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Reframe error when using summarize to get remove na's from rows

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I am having an awful lot of trouble trying to get summarized rows from a data frame that has rows with duplicated timestamps, but has different data in each row, so that some variables are NA, and others are valid data. The goal is to get one row per timestamp, with all valid columns filled in. Also, some of the duplicated timestamps have data in both rows, so both rows need to be returned in this case. Here is an example input data:

input_data <- structure(list(File_Category = c("OneMinute", "OneMinuteExtra", 
"OneMinute", "OneMinuteExtra", "OneMinute", "OneMinuteExtra", 
"OneMinute", "OneMinuteExtra", "OneMinute", "OneMinuteExtra", 
"OneMinute", "OneMinuteExtra"), File_Station_Name = c("Lincoln 1500 N 45th", 
"Lincoln 1500 N 45th", "Lincoln 1500 N 45th", "Lincoln 1500 N 45th", 
"Lincoln 1500 N 45th", "Lincoln 1500 N 45th", "Lincoln 1500 N 45th", 
"Lincoln 1500 N 45th", "Lincoln 1500 N 45th", "Lincoln 1500 N 45th", 
"Lincoln 1500 N 45th", "Lincoln 1500 N 45th"), TIMESTAMP = c("2019-09-05 00:57:00", 
"2019-09-05 00:57:00", "2019-09-05 00:58:00", "2019-09-05 00:58:00", 
"2019-09-05 00:59:00", "2019-09-05 00:59:00", "2019-09-05 01:00:00", 
"2019-09-05 01:00:00", "2019-09-05 01:01:00", "2019-09-05 01:01:00", 
"2019-09-05 01:02:00", "2019-09-05 01:02:00"), RECORD = c(423L, 
423L, 424L, 424L, 425L, 425L, 426L, 426L, 427L, 427L, 428L, 428L
), Ta_2m_Avg = c(23.05, 22.93, 23.05, NA, 22.99, NA, 22.97, NA, 
22.97, NA, 22.93, NA), TaMax_2m = c(23.2, 23.01, 23.2, NA, 23.1, 
NA, 23.11, NA, 23.11, NA, 23.01, NA), TaMin_2m = c(22.96, 22.83, 
22.96, NA, 22.92, NA, 22.92, NA, 22.92, NA, 22.83, NA), RH_2m_Avg = c(64.07, 
65, 64.07, NA, 63.84, NA, 64.07, NA, 64.5, NA, 65, NA), RHMax_2m = c(64.41, 
65.28, 64.41, NA, 63.91, NA, 64.25, NA, 65.11, NA, 65.28, NA), 
    RHMin_2m = c(63.85, 64.71, 63.85, NA, 63.75, NA, 63.82, NA, 
    64.11, NA, 64.71, NA), Dp_2m_Avg = c(15.89, 16.01, 15.89, 
    NA, 15.78, NA, 15.82, NA, 15.93, NA, 16.01, NA), DpMax_2m = c(15.99, 
    16.14, 15.99, NA, 15.91, NA, 15.98, NA, 16.08, NA, 16.14, 
    NA), DpMin_2m = c(15.78, 15.88, 15.78, NA, 15.69, NA, 15.71, 
    NA, 15.78, NA, 15.88, NA), HeatIndex_2m_Avg = c(23.06, 22.96, 
    23.06, NA, 23, NA, 22.98, NA, 22.99, NA, 22.96, NA), HeatIndexMax_2m = c(23.22, 
    23.04, 23.22, NA, 23.12, NA, 23.13, NA, 23.13, NA, 23.04, 
    NA), WindChill_2m_Avg = c(24.85, 26.72, 24.85, NA, 24.67, 
    NA, 24.88, NA, 25.9, NA, 26.72, NA), WindChillMin_2m = c(24.6, 
    25.01, 24.6, NA, 24.42, NA, 24.75, NA, 24.6, NA, 25.01, NA
    ), WndAveSpd_3m = c(0.752, 0.044, 0.752, NA, 0.969, NA, 0.564, 
    NA, 0.419, NA, 0.044, NA), WndVecMagAve_3m = c(0.715, 0.044, 
    0.715, NA, 0.959, NA, 0.552, NA, 0.418, NA, 0.044, NA), WndAveDir_3m = c(147.7, 
    120.3, 147.7, NA, 140.2, NA, 128.1, NA, 140.4, NA, 120.3, 
    NA), WndAveDirSD_3m = c(18.08, 0.176, 18.08, NA, 8.27, NA, 
    11.74, NA, 3.204, NA, 0.176, NA), WndMaxSpd5s_3m = c(1.1, 
    0.3, 1.1, NA, 1.366, NA, 0.7, NA, 1, NA, 0.3, NA), WndMax_5sec_Dir_3m = c(157.8, 
    120.2, 157.8, NA, 126.9, NA, 139.5, NA, 142.7, NA, 120.2, 
