Last updated: 2018-02-22
Code version: d3c2fe9
library(dplyr)
library(data.table)
library(magrittr)
library(purrr)
library(here) # for tracking working directory
coinflip <- function(num){
sample(x = c(1,2), size = num, replace = TRUE)
}
Make dice roll function
diceroll <- function(num) {
sample(x = 1:6, size = num, replace = TRUE)
}
diceroll(4)
[1] 5 6 5 6
diceroll2 <- function(num, sides) {
sample(x = 1:sides, size = num, replace = TRUE)
}
diceroll2(num = 4, sides = 4)
[1] 2 1 2 3
Take dice with 6 sides, which was diceroll
Let’s roll the dice 1, 10, 100, 1000 and 10000 times, calculate the mean, and do this 1000 times
We will use the package purrr
to map functions to the list of number of throws
require(magrittr)
require(purrr)
nsim = 1000
nthrow = 10^(0:4)
nthrows <- rep(nthrow, times = nsim)
nthrow %>%
map(diceroll) %>%
map_dbl(mean)
[1] 5.0000 2.8000 3.6700 3.5860 3.4904
means <- list(nthrows) %>%
pmap(diceroll) %>% # apply diceroll to each row of nthrows, returns a list
map_dbl(mean) # apply mean to each element of the list, return a double vector
df <- data.frame(nthrows, means)
Visualize results
require(ggplot2)
df %>%
ggplot(aes(x = nthrows, y = means)) +
geom_point(alpha = .1) +
scale_x_log10() + theme_minimal() +
ggtitle("Means of 1000 simulations of n-dice throws") +
labs(x = "Number of throws")
Now with uniform sampling on logarithmic scale
nthrows <- round(10^runif(min = 0, max = 4, n = nsim))
means <- list(nthrows) %>%
pmap(diceroll) %>% # apply diceroll to each row of nthrows, returns a list
map_dbl(mean) # apply mean to each element of the list, return a double vector
df <- data.frame(nthrows, means)
df %>%
ggplot(aes(x = nthrows, y = means)) +
geom_point(alpha = .1) +
scale_x_log10() + theme_minimal() +
ggtitle("Means of 1000 simulations of n-dice throws") +
labs(x = "Number of throws")
Or with base-R plot
plot(means~nthrows, data = df, log = "x")
Get data
smokedat <- data.frame(
id = 1:20,
smoking.status = c(NA, "never smoked", "never smoked", "never smoked", "never smoked", "never smoked", "never smoked", "has smoked", "has smoked", "has smoked", "has smoked", "has smoked", "has smoked", "currently smoking", "currently smoking", "currently smoking", "currently smoking", "currently smoking", "currently smoking", "currently smoking"),
outcome = c("no hart infarction", NA, "no hart infarction", "no hart infarction", "no hart infarction", "no hart infarction", "hart infarction", "no hart infarction", "no hart infarction", "no hart infarction", "no hart infarction", "hart infarction", "hart infarction", "hart infarction", "no hart infarction", "no hart infarction", "no hart infarction", "hart infarction", "hart infarction", "hart infarction")
)
View
smokedat
id smoking.status outcome
1 1 <NA> no hart infarction
2 2 never smoked <NA>
3 3 never smoked no hart infarction
4 4 never smoked no hart infarction
5 5 never smoked no hart infarction
6 6 never smoked no hart infarction
7 7 never smoked hart infarction
8 8 has smoked no hart infarction
9 9 has smoked no hart infarction
10 10 has smoked no hart infarction
11 11 has smoked no hart infarction
12 12 has smoked hart infarction
13 13 has smoked hart infarction
14 14 currently smoking hart infarction
15 15 currently smoking no hart infarction
16 16 currently smoking no hart infarction
17 17 currently smoking no hart infarction
18 18 currently smoking hart infarction
19 19 currently smoking hart infarction
20 20 currently smoking hart infarction
?table
table(smokedat$smoking.status, smokedat$outcome)
hart infarction no hart infarction
currently smoking 4 3
has smoked 2 4
never smoked 1 4
Modify the previous command, to also display the numbers of missing.
table(smokedat$smoking.status, smokedat$outcome, useNA = "always")
hart infarction no hart infarction <NA>
currently smoking 4 3 0
has smoked 2 4 0
never smoked 1 4 1
<NA> 0 1 0
These tables are rather simple, we would like to add row and column totals as well as proportions.
Assign the table created in C) to an object called smoketab. Use the book, R forum, Google, etc. to find the commands to calculate row and column sums. In the same way, find the commands to bind rows and columns. Apply the above commands to create smoketab2, with the row totals added to smoketab as a new column. In addition, create smoketab3 with the column totals added to smoketab2 as a new row.
smoketab <- table(smokedat$smoking.status, smokedat$outcome, useNA = "always")
smoketab2 <- cbind(smoketab, total = rowSums(smoketab))
smoketab3 <- rbind(smoketab2, total = colSums(smoketab2))
smoketab3
hart infarction no hart infarction <NA> total
currently smoking 4 3 0 7
has smoked 2 4 0 6
never smoked 1 4 1 6
<NA> 0 1 0 1
total 7 12 1 20
Calculate row proportions for smoketab3 (hint: You can divide smoketab3 by the row totals). Store the new table in an object called smoketab3.RP.
Each row get’s multiplied with 1 divided by the sum of the columns
We can create a matrix with 1 over the column totals, and do element-wise product
totals <- tail(smoketab3, 1)
total_matrix <- matrix(rep(1/totals, nrow(smoketab3)),
nrow = nrow(smoketab3), byrow = T)
total_matrix
[,1] [,2] [,3] [,4]
[1,] 0.1428571 0.08333333 1 0.05
[2,] 0.1428571 0.08333333 1 0.05
[3,] 0.1428571 0.08333333 1 0.05
[4,] 0.1428571 0.08333333 1 0.05
[5,] 0.1428571 0.08333333 1 0.05
smoketab3.RP <- smoketab3 * total_matrix
smoketab3.RP
hart infarction no hart infarction <NA> total
currently smoking 0.5714286 0.25000000 0 0.35
has smoked 0.2857143 0.33333333 0 0.30
never smoked 0.1428571 0.33333333 1 0.30
<NA> 0.0000000 0.08333333 0 0.05
total 1.0000000 1.00000000 1 1.00
Calculate columns proportions as well, in the same manner (note: you will need to transpose the table, and then transpose back afterwards). Store the new table in an object called smoketab3.CP.
col_totals <- smoketab3[, "total"]
col_total_matrix <- matrix(rep(1/col_totals, each = ncol(smoketab3)),
nrow = nrow(smoketab3), byrow = T)
col_total_matrix
[,1] [,2] [,3] [,4]
[1,] 0.1428571 0.1428571 0.1428571 0.1428571
[2,] 0.1666667 0.1666667 0.1666667 0.1666667
[3,] 0.1666667 0.1666667 0.1666667 0.1666667
[4,] 1.0000000 1.0000000 1.0000000 1.0000000
[5,] 0.0500000 0.0500000 0.0500000 0.0500000
smoketab3.CP <- smoketab3 * col_total_matrix
smoketab3.CP
hart infarction no hart infarction <NA> total
currently smoking 0.5714286 0.4285714 0.0000000 1
has smoked 0.3333333 0.6666667 0.0000000 1
never smoked 0.1666667 0.6666667 0.1666667 1
<NA> 0.0000000 1.0000000 0.0000000 1
total 0.3500000 0.6000000 0.0500000 1
using the list() command, produce a list containing smoketab3, smoketab3.RP and smoketab3.CP. Name these three components “tab”, “rowprop” and “colprop”, respectively.
tab_list <- list(tab = smoketab3, rowprop = smoketab3.RP, colprop = smoketab3.CP)
Now make an improved table function yourself. Note: build a function that uses the arguments variable1, variable2 and dat, to produce a list such as you made in A) - G). variable1 and variable2 should be character values (containing text between quotes). Note that selection of columns or rows can also be done e.g. using smokedat[,“smoking.status”] (try it).
table2 <- function(data, variable1, variable2) {
x <- data[[variable1]]
y <- data[[variable2]]
# use dnn to preserve variable names
tab <- table(x, y, dnn = c(variable1, variable2), useNA = "always")
row_totals <- rowSums(tab)
tab2 <- cbind(tab, total = row_totals)
col_totals <- colSums(tab2)
tab3 <- rbind(tab2, total = col_totals)
# update col and row totals
row_totals <- rowSums(tab3) / 2
col_totals <- colSums(tab3) / 2
row_total_matrix <- matrix(rep(row_totals, each = ncol(tab3)),
nrow = nrow(tab3), byrow = T)
col_total_matrix <- matrix(rep(col_totals, each = nrow(tab3)),
nrow = nrow(tab3), byrow = F)
tab3.RP <- tab3 / row_total_matrix
tab3.CP <- tab3 / col_total_matrix
tabs <- list(tab = tab3, rowprop = tab3.RP, colprop = tab3.CP)
return(tabs)
}
table2(smokedat, "smoking.status", "outcome")
$tab
hart infarction no hart infarction <NA> total
currently smoking 4 3 0 7
has smoked 2 4 0 6
never smoked 1 4 1 6
