# How to Calculate Summary Statistics by Group in R

There are two basic ways to calculate summary statistics by group in R:

Method 1: Use tapply() from Base R

```tapply(df\$value_col, df\$group_col, summary)
```

Method 2: Use group_by() from dplyr Package

```library(dplyr)

df %>%
group_by(group_col) %>%
summarize(min = min(value_col),
q1 = quantile(value_col, 0.25),
median = median(value_col),
mean = mean(value_col),
q3 = quantile(value_col, 0.75),
max = max(value_col))```

The following examples show how to use each method in practice.

### Method 1: Use tapply() from Base R

The following code shows how to use the tapply() function from base R to calculate summary statistics by group:

```#create data frame
df <- data.frame(team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'),
points=c(99, 68, 86, 88, 95, 74, 78, 93),
assists=c(22, 28, 31, 35, 34, 45, 28, 31),
rebounds=c(30, 28, 24, 24, 30, 36, 30, 29))

#calculate summary statistics of 'points' grouped by 'team'
tapply(df\$points, df\$team, summary)

\$A
Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
68.00   81.50   87.00   85.25   90.75   99.00

\$B
Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
74.0    77.0    85.5    85.0    93.5    95.0
```

### Method 2: Use group_by() from dplyr Package

The following code shows how to use the group_by() and summarize() functions from the dplyr package to calculate summary statistics by group:

```library(dplyr)

#create data frame
df <- data.frame(team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'),
points=c(99, 68, 86, 88, 95, 74, 78, 93),
assists=c(22, 28, 31, 35, 34, 45, 28, 31),
rebounds=c(30, 28, 24, 24, 30, 36, 30, 29))

#calculate summary statistics of 'points' grouped by 'team'
df %>%
group_by(team) %>%
summarize(min = min(points),
q1 = quantile(points, 0.25),
median = median(points),
mean = mean(points),
q3 = quantile(points, 0.75),
max = max(points))

# A tibble: 2 x 7
team    min    q1 median  mean    q3   max

1 A        68  81.5   87    85.2  90.8    99
2 B        74  77     85.5  85    93.5    95
```

Notice that both methods return the exact same results.

It’s worth noting that the dplyr approach will likely be faster for large data frames but both methods will perform similarly on smaller data frames.