it is not a tibble
is_tibble(valid_respondents)
[1] FALSE
dput(head(valid_respondents))
structure(list(date = c("2021-10-22T15:25:11Z", "2021-10-22T15:34:54Z",
"2021-10-22T15:39:14Z", "2021-10-22T15:41:16Z", "2021-10-22T15:52:30Z",
"2021-10-22T15:52:40Z"), predict_5_increase = c(10L, 50L, 20L,
10L, 20L, 40L), predict_5_same = c(20L, 20L, 10L, 20L, 20L, 30L
), predict_5_decrease = c(70L, 30L, 70L, 70L, 60L, 30L), open_5_day = c("It seems to overall trend downward, and I really doubt it'll go up.",
"It may look like there is a downturn but the overall trend is upwards. It may also go sideways because five days is not a very long.",
"Based off the history of the energy sector has been doing down for awhile. Since it was on a downward track (not that long) after a short upward track I figured it would more than likely keep going down for a little while before going back up.",
"Only one that previous state", "5 days for Red line is reversal for decrease",
"red line is not stayed for same place, and the red line was up and down for the energy."
), predict_30_increase = c(10L, 30L, 40L, 10L, 80L, 40L), predict_30_same = c(20L,
20L, 10L, 20L, 0L, 20L), predict_30_decrease = c(70L, 50L, 50L,
70L, 20L, 40L), open_30_day = c("I don't think, since the graph covers about two years, that there will be any major unforeseen changes in 30 days.",
"After a month I think the energy index will continue to go down. There is not much information to go on",
"This one is a little harder for me to predict because it had been going up a little longer, however still based on the time before the upwards trend that it had been going down, I think it's a little more likely to decrease in this timeframe.",
"maximum chance about previous state", "Future days positive in energy index in reversal",
"red line is not stayed for same place, and the red line was up and down for the energy."
), education = c("12th grade", "Associates degree academic, between 1 and 2 years of college",
"Associates degree vocational, 3 or more years of college", "Bachelor's degree",
"Master's degree", "Master's degree"), television = c("Never",
"Daily", "1-3 times a year", "Daily", "Daily", "1 day a week"
), radio = c("Never", "Never", "1-3 times a month", "4 days a week",
"5 days a week", "1-3 times a month"), print = c("Never", "2 days a week",
"1-3 times a month", "6 days a week", "Daily", "1-3 times a month"
), comp_smart_tab = c("Daily", "Daily", "Daily", "Daily", "Daily",
"1 day a week"), dig_web_app = c("Daily", "Daily", "Daily", "Daily",
"Daily", "1 day a week"), dig_SOME = c("Never", "Daily", "Daily",
"Daily", "Daily", "1-3 times a month"), dig_internet = c("Never",
"6 days a week", "6 days a week", "Daily", "Daily", "1-3 times a month"
), dig_podcast = c("Never", "3 days a week", "1-3 times a month",
"6 days a week", "6 days a week", "1 day a week"), age = c(22L,
60L, 36L, 62L, 58L, 25L), gender = c("Man", "Man", "Woman", "Man",
"Man", "Woman"), Total.time = c(108.58, 218.58, 274.24, 293.82,
397.98, 269.92), sum_predict_5 = c(100L, 100L, 100L, 100L, 100L,
100L), sum_predict_30 = c(100L, 100L, 100L, 100L, 100L, 100L)), row.names = c(NA,
6L), class = "data.frame")