<NA> 0 1 0 1
total 7 12 1 20
$rowprop
hart infarction no hart infarction <NA> total
currently smoking 0.5714286 0.4285714 0.0000000 1
has smoked 0.3333333 0.6666667 0.0000000 1
never smoked 0.1666667 0.6666667 0.1666667 1
<NA> 0.0000000 1.0000000 0.0000000 1
total 0.3500000 0.6000000 0.0500000 1
$colprop
hart infarction no hart infarction <NA> total
currently smoking 0.5714286 0.25000000 0 0.35
has smoked 0.2857143 0.33333333 0 0.30
never smoked 0.1428571 0.33333333 1 0.30
<NA> 0.0000000 0.08333333 0 0.05
total 1.0000000 1.00000000 1 1.00
In the book, R forum, internet or elsewhere, can you find a crosstable function that provides similar functionality (row and column totals and proportions)?
I googled for ‘r package table with proportions and margins’
Found: gmodels on the website Quick-R
Works like:
gmodels::CrossTable(smokedat$smoking.status, smokedat$outcome)
Cell Contents
|-------------------------|
| N |
| Chi-square contribution |
| N / Row Total |
| N / Col Total |
| N / Table Total |
|-------------------------|
Total Observations in Table: 18
| smokedat$outcome
smokedat$smoking.status | hart infarction | no hart infarction | Row Total |
------------------------|--------------------|--------------------|--------------------|
currently smoking | 4 | 3 | 7 |
| 0.600 | 0.382 | |
| 0.571 | 0.429 | 0.389 |
| 0.571 | 0.273 | |
| 0.222 | 0.167 | |
------------------------|--------------------|--------------------|--------------------|
has smoked | 2 | 4 | 6 |
| 0.048 | 0.030 | |
| 0.333 | 0.667 | 0.333 |
| 0.286 | 0.364 | |
| 0.111 | 0.222 | |
------------------------|--------------------|--------------------|--------------------|
never smoked | 1 | 4 | 5 |
| 0.459 | 0.292 | |
| 0.200 | 0.800 | 0.278 |
| 0.143 | 0.364 | |
| 0.056 | 0.222 | |
------------------------|--------------------|--------------------|--------------------|
Column Total | 7 | 11 | 18 |
| 0.389 | 0.611 | |
------------------------|--------------------|--------------------|--------------------|
If you still have time, make an improved version of the function that you made in H) that includes rounding of the decimals in rowprop and colprop.
table3 <- function(data, variable1, variable2, ndigits = NULL) {
x <- data[[variable1]]
y <- data[[variable2]]
# use dnn to preserve variable names
tab <- table(x, y, dnn = c(variable1, variable2), useNA = "always")
row_totals <- rowSums(tab)
tab2 <- cbind(tab, total = row_totals)
col_totals <- colSums(tab2)
tab3 <- rbind(tab2, total = col_totals)
# update col and row totals
row_totals <- rowSums(tab3) / 2
col_totals <- colSums(tab3) / 2
row_total_matrix <- matrix(rep(row_totals, each = ncol(tab3)),
nrow = nrow(tab3), byrow = T)
col_total_matrix <- matrix(rep(col_totals, each = nrow(tab3)),
nrow = nrow(tab3), byrow = F)
tab3.RP <- tab3 / row_total_matrix
tab3.CP <- tab3 / col_total_matrix
tabs <- list(tab = tab3, rowprop = tab3.RP, colprop = tab3.CP)
# no rounding
if (is.null(ndigits)) return(tabs)
# rounding
tabs_rounded <- tabs %>%
purrr::map(round, ndigits)
return(tabs_rounded)
}
table3(smokedat, "smoking.status", "outcome")
$tab
hart infarction no hart infarction <NA> total
currently smoking 4 3 0 7
has smoked 2 4 0 6
never smoked 1 4 1 6
<NA> 0 1 0 1
total 7 12 1 20
$rowprop
hart infarction no hart infarction <NA> total
currently smoking 0.5714286 0.4285714 0.0000000 1
has smoked 0.3333333 0.6666667 0.0000000 1
never smoked 0.1666667 0.6666667 0.1666667 1
<NA> 0.0000000 1.0000000 0.0000000 1
total 0.3500000 0.6000000 0.0500000 1
$colprop
hart infarction no hart infarction <NA> total
currently smoking 0.5714286 0.25000000 0 0.35
has smoked 0.2857143 0.33333333 0 0.30
never smoked 0.1428571 0.33333333 1 0.30
<NA> 0.0000000 0.08333333 0 0.05
total 1.0000000 1.00000000 1 1.00
table3(smokedat, "smoking.status", "outcome", ndigits = 2)
$tab
hart infarction no hart infarction <NA> total
currently smoking 4 3 0 7
has smoked 2 4 0 6
never smoked 1 4 1 6
<NA> 0 1 0 1
total 7 12 1 20
$rowprop
hart infarction no hart infarction <NA> total
currently smoking 0.57 0.43 0.00 1
has smoked 0.33 0.67 0.00 1
never smoked 0.17 0.67 0.17 1
<NA> 0.00 1.00 0.00 1
total 0.35 0.60 0.05 1
$colprop
hart infarction no hart infarction <NA> total
currently smoking 0.57 0.25 0 0.35
has smoked 0.29 0.33 0 0.30
never smoked 0.14 0.33 1 0.30
<NA> 0.00 0.08 0 0.05
total 1.00 1.00 1 1.00
Load and examine the haartdat dataset. Note: This can be done using the function load().
load(here("data", "haartdat.rda"))
str(haartdat)
'data.frame': 19175 obs. of 10 variables:
$ patient : int 1 1 1 1 1 1 1 1 1 1 ...
$ tstart : num -100 0 100 200 300 400 500 600 700 800 ...
$ fuptime : num 0 100 200 300 400 500 600 700 800 900 ...
$ haartind: num 0 0 0 0 0 0 0 1 1 1 ...
$ event : num 0 0 0 0 0 0 0 0 0 0 ...
$ sex : num 1 1 1 1 1 1 1 1 1 1 ...
$ age : num 22 22 22 22 22 22 22 22 22 22 ...
$ cd4.sqrt: num 23.8 25.6 23.5 24.2 23.2 ...
$ endtime : num 2900 2900 2900 2900 2900 2900 2900 2900 2900 2900 ...
$ dropout : num 0 0 0 0 0 0 0 0 0 0 ...
Try out the split() command in the following manner: * Split haartdat into a list of individual datasets, based on the patient ID * Name this list datlist * Look at some components in this list, e.g. the first, second or 20th component
uniqueN(haartdat$patient)
[1] 1200
datlist <- base::split(haartdat, haartdat$patient)
datlist[[1]]
patient tstart fuptime haartind event sex age cd4.sqrt endtime dropout
1 1 -100 0 0 0 1 22 23.83275 2900 0
2 1 0 100 0 0 1 22 25.59297 2900 0
3 1 100 200 0 0 1 22 23.47339 2900 0
4 1 200 300 0 0 1 22 24.16609 2900 0
5 1 300 400 0 0 1 22 23.23790 2900 0
6 1 400 500 0 0 1 22 24.85961 2900 0
7 1 500 600 0 0 1 22 25.94224 2900 0
8 1 600 700 1 0 1 22 26.03843 2900 0
9 1 700 800 1 0 1 22 26.72078 2900 0
10 1 800 900 1 0 1 22 27.47726 2900 0
11 1 900 1000 1 0 1 22 23.87467 2900 0
12 1 1000 1100 1 0 1 22 28.30194 2900 0
13 1 1100 1200 1 0 1 22 25.72936 2900 0
14 1 1200 1300 1 0 1 22 22.62742 2900 0
15 1 1300 1400 1 0 1 22 30.16621 2900 0
16 1 1400 1500 1 0 1 22 23.04344 2900 0
17 1 1500 1600 1 0 1 22 25.09980 2900 0
18 1 1600 1700 1 0 1 22 23.62202 2900 0
19 1 1700 1800 1 0 1 22 23.49468 2900 0
20 1 1800 1900 1 0 1 22 26.57066 2900 0
21 1 1900 2000 1 0 1 22 28.47806 2900 0
22 1 2000 2100 1 0 1 22 20.59126 2900 0
23 1 2100 2200 1 0 1 22 23.87467 2900 0
24 1 2200 2300 1 0 1 22 25.53429 2900 0
25 1 2300 2400 1 0 1 22 22.56103 2900 0
26 1 2400 2500 1 0 1 22 21.93171 2900 0
27 1 2500 2600 1 0 1 22 28.23119 2900 0
28 1 2600 2700 1 0 1 22 21.97726 2900 0
29 1 2700 2800 1 0 1 22 22.84732 2900 0
30 1 2800 2900 1 0 1 22 26.85144 2900 1
datlist[[10]]
patient tstart fuptime haartind event sex age cd4.sqrt endtime dropout
258 10 -100 0 0 0 0 35 16.21727 2900 0
259 10 0 100 0 0 0 35 18.73499 2900 0
260 10 100 200 1 0 0 35 14.42221 2900 0
261 10 200 300 1 0 0 35 17.88854 2900 0
262 10 300 400 1 0 0 35 17.97220 2900 0
263 10 400 500 1 0 0 35 16.94107 2900 0
264 10 500 600 1 0 0 35 17.05872 2900 0
265 10 600 700 1 0 0 35 18.38478 2900 0
266 10 700 800 1 0 0 35 20.00000 2900 0
267 10 800 900 1 0 0 35 14.69694 2900 0
268 10 900 1000 1 0 0 35 15.68439 2900 0
269 10 1000 1100 1 0 0 35 18.60108 2900 0
270 10 1100 1200 1 0 0 35 15.87451 2900 0
271 10 1200 1300 1 0 0 35 15.90597 2900 0
272 10 1300 1400 1 0 0 35 16.15549 2900 0
273 10 1400 1500 1 0 0 35 14.59452 2900 0
274 10 1500 1600 1 0 0 35 18.81489 2900 0
275 10 1600 1700 1 0 0 35 14.03567 2900 0
276 10 1700 1800 1 0 0 35 16.12452 2900 0
277 10 1800 1900 1 0 0 35 15.16575 2900 0
278 10 1900 2000 1 0 0 35 14.10674 2900 0
279 10 2000 2100 1 0 0 35 14.56022 2900 0
280 10 2100 2200 1 0 0 35 15.68439 2900 0
281 10 2200 2300 1 0 0 35 13.45362 2900 0
282 10 2300 2400 1 0 0 35 12.76715 2900 0
283 10 2400 2500 1 0 0 35 15.29706 2900 0
284 10 2500 2600 1 0 0 35 13.22876 2900 0
285 10 2600 2700 1 0 0 35 13.60147 2900 0
286 10 2700 2800 1 0 0 35 10.58301 2900 0
287 10 2800 2900 1 0 0 35 11.00000 2900 1
length(datlist)
[1] 1200
Reverse the splitting operation by unsplit(), name the resulting object haartdat2. Are haartdat and haartdat2 identical? (Hint: take a summary of haartdat - haartdat2).
haartdat2 <- unsplit(datlist, haartdat$patient)
summary(haartdat2 - haartdat)
patient tstart fuptime haartind event sex
Min. :0 Min. :0 Min. :0 Min. :0 Min. :0 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
Median :0 Median :0 Median :0 Median :0 Median :0 Median :0
Mean :0 Mean :0 Mean :0 Mean :0 Mean :0 Mean :0
3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
Max. :0 Max. :0 Max. :0 Max. :0 Max. :0 Max. :0
age cd4.sqrt endtime dropout
Min. :0 Min. :0 Min. :0 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.:0
Median :0 Median :0 Median :0 Median :0
Mean :0 Mean :0 Mean :0 Mean :0
3rd Qu.:0 3rd Qu.:0 3rd Qu.:0 3rd Qu.:0
Max. :0 Max. :0 Max. :0 Max. :0
All zeros, so equal, check dimensions
dim(haartdat)
[1] 19175 10
dim(haartdat2)
[1] 19175 10
Suppose we want to determine the minimum of the CD4 counts for each patient. Make an individual dataset inddat, containing one row for each patient, with only the patient ID (HINT: use unique()). Split the CD4 measurements in haartdat per individual, name this object cd4.split. Using sapply(), take the minimum of CD4 count for each individual, and add the resulting vector to inddat.
inddat <- unique(haartdat$patient)
cd4.split <- split(haartdat$cd4.sqrt, haartdat$patient)
cd4s <- sapply(cd4.split, min)
inddat <- data.frame(patient = inddat, min_cd4 = cd4s)
head(inddat)
patient min_cd4
1 1 20.59126
2 2 18.05547
3 3 22.95648
4 4 19.64688
5 5 15.87451
6 6 23.62202
We can do this we fewer lines of code using dplyr
haartdat %>%
group_by(patient) %>%
summarize(min_cd4 = min(cd4.sqrt))
# A tibble: 1,200 x 2
patient min_cd4
<int> <dbl>
1 1 20.6
2 2 18.1
3 3 23.0
4 4 19.6
5 5 15.9
6 6 23.6
7 7 13.1
8 8 9.54
9 9 16.2
10 10 10.6
# ... with 1,190 more rows
Or using data.table
setDT(haartdat) # make data.table (only need to do this once)
haartdat[, list(min_cd4 = min(cd4.sqrt)), by = "patient"]
patient min_cd4
1: 1 20.59126
2: 2 18.05547
3: 3 22.95648
4: 4 19.64688
5: 5 15.87451
---
1196: 1196 16.58312
1197: 1197 19.00000
1198: 1198 10.29563
1199: 1199 20.12461
1200: 1200 19.74842
Similarly to D), calculate for each patient the maximum of haartind, indicating if somewhere during the follow-up HAART was given. Add the resulting vector to inddat.
Let’s use data.table
inddat <- haartdat[, list(
min_cd4 = min(cd4.sqrt),
haart = max(haartind)
), by = "patient"]
head(inddat)
patient min_cd4 haart
1: 1 20.59126 1
2: 2 18.05547 1
3: 3 22.95648 1
4: 4 19.64688 1
5: 5 15.87451 1
6: 6 23.62202 1
table(inddat$haart)
0 1
824 376
We would also like to compute for each patient the cumulative amount of HAART treatment at each time point. Hint: to transform a list object (e.g. listname) to a single numeric vector, you can use the command do.call(c, listname) (of course, this only works when the list only contains numeric values).
We can get this by sorting on timepoint, and then calculating a cumulative sum of haart for each patient
haartdat[order(tstart), cum_haart:=cumsum(haartind), by = "patient"]
haartdat[order(patient, tstart)][patient %in% unique(patient)[1:2]]
patient tstart fuptime haartind event sex age cd4.sqrt endtime dropout
1: 1 -100 0 0 0 1 22 23.83275 2900 0
2: 1 0 100 0 0 1 22 25.59297 2900 0
3: 1 100 200 0 0 1 22 23.47339 2900 0
4: 1 200 300 0 0 1 22 24.16609 2900 0
5: 1 300 400 0 0 1 22 23.23790 2900 0
6: 1 400 500 0 0 1 22 24.85961 2900 0
7: 1 500 600 0 0 1 22 25.94224 2900 0
8: 1 600 700 1 0 1 22 26.03843 2900 0
9: 1 700 800 1 0 1 22 26.72078 2900 0
10: 1 800 900 1 0 1 22 27.47726 2900 0
11: 1 900 1000 1 0 1 22 23.87467 2900 0
12: 1 1000 1100 1 0 1 22 28.30194 2900 0
13: 1 1100 1200 1 0 1 22 25.72936 2900 0
14: 1 1200 1300 1 0 1 22 22.62742 2900 0
15: 1 1300 1400 1 0 1 22 30.16621 2900 0
16: 1 1400 1500 1 0 1 22 23.04344 2900 0
17: 1 1500 1600 1 0 1 22 25.09980 2900 0
18: 1 1600 1700 1 0 1 22 23.62202 2900 0
19: 1 1700 1800 1 0 1 22 23.49468 2900 0
20: 1 1800 1900 1 0 1 22 26.57066 2900 0
21: 1 1900 2000 1 0 1 22 28.47806 2900 0
22: 1 2000 2100 1 0 1 22 20.59126 2900 0
23: 1 2100 2200 1 0 1 22 23.87467 2900 0
24: 1 2200 2300 1 0 1 22 25.53429 2900 0
25: 1 2300 2400 1 0 1 22 22.56103 2900 0
26: 1 2400 2500 1 0 1 22 21.93171 2900 0
27: 1 2500 2600 1 0 1 22 28.23119 2900 0
28: 1 2600 2700 1 0 1 22 21.97726 2900 0
29: 1 2700 2800 1 0 1 22 22.84732 2900 0
30: 1 2800 2900 1 0 1 22 26.85144 2900 1
31: 2 -100 0 0 0 0 21 18.05547 3300 0
32: 2 0 100 0 0 0 21 20.46949 3300 0
33: 2 100 200 1 0 0 21 22.89105 3300 0
34: 2 200 300 1 0 0 21 26.64583 3300 0
35: 2 300 400 1 0 0 21 22.02272 3300 0
36: 2 400 500 1 0 0 21 22.67157 3300 0
37: 2 500 600 1 0 0 21 24.91987 3300 0
38: 2 600 700 1 0 0 21 23.08679 3300 0
39: 2 700 800 1 0 0 21 22.93469 3300 0
40: 2 800 900 1 0 0 21 28.00000 3300 0
41: 2 900 1000 1 0 0 21 24.69818 3300 0
42: 2 1000 1100 1 0 0 21 25.09980 3300 0
43: 2 1100 1200 1 0 0 21 28.40775 3300 0
44: 2 1200 1300 1 0 0 21 27.11088 3300 0
45: 2 1300 1400 1 0 0 21 28.33725 3300 0
46: 2 1400 1500 1 0 0 21 25.39685 3300 0
47: 2 1500 1600 1 0 0 21 26.22975 3300 0
48: 2 1600 1700 1 0 0 21 26.98148 3300 0
49: 2 1700 1800 1 0 0 21 28.03569 3300 0
50: 2 1800 1900 1 0 0 21 27.42262 3300 0
51: 2 1900 2000 1 0 0 21 27.82086 3300 0
52: 2 2000 2100 1 0 0 21 26.98148 3300 0
53: 2 2100 2200 1 0 0 21 29.05168 3300 0
54: 2 2200 2300 1 0 0 21 29.46184 3300 0
55: 2 2300 2400 1 0 0 21 28.35489 3300 0
56: 2 2400 2500 1 0 0 21 26.68333 3300 0
57: 2 2500 2600 1 0 0 21 23.10844 3300 0
58: 2 2600 2700 1 0 0 21 25.47548 3300 0
59: 2 2700 2800 1 0 0 21 28.80972 3300 0
60: 2 2800 2900 1 0 0 21 27.80288 3300 0
61: 2 2900 3000 1 0 0 21 26.83282 3300 0
62: 2 3000 3100 1 0 0 21 28.74022 3300 0
63: 2 3100 3200 1 0 0 21 27.38613 3300 0
64: 2 3200 3300 1 0 0 21 28.35489 3300 0
patient tstart fuptime haartind event sex age cd4.sqrt endtime dropout
cum_haart
1: 0
2: 0
3: 0
4: 0
5: 0
6: 0
7: 0
8: 1
9: 2
10: 3
11: 4
12: 5
13: 6
14: 7
15: 8
16: 9
17: 10
18: 11
19: 12
20: 13
21: 14
22: 15
23: 16
24: 17
25: 18
26: 19
27: 20
28: 21
29: 22
30: 23
31: 0
32: 0
33: 1
34: 2
35: 3
36: 4
37: 5
38: 6
39: 7
40: 8
41: 9
42: 10
43: 11
44: 12
45: 13
46: 14
47: 15
48: 16
49: 17
50: 18
51: 19
52: 20
53: 21
54: 22
55: 23
56: 24
57: 25
58: 26
59: 27
60: 28
61: 29
62: 30
63: 31
64: 32
cum_haart
The goal of this exercise is to simulate longitudinal patient data (n = 100). As an example, we use the development of CD4 count in HIV positive individuals. We consider development since the moment of HIV seroconversion (= t0). Typically, CD4 count decreases over time after seroconversion. For now, we assume that the square root of CD4 count decrease linearly over time. We further assume each patient has a different intercept and slope for sqrt(CD4).
Build a data frame called basdat, containing patient IDs 1, 2, …, 100.
basdat <- data.frame(id = 1:100)
From a bivariate normal distribution with means (26, -2), variances (5, 0.25) and covariance -0.5, draw 100 samples. These represent the patient-level intercepts and slopes. Paste the values to basdat. Make sure the column names in basdat are informative. Also make sure that the class of basdat remains “data.frame”.
The covariance of \(<0\) means that we model that patients with higher intercepts (‘baseline CD4 count’), the slope is more negative, so the CD4 count goes down faster.
require(mvtnorm)
means <- c(26, -2)
variances <- c(5, 0.25)
covariance <- -0.5
cov_mat <- matrix(c(variances[1], covariance, covariance, variances[2]),
nrow = 2, byrow = T)
set.seed(2)
samp1 <-rmvnorm(n = nrow(basdat), mean = means, sigma = cov_mat)
str(samp1)
num [1:100, 1:2] 24 29.7 25.8 27.6 30.4 ...
Check means, variances and covariance
mean(samp1[, 1]); var(samp1[, 1])
[1] 26.23858
[1] 5.442759
mean(samp1[, 2]); var(samp1[, 2])
[1] -2.064848
[1] 0.3031027
cov(samp1[, 1], samp1[, 2])
[1] -0.5774269
Plot
plot(samp1[,1], samp1[, 2])
Add to data.frame
basdat %<>%
mutate(intercept = samp1[, 1], slope = samp1[, 2])
head(basdat)
id intercept slope
1 1 23.96704 -1.747633
2 2 29.74817 -2.819619
3 3 25.79658 -1.923623
4 4 27.62208 -2.242741
5 5 30.44786 -2.432938
6 6 26.74836 -1.621789
We (simply) assume these patients are all followed for 10 years. However, the number of CD4 measurements differs between patients. From a Poisson distribution, draw a vector of numbers of measurements, with a mean of 5 (= lambda). Make sure these draws will be a new column of basdat. Call the column nmeas.
basdat %<>%
mutate(nmeas = rpois(nrow(basdat), lambda = 5))
Check for zeros in nmeas, convert these all to 1 for simplicity. Note that in reality, allowing for the zeros will be more realistic.
Use data.table
setDT(basdat)
basdat[nmeas == 0, .N]
[1] 1
Change to 1
basdat[nmeas == 0, nmeas:=1]
Now we will simulate and plot longitudinal data for the first patient (we will repeat the process for the patients 2-100 later). Make a dataframe “longdat”, which will contain the simulated CD4 measurements. At first put in this dataframe the patient ID, repeated according to the value of nmeas for patient 1. Hint: you can use rep(). Also put in the individual slope and intercept for patient 1.
longdat <- basdat[id == 1, {
.SD %>% # take the columns as specified by .SDcols
map(rep, nmeas) # repeat each one nmeas times
}, .SDcols = c("id", "intercept", "slope")]
In longdat, simulate the measurement times, drawn from the uniform distribution on (0, 10).
For readibility, sort on time
longdat[, times:=runif(n = .N, min = 0, max = 10)]
setorder(longdat, times)
print(longdat)
id intercept slope times
1: 1 23.96704 -1.747633 2.344975
2: 1 23.96704 -1.747633 3.426912
3: 1 23.96704 -1.747633 4.296028
4: 1 23.96704 -1.747633 5.599569
5: 1 23.96704 -1.747633 6.920055
6: 1 23.96704 -1.747633 7.367007
In longdat, compute the “true” square root of CD4, based on the intercept, slope and time. To this “true” value, add normally distributed noise with mean = 0 and SD = 1. This will give the simulated CD4 measurements (on the square root scale). Now compute measured CD4 count itself by squaring.
With dplyr, we can nicely mutate variables after each other
longdat %<>%
mutate(true_CD4 = intercept + slope * times,
CD4_sqrt = true_CD4 + rnorm(nrow(longdat), mean = 0, sd = 1),
CD4 = CD4_sqrt^2) %>% data.table()
head(longdat)
id intercept slope times true_CD4 CD4_sqrt CD4
1: 1 23.96704 -1.747633 2.344975 19.86888 19.54934 382.1767
2: 1 23.96704 -1.747633 3.426912 17.97806 18.82151 354.2491
3: 1 23.96704 -1.747633 4.296028 16.45916 16.94282 287.0590
4: 1 23.96704 -1.747633 5.599569 14.18105 13.87738 192.5816
5: 1 23.96704 -1.747633 6.920055 11.87332 11.58823 134.2870
6: 1 23.96704 -1.747633 7.367007 11.09222 12.07265 145.7489
Try to make a pretty plot of measured CD4 count against follow-up time for patient 1. Suggestion: use xlim and ylim, to at least display 0 for both axes.
longdat %>%
ggplot(aes(x = times, y = CD4)) +
geom_line() +
lims(x = c(0, max(longdat$times)), y = c(0, max(longdat$CD4))) +
theme_minimal()
Some more fun: put together all the code used to simulate and plot the data for patient 1. Now replace the number 1, indicating patient 1, by the letter i. Try: with first defining i <- 1, the same plot as before will be produced. In the main title of the plot, make sure that the patient ID (based on i) is plotted. (HINT: find out how to use paste()). Try it also with i <- 2. Now put your syntax within a “loop” with i ranging from 1 to 100. (Hint: place for (i in 1:100){ before and } after your code). Final step, make sure the output of the loop is send to a PDF plot. Look at the plot, scroll through. Be proud.
By writing the code with data.table, it is now easy to do this for each patient
longdat2 <- basdat[, {
.SD %>% # take the columns as specified by .SDcols
map(rep, nmeas) # repeat each one nmeas times
}, .SDcols = c("intercept", "slope"), by = "id"]
print(longdat2)
id intercept slope
1: 1 23.96704 -1.747633
2: 1 23.96704 -1.747633
3: 1 23.96704 -1.747633
4: 1 23.96704 -1.747633
5: 1 23.96704 -1.747633
---
536: 100 24.22942 -2.187417
537: 100 24.22942 -2.187417
538: 100 24.22942 -2.187417
539: 100 24.22942 -2.187417
540: 100 24.22942 -2.187417
Simulate times. Note that it does not matter whether we do this for each patient separately or all at the same time.
longdat2[, times:=runif(n = .N, min = 0, max = 10)]
setorder(longdat2, times)
Add simulated CD4 counts, also not needed to do this by patient, since for each line we have a time, intercept and slope
longdat2 %<>%
mutate(true_CD4 = intercept + slope * times,
CD4_sqrt = true_CD4 + rnorm(n(), mean = 0, sd = 1), # n() counts the number of rows
CD4 = CD4_sqrt^2) %>%
data.table()
Create a plot of all patients at the same time
longdat2 %>%
mutate(id = factor(id)) %>%
ggplot(aes(x = times, y = CD4, col = id)) +
geom_line() +
lims(x = c(0, max(longdat2$times)), y = c(0, max(longdat2$CD4))) +
theme_minimal()
Create a plot for each patient separately (only 4 are shown here)
longdat2[id %in% unique(id)[1:4]] %>%
mutate(id = factor(id)) %>%
ggplot(aes(x = times, y = CD4)) +
geom_line() +
lims(x = c(0, max(longdat2$times)), y = c(0, max(longdat2$CD4))) +
theme_minimal() +
facet_wrap(~id, scales = "fixed")
To create pdfs separately for each patient, we will use looping.
Let’s only use 3 patients here
for (i in 1:3) {
longdat2[id == i] %>%
ggplot(aes(x = times, y = CD4)) +
geom_line() +
lims(x = c(0, max(longdat2$times)), y = c(0, max(longdat2$CD4))) +
theme_minimal() +
ggtitle("CD4 number over time", paste0("patient ", i))
ggsave(filename = here("figs", paste0("patient_", i, ".pdf")), device = "pdf")
}
Or to put them all in one file
plots <- vector("list", nrow(basdat))
for (i in 1:nrow(basdat)) {
plots[[i]] <-
longdat2[id == i] %>%
ggplot(aes(x = times, y = CD4)) +
geom_line() +
lims(x = c(0, max(longdat2$times)), y = c(0, max(longdat2$CD4))) +
theme_minimal() +
ggtitle("CD4 number over time", paste0("patient ", i))
}
To save them in a single pdf with one page per patient (this takes some time)
require(gridExtra)
pdf(here("figs", "cd4_vs_time.pdf"), onefile = T)
for (i in seq(length(plots))) {
grid.arrange(plots[[i]])
}
dev.off()
Now be proud ;)
These cost a lot of time to render and are put in a separate file. Find them here: assignments Simulation Studies
require(haven)
rtsbp <- haven::read_spss(here::here("data", "RTSBP.sav"))
rtsbp
# A tibble: 33 x 3
groep SBP RT
<dbl+lbl> <dbl> <dbl>
1 1 66.0 7.00
2 1 52.0 10.0
3 1 72.0 18.0
4 1 67.0 4.00
5 1 69.0 10.0
6 1 71.0 13.0
7 1 88.0 21.0
8 1 68.0 12.0
9 1 59.0 9.00
10 1 73.0 65.0
# ... with 23 more rows
require(magrittr)
rtsbp %>%
boxplot(RT~groep, data = .)
With K-S test
ks.test(rtsbp$RT[rtsbp$groep==1], rtsbp$RT[rtsbp$groep==2])
Warning in ks.test(rtsbp$RT[rtsbp$groep == 1], rtsbp$RT[rtsbp$groep == 2]):
cannot compute exact p-value with ties
Two-sample Kolmogorov-Smirnov test
data: rtsbp$RT[rtsbp$groep == 1] and rtsbp$RT[rtsbp$groep == 2]
D = 0.54231, p-value = 0.01943
alternative hypothesis: two-sided
ks0 <- ks.test(rtsbp$RT[rtsbp$groep==1], rtsbp$RT[rtsbp$groep==2])
n <- nrow(rtsbp)
n1 <- min(table(rtsbp$groep))
y <- rtsbp$RT
nperm <- 999
ks <- numeric(nperm)
for (i in 1:nperm) {
g <- sample(n, n1, replace = F)
ks[i] <- ks.test(y[g], y[-g], exact = F)$statistic
}
# two-sided permutation p-value
(1 + sum(abs(ks) >= abs(ks0$statistic)))/(nperm+1)
[1] 0.006
hist(ks)
abline(v = ks0$statistic, lty = 2)
sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] haven_1.1.1 bindrcpp_0.2 mvtnorm_1.0-7
[4] ggplot2_2.2.1 here_0.1 purrr_0.2.4
[7] magrittr_1.5 data.table_1.10.4-3 dplyr_0.7.4
loaded via a namespace (and not attached):
[1] Rcpp_0.12.15 pillar_1.1.0 compiler_3.4.3 git2r_0.21.0
[5] plyr_1.8.4 bindr_0.1 forcats_0.2.0 tools_3.4.3
[9] digest_0.6.15 evaluate_0.10.1 tibble_1.4.2 gtable_0.2.0
[13] pkgconfig_2.0.1 rlang_0.1.6 cli_1.0.0 yaml_2.1.16
[17] stringr_1.2.0 knitr_1.19 hms_0.4.1 gtools_3.5.0
[21] rprojroot_1.3-2 grid_3.4.3 glue_1.2.0 R6_2.2.2
[25] rmarkdown_1.8 gdata_2.18.0 readr_1.1.1 backports_1.1.2
[29] scales_0.5.0 htmltools_0.3.6 gmodels_2.16.2 MASS_7.3-48
[33] assertthat_0.2.0 colorspace_1.3-2 labeling_0.3 utf8_1.1.3
[37] stringi_1.1.6 lazyeval_0.2.1 munsell_0.4.3 crayon_1.3.4
This R Markdown site was created with workflowr


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Self-similar, space-filling dragon curve, approximated.
Complexity: {{dragon.n}}
Squiggle Style: Sharp corners Truchet squiggle Smooth squiggle


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{{ launch.build.branch | cut:true:11:'..'}}
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{{ launch.build.branch }}
{{ launch.parameters.env.BRANCH || launch.parameters.env.branch || launch.parameters.env.CORE_APP }}
|
{{ launch.created | date:"dd.MM HH:mm" }} | {{ launch.counts.passed }} {{ launch.counts.passed }} {{ launch.counts.skipped }} {{ launch.counts.skipped }} {{ launch.counts.failed }} {{ launch.counts.failed }} {{ launch.counts.blocked }} {{ launch.counts.blocked }} {{ launch.counts.blocked }} | {{ launch.state | states }} | {{ launch.percent_of_failed | number }} | {{ launch.comments.count }} |


I should stick to the top and bottom of #foo1. I'm not a child of any #foo's.
Go to Sticky DocsI should stick to the top of #foo4 and the top of #foo6.
I'm a child of #foo4
I should stick to the top of #foo6 and the bottom of #foo8. I'm a child of #foo7.
Go to Sticky DocsI should stick to the top of #foo10 and the bottom of #foo11. I should be stick to the window bottom with a bottom margin of 1em (default bottom margin).
Go to Sticky DocsI'm big to force scroolbar
I should stick to the top of #foo12. I should be stick until window end because only data-top-anchor is set.
Go to Sticky DocsI'm big to force scroolbar
I should stick to the top and bottom of #foo3.

When I'm not stuck I jump to the top because I'm not a child of the top anchor, is this my normal behavior?


Holds static data from the Unicode database
Methods
Constants
| ATTRIBUTES | = | :codepoints, :composition_exclusion, :composition_map, :boundary, :cp1252 |
Public Class methods
Returns the directory in which the data files are stored
[ show source ]
# File vendor/rails/activesupport/lib/active_support/multibyte/unicode_database.rb, line 59 59: def self.dirname 60: File.dirname(__FILE__) + '/../values/' 61: end
Returns the filename for the data file for this version
[ show source ]
# File vendor/rails/activesupport/lib/active_support/multibyte/unicode_database.rb, line 64 64: def self.filename 65: File.expand_path File.join(dirname, "unicode_tables.dat") 66: end
[ show source ]
# File vendor/rails/activesupport/lib/active_support/multibyte/unicode_database.rb, line 16 16: def initialize 17: @codepoints = Hash.new(Codepoint.new) 18: @composition_exclusion = [] 19: @composition_map = {} 20: @boundary = {} 21: @cp1252 = {} 22: end
Public Instance methods
[ show source ]
# File vendor/rails/activesupport/lib/active_support/multibyte/unicode_database.rb, line 46 46: def ===(other) 47: detect { |i| i === other } ? true : false 48: end
Loads the Unicode database and returns all the internal objects of UnicodeDatabase.
[ show source ]
# File vendor/rails/activesupport/lib/active_support/multibyte/unicode_database.rb, line 36 36: def load 37: begin 38: @codepoints, @composition_exclusion, @composition_map, @boundary, @cp1252 = File.open(self.class.filename, 'rb') { |f| Marshal.load f.read } 39: rescue Exception => e 40: raise IOError.new("Couldn't load the Unicode tables for UTF8Handler (#{e.message}), ActiveSupport::Multibyte is unusable") 41: end 42: 43: # Redefine the === method so we can write shorter rules for grapheme cluster breaks 44: @boundary.each do |k,_| 45: @boundary[k].instance_eval do 46: def ===(other) 47: detect { |i| i === other } ? true : false 48: end 49: end if @boundary[k].kind_of?(Array) 50: end 51: 52: # define attr_reader methods for the instance variables 53: class << self 54: attr_reader(*ATTRIBUTES) 55: end 56: end


My Morning
My morning was lacking in coffee, therefore my morning was not great.
- I didn't have breakdast
- I should have had breakfast
- I need coffee
My morning was lacking in coffee, therefore my morning was not great.
- I didn't have breakdast
- I should have had breakfast
- I need coffee


Testing if a JS object C is a cLASS can only be done with (C.constructor === cLASS) and not with (C instanceof cLASS).
C
(C.constructor === cLASS)
(C instanceof cLASS)


WITNESS LIST Appropriations-S/C on Criminal Justice Committee (Subcommittee on Interim Charge Number Two) September 9, 2002-10:30A Charge Two On: Baldwin, Howard (OAG) Bishop, Joani (Comptroller's Office for Private Security) Blalock, Michael (Adjutant General) Boyd, Jeffrey (OAG) Bryant, Cynthia (OAG) Cowherd, Mary (OCA) Dozier, Jim (Law Enforcement Officer Standards) Fox, Jeanne (TABC) Garza, Rolando (TABC) Grumbles, Cliff (Private Security) Hamilton, Gregory (TABC) Johnson, Gregory (Adjutant General) Julian, Terry (Jail Standards) Mangum, James (Military Facilities) Marcovich, Carrice (OCA) Miranda-Clark, Angela (OCA) Munoz, Ana (Fire Protection) Samulin, Michael (Private Security) Soteriou, Jake (Fire Protection) Steen, John (TABC) Vogel, Peter (OCA) Wells, John (Military Facilities) Yarbrough, Randy (TABC) Registering, but not testifying: On: Nance, Lisa (Comptroller's Office for Private Security)


{% load display_top %} {% load display_bottom %} {% display_top %} tournaments: the tournament you requested is no longer with us. pause now for a time of mourning. {% display_bottom %}


“Buddha vīra namo tyatthu,
sabbasattānamuttama;
Yo maṁ dukkhā pamocesi,
aññañca bahukaṁ janaṁ.

Supreme among all beings,
who released me from suffering,
and many other beings as well.
Sabbadukkhaṁ pariññātaṁ,
Hetutaṇhā visositā;
Bhāvito aṭṭhaṅgiko maggo,
Nirodho phusito mayā.
All suffering is fully understood;
craving—its cause—is dried up;
the eightfold path has been developed;
and cessation has been realized by me.
Mātā putto pitā bhātā,
ayyakā ca pure ahuṁ;
Yathābhuccamajānantī,
saṁsariṁhaṁ anibbisaṁ.
Previously I was a mother, a son,
a father, a brother, and a grandmother.
Failing to grasp the true nature of things,
I’ve transmigrated without reward.
Diṭṭho hi me so bhagavā,
antimoyaṁ samussayo;
Vikkhīṇo jātisaṁsāro,
natthi dāni punabbhavo.
Since I have seen the Blessed One,
this bag of bones is my last.
Transmigration through births is finished,
now there’ll be no more future lives.
Āraddhavīriye pahitatte,
Niccaṁ daḷhaparakkame;
Samagge sāvake passe,
Esā buddhāna vandanā.
I see the disciples in harmony,
energetic and resolute,
always staunchly vigorous—
this is homage to the Buddhas!
Bahūnaṁ vata atthāya,
Māyā janayi gotamaṁ;
Byādhimaraṇatunnānaṁ,
Dukkhakkhandhaṁ byapānudī”ti.
It was truly for the benefit of many
that Māyā gave birth to Gotama.
He swept away the mass of suffering
for those stricken by sickness and death.
… Mahāpajāpatigotamī therī ….


We redefine users!
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- embrace world-class convergence


|
| def | test_run (self) |
| |
| def | setUp (self) |
| |
| def | check_binary_availability (self, binary_name) |
| |
| def | gen_command (self, binary_name, args_string, output_root, log_dir=None) |
| |
| def | gen_command_flagfile (self, binary_name) |
| |
| def | gen_args_flagfile (self, binary_name) |
| |
| def | run_app (self, binary_name, args=None, log_file=None) |
| |
| def | assert_within_atol (self, actual, desired, tolerance) |
| |
| def | assert_within_rtol (self, actual, desired, tolerance) |
| |
| def | check_metrics (self, record, setup=None) |
| |
| def | check_correct_error (self, setup=None) |
| |
| def | check_against_truth (self, truth, output) |
| |
Unit test class for GenerateCameraOverlaps.
Attributes:
name (str): String representation of the class name.


Getting started¶
Cnerator is a C source code generation tool. It can be used to generate large amounts of standard ANSI/ISO C source code, ready to be compiled by any standard language implementation. Cnerator is highly customizable to generate all the syntactic constructs of the C language, necessary to build accurate predictive models with machine learning algorithms.
Installation¶
You need a Python 3.7+ standard implementation.
The only additional package to install is numpy
:
pip install numpy
Usage¶
Then, you may just run Cnerator
with no arguments to generate a random C program in the out
directory:
python cnerator.py
There are plenty of options to customize Cnerator.
To specify the probability of a particular language
construct, you can use the -p
or --probs
option.
The following command sets to 20% the probability of
generating a function invocation when a new expression is required:
python cnerator.py -p "call_prob = {True: 0.2, False: 0.8}"
If more sophisticated probabilities are required, you can specify them in a JSON file and pass it as
a parameter (see the [documentation](documentation#probability-specification-files) to know the JSON file format).
The following line passes an example JSON file in the json/probabilities
folder where
different probability distributions are specified for some syntactic constructs:
python cnerator.py -P json/probabilities/example_probs.json
Cnerator also provides allows the user to control the number and characteristics of all the functions to be generated. A JSON file is used for that purpose (see the [documentation](documentation#function-generation-files)). The following command makes Cnerator generate one function for each high-level return type in the C programming language:
python cnerator.py -f json/functions/1-function-each-type.json
Sometimes, the user needs the output program to fulfill some requirements not guaranteed by the
stochastic generation process.
To this aim, Cnerator allows
the specification of an ordered collection of Python
post-process specification files (see the [documentation](documentation#post-processing-specification-files)).
These post-processing files can modify the generated code to satisfy those requirements.
The following execution generates a random program and then executes two visitors:
one to rename func
functions to proc
(and their invocations) when they return void
;
and another one to add a __RETURN__
label before each return statement:
python cnerator.py -V visitors.func_to_proc:visitors.return_instrumentation
To see all the options, just run the -h
or --help
options.


|
NANO102/112 BSP V3.03.003
The Board Support Package for Nano102/112 Series
|
Modules | |
| PDMA Exported Functions | |
Macros | |
| #define | PDMA_WIDTH_8 0x00080000UL |
| #define | PDMA_WIDTH_16 0x00100000UL |
| #define | PDMA_WIDTH_32 0x00000000UL |
| #define | PDMA_SAR_INC 0x00000000UL |
| #define | PDMA_SAR_FIX 0x00000020UL |
| #define | PDMA_SAR_WRA 0x00000030UL |
| #define | PDMA_DAR_INC 0x00000000UL |
| #define | PDMA_DAR_FIX 0x00000080UL |
| #define | PDMA_DAR_WRA 0x000000C0UL |
| #define | PDMA_SPI0_TX 0x00000000UL |
| #define | PDMA_SPI1_TX 0x00000001UL |
| #define | PDMA_UART0_TX 0x00000002UL |
| #define | PDMA_UART1_TX 0x00000003UL |
| #define | PDMA_TMR0_TX 0x00000009UL |
| #define | PDMA_TMR1_TX 0x0000000AUL |
| #define | PDMA_TMR2_TX 0x0000000BUL |
| #define | PDMA_TMR3_TX 0x0000000CUL |
| #define | PDMA_SPI0_RX 0x00000010UL |
| #define | PDMA_SPI1_RX 0x00000011UL |
| #define | PDMA_UART0_RX 0x00000012UL |
| #define | PDMA_UART1_RX 0x00000013UL |
| #define | PDMA_ADC 0x00000016UL |
| #define | PDMA_PWM0_CH0 0x00000019UL |
| #define | PDMA_PWM0_CH2 0x0000001AUL |
| #define | PDMA_MEM 0x0000001FUL |


Best Christmas TV ads 2019
November means Christmas TV ads and we've got all the best one for 2019. Watch them all in one place.
by Chris MartinIt's November so it's time for all the Christmas themed commercials to air for the first time. It's another big shootout between the big names including John Lewis, Sainsbury's, M&S and more. Here are the pick of the best Christmas TV ads 2019.
Last year brought along John Lewis' Elton John advert, Aldi's Kevin the carrot and Iceland's banned effort. There's a lot to live up to with these festival stories and first up this year is Asda, Amazon and a few others even including Walkers, but we'll add more as they launch. Excitable Edgar is the boxoffice effort from John Lewis and Waitrose.
As well as the best 2019 ads, we've also got a collection of the best Christmas ads ever including all the classics.
Best Christmas ads 2019
John Lewis & Waitrose: Excitable Edgar
Sainsburys: Nicholas The Sweep
Argos: The Book of Dreams
Joules, Wallace & Grommit: Christmas at the click of a button
Barbour: 125 Years of Blooming Barbour Christmases
M&S: Go Jumpers For Christmas
Asda: Let's Make Christmas Extra Special
Amazon: Brand
Iceland: #Magicoffrozen
Walkers: All Mariah Carey wants this Christmas
IKEA: Silence The Critics
McDonald's: #ReindeerReady
Prezzo: Meet Frank
Aldi: Kevin The Carrot
Very: Get More Out of Giving
Samsung: Connect Your Galaxy
Best Christmas TV ads ever
We've also rounded up what we think are the best Christmas TV ads ever are, spanning back to the early 90s, to see how things have changed over the years.
There are some classics including John Lewis' The Bear and the Hare as well as Sainsbury's iconic Christmas is for sharing. 2014 was a vintage year.


I've done speed a few times since earlier this fall--September or so. Prior to doing speed, I had only ever smoked weed; I did meth with my roommates, essentially just out of curiousity. The first time I did it, we did lines, which was a new experience for me. Some people find snorting speed to be very uncomfortable, even painful, and it is most definitely an unpleasant sensation, but not horrible. I was amazed at how quickly the drug hit me: I did a line, felt a nasty chemical drip in the back of my throat, and instantly felt like I'd been wound up to run around at a thousand miles an hour. So awake, wanting to run around, move, fix things, talk forever--an incredibly excited, happy, powerful feeling.
Shortly after doing the line, I left with another of my roommates to go hang out with some friends of ours who live nearby. I was very excited and energetic feeling, very happy to be doing things, anything. I was very talkative and much more outgoing than I usually am once we got to the party. We all did some tequila shots--three or four, I think. Not a lot, but enough where I should have been pretty buzzed if not verging on drunk, but I didn't really feel the alcohol at all. I danced pretty much the whole night, made lots of conversation--much more than usual!--and basically felt really excited and happy.
By the time everyone was leaving, it was probably about four or five hours later. I was bummed that the night was ending; I was still ready to party, party, party! I went home and stayed up with my roommates for a little longer. Then I decided that I could go to bed, so I tried. I could not have fallen asleep then for any amount of money. I was wide awake, and yet I wanted to sleep. I get this feeling whenever I do speed, and it may be one of the worst aspects of the usage for me. I decided to get up and do something. First I decided I wanted to go for a walk (this is at about 5AM or so), so I leave to go for a walk at the nearby university.
 I felt like I was being watched, or followed, like someone was going to 'get' me. I thought I heard footsteps nearby. I decided maybe I would feel better back home, but even inside my own apartment, I was freaked out, jumping at the littlest noise, worrying frantically. This was a huge, uncomfortable full-fledged bout of paranoia. It made time go by very slowly, and I started to get the feeling that I had always felt this jittery, nervous, anxious feeling--I honestly felt like I was going a tiny bit crazy.
I finally managed to fall asleep for a couple of hours a little later in the morning. I felt awful and dragged out the rest of the day, and the day after that--for me, the comedown off of speed is always awful. My roommates tell me that this gets 'better' if you take it more, and also that they are able to sleep and eat semi-normally on it. Of course, doing this means taking more speed, which is not a good idea, by anyone's judgement that I've ever heard speak of it. I forgot to mention that, obviously, speed is a major major appetite suppressant, to the same extent that it won't let me sleep, it just doesn't occur to me that I need food or rest, and when someone suggests it, the idea just sounds generally unappealing.
I have done speed a few more times since, and haven't had such a bad experience with paranoia, which I think was brought on by me being awake and by myself for several hours. My best experience with the drug was probably the last time I took it: I stayed up with on of my roommates and we pretty much smoked it all night. We talked for nine straight hours--it was a really cool experience, and I enjoyed being up like that. However, as usual, my comedown was awful--I felt run down for a week, week and a half. Also since about the third or fourth time I personally took it, I get awful jaw clenching on it, where I glench my jaw compulsively. This has happened to me to the point where my jaw is sore for four or five days, to the point where it's uncomfortable to eat. Not fun.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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YAABA A film review by Randy Parker Copyright 1996 Randy Parker
RATING: *** (out of ****)
(Review written in 1990)
Set and filmed in Africa, YAABA is an enchanting slice of life which doesn't cater in any way to American audiences. The movie, which won the International Critics Award at the 1989 Cannes Film Festival, takes us into the world of Bila, a rambunctious boy living in a small village. You could think of Bila as an African Huck Finn. He is the village rascal--good natured but always getting into trouble, usually with his stern father. Bila gets in especially hot water when he befriends Sana, an old woman whom the villagers have shunned as a witch. Sana lives by herself away from the village, but Bila brings her food and keeps her company; he affectionately calls her Yaaba, or grandmother. When he's not visiting Sana, Bila either plays with his flirtatious friend Nopoko or fights with the other boys in the village.
The movie's charm lies not in its plot, which is virtually nonexistent, but in its wonderful characters. YAABA is set in an exotic culture, but the personalities within the culture are universal. There's the town drunk, the kind mother, the bitchy wife, and various other human archetypes. YAABA is a unique opportunity to visit Africa without having to spend your life savings on air-fare.
--- Randy Parker<EMAIL_ADDRESS>http://www.shoestring.org
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