90a6 --- /dev/null +++ b/problem_sets/week_04/ps4-worked_solution.html @@ -0,0 +1,426 @@ + + + + + + + + + + + + + + + +Week 4 Problem set: Worked solutions + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

Programming challenges

+
+

PC2

+
## You'll need to edit these first lines to work on your own machine
+##
+## Note that for working with .Rmd files interactively in Rstudio you may find it easier to do this 
+## using the drop down menus: "Session" → "Set Working Directory" → "To Source File Location" 
+##
+
+## setwd("~/Documents/Teaching/2019/stats/")
+## list.files("data/week_04")
+
+mobile <- read.csv("data/week_04/COS-Statistics-Mobile_Sessions.csv")
+total <- read.csv("data/week_04/COS-Statistics-Gov-Domains-Only.csv")
+
+
+summary.df <- function (d) {
+    print(nrow(d))
+    print(ncol(d))
+    print(head(d))
+    print(d[sample(seq(1, nrow(d)), 5),])
+}
+
+## run these two lines a few times to look at the numbers
+summary.df(mobile)
+
## [1] 231
+## [1] 8
+##   Operating_System Sessions New_Sessions New_Users Bounce_Rate
+## 1              iOS   332291        47.75    158674       60.79
+## 2          Android   170107        45.53     77453       58.14
+## 3          Windows    27325        44.76     12231       44.60
+## 4    Windows Phone    10109        45.71      4621       59.01
+## 5       BlackBerry     1375        39.27       540       62.98
+## 6        (not set)      408        83.09       339       72.30
+##   PagesPerSession AvgSessionDuration                  Month
+## 1            2.34            0:02:11 01/01/2015 12:00:00 AM
+## 2            2.98            0:03:53 01/01/2015 12:00:00 AM
+## 3            3.26            0:02:40 01/01/2015 12:00:00 AM
+## 4            2.14            0:01:45 01/01/2015 12:00:00 AM
+## 5            2.10            0:02:24 01/01/2015 12:00:00 AM
+## 6            1.82            0:01:01 01/01/2015 12:00:00 AM
+##     Operating_System Sessions New_Sessions New_Users Bounce_Rate
+## 51           Samsung       59        44.07        26       77.97
+## 160            Nokia       12       100.00        12      100.00
+## 162       Firefox OS        6       100.00         6        0.00
+## 224        (not set)      419        87.11       365       74.22
+## 172               LG        6       100.00         6        0.00
+##     PagesPerSession AvgSessionDuration                  Month
+## 51             1.54            0:03:01 04/01/2015 12:00:00 AM
+## 160            1.00            0:00:00 10/01/2015 12:00:00 AM
+## 162            2.00            0:00:02 10/01/2015 12:00:00 AM
+## 224            1.37            0:01:09 08/01/2016 12:00:00 AM
+## 172            1.83            0:01:37 12/01/2015 12:00:00 AM
+
summary.df(total)
+
## [1] 1242
+## [1] 7
+##                    domain pageviews unique.pageviews average.time.on.page
+## 1        www.seattle.gov/   3525737          2689843              0:01:19
+## 2       www2.seattle.gov/   2158182           125984              0:01:12
+## 3       web6.seattle.gov/    367871           204803              0:01:18
+## 4 spdblotter.seattle.gov/    117645            91076              0:01:14
+## 5       web1.seattle.gov/     79529            32258              0:01:09
+## 6       find.seattle.gov/     78611            62516              0:00:39
+##   bounce.rate exit.percent                  month
+## 1       50.86        36.53 04/01/2015 12:00:00 AM
+## 2       41.69         4.53 04/01/2015 12:00:00 AM
+## 3       40.66        23.23 04/01/2015 12:00:00 AM
+## 4       69.29        46.42 04/01/2015 12:00:00 AM
+## 5       59.57        18.76 04/01/2015 12:00:00 AM
+## 6       25.67        21.74 04/01/2015 12:00:00 AM
+##                              domain pageviews unique.pageviews
+## 963    sdotperformance.seattle.gov/        20               15
+## 1022            obrien.seattle.gov/       283              241
+## 914     lc317web.light.seattle.gov/         4                4
+## 455        mayormurray.seattle.gov/      9896             8925
+## 744  councilconnection.seattle.gov/         2                2
+##      average.time.on.page bounce.rate exit.percent                  month
+## 963               0:00:13       81.82        55.00 01/01/2016 12:00:00 AM
+## 1022              0:01:51        0.61         0.34 02/01/2016 12:00:00 AM
+## 914               0:00:04        0.00        50.00 01/01/2016 12:00:00 AM
+## 455               0:03:23       90.59        87.12 06/01/2015 12:00:00 AM
+## 744               0:00:00        1.00         1.00 09/01/2015 12:00:00 AM
+
## PC3. Using the top 5000 dataset, create a new data frame that one
+## column per month (as described in the data) and a second column is
+## the total number of views made to all pages in the dataset over
+## that month.
+
+## first create a table/array using tapply
