R Function involving two for loops - baseball data - r

Question

For those into sports, I am working on a function that adds a column with the pitch count for a game in a given season for a pitcher.
For example's sake, data used is a data frame called pitcher that contains a game_date and sv_id (date/timestamp or the pitch). My goal is to order the sv_id in ascending order for each unique game_date and then add a column with a numbering system for this order. So for example, if for game_date=9/9/2018 there were 3 pitches thrown with sv_id's equal to 090918_031456, 090918_031613, and 090918_031534, I would first want to sort this data into chronological order (090918_031456,090918_031534,090918_031613) and then have a new column with the values 1,2,3 respectively to act as a pitch count. Below is my function so far. I originally thought I would make a list of lists but now I am not sure that is the right way to go about this. Please help! This is also my first time posting on here so any advice is appreciated. Thank you!!!
` pitchCount <- function(game_date, sv_id){
gameUnique<-unique(pitcher$game_date)
PC<-list()
for (j in 1:length(gameUnique)){
PCLocal<-filter(pitcher,game_date==gameUnique[j])
PCLocal[order(PCLocal$sv_id),]
for (i in 1:length(PCLocal$sv_id)){
PCLocal$PC[i]=i
}
PC[j]=PCLocal$PC
}
return(PC)
}
pitch.Count <- pitchCount(pitcher$game_date,pitcher$sv_id)
pitcher$PC<-pitch.Count
`

Answer 1

So you want to count pitches as they come in order, right? Should be no need for a loop. In R, loops are rarely needed.
Check if this is what you want. A tidyverse/dplyr solution.
The sv_id variable is in a format that can be converted to POSIX (a type of date format). This makes it simple to sort in order.
library(tidyverse)
# Create data_frame
pitcher <- data_frame(game_date = as.Date(c("2018-09-09", "2018-09-09", "2018-09-09")),
sv_id = c("090918_031456", "090918_031613", "090918_031534"))
# First, convert sv_id strings to POSIX format (this can be done in the code below but this makes it clearer.
pitcher$sv_id <- as.POSIXct(c("090918_031456", "090918_031613", "090918_031534"), format = "%y%m%d_%H%M%S", tz = "GMT")
# Create pitch count
pitcher %>%
arrange(sv_id) %>%
mutate(Count = 1, pitchcount = cumsum(Count), Count = NULL)
# A tibble: 3 x 3
game_date sv_id pitchcount
<date> <dttm> <dbl>
1 2018-09-09 2009-09-18 03:14:56 1
2 2018-09-09 2009-09-18 03:15:34 2
3 2018-09-09 2009-09-18 03:16:13 3

Answer 2

Try using data.table.
library(data.table)
pitcher_dt <- data.table(pitcher)
> pitcher_dt
game_date sv_id
1: 2018-01-02 090918_031456
2: 2018-01-02 090918_031613
3: 2018-01-02 090918_031534
We can add Count column by := and add a position of 'sv_id' by order(sv_id).
pitcher_dt [, Count := order(sv_id)]
> pitcher_dt
game_date sv_id Count
1: 2018-01-02 090918_031456 1
2: 2018-01-02 090918_031613 3
3: 2018-01-02 090918_031534 2
Since Count only puts the position of 'sv_id', in this case (1,3,2), we can either sort 'Count' or 'sv_id' in ascending order
pitcher_dt[,order(Count)] or pitcher_dt[,order(sv_id)]
> pitcher_dt[order(Count)]
game_date sv_id Count
1: 2018-01-02 090918_031456 1
2: 2018-01-02 090918_031534 2
3: 2018-01-02 090918_031613 3
For me, it is easy to manipulate data with data.table. But, you can also use dplyr.
Introduction to data.table is a good start to learn about data.table.

Answer 3

I am not sure how is your data looks like, but I assume the following from your description
> df
# A tibble: 9 x 2
game_date sv_id
<date> <chr>
1 2018-09-09 090918_031456
2 2018-09-09 090918_031613
3 2018-09-09 090918_031534
4 2018-05-17 090918_031156
5 2018-05-17 090918_031213
6 2018-06-30 090918_031177
7 2018-06-30 090918_031211
8 2018-06-30 090918_031144
9 2018-06-30 090918_031203
Then you use dplyr to do generate your target
library(dplyr)
df <- df %>%
group_by(game_date) %>%
mutate(count = n_distinct(sv_id)) %>% #count sv_id with each game_date
arrange(desc(sv_id))
The output is:
# A tibble: 9 x 3
# Groups: game_date [3]
game_date sv_id count
<date> <chr> <int>
1 2018-06-30 090918_031144 4
2 2018-05-17 090918_031156 2
3 2018-06-30 090918_031177 4
4 2018-06-30 090918_031203 4
5 2018-06-30 090918_031211 4
6 2018-05-17 090918_031213 2
7 2018-09-09 090918_031456 3
8 2018-09-09 090918_031534 3
9 2018-09-09 090918_031613 3
I hope this could help