How Calculate Growth Rate in Long Format Data Frame

How calculate growth rate in long format data frame?

For these sorts of questions ("how do I compute XXX by category YYY")? there are always solutions based on by(), the data.table() package, and plyr. I generally prefer plyr, which is often slower, but (to me) more transparent/elegant.

df <- data.frame(Category=c(rep("A",6),rep("B",6)),
  Year=rep(2010:2015,2),Value=1:12)

library(plyr)
ddply(df,"Category",transform,
         Growth=c(NA,exp(diff(log(Value)))-1))

The main difference between this answer and @krlmr's is that I am using a geometric-mean trick (taking differences of logs and then exponentiating) while @krlmr computes an explicit ratio.

Mathematically, diff(log(Value)) is taking the differences of the logs, i.e. log(x[t+1])-log(x[t]) for all t. When we exponentiate that we get the ratio x[t+1]/x[t] (because exp(log(x[t+1])-log(x[t])) = exp(log(x[t+1]))/exp(log(x[t])) = x[t+1]/x[t]). The OP wanted the fractional change rather than the multiplicative growth rate (i.e. x[t+1]==x[t] corresponds to a fractional change of zero rather than a multiplicative growth rate of 1.0), so we subtract 1.

I am also using transform() for a little bit of extra "syntactic sugar", to avoid creating a new anonymous function.

Calculating yearly growth-rates from quarterly, long form data in r

I think you need something like this:

library(dplyr)
df %>%
  #group by sector and country
  group_by(Sector, Country) %>%
  #calculate growth as (quarter / 5-period-lagged quarter) - 1
  mutate(growth = Income / lag(Income, 4) - 1)

Output

Source: local data frame [32 x 5]
Groups: Sector, Country [4]

   Sector Country Quarter Income    growth
   (fctr)  (fctr)   (int)  (int)     (dbl)
1       A     USA       1     20        NA
2       A     USA       2     21        NA
3       A     USA       3     22        NA
4       A     USA       4     23        NA
5       A     USA       5     24 0.2000000
6       A     USA       6     25 0.1904762
7       A     USA       7     26 0.1818182
8       A     USA       8     27 0.1739130
9       B     USA       1     28        NA
10      B     USA       2     29        NA
..    ...     ...     ...    ...       ...

Calculating rates when data is in long form

You can use group_by to calculate this for each squirrel:

group_by(df, squirrel) %>% 
    mutate(g.rate = (wt - nth(wt, which.min(is.na(wt)))) / 
                    (age - nth(age, which.min(is.na(wt)))))

That leaves NaNs where the age term is zero, but you can change those to NAs if you want with df$g.rate[is.nan(df$g.rate)] <- NA.

Calculating growth rates across rows by groups in the long format (relative to a base group) in R

To compute based on the first in each group use, well, first. This is valid if Baseline is always the first in each group.

df<- structure(list(
  country = c("CAN", "CAN", "CAN", "CAN", "CAN", 
              "CAN", "CAN", "CAN", "CAN", "CAN", "CAN", "CAN", "CAN", "CAN", 
              "CAN", "NCOA", "NCOA", "NCOA", "NCOA", "NCOA"), 
  year = c("2020", "2020", "2020", "2020", "2020", "2025", "2025", 
           "2025", "2025", "2025", "2030", "2030", "2030", "2030", "2030", 
           "2020", "2020", "2020", "2020", "2020"), 
  scenario = c("Baseline", "BCA", "Full BCA", "NDC", "Partial BCA", 
               "Baseline", "BCA", "Full BCA", "NDC", "Partial BCA", 
               "Baseline", "BCA", "Full BCA", "NDC", "Partial BCA", "Baseline", 
               "BCA", "Full BCA", "NDC", "Partial BCA"), 
  value = c(50527.8708215592, 50487.4619290311, 50485.0924261504, 
            50489.4453487844, 50486.1975947164, 55845.9708589775, 55070.2745559464, 
            55133.107605613, 55153.4525662034, 55065.0036253937, 61463.2383809614, 
            59893.8712077455, 59971.8726308887, 59936.72156767, 59875.7762254252, 
            338418.917408225, 338420.617142445, 338428.007621131, 338419.514027857, 
            338427.263672463)), row.names = c(NA, -20L), 
  class = c("tbl_df", "tbl", "data.frame"))

suppressPackageStartupMessages(library(dplyr))

