使用上一行信息在R中循环文件

Question

I want to calculate snow storage for the daily time step and add the previous day information and written simple code. The data looks like this

elev = seq(550, 1000, 50)
# Elevation in sequence 
D <- read.csv('data.csv', stringsAsFactors=T, header=T)
# Data File 
Z <- 550

#Elevation 
> head(D)
#Table Data 

   Date        T_min T_max  P   J_day   Prain  Psnow  **Snow accum**
1 1995/08/01  -4     -2    0.4   213      0.2   0.2    **0.2**
2 1995/08/02  -12    -6    0.0   214      0.0   0.0    **0.2**
3 1995/08/03  -5      2    4.2   215      2.6   1.2    **1.4**
4 1995/08/04  -2      5    3.2   216      3.0   0.2    **1.6**
5 1995/08/05  -8     -3    0.0   217      0.0   0.0    **0.0**

I distributed T_max and T_min and P snow in each elevation zone using the loop

C_Temp = array(dim=c(length(D$T_max), length(elev)))

C_Ppt = array(dim=c(length(D$P),length(elev)))

for (i in 1:length(elev)){
      C_Temp[,i] = D$T_max - Tg*(elev[i]-Z)}

for (i in 1:length(elev)){
      C_Ppt[,i] = D$P*(1+ Pg*(elev[i]-Z))}

Similarly, I want to calculate the Snow accumulation separating snow and rain using the code shown in table above.As in format it is not cumulative addition and snow melt depends on temperature as well.

SS<-array(dim=c(length(D$P), length(elev)))

for (i in 1:length(elev)){
     SS[i+1]<-PG[i-1]+PG }

Where PG is the Psnow and SS is the snow accumulation (Snow accum).

I get an error:

dims [product 36530] do not match the length of object [0]

I don't know how to use previous row information, ie 0.2 of Psnow, to calculate the S accumulation for next day using loop and since the file is very big I cannot do it manually.

Answer 1

Let df be your data frame. Than this can be achieved as

df
   structure(list(Date = structure(c(1L, 2L, 3L, 4L, 5L, 5L, 5L), .Label = c("8/1/1995", 
"8/2/1995", "8/3/1995", "8/4/1995", "8/5/1995"), class = "factor"), 
    T_min = c(-4L, -12L, -5L, -2L, -8L, 8L, -7L), T_max = c(-2L, 
    -6L, 2L, 5L, -3L, -3L, -3L), P = c(0.4, 0, 4.2, 3.2, 0, 0, 
    0), J_day = c(213L, 214L, 215L, 216L, 217L, 217L, 217L), 
    Prain = c(0.2, 0, 2.6, 3, 0, 0, 0), Psnow = c(0.2, 0, 1.2, 
    0.2, 0, 5, 10), X..Snow = structure(c(2L, 2L, 3L, 4L, 1L, 
    1L, 1L), .Label = c("**0.0**", "**0.2**", "**1.4**", "**1.6**"
    ), class = "factor")), .Names = c("Date", "T_min", "T_max", 
"P", "J_day", "Prain", "Psnow", "X..Snow"), class = "data.frame", row.names = c(NA, 
-7L))
head(df)
# Get all the rows satisfying your condition
df_snow<-subset(df, df$T_min<0)
df_snow
# Get columnwise cumulative sum
df_snow$AccuSnow<-cumsum(df_snow$Psnow)
df_snow

# If you want, you can merge now
df.final<-merge(df, df_snow, all=T)
df.final

Hope this helps.