+total.views.bymonth.tbl <- tapply(total$pageviews, total$month, sum)
+total.views.bymonth.tbl
+
##                        01/01/2015 12:00:00 AM 01/01/2016 12:00:00 AM 
+##                     NA                6350440                3471121 
+## 02/01/2015 12:00:00 AM 02/01/2016 12:00:00 AM 03/01/2015 12:00:00 AM 
+##                5820453                3366834                6609602 
+## 03/01/2016 12:00:00 AM 04/01/2015 12:00:00 AM 04/01/2016 12:00:00 AM 
+##                4087054                6481483                3644750 
+## 05/01/2015 12:00:00 AM 06/01/2015 12:00:00 AM 07/01/2015 12:00:00 AM 
+##                6544055                6952488                8084318 
+## 08/01/2015 12:00:00 AM 09/01/2015 12:00:00 AM 10/01/2015 12:00:00 AM 
+##                7045189                3067760                2961681 
+## 12/01/2015 12:00:00 AM 
+##                5745045
+
## now construct a data frame
+total.views <- data.frame(months=names(total.views.bymonth.tbl),
+                          total=total.views.bymonth.tbl)
+
+## zero out the rownames so it looks a bit better (this would all work
+## the same if i didn't do this part)
+rownames(total.views) <- NULL
+
+head(total.views)
+
##                   months   total
+## 1                             NA
+## 2 01/01/2015 12:00:00 AM 6350440
+## 3 01/01/2016 12:00:00 AM 3471121
+## 4 02/01/2015 12:00:00 AM 5820453
+## 5 02/01/2016 12:00:00 AM 3366834
+## 6 03/01/2015 12:00:00 AM 6609602
+
## PC4. Using the mobile dataset, create a new data frame where one
+## column is each month described in the data and the second is a
+## measure (estimate?) of the total number of views made by mobiles
+## (all platforms) over each month. This will will involve at least
+## two steps since total views are included. You'll need to first use
+## the data there to create a measure of the total views per platform.
+
+## first, multiply sessions by pages per session to get an estimate of
+## total pages
+mobile$total.pages <- mobile$Sessions * mobile$PagesPerSession 
+
+# see above, this is more or less copy/pasted from above
+mobile.views.bymonth.tbl <- tapply(mobile$total.pages, mobile$Month, sum)
+mobile.views.bymonth.tbl
+
##                        01/01/2015 12:00:00 AM 01/01/2016 12:00:00 AM 
+##                     NA              1399185.6               668275.2 
+## 02/01/2015 12:00:00 AM 02/01/2016 12:00:00 AM 03/01/2015 12:00:00 AM 
+##              1275315.2               592607.8              1402086.4 
+## 03/01/2016 12:00:00 AM 04/01/2015 12:00:00 AM 04/01/2016 12:00:00 AM 
+##               800842.8              1381295.1               788533.7 
+## 05/01/2015 12:00:00 AM 06/01/2015 12:00:00 AM 07/01/2015 12:00:00 AM 
+##              1605914.9              1722519.5              1988848.0 
+## 07/01/2016 12:00:00 AM 08/01/2015 12:00:00 AM 08/01/2016 12:00:00 AM 
+##               878142.6              1741067.8               912435.4 
+## 09/01/2015 12:00:00 AM 10/01/2015 12:00:00 AM 12/01/2015 12:00:00 AM 
+##               564453.5              1285288.0              1223414.0
+
mobile.views <- data.frame(months=names(mobile.views.bymonth.tbl),
+                           mobile=mobile.views.bymonth.tbl)
+
## PC5. Merge your two datasets together into a new dataset with
+## columns for each month, total views (across the top 5000 pages) and
+## total mobile views. Are there are missing data? Can you tell why?
+
+### TODO cleanup variable names to match
+
+views <- merge(mobile.views, total.views, all.x=TRUE, all.y=TRUE, by="months")
+
+## these don't sort well at the moment because they're not really
+## dates, so lets recode them
+views$months <- as.Date(views$months, format="%m/%d/%Y %H:%M:%S")
+
+## as then sort them
+views <- views[sort.list(views$months),]
+
+## there's one line that is all missing, so lets drop that
+views <- views[apply(views, 1, function (x) {!all(is.na(x))}),]
+
+## inspect it, looks like there's some missing data. lets drop
+## that. there are a few ways but complete.cases() might make most
+## cases
+views.complete <- views[complete.cases(views),]
+
## PC6. Create a new column in your merged dataset that describes your
+## best estimate of the proportion (or percentage, if you really
+## must!) of views that comes from mobile. Be able to talk about the
+## assumptions you've made here. Make sure that date, in this final
+## column, is a date or datetime object in R.
+
+views.complete$pct.mobile <- views.complete$mobile / views.complete$total
+    
+## PC6. Graph this over time and be ready to describe: (a) your best
+## estimate of the proportion of views from mobiles to the Seattle
+## City website over time and (b) an indication of whether it's going
+## up or down.
+
+library(ggplot2)
+ggplot(data=views.complete) + aes(x=months, y=pct.mobile) + geom_point() + scale_y_continuous(limits=c(0, 1))
+