df %>%
  group_by(country, year) %>%
  mutate(growth = (value/first(value) - 1)*100)
#> # A tibble: 20 × 5
#> # Groups:   country, year [4]
#>    country year  scenario      value    growth
#>    <chr>   <chr> <chr>         <dbl>     <dbl>
#>  1 CAN     2020  Baseline     50528.  0       
#>  2 CAN     2020  BCA          50487. -0.0800  
#>  3 CAN     2020  Full BCA     50485. -0.0847  
#>  4 CAN     2020  NDC          50489. -0.0760  
#>  5 CAN     2020  Partial BCA  50486. -0.0825  
#>  6 CAN     2025  Baseline     55846.  0       
#>  7 CAN     2025  BCA          55070. -1.39    
#>  8 CAN     2025  Full BCA     55133. -1.28    
#>  9 CAN     2025  NDC          55153. -1.24    
#> 10 CAN     2025  Partial BCA  55065. -1.40    
#> 11 CAN     2030  Baseline     61463.  0       
#> 12 CAN     2030  BCA          59894. -2.55    
#> 13 CAN     2030  Full BCA     59972. -2.43    
#> 14 CAN     2030  NDC          59937. -2.48    
#> 15 CAN     2030  Partial BCA  59876. -2.58    
#> 16 NCOA    2020  Baseline    338419.  0       
#> 17 NCOA    2020  BCA         338421.  0.000502
#> 18 NCOA    2020  Full BCA    338428.  0.00269 
#> 19 NCOA    2020  NDC         338420.  0.000176
#> 20 NCOA    2020  Partial BCA 338427.  0.00247

^{Created on 2022-05-08 by the reprex package (v2.0.1)}

How to calculate growth in percentage between rows in a Pandas DataFrame?

Use Series.pct_change with multiple by 100 with Series.mul and rounding by Series.round:

df['C'] = df.B.pct_change().mul(100).round(2)
print (df)
   A    B      C
0  1  100    NaN
1  2  150  50.00
2  3  200  33.33

For percentage add map with format and processing with missing values with NaN != NaN:

df['C'] = df.B.pct_change().mul(100).round(2).map(lambda x: '{0:g}%'.format(x) if x==x else x)
print (df)
   A    B       C
0  1  100     NaN
1  2  150     50%
2  3  200  33.33%

How to compute growth rate (1- and 3-year horizon) from panel data in R

@Dan Do you use any packages? I recommend you using zoo and data.table packages and transform dates in the following way:

DT[, YearNumeric := as.numeric(YEAR)]
DT[, PreviousYearLoanNet := .SD[match(YearNumeric - 1, .SD$YearNumeric), LOANS_NET], by=CODE]

Here, you create a column with previous (-1 year) loan values. Then you create a new column with growth:

DT[,Growth1Y:= (YEARLOANNET- PreviousYearLoanNet)/PreviousYearLoanNet]

And then you do whatever you want:) Cheers!

Quarterly Year over Year Growth Rate

Here's a very simple solution:

YearOverYear<-function (x,periodsPerYear){
    if(NROW(x)<=periodsPerYear){
        stop("too few rows")
    }
    else{
        indexes<-1:(NROW(x)-periodsPerYear)
        return(c(rep(NA,periodsPerYear),(x[indexes+periodsPerYear]-x[indexes])/x[indexes]))
    }
}

> cbind(df,YoY=YearOverYear(df$value,4))
         date value         YoY
1  2000-01-01  1592          NA
2  2000-04-01  1825          NA
3  2000-07-01  1769          NA
4  2000-10-01  1909          NA
5  2001-01-01  2022  0.27010050
6  2001-04-01  2287  0.25315068
7  2001-07-01  2169  0.22611645
8  2001-10-01  2366  0.23939235
9  2002-01-01  2001 -0.01038576
10 2002-04-01  2087 -0.08745081
11 2002-07-01  2099 -0.03227294
12 2002-10-01  2258 -0.04564666

Finding Growth in Dataframe in R

Assuming that you have more years, dplyr handles it beautifully.

library(dplyr)
growth <- function(x)x/lag(x)-1
df %>% 
  group_by(Website) %>% 
  mutate_each(funs(growth), V1, V2)
#    Website Year  V1  V2
#1 Website 1 2013  NA  NA
#2 Website 2 2013  NA  NA
#3 Website 3 2013  NA  NA
#4 Website 1 2014 1.0 2.0
#5 Website 2 2014 0.5 1.0
#6 Website 3 2014 0.4 0.5

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