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r - NA values and extra rows when spreading repeated measures of multiple variables into wide format?

问题描述

With the data below (included with dput), I have a varying number of repeat Lat and Long locations for three individuals and would like to spread them into wide format using dplyr.

The data look like so:

> head(Dat)
  IndIDII      IndYear  WintLat  WintLong
1 BHS_265 BHS_265-2015 47.61025 -112.7210
2 BHS_265 BHS_265-2016 47.59884 -112.7089
3 BHS_770 BHS_770-2016 42.97379 -109.0400
4 BHS_770 BHS_770-2017 42.97129 -109.0367
5 BHS_770 BHS_770-2018 42.97244 -109.0509
6 BHS_377 BHS_377-2015 43.34744 -109.4821

This post provided a slick solution that has been a big help. Nonetheless, I am unable to obtain my desired result. Modifying the code I have the following:

Dat %>%  
  group_by(IndIDII) %>%
  #Make YearNum (as intiger not calnader year) for each IndIDII
  mutate(YearNum = row_number()) %>% 
  gather(Group, LatLong, c(WintLat,  WintLong)) %>% 
  unite(GroupNew, YearNum, Group, sep = "-") %>% 
  spread(GroupNew, LatLong) %>% 
  as.data.frame()

Which produces a nearly correct result, but has multiple rows for each IndIDII, each containing the lat and long for a single year.

  IndIDII      IndYear 1-WintLat 1-WintLong 2-WintLat 2-WintLong 3-WintLat 3-WintLong 4-WintLat 4-WintLong
1 BHS_265 BHS_265-2015  47.61025  -112.7210        NA         NA        NA         NA        NA         NA
2 BHS_265 BHS_265-2016        NA         NA  47.59884  -112.7089        NA         NA        NA         NA
3 BHS_377 BHS_377-2015  43.34744  -109.4821        NA         NA        NA         NA        NA         NA
4 BHS_377 BHS_377-2016        NA         NA  43.35559  -109.4445        NA         NA        NA         NA
5 BHS_377 BHS_377-2017        NA         NA        NA         NA  43.35195  -109.4566        NA         NA
6 BHS_377 BHS_377-2018        NA         NA        NA         NA        NA         NA  43.34765  -109.4892
7 BHS_770 BHS_770-2016  42.97379  -109.0400        NA         NA        NA         NA        NA         NA
8 BHS_770 BHS_770-2017        NA         NA  42.97129  -109.0367        NA         NA        NA         NA
9 BHS_770 BHS_770-2018        NA         NA        NA         NA  42.97244  -109.0509        NA         NA

I am trying to have all lat and longs for an IndIDII in a single row (i.e. wide format) as shown below. NA values will appear when individuals have fewer than the max number of years. I suspect the issue is with the GroupNew variable and have tried different options, but to no avail...

enter image description here

Dat <- structure(list(IndIDII = c("BHS_265", "BHS_265", "BHS_770", "BHS_770", 
"BHS_770", "BHS_377", "BHS_377", "BHS_377", "BHS_377"), IndYear = c("BHS_265-2015", 
"BHS_265-2016", "BHS_770-2016", "BHS_770-2017", "BHS_770-2018", 
"BHS_377-2015", "BHS_377-2016", "BHS_377-2017", "BHS_377-2018"
), WintLat = c(47.6102519805014, 47.5988417247191, 42.9737859090909, 
42.9712914772727, 42.9724390816327, 43.3474354347826, 43.3555934579439, 
43.3519543396226, 43.3476466990291), WintLong = c(-112.720994832869, 
-112.708887595506, -109.039964727273, -109.036693522727, -109.050923061224, 
-109.482114456522, -109.444522149533, -109.45659254717, -109.489241553398
)), class = "data.frame", row.names = c(NA, -9L))

标签： rdplyr

解决方案

你快到了。lat和long进入不同的行，因为它们是IndYear不同的。由于您仅在 final 中保留IndYearfor each的第一个值，因此 add将为您提供所需的结果。IndiDIIdata.frameIndYear = first(IndYear)

Dat %>%  
    group_by(IndIDII) %>%
    mutate(YearNum = row_number(), IndYear = first(IndYear)) %>% 
    gather(Group, LatLong, c(WintLat,  WintLong)) %>% 
    unite(GroupNew, YearNum, Group, sep = "-") %>% 
    spread(GroupNew, LatLong) %>% 
    as.data.frame()

#   IndIDII      IndYear 1-WintLat 1-WintLong 2-WintLat 2-WintLong 3-WintLat 3-WintLong 4-WintLat 4-WintLong
# 1 BHS_265 BHS_265-2015  47.61025  -112.7210  47.59884  -112.7089        NA         NA        NA         NA
# 2 BHS_377 BHS_377-2015  43.34744  -109.4821  43.35559  -109.4445  43.35195  -109.4566  43.34765  -109.4892
# 3 BHS_770 BHS_770-2016  42.97379  -109.0400  42.97129  -109.0367  42.97244  -109.0509        NA         NA

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