For loop to read in multiple tables from SQLite database - sql

Question

I would like to create a for loop that reads in multiple tables from a SQLite database. I would like it to either read the first 300 tables, but ideally I would like to get it to read 300 random tables from my database into R.
For each table read in, I would like it to go through the written code, save the graph at the end then start over with a new table. If possible I would like the all of the tables to be on the same graph. I have written the code for a single table, but I am unsure as to how I could proceed from here.
for (i in 1:300){
# Reads the selected table in database
ind1 <- dbReadTable(mydb, i)
# Formats the SQL data to appropriate R data structure
cols <- c("Mortality", "AnimalID", "Species", "Sex", "CurrentCohort",
"BirthYear", "CaptureUnit","CaptureSubunit",
"CaptureArea", "ProjectName")
ind[cols] <- lapply(ind[cols], factor) ## as.factor() could also be used
ind$DateAndTime <- as.POSIXct(ind$DateAndTime, tz = "UTC",
origin = '1970-01-01')
# Converts the Longitude and Latitude to UTMs
ind <- convert_utm(ind1)
ind_steps <- ind %>%
# It's always a good idea to *double check* that your data are sorted
# properly before using lag() or lead() to get the previous/next value.
arrange(AnimalID, DateAndTime) %>%
# If we group_by() AnimalID, lead() will insert NAs in the proper
# places when we get to the end of one individual's data and the beginning
# of the next
group_by(AnimalID) %>%
# Now rename our base columns to reflect that they are the step's start point
rename(x1 = utm_x,
y1 = utm_y,
t1 = DateAndTime) %>%
# Attach the step's end point
mutate(x2 = lead(x1),
y2 = lead(y1),
t2 = lead(t1)) %>%
# Calculate differences in space and time
mutate(dx = x2 - x1,
dy = y2 - y1,
DateAndTime = as.numeric(difftime(t2, t1, units = "hours"))) %>%
# Calculate step length
mutate(sl = sqrt(dx^2 + dy^2)) %>%
# Calculate absolute angle
mutate(abs_angle = (pi/2 - atan2(dy, dx)) %% (2*pi)) %>%
# Calculate relative angle
mutate(rel_diff = (abs_angle - lag(abs_angle)) %% (2*pi),
rel_angle = ifelse(rel_diff > pi, rel_diff - 2*pi, rel_diff)) %>%
# Drop this uneccesary column
select(-rel_diff) %>%
# Drop incomplete final step
filter(!is.na(x2))
ind_steps <- ind_steps %>%
mutate(NSD = (x2 - x1[1])^2 + (y2 - y1[1])^2)
# Plot NSD
ind_steps %>%
ggplot(aes(x = t2, y = NSD)) +
geom_line() +
theme_bw()
}
Any help would be greatly appreciated!

Answer 1

If there are 1000 tables you can use sample to get random 300 from them, create a list with length 300 to store the plots and if you want to plot them together you can use cowplot::plot_grid.
random_tables <- sample(1000, 300, replace = TRUE)
plot_list <- vector('list', 300)
for (i in seq_along(random_tables)){
# Reads the selected table in database
ind1 <- dbReadTable(mydb, random_tables[i])
#...Rest of the code
#....
#....
# Plot NSD
plot_list[[i]] <- ggplot(ind_steps, aes(x = t2, y = NSD)) +
geom_line() + theme_bw()
}
cowplot::plot_grid(plotlist = plot_list, nrow = 30, ncol = 10)