how to count the number of rows under certain condition?

[Janicehau]

Hi, here is my table. The Y column is the number of different sites and the X column is the number of times where the animals have been spotted. I want to know for each site, how many days there are animals are being spotted(where the number is <1) I am new to R so I am not sure which code I need to use.

Thank you so much!

[williaml]

Hi, perhaps something like this. I have created a pretend dataset, but next time provide a reproducible dataset.

library(tidyverse)

# pretend dataset ---------------------------------
df <- tibble(site_name = c("a", "b", "c"),
             `2020-09-30` = c(0, 1, 0),
             `2020-10-01` = c(1, 2, 0))


# pivot then only the ones less than 1 -------------
df %>% 
  pivot_longer(-site_name, names_to = "dates", values_to = "count") %>% 
  filter(count < 1)


# # A tibble: 3 x 3
# site_name dates      count
# <chr>     <chr>      <dbl>
#  1 a         2020-09-30     0
# 2 c         2020-09-30     0
# 3 c         2020-10-01     0

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[technocrat]

Run this to see an alternative approach that emphasizes thinking about data organization with a view to its desired uses beforehand, then building function objects to transform a data object into an object containing the desired result. Because of all the punctuation, eyes tend to glaze over, but in the longer run there really is far less to remember than with a focus on a recipe of do this then do that.

Couldn't do a reprex because RStudio is misbehaving.

# create fake data
basket <- c(0:3,NA)
make_fakes <- function(x) t(sample(basket,length(the_dates), replace = TRUE))
the_dates <- seq.Date(from = as.Date('2020-09-01'), to = as.Date('2020-10-31'), by = 'days') 
observations <- length(the_dates)
d <- sapply(1:observations,make_fakes) |> data.frame()
the_sites <- paste0("TUW",1:61) 
colnames(d) <- the_sites
d <- cbind(the_dates,d)
# d, the fake data data frame complete

# over all dates, count the number
# of times that each variable was
# below 1 and not NA
# 
# because
NA < 1

# after removing NAs, sum the number of rows in a
# column that are less than 0
count_lows <- function(x) sum(d[x][[1]] < 1, na.rm = TRUE)
# make a data frame
result <- data.frame(low_counts = sapply(2:(dim(d)[2]),count_lows))
# add in the corresponding site names
low_counts$sites <- the_sites
# swap first and second column positions
low_counts <- low_counts[c(2,1)]
low_counts
# bonus: convert to time series and compare plots
the_start = c(2022, as.numeric(format(the_dates[1], "%j")))
series <- ts(d[2],start = the_start, frequency = 365)
par(mfrow = c(2,2))
plot(series, type = "l")
plot(series, type = "p")
plot(series, type = "h")
plot(series, type = "s")
series <- ts(d[3],start = the_start, frequency = 365)
par(mfrow = c(2,2))
plot(series, type = "l")
plot(series, type = "p")
plot(series, type = "h")
plot(series, type = "s")