+
+
+
+

Statistical questions

+
+
+

Empirical paper questions

+
+ + + + +
+ + + + + + + + diff --git a/r_lectures/w04-R_lecture.Rmd b/r_lectures/w04-R_lecture.Rmd new file mode 100644 index 0000000..c7c84ff --- /dev/null +++ b/r_lectures/w04-R_lecture.Rmd @@ -0,0 +1,59 @@ +--- +title: "Week 3 R lecture" +subtitle: "Statistics and statistical programming \nNorthwestern University \nMTS 525" +author: "Aaron Shaw" +date: "April 18, 2019" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +This week, we'll focus on one more way to manage date-time objects and some ways to generate distributions. + +## as.Date + +First, something I meant to include in last week's materials. The `as.Date()` function provides an alternative to `as.POSIX()` that is far more memorable and readable, but far less precise. Note that it truncates the time of day and the timezone from the ouput + +```{r} +m <- "2019-02-21 04:35:00" +class(m) + +a.good.time <- as.Date(m, format="%Y-%m-%d %H:%M:%S", tz="CDT") +class(a.good.time) +a.good.time +``` + +## Distribution functions +distribution functions: lets focus on *unif(): the key is on page 222 of Verzani +The “d” functions return the p.d.f. of the distribution +dunif(x=1, min=0, max=3) # 1/3 of the area is the to the left 1 +The “p” functions return the c.d.f. of the distribution. +dunif(q=2, min=0, max=3) #1/(b-a) is 2/3 +The “q” functions return the quantiles. +qunif(p=0.5, min=0, max=3) # half way between 0 and 3 + +The “r” functions return random samples from a distribution. +runif(n=1, min=0, max=3) # a random value in [0,3] + +## Doing simple simulations with random data +runif() +rnorm() + +## A quick simulation + +In case you don't believe the central limit theorem, let's put together a quick simulation to illustrate it in R. + +Write a function to repeatedly take the mean of a sample. + +Experiment by changing the size of the sample + + +## Quantile quantile plots + + +## Binomial and factorial functions +Choose, factorial + +