    NA), PresAvg_1pnt5m = c(977.1187, 977.1306, 977.1187, NA, 
    977.1209, NA, 977.127, NA, 977.1389, NA, 977.1306, NA), PresMax_1pnt5m = c(977.1798, 
    977.1832, 977.1798, NA, 977.1459, NA, 977.1791, NA, 977.15, 
    NA, 977.1832, NA), PresMin_1pnt5m = c(977.0795, 977.1168, 
    977.0795, NA, 977.0795, NA, 977.0835, NA, 977.0835, NA, 977.1168, 
    NA), Solar_2m_Avg = c(2.414, 0.849, 2.414, NA, 2.207, NA, 
    1.623, NA, 1.185, NA, 0.849, NA), Rain_1m_Tot = c(0L, 0L, 
    0L, NA, 0L, NA, 0L, NA, 0L, NA, 0L, NA), Ts_bare_10cm_Avg = c(28.46, 
    28.39, 28.46, NA, 28.44, NA, 28.42, NA, 28.41, NA, 28.39, 
    NA), TsMax_bare_10cm = c(28.47, 28.4, 28.47, NA, 28.45, NA, 
    28.43, NA, 28.41, NA, 28.4, NA), TsMin_bare_10cm = c(28.45, 
    28.38, 28.45, NA, 28.43, NA, 28.42, NA, 28.4, NA, 28.38, 
    NA), BattVolts_Min = c(12.76, 12.75, 12.76, NA, 12.76, NA, 
    12.76, NA, 12.75, NA, 12.75, NA), LithBatt_Min = c(3.447, 
    3.447, 3.447, NA, 3.447, NA, 3.447, NA, 3.447, NA, 3.447, 
    NA), MaintMode = c(0L, 0L, 0L, NA, 0L, NA, 0L, NA, 0L, NA, 
    0L, NA), Ta_10m_Avg = c(NA, NA, NA, 23.65, NA, 23.7, NA, 
    23.64, NA, 23.58, NA, 23.45), TaMax_10m = c(NA, NA, NA, 23.8, 
    NA, 23.99, NA, 23.94, NA, 23.9, NA, 23.59), TaMin_10m = c(NA, 
    NA, NA, 23.55, NA, 23.57, NA, 23.52, NA, 23.43, NA, 23.34
    ), RH_10m_Avg = c(NA, NA, NA, 60.32, NA, 60.5, NA, 60.68, 
    NA, 61.27, NA, 61.65), RHMax_10m = c(NA, NA, NA, 60.46, NA, 
    60.78, NA, 60.84, NA, 61.64, NA, 61.82), RHMin_10m = c(NA, 
    NA, NA, 60.18, NA, 60.31, NA, 60.53, NA, 60.76, NA, 61.54
    ), Dp_10m_Avg = c(NA, NA, NA, 15.52, NA, 15.61, NA, 15.61, 
    NA, 15.7, NA, 15.67), DpMax_10m = c(NA, NA, NA, 15.7, NA, 
    15.96, NA, 15.92, NA, 16.02, NA, 15.82), DpMin_10m = c(NA, 
    NA, NA, 15.39, NA, 15.44, NA, 15.46, NA, 15.47, NA, 15.55
    ), HeatIndex_10m_Avg = c(NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA), HeatIndexMax_10m = c(NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA), WindChill_10m_Avg = c(NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), WindChillMin_10m = c(NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), WndAveSpd_10m = c(NA, 
    NA, NA, 1.266, NA, 1.005, NA, 1.188, NA, 0.394, NA, 0.508
    ), WndVecMagAve_10m = c(NA, NA, NA, 1.247, NA, 0.969, NA, 
    1.163, NA, 0.377, NA, 0.499), WndAveDir_10m = c(NA, NA, NA, 
    147.9, NA, 152, NA, 139, NA, 126.9, NA, 143.5), WndAveDirSD_10m = c(NA, 
    NA, NA, 9.94, NA, 15.42, NA, 11.81, NA, 16.94, NA, 11.08), 
    WndMaxSpd5s_10m = c(NA, NA, NA, 1.799, NA, 1.599, NA, 1.633, 
    NA, 0.8, NA, 0.8), WndMax_5sec_Dir_10m = c(NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA), WndMaxSpd5s_Dir_10m = c(NA, 
    NA, NA, 143.4, NA, 144.4, NA, 131.3, NA, 95.8, NA, 160.7), 
    source = c("D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min_extra.csv.backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min_extra.csv.backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min_extra.csv.backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min_extra.csv.backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min_extra.csv.backup"
    )), class = "data.frame", row.names = c(NA, -12L))

