2analyseaccounts.Rmd

---
title: "Council accounts"
output: html_notebook
---

# Council accounts - analysis

With the accounts downloaded, cleaned, and filtered into a subset (see other notebook in this folder), this notebook deals with the analysis.

## Totals spent

We can calculate a basic total for a column:

```{r}
table(reservesdataonly.councils[,5])
sum(reservesdataonly.councils[,5])
```

But the column needs to be numeric first:

```{r}
sum(reservesdataonly.councils[,6])
```

```{r}
#Parse as integers
parse_integer(reservesdataonly.councils[,6])
#Replace column with numeric version
reservesdataonly.councils[,6] <- parse_integer(reservesdataonly.councils[,6])
#Sum - this returns NA
sum(reservesdataonly.councils[,6])
#Table to show frequency of numbers
table(reservesdataonly.councils[,6])
```

## Comparing two columns for different years

Let's compare the unallocated financial reserves for 2017 and 2018. First, find the columns:

```{r}
colnames(reservesdataonly.councils)
```

Columns 15 and 19 seem to be the key ones:

```{r}
#colnames(reservesdataonly.councils)[grepl(".*unallocated.*",colnames(reservesdataonly.councils))]
colnames(reservesdataonly.councils[15])
colnames(reservesdataonly.councils[19])
```

As before, these are probably stored as character, so let's check that and then convert to number:

```{r}
reservesdataonly.councils[,15]
summary(reservesdataonly.councils[,15])
reservesdataonly.councils[,15] <- parse_number(reservesdataonly.councils[,15])
summary(reservesdataonly.councils[,15])
```

Then repeat for column 19:

```{r}
reservesdataonly.councils[,19]
summary(reservesdataonly.councils[,19])
reservesdataonly.councils[,19] <- parse_number(reservesdataonly.councils[,19])
summary(reservesdataonly.councils[,19])
```

Let's create a column showing the change:


```{r}
reservesdataonly.councils$unallocated_reserves_change <- parse_integer(reservesdataonly.councils[,19])-parse_integer(reservesdataonly.councils[,15])
```

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