I have a time series data for neighbourhood and crime rate per year (example below).

```
Neighbourhood year rate
1 2009 43.5
1 2010 34.7
1 2011 40.8
2 2009 28.9
2 2010 33.8
2 2011 24.4
. . .
. . .
```

**I applied spline regression by group (for each neighbourhood) by using plyr and ggplot. Both show plots for each Neighbourhood but does not show the model line (spline curve)**

```
library(ggplot2)
require(stats)
# First try:
# run spline regression by groupe: (for each Neighbourhood)
models <- dlply(crime_df, "Neighbourhood", function(crime_df)
lm(formula = crime_df$rate ~ bs(crime_df$year, 6 )))
#Extract coefficients
ldply(models, coefficients)
# get a plot for each neighbourhood
d_ply(crime_df, "Neighbourhood", transform, plot(year, rate, main = unique(Neighbourhood), pch= 19))
lines(crime_df$year, fitted(models)) # to put spline curve on these plots but does not work:
# Second try:
# run spline regression by groupe: (for each Neighbourhood) by using tidy
# I used geom_smooth to plot the model (spline curve) but did not work
crime_df %>%
ggplot(aes(x = year, y = rate, group = Neighbourhood)) +
geom_point(color = palette_light()[[2]]) +
geom_smooth(method = lm, formula = crime_df$rate ~ splines::bs(crime_df$year, 6), se = FALSE) +
labs(title = "spline") +
theme_tq() +
facet_wrap(~ Neighbourhood, scale = "free_y", ncol = 3) +
scale_x_date(date_labels = "%Y")
```