Desired output:

desired_output <- structure(list(File_Category = c("OneMinute", "OneMinuteExtra", 
"OneMinute", "OneMinute", "OneMinute", "OneMinute", "OneMinute"
), File_Station_Name = c("Lincoln 1500 N 45th", "Lincoln 1500 N 45th", 
"Lincoln 1500 N 45th", "Lincoln 1500 N 45th", "Lincoln 1500 N 45th", 
"Lincoln 1500 N 45th", "Lincoln 1500 N 45th"), TIMESTAMP = c("2019-09-05 00:57:00", 
"2019-09-05 00:57:00", "2019-09-05 00:58:00", "2019-09-05 00:59:00", 
"2019-09-05 01:00:00", "2019-09-05 01:01:00", "2019-09-05 01:02:00"
), RECORD = c(423L, 423L, 424L, 425L, 426L, 427L, 428L), Ta_2m_Avg = c(23.05, 
22.93, 23.05, 22.99, 22.97, 22.97, 22.93), TaMax_2m = c(23.2, 
23.01, 23.2, 23.1, 23.11, 23.11, 23.01), TaMin_2m = c(22.96, 
22.83, 22.96, 22.92, 22.92, 22.92, 22.83), RH_2m_Avg = c(64.07, 
65, 64.07, 63.84, 64.07, 64.5, 65), RHMax_2m = c(64.41, 65.28, 
64.41, 63.91, 64.25, 65.11, 65.28), RHMin_2m = c(63.85, 64.71, 
63.85, 63.75, 63.82, 64.11, 64.71), Dp_2m_Avg = c(15.89, 16.01, 
15.89, 15.78, 15.82, 15.93, 16.01), DpMax_2m = c(15.99, 16.14, 
15.99, 15.91, 15.98, 16.08, 16.14), DpMin_2m = c(15.78, 15.88, 
15.78, 15.69, 15.71, 15.78, 15.88), HeatIndex_2m_Avg = c(23.06, 
22.96, 23.06, 23, 22.98, 22.99, 22.96), HeatIndexMax_2m = c(23.22, 
23.04, 23.22, 23.12, 23.13, 23.13, 23.04), WindChill_2m_Avg = c(24.85, 
26.72, 24.85, 24.67, 24.88, 25.9, 26.72), WindChillMin_2m = c(24.6, 
25.01, 24.6, 24.42, 24.75, 24.6, 25.01), WndAveSpd_3m = c(0.752, 
0.044, 0.752, 0.969, 0.564, 0.419, 0.044), WndVecMagAve_3m = c(0.715, 
0.044, 0.715, 0.959, 0.552, 0.418, 0.044), WndAveDir_3m = c(147.7, 
120.3, 147.7, 140.2, 128.1, 140.4, 120.3), WndAveDirSD_3m = c(18.08, 
0.176, 18.08, 8.27, 11.74, 3.204, 0.176), WndMaxSpd5s_3m = c(1.1, 
0.3, 1.1, 1.366, 0.7, 1, 0.3), WndMax_5sec_Dir_3m = c(157.8, 
120.2, 157.8, 126.9, 139.5, 142.7, 120.2), PresAvg_1pnt5m = c(977.1187, 
977.1306, 977.1187, 977.1209, 977.127, 977.1389, 977.1306), PresMax_1pnt5m = c(977.1798, 
977.1832, 977.1798, 977.1459, 977.1791, 977.15, 977.1832), PresMin_1pnt5m = c(977.0795, 
977.1168, 977.0795, 977.0795, 977.0835, 977.0835, 977.1168), 
    Solar_2m_Avg = c(2.414, 0.849, 2.414, 2.207, 1.623, 1.185, 
    0.849), Rain_1m_Tot = c(0L, 0L, 0L, 0L, 0L, 0L, 0L), Ts_bare_10cm_Avg = c(28.46, 
    28.39, 28.46, 28.44, 28.42, 28.41, 28.39), TsMax_bare_10cm = c(28.47, 
    28.4, 28.47, 28.45, 28.43, 28.41, 28.4), TsMin_bare_10cm = c(28.45, 
    28.38, 28.45, 28.43, 28.42, 28.4, 28.38), BattVolts_Min = c(12.76, 
    12.75, 12.76, 12.76, 12.76, 12.75, 12.75), LithBatt_Min = c(3.447, 
    3.447, 3.447, 3.447, 3.447, 3.447, 3.447), MaintMode = c(0L, 
    0L, 0L, 0L, 0L, 0L, 0L), Ta_10m_Avg = c(NA, NA, 23.65, 23.7, 
    23.64, 23.58, 23.45), TaMax_10m = c(NA, NA, 23.8, 23.99, 
    23.94, 23.9, 23.59), TaMin_10m = c(NA, NA, 23.55, 23.57, 
    23.52, 23.43, 23.34), RH_10m_Avg = c(NA, NA, 60.32, 60.5, 
    60.68, 61.27, 61.65), RHMax_10m = c(NA, NA, 60.46, 60.78, 
    60.84, 61.64, 61.82), RHMin_10m = c(NA, NA, 60.18, 60.31, 
    60.53, 60.76, 61.54), Dp_10m_Avg = c(NA, NA, 15.52, 15.61, 
    15.61, 15.7, 15.67), DpMax_10m = c(NA, NA, 15.7, 15.96, 15.92, 
    16.02, 15.82), DpMin_10m = c(NA, NA, 15.39, 15.44, 15.46, 
    15.47, 15.55), HeatIndex_10m_Avg = c(NA, NA, NA, NA, NA, 
    NA, NA), HeatIndexMax_10m = c(NA, NA, NA, NA, NA, NA, NA), 
    WindChill_10m_Avg = c(NA, NA, NA, NA, NA, NA, NA), WindChillMin_10m = c(NA, 
    NA, NA, NA, NA, NA, NA), WndAveSpd_10m = c(NA, NA, 1.266, 
    1.005, 1.188, 0.394, 0.508), WndVecMagAve_10m = c(NA, NA, 
    1.247, 0.969, 1.163, 0.377, 0.499), WndAveDir_10m = c(NA, 
    NA, 147.9, 152, 139, 126.9, 143.5), WndAveDirSD_10m = c(NA, 
    NA, 9.94, 15.42, 11.81, 16.94, 11.08), WndMaxSpd5s_10m = c(NA, 
    NA, 1.799, 1.599, 1.633, 0.8, 0.8), WndMax_5sec_Dir_10m = c(NA, 
    NA, NA, NA, NA, NA, NA), WndMaxSpd5s_Dir_10m = c(NA, NA, 
    143.4, 144.4, 131.3, 95.8, 160.7), source = c("D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup", 
    "D:\\Data_Collected_11202023\\Data_Collected/0034/0034_1min.csv (2).backup"
    )), class = "data.frame", row.names = c(NA, -7L))

I get errors using just about everything. Here's an example.

result <- input_data %>%
      group_by(TIMESTAMP) %>%
      summarize(across(File_Category:File_Station_Name, first),
                across(RECORD, first),
                across(Ta_2m_Avg:WndMaxSpd5s_Dir_10m, na.omit),
                across(source, first))

Error in names(dots)[[i]] : subscript out of bounds

I know I've asked similar questions before. Answers that have helped me in the past don't seem to work for this. I don't know why. I've read in the data using read_csv so that TIMESTAMP is posixct class. Thank you.

r dplyr summarize across
Original Q&A
1

There are 1 best solutions below

2
On BEST ANSWER

Here's a solution using your example input data. It's not particularly tidy, but maybe you can build on this.

input_data$TIMESTAMP <- as.POSIXct(input_data$TIMESTAMP, 
                                   format = "%Y-%m-%d %H:%M:%S", 
                                   tz = "UTC")

desired_output$TIMESTAMP <- as.POSIXct(desired_output$TIMESTAMP, 
                                       format = "%Y-%m-%d %H:%M:%S", 
                                       tz = "UTC")

library(dplyr)
library(tidyr)

result <- input_data %>%
  group_by(TIMESTAMP) %>%
  # replace NAs with unique values per group (creates duplicates)
  fill(Ta_2m_Avg:WndMaxSpd5s_Dir_10m, .direction = "downup") %>% 
  # remove rows based on duplicates in column range
  # select columns range by index or by name
  # .[which(!duplicated(.[,c(which(colnames(.) == "Ta_2m_Avg"):which(colnames(.) == "WndMaxSpd5s_Dir_10m"))])),]
 .[which(!duplicated(.[,c(5:54)])),]

# confirm
library(diffdf)
diffdf(desired_output, result)
# > No issues were found!

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