times = c("2015-12-30 20:00:00", "2016-01-06 20:00:00",
          "2016-01-08 20:00:00", "2016-01-11 20:00:00",
          "2016-01-13 20:00:00", "2016-01-14 20:00:00",
          "2016-01-15 20:00:00", "2016-01-18 20:00:00",
          "2016-01-20 20:00:00", "2016-01-21 20:00:00",
          "2016-01-25 20:00:00")
counts = c(7, 14, 61, 1, 2, 66, 10, 35, 1, 304, 2)
df <- data.frame(timestamp = as.POSIXct(times, format="%Y-%m-%d %H:%M:%S",
                                  tz="Pacific/Auckland"),
           count = counts)

hist(df$count)

qqnorm(df$count)
qqline(df$count)

lambda <- geoR::boxcoxfit(df$count)$lambda
df$transformed <- car::bcPower(df$count, lambda=lambda)

timestamp count transformed      zscore
1  2015-12-30 20:00:00     7   1.7922836 -0.14446864
2  2016-01-06 20:00:00    14   2.3618561  0.22598616
3  2016-01-08 20:00:00    61   3.4646761  0.94326978
4  2016-01-11 20:00:00     1   0.0000000 -1.31018523
5  2016-01-13 20:00:00     2   0.6729577 -0.87248782
6  2016-01-14 20:00:00    66   3.5198741  0.97917102
7  2016-01-15 20:00:00    10   2.0895953  0.04890541
8  2016-01-18 20:00:00    35   3.0646823  0.68311037
9  2016-01-20 20:00:00     1   0.0000000 -1.31018523
10 2016-01-21 20:00:00   304   4.5195550  1.62937200
11 2016-01-25 20:00:00     2   0.6729577 -0.87248782