Q:

R - Create dataframe from a list of data with different number of columns

I have a dataset showing date, name of the location and values in the same list.  My goal is to generate a dataframe in a loop where columns show the value for each date and the name of the location are the rows.  
However, the dates don't always match.  Missing values can be NA.  
Furthermore, I only need the second value (value2). 
What's the easiest way to do this? 
Sample data:
data <- structure(list(date = structure(c(7L, 4L, 6L, 15L, 3L, 1L, 13L, 
2L, 16L, 11L, 7L, 14L, 8L, 4L, 6L, 15L, 3L, 9L, 10L, 1L, 12L, 
5L, 7L, 14L, 8L, 4L, 6L, 15L, 9L, 10L, 1L, 13L, 2L, 16L, 11L), .Label = c("01.10.2013", 
"01.10.2015", "08.10.2010", "13.09.2007", "16.09.2003", "17.09.2008", 
"20.09.2004", "21.09.2006", "23.09.2011", "26.09.2012", "26.09.2017", 
"27.08.2001", "29.09.2014", "30.08.2005", "30.09.2009", "30.09.2016"
), class = "factor"), name = structure(c(1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("P1", 
"P2", "P3"), class = "factor"), value = c(14L, 453L, 345L, 765L, 
87L, 45L, 4L, 12L, 2L, 6L, 84L, 11L, 87L, 45L, 4L, 12L, 12L, 
14L, 453L, 345L, 51L, 50L, 123L, 34L, 75L, 74L, 42L, 2L, 123L, 
42L, 12L, 6L, 9L, 4L, 1L), value2 = c(0.046, 0.003, 0.022, 0.016, 
-0.035, 0.032, 0.013, -0.001, 0.018, -0.006, 0.017, -0.001, 0.02, 
0, 0.009, 0.191, 0.169, 0.191, 0.286, 0.324, 0.426, 0.35, 0.212, 
0.107, 0.081, 0.034, 0.084, 0.092, 0.054, 0.019, 0.022, 0.017, 
0.018, 0.002, 0.017)), .Names = c("date", "name", "value", "value2"
), class = "data.frame", row.names = c(NA, -35L))
So far, I've tried different things I found on the internet but none has worked for me. 
First, I generated a dataframe with all unique dates found in the list
data$date <- as. Date(data$date, format="%d.%m.%Y")
uniquedates <- as.data.frame(unique(sort(data$date)))
colnames(uniquedates) <- c("date")

Next, I split up the data by name. 
split <- split(data, data$name)

Finally, I tried get the date and value2 from every split and merge them together in a loop. 
for (i in seq_along(split)) {
  point <- split[[i]][,c("date","value2")]
  name <- as.character(unique(split[[i]]$name))
  colnames(merge)[colnames(merge) == "value2"] <- name
  merge <- merge(x=uniquedates, y = point, by='date', all.x = TRUE)
}

This is the result I'm looking for:
date        P1      P2      P3
27.08.2001  NA      0.426   NA
16.09.2003  NA      NA      0.35
20.09.2004  0.046   0.017   0.212
30.08.2005  NA     -0.001   0.107
21.09.2006  NA      0.02    0.081
13.09.2007  0.003   0       0.034
17.09.2008  0.022   0.009   0.084
30.09.2009  0.016   0.191   0.092
08.10.2010  -0.035  0.169   NA
23.09.2011  NA      0.191   0.054
26.09.2012  NA      0.286   0.019
01.10.2013  0.032   0.324   0.022
29.09.2014  0.013   NA      0.017
01.10.2015  -0.001  NA      0.018
30.09.2016  0.018   NA      0.002
26.09.2017  -0.006  NA      0.017

A:

Look into the reshape2 package and the dcast and melt methods.     
library(dplyr)
library(reshape2)

data2 = data%>%
  mutate(date = as. Date(date,format = '%d.%m.%Y'))%>% #Convert date to a date time object
  select(-value)%>% #Remove value because we dont need it
  dcast(date~name,value.var = "value2") # Pivot the